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EZE Cloud Consulting Advances AI-first Strategy with Agile In-house Deployment of Workday GO
SINGAPORE; BENGALURU, INDIA; AND SAN FRANCISCO, USA--( BUSINESS WIRE )-- Leading by example, EZE Cloud Consulting, an official Services, Sales, and Innovation Partner of Workday, Inc., the enterprise AI platform for managing people, finance, and agents, has implemented the complete Workday Human Capital Management (HCM) and Workday Financial Management suite to power its own global operations across its people and finances.
Relativity Expands AI Capabilities and APAC Presence
Relativity is integrating AI into its platform, enhancing its Relativity One system for better scalability and defensibility in the APAC region. Plans include a Singapore entity and expanded AI features like aiR Assist, aiming to meet rising demands from increasing data volumes and regulatory complexities.
An AI Screw-Up By... Sullivan & Cromwell? - by David Lat
So that’s my compliment for S&C. Here’s my criticism: considering how much time has passed since the earliest AI errors—Avianca-gate took place three years ago next month—mistakes are less and less excusable (to the extent that they ever were). After all, as noted by Joe Patrice, there are tech tools out there—some of them powered by AI (a third irony?
Alicia Lyttle - AI InnoVision | LinkedIn
Within 45 days, she generated $2,400. That moment wasn’t luck. It was proof.
Blend's Ozgur Dogan Named CEO as Enterprise AI Adoption Accelerates
Blend has grown to more than 1,300 professionals across North America, Europe, India, and Latin America, working with Fortune 1000 and high-growth companies to deploy advanced analytics and AI solutions. As organizations move beyond experimentation and into enterprise deployment, demand continues ...
Decoding ROI from AI | PwC
Just 20% of companies are capturing 74% of all AI-driven value. We've decoded how, so you can harness AI to drive productivity, reinvention, and growth.
Google’s new Deep Research and Deep Research Max agents can search the web and your private data
Google on Monday unveiled the most significant upgrade to its autonomous research agent capabilities since the product's debut, launching two new agents — Deep Research and Deep Research Max — that for the first time allow developers to fuse open web data with proprietary enterprise information through a single API call, produce native charts and infographics inside research reports, and connect to arbitrary third-party data sources through the Model Context Protocol (MCP). The release, built on Google's Gemini 3.1 Pro model, marks an inflection point in the rapidly intensifying race to build AI systems that can autonomously conduct the kind of exhaustive, multi-source research that has traditionally consumed hours or days of human analyst time. It also represents Google's clearest bid yet to position its AI infrastructure as the backbone for enterprise research workflows in finance, life sciences, and market intelligence — industries where the stakes of getting information wrong are extraordinarily high. "We are launching two powerful updates to Deep Research in the Gemini API, now with better quality, MCP support, and native chart/infographics generation," Google CEO Sundar Pichai wrote on X. "Use Deep Research when you want speed and efficiency, and use Max when you want the highest quality context gathering & synthesis using extended test-time compute — achieving 93.3% on DeepSearchQA and 54.6% on HLE." Both agents are available starting today in public preview via paid tiers of the Gemini API, accessible through the Interactions API that Google first introduced in December 2025. Why Google built two research agents instead of one The launch introduces a tiered architecture that reflects a fundamental tension in AI agent design: the tradeoff between speed and thoroughness. Deep Research, the standard tier, replaces the preview agent Google released in December and is optimized for low-latency, interactive use cases. It delivers what Google describes as significantly reduced latency and cost at higher quality levels compared to its predecessor. The company positions it as ideal for applications where a developer wants to embed research capabilities directly into a user-facing interface — think a financial dashboard that can answer complex analytical questions in near-real time. Deep Research Max occupies the opposite end of the spectrum. It leverages extended test-time compute — a technique where the model spends more computational cycles iteratively reasoning, searching, and refining its output before delivering a final report. Google designed it for asynchronous, background workflows: the kind of task where an analyst team kicks off a batch of due diligence reports before leaving the office and expects exhaustive, fully sourced analyses waiting for them the next morning. The Google DeepMind team framed the distinction on X: "Deep Research: Optimized for speed and efficiency. Perfect for interactive apps needing quicker responses. Deep Research Max: It uses extra time to search and reason. Ideal for exhaustive context gathering and tasks happening in the background." "Deep Research was our first hosted agent in the API and has gained a ton of traction over the last 3 months, very excited for folks to test out the new agents and all the improvements, this is just the start of our agents journey," Logan Kilpatrick, who leads developer relations for Google's AI efforts, wrote on X. MCP support lets the agents tap into private enterprise data for the first time Perhaps the most consequential feature in today's release is the addition of Model Context Protocol support, which transforms Deep Research from a sophisticated web research tool into something more closely resembling a universal data analyst. MCP , an emerging open standard for connecting AI models to external data sources, allows Deep Research to securely query private databases, internal document repositories, and specialized third-party data services — all without requiring sensitive information to leave its source environment. In practical terms, this means a hedge fund could point Deep Research at its internal deal-flow database and a financial data terminal simultaneously, then ask the agent to synthesize insights from both alongside publicly available information from the web. Google disclosed that it is actively collaborating with FactSet, S&P, and PitchBook on their MCP server designs, a signal that the company is pursuing deep integration with the data providers that Wall Street and the broader financial services industry already rely on daily. The goal, according to the blog post authored by Google DeepMind product managers Lukas Haas and Srinivas Tadepalli, is to "let shared customers integrate financial data offerings into workflows powered by Deep Research, and to enable them to realize a leap in productivity by gathering context using their exhaustive data universes at lightning speed." This addresses one of the most persistent pain points in enterprise AI adoption: the gap between what a model can find on the open internet and what an organization actually needs to make decisions. Until now, bridging that gap required significant custom engineering. MCP support, combined with Deep Research's autonomous browsing and reasoning capabilities, collapses much of that complexity into a configuration step. Developers can now run Deep Research with Google Search, remote MCP servers, URL Context, Code Execution, and File Search simultaneously — or turn off web access entirely to search exclusively over custom data. The system also accepts multimodal inputs including PDFs, CSVs, images, audio, and video as grounding context. Native charts and infographics turn AI reports into stakeholder-ready deliverables The second headline feature — native chart and infographic generation — may sound incremental, but it addresses a practical limitation that has constrained the usefulness of AI-generated research outputs in professional settings. Previous versions of Deep Research produced text-only reports. Users who needed visualizations had to export the data and build charts themselves, a friction point that undermined the promise of end-to-end automation. The new agents generate high-quality charts and infographics inline within their reports, rendered in HTML or Google's Nano Banana format, dynamically visualizing complex datasets as part of the analytical narrative. "The agent generates HTML charts and infographics inline with the report. Not screenshots. Not suggestions to 'visualize this data.' Actual rendered charts inside the markdown output," noted AI commentator Shruti Mishra on X, capturing the practical significance of the change. For enterprise users — particularly those in finance and consulting who need to produce stakeholder-ready deliverables — this transforms Deep Research from a tool that accelerates the research phase into one that can potentially produce near-final analytical products. Combined with a new collaborative planning feature that lets users review, guide, and refine the agent's research plan before execution, and real-time streaming of intermediate reasoning steps, the system gives developers granular control over the investigation's scope while maintaining the transparency that regulated industries demand. How Deep Research evolved from a consumer chatbot feature to enterprise platform infrastructure Today's release crystallizes a strategic narrative Google has been building for months: Deep Research is not merely a consumer feature but a piece of infrastructure that powers multiple Google products and is now being offered to external developers as a platform. The blog post explicitly notes that when developers build with the Deep Research agent, they tap into "the same autonomous research infrastructure that powers research capabilities within some of Google's most popular products like Gemini App, NotebookLM, Google Search and Google Finance." This suggests that the agent available through the API is not a stripped-down version of what Google uses internally but the same system, offered at platform scale. The journey to this point has been remarkably rapid. Google first introduced Deep Research as a consumer feature in the Gemini app in December 2024, initially powered by Gemini 1.5 Pro. At the time, the company described it as a personal AI research assistant that could save users hours by synthesizing web information in minutes. By March 2025, Google upgraded Deep Research with Gemini 2.0 Flash Thinking Experimental and made it available for anyone to try. Then came the upgrade to Gemini 2.5 Pro Experimental, where Google reported that raters preferred its reports over competing deep research providers by more than a 2-to-1 margin. The December 2025 release was the pivot to developer access, when Google launched the Interactions API and made Deep Research available programmatically for the first time, powered by Gemini 3 Pro and accompanied by the open-source DeepSearchQA benchmark. The underlying model driving today's improvements is Gemini 3.1 Pro, which Google released on February 19, 2026. That model represented a significant leap in core reasoning: on ARC-AGI-2, a benchmark evaluating a model's ability to solve novel logic patterns, 3.1 Pro scored 77.1% — more than double the performance of Gemini 3 Pro. Deep Research Max inherits that reasoning foundation and layers autonomous research behaviors on top of it, achieving 93.3% on DeepSearchQA (up from 66.1% in December) and 54.6% on Humanity's Last Exam (up from 46.4%). Google faces a crowded field of competitors building autonomous research agents Google is not operating in a vacuum. The launch arrives amid intensifying competition in the autonomous research agent space. OpenAI has been developing its own agent capabilities within ChatGPT under the codename Hermes, which includes an agent builder, templates, scheduling, and Slack integration, according to reports circulating on social media. Perplexity has built its business around AI-powered research. And a growing ecosystem of startups is attacking various slices of the automated research workflow. What distinguishes Google's approach is the combination of its search infrastructure — which gives Deep Research access to the broadest and most current index of web information available — with the MCP-based connectivity to enterprise data sources. No other company currently offers a research agent that can simultaneously query the open web at Google Search's scale and navigate proprietary data repositories through a standardized protocol. The pricing structure also signals Google's intent to drive adoption: according to Sim.ai, which tracks model pricing, the Deep Research agent in the December preview was priced at $2 per million input tokens and $2 per million output tokens with a 1 million token context window — positioning it as cost-competitive for the volume of research output it generates. Not everyone greeted the announcement with unalloyed enthusiasm, however. Several users on X noted that the new agents are available only through the API, not in the Gemini consumer app. "Not on Gemini app," observed TestingCatalog News, while another user wrote, "Google keeps punishing Gemini App Pro subscribers for some reason." Others raised concerns about the presentation of benchmark results, with one user arguing that Google's charts could be "misleading" in how they represent percentage improvements. These complaints point to a broader tension in Google's AI strategy: the company is increasingly directing its most advanced capabilities toward developers and enterprise customers who access them through APIs, while consumer-facing products sometimes lag behind. What Deep Research Max means for finance, biotech, and the future of knowledge work The practical implications of today's launch are most immediately felt in industries that depend on exhaustive, multi-source research as a core business function. In financial services, where analysts routinely spend hours assembling due diligence reports from scattered sources — SEC filings, earnings transcripts, market data terminals, internal deal memos — Deep Research Max offers the possibility of automating the initial research phase entirely. The FactSet, S&P, and PitchBook partnerships suggest Google is serious about making this work with the data infrastructure that financial professionals already use. In life sciences, the blog post notes that Google has collaborated with Axiom Bio, which builds AI systems to predict drug toxicity, and found that Deep Research unlocked new levels of initial research depth across biomedical literature. In market research and consulting, the ability to produce stakeholder-ready reports with embedded visualizations and granular citations could compress project timelines from days to hours. The key question is whether the quality and reliability of these automated outputs will meet the standards that professionals in these fields demand. Google's benchmark numbers are impressive, but benchmarks measure performance on standardized tasks — real-world research is messier, more ambiguous, and often requires the kind of judgment that remains difficult to automate. Deep Research and Deep Research Max are available now in public preview via paid tiers of the Gemini API, with availability on Google Cloud for startups and enterprises coming soon. Eighteen months ago, Deep Research was a feature that helped grad students avoid drowning in browser tabs. Today, Google is betting it can replace the first shift at an investment bank. The distance between those two ambitions — and whether the technology can actually close it — will define whether autonomous research agents become a transformative category of enterprise software or just another AI demo that dazzles on benchmarks and disappoints in the conference room.
What AI model should you use for revenue intelligence? Von says all the big ones, and it will automate mixing and matching for you
Looking at enterprise AI adoption, VentureBeat has anecdotally observed a fairly wide divergence when it comes to specific roles: For those who build—engineers and developers—the arrival of AI has been transformative, moving through the workflow with the speed of tools like Claude Code and Cursor to automate the heavy lifting of syntax and architecture. Yet, for those who sell, the "revenue stack" has remained a fragmented collection of data silos, manual CRM entries, and anecdotal reporting. Von, a new AI platform emerging from the team behind process automation startup Rattle, aims to bridge this gap. By positioning itself not as another "point solution" but as a foundational "intelligence layer," Von seeks to do for Go-To-Market (GTM) teams what the modern IDE has done for the developer: provide a single, reasoning interface that understands the entire business context. “AI has revolutionized the workflow for people who build things, but there is nothing that has revolutionized the workflow for people who sell those things," Von CEO Sahil Aggarwal said in a recent video call interview with VentureBeat. "That is what we are trying to build with Von”. Technology: The context graph and multi-model engine At the core of Von’s capability is a departure from the traditional "search bar" approach to enterprise AI. While standard LLMs often struggle with the sprawling, unstructured nature of sales data, Von begins its deployment by building a "context graph" of a company’s entire business. This process involves ingesting structured data from CRMs like Salesforce and HubSpot, alongside unstructured data from call recorders (Gong, Zoom, Chorus), email threads, and internal documentation. "Once Von builds this context graph, it will understand your business better than anyone else in the company," Aggarwal said. This understanding is rooted in a company’s specific "ontology"—the unique language of its deal stages, territory definitions, and institutional knowledge. "We train these foundational models on a company’s own business and ontology to make the model work for them," the CEO addded. Instead of relying on a single large language model, Von utilizes a "mixture of models" strategy to optimize performance and cost. In this architecture, Anthropic's Claude is deployed for high-level reasoning and "thinking," ChatGPT handles bulk data processing, and Google’s Gemini is utilized for generating creative assets such as decks and reports. This technical approach allows Von to resolve a common frustration in Sales Operations: the gap between what is logged in a CRM and what actually happened in a meeting. By cross-referencing call transcripts with Salesforce records, the system can identify discrepancies in "lost reasons" or verify deal health based on sentiment rather than just a rep’s manual update. From reporting queues to AI headcount Von is designed to function as an "AI Data Scientist" or a "VP of RevOps" that lives on top of the enterprise's existing revenue tracking tools. During an initial product demonstration, Aggarwal showed how the platform could analyze 101 SMB accounts to identify churn risk in just over three minutes—a task he estimates would take a human analyst one to two weeks. The platform’s primary interface resembles a chat environment, but the outputs are designed to be actionable revenue assets. Key functionalities include: Deal Health Monitoring: Cross-referencing calls and emails to surface "risky" commits that might otherwise go unnoticed until the end of a quarter. Automated Briefing: Generating pre-call context docs that draw from the entire history of an account, ensuring reps are briefed on every previous touchpoint. Win/Loss Analysis: Clustered analysis of transcripts to find the "true" reasons for lost deals, often finding that the recorded reason in the CRM does not match the customer's actual feedback. Revenue Operations Automation: Handling "low-level" Salesforce admin tasks, such as creating flows, validation rules, or cleaning up account territories. The goal is to shift Revenue Operations (RevOps) from a "reporting queue" that handles ad-hoc data requests into an infrastructure layer. As Kieran Snaith, SVP of Revenue Operations at Qualified, noted in a Von testimonial blog post, the goal is to allow leaders to "run the business in chat," asking complex questions about forecast confidence or pipeline risk and receiving data-backed answers instantly. Pivoting into 'the next Salesforce' Von is operated by Rattle Software Inc., a company that previously found success with "Rattle," a mid-seven-figure revenue business focused on Salesforce-Slack integrations. Aggarwal describes Von as a significant pivot toward a larger opportunity, aiming to build "the next Salesforce". The business has seen rapid early traction, reportedly crossing $500,000 in revenue within its first eight weeks of launch, with projections to reach $10 million in its first year. The product is governed by a commercial, proprietary license typical of enterprise SaaS. Unlike open-source tools, Von’s "restricted" license means the underlying source code and the "context graph" technology are proprietary to Rattle Software Inc.. Users are granted a non-transferable, non-exclusive right to use the software for internal business purposes, with the company maintaining all rights, title, and interest in the service. This philosophy of deep integration extends to the broader SaaS ecosystem, where Aggarwal observes, "Point solutions in SaaS are essentially dead. They will have a very hard time surviving in this world, because point solutions can now be white-coded within a company." Pricing follows a hybrid model of per-seat subscriptions and consumption-based credits. This structure is designed to scale with the persona using the tool; for instance, a Chief Revenue Officer (CRO) seat may cost $1,000 per month for deep strategic analysis, while individual seller seats may be as low as $20 per month for basic research and follow-up tasks. The company is currently backed by several tier-one venture capital firms, including Sequoia Capital, Lightspeed, Insight Partners, and GV (Google Ventures). Early adopter reaction The reaction from early adopters highlights a shift in how AI is being integrated into the sales org. Taylor Kelly, Head of Revenue Operations at Tapcart, remarked that "Von handles the analysis and insights that would normally require hiring another full-time analyst," specifically citing its ability to handle complex Salesforce configurations and deal risk assessments. Similarly, Evan Briere, VP of Partnerships at DemandScience, noted that Von’s direct connection to data sources makes it "actually applicable" compared to more "theoretical" horizontal AI tools like ChatGPT. Other community feedback from the platform’s early users includes: CJ Oordt, Sales Director at Coalesce: Described it as a "research assistant who knows every conversation and note". Rob Janke, Director of Revenue Operations at QuickNode: Stated that Von "solved this gap before we could even start building it ourselves". Sydney, Head of Renewals at 15Five: Highlighted its impact on renewal intelligence, allowing her to analyze actual conversation signals across an entire book of business in minutes. The prevailing sentiment among these users is that Von serves as "additional headcount" rather than just a tool. This mirrors the company’s internal metrics, which report that Von is already completing over 10,000 revenue tasks per week for its customer base. An autonomous revenue org The introduction of Von signals a maturing of AI in the enterprise. We are moving past the era of "AI as a feature"—where a chatbot is simply bolted onto an existing CRM—toward "AI as a persona". By training foundational models on a company’s specific business logic, Von is attempting to create a system that doesn't just return data but offers "judgment calls".As organizations look toward the rest of 2026, the challenge for RevOps leaders will be one of trust and infrastructure. If Von can maintain its claimed 95% accuracy in predicting deal outcomes, the role of the human salesperson will inevitably shift toward higher-value relationship management, leaving the "data science" of sales to the agents. For now, Von remains a high-growth experiment in whether the "intelligence layer" can finally bring the same level of revolutionary workflow to the people who sell as it has to the people who build.
The AI governance mirage: Why 72% of enterprises don’t have the control and security they think they do
Decision makers at 72% of organizations claim to have two or more AI platforms that they identify as their "primary" layer, according to a survey of 40 enterprise companies conducted by VentureBeat last month, revealing real gaps in security and control. For enterprise management and technical leaders, and especially security leaders, these multiple AI platforms extend the attack surfaces of most enterprises at a time when AI-driven attacks have become increasingly potent. The multiple platforms — which include offerings from hyperscaler or AI labs like Microsoft Azure, Google, OpenAI or Anthropic, or big application companies like Epic, Workday or ServiceNow — reflect a state of sprawl that has emerged as these big software providers rush to offer their own AI to their enterprise customers. Those customers, in their own rush to scale AI, are finding they aren’t building a singular strategy — in fact they may be building a collection of contradictions. The strategic paradox: why leading enterprises are building around their vendors For example, take the strategic paradox faced by Mass General Brigham (MGB) hospital system, which has 90,000 employees and is the largest employer in Massachusetts. The hospital system last year had to shut down an uncontrolled number of internal proof of concepts that had sprouted up as employees had gotten carried away with AI projects, said CTO Nallan “Sri” Sriraman at the VentureBeat AI Impact event in Boston on March 26, which focused on the challenges of scaling AI. Instead, the company decided it was better to wait for the software giants it already uses to deliver on their AI roadmaps. Since these companies have so many resources, and were making AI a top priority themselves, it made no sense for MGB to try to build its own AI layer that would be duplicative, he said. "Why are we building it ourselves?" he asked. "Leverage it." Yet, even then, Sriraman’s team has been forced to build workarounds, where those companies haven’t done enough. For example, MGB has just completed a “full-scaled” custom build around Microsoft’s Copilot — to get essentially everything offered by that tool — by putting a "skin" around Copilot to handle the safety and data privacy concerns the major model providers haven't yet mastered. Specifically, MGB needed a way for employees to prompt the AI and not have their protected health information (PHI) leaked back to the Copilot LLM provider, OpenAI. The new secure platform, which can support up to 30,000 users, is really the ultimate contradiction: Even though the company has a mandate to leverage the AI provided by the bigger companies, it needs to build around its failures. The contradiction goes even further. These software vendors used by MGB — which also include Epic, Workday and ServiceNow — are all now building agents for their AI, all operating differently. So MGB has to invest in building a “control plane that coordinates and orchestrates all of these agents,” Sriraman said. “That’s where our investment is going to be.” He noted that companies like his are “discovering and experimenting as the landscape keeps shifting." The marketplace is "still nascent," he said, which makes decisions difficult. The "six blind men" problem Sriraman explained the current vendor landscape with an analogy: "When you ask six blind men to touch an elephant and say, what does this elephant look like?" Sriraman said. "You're gonna get six different answers." What emerges from the research VentureBeat conducted in the first quarter, along with conversations like the one in Boston, is a situation that we at VentureBeat are calling a “governance mirage.” While many enterprises say they have adequate governance, in reality they haven’t created clear accountability or specific guardrails, evaluations or security processes to ensure that governance. The data of disconnect: confidence vs. systematic oversight The research comes from surveys across January, February and March by VentureBeat of enterprise companies with 100 or more employees, with 40 to 70 qualified respondents per topic area — covering agentic orchestration, AI security, RAG and governance. The data lacks statistical significance in many areas and should be treated as directional. The research on governance found that a majority, or 56%, of respondents said they are “very confident” that they’d detect a misbehaving AI model, suggesting that most decision-makers believe they have sufficient basic governance at their companies. However, nearly a third of respondents have no systematic mechanism to detect AI misbehavior until it surfaces through users or audits. In a world where telemetry leakage accounts for 34% of GenAI incidents (Wiz), and the global average breach cost has hit $4.4M (IBM 2025 Cost of a Data Breach), finding out after the damage is done is the default for too many companies. Moreover, 43% of respondents say a central team owns AI governance. That sounds reassuring — until you look at what’s happening everywhere else. Twenty-three percent say governance is unclear or actively contested between teams. Twenty percent say each platform team governs independently. Six percent say no one has formally addressed it. The rest said they were unsure who owned it. More telling is the barrier data. When asked about the single biggest obstacle to governing AI across platforms, “no single owner or accountable team” ranked second at 29% — just behind vendor opacity. Accountability structure and lack of vendor transparency are the two dominant failure modes, and they compound each other: Without a central owner, no one has the mandate to demand transparency from the vendors. The day-two bill: managing sprawl, creep, and lock-in The scaling trap: Red Hat’s warning Brian Gracely, Senior Director at Red Hat, who also spoke at the VentureBeat Boston event last month, addressed the infrastructure side of this sprawl, warning that many enterprises are falling into a trap of deceptive initial wins. Gracely noted that the barrier to entry is almost nonexistent at the start, with nearly anyone able to spin up a project using a credit card and an API key. "Day zero is very, very easy," Gracely said. "Day two is when the bill comes due." Red Hat is positioning its software layer (OpenShift AI) as the necessary buffer to prevent enterprises from getting buried in a single provider's proprietary ecosystem. Gracely’s point is direct: If your control system is built entirely inside one cloud provider’s toolset, you are effectively "renting a cage." The illusion of speed in the early pilot phase often hides a technical debt that becomes obvious the moment you try to move your AI work to a different platform. Gracely illustrated this with a recent example. A senior leader from Red Hat’s centralized CTO office spent part of her vacation contributing to an open-source agent project called OpenClaw, which became widely popular in the first quarter. Within days of her name appearing as a project maintainer, Red Hat was fielding calls from major New York banks. Their problem was immediate: They realized they already had upwards of 10,000 employees bringing "claws" — agent-based tools — into their infrastructure with zero centralized oversight. Breaches caused by employees working on these sorts of unapproved technologies are costly. These so-called “shadow AI” incidents cost on average $670K more than standard incidents, according to IBM. Red Hat’s Gracely noted that while organizations can try to shut down these unapproved ports, they eventually have to figure out how to make them productive and secure — a task that requires a serious investment in an orchestration or platform layer. The dynamic defensive: MassMutual’s refusal to bet While some enterprise companies seek an "AI operating system" that oversees all of their AI technologies and apps, others are simply refusing to sign the check. Sears Merritt, CIO and head of enterprise technology at MassMutual, is managing the governance conundrum by intentionally staying in a state of high-velocity flexibility. "Things are so dynamic, it’s hard to know which of the AI vendors will end up on top," Merritt said at the Boston event. For that reason, MassMutual is refusing to enter any long-term contracts with AI vendors. Merritt’s strategy of “dynamic defensive” highlights a core finding of our research: Vendor popularity is changing radically month to month. Anthropic, for example, went from 0% in January to nearly 6% in February, in the number of respondents reporting what agent orchestration technology they were using. Again, the sample size was small, at 70 respondents. Still, even if directional, the dynamic landscape suggests picking a "primary" winner today is a fool’s errand. The January figure likely reflects survey composition: Respondents represent the broader enterprise market, not the developer community where Anthropic has seen its strongest early traction. Until recently, most organizations had signed up early with leaders like Microsoft and OpenAI as their main orchestration providers, due to their early lead with Copilot. Our finding that Anthropic is just now pushing into enterprise agent orchestration may be a confirmation of the recent excitement around that platform. One possible explanation is that enterprises already using Claude for model inference are now routing through Anthropic's native tooling rather than third-party frameworks — though the sample is too small to draw firm conclusions. The rise of “platform creep” The leading providers are also shifting toward "managed agents," as reflected by Anthropic’s recent announcement. This offering suggests possible continued platform creep, whereby providers like OpenAI and Anthropic take over more and more of the AI infrastructure — most specifically, in this case, the memory of agentic session details. And there the trap is set. Once your session data and orchestration live inside a provider's proprietary database, you aren't just using a model; you are living in its ecosystem. Moreover, persistent agent memory is a prime target for memory poisoning via injected instructions that influence every future interaction. And when that memory lives in a provider's database, you lose your own forensic capability. The security irony: The fox guarding the hen house We are seeing this platform creep in our data as well. The most jarring finding in our Q1 data is what we call the "Security Irony": the fact that the providers most responsible for creating enterprise AI risk are the same ones enterprises are using to manage it. Respondents said the top selection criterion for AI orchestration platforms was “security and permissions generally” (37.1%), beating out other criteria like cost, flexibility, control and ease of development. Yet, the market is choosing convenience over sovereignty. According to our survey, 26% of enterprises in February were using OpenAI as their primary security solution — the very same provider whose models create the risks they are trying to secure. That trend only seemed to strengthen in March, though, as stated before, we want to be careful. Our sample size is small, and this data should only be taken as directional. It’s not clear whether enterprises are choosing OpenAI as a security solution, or just relying on its built-in security features offered by Microsoft Azure (which partnered with OpenAI when it pushed its Copilot solution aggressively in 2024) because customers were already on that platform. Beyond the data, there are anecdotal signs that OpenAI's enterprise position may be shifting. Anthropic's Claude Code drew significant attention among developers early this year alongside the Claude 4.6 model. The subsequent announcement of Mythos, its security-focused model, prompted interest from enterprise security teams given its ability to identify vulnerabilities. OpenAI has also announced a security-focused model, GPT-5.4-Cyber. Our data may also point to a drop in OpenAI’s relative position in a few enterprise AI categories. One area was data-retrieval, where OpenAI again leads among third-party providers, but we saw an increase in the number of respondents instead using in-house solutions for retrieval — perhaps a sign that AI models and agents are getting better at natively being able to use tools to call directly to companies’ existing databases, and that custom code is often a way companies are building this in. However, here again we feel our data is at best directional for now. We are asking the fox to guard the hen house. Hyperscaler security features (like those from OpenAI, Azure, and Google) are winning, because they are already integrated into the platforms enterprises are using. But it creates a single-provider dependency. As agents gain the power to modify documents, call APIs and access databases, the “governance mirage" suggests we have control, while the data shows we are simply clicking "I agree" on whatever the hyperscalers offer. The resulting risks, however, include content injection, privilege escalation and data exfiltration. The path forward: toward a unified control plane The search for the "Dynatrace for AI" So, what is the way out? Sriraman argued that the industry desperately needs a "central observability platform" — a "Dynatrace for AI" — that provides full end-to-end visibility, including model drift and safety prompting, agent behavior analytics, privilege escalation alerts, and forensic logging. He is currently working with a number of potential providers to deliver on this. The “swivel chair” warning Sriraman warned that without a unified control plane, enterprises are at risk of sliding back into a fragmented "swivel chair" world — reminiscent of the early, inefficient days of Robotic Process Automation (RPA) — where employees are forced to constantly jump between different siloed AI tools to finish a single workflow. "We don’t want to create a world where you have to switch to do something here and then go back to the platform to do something else," he said. But that desire for a single control plane conflicts with the desire to avoid lock-in. Our data shows the market has settled on the “hybrid control plane.” In other words, the most popular situation among our respondents (at 34.3%), was to use model provider-native solutions like Copilot Studio or OpenAI assistants for some workflows, while also running external options like LangGraph or custom orchestration for others. Smaller numbers of companies reported being more dogmatic here, whether that be deliberately removing the model provider from the orchestration layer entirely, relying only on custom orchestration tools, or relying only on the model provider’s technology Enterprises trust no single provider enough to give them full control, yet they lack the engineering capacity to build entirely from scratch. The bottom line: The “big red button” Visibility and integration are only half the battle. In a high-stakes industry like healthcare, Sriraman argues that any legitimate control plane must also offer a hard-stop capability. "We need a big red button," he said. "Kill it. We should be able to have that … without that, don't put anything in the operational setting." In fact, such a kill switch was formally called for by the security community group OWASP as part of a recommended security framework. The “governance mirage” is the belief that you can scale AI without deciding who owns the control and security plane. If you are one of the 72% of organizations claiming multiple "primary" platforms, be careful because you may not have a strategy; you may have a conflict of interest. It suggests that the winner of the war between the AI behemoths — OpenAI, Anthropic, Google, Microsoft, etc. — won’t necessarily be the one with the best model, but the one that manages to sit above the models and help enterprises enforce a single version of the truth. That may be difficult to achieve, though, given that companies won’t want lock-in with a single player. The data suggests enterprises are already resisting that outcome — and may need to formalize that resistance. Enterprises arguably need to own their control plane with independent security instrumentation, not wait for a vendor to win that role for them.
Elite law firm Sullivan & Cromwell admits to AI ‘hallucinations’
Firm whose partners bill more than $2,000 per hour apologises to judge for software-driven errors in bankruptcy case
Sullivan & Cromwell law firm apologizes for AI 'hallucinations' in court filing | Reuters
In a letter dated April 18, Andrew Dietderich, co-head of the firm's global restructuring group, said the errors included AI "hallucinations" - instances in which AI makes up case citations, misquotes the law or generates non-existent legal sources.
GC AI powers legal workflows for 1,500 companies, saving lawyers 14 hours a week with Claude
GC AI leverages Claude to streamline legal tasks like drafting and research, demonstrating measurable enterprise ROI through significant time savings.
OpenAI leans on global consultancies to expand Codex use in large companies
OpenAI leans on global consultancies to expand Codex use in large companies
Beyond the Model — Why Responsible AI Must Address Workforce Impact
For the fifth year in a row, MIT Sloan Management Review and Boston Consulting Group (BCG) have assembled an international panel of AI experts that includes academics and practitioners to help us understand how responsible artificial intelligence (RAI) is being implemented across organizations worldwide. In prior years, we examined organizational RAI maturity; third-party, generative, and […]
The Wharton Blueprint for AI Agent Adoption - Knowledge at Wharton
The main barriers to adoption are now psychological. Using an agent requires believing it can perform tasks correctly, trusting it with sensitive information, and relinquishing control. Created by Wharton Human-AI Research and Science Says, this Blueprint bridges that gap by drawing on behavioral science in human-AI interaction, guidance from Wharton faculty, and lessons from organizations deploying ...
The role of coaching in AI adoption | The AI Journal
When organisations hit friction ... or increase spending. But this misdiagnoses the problem. Integrating AI into everyday working life requires shifts in judgement, habits, and confidence that do not emerge automatically from a training session. The challenge becomes less about deploying tools and ...
B2B pricing: Navigating the next phase of the AI revolution
Agentic AI is transforming B2B pricing by enabling automated, optimized workflows for quotes and discounts, though success requires strong data foundations.
Moving beyond Principles: Identifying Actionable AI Fairness Practices
Because artificial intelligence (AI) increasingly mediates organizational work, fairness has become a critical governance challenge. Existing frameworks often prioritize abstract ethical principles rather than fairness-specific ones and lack actionable guidance across the entire AI lifecycle. This study addresses the principles-to-practice gap in AI fairness governance.
AI Agent Slop Overwhelms Workers
The agent mania gripping the industry is producing a real problem: AI workslop. As OpenClaw founder Peter Steinberger put it, agents without strong human direction still produce slop. The bottleneck isn't technology.
How AI Helps the Best and Hurts the Rest
Mark Shaver/theispot.com Can generative AI serve as an effective adviser for business owners and entrepreneurs? Intuitive chat-based natural language interfaces mean that anyone who can read and write can use GenAI tools for a wide range of tasks, even if they lack technical skills. This has obvious appeal for entrepreneurs and small business owners, many […]
KWBench: Measuring Unprompted Problem Recognition in Knowledge Work
arXiv:2604.15760v1 Announce Type: new Abstract: We introduce the first version of KWBench (Knowledge Work Bench), a benchmark for unprompted problem recognition in large language models: can an LLM identify a professional scenario before attempting to solve it. Existing frontier benchmarks have saturated, and most knowledge-work evaluations to date reduce to extraction or task completion against
From hours to minutes: How Agentic AI gave marketers time back for what matters
This AWS case study demonstrates how agentic AI can shrink web page assembly from hours to minutes by coordinating content planning, layout selection, and publishing.
The hidden ROI of AI: What leaders should actually measure
How enterprises can bridge the gap between AI experimentation and real business impact.
The AI layoff trap: How cutting headcount could backfire on corporate America
HR leaders warn that using AI to justify layoffs is a shortsighted strategy.
Imperfectly Cooperative Human-AI Interactions: Comparing the Impacts of Human and AI Attributes in Simulated and User Studies
arXiv:2604.15607v1 Announce Type: cross Abstract: AI design characteristics and human personality traits each impact the quality and outcomes of human-AI interactions. However, their relative and joint impacts are underexplored in imperfectly cooperative scenarios, where people and AI only have partially aligned goals and objectives. This study compares a purely simulated dataset comprising 2,000 simulations and a parallel human subjects experiment involving 290 human participants to investigate these effects across two scenario categories: (1) hiring negotiations between human job candidates and AI hiring agents; and (2) human-AI transactions wherein AI agents may conceal information to maximize internal goals. We examine user Extraversion and Agreeableness alongside AI design characteristics, including Adaptability, Expertise, and chain-of-thought Transparency. Our causal discovery analysis extends performance-focused evaluations by integrating scenario-based outcomes, communication analysis, and questionnaire measures. Results reveal divergences between purely simulated and human study datasets, and between scenario types. In simulation experiments, personality traits and AI attributes were comparatively influential. Yet, with actual human subjects, AI attributes -- particularly transparency -- were much more impactful. We discuss how these divergences vary across different interaction contexts, offering crucial insights for the future of human-centered AI agents.
Only 39% of tech leaders confident of positive returns from AI investments, ETHRWorld
AI Investment: The report has outlined six key shifts needed to realise AI value, including building AI-first data and analytics capabilities, redesigning teams for human and AI collaboration, and strengthening context and data infrastructure to support AI systems.
Professionals Share the Skills New Workers Need to Succeed - Beyond AI - CPA Practice Advisor
36% believe new job seekers should be prepared to demonstrate knowledge of AI tools when applying to roles and 37% caution against using AI to overstate skills or experience.
$29.6+ BN Life Sciences Consulting Services Market Forecasts to 2034: Rising Drug Complexity, AI & Big Data Adoption, Demand for Real-World Evidence, and Biotech Expansion with Novel Therapies
Life sciences consulting services market is projected to reach $ 29.6 BN by 2034 from its value of $ 14.1 BN in 2024, at a CAGR of 7.7% from 2025 to 2034...
2026 AI Impact Survey Report | Grant Thornton
Most organizations are scaling AI they cannot explain, measure or defend.
Why AI agents won’t replace work as fast as expected
Enterprise AI is racing ahead, but control remains a bottleneck. This article examines why agents are harder to deploy than expected and the role of security, governance, and orchestration in their success.
Leaders Can No Longer Fake an AI Strategy - CEOWORLD magazine
The era of corporate AI theater is ending. A recent Wharton Human-AI Research and GBK Collective study finds that 82% of enterprise leaders use generative AI at least weekly and 46% use it daily. That is no longer experimentation. Yet McKinsey’s 2025 state of AI survey shows a stubborn gap ...
EOR & Compliance Digest, April 19: Netherlands Ends the Contractor Free Pass as EU Deadline Looms
Contractor misclassification rules update: Netherlands full enforcement, EU Platform Work Directive, Chile 42-hour law, Germany Blue Card changes.
The Cognitive Tax of AI - by Dirk Shaw - FUTUREMINDED
This matters most in the terrain of adjacent expertise. AI does not just help people do more of their own work. It lets them operate across roles. A strategist can now produce design. A designer can now write positioning. A marketer can now generate research.
2 AI Prompts + Claude Design to Ship a Conversion-Focused Landing Page This Weekend
You’re reading Excellent AI Prompts. AI prompts, skills, frameworks, and agentic workflows for professionals who use AI to earn more, think sharper, and live better.
LinkedIn | LinkedIn
How can you take control of your career as AI reshapes the workplace? With Open to Work! 📘 LinkedIn's Ryan Roslansky and Aneesh Raman are here with a practical guide on how to get ahead and embr.
Governing Reflective Human-AI Collaboration: A Framework for Epistemic Scaffolding and Traceable Reasoning
arXiv:2604.14898v1 Announce Type: cross Abstract: Large language models have advanced rapidly, from pattern recognition to emerging forms of reasoning, yet they remain confined to linguistic simulation rather than grounded understanding. They can produce fluent outputs that resemble reflection, but lack temporal continuity, causal feedback, and anchoring in real-world interaction. This paper proposes a complementary approach in which reasoning is treated as a relational process distributed between human and model rather than an internal capability of either. Building on recent work on "System-2" learning, we relocate reflective reasoning to the interaction layer. Instead of engineering reasoning solely within models, we frame it as a cognitive protocol that can be structured, measured, and governed using existing systems. This perspective emphasizes collaborative intelligence, combining human judgment and contextual understanding with machine speed, memory, and associative capacity. We introduce "The Architect's Pen" as a practical method. Like an architect who thinks through drawing, the human uses the model as an external medium for structured reflection. By embedding phases of articulation, critique, and revision into human-AI interaction, the dialogue itself becomes a reasoning loop: human abstraction -> model articulation -> human reflection. This reframes the question from whether the model can think to whether the human-AI system can reason. The framework enables auditable reasoning traces and supports alignment with emerging governance standards, including the EU AI Act and ISO/IEC 42001. It provides a practical path toward more transparent, controllable, and accountable AI use without requiring new model architectures.
Startup Offers AI Tools to Find New Jobs for Workers Displaced by AI
Pelgo says it can provide placement services quicker and cheaper using AI
The Missing Knowledge Layer in AI: A Framework for Stable Human-AI Reasoning
arXiv:2604.14881v1 Announce Type: cross Abstract: Large language models are increasingly integrated into decision-making in areas such as healthcare, law, finance, engineering, and government. Yet they share a critical limitation: they produce fluent outputs even when their internal reasoning has drifted. A confident answer can conceal uncertainty, speculation, or inconsistency, and small changes in phrasing can lead to different conclusions. This makes LLMs useful assistants but unreliable partners in high-stakes contexts. Humans exhibit a similar weakness, often mistaking fluency for reliability. When a model responds smoothly, users tend to trust it, even when both model and user are drifting together. This paper is the first in a five-paper research series on stabilising human-AI reasoning. The series proposes a two-layer approach: Parts II-IV introduce human-side mechanisms such as uncertainty cues, conflict surfacing, and auditable reasoning traces, while Part V develops a model-side Epistemic Control Loop (ECL) that detects instability and modulates generation accordingly. Together, these layers form a missing operational substrate for governance by increasing signal-to-noise at the point of use. Stabilising interaction makes uncertainty and drift visible before enforcement is applied, enabling more precise capability governance. This aligns with emerging compliance expectations, including the EU AI Act and ISO/IEC 42001, by making reasoning processes traceable under real conditions of use. The central claim is that fluency is not reliability. Without structures that stabilise both human and model reasoning, AI cannot be trusted or governed where it matters most.
AI can power small teams to boost output, write @saraheneedleman in @BusinessInsider.
AI can power small teams to boost output, write @saraheneedleman in @BusinessInsider. https://africa.businessinsider.com/careers/snaps-layoffs-highlight-growing-work-trend-ai-powered-tiny-teams/xw8stkt Here's my quote: "The winners won't simply be the leanest organizations," [Brynjolfsson] said. "They'll be the ones that best redesign work so humans and AI complement each other."
Marsh Aims to Be 'AI Winner' by Focusing on Gains in Growth, Productivity, Efficiency
By L.S. Howard | April 17, 2026 ... Marsh aims to be an “AI winner” through growth, productivity and efficiency gains, according to John Doyle, president and CEO of the broker and risk adviser.
How CIOs can tackle AI ownership | CIO Dive
As tech execs gain more control over AI, they must become enablers for its adoption, taking a leadership role around governance and ROI.
Nearly 75pc of AI’s economic value captured by just 20pc of companies
PwC has published a report exploring how only a fraction of organisations are fully capturing the economic value of artificial intelligence.
Deloitte to trim benefits for ‘center’ workforce amid AI shift
This category includes internal-facing roles such as administrative staff, IT support, and finance teams
When Creating an AI Strategy, Don't Overlook Employee Perception
This article discusses how AI strategy is influenced by whether employees view the technology as a tool for augmentation or a threat to their jobs.
Task maxing or task masking? | The AI productivity paradox
While 80% of leaders say AI is already driving productivity gains, most are still measuring that productivity by hours and tasks.
Reveal Survey Identifies the Most In-Demand Technology Jobs and Skills
AI, Cybersecurity, and Data Analysts Are Most In-Demand Jobs as Talent Shortages Impact Technology Leaders...
AI News Digest, April 16: AI Agents Are Now Running Payroll. The Governance Gap Is Real.
AI payroll agents are now live across 40+ countries. Apr 16: enterprise agent sprawl, the HR readiness gap, and EU AI Act's political fight ahead.
Dublin’s Audrey AI closes $1.8m pre-seed funding round
The investment will fund growth across auditing and engineering, with expansion expected across Ireland and the UK. Read more: Dublin’s Audrey AI closes $1.8m pre-seed funding round
Harvey’s 30-year-old CEO says failing is a ‘good way to learn’ and destroying his ego led to an $11 billion success
Winston Weinberg says he learns from both his wins and losses to stay on top in a hypercompetitive AI startup market.
The $10 Billion Startup Training AI to Replace the White-Collar Workforce
Mercor is promising to replicate most professional work. It was also co-founded by twentysomethings who previously never held a real job.
How Guidesly Built AI-Generated Trip Reports for Outdoor Guides on AWS
Guidesly's Jack AI leverages AWS services like Amazon Bedrock and SageMaker to automate the creation of marketing-ready trip reports and social content for outdoor guides.
A Comparative Study of Dynamic Programming and Reinforcement Learning in Finite Horizon Dynamic Pricing
arXiv:2604.14059v1 Announce Type: new Abstract: This paper provides a systematic comparison between Fitted Dynamic Programming (DP), where demand is estimated from data, and Reinforcement Learning (RL) methods in finite-horizon dynamic pricing problems. We analyze their performance across environments of increasing structural complexity, ranging from a single typology benchmark to multi-typology settings with heterogeneous demand and inter-temporal revenue constraints. Unlike simplified comparisons that restrict DP to low-dimensional settings, we apply dynamic programming in richer, multi-dimensional environments with multiple product types and constraints. We evaluate revenue performance, stability, constraint satisfaction behavior, and computational scaling, highlighting the trade-offs between explicit expectation-based optimization and trajectory-based learning.
When Reasoning Models Hurt Behavioral Simulation: A Solver-Sampler Mismatch in Multi-Agent LLM Negotiation
arXiv:2604.11840v1 Announce Type: cross Abstract: Large language models are increasingly used as agents in social, economic, and policy simulations. A common assumption is that stronger reasoning should improve simulation fidelity. We argue that this assumption can fail when the objective is not to solve a strategic problem, but to sample plausible boundedly rational behavior. In such settings, reasoning-enhanced models can become better solvers and worse simulators: they can over-optimize for strategically dominant actions, collapse compromise-oriented terminal behavior, and sometimes exhibit a diversity-without-fidelity pattern in which local variation survives without outcome-level fidelity. We study this solver-sampler mismatch in three multi-agent negotiation environments adapted from earlier simulation work: an ambiguous fragmented-authority trading-limits scenario, an ambiguous unified-opposition trading-limits scenario, and a new-domain grid-curtailment case in emergency electricity management. We compare three reflection conditions, no reflection, bounded reflection, and native reasoning, across two primary model families and then extend the same protocol to direct OpenAI runs with GPT-4.1 and GPT-5.2. Across all three experiments, bounded reflection produces substantially more diverse and compromise-oriented trajectories than either no reflection or native reasoning. In the direct OpenAI extension, GPT-5.2 native ends in authority decisions in 45 of 45 runs across the three experiments, while GPT-5.2 bounded recovers compromise outcomes in every environment. The contribution is not a claim that reasoning is generally harmful. It is a methodological warning: model capability and simulation fidelity are different objectives, and behavioral simulation should qualify models as samplers, not only as solvers.
Thoma Bravo Signs Multiyear Deal With Google for AI Adoption
Software investor Thoma Bravo struck a strategic partnership with Alphabet Inc.’s Google Cloud to help the private equity firm’s portfolio companies accelerate their adoption of artificial intelligence.
The CEO chatbot era is coming
Many executives will be following Meta’s plans for an AI version of Mark Zuckerberg with interest
Memory as Metabolism: A Design for Companion Knowledge Systems
arXiv:2604.12034v1 Announce Type: new Abstract: Retrieval-Augmented Generation remains the dominant pattern for giving LLMs persistent memory, but a visible cluster of personal wiki-style memory architectures emerged in April 2026 -- design proposals from Karpathy, MemPalace, and LLM Wiki v2 that compile knowledge into an interlinked artifact for long-term use by a single user. They sit alongside production memory systems that the major labs have shipped for over a year, and an active academic lineage including MemGPT, Generative Agents, Mem0, Zep, A-Mem, MemMachine, SleepGate, and Second Me. Within a 2026 landscape of emerging governance frameworks for agent context and memory -- including Context Cartography and MemOS -- this paper proposes a companion-specific governance profile: a set of normative obligations, a time-structured procedural rule, and testable conformance invariants for the specific failure mode of entrenchment under user-coupled drift in single-user knowledge wikis built on the LLM wiki pattern. The design principle is that personal LLM memory is a companion system: its job is to mirror the user on operational dimensions (working vocabulary, load-bearing structure, continuity of context) and compensate on epistemic failure modes (entrenchment, suppression of contradicting evidence, Kuhnian ossification). Five operations implement this split -- TRIAGE, DECAY, CONTEXTUALIZE, CONSOLIDATE, AUDIT -- supported by memory gravity and minority-hypothesis retention. The sharpest prediction: accumulated contradictory evidence should have a structural path to updating a centrality-protected dominant interpretation through multi-cycle buffer pressure accumulation, a failure mode no existing benchmark captures. The safety story at the single-agent level is partial, and the paper is explicit about what it does and does not solve.
Building trust in the AI era with privacy-led UX
The practice of privacy-led user experience (UX) is a design philosophy that treats transparency around data collection and usage as an integral part of the customer relationship. An undertapped opportunity in digital marketing, privacy-led UX treats user consent not as a tick-box compliance exercise, but rather as the first overture in an ongoing customer relationship.…
Narrative-Driven Paper-to-Slide Generation via ArcDeck
arXiv:2604.11969v1 Announce Type: new Abstract: We introduce ArcDeck, a multi-agent framework that formulates paper-to-slide generation as a structured narrative reconstruction task. Unlike existing methods that directly summarize raw text into slides, ArcDeck explicitly models the source paper's logical flow. It first parses the input to construct a discourse tree and establish a global commitment document, ensuring the high-level intent is preserved. These structural priors then guide an iterative multi-agent refinement process, where specialized agents iteratively critique and revise the presentation outline before rendering the final visual layouts and designs. To evaluate our approach, we also introduce ArcBench, a newly curated benchmark of academic paper-slide pairs. Experimental results demonstrate that explicit discourse modeling, combined with role-specific agent coordination, significantly improves the narrative flow and logical coherence of the generated presentations.
Narrative over Numbers: The Identifiable Victim Effect and its Amplification Under Alignment and Reasoning in Large Language Models
arXiv:2604.12076v1 Announce Type: cross Abstract: The Identifiable Victim Effect (IVE) $-$ the tendency to allocate greater resources to a specific, narratively described victim than to a statistically characterized group facing equivalent hardship $-$ is one of the most robust findings in moral psychology and behavioural economics. As large language models (LLMs) assume consequential roles in humanitarian triage, automated grant evaluation, and content moderation, a critical question arises: do these systems inherit the affective irrationalities present in human moral reasoning? We present the first systematic, large-scale empirical investigation of the IVE in LLMs, comprising N=51,955 validated API trials across 16 frontier models spanning nine organizational lineages (Google, Anthropic, OpenAI, Meta, DeepSeek, xAI, Alibaba, IBM, and Moonshot). Using a suite of ten experiments $-$ porting and extending canonical paradigms from Small et al. (2007) and Kogut and Ritov (2005) $-$ we find that the IVE is prevalent but strongly modulated by alignment training. Instruction-tuned models exhibit extreme IVE (Cohen's d up to 1.56), while reasoning-specialized models invert the effect (down to d=-0.85). The pooled effect (d=0.223, p=2e-6) is approximately twice the single-victim human meta-analytic baseline (d$\approx$0.10) reported by Lee and Feeley (2016) $-$ and likely exceeds the overall human pooled effect by a larger margin, given that the group-victim human effect is near zero. Standard Chain-of-Thought (CoT) prompting $-$ contrary to its role as a deliberative corrective $-$ nearly triples the IVE effect size (from d=0.15 to d=0.41), while only utilitarian CoT reliably eliminates it. We further document psychophysical numbing, perfect quantity neglect, and marginal in-group/out-group cultural bias, with implications for AI deployment in humanitarian and ethical decision-making contexts.
Identifying and Mitigating Gender Cues in Academic Recommendation Letters: An Interpretability Case Study
arXiv:2604.12337v1 Announce Type: cross Abstract: Letters of recommendation (LoRs) can carry patterns of implicitly gendered language that can inadvertently influence downstream decisions, e.g. in hiring and admissions. In this work, we investigate the extent to which Transformer-based encoder models as well as Large Language Models (LLMs) can infer the gender of applicants in academic LoRs submitted to an U.S. medical-residency program after explicit identifiers like names and pronouns are de-gendered. While using three models (DistilBERT, RoBERTa, and Llama 2) to classify the gender of anonymized and de-gendered LoRs, significant gender leakage was observed as evident from up to 68% classification accuracy. Text interpretation methods, like TF-IDF and SHAP, demonstrate that certain linguistic patterns are strong proxies for gender, e.g. "emotional'' and "humanitarian'' are commonly associated with LoRs from female applicants. As an experiment in creating truly gender-neutral LoRs, these implicit gender cues were remove resulting in a drop of up to 5.5% accuracy and 2.7% macro $F_1$ score on re-training the classifiers. However, applicant gender prediction still remains better than chance. In this case study, our findings highlight that 1) LoRs contain gender-identifying cues that are hard to remove and may activate bias in decision-making and 2) while our technical framework may be a concrete step toward fairer academic and professional evaluations, future work is needed to interrogate the role that gender plays in LoR review. Taken together, our findings motivate upstream auditing of evaluative text in real-world academic letters of recommendation as a necessary complement to model-level fairness interventions.
A16z’s Ben Horowitz sees ‘AI anxiety’ consuming Silicon Valley founders. Workers’ fear of something else is killing adoption
The a16z cofounder said the “laws of physics” have changed for founders—and “AI anxiety” is real. Workers are experiencing something much darker.
Do data and AI talent needs conflict with a workforce seeking stability?
A new report shows that many organisations plan to increase the size of their data teams – however, many professionals say they are unlikely to change employers in 2026. Read more: Do data and AI talent needs conflict with a workforce seeking stability?
HubSpot Unveils AI Visibility Tool to Revolutionize Marketing Engagement
HubSpot introduces a pioneering AI visibility tool within its Marketing Hub to track brand presence in AI-driven content and optimize engagement.
The org chart isn’t ready: How AI exposed the hidden crisis inside the American corporation
A landmark KPMG index finds boards demanding transformation, execs spending billions on tech, and the people caught in the middle burning out.
Exclusive: Handshake, Mercor Revenue Surges
Handshake and Mercor revenue surges on demand for human contractors to train AI, as reported by The Information.
A Methodological Guide on Using Large Language Models for Text Annotation in the Social Sciences and Humanities with Python and R
arXiv:2604.09638v1 Announce Type: new Abstract: Large language models (LLMs) have become an essential tool for social science and humanities (SSH) researchers who work with text. One particularly valuable application is automating text annotation, a traditionally time-consuming step in preparing data for empirical analysis. Yet many SSH researchers face two challenges: getting started with LLMs and understanding how to address their limitations. Practically, the rapid pace of model development can make LLMs seem inaccessible or intimidating, while even experienced users may overlook how annotation errors can bias downstream statistical analyses (e.g., regression estimates and $p$-values), even when annotation accuracy appears high. This paper provides a comprehensive, step-by-step methodological guide for using LLMs for text annotation in SSH research, with clear Python and R code snippets. We cover (1) how LLMs work and what they can and cannot do; (2) how to identify an LLM-suitable research project and establish minimum data and computational requirements; (3) how to design prompts and run annotation tasks; (4) how to evaluate annotation quality and iteratively refine prompts without overfitting; (5) how to integrate LLM annotations into downstream statistical analyses while accounting for annotation error; and (6) how to manage cost, efficiency, and reproducibility when scaling up annotation. Throughout, we provide intuitive methodological reasoning, concrete examples, code snippets, and best-practice guidance to help researchers confidently and transparently incorporate LLM-based annotation into their scientific workflows.
Help Without Being Asked: A Deployed Proactive Agent System for On-Call Support with Continuous Self-Improvement
arXiv:2604.09579v1 Announce Type: new Abstract: In large-scale cloud service platforms, thousands of customer tickets are generated daily and are typically handled through on-call dialogues. This high volume of on-call interactions imposes a substantial workload on human support analysts. Recent studies have explored reactive agents that leverage large language models as a first line of support to interact with customers directly and resolve issues. However, when issues remain unresolved and are escalated to human support, these agents are typically disengaged. As a result, they cannot assist with follow-up inquiries, track resolution progress, or learn from the cases they fail to address. In this paper, we introduce Vigil, a novel proactive agent system designed to operate throughout the entire on-call life-cycle. Unlike reactive agents, Vigil focuses on providing assistance during the phase in which human support is already involved. It integrates into the dialogue between the customer and the analyst, proactively offering assistance without explicit user invocation. Moreover, Vigil incorporates a continuous self-improvement mechanism that extracts knowledge from human-resolved cases to autonomously update its capabilities. Vigil has been deployed on Volcano Engine, ByteDance's cloud platform, for over ten months, and comprehensive evaluations based on this deployment demonstrate its effectiveness and practicality. The open source version of this work is publicly available at https://github.com/volcengine/veaiops.
What happened when AI ran into the cold hard reality of the legal profession
Hallucinations don't fly in a court of law.
CEOs are betting AI will augment work rather than displace all workers
CEOs are betting AI will augment work rather than displace all workers Key Points - The debate over the future of work even extends inside the corridors of a major AI provider. - Speaking Monday at the Semafor World Economy conference in Washington, D.C., Anthropic co-founder Jack Clark dismissed Anthropic CEO Dario Amodei’s argument that AI could drive the unemployment rate as high as 20% in the next five years. Jack Clark, co-founder of Anthropic, at the Semafor World Economy Summit during the International Monetary Fund (IMF) and World Bank Spring meetings in Washington, DC, US, on Monday, April 13, 2026. Samuel Corum | Bloomberg | Getty Images The effect artificial intelligence will have on the labor market has left workers and job seekers alike worried about their future. Top executives, however, are optimistic that the technology can continue to augment workloads rather than e
New Research Finds Workers Are Leveraging AI for Career Mobility as Employers Struggle to Keep Pace
New Research Finds Workers Are Leveraging AI for Career Mobility as Employers Struggle to Keep Pace Accessibility Statement Skip Navigation New University of Phoenix Career Institute® Career Optimism Index® study points to an emerging shift in workforce power dynamics: while employees "job hug" in a stabilizing but constrained labor market, many are quietly using AI to build skills, boost confidence, and explore career mobility – creating new talent retention risks for employers. PHOENIX, April 14, 2026 /PRNewswire/ -- University of Phoenix Career Institute® today released its sixth annual Career Optimism Index® recurring national workforce research study of 5,000 U.S. working adults and 1,000 employers fielded January 21–February 6, 2026. The study found that while workers appear to be "job hugging" in a stabilizing labor market where mob
AI Is No Longer Optional at Work. For a while, AI felt like an advantage. | by Yevgeniy Maliyev | Apr, 2026 | Medium
AI Is No Longer Optional at Work. For a while, AI felt like an advantage. | by Yevgeniy Maliyev | Apr, 2026 | Medium Sign up Get app Sign up Press enter or click to view image in full size # AI Is No Longer Optional at Work 7 min read Just now -- Share For a while, AI felt like an advantage. Something extra. Something useful. Something curious people experimented with before everyone else caught up. If you used it well, you moved faster. You wrote quicker. You summarized better. You researched more efficiently. You automated things other people still did manually. That was the first phase. In that phase, AI looked like a skill. Now it’s starting to look like something else. A requirement. And that changes everything. Because the moment a tool stops being optional, it stops feeling like a personal advantage and starts becoming part of the new baseline. That is where man
The Hourglass Revolution: A Theoretical Framework of AI's Impact on Organizational Structures in Developed and Emerging Markets
arXiv:2604.09623v1 Announce Type: cross Abstract: This paper presents a theoretical framework examining how artificial intelligence (AI) transforms organizational structures, introducing an "hourglass" configuration that emerges as AI assumes traditional middle management functions. The analysis identifies three key mechanisms algorithmic coordination, structural fluidity, and hybrid agency that demonstrate how AI enables organizational forms transcending traditional structural boundaries. These mechanisms illustrate how AI enables new modes of organizing to go beyond existing structural boundaries. Drawing on institutional theory and digital transformation research, we examine how these mechanisms operate differently in developed and emerging markets, producing distinct patterns of structural transformation. Our framework offers three important theoretical contributions: (1) conceptualizing algorithmic coordination as a unique form of organizational integration, (2) explaining how structural fluidity allows organizations to achieve stability and adaptability at the same time, and (3) the theoretical argument that hybrid agency surpasses traditional, human centric forms of organizational capabilities. Our analysis shows that while the move to AI enabled strategies overall seems quite global, successful application will need to pay sufficient attention to the technological capabilities, cultural dimensions, and contexts of the market.
LLM Nepotism in Organizational Governance
arXiv:2604.09620v1 Announce Type: new Abstract: Large language models are increasingly used to support organizational decisions from hiring to governance, raising fairness concerns in AI-assisted evaluation. Prior work has focused mainly on demographic bias and broader preference effects, rather than on whether evaluators reward expressed trust in AI itself. We study this phenomenon as LLM Nepotism, an attitude-driven bias channel in which favorable signals toward AI are rewarded even when they are not relevant to role-related merit. We introduce a two-phase simulation pipeline that first isolates AI-trust preference in qualification-matched resume screening and then examines its downstream effects in board-level decision making. Across several popular LLMs, we find that resume screeners tend to favor candidates with positive or non-critical attitudes toward AI, discriminating skeptical, human-centered counterparts. These biases suggest a loophole: LLM-based hiring can produce more homogeneous AI-trusting organizations, whose decision-makers exhibit greater scrutiny failure and delegation to AI agents, approving flawed proposals more readily while favoring AI-delegation initiatives. To mitigate this behavior, we additionally study prompt-based mitigation and propose Merit-Attitude Factorization, which separates non-merit AI attitude from merit-based evaluation and attenuates this bias across experiments.
Germany’s Nesto secures €11 million to scale its AI workforce management platform for hospitality
Nesto, a Karlsruhe-based startup building an AI-driven platform for workforce management in the hospitality industry, has secured €11 million in growth equity funding from Expedition Growth Capital. With this capital, the company plans to support continued product investment and accelerate its expansion across Europe. Dr Theodor Ackbarow, co-founder and Executive Chairperson of Nesto, “We are […]
Germany’s Zell raises €500k to automate sales management workflows with AI
Berlin-based Zell, an AI startup automating sales management workflows, has raised €500k in order to accelerate commercial growth, expand the team with new hires across tech and sales, further develop its AI engine, and drive expansion across Europe beyond the DACH and Italian markets. The round was co-led by P3 Ventures, SkyDeck Europe, UC Berkeley […]
How AI is rewriting the ERP investment playbook | TechRadar
AI breaks the link between effort and outcomes. If automation removes 40% of manual effort but the contract remains anchored on ‘hours billed’, the economic benefit is absorbed by the supplier rather than the client. Procurement must pivot toward outcome-based models where partners are rewarded for business ...
PwC Study Finds 20% of Companies Capture 74% of AI’s Economic Value - NervNow™
PwC Study Finds 20% of Companies Capture 74% of AI’s Economic Value - NervNow™ ## Share your love A global survey of 1,217 executives by PwC shows AI leaders use the technology to pursue growth and reinvent business models, not just cut costs. A small group of companies is capturing the bulk of AI’s financial returns while most businesses remain stuck in pilot mode, according to the 2026 AI Performance Study by PwC, released Monday. The study, based on interviews with 1,217 senior executives at large, publicly listed companies across 25 sectors and multiple regions, found that 74% of AI’s economic value is being captured by just 20% of organizations. ALSO READ: Harvard Meets Bairong: Chinese AI Firm Bets Big on Silicon-Based Employees The divide comes down to intent. Top-performing com
Anthropic Expands Into Enterprise Software, Launches AI Tools for Security and Automation
Anthropic is expanding its focus from AI research to enterprise software and security, launching tools for legal, financial, HR, and sales automation.
Focus on AI for growth, not just productivity: IBM CHRO
Focus on AI for growth, not just productivity: IBM CHRO - About HR Executive - Advertise with us - Awards - Privacy Policy Search type here... SEARCH # IBM CHRO: Focus on AI productivity at your own risk Emerging HR Tech Future-ready workforce HR Technology Date: April 13, 2026 Share post: Nickle LaMoreaux, IBM The word “productivity” has likely surfaced in more workforce strategy meetings than ever before during recent months, as business leaders press for transformation to maximize efficien
Baird turns cautious on engineering firms ahead of earnings, downgrades Parsons (PSN:NYSE) | Seeking Alpha
Baird warns macro volatility may hit E&C Q1 earnings; favors AI/infrastructure contractors and flags PSN risks.
Scaffolding Human-AI Collaboration: A Field Experiment on Behavioral Protocols and Cognitive Reframing
arXiv:2604.08678v1 Announce Type: new Abstract: Organizations have widely deployed generative AI tools, yet productivity gains remain uneven, suggesting that how people use AI matters as much as whether they have access. We conducted a field experiment with 388 employees at a Fortune 500 retailer to test two scaffolding interventions for human-AI collaboration. All participants had access to the same AI tool; we varied only the structure surrounding its use. A behavioral scaffolding intervention (a structured protocol requiring joint AI use within pairs) was associated with lower document quality relative to unstructured use and substantially lower document production. A cognitive scaffolding intervention (partnership training that reframed AI as a thought partner) was associated with higher individual document quality at the top of the distribution. Treatment participants also showed greater positive belief change across the session, though sensitivity analyses suggest this likely reflects recovery from carry-over effects rather than genuine training-induced shifts. Both findings are subject to design limitations including an AM/PM session confound, differential attrition, and LLM grading sensitivity to document length.
AI went viral among attorneys. We have the numbers on what happened next
Not viral as in cat videos. Viral as in we need a vaccine Opinion For a sector at the heart of US economic growth, AI claims and counter-claims remain curiously hard to reconcile. Models are improving at the speed of light, AI firms claim, yet the message from the codeface remains that benefits are still more than balanced by the downsides.…
FutureFit AI Announces Strategic Investment to Help Governments and Industries Navigate AI's Impact on People & Jobs
FutureFit AI Announces Strategic Investment to Help Governments and Industries Navigate AI's Impact on People & Jobs Accessibility Statement Skip Navigation NEW YORK, April 13, 2026 /PRNewswire/ -- FutureFit AI, a global leader in AI-powered workforce development technology, today announced an investment from Achieve Partners, led by investor and author Ryan Craig, to accelerate its mission of helping more people navigate to better jobs faster and cheaper at scale. This investment marks an important step forward in expanding FutureFit AI's ability to scale talent marketplaces across new regions, close critical talent gaps in growth sectors, and expand access to good jobs in partnership with governments and industry organizations. Continue Reading FutureFit AI logo "As a society, we're continuing to underestimate what is needed to help pe
This is the hidden reason your AI investments are failing, according to Brené Brown | Fortune
This is the hidden reason your AI investments are failing, according to Brené Brown | Fortune Newsletters Fortune Workplace Innovation # This is the hidden reason your AI investments are failing, according to Brené Brown By Kristin Stoller Editorial Director, Fortune Live Media By Kristin Stoller Editorial Director, Fortune Live Media April 13, 2026, 8:04 AM ET Add us on Brené Brown and BetterUp CEO Alexi Robichaux explain why leaders who build trust and coach employees see dramatically better returns on their AI investments.Courtesy of BetterUp Good morning! --- About the Author By Kristin Stoller Editorial Director, Fortune Live Media Kristin Stoller is an editorial director at Fortune focused on expanding Fortune's C-suite
HR investment in AI is huge, but many don't see big results
HR investment in AI is huge, but many don't see big results - About HR Executive - Advertise with us - Awards - Privacy Policy Search type here... SEARCH # HR investment in AI is booming, but most companies aren’t seeing meaningful results AI and machine learning Emerging HR Tech Guest viewpoints Date: April 13, 2026 Share post: Credit: Adobe Stacey Cadigan is a partner at global AI-centered technology research and advisory firm ISG, where she helps clients meet
Designing the agentic AI enterprise for measurable performance
Presented by Edgeverve Smart, semi‑autonomous AI agents handling complex, real‑time business work is a compelling vision. But moving from impressive pilots to production‑grade impact requires more than clever prompts or proof‑of‑concept demos. It takes clear goals, data‑driven workflows, and an enterprise platform that balances autonomy, governance, observability, and flexibility with hard guardrails from day one. From pilots to the “operational grey zones” The next wave of value sits in the connective tissue between applications — those operational grey zones where handoffs, reconciliations, approvals, and data lookups still rely on humans. Assigning agents to these paths means collapsing system boundaries, applying intelligence to context, and re‑imagining processes that were never formally automated. Many pilots stall because they start as lab experiments rather than outcome‑anchored designs tied to production systems, controls, and KPIs. Start with outcomes, not algorithms. Translate organizational KPIs (cash‑flow, DSO, SLA adherence, compliance hit rates, MTTR, NPS, claims leakage, etc.) into agent goals, then cascade them into single‑agent and multi‑agent objectives. Only after goals are explicit should you select workflows and decompose tasks. Pick targets, then decompose the work What does “target” actually mean? In agentic programs, a target is a business outcome and the use case that moves it. For example, “reduce unapplied cash by 20%” target outcome; “cash application and exceptions handling” use case. With the use case in hand, perform persona‑level task decomposition: map the human role (e.g., cash applications analyst, facilities coordinator), enumerate their tasks, and identify which are ripe for agentification (data retrieval, matching, policy checks, decision proposals, transaction initiation). Delivering on those tasks requires a data‑embedded workflow fabric that can read, write, and reason across enterprise systems while honoring permissions. Data must be AI‑ready, discoverable, governed, labeled where needed, augmented for retrieval (RAG), and policy‑protected for PII, PCI, and regulatory constraints. Integration goes beyond APIs APIs are one mode of integration, not the only one. Robust agent execution typically blends: Stable APIs with lifecycle management for core systems Event‑driven triggers (streams, webhooks, CDC) to react in real time UI/RPA fallbacks where APIs don’t exist Search/RAG connectors for documents and knowledge bases Policy management across tools and actions to enforce entitlements and segregation of duties The north star is integration reliability — built on idempotency, retries, circuit-breakers, and standardized tool schemas — so agents don’t “hallucinate” actions the enterprise can’t verify. A quick example: finance and facilities, in production Inside our organization, we deployed specialized agents in a live CFO environment and in building maintenance. In finance, seven agents interacted with production systems and real accountability structures. Year‑one outcomes included: >3% monthly cash‑flow improvement, 50% productivity gain in affected workflows, 90% faster onboarding, a shift from account‑level handling to function‑level orchestration, and a $32M cash‑flow lift. These results don’t guarantee gains everywhere; they show that designing products can deliver measurable outcomes on a scale. The four design pillars: Autonomy, governance, observability & evals, flexibility 1) Autonomy: right‑size it to the risk Autonomy exists on a spectrum. Early efforts often automate well‑bounded tasks; others pursue research/analysis agents; increasingly, teams target mission‑critical transactional agents (payments, vendor onboarding, pricing changes). The rule: match autonomy to risk, and encode the operating mode suggest‑only, propose‑and‑approve, or execute‑with‑rollback per task. 2) Governance: guardrails by design, not as bolt‑ons Unbounded agents create unacceptable risk. Build guardrails into the plan: Policy & permissions: tie tools/actions to identity, scopes, and SoD rules. Human‑in‑the‑loop (HITL): where mission‑critical thresholds are crossed (amount, vendor risk, regulatory exposure). Agent lifecycle management: versioning, change control, regression gates, approval workflows, and sunsetting. Third‑party agent orchestration: vet external agents like vendors, capabilities, scopes, logs, SLAs. Incident and rollback: kill‑switches, safe‑mode, and compensating transactions. This is how you scale innovation safely while protecting brand, compliance, and customers. 3) Observability & evaluations: trust comes from telemetry Production agents need the same rigor as any core platform: Telemetry: capture full execution traces across perception, planning, tool use, action supported by structured logs and replay. Offline evals: cenario tests, red‑teaming, bias and safety checks, cost/performance benchmarks; baseline vs. challenger comparisons. Online evals: shadow mode, A/B, canary releases, guardrail breach alerts, human feedback loops. Explainability & auditability: why was an action taken, which data/tools were used, and who approved. 4) Flexibility: assume volatility, design for swap‑ability Models, tools, and vendors change fast. Treat agentic capability as platform currency: create an environment where teams can evaluate, select, and swap models/tools without tearing down the build. Use a model router, tool registry, and contract‑first interfaces so upgrades are controlled experiments, not rewrites. The agent platform fabric: how platformization turns goals into outcomes A true agentic enterprise requires a platform fabric that transforms goals into outcomes, not a patchwork of isolated pilots. This platform anchors enterprise‑to‑agent KPI cascades, drives task decomposition and multi‑agent planning, and provides governed tooling and data access across APIs, RPA, search, and databases. It centralizes knowledge and memory through RAG and vector stores, enforces enterprise controls via a policy engine, and manages performance and safety through a unified model layer. It supports robust orchestration of first‑ and third‑party agents with common context, embeds deep observability and evaluation pipelines, and applies disciplined release engineering from sandbox to GA. Finally, it ensures long‑term resilience through lifecycle management versioning, deprecation, incident playbooks, and auditable histories. Guardrails in action: a BFSI example Consider payments exception handling in banking — high stakes, regulated, and customer‑visible. An agent proposes a resolution (e.g., auto‑reconcile or escalate) only when: The transaction falls below risk thresholds; above them, it triggers HITL approval. All policy checks (KYC/AML, velocity, sanctions) pass. Observability hooks record rationale, tools invoked, and data used. Rollback/compensation is defined if downstream failures occur. This pattern generalizes to vendor onboarding, pricing overrides, or claims adjudication — mission‑critical work with explicit safety rails. Scale beyond pilots Scaling agentic AI beyond pilots demands disciplined readiness across nine fronts: leaders must clarify which KPIs matter and how agent goals ladder into them, determine which persona tasks are agentified versus remain human‑led, and align each with the right autonomy mode from suggest‑only to propose‑and‑approve to execute‑with‑rollback. They must embed governance guardrails, including HITL points and lifecycle controls; ensure robust observability and evaluation via telemetry, replay, audits, and offline/online tests; and verify data readiness, with governed, policy‑protected, retrieval‑augmented data flows. Integration must be reliable, with API lifecycle management, event triggers, and RPA/other fallbacks. The underlying platform should enable model swap‑ability and orchestration of first‑ and third‑party agents without rebuilding. Finally, measurement must focus on true operational impact cash flow, cycle times, quality, and risk reduction rather than task counts. The takeaway Agentic AI is not a shortcut; it’s a new system of work. Enterprises that approach it with platform discipline aligning autonomy with risk, embedding governance and observability, and designing for swap‑ability will convert pilots into production impact. Those that don’t keep accumulating impressive but disconnected demos. The difference isn’t how fast you ship an agent; it’s how deliberately you design the enterprise around it. N. Shashidar is SVP & Global Head, Product Management at EdgeVerve. Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.
AI's Next Operating Model
Bain's report argues that long-running agents with persistence and operational memory could shift AI from a transactional tool into an ongoing layer of organizational capability.
Salesforce and ServiceNow Compete in AI-Powered Helpdesk
How Salesforce and ServiceNow are squaring off in the battle for the helpdesk. Benioff banks on user engagement while McDermott wants to govern AI agents
Staffing firms using AI see double revenue growth, report says - Outsource Accelerator
AI-driven recruitment is rapidly reshaping the global staffing industry, with firms using AI twice as likely to report revenue growth last year.
ServiceNow incorporates native AI in all its products - Asia Tech Journal
ServiceNow incorporates native AI in all its products - Asia Tech Journal ServiceNow has announced that its entire product portfolio will feature built-in artificial intelligence capabilities. All ServiceNow solutions now include native AI, data connectivity, workflow execution, security and governance natively. This shift enables organizations to accelerate their AI goals and helps ensure they get the most value from the technology by bringing together the essential components needed for enterprise-scale deployment: a conversational gateway (EmployeeWorks), connected data for cross-business context (Workflow Data Fabric), visibility and governance (AI Control Tower), and autonomous workflows that can move from assisting people to acting on their behalf. The company is also introducing Context Engine, an enterprise context solution that connects relationships, protocols, and resolution
Executive survey on AI, risk and growth in 2026
PwC’s April 2026 C-Suite Outlook explores how executives are navigating policy shifts and geopolitical uncertainty—and why competitive advantage is harder to achieve.
Gaps are emerging between AI’s promise and delivery: Is there an opportunity in this for India’s IT firms? | Mint
The AI revolution has entered a turbulent phase, where promise runs ahead of delivery. Productivity gains are real but messy. As sundry businesses struggle to make the most of AI, Indian IT service companies could make themselves useful.
40% of AI productivity gains lost to rework for errors - CIO
40% of AI productivity gains lost to rework for errors | CIO # 40% of AI productivity gains lost to rework for errors News Analysis Apr 13, 20265 mins ## AI tools are helping organizations get work done faster, but there’s a good chance that your most engaged and talented employees are the ones quietly tasked with the burden of AI-cleanup. Credit: Rob Schultz / Shutterstock While the promise of en
LinkedIn Enters AI Training Market, Challenges Startups - Business Insider
LinkedIn is launching an AI labor marketplace, offering up to $150 an hour for AI training, challenging startups like Mercor and Surge AI.
Bain & Co vulnerability exposed by hacker a month after McKinsey
CodeWall says it gained access to consultant’s Pyxis platform using a username and password from public web code
Stop Building AI Tools Nobody Uses: The ADOPT Framework for AI ...
Stop Building AI Tools Nobody Uses: The ADOPT Framework for AI Adoption - Momentum Nexus Blog Here’s a stat that should reframe how you think about AI investment: 88% of AI agents fail to reach production. Not because the technology breaks. Because nobody uses them. That number comes from recent enterprise adoption data, and it matches a pattern I’ve seen repeatedly across B2B SaaS companies at Momentum Nexus. A founder invests weeks building or buying an AI tool. The demo is impressive. The pilot looks promising. Then three months later, the team has quietly reverted to their old spreadsheets and manual processes. The AI tool sits there, fully functional, completely unused. This is the AI adoption gap, and it’s the most expensive problem in SaaS right now. BCG’s research quantified this precisely: more than 85% of employees remain at stages two and three of AI maturity (using AI as
PwC: Why Most AI Value is Going to Just 20% of Companies - EME Outlook Magazine
PwC: Why Most AI Value is Going to Just 20% of Companies - EME Outlook Magazine New PwC 2026 AI Performance Study finds leading businesses are using AI for growth rather than just productivity. Contents - PwC’s 2026 AI Performance Study - AI leaders are prioritising growth over efficiency - Workflow redesign is key to stronger AI performance - Automation is increasing across leading businesses - Governance and trust underpin AI success - Gap between AI leaders and laggards may continue to widen ## PwC’s 2026 AI Performance Study A small group of businesses is capturing the majority of artificial intelligence’s economic benefits, according to new research from PwC, highlighting a widening gap between AI leaders and the rest of the market. PwC’s 2026 AI Performance Study found that 74% of AI’s economic value is being captured by just 20% of organisations, suggesting that while many b
Gaps are emerging between AI’s promise and delivery: Is there an opportunity in this for India’s IT firms? | Mint
Gaps are emerging between AI’s promise and delivery: Is there an opportunity in this for India’s IT firms? | Mint # Gaps are emerging between AI’s promise and delivery: Is there an opportunity in this for India’s IT firms? 4 min read13 Apr 2026, 04:01 PM IST Adopting AI thoughtfully requires investment not just in tools but in training, governance and organizational design. (HT) Summary The AI revolution has entered a turbulent phase, where promise runs ahead of delivery. Productivity gains are real but messy. As sundry businesses struggle to make the most of AI, Indian IT service companies could make themselves useful. Gift this articleGet updates on This is a Mint Premium article gifted to you.Subscribe to enjoy similar stories. Subscribe now Technological revolutions have a familiar rhythm. First comes excitement, then investment, and then an awkward phase of reality refusing
Introducing RobustiPy: An efficient next generation multiversal library with model selection, averaging, resampling, and explainable artificial intelligence
arXiv:2506.19958v3 Announce Type: replace-cross Abstract: Scientific inference is often undermined by the vast but rarely explored "multiverse" of defensible modelling choices, which can generate results as variable as the phenomena under study. We introduce RobustiPy, an open-source Python library that systematizes multiverse analysis and model-uncertainty quantification at scale. RobustiPy unifies bootstrap-based inference, combinatorial specification search, model selection and averaging, joint-inference routines, and explainable AI methods within a modular, reproducible framework. Beyond exhaustive specification curves, it supports rigorous out-of-sample validation and quantifies the marginal contribution of each covariate. We demonstrate its utility across five simulation designs and ten empirical case studies spanning economics, sociology, psychology, and medicine, including a re-analysis of widely cited findings with documented discrepancies. Benchmarking on ~672 million simulated regressions shows that RobustiPy delivers state-of-the-art computational efficiency while expanding transparency in empirical research. By standardizing and accelerating robustness analysis, RobustiPy transforms how researchers interrogate sensitivity across the analytical multiverse, offering a practical foundation for more reproducible and interpretable computational science.
Driving AI Value Across the Enterprise - Paid Program - WSJ
Paid Program: Driving AI Value Across the Enterprise # Driving AI Value Across the Enterprise AI creates value when intelligent systems and human judgment are engineered to work as one integrated operating model. Quick Summary read more - Scaling AI across an enterprise presents significant complexities beyond initial small-scale pilots, often hindered by technical debt and legacy organizational structures. - Companies are shifting toward agentic workflows that focus on redesigning how people and products interact to drive measurable enterprise growth or cost reduction. - Publicis Sapient provides deep expertise and a suite of enterprise AI products and services designed to modernize legacy technology systems, accelerate the software development lifecycle process, build and orchestrate agents, and automate IT operations for meaningful enterprise-level value. An artificial-intellige
AI in Distributed Sales Channels
The article argues that AI companions, local-language voice and text interfaces, dynamic route planning, personalized recommendations, and digital sales agents can improve frontline selling in emerging markets.
Enoch O. - Ivy Flow AI and Automation | LinkedIn
Let’s be clear , AI won’t replace your business. But a competitor leveraging AI most certainly will. The truth?
AI Might Be Giving Lawyers Their Busiest Years
AI might be giving lawyers their busiest years right before making them obsolete
2026 AI Impact Survey Report | Grant Thornton
Most organizations are scaling AI they cannot explain, measure or defend.
Upskill us, don't spy on us: AI rollout fuels surveillance fears in India Inc - BusinessToday
AI adoption is accelerating across India Inc., but rising surveillance anxiety is creating a new workplace tension
How will AI change the org chart?
At the micro level, there is evidence that it pays to collapse the layers of command
Nicolas Payette - Specira AI | LinkedIn
We're obsessed with what Gen AI can generate. Much less attention goes to what we should actually tell it to generate in the first place. In every enterprise AI project I've seen go sideways
Clients’ barrage of AI-generated queries risks pushing up lawyers’ fees
Firms may raise prices for fixed-fee contracts if clients keep sending flurries of emails and letters
KPMG report finds enterprise disconnect between AI and its ROI | CIO
Analysts point to many reasons for the challenge in achieving ROI for AI, but much of the issue is that it is replacing services that have never been effectively measured.
Workday seeks to dismiss expanded allegations of discrimination over AI software
Workday is moving to dismiss new discrimination claims in a lawsuit regarding its AI job-screening software, arguing the plaintiffs have exceeded the scope permitted by the court.
The Future of AI for Sales is Diverse and Distributed
True creativity and innovation will come from human-agent collaboration, with one human and millions of agents.
AI Made Data Teams Faster. Now They Have to Be Better.
AI will happily give you something technically correct and visually noisy. It will not ask whether someone can process it in five seconds or whether it makes a point. Frequently, I find that AI generates a different type of chart than would be ideal for the purposes I want to convey, e.g.
Transforming the Voice of the Customer: Large Language Models for Identifying Customer Needs
arXiv:2503.01870v2 Announce Type: replace-cross Abstract: Identifying customer needs (CNs) is fundamental to product innovation and marketing strategy. Yet for over thirty years, Voice-of-the-Customer (VOC) applications have relied on professional analysts to manually interpret qualitative data and formulate "jobs to be done." This task is cognitively demanding, time-consuming, and difficult to scale. While current practice uses machine learning to screen content, the critical final step of precisely formulating CNs relies on expert human judgment. We conduct a series of studies with market research professionals to evaluate whether Large Language Models (LLMs) can automate CN abstraction. Across various product and service categories, we demonstrate that supervised fine-tuned (SFT) LLMs perform at least as well as professional analysts and substantially better than foundational LLMs. These results generalize to alternative foundational LLMs and require relatively "small" models. The abstracted CNs are well-formulated, sufficiently specific to guide innovation, and grounded in source content without hallucination. Our analysis suggests that SFT training enables LLMs to learn the underlying syntactic and semantic conventions of professional CN formulation rather than relying on memorized CNs. Automation of tedious tasks transforms the VOC approach by enabling the discovery of high-leverage insights at scale and by refocusing analysts on higher-value-added tasks.
Anthropic Unveils Claude Managed Agents for Streamlined AI Deployment
Anthropic has launched a public beta for Claude Managed Agents, designed to provide robust infrastructure for complex, multi-step enterprise AI tasks.
57% Gen Z want higher pay for key skills as AI reshapes workplaces - India Today
Mercer’s Global Talent Trends 2026 report shows that Gen Z workers in India are increasingly linking pay to skills in demand. At the same time, companies are rapidly redesigning workplaces around AI, as trust and fairness concerns grows among employees.
Managers and Executives Disagree on AI—and It’s Costing Companies
An HBR article highlighting how internal misalignment between executives and middle managers regarding AI incentives is slowing down organizational adoption and execution.
Governance & compliance block MSP AI adoption, report says
Governance & compliance block MSP AI adoption, report says ChannelLife UK - Industry insider news for technology resellers # Governance & compliance block MSP AI adoption, report says Fri, 10th Apr 2026 (Yesterday) By Catherine Knowles, News Editor AvePoint has published research with Omdia showing that governance and compliance are the main barriers to AI adoption among managed service providers. In the study, 51% of MSPs identified them as the biggest obstacle. The findings highlight a gap between investment in AI readiness services and the ability to deliver them. While 94% of MSPs said they are investing in automation for AI data readiness and compliance, only 43% said they had reached high maturity in delivering AI-ready data environments to customers. The research was based on a survey of 333 MSPs across North America, Europe, t
MSPs face AI adoption hurdle over governance rules
MSPs face AI adoption hurdle over governance rules SecurityBrief New Zealand - Technology news for CISOs & cybersecurity decision-makers # MSPs face AI adoption hurdle over governance rules Fri, 10th Apr 2026 (Yesterday) By Catherine Knowles, News Editor AvePoint has published research with Omdia on AI readiness among managed service providers, finding that governance and compliance are the main barriers to AI adoption for MSP customers. The study surveyed 333 MSPs across North America, EMEA and APAC, examining governance, compliance, automation maturity and scaling challenges. It points to a widening gap between demand for AI services and providers' ability to deliver them consistently. More than half of respondents, 51%, identified data governance and compliance challenges as the primary obstacle to AI adoption. That put them well
Meet ‘trendslop,’ the new, AI-fueled scourge of workplace consultants everywhere
Some economists have deemed consultants useless, but AI assistance could just be the old challenge in new clothing.
Over-reliance on AI is weakening workforce skills, warns recruiter
Over-reliance on AI is weakening workforce skills, warns recruiter X Try from €0.25 / day Employers need to ensure their teams use AI as a tool to speed up basic tasks, while not developing a reliance that dulls their ability to make decisions and solve problems. This is the view of Dr Ryne Sherman, chief science officer at Hogan Assessments, a specialist in talent acquisition and development. Dr Sherman cites the latest Microsoft Work Trend Index finding that more than 75% of knowledge workers are already using Artificial Intelligence (AI) at work. In this Q&A interview, Dr Sherman advises that, as AI tools become embedded in day-to-day work across Ireland, a new workplace trend is emerging: the rise of so-called “AI zombies”. He advises employers and employees alike to beware of the dangers of over-reliance on AI in their daily functions, notably cautioning against drifting into
Chapter 8 (Agentic AI Engineering Blog Series): Observability, Metrics & GTM | by Jyoti Dabass, Ph.D. | AI in Plain English | Apr, 2026 | Medium
Chapter 8 (Agentic AI Engineering Blog Series): Observability, Metrics & GTM | by Jyoti Dabass, Ph.D. | AI in Plain English | Apr, 2026 | Medium Sign up Get app Sign up ## AI in Plain English Simplifying AI and tech for everyone. Learn the basics and stay up-to-date on the latest trends. Member-only story # Chapter 8 (Agentic AI Engineering Blog Series): Observability, Metrics & GTM 15 min read 20 hours ago -- Share How to measure if your agent is actually working — and how to sell it You’ve spent seven chapters building agents that think, remember, use tools, and collaborate. But building is only half the job. The other half is knowing whether what you built actually works — and turning it into something people will pay for. That’s what this chapter is about. We’ll cover three things in order: observability (seeing inside your agent while it runs), metrics (numbers that tel
5년 간 AI 산업 채용 공고 112% 증가...잡코리아 분석 < AI·엔터프라이즈 < 기사본문 - 디지털투데이 (DigitalToday)
5년 간 AI 산업 채용 공고 112% 증가...잡코리아 분석 < AI·엔터프라이즈 < 기사본문 - 디지털투데이 (DigitalToday) ## 본문영역 이전 기사보기 다음 기사보기 5년 간 AI 산업 채용 공고 112% 증가...잡코리아 분석 바로가기 복사하기 본문 글씨 줄이기 본문 글씨 키우기 스크롤 이동 상태바 [사진: 잡코리아] [디지털투데이 황치규 기자]AI·데이터 기반 HR테크 플랫폼 잡코리아(운영 법인 웍스피어)가 10일 올해 1분기 AI 산업 채용 공고 현황을 분석해 공개했다. 잡코리아 공고 데이터에 따르면, 올해 1분기 ‘AI’ 키워드가 포함된 공고 수는 5년 전 대비 112% 증가한 것으로 나타났다. 특히 신입직 공고는 162% 늘어나며 경력직뿐 아니라 신입 채용도 AI 인재 수요가 확대되는 흐름이 확인됐다. 지역별로는 비수도권(232%) 증가율이 수도권(110%)을 크게 웃돌며, AI 채용 수요가 특정 지역을 넘어 전국적으로 확산되고 있는 것으로 분석됐다. 이 같은 흐름은 과거 개발자 채용이 활발했던 5년 전과 비교해 AI 직무 중심으로 산업 트렌드가 변화했기 때문이다. 당시에는 서비스 개발, 인프라 구축 등 전통적인 개발 직무 중심 채용이 주를 이뤘고, AI 인재 수요는 일부 연구개발 조직에 한정됐다. 김요섭 잡코리아 최고기술책임자(CTO)는 "AI는 이제 특정 산업이 아닌 전 산업 기본 역량으로 자리잡고, 채용 시장에도 그 변화가 데이터를 통해 명확하게 나타나고 있다"며 "잡코리아는 AI 기반 추천, 에이전트 등 통합 AI 생태계를 구축해 더 빠르고 정확한 일자리 연결을 만들기 위해 노력하고, AI 중심으로 채용 패러다임을 바꾸는 업계 내 선두주자 역할을 할 것"이라고 말했다. #### 키워드 저작권자 © 디지털투데이 (DigitalToda
Accenture Invests in Replit to Accelerate AI-Driven Software Development
Accenture Invests in Replit to Accelerate AI-Driven Software Development Oops, something went wrong # Accenture Invests in Replit to Accelerate AI-Driven Software Development Accenture plc (NYSE: ACN) is included among the 15 Best Cheap Dividend Stocks to Buy. Accenture Invests in Replit to Accelerate AI-Driven Software Development Carol Gauthier/Shutterstock.com On April 9, Accenture plc (NYSE:ACN) said it has invested in Replit through Accenture Ventures. The goal is to help enterprises speed up the creation of digital platforms using AI-driven software development. As part of the investment, the two companies are also entering into a strategic partnership. As companies push toward AI-led transformation, the way software is built is starting to change. Tradi
Investing in part of the workforce creates an AI skills gap, finds report
Forrester’s research has shown that a failure to commit to long-term, inclusive AI education can greatly impact an organisation. Read more: Investing in part of the workforce creates an AI skills gap, finds report
Cristian Cruz - Business Consultant Focused on AI, Sales ...
Congrats Eric Moore! I believe this will be one of the standard AI platforms for industries where Ethical Reasoning, Compliance, and Accountability are Paramount.
The AI job loss story is all about bundles
Evidence on white-collar work displacement is beginning to match the theory
MindsDB Anton
Business intelligence that doesn't just answer — it acts.
Listen up: AI’s communication blind spot
How we hear other humans is more crucial than ever
Patlytics Secures $40M to Revolutionize Patent Law with AI, Targets Global Expansion
Patlytics has secured $40 million in Series B funding to enhance its AI platform for patent lifecycle management, targeting Am Law 100 firms.
Patlytics Secures $40M Funding
Patlytics has secured $40 million in Series B funding, led by SignalFire, to enhance its AI platform for patent lifecycle management.
Grounding Your LLM
Grounding Your LLM: A Practical Guide to RAG for Enterprise Knowledge Bases.
Paylocity Acquires Grayscale Labs
Paylocity acquires Grayscale Labs to enhance AI-driven recruiting, aiming for faster candidate engagement in high-volume hiring.
BDI-Kit Demo: A Toolkit for Programmable and Conversational Data Harmonization
arXiv:2604.06405v1 Announce Type: new Abstract: Data harmonization remains a major bottleneck for integrative analysis due to heterogeneity in schemas, value representations, and domain-specific conventions. BDI-Kit provides an extensible toolkit for schema and value matching. It exposes two complementary interfaces tailored to different user needs: a Python API enabling developers to construct harmonization pipelines programmatically, and an AI-assisted chat interface allowing domain experts to harmonize data through natural language dialogue. This demonstration showcases how users interact with BDI-Kit to iteratively explore, validate, and refine schema and value matches through a combination of automated matching, AI-assisted reasoning, and user-driven refinement. We present two scenarios: (i) using the Python API to programmatically compose primitives, examine intermediate outputs, and reuse transformations; and (ii) conversing with the AI assistant in natural language to access BDI-Kit's capabilities and iteratively refine outputs based on the assistant's suggestions.
AI focus shifts from potential to payback | CIO
Today's AI initiatives are evolving from pilots to being embedded into daily operations. They must deliver real value back to the business in measurable outcomes, cost savings, or productivity gains.
AI in the Boardroom: Which Industries Are Leaning In, Which Are Holding Back - CEOWORLD magazine
AI In The Boardroom: From Curiosity To Corporate Power The most sophisticated boards in the world are no longer asking whether artificial intelligence belongs in the boardroom; they are quietly testing how far it can go. Directors are experimenting with tools that summarise dense board packs ...
Back to the Beginnings of AI at Work | Digital Data Design Institute at Harvard
What a Landmark AI Study Tells Us About When to Trust, and When Not to Trust, AI Listen to this article: In September 2023, a working paper out of Harvard
With ROSS, Westlaw hearing on the horizon, fair use ruling draws attention
ROSS Intelligence is feeling buoyed by a new precedential holding that online publication of standards incorporated in the International Building Code is sufficiently transformative to excuse admitted copying.
Cutting employee compensation to pay for for AI unsustainable strategy | Employee Benefit News
Cutting employee compensation to pay for for AI unsustainable strategy | Employee Benefit News # Cutting employee compensation to invest in AI could backfire Published April 09, 2026, 11:00 a.m. EDT | Updated April 09, 2026, 11:00 a.m. EDT 3 Min Read Adobe Stock - Key Insight: Learn why prioritizing AI spend over employee pay may backfire strategically. - What's at Stake: Cost-cutting for AI could erode engagement, productivity, and long-term innovation capacity. - Supporting Data: Survey: 54% plan compensation cuts; 92% prioritize AI over employee satisfaction.Source: Bullets generated by AI with editorial review Organizations are in such a rush to win the AI race that they're willing to sacrifice employees' paychecks to do it — but that business-minded thinking is flawed. Processing C
AI job layoff career damage explained: Lost your job to AI? Experts warn the career damage could be permanent and hard to reverse - The Economic Times
AI job layoff career damage explained: Lost your job to AI? Experts warn the career damage could be permanent and hard to reverse - The Economic Times FEATURED FUNDS★★★★★Motilal Oswal Midcap Fund Direct-Growth5Y Return23.4 % Invest Now Business News› News› International› US News›Lost your job to AI? Experts warn the career damage could be permanent and hard to reverse ##### The Economic Times daily newspaper is available online now. # Lost your job to AI? Experts warn the career damage could be permane
The Secret to Mastering AI Is Getting the Division of Labor Right
Step one: Don’t start by outsourcing your thinking.
AI-as-a-service vs lab-as-a-service: What’s the difference and how they are changing AI adoption - The Economic Times
Businesses are now deciding whether to buy AI tools or build custom systems. AI-as-a-service offers quick solutions for simple tasks. Lab-as-a-service provides embedded teams for complex, tailored AI. This shift is crucial for high-trust, domain-specific AI applications.
Microsoft and Publicis Groupe expand AI marketing partnership, deploy Copilot tools to 114,000 Publicis employees
Microsoft and Publicis Groupe expand AI marketing partnership, deploy Copilot tools to 114,000 Publicis employees Back to: All AI News # Microsoft and Publicis Groupe expand AI marketing partnership, deploy Copilot tools to 114,000 Publicis employees Microsoft and Publicis Groupe expanded their partnership April 8 to build an AI-powered marketing platform combining Azure, Copilot tools, and Epsilon's identity data. Publicis is also deploying Microsoft 365 Copilot across its 114,000+ employees. Categorized in: AI News Marketing Published on: Apr 09, 2026 Become an AI Expert today ## Microsoft and Publicis Groupe Expand Partnership to Build AI-Powered Marketing Platform Microsoft and Publicis Groupe announced an expansio
An AI Agent Is Saving a Leader at IBM Consulting Hours Every Week - Business Insider
An AI Agent Is Saving a Leader at IBM Consulting Hours Every Week - Business Insider You're currently following this author! Want to unfollow? Unsubscribe via the link in your email. IBM's global consulting business has nearly 150,000 employees. Artur Widak/NurPhoto via Getty Images 2026-04-09T18:06:55.713Z Share Copy link Email Facebook WhatsApp X LinkedIn Bluesky Threads Save Saved - IBM's Dave McCann is using an AI agent to prepare for client meetings. - He said "Di
White-collar workers are quietly rebelling against AI as 80% outright refuse adoption mandates
White-collar workers are quietly rebelling against AI as 80% outright refuse adoption mandates Something went wrong # White-collar workers are quietly rebelling against AI as 80% outright refuse adoption mandates AI adoption is in its quiet quitting era. · Fortune · Getty Images There was a moment, not long ago, when “ shadow AI” felt like a good-news story. Workers were sneaking ChatGPT and Claude past the IT department, using personal accounts to do what used to take hours in minutes. An MIT study published last year found that employees at more than 90% of companies were using personal chatbot accounts for daily tasks — often without approval — even as only 40% of those same companies had official LLM subscriptions. The shadow economy was booming. Management called it a governance problem. The
White-collar industries bet on a secret weapon against AI: trust
Executives across finance and cyber defence map where Anthropic’s Claude ‘plug-ins’ will — and will not — change work
White-collar workers are quietly rebelling against AI
Reports indicate that a significant portion of white-collar employees are resisting the adoption of AI tools in the workplace.
Only 14% of firms have clear AI strategy, study finds
Only 14% of firms have clear AI strategy, study finds IT Brief New Zealand - Technology news for CIOs & IT decision-makers # Risk & Compliance # Only 14% of firms have clear AI strategy, study finds Thu, 9th Apr 2026 (Yesterday) By Shannon Williams, News Editor Altimetrik and HFS Research have published research showing that only 14% of Global 2000 companies have a documented AI strategy with clea
How to Write User Stories Faster with AI? - by Dejan Majkic
Turn seven hours of tedious ticket-writing into minutes with AI , and get back to the strategic product work that actually matters.
Accenture acquires Spain’s Keepler to deepen AI and data capabilities in Spain and EMEA
Accenture has acquired Keepler Data Tech, a Spanish cloud-native AI and data startup, in order to further strengthen and expand Accenture’s ability to scale AI for clients in Spain and beyond. Today’s acquisition is part of Accenture’s ongoing investments in AI to accelerate clients’ reinvention. Keepler is the latest in a series of strategic acquisitions […] The post Accenture acquires Spain’s Keepler to deepen AI and data capabilities in Spain and EMEA appeared first on EU-Startups.
Survey: AI Adoption Outpaces Integration in Sales Orgs - Radio Ink
A new LeadG2 survey of 154 revenue professionals finds AI is nearly universal but deep integration, consistent messaging, and frontline adoption remain out of reach.
The $0 GTM Stack: How First-Time Founders Are Using AI to Do What Agencies Charge $80k For
Founders who are pulling real results from AI are using it upstream — at the positioning layer, before a single piece of content gets written. They’re using it to think, pressure-test, and sharpen. The content follows naturally.
Sprinklr Launches AI Update
Sprinklr has launched its Spring '26 Release (26.4), a significant AI-powered update to its Unified-CXM platform, focusing on autonomous AI agents and scalable enterprise AI.
Lessie AI
Search, Reach and Connect - Find the perfect fit, 10x faster.
AI Knowledge Management Tools Surge in Enterprise Adoption Amid 2026 Digital Transformation Wave
Data privacy remains paramount, with 2026 seeing heightened FTC enforcement on AI training datasets. Tools must comply with NIST frameworks, or face fines akin to recent OpenAI probes. Hallucination risks—where AI generates false info—persist, though retrieval-augmented generation (RAG) in top tools mitigates this to under 5% error rates, per benchmarks. Integration friction with legacy systems like Oracle or SAP slows rollout for 40% of enterprises...
How AI is forcing a rethink of services, skills and pricing | Accounting Today
The accounting community is focused on how AI is changing what accountants do. But the truth is that AI is changing what accounting professionals are for.
Sprinklr Launches Major AI Update, Enhancing Unified-CXM with Autonomous Agents and Scalable Enterprise AI
Sprinklr has launched its Spring ’26 Release, a significant AI-powered update to its Unified-CXM platform, focusing on autonomous AI agents and scalable enterprise AI.
Context Collapse: Barriers to Adoption for Generative AI in Workplace Settings
arXiv:2604.05151v1 Announce Type: new Abstract: As generative AI technologies are pressed into service in workplace settings, current approaches to account for the contexts in which such technologies are used fall short of users' expectations and needs. This paper empirically demonstrates, through expert interviews, both how these tools fail to account for users' context and how users deploy concrete strategies address such failures. The paper analyzes how context is variously conceptualized by tool developers, users, and social scientists to identify specific pitfalls inherent in computational approaches to context. Multiple distinct contexts tend to collapse into one another or rot, degrading over time, reducing the utility of any efforts to account for context. The paper concludes with a provocation to shift from an indiscriminate collection of context-relevant data toward a more interactional set of practices to embed GenAI systems more appropriately into users' contexts of use.
AI-Augmented Peer Review and Scientific Productivity: A Cross-Country Panel and SEM Analysis
arXiv:2604.05463v1 Announce Type: new Abstract: This study empirically investigates the impact of AI-augmented peer review systems on scientific productivity using panel data from OECD countries. While prior research has highlighted inefficiencies in traditional peer review, little empirical work has quantified the systemic impact of AI integration at the national level. We construct a novel AI Review Capability Index (AIRC) and examine its effects on research productivity, reproducibility, and innovation output. Using fixed-effects regression and structural equation modeling (SEM), we show that AI-assisted evaluation significantly enhances productivity and reduces variance in research quality. Results indicate that a one standard deviation increase in AIRC is associated with an 18-25% increase in scientific productivity, mediated through improvements in review efficiency and reproducibility. This paper provides the first cross-country empirical validation of AI-augmented scientific evaluation systems and contributes to the emerging literature on AI as a structural driver of knowledge production.
PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing
arXiv:2604.05018v1 Announce Type: new Abstract: Synthesizing unstructured research materials into manuscripts is an essential yet under-explored challenge in AI-driven scientific discovery. Existing autonomous writers are rigidly coupled to specific experimental pipelines, and produce superficial literature reviews. We introduce PaperOrchestra, a multi-agent framework for automated AI research paper writing. It flexibly transforms unconstrained pre-writing materials into submission-ready LaTeX manuscripts, including comprehensive literature synthesis and generated visuals, such as plots and conceptual diagrams. To evaluate performance, we present PaperWritingBench, the first standardized benchmark of reverse-engineered raw materials from 200 top-tier AI conference papers, alongside a comprehensive suite of automated evaluators. In side-by-side human evaluations, PaperOrchestra significantly outperforms autonomous baselines, achieving an absolute win rate margin of 50%-68% in literature review quality, and 14%-38% in overall manuscript quality.
ActivTrak Launches AI Insights to Give Leaders a Clear View of How AI Changes Work
ActivTrak Launches AI Insights to Give Leaders a Clear View of How AI Changes Work Accessibility Statement Skip Navigation New capabilities connect AI usage to productivity, capacity and business performance — helping organizations understand what's actually working AUSTIN, Texas, April 8, 2026 /PRNewswire/ -- ActivTrak today announced AI Insights, a new set of work intelligence capabilities that gives organizations a clear, data-driven view of how AI is used across the business — and whether it's driving meaningful results. As companies rapidly deploy AI tools, most still rely on fragmented, t
LLM-referred traffic converts at 30-40% — and most enterprises aren't optimizing for it
For more than two decades, digital discovery has operated on a simple model: search, scan, click, decide. That worked when humans were the ones doing the web searching; but with the advent of AI agents, the primary consumer of information is no longer always human. This is giving rise to a new paradigm: Answer engine optimization (AEO), also referred to as generative engine optimization (GEO). Because agents look at data much differently than humans do, success is no longer defined by rankings and clicks, but whether content is understood, selected, and cited by AI systems. The SEO model that the web was built on simply isn’t going to cut it anymore, and enterprises need to prepare now. How LLMs interpret web content Traditional SEO is built around keywords, rankings, page-level optimization, and click-through rates. Users manually search across multiple sources and click around to get what they need. Simple, but sometimes frustrating and a definite time suck. But AEO operates on a whole different level. Agents are increasingly taking over users’ workflows: Claude Code, OpenClaw, CrewAI, Microsoft Copilot, AutoGen, LangChain, Agent Bricks, Agentforce, Google Vertex, Perplexity’s web interface, and whatever else comes along. These agents do not “browse” the web the way humans do. They analyze user intent based not just on phrasing, but persistent memory and context from past sessions (rather than simple autocomplete). They require materials that are concise, structured, and to the point. What’s more, agents are moving beyond browsing to delegation, handling more downstream work. What started as “search, read, decide,” evolves to “agent retrieves, agent summarizes, human decides” (and, beyond that, “agent acts → human validates”). “In practice, AEO begins where SEO stops,” said Dustin Engel, founder of consultancy company Elegant Disruption. “AEO is the next layer of discovery,” or “zero-click discovery.” In this new world where agents synthesize answers, users may never even see an enterprise’s website, click-through rates decline, and attribution and citability (rather than pure visibility, or showing up at the top of a list of blue links) become critical. “The new default is closer to a citation map: Where the model is pulling from, how often you show up, and how you are described,” Engel said. Some, like Adam Yang of Q&A platform Quora, argue that AEO is already becoming the default over SEO. This is for “a certain class of queries,” Yang notes. Any question where the user wants a synthesized answer — "what's the best approach to X," "compare these two options," "what do I need to know about Y" — is increasingly resolved by an AI without a click. Google's own AI Overviews are already accelerating this on the consumer side, many analysts note. “SEO isn't dead,” Yang said. “But the optimization target has shifted from ‘rank on page 1’ to ‘get cited in the answer.’” How devs are already using AI agents Are there scenarios where regular search/Googling is still the best option? “Absolutely,” said analyst Wyatt Mayham of Northwest AI Consulting. Notably, for personal tasks like finding nearby restaurants or local service providers. The interface is “just better” in those cases because it integrates maps, reviews, and photos. “That experience is hard to beat right now,” he said. For work-related research, though, he says he’s “barely” using traditional search anymore, and it’s getting “closer to zero” every month. “When I need to understand a company or a person professionally, agents do it faster and give me a more useful output than a page of blue links ever did,” he said. His firm uses autonomous agents “heavily,” and built a Claude Skills function that powers its sales operation. Before a discovery call with a prospect, team members can trigger a skill that pulls the contact’s LinkedIn profile, scrapes their company website, grabs relevant info from sources like ZoomInfo, and crafts a clear picture of their revenue, team size, tech stack, and pain points. “By the time I get on a call, I have a tailored research brief ready to go without spending 30 to 45 minutes manually Googling around,” Mayham said. The big advantage is that these tools run in the background, he noted. You don’t have to sit clicking through browser tabs: You just tell the agent what you need, it does it, and you get a structured output that’s actually useful. “It's collapsed what used to be a full hour of sales prep into a few minutes,” Mayham said. Carlos Dutra, CEO and founder of IT consultancy Vindler Solutions, said Claude Code has “genuinely changed” his daily workflow. He uses it for most of his coding work, and what surprised him wasn't the speed, but the fact that he didn’t need to open and keep track of browser tabs. “Not because I'm lazy, but because the answers are better,” he said. He still uses Google for some tasks: Pricing pages, recent news, anything that needs to be current. “But for technical reasoning? Agents have mostly replaced search for me personally,” he said. Quora’s Yang has had a similar experience. He’s been using Claude Code daily for the past few months, primarily for content strategy, knowledge management, and competitive research. Workflows that used to take him half a day now take 30 minutes. But what’s been most advantageous is that he can now run research and synthesis tasks in parallel that he previously had to do sequentially. Also helpful is that agents’ context retention across sessions is “meaningfully better” than web-based tools. When he needs to understand a concept, map a competitive landscape, or synthesize industry trends, Claude or Perplexity are the go-to before opening a browser tab. “I've started treating agent search as my first stop, not Google. Traditional search is now where I verify, not where I discover.” The kinks are real, though. Mayham pointed out that LinkedIn, in particular, is “aggressive” about blocking automated access, and many other sites have (or are implementing) similar protections. Users will hit walls when agents can't get through, so a fallback plan is important for those relying on agents. “The reliability isn't 100% yet, and that's probably the biggest thing holding broader adoption back,” he said. Mayham’s advice for other devs: Stop chasing shiny objects. A new AI tool launches “practically every day,” and many (experienced devs included) are jumping from platform to platform without ever going deep with any of them. “Pick a model, go deep, build real workflows on it,” he emphasized. “You'll get more value from mastery of one platform than surface-level experimentation across five.” How enterprises can compete in an AEO-driven world When AI agents do the searching, the rules change. The question is no longer whether your content ranks on the first page, it's whether the model selects you as the source when generating an answer. Structure matters much more than it used to. Content should: Be organized around conversational intent, provide direct answers, and mirror real user questions and follow-ups; Be authoritative and reflect strong expertise; Be fresh (and, when necessary, regularly refreshed); Have clear headers and established FAQ schema. Another must is maintaining a strong brand presence across the forums and platforms — Wikipedia, Reddit, LinkedIn, industry publications — that models are trained on. Enterprises might also consider investing in original data, like research. In Mayham’s experience, when a business gets recommended by an LLM during a search-style query, the conversion rate is “dramatically higher” than traditional channels. For his company, LLM-referred traffic is converting at 30 to 40%, which “blows away what we see from SEO or paid social.” “The intent signal is just different when someone is having a conversation with an AI and it recommends you by name.” Discoverability inside LLMs will matter as much as Google rankings, “maybe more,” Mayham said. “It's a whole new surface for customer acquisition that most businesses aren't even thinking about yet.” Vindler's Dutra agreed that the “uncomfortable truth” is that most enterprise content is becoming “basically invisible” in agent-driven queries. “AEO is about whether your content survives being chunked, embedded, and semantically retrieved,” he said. The companies getting ahead aren’t doing anything “exotic,” he noted. They have clean, declarative content that doesn’t require context to understand. Those still writing copy stuffed with keywords are going to fall behind because LLMs care about semantic clarity. A quick test he gives clients: Ask an LLM a question your page is supposed to answer, without giving it the URL. “If it can't construct the answer from your content, you have a problem.” Jeff Oxford of SEO agency Visibility Labs offers valuable step-by-step advice: Engage in conversations on Reddit, which is one of the most-cited domains in AI search. Enterprises should establish a positive reputation on Reddit, and engage on any relevant threads where customers are asking for recommendations. Build a strong YouTube presence. According to Ahrefs, which tracks internet behavior, YouTube mentions have the “strongest correlation” with AI visibility across ChatGPT, AI Mode, and AI Overviews. “This makes sense, since both Google and OpenAI have trained their models on YouTube transcripts,” Oxford said, “and YouTube is the most-cited domain in Google's AI products.” Invest in digital PR and brand mentions; the latter is the second-highest correlated factor with AI visibility. “Brands need to improve their digital presence by being in as many places as possible,” Oxford said. Create content aligned with AI citation patterns. Enterprises should audit the prompts and topics where AI search engines are surfacing competitors, then create authoritative content on those same topics. “The goal is to become a source that AI models consider worth citing,” he noted. Still, there may be a lot of unnecessary hype around how drastically enterprises need to change, said Shashi Bellamkonda, principal research director at consultancy firm Info-Tech Research Group. Those following best practices of producing content that their audience actually needs, written by experts and showcasing expert opinion, are in a good position to be cited in AI-powered search. He pointed out that Google developed an EEAT framework (experience, expertise, authority, and trust) to evaluate content quality and helpfulness and help algorithms identify reliable, high-quality information. To stand out, enterprises should use structured data and schema to signal the context: Is this an article, a research study, a product overview? “Original long-form content will be valued by AI-powered answer engines,” Bellamkonda said. “Copycat strategies or trying to game the system are taboo in this era.” Experts should also share their thoughts across several channels, and "About Us" pages must be “robust” and include bios highlighting thought leaders’ expertise. “Ultimately, the reputation of AI-powered search is in making sure the user likes the search rather than what you think they should read,” Bellamkonda said. “So a good focus on the end user is a great way to succeed.”
AI Helps Scale Qualitative Customer Research
Generative AI can modernize qualitative customer research by making interviews and insight gathering faster, cheaper, and easier to scale than traditional methods. AI moderators can shorten the loop between customer signals and business decisions, capture richer voice-based feedback, and open a high-ROI use cases for enterprises.
Executives Blame Internal Chaos for Enterprise AI Gaps | PYMNTS.com
For large businesses, artificial intelligence promises to serve as a path to increased productivity and bigger profits. But C-suites are often pushing for
AI Success Depends on a Skills-First Workforce Strategy - HRMorning
AI Success Depends on a Skills-First Workforce Strategy: 4 Strategies Across industries, organizations are experimenting with artificial intelligence (AI). Pilot programs are underway. Proofs of concept are promising. Yet many companies remain stuck in experimentation mode, unable to transition from pilot to true production-grade AI applications and AI success. The barrier is rarely the technology itself. It’s skilled talent. ## Building the Workforce for AI Success Production-ready AI systems require skilled professionals who can build, deploy, integrate, and continuously improve them. And in today’s market, experienced AI developers and engineers are in short supply. They also often come at a premium cost. For HR leaders, this creates a pressing question: How do we build the talent we need without entering an unsustainable bidding war? The answer lies in a deliberate reskilling of
Colorado’s AI Law Is Here: What Employers Need to Know About the Colorado Artificial Intelligence Act (CAIA) | Adams & Reese - JDSupra
Colorado has enacted the nation's first comprehensive AI statute regulating "high‑risk" systems used in making employment decisions. Effective February 1, 2026, the law specifically targets...
ROI is about more than profitability when it comes to AI adoption – here’s what enterprises are looking for | IT Pro
A survey from KPMG suggests enterprises are measuring more than just financial returns
As Republicans embrace AI in campaigning, Democrats bet on a backlash | Semafor
As Republicans embrace AI in campaigning, Democrats bet on a backlash | Semafor Intelligence for the New World Economy --- --- # View / As Republicans embrace AI in campaigning, Democrats bet on a backlash Politics Reporter, Semafor Apr 8, 2026, 2:22pm EDT Share NRSC/YouTube Sign up for Semafor Washington, DC: What the White House is reading. Read it now. Email addressSign Up In this article: ### David’s view ### Room for Disagreement ### Notable ### David’s view Republicans are very comfortable using AI for almost anything in politics, and they’ll say so. Democrats are not very comfortable, and they won’t talk about it. We’re only starting to see how those different views might change campaigning. But Republicans are optimistic that their approach will win out. “Professionally we have an advantage, because we’r
Gain Consumer Insight With Generative AI
Stuart Kinlough/Ikon Images Marketing leaders often face a dilemma: Deriving the insights they need in order to make confident decisions can cost tens of thousands of dollars and involve several months of data gathering and analysis, by which time market conditions may have shifted. Can generative AI fundamentally reshape this calculus? Drawing on recent research, […]
Managers and Executives Disagree on AI—and It’s Costing Companies
Managers and Executives Disagree on AI—and It’s Costing Companies SKIP TO CONTENT # Managers and Executives Disagree on AI—and It’s Costing Companies by Jeremy Korst, Stefano Puntoni and Prasanna Tambe April 8, 2026 HBR Staff/Nongnuch Pitakkorn/Getty Images - Post - Post - Share - Save - Get PDF - Buy Copies - Print ## Summary. Leer en español [Ler em português](https://hbr.org/2026/04/ma
Forrester finds AI training gap stalls workplace gains
Forrester finds AI training gap stalls workplace gains IT Brief UK - Technology news for CIOs & IT decision-makers # Risk & Compliance # Forrester finds AI training gap stalls workplace gains Wed, 8th Apr 2026 (Today) By Sean Mitchell, Publisher Forrester has published research showing that most employees are not prepared to use workplace AI effectively. Only 16% achieved a high Artificial Intelligence Quotient in 2025. The findings highlight a gap between the spread of generative AI tools and the readiness of the people expected to use them. While 68% of organisations reported using generative AI in deployed production applications, the share of workers with a high AIQ rose only slightly, from 12% in 2024 to 16% in 2025. Forrester defines AIQ as a measure of employee readiness across four
The tech and IT skills gap continues to evolve. Employers, what’s your action plan?
The tech and IT skills gap continues to evolve. Employers, what’s your action plan? # The tech and IT skills gap continues to evolve. Employers, what’s your action plan? By Ryan M. Sutton, Executive Director, Technology, Robert Half Technology continues to accelerate at a rapid pace, and the skills required to support it are evolving just as quickly. While the technology skills gap has been a significant issue for many years, challenges are growing as organizations need to maintain proficiency in core, existing systems while also building new skills to support growing modernization efforts and priority projects, like security, AI and data. Recent Robert Half research shows just how concentrated these gaps have become. 72% of technology leaders report a skills gap within their department, and 77% say the im
Experts to advise on AI-automated contract templates sought by EU Commission
The European Commission is seeking an expert group to develop model contract terms and guidance for AI-driven automated contracts, focusing on legal validity and liability for SMEs.
Indian IT Companies Surge Ahead in AI Leadership as Automation Revolutionizes the Industry, ETEnterpriseai
India's top IT firms are reshaping their leadership to prioritize artificial intelligence, implementing new roles and units to accommodate growing automation demands. A report reveals significant changes across key players like TCS, Infosys, and Wipro, signaling a shift in focus towards AI ...
Why AI Implementation Fails Without Structured Visibility | Press Releases | norfolkdailynews.com
Why AI Implementation Fails Without Structured Visibility | Press Releases | norfolkdailynews.com You have permission to edit this article. --- --- Why AI Tools Fail Without the Right Business Structure Victoria, Canada - April 8, 2026 / Chief AI Advisors /"/> Why AI Tools Fail Without the Right Business Structure Victoria, Canada - April 8, 2026 / Chief AI Advisors / Businesses across industries are accelerating investments in artificial intelligence, adopting tools designed to automate workflows, improve efficiency, and enhance customer engagement. Yet despite this momentum, many organizations are seeing limited return on these efforts. The issue is not the technology itself. It is the lack of foundational structure required to support it. AI implementation is often approached as a tool decision rather than a systems decision. Businesses deploy automation platforms, integrat
Clarvos unveils AI-driven workflow platform to streamline marketing for smaller businesses
Marketing technology startup Clarvos LLC today announced an early access launch of its new agentic marketing workflow platform designed to simplify how growing small and midsized businesses plan, create and run marketing campaigns. The company says the new platform combines audience discovery, creative generation and campaign execution into a single system to help businesses maintain […] The post Clarvos unveils AI-driven workflow platform to streamline marketing for smaller businesses appeared first on SiliconANGLE.
Mahesh Babu Boddupalli - Hema AI Consulting | LinkedIn
Description: As the Talent Acquisition Lead for the Lueinhire project, I have been instrumental in developing and implementing an AI -driven recruitment platform designed to streamline and automate the hiring process.
AIMG Report Finds 87% of Enterprises Using AI, 19% Fully Data-Ready - The Herald News
AIMG Report Finds 87% of Enterprises Using AI, 19% Fully Data-Ready - The Herald News | This page contains press release content distributed by XPR Media. Members of the editorial and news staff of the USA TODAY Network were not involved in the creation of this content. | | --- | # AIMG Report Finds 87% of Enterprises Using AI, 19% Fully Data-Ready Distributed by EIN Presswire $560B Enterprise AI
Its an important time for the AI labs to build interfaces around the goal of "job augmentation through AI" rather than building "job replacement through AI." Chatbots were mostly augments, requiring a human to work. Agentic work patterns are still in flux & could center humans.
Its an important time for the AI labs to build interfaces around the goal of "job augmentation through AI" rather than building "job replacement through AI." Chatbots were mostly augments, requiring a human to work. Agentic work patterns are still in flux & could center humans.
Predflow AI
Your AI agent for ad performance.
Bounded by Risk, Not Capability: Quantifying AI Occupational Substitution Rates via a Tech-Risk Dual-Factor Model
arXiv:2604.04464v1 Announce Type: cross Abstract: The deployment of Large Language Models (LLMs) has ignited concerns about technological unemployment. Existing task-based evaluations predominantly measure theoretical "exposure" to AI capabilities, ignoring critical frictions of real-world commercial adoption: liability, compliance, and physical safety. We argue occupations are not eradicated instantaneously, but gradually encroached upon via atomic actions. We introduce a Tech-Risk Dual-Factor Model to re-evaluate this. By deconstructing 923 occupations into 2,087 Detailed Work Activities (DWAs), we utilize a multi-agent LLM ensemble to score both technical feasibility and business risk. Through variance-based Human-in-the-Loop (HITL) validation with an expert panel, we demonstrate a profound cognitive gap: isolated algorithmic probabilities fail to encapsulate the "institutional premium" imposed by experts bounded by professional liability. Applying a strictly algorithmic baseline via mathematical bottleneck aggregation, we calculate Relative Occupational Automation Indices ($OAI$) for the U.S. labor market. Our findings challenge the traditional Routine-Biased Technological Change (RBTC) hypothesis. Non-routine cognitive roles highly dependent on symbolic manipulation (e.g., Data Scientists) face unprecedented exposure ($OAI \approx 0.70$). Conversely, unstructured physical trades and high-stakes caretaking roles exhibit absolute resilience, quantifying a profound "Cognitive Risk Asymmetry." We hypothesize the emergent necessity of a "Compliance Premium," indicating wage resilience increasingly tied to risk-absorption capacity. We frame these findings as a cross-sectional diagnostic of systemic vulnerability, establishing a foundation for subsequent Computable General Equilibrium (CGE) econometric modeling involving dynamic wage elasticity and structural labor reallocation.
Anthropic's research shows that AI can already do a huge portion of many jobs; its top economist talks about how that could shape the future of work | Fortune
Peter McCrory talks about Anthropic's latest analysis of AI's role in occupations from computer programming to groundskeeping.
Is AI quietly killing the value of being pretty good at things?
An exploration of how AI automation might be devaluing mid-level human skills and expertise in various professional fields.
Closing the data security maturity gap: Embedding protection into enterprise workflows
Strategies for improving enterprise data security by integrating protective measures directly into existing business processes.
Democratizing Marketing Mix Models (MMM) with Open Source and Gen AI
A practical system design combining open-source Bayesian MMM and GenAI for transparent, vendor independent marketing analytics insights.
AI is transforming work—and talent strategy must keep up | Fortune
As AI absorbs routine tasks, ... trends, and driving change. In an augmented workforce, AI scales execution while humans focus on judgment, context, and leadership growth. This balance won’t emerge on its own. It must be designed. Today’s CHROs are not just stewards of policy and process. They are architects of how work is designed, how skills are developed, ...
From 4 Weeks to 45 Minutes: Designing a Document Extraction System for 4,700+ PDFs
How a hybrid PyMuPDF + GPT-4 Vision pipeline replaced £8,000 in manual engineering effort, and why the latest models weren't the answer.
Enabling agent-first process redesign
Unlike static, rules-based systems, AI agents can learn, adapt, and optimize processes dynamically. As they interact with data, systems, people, and other agents in real time, AI agents can execute entire workflows autonomously. But unlocking their potential requires redesigning processes around agents rather than bolting them onto fragmented legacy workflows using traditional optimization methods. Companies…
Auto-Research for Legal Agents
This article demonstrates how AI self-improving agent loops can significantly enhance legal agent performance, turning complex drafting workflows into highly automated tasks.
Natter lands $23M to encourage employees to open up about their organizations through AI video interviews
Conversation intelligence startup Natter said today it has raised $23 million in funding to replace the traditional surveys and focus groups used by organizations to obtain insights from their employees. The round was led by Renegade Partners and saw participation from Kindred Capital, Costanoa Ventures, Rackhouse Ventures, Village Global and Asymmetric Capital Partners, plus a […] The post Natter lands $23M to encourage employees to open up about their organizations through AI video interviews appeared first on SiliconANGLE.
Take-up of AI ‘is outpacing firms’ ability to manage change’ | Irish Independent
Take-up of AI ‘is outpacing firms’ ability to manage change’ | Irish Independent Search for articles The survey shows many CIOs lack strong confidence in their organisation's roadmap for handling AI Donal O'Donovan Yesterday at 06:30 The push for AI adoption in industry is outpacing the ability of businesses or their leaders to manage, a survey of senior tech executives indicates. The survey of more than 1,000 chief information officers (CIOs), including 100 in Ireland, found almost 90pc don’t believe their own firms have the internal technical capability to match AI roll-out ambitions. It also shows a large majority believe there is an AI bubble. The research for tech services provider Logicalis was undertaken by Vanson Bourne. The results show nine out of 10 global CIOs admit to “learning on the go” when it comes to AI, meaning they are essentially working with technology befor
The Agentic AI: How Autonomous AI Systems Are Rewriting the Rules of Work, Business, and Technology | by Nithin Narla | Apr, 2026 | Towards AI
The Agentic AI: How Autonomous AI Systems Are Rewriting the Rules of Work, Business, and Technology | by Nithin Narla | Apr, 2026 | Towards AI Sign up Get app Sign up ## Towards AI Follow publication We build Enterprise AI. We teach what we learn. Join 100K+ AI practitioners on Towards AI Academy. Free: 6-day Agentic AI Engineering Email Guide: https://email-course.towardsai.net/ Follow publication # The Agentic AI: How Autonomous AI Systems Are Rewriting the Rules of Work, Business, and Technology ## From chatbots to digital workers — inside the $10.9 billion revolution that’s turning AI from a tool you use into a colleague that works alongside you. 13 min read 21 hours ago -- 1 Listen Share Press enter or click to view image in full size Last week, we broke down the state of generative AI in 2026 — the models, the money, the infra
OpenAI Shares Superintelligence Vision for Workers, Calls for Four-Day Workweeks and Bonuses | Technology News
OpenAI Shares Superintelligence Vision for Workers, Calls for Four-Day Workweeks and Bonuses | Technology News English Edition More - Home - Ai - Ai News - OpenAI Shares Superintelligence Vision for Workers, Calls for Four Day Workweeks and Bonuses # OpenAI Shares Superintelligence Vision for Workers, Calls for Four-Day Workweeks and Bonuses OpenAI published its “Industrial Policy for the Intelligence Age” report, sharing its views on how AI should shape the world. Written by Akash Dutta, Edited by Ketan Pratap| Updated: 7 April 2026 13:43 IST Photo Credit: Unsplash/Levart_Photographer OpenAI also suggested adding higher taxes on capital gains and corporate income [Click Her
AI startups court law students in fight for lawyer market | Reuters
AI startups court law students in fight for lawyer market | Reuters Exclusive news, data and analytics for financial market professionalsLearn more aboutRefinitiv Figurines with computers and smartphones are seen in front of the words "Artificial Intelligence AI" in this illustration created on February 19, 2024. REUTERS/Dado Ruvic/Illustration//File Photo Purchase Licensing Rights, opens new tab April 7 (Reuters) - As artificial intelligence companies compete for U.S. legal industry customers, two leading legal AI startups are working to win over law students who will soon populate the country's law firms and corporate legal depart
AI Is Ready. Are Your Workers?
AI Is Ready. Are Your Workers? Leadership Reworked's leadership channel brings together the latest trends and best practices to guide the leaders of today and tomorrow. Hear from the leading voices in the space on organizational design, modern management, corporate culture and more. EditorialThe Biggest Threats to Your Company Aren't What You ThinkRead now EditorialSilent Struggles: How AI Is Fueling a Hidden Workforce CrisisRead now EditorialWhen the World Feels Heavy, Your Leadership Matters More Than EverRead now [The Antipatterns Blocking Your Future of Work Transformation](https://reworked.co/
No one is going to want to give up their Claude (or ChatGPT or Gemini). But they will also be extremely nervous about the implications of AI overall. To that extent the "pivot to enterprise" is going to result in a lot more anxiety than when the focus was on consumer assistants.
No one is going to want to give up their Claude (or ChatGPT or Gemini). But they will also be extremely nervous about the implications of AI overall. To that extent the "pivot to enterprise" is going to result in a lot more anxiety than when the focus was on consumer assistants.
Why Enterprise AI Adoption and ROI Matter | Vistage
It documents a cohort of leaders ... adopt AI, success depends on embedding ROI metrics, tightening guardrails, and funding internal capabilities where they matter most. The OECD’s cross-country portrait shows that adoption is concentrated in larger and data-mature firms and in sectors such as ICT and professional services, where users tend to be more productive. That nuance reconciles the headlines: value arrives first where workflows are digital, measurable, and well-informed ...
Many AI-First Companies Still Make Money the Old-Fashioned Way—Here's How
Many AI-First Companies Still Make Money the Old-Fashioned Way—Here's How # B2B AI & SaaS Executive Intelligence SubscribeSign in # Many AI-First Companies Still Make Money the Old-Fashioned Way. Here’s How. ### Don’t believe the myth about the death of SaaS. Instead, remember the 95 percent rule. Apr 07, 2026 11 Share A version of this article originally appeared in Inc. Magazine. Adil Husain is the Founder and Editor-in-Chief of The Intelligence Council. A brutal market reset in February colloquially dubbed “Black Tuesday for Software” wiped out nearly $1 trillion in market value. [Wall Street remains panicked](https://www.bloomberg.co
Beyond Predefined Schemas: TRACE-KG for Context-Enriched Knowledge Graphs from Complex Documents
arXiv:2604.03496v1 Announce Type: new Abstract: Knowledge graph construction typically relies either on predefined ontologies or on schema-free extraction. Ontology-driven pipelines enforce consistent typing but require costly schema design and maintenance, whereas schema-free methods often produce fragmented graphs with weak global organization, especially in long technical documents with dense, context-dependent information. We propose TRACE-KG (Text-dRiven schemA for Context-Enriched Knowledge Graphs), a multimodal framework that jointly constructs a context-enriched knowledge graph and an induced schema without assuming a predefined ontology. TRACE-KG captures conditional relations through structured qualifiers and organizes entities and relations using a data-driven schema that serves as a reusable semantic scaffold while preserving full traceability to the source evidence. Experiments show that TRACE-KG produces structurally coherent, traceable knowledge graphs and offers a practical alternative to both ontology-driven and schema-free construction pipelines.
Modus secures $85M to expand AI-powered audit and accounting partnerships
Artificial intelligence-native audit technology startup Modus Audit Inc. announced today that it has raised $85 million in new funding to accelerate product development and to support its strategy of investing in and partnering with growing audit-first accounting firms. Founded in 2025, Modus is aiming to empower the AI-native accounting firm by combining AI, deep regulatory […] The post Modus secures $85M to expand AI-powered audit and accounting partnerships appeared first on SiliconANGLE.