AI Daily Brief
Professional Services
Latest Intelligence
The latest AI stories, analysis and developments relevant to Professional Services — curated daily by Best Practice AI.
Use Casesfor Professional Services
200 articles
Digital Law & Regulation
Digital Law & Regulation.
We cannot have the AI model advising clients. They might think they don’t need us
A behind-the-scenes look at the work of Rutherford Hall, critical communications strategist
CASCADE: Case-Based Continual Adaptation for Large Language Models During Deployment
arXiv:2605.06702v1 Announce Type: new Abstract: Large language models (LLMs) have become a central foundation of modern artificial intelligence, yet their lifecycle remains constrained by a rigid separation between training and deployment, after which learning effectively ceases. This limitation contrasts with natural intelligence, which continually adapts through interaction with its environment. In this paper, we formalise deployment-time learning (DTL) as the third stage in the LLM lifecycle that enables LLM agents to improve from experience during deployment without modifying model parameters. We present CASCADE (CASe-based Continual Adaptation during DEployment), a general and principled framework that equips LLM agents with an explicit, evolving episodic memory. CASCADE formulates experience reuse as a contextual bandit problem, enabling principled exploration-exploitation trade-offs and establishing no-regret guarantees over long-term interactions. This design allows agents to accumulate, select, and refine task-relevant cases, transforming past experience into actionable knowledge. Across 16 diverse tasks spanning medical diagnosis, legal analysis, code generation, web search, tool use, and embodied interaction, CASCADE improves macro-averaged success rate by 20.9% over zero-shot prompting while consistently outperforming gradient-based and memory-based baselines. By reframing deployment as an adaptive learning process, this work establishes a foundation for continually improving AI systems.
Do you need a chief AI officer? Here's how the tech is changing boardrooms
AI may now be coming for the C-suite, according to a report published Monday by IBM, which found that most companies were now staffing chief AI officer roles.
When Does Critique Improve AI-Assisted Theoretical Physics? SCALAR: Structured Critic--Actor Loop for Agentic Reasoning
arXiv:2605.06772v1 Announce Type: new Abstract: As large language models (LLMs) show increasing promise on research-level physics reasoning tasks and agentic AI becomes more common, a practical question emerges: How does the interaction between researchers and agents affect the results? We study this using SCALAR (Structured Critic--Actor Loop for AI Reasoning), an Actor--Critic--Judge pipeline applied to quantum field theory and string theory problems. The Actor proposes solutions, the Critic provides iterative feedback, and an independent Judge evaluates the transcript against reference solutions. We vary the Actor persona, the Critic feedback strategy, and the Actor model family and scale. Multi-turn dialogue improves over single-shot attempts throughout, but both the mechanism of improvement and the value of different prompting choices depend strongly on the Actor--Critic pairing. Increasing the scale within one model family (e.g. from the 8B-parameter DeepSeek-R1 variant to DeepSeek-R1 70B) improves some easier-problem behavior, but does not remove the hardest bottleneck we observe. Critic feedback strategy matters most clearly in the asymmetric Actor--Critic setting (e.g., a lightweight Haiku Actor guided by a stronger Sonnet Critic), where constructive feedback improves mean-score outcomes. In same-family Actor--Critic settings, strategy effects are weaker: lenient feedback is sometimes favored, while strict and adversarial feedback are not beneficial. Taken together, SCALAR provides a controlled testbed for evaluating which interaction structures help or hinder AI-driven scientific discovery.
From hype to habit: how organisations can operationalise AI in HR - HR Leader
Most organisations have moved past asking whether AI matters. The conversation now is whether it’s genuinely becoming part of how work gets done.
Women at the sharp end as AI takes over administrative roles
Female-dominated clerical work is among the most vulnerable to automation, and labour market losses are already being felt
Will AI Democratize Skills? - Times Kuwait
By María Lombardi Special to The Times Kuwait Recent AI advances have created widespread expectations of substantial productivity gains. Early studies, such as one showing that AI increased the productivity of customer-support agents by 15% on average (with less-experienced workers getting ...
Workslop: Adoption Gap Costs Firms
Microsoft expert: stop workslop in Microsoft three sixty five Copilot with adoption fixes to restore value and safe AI
Accenture CEO Julie Sweet on Corporate America problem: All the data is showing companies are investing in AI, but … - The Times of India
Tech News News: Accenture CEO Julie Sweet has flagged a growing gap in Corporate America’s approach to artificial intelligence (AI). She noted that while companies ar.
All Those A.I. Note Takers? They’re Making Lawyers Very Nervous.
A trendy productivity hack, A.I. note takers are capturing every joke and offhand comment in many meetings. They could also potentially waive attorney-client privilege.
Eightfold AI Integrates with Oracle Cloud
Eightfold AI has integrated its AI Interviewer with Oracle Fusion Cloud Recruiting to facilitate automated, skills-based hiring.
Will AI level up skills — or widen inequality? | Arab News
Recent advances in artificial intelligence have created widespread expectations of substantial productivity gains. Early studies, such as one showing that AI increased the productivity of customer support agents by 15 percent on average — with less-experienced workers getting a much bigger ...
EU AI Act Update: New Deadlines & Compliance Priorities
Sandboxes may still become a valuable ... with regulatory engagement. But they should not be the only compliance strategy. The agreement is still provisional and must be formally endorsed by both the Council and Parliament before legal and linguistic revision and final adoption. However, the direction is clear enough for companies to act. The simplification package should be treated as the starting point for practical compliance planning, not as a reason to postpone it. ... Update internal AI Act compliance ...
AI interviews creating negative experience for Irish jobseekers, finds report
New research from Greenhouse shows that of all the regions surveyed, Ireland-based jobseekers had the worst opinion of AI in the interview process. Read more: AI interviews creating negative experience for Irish jobseekers, finds report
Redesigning Your Marketing Organization for the Agentic Age
As AI accelerates product development and expands marketing’s responsibilities, most marketing organizations are struggling to keep up because their operating model—sequential, siloed, and coordination-heavy—hasn’t changed. The solution is a new structure built for human-agent ...
The Enterprise Automation Playbook: AI Models, Use Cases, and Frameworks That Deliver Real Impact: By Quadri Owolabi
Automation is no longer a tactical efficiency lever. With AI embedded, it becomes an enterprise c
Parloa Builds Service Agents Customers Want to Talk To
Parloa’s AI Agent Management Platform utilizes OpenAI models to design, simulate, and run voice-driven customer service agents for enterprise applications.
A Few Good Clauses: Comparing LLMs vs Domain-Trained Small Language Models on Structured Contract Extraction
arXiv:2605.05532v1 Announce Type: cross Abstract: This paper evaluates whether a domain trained Small Language Model (SLM) can outperform frontier Large Language Models on structured contract extraction at radically lower cost. We test Olava Extract, a self hosted legal domain Mixture of Experts model, against five frontier models. Olava Extract achieved the strongest aggregate performance in t
Partial Evidence Bench: Benchmarking Authorization-Limited Evidence in Agentic Systems
arXiv:2605.05379v1 Announce Type: new Abstract: Enterprise agents increasingly operate inside scoped retrieval systems, delegated workflows, and policy-constrained evidence environments. In these settings, access control can be enforced correctly while the system still produces an answer that appears complete even though material evidence lies outside the caller's authorization boundary. This paper introduces Partial Evidence Bench, a deterministic benchmark for measuring that failure mode. The benchmark ships three scenario families -- due diligence, compliance audit, and security incident response -- with 72 tasks total, ACL-partitioned corpora, oracle complete answers, oracle authorized-view answers, oracle completeness judgments, and structured gap-report oracles. It evaluates systems along four surfaces: answer correctness, completeness awareness, gap-report quality, and unsafe completeness behavior. Checked-in baselines show that silent filtering is catastrophically unsafe across all shipped families, while explicit fail-and-report behavior eliminates unsafe completeness without collapsing the task into trivial abstention. Preliminary real-model runs show model-dependent and scenario-sensitive differences in whether systems overclaim completeness, conservatively underclaim, or report incompleteness in an enterprise-usable form. The benchmark's broader contribution is to make a governance-critical agent failure measurable without human judges or contamination-prone static corpora.
900 CEOs Surveyed: 80% believe their job is at risk if AI fails this year
A recent survey reveals widespread anxiety among CEOs regarding the potential failure of AI initiatives and the impact on their leadership roles.
Ex-Hedge Fund Adviser Bets on AI-Backed Consultancy for Rich
When Tony Cowell retired in 2023 from a three-decade career advising private equity and hedge funds from the Cayman Islands at KPMG LLP, AI was still relatively nascent.
Aon study reveals disconnect between AI ambitions and workforce investment among employers | Noah News
Despite widespread recognition that AI success depends on people, many organisations prioritise automation over reskilling, risking misalignment between technology deployment and workforce needs, says Aon’s latest...
Why USEReady believes AI orchestration will define the next decade of enterprise transformation
USEReady CEO Uday Hegde explains why AI orchestration, governance, and business outcomes matter more than standalone AI pilots for enterprises. Interviews Emerging Tech
Why Enterprise AI Success Depends on More Than Models: Insights from Qlik and Deloitte - CDO Magazine
Why Enterprise AI Success Depends on More Than Models: Insights from Qlik and Deloitte - CDO Magazine
The job market is healing for everyone—except in the office | Fortune
The April jobs report showed America is hiring again at the strongest pace in over a year, but white-collar sectors keep shedding workers, and AI may be part of why.
AI adds burden on customer service agents, experts warn - Outsource Accelerator
AI was supposed to ease the pressure on customer service teams. Instead, many companies are pushing agents into more emotionally intense work.
Why Talent Transformation Is the Missing Focus of Enterprise AI | Databricks Blog
- AI is rapidly reshaping enterprises, making workforce-wide upskilling essential to stay competitive - Enterprises investing in upskilling as a continuous journey, as opposed to a one-time event, are seeing the strongest returns on investment - To accelerate AI adoption for enterprises of ...
ServiceNow Aims for $30B Revenue by 2030 with AI Leading the Charge
ServiceNow aims for $30 billion in subscription revenue by 2030, leveraging AI to contribute over 30% of its annual contract value.
AI legal-tech start-ups’ alternative career path for lawyers
The opportunity to join a dynamic business and influence the wider legal sector holds increasing appeal
Making the Invisible Visible: Understanding the Mismatch Between Organizational Goals and Worker Experiences in AI Adoption
arXiv:2605.03078v1 Announce Type: new Abstract: While AI is often introduced into organizations to drive innovation and efficiency, many adoption efforts fail as workers resist and struggle to integrate these systems. These failures point to a deeper issue: workers, the very people expected to collaborate with AI, are often invisible in decisions about how AI is designed and used. Drawing on interviews with professionals who interact with AI systems daily in healthcare, finance, and management, we examine the disconnect between organizational expectations and worker experiences. We identify key barriers, including poor usability and interoperability, misaligned expectations, limited control, and insufficient communication. These challenges highlight a gap between how organizations implement AI and the evolving worker needs, tasks, and workflows that it fails to support. We argue that successful adoption requires recognizing workers as central to AI integration and propose adaptation strategies at the individual, task, and organizational levels to better align AI systems with real-world practices.
Dyna Software's AI assistant promises to massage your toughest ServiceNow configs
The tool is meant to take the place of 80% of the work that requires ServiceNow dev teams
Why AI breaks without context — and how to fix it
Presented by Zeta Global The gap between what AI promises and what it delivers is not subtle. The same model can produce precise, useful output in one system and generic, irrelevant results in another. The issue is not the model. It's the context. Most enterprise systems were not built for how AI operates. Data is scattered across tools. Identity is inconsistent. Signals arrive late or not at all. Systems record events but fail to connect them into a continuous view. AI depends on that continuity. Without it, the model fills in the gaps so the result looks polished but lacks relevance. This is where most teams get stuck. A better model does not fix fragmented, stale, or commoditized data. Gartner estimates organizations lose an average of $12.9 million annually due to poor data quality. AI does not solve that problem, it surfaces it faster and at a greater scale. The mirror test There is a fast diagnostic test for this. Give your AI a perfect, high-intent customer signal and see what comes back. If the output is generic or irrelevant, the model needs work. But if the model produces something sharp and useful on clean data, and then falls apart on real production data, the problem is the data. In practice, it is almost always the second scenario. AI functions like a magnifying glass, so strong data systems become dramatically more powerful, and the weak ones become dramatically more visible. Organizations that have been coasting on fragmented, poorly integrated customer data can no longer hide behind reporting lag and manual interpretation. The AI renders the problem in plain sight. Context is the new identity layer This is really where the next evolution gets interesting. Even after you solve the data quality problem, there is still a second shift underway in how customer profiles are built and used. For years, enterprise data systems stored content: transactions in CRMs, demographics in data warehouses, campaign responses in marketing platforms. These records described what had already happened. They were useful for reporting but were not built for AI. AI requires context. Context is not a static record. It is a current view of the customer including recent behavior, cross-channel signals, and emerging intent. The thread that connects one interaction to the next. Identity tells you who someone is. Context tells you what they are doing and what they are likely to do next. Consider a simple example: ask an AI to recommend a beach vacation destination, and it might suggest Hawaii or Florida. Tell it you have three children, and it surfaces family-friendly options. Give it access to your recent search patterns, your affordability signals, and where you have been searching over the past year, and the recommendation changes entirely because the model is no longer working from demographic categories but from a live picture of who you are and what you are doing right now. Most enterprise systems were built to store state, not maintain context. They capture events, but they don’t maintain continuity between them. That’s the gap AI exposes. But for practitioners, the challenge is not conceptual; it is architectural. Context does not live in a single system. It is fragmented across event streams, product analytics tools, CRMs, data warehouses, and real-time pipelines. Stitching that into something an AI system can actually use requires moving from batch-oriented data models to streaming or near-real-time architectures, where signals are continuously ingested, resolved, and made available at inference time. This is where many AI initiatives stall. The model is ready, but the context layer is not operationalized. Systems are not designed to retrieve the right signals within milliseconds, or to resolve identity across channels in real time. Without that, “context” remains theoretical rather than actionable. Architectures like Model Context Protocol (MCP) are accelerating this shift by giving AI systems a way to pass memory about a user between applications, essentially threading a continuous line of context around an individual across different interactions. The result is a profile that becomes richer and more predictive over time, one that creates a line of continuity between what someone has done, what they are doing now, and what they are likely to do next. When that identity layer is strong, the same model produces better outcomes. When it is weak, no model can compensate. The compounding advantage Organizations that built first-party data systems and durable identity infrastructure before the AI wave are now benefiting from a compounding effect. Better data trains smarter models. Smarter models attract more consented users. More consented users generate richer behavioral signals. Competitors without that foundation cannot replicate this, regardless of which model they are running. The gap is structural, not algorithmic, and because identity systems improve incrementally over time, the organizations that started investing earlier have advantages that are genuinely hard to close. What this means in practice The practical implication is a shift in where AI investment goes. The organizations getting consistent results from AI are treating it as a processing layer for a living data system, not as a standalone capability to be bolted onto existing infrastructure. For builders and operators, this translates into a different set of priorities than the last two years of AI experimentation: First, instrument for real-time signals. Batch pipelines and nightly refreshes are not sufficient when AI systems are expected to respond to user intent as it happens. Teams need event-driven architectures that capture and surface behavioral signals in near real time. Second, make context retrievable at inference time. It is not enough to store data in a warehouse. Systems must be designed so that relevant context can be resolved and injected into prompts or retrieved by agents within milliseconds. Third, invest in identity resolution as infrastructure. Connecting fragmented signals across devices and channels so the system understands real individuals rather than anonymous interactions is foundational, not optional. Fourth, treat governance and consent as part of system design. First-party data built on trust is not just safer; it is more durable and ultimately more valuable than third-party data that competitors can access. These investments are less visible than a new model launch and are also far harder to copy. The real race Models are now interchangeable. The difference will come from who can operationalize context at scale and treat the model as a processing layer, not the advantage. That advantage comes from years of investment in identity infrastructure, first-party data, and systems that keep customer context current. The organizations that win won’t be the ones with better prompts. They’ll be the ones whose systems understand the customer before the prompt is ever written. Neej Gore is Chief Data Officer at Zeta Global. 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 Adoption Fuels Growth at IT Giant Coforge | Technology
Coforge's Q4 earnings report reveals rising profits and revenue growth despite AI-driven industry disruptions. The IT firm sees AI creating new high-margin opportunities, likening its impact to past cloud migrations. Coforge projects robust growth, emphasizing AI's role in expanding revenue ...
AI and The Magic Loop - by Ethan Evans and Jason P. Yoong
This idea applies to all kinds of skills that used to require deep expertise, not just coding. AI tools can perform basic legal analysis, construct financial models, create user interface mock-ups, and much more.
Why You Shouldn’t Treat AI Agents Like Employees
This HBR article argues that organizations should treat AI agents as tools rather than employees, focusing on accountability, quality control, and escalation paths.
Cheap Expertise: Mapping and Challenging Industry Perspectives in the Expert Data Gig Economy
arXiv:2605.03295v1 Announce Type: new Abstract: Demand for expert-annotated data on the part of leading AI labs has created an expert gig economy with the potential to reshape white collar work and society's understanding of expertise. In this research, we study the vision for the future of expertise described in the public communication of five industry data annotation organizations and their CEOs, as reflected on social media feeds and public appearances on podcasts. We find that the industry envisions AI expertise as cheap, meaning that it can offer a better return on investment than human expertise. Human expertise, meanwhile, is viewed as an extractable resource, the value of which can be judged relative to AI expertise. Finally, institutional expertise (such as that created or possessed by universities and corporations) is viewed as in need of liberation or reform, such that it can be incorporated into the latest artificial intelligence systems. Our findings have implications for human experts, whose professional lives may be transformed and revalued by this industry, as well as for societal institutions that mediate expertise. We close this work with a series of provocations intended to elicit consideration of how society can best approach an AI-driven expert gig economy and the cheap expertise it intends to produce.
AI adoption ROI: why adoption determines enterprise performance
AI adoption ROI depends on workflow execution, not usage. Learn how to connect AI to real business outcomes and enterprise performance.
As if the Great Flattening wasn't enough — now managers are in charge of the AI revolution
While leaders have floated a future ... manage AI. Early on, many organizations relied on simplistic tracking measures, such as whether employees logged in to a tool. Yet those metrics didn't necessarily translate into meaningful productivity gains, BCG's Bedard said. Managers are facing questions of "How do you roll out this tool to get the work done even better, and faster, and more effectively?" said Kotter. At JPMorgan, the engineer said this push has changed how he ...
Call my agents: snapshots from the future of work
Staff are often more willing than leaders to move quickly on AI and automation
Market research is too slow for the AI era, so Brox built 60,000 identical 'digital twins' of real people you can survey instantly, repeatedly
In a world where a viral TikTok video can cause a brand to trend globally in mere hours, the traditional market research cycle — often spanning 12 weeks — is becoming a liability. The lag between a survey question and the answers from a wide (or targeted) pool of respondents has become a primary bottleneck for Fortune 500 decision-makers who are forced to navigate volatile geopolitical and economic shifts with data that is frequently outdated by the time it reaches a slide deck, as industry experts have observed. Brox, a predictive human intelligence startup, recently announced a strategic funding round following a year where they reported 10X revenue growth. Their proposition is as ambitious as it is technical: the creation of a "parallel universe" populated by 60,000 digital twins of real, living human beings and their entire demographic profiles and consumer preferences, allowing enterprises to run unlimited experiments in hours rather than months. “These digital twins are one-to-one replicas of actual, real individuals," said Brox CEO Hamish Brocklebank in a recent video call interview with VentureBeat. "We recruit real people like a normal panel company does, pay them to interview them, and capture all the data around them — fully consent-driven.” The company, currently a lean 14-person operation, is positioning itself as the antithesis of the "insane" research industry. By replacing statistical models with behavioral replicas, Brox aims to transform how the world’s largest banks and pharmaceutical giants anticipate human reactions to high-stakes global and market-shifting events, or narrow, targeted product releases and personnel news, and everything in between. The kinds of surveys and specific questions that Brox asks its digital twins are completely open-ended and can be customized to fit any conceivable business customer's use cases and goals. According to Brocklebank, examples of survey questions include: “What happens if America invades Iran or Greenland? Will depositors at Bank of America put more money into their account or take more money out? Or, in pharmaceuticals, if RFK Jr. says something next week, will that make people more likely to take vaccines or less likely?” Not synthetic people — AI copies of real ones The core differentiator of Brox’s technology lies in the fidelity of its input data. While many competitors in the "digital audience" space rely on purely synthetic identities — generic personas generated by Large Language Models (LLMs ) — Brocklebank argues that these methods inevitably produce "AI slop". Purely synthetic audiences often cluster around a tight distribution of answers, over-indexing for "correct" or "healthy" behaviors (such as eating broccoli) because of inherent biases in the underlying models. Brox’s "Digital Twins" are instead one-to-one behavioral replicas of real individuals who have been recruited and interviewed with exhaustive depth. The process is intensive: Deep Interviews: The company conducts hours of real and AI-driven interviews with each participant. Psychological Depth: The data collection seeks to understand fundamental "decision drivers," including upbringing, relationships, and even marital stability. Data Density: For some twins, Brox maintains up to 300 pages of text data, representing what Brocklebank calls "the deepest per person data set that exists". To solve the "black box" problem common in AI, Brox utilizes a "reasoning chain" for its predictive outputs. When a digital twin predicts a reaction — such as how a $2 billion net-worth individual might respond to a specific interest rate hike — the model introspects and provides a step-by-step explanation for that decision. This allows clients to understand not just what will happen, but the underlying psychology of why it is happening. Scaling the "unscalable" interview The product offering is currently live in the US, UK, Japan, and Turkey. Brox has successfully digitized specific, high-value cohorts that are traditionally difficult for researchers to access. This includes a panel of "high-net-worth" individuals (those worth over $5 million) and specialized medical professionals like dermatologists — including a multibillionaire. However, the largest value for customers is likely in the aggregate mass of all individuals that can be polled en masse and/or segmented across demographics, especially those of medium and lower income levels, whose purchasing power and decision-making is more constrained and whose market- One of the more unique aspects of the Brox platform is its incentive structure. To ensure twins remain up-to-date, real-world counterparts are re-contacted frequently. For high-value individuals who are not motivated by small cash payments, Brox has issued Stock Appreciation Rights (SARs), essentially making these participants "investors" in the company’s success to ensure they continue to provide high-fidelity personal updates. The platform’s use cases currently focus on two primary sectors: Pharmaceuticals: Predicting vaccine hesitancy or how physicians might react to new biologics based on shifting political climates. Finance: Simulating how depositors at major banks might move funds in response to geopolitical events, such as conflicts in the Middle East. As for why go to the trouble of interviewing and digitally cloning real people instead of just creating wholly fictitious, synthetic audience characters and personas using LLMs and other AI models, Brocklebank offered his perspective. “You can create 10,000 truly synthetic digital twins, but the answers will still normalize into a very tight distribution, which is not realistic when you’re actually asking real people," Brocklebank said. By maintaining a pre-built audience of 60,000 twins, the company enables clients to bypass the recruitment phase of research. A large US bank or a global pharma giant can now "query" the digital population and receive a validated analysis in a matter of hours. Pricing and accessibility Unlike traditional research firms that charge on a per-project or per-respondent basis, Brox operates as a high-end Software-as-a-Service (SaaS) platform with enterprise-level commercial licensing. The company avoids the "seat" or "usage" limits that often hinder rapid experimentation within large organizations. Pricing Tiers: Subscriptions are sold as blanket flat fees, starting at a minimum of $100,000 per year. Top-Tier Contracts: For larger deployments involving multiple teams and global data access, contracts scale up to $1.5 million per year. Usage Rights: Clients are granted unlimited usage during the contract period. This allows them to run thousands of simulations without worrying about incremental costs, encouraging a culture of "testing everything" before deployment. From a legal and privacy standpoint, the digital twins are built on a "fully consent-driven" framework. While the twins can be traced back to real human data for internal validation, the platform is designed to provide aggregated behavioral insights that protect the anonymity of the participants while maintaining the predictive power of their digital replicas. Rejecting the rise of Kalshi, Polymarket and 'prediction markets' The tech industry has recently seen a surge in valuations and interest in "prediction markets" like PolyMarket and Kalshi, which allow users to bet on the outcomes of various global events. However, the leadership at Brox maintains a distinct distance from these platforms, citing a "personal disdain" for betting markets from both a moral and intellectual perspective. Brocklebank argues that while betting markets can predict outcomes (e.g., who wins an election), they offer zero utility for business decision-makers because they fail to provide the "why". Knowing there is a 60% chance of a certain candidate winning does not help a company adjust its consumer strategy; knowing why a specific cohort of depositors is feeling anxious does. Investors including Scribble Ventures, Wonder Ventures, and Vela Partners have backed this "human-first" approach to AI, betting that the moat created by deep human data will prove more resilient than the commoditized models of synthetic data providers. As Brox prepares for launches in the Middle East and APAC, the company is moving toward its ultimate goal: simulating the entire world as a "parallel universe" for risk-free decision-making.
Slower growth, tougher macro jeopardise salary hike cycles for IT cos | Tech News - Business Standard
Economic uncertainty, slower growth and pressure on margins are prompting IT services firms to delay salary hikes and offer lower increments, impacting employee morale across the sector
Calibrate AI Use to the Decision at Hand | MIT Sloan Management Review
Executives should understand what kinds of decisions are best supported by analytical AI versus generative AI.
AI in Business Process Management: ROI, Costs, and Real Business Value
Discover if AI in BPM is worth the investment for U.S. businesses. Explore real costs, ROI, productivity gains, and how AI drives faster process improvement and operational efficiency.
Managers Drive Employee Adoption of AI Tools | Let's Data Science
Business Insider reports that middle managers are being tasked with increasing employee use of AI inside firms, as companies seek measurable efficiency gains. Per Business Insider, managers are using usage dashboards, flagging low adoption, and offering concrete ideas to help staff adopt AI ...
AI Is Expanding Employee Agency. Why Most Organizations Block It
AI is expanding employee agency at work, but outdated structures, metrics and incentives are blocking it, limiting the real value organizations can capture.
Process Intelligence Key to AI ROI
Enterprise AI is hitting a wall most teams cannot see, and process intelligence is the missing layer that maps how work happens before agents automate it. Without that foundation, companies risk scaling inefficiency.
Anthropic, Blackstone, Goldman Sachs to create AI services company
Anthropic has partnered with investment giants Blackstone, Hellman & Friedman, and Goldman Sachs to form a new AI services company. Read more: Anthropic, Blackstone, Goldman Sachs to create AI services company
AI-driven pricing pressure, cost cuts reshape India IT outlook
A recent report by Kotak Institutional Equities highlights how AI-led productivity gains are reshaping India's IT services sector, creating pricing pressure, evolving business models, and navigating challenges amidst rising competition.
4 ways HR uses AI in hiring, according to talent professionals | HR Dive
Inefficient implementation, however, may be cutting into employers’ ROI.
How process intelligence could decide AI's ROI
This podcast episode explores why process intelligence is the essential foundation for mapping workflows before implementing AI agents to avoid scaling inefficiency.
Anthropic And OpenAI Are Both Launching Joint Ventures for Enterprise AI Services
Anthropic and OpenAI are creating enterprise AI service ventures to help companies deploy Generative AI through deeper consulting, engineering, and implementation support.
Who Wins in the AI Services Economy? Join Our Webinar Discussion - Newsweek
“The opportunity is increasing,” Noshir Kaka says. “But so is the competition.” AI Impact Forum looks at what’s next.
Organizational Theory Is Essential for Managing the Complexity of Multi-Agent AI Systems
Current approaches to agentic systems rely too heavily on technical control planes, ignoring the necessary organizational frameworks. Effective deployment requires integrating management principles like decision rights and spans of control.
Bosses Want You to Use AI but They're Not Setting a Good Example, Study Says - CNET
Executives have been pushing AI for years. But leadership-led workplace culture may be preventing effective adoption, Microsoft found.
The enterprise productivity fix hiding in the pit lane - SiliconANGLE
F1 offers an extreme proxy for enterprise performance — every decision is data-driven, every week demands measurable progress and the cost of poor knowledge flow is visible in race results, Boyagi noted. The same systemic barriers blocking software teams, including unclear priorities and AI that ...
Anthropic comes for the midmarket software spend
Backed by private equity and banking giants, it will build custom AI systems for business bottlenecks There’s gold in midmarket IT spend, and Anthropic - backed by private equity and banking heavyweights and tapping its Claude Partner Network - is coming for it.…
Enter Just Tripled Its Valuation to $1.2 Billion and the Brazilian Legal AI Company’s Growth Reveals Why Emerging Markets Create Different AI Moats Than Silicon Valley Expects – Startup Fortune
The founder-market fit argument is not just biographical decoration. It explains why Enter can sell into Itaú’s legal department while a US-based legal AI startup would struggle to navigate the relationship, regulatory, and language requirements that those accounts demand. The valuation ...
Agentic Coding Is a Trap
A critical look at the current trend of agentic coding and why it may be problematic for developers.
Council Post: It’s Time To Retire The Corporate Training Course. AI Has Already Replaced It
If AI changes how people learn, it also changes how organizations can measure it.
A new dashboard tracks how much KPMG workers use AI. They say it's easy to game the system.
KPMG says it hopes the dashboard encourages more "frequent and sophisticated" AI use among its US advisory's 10,000 workers.
LTM to Revolutionise IT Services with 'Blueverse Credit' AI Pricing Model in FY27 - Rediff.com Business
IT services major LTM is set to introduce a new pricing framework, 'Blueverse Credit', in the first quarter of FY27, aiming to align monetisation with the growing adoption of agentic artificial intelligence.
Recruiters turn to AI in quest to find the perfect connection
Technology is being used to ‘clear the decks for human moments’
Alvarez & Marsal Wants to Make $3.5 Billion From AI Work by 2028
Alvarez & Marsal wants to generate 50% of its revenue from artificial intelligence work by 2028, which would represent as much as $3.5 billion in earnings.
Building an agentic AI strategy that pays off - without risking business failure | ZDNET
Companies are chasing tenfold AI gains, but many projects are failing fast. We break down the real risks and show you how to turn agentic AI into reliable, profitable outcomes.
How an Organizational Shift Can Unlock Real Value from a Stalled AI Strategy - SPONSOR CONTENT FROM PUBLICIS SAPIENT
Beyond incorporating new agentic ... AI agents to act within context, experienced builders and domain experts are critical for embedding engineering, data science, and domain expertise into this AI to ensure the scalability, good governance, and sustainability of these new systems. This integration of technology and expertise allows organizations to extend AI beyond pilots and into ongoing operations. The partnership-driven model is the connection ...
AI agents for SMBs - by Robin Lowe - The Lowe-Down
At its core, an AI agent is software that can plan, make decisions, and take action toward a goal with limited supervision.
From Burnt-Out Freelancer to Productized Operator (With AI Doing the Boring Bits)
AI works best when your service is already productized.
Job Seekers Are Using AI During Interviews - Newsweek
Roughly 22 percent of candidates are already using AI during real‑time interviews, according to a new survey.
A-Cube lands €4 million to support e-invoicing and real-time tax reporting expansion from Italy
A-Cube, a Milan startup specialising in automated digital tax compliance, has secured a €4 million investment round to support product expansion, develop solutions adjacent to digital tax reporting, and integrate AI models into its platform. The funding was led by P101 SGR, with participation from Sella Direct Ventures. “Global taxation is undergoing a radical shift, […]
Extracting Contract Insights with PwC’s AI-Driven Annotation on AWS
A use case detailing an AI-driven contract analysis system that extracts structured insights and supports natural language questioning, useful for legal and procurement teams.
AI Created 8 High-Paying Jobs You Are Probably Already Qualified For
Build and deploy AI systems into real products and workflows.
TruMatch - AI-Native Recruitment Agency
We cannot provide a description for this page right now
5 Management Tips in the Age of AI | by Jonathan Fulton | Jonathan’s Musings | May, 2026 | Medium
I’ve seen engineers fall into what I call “tokenmaxing” — measuring their productivity by tokens consumed rather than problems solved. It’s the AI equivalent of typing faster instead of thinking better
Ai Job Impact: People are losing jobs to AI—But this quiet trait could save yours, shares ex-Oracle boss - The Times of India
Relationships News: In a world that’s racing faster every day—driven by automation, artificial intelligence, and the constant push for speed—there’s a softer, more human .
AI-Specific Legal Protections
Courts are increasingly imposing AI-specific protective order restrictions to safeguard confidential information, with Model 3 being a preferred approach. Practitioners must proactively ensure AI tool compliance.
AI-Specific Legal Protections
Courts are increasingly imposing AI-specific protective order restrictions to safeguard confidential information, with Model 3 being a preferred approach. Practitioners must proactively ensure AI tool
AlphaSignal Workshop on Harness Engineering
AlphaSignal is launching a workshop on Harness Engineering, a discipline behind AI agents that work in production, on May 14th. The workshop will cover topics such as why agents break in production an
AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights
A new research paper explores the implications of AI self-preferencing within algorithmic hiring processes.
AI is coming for your mind - by Khe Hy
Claude Cowork (and now Codex for Everything) have shifted the narrative on AI adoption. AI ’s ability to do real work now has the potential to expand well beyond software engineers (and the bill is coming due)!
What IT Executives Are So Keen with “AI replace programmers” (Part-2): Programmers tend to switch jobs frequently | by Zhimin Zhan | May, 2026 | Medium
What IT Executives Are So Keen with “AI replace programmers” (Part-2): Programmers tend to switch jobs frequently. Frequent staff turnover is a major headache and costly for software development companies.
This 12-Step AI Stack Is How Smart Businesses Operate in 2026
What customers want, what competitors are doing, what’s trending — AI can summarize it in minutes.
You're only using Claude Code at 5% of its capacity. The other 95% is the features you don’t even know about. Most people learned AI through chatbots.
AI Prototyping Investment Framework
Teams building AI are running into the same problem: they cannot align on what to build until it is visible, and by then, time is lost. Prototyping fixes that. This guide offers a framework to choose the right AI prototype for your business.
How to Get Hired in the AI Era
How to Get Hired in the AI Era
“If Figma and Lovable had a child that became an architect”: Synaps raises €3.06 million to rival AutoCAD
Five months after launching its first beta version, Vienna-based AI canvas for architects, Synaps, has announced its €3. 06 million ($3. 6 million) pre-Seed investment round from US-based Plug and Play and Fil Rouge, among others.
‘Awkward and humiliating’: UK job hunters share frustration with AI interviews
People describe unnatural process as survey finds nearly half of job seekers have been interviewed by AI Nearly half (47%) of UK job seekers have had an AI interview, research from the hiring platform Greenhouse has found. In its survey of 2,950 active job seekers, including 1,132 UK-based workers, with additional respondents from the US, Germany, Australia and Ireland, it found that 30% of UK candidates had walked away from a hiring process because it included an AI interview. Continue reading...
Accenture Invests in Netomi
Accenture Ventures spearheads investment in Netomi, an AI platform enhancing customer experiences, signaling a shift from pilot projects to scalable AI solutions.
“A model that produces code which compiles and passes the tests it was given is not the same as a model that produces correct, secure, maintainable, well-architected software”
I end up having to correct things ("No, didn't I see similar code earlier that should be refactored to reuse this?" or "No, that is not an efficient way to approach this") frequently -- often the same sort of corrections I would use with a junior dev (back when junior devs were a thing in this industry). But *with* continual corrections and guidance and not blindly accepting proposed implementations and changes, I've found the AI augmented approach to substantially increase the output of *high quality* code that I produce.
The Psychological Costs of Adopting AI
AI adoption strategies are overwhelmingly framed around productivity and efficiency. But that lens misses a critical constraint: the psychological cost of working with AI. New research shows that “psychological debt”—a cluster of six negative effects including cognitive offloading, reduced ...
From Chatbots to Agents: How Agentic AI Is Taking Over Admin Workflows in 2026 - AttendanceBot Blog
Discover how agentic AI in HR is transforming admin workflows in 2026 - from scheduling automation to attendance tracking and more.
Colorado’s AI Act Rewrite Moves Forward: A New Draft For Employers
Colorado’s latest AI draft moves regulation beyond “high-risk systems” and toward how decisions are made, with direct implications for employers.
Beyond the Model — Why Responsible AI Must Address Workforce Impact | MIT Sloan Management Review
How much should responsible AI efforts focus on the technology’s displacement of human workers?
Accenture Ventures Fuels Netomi's AI Expansion in Customer Experience with Strategic Investment
Accenture Ventures is investing in Netomi to help scale AI solutions for customer experience, with support from Adobe Ventures and board member Jeffrey Katzenberg.
Can AI Actually Run Your Marketing? - Rectified
Think about it this way. QuickBooks may cost you $5,000–$10,000 a year, but your accountant costs $60,000–$100,000. Those are the “service dollars.” The new play for AI -native companies is to capture both: offer the tool and the expertise, at a fraction of what it would cost to hire a team.
Dublin’s Version 1 to acquire CreateFuture consultancy
Version 1 said the merger would create a ‘combined organisation of 4,250 employees with annual revenues of more than €500m’. Read more: Dublin’s Version 1 to acquire CreateFuture consultancy
AI adoption raises psychosocial risks at work, ILO warns | Human Resources Director
New ILO paper reveals intrusive surveillance, loss of job autonomy as risks emerging from AI deployment
Hitting the ‘GenAI wall’: Where generative AI stops working, and what it means for your talent strategy
Companies that assume GenAI eliminates the need for expertise will hit the AI wall—and wonder why their workforce transformation has stalled.
Hidden IT problems are quietly creating risk, shadow IT, and lost productivity
Presented by TeamViewer Enterprise technology failures are largely invisible. Research from TeamViewer, based on a global survey of 4,200 managers and employees, finds that the majority of digital dysfunction never reaches the IT help desk. Employees work around slow applications, failed logins, and intermittent glitches rather than reporting them, leaving organizations without an accurate pictu
Microsoft is delving deeper into professional services workflows with the release of a new AI-powered "Legal Agent" embedded in Microsoft Word, demonstrating how generative AI is transitioning from...
# Microsoft is delving deeper into professional services workflows with the release of a new AI-powered "Legal Agent" embedded in Microsoft Word, demonstrating how generative AI is transitioning from... | Fintech Association Of Kenya Published: 2026-05-01T05:33:39+00:00 ## Summary Microsoft has introduced a new AI-powered "Legal Agent" embedded in Microsoft Word, designed to support legal professionals directly in their work. The agent can help review the contract and provide key insights about its terms and allocation of risk. It can also assist in preparing a fully redefined version of the document. The tool is now available for purchase. ## Story Microsoft is delving deeper into professional services workflows with the release of a new AI-powered "Legal Agent" embedded in Microsoft Word, demonstrating how generative AI is transitioning from... | Fintech Association Of Kenya Agree
Forget productivity: Here are 5 strategic shifts that drive real AI value | ZDNET
While the CIO I mentioned earlier ... clear, measurable benefits from an AI implementation, Lovelock said executives who don't see the value of extra downtime could be missing a trick. Also: 6 ways to stop cleaning up after AI - and keep your productivity gains · "When people are eight hours head-down, you don't have a corporate ...
The Real ROI of AI Automation for Enterprise in 2026 - SEM Nexus
Struggling to justify your AI budget? Discover how to calculate the true ROI of AI automation for enterprise in 2026, and avoid the trap of expensive, low-return deployments.
Private Equity's AI Moment: The Greatest Value Lever in Decades -- and the Hardest to Pull | The Manila Times
The following article is authored by Neil Dhar, Senior Vice President, IBM Consulting Americas
Employees Are Relying on AI for Personal Support. That’s Risky.
Employees Are Relying on AI for Personal Support. That’s Risky. SKIP TO CONTENT # Employees Are Relying on AI for Personal Support. That’s Risky. Research shows how AI can weaken human connection at work. Leaders should step in to prevent this. by Constance Noonan Hadley and Sarah L. Wright From the Magazine (May–June 2026) Anton Repponen ## Summary. Leer en español Ler em português - Post - Post - Share - Save - Get PDF - Buy Copies - Print We’ve entered a new era of organizational life when, for the first time in history, people can turn to something other than a fellow human for conversation and support during the workday. They now can engage with AI. But how are employees using AI for social purposes? How is that usage affecting them? In our ongoing research we’ve been trying to answer those questions and understand where this new dynamic might take us in the future. Read m
Casa, a Handyman Start-Up, Aims to Automate Home Maintenance
Casa, a company founded by former Uber executives, says it uses artificial intelligence and a stable of handymen to take care of members’ homes.
AI to ROI Reports & Data: KPMG's Global AI Pulse Q1-26
The Technology, Media, and Telecommunications sector currently sets the pace for scaling. Organizations in this sector report the highest average investment at $245 million. TMT firms achieve high value by embedding AI -native architectures directly into their core products.
Swedish legal AI startup Legora raises €42 million extension to Series D – bringing total to over €500 million
Stockholm’s Legora today announced a €42 million ($50 million) extension of its previously announced Series D financing, bringing the total round to €513 million ($600 million) in equity and valuing the company at €4. 7 billion ($5. 6 billion) post-money.
Mphasis CEO on Business Strategy, AI Investments
Nitin Rakesh, CEO at Indian IT firm Mphasis, discusses the company's business strategy and its investments in AI. He speaks with Haidi Stroud-Watts on Bloomberg: The Asia Trade. (Source: Bloomberg)
Cognizant Acquires Astreya
Cognizant announces its acquisition of Astreya to enhance AI infrastructure and data center services, aiming to boost its AI-focused managed services.
From Exposure to Adoption: Generative AI in European Workplaces
arXiv:2604.18849v3 Announce Type: replace Abstract: This study examines who adopts generative AI and whether early adoption has begun to reshape the task content of jobs across 35 European countries. Adoption ranges from under 3% to 25%. Occupational exposure strongly predicts uptake, but AI does not diffuse passively along exposure lines. At the worker level, skills, abstract task content, and e
Is AI increasing access to justice?
A steep increase in ‘vibe litigation’ appears to be expanding the market for legal activity
Audit Yourself to Get More From GenAI
Carolyn Geason-Beissel/MIT SMR | Getty Images More than a year into using generative AI daily, I wondered whether I was getting the most out of my AI use. There was no benchmark or feedback loop, and no one was grading my sessions with ChatGPT and Claude — until I created a self-audit. I did what […]
Resume-ing Control: (Mis)Perceptions of Agency Around GenAI Use in Recruiting Workflows
arXiv:2604.26851v1 Announce Type: new Abstract: When generative AI (genAI) systems are used in high-stakes decision-making, its recommended role is to aid, rather than replace, human decision-making. However, there is little empirical exploration of how professionals making high-stakes decisions, such as those related to employment, perceive their agency and level of control when working with genAI systems. Through interviews with 22 recruiting professionals, we investigate how genAI subtly influences control over everyday workflows and even individual hiring decisions. Our findings highlight a pressing conflict: while recruiters believe they have final authority across the recruiting pipeline, genAI has become an invisible architect that shapes the foundational building blocks of information used for evaluation, from defining a job to determining good interview performances. The decision of whether or not to adopt was also often outside recruiters' control, with many feeling compelled to adopt genAI due to calls to integrate AI from higher-ups in their business, to combat applicant use of AI, and the individual need to boost productivity. Despite a seemingly seismic shift in how recruiting happens, participants only reported marginal efficiency gains. Such gains came at the high cost of recruiter deskilling, a trend that jeopardizes the meaningful oversight of decision-making. We conclude by discussing the implications of such findings for responsible and perceptible genAI use in hiring contexts.
How Big Four Leaders Are Using AI in Their Day-to-Day Work - Business Insider
Business Insider asked leaders at the Big Four firms PwC, EY, and KPMG how they're using AI in their day-to-day work.
Cognizant to Acquire Astreya, Bolster AI Infrastructure in Strategic Expansion
Cognizant announces its acquisition of Astreya to enhance AI infrastructure and data center services, aiming to boost its AI-focused managed services.
Persuadability and LLMs as Legal Decision Tools
arXiv:2604.26233v1 Announce Type: new Abstract: As Large Language Models (LLMs) are proposed as legal decision assistants, and even first-instance decision-makers, across a range of judicial and administrative contexts, it becomes essential to explore how they answer legal questions, and in particular the factors that lead them to decide difficult questions in one way or another. A specific feature of legal decisions is the need to respond to arguments advanced by contending parties. A legal decision-maker must be able to engage with, and respond to, including through being potentially persuaded by, arguments advanced by the parties. Conversely, they should not be unduly persuadable, influenced by a particularly compelling advocate to decide cases based on the skills of the advocates, rather than the merits of the case. We explore how frontier open- and closed-weights LLMs respond to legal arguments, reporting original experimental results examining how the quality of the advocate making those arguments affects the likelihood that a model will agree with a particular legal point of view, and exploring the factors driving these results. Our results have implications for the feasibility of adopting LLMs across legal and administrative settings.
AI-Driven Agreement Workflows Boost ROI by Nearly 30%, Says New Study
A recent study by DocuSign in collaboration with Deloitte highlights a significant opportunity for small business owners looking to harness the power of artificial intelligence (AI). The report,
Why AI Adoption Stalls: The Organizational Challenge Behind the Technology | IBTimes
Why AI Adoption Stalls: The Organizational Challenge Behind the Technology | IBTimes rGen Consulting rGen Consulting, a management consulting firm focused on strategy, artificial intelligence, analytics, and business transformation, offers a clear perspective on AI adoption. In rGen's view, many AI initiatives underdeliver not because the technology lacks capability, but because organizations have not yet evolved the human behaviors, skills, processes, and governance needed to use it effectively. The firm's aim is to help clients build AI capability and move from isolated experimentation to broader, more durable adoption. AI capability is advancing fast. In some domains, it is solving problems that were out of reach just a few years ago. But inside most organizations, its use remains modest, often limited to drafting emails, summarizing meetings, or speeding up basic tasks, basically
Slalom targets AI transformation that earns its spend - SiliconANGLE
Slalom targets AI transformation that earns its spend - SiliconANGLE SHARE Coverage from SiliconANGLE's livestreaming video studiohelp_outline UPDATED 10:34 EDT / APRIL 30 2026 AI ### No more waiting: Enterprise AI transformation has become the top CEO mandate SHARE Enterprise AI transformation is clearing the proof-of-concept stage for many organizations, with execution at scale becoming the new challenge that IT departments alone can’t handle. Governance, talent and organizational structure are required to meet C-suite mandates, according to Daniel Prager(pictured, right), global partner development lead for Google Cloud at Slalom Consulting LLC, a global business and technology consulting firm. Working closely with customers navigating the gap between AI ambition and measurable business impact, Slalom sees these same friction points as the forces most likely to slow even the b
AI-led process modernization leads to measurable ROI - SiliconANGLE
AI-led process modernization transforms manual invoice approvals into governed, scalable workflows, delivering measurable ROI and operational efficiency.
When AI Decides and Human Signs Off | by Leslie Sultani | Apr, 2026 | Medium
When AI Decides and Human Signs Off | by Leslie Sultani | Apr, 2026 | Medium Sign up Get app Sign up # When AI Decides and Human Signs Off ## The design problem most AI companies aren’t solving 14 min read 1 hour ago -- Share Press enter or click to view image in full size There’s a design principle underneath every high-stakes AI product: AI is the decision support. The human is the decision maker. Those are different jobs. The AI surfaces information, surfaces risk, surfaces patterns a person couldn’t find alone. The human takes that and decides what to do. That is the contract: the AI provides the evidence, but the human owns the decision. Most AI products in legal, healthcare, and criminal justice contexts are violating that contract by design. Not maliciously. They just weren’t built around it. They were built around making AI output look good and feel fast, and then a h
New Survey from Harvard Business Review Analytic Services Finds AI Adoption Remains High, Yet Value May Lag Without Modernization and Workflow Integration
New Survey from Harvard Business Review Analytic Services Finds AI Adoption Remains High, Yet Value May Lag Without Modernization and Workflow Integration Accessibility Statement Skip Navigation A critical AI success gap is emerging for organizations, with 30% surveyed seeing an impact on new revenue streams. ORLANDO, Fla., April 30, 2026 /PRNewswire/ -- Most organizations have moved beyond experimenting with artificial intelligence, but few are realizing its full value. New research from Harvard Business Review Analytic Services, sponsored by Appian, finds that while 59% of organizations (who are moving forward with AI to some extent) have AI in production, the majority are currently focused on incremental gains that prioritize efficiency and productivity over top-line growth. Notably, AI has the strongest impact in bolstering productivity, not enabling growth. Respondents indicated
AI adoption remains high but value lags without modernization and workflow integration – Intelligent CIO North America
AI adoption remains high but value lags without modernization and workflow integration – Intelligent CIO North America Light Dark Get Industry Insights & Whitepapers! # AI adoption remains high but value lags without modernization and workflow integration A new survey highlights a growing gap between widespread AI adoption and the ability of organisations to generate meaningful business value from it. Most organizations have moved beyond experimenting with artificial intelligence, but few are realizing its full value. New research from Harvard Business Review Analytic Services, sponsored by Appian, finds that while 59% of organizations (who are moving forward with AI to some extent) have AI in production, the majority are currently focused on incremental gains that prioritize efficiency and productivity over top-line growth. New research from Harvard Business Review Analytic Servic
Deloitte’s AI Forum 2026: The shift from AI adoption to return on intelligence - CNBC TV18
At Deloitte’s AI Forum 2026, over 300 leaders from 114 companies explored how enterprises can move beyond AI adoption to measurable ROI and return on intelligence, focusing on growth, trust and value creation.
What is agentic AI and should HR be using it?
Software companies are promoting agentic AI alongside their longstanding HR systems, but what does it do and does HR need it?
Empathetic Leadership Can Make or Break AI Adoption | Harvard Business Review | 32 comments
Empathetic Leadership Can Make or Break AI Adoption | Harvard Business Review | 32 comments Agree & Join LinkedIn By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy. # Harvard Business Review’s Post 14,582,100 followers 8h Research has now made the business case for empathy clear: Employees at empathic companies do better, working harder, collaborating more efficiently, and generating stronger ideas. Without empathetic leadership, it’s impossible to maximize AI’s potential. Companies are failing to leverage AI because many executives have forgotten that technology only works through people, and people work best when they feel cared for. Wise leaders can gain an AI advantage by leaning into connection. Here’s what can help. Empathetic Leadership Can Make or Break AI Adoption hbr.org There’s something in this — but emp
Companies accelerating digital investments, but struggling to demonstrate their business impact - study - Business Review
Companies accelerating digital investments, but struggling to demonstrate their business impact - study - Business Review # Companies accelerating digital investments, but struggling to demonstrate their business impact – study Anca Alexe 30/04/2026 | 12:10 Companies are accelerating investments in digital transformation, but struggle to clearly demonstrate the value generated by these initiatives, according to the study “Digital Value: How AI and Digital Transformation Finally Pay Off” by international management consulting company Horváth. The findings show a strong increase in digitalisation budgets, but also highlight gaps in strategy and performance measurement. Key findings of the study: - 67% of companies are increasing their digitalisation budgets, with an average rise of around 30% - Around 30% of investments are directed towards artificial intelligence (AI) initiatives -
Inside India newsletter: AI is exposing cracks in India’s growth story as it hits high-paying IT jobs
Inside India newsletter: AI is exposing cracks in India’s growth story as it hits high-paying IT jobs Key Points - 10 million to 15 million people working in the information technology sector have anchored India's aspirational middle class—buying homes, taking flights, driving consumption. - For the last five years, gross hiring of IT firms averaged around 230,000, but in the financial year ending in March 2026, they added around 170,000: analyst. - Ten-plus years of 'Make in India' has not yet triggered a manufacturing renaissance, an expert said. This report is from this week's "Inside India" newsletter, which brings you timely, insightful news and market commentary on the emerging powerhouse — Subscribe today Hello, this is Priyanka Salve, writing to you from Singapore. Welcome to the latest edition of " Inside India" — your one-stop destination for stories and developments from
Employers are blindsiding candidates with AI interviews—and scaring them off - Fast Company
Employers are blindsiding candidates with AI interviews—and scaring them off - Fast Company Try our mini crossword with a business twist: Play Fast Company Mini Crossword LOGIN | - IBM - Texas A&M University Listen to this Article More info 0:00 / 0:00 It’s no secret that artificial intelligence has penetrated every aspect of the hiring process—even the elements that should necessitate a human touch, like conducting interviews. The vast majority of companies already rely on AI to sift through applications and resumes, but many of them are now also using it for screening calls and initial interviews. The AI interview has grown so ubiquitous, in fact, that a new report from the hiring platform Greenhouse found that nearly two-thirds of job seekers have been interviewed by AI during the hiring process—an increase of 13 percentage points from just six months ago. But that doesn’t mea
Managing Agentic AI in Real‑World Use: From Outputs to Actions | Wiley Rein LLP - JDSupra
Agentic artificial intelligence (AI) is the next frontier for companies and organizations that are using AI. Agentic AI can select and carry out actions on a user’s behalf based on instructions,...
From Operators to Orchestrators: Deloitte's 2026 Global Technology Leadership Study Reveals a New Mandate for Tech Leaders
From Operators to Orchestrators: Deloitte's 2026 Global Technology Leadership Study Reveals a New Mandate for Tech Leaders Accessibility Statement Skip Navigation A new era of technology leadership is here, but most enterprises are not yet equipped to keep pace NEW YORK, April 30, 2026 /PRNewswire/ -- Key takeaways | Explore Key Insights Continue Reading Tech leaders are no longer measured by uptime or delivery alone but rather their ability to drive enterprise value. There is a widening gap between ambition and capability in scaling new tech - The mandate has fundamentally shifted: The majority of tech leaders (79%) cite driving business outcomes as their top priority, signaling a definitive move from running systems to creating enterprise value. - There's a new standard for tech leadership: As AI raises the stakes, success will likely require more than technical expertise. Tech
AI & Tech Brief: The “turning point” in the AI economy - The Washington Post
Exclusive: A new survey by Harvard Business Review Analytics shows that businesses are using AI to improve efficiency in back-office operations — but not for revenue growth or business competitiveness.
AI-Powered Manifest OS Secures Funding
Manifest OS, an AI-powered platform, is set to revolutionize legal billing by replacing the billable-hour model with outcomes-based pricing, starting with business immigration.
Cognizant forecasts quarterly revenue below estimates on cautious IT spending
Cognizant Technology forecast quarterly revenue below Wall Street estimates on Wednesday, signaling cautious client spending on its IT services amid macroeconomic uncertainty, sending its shares down about 6% in premarket trading.
LinkedIn's AI hiring agents on track for $450 million in yearly revenue
LinkedIn, the social network for professionals owned by Microsoft, on Wednesday said that its hiring products using what is known as agentic AI are on track to generate $450 million in sales in the coming year.
Cognizant to buy Astreya for about $600 million
Cognizant Technology has agreed to buy Astreya, an IT services and technology provider focused on AI infrastructure and data center services, in a deal valued at around $600 million, the company told Reuters.
The Agentic Sales Engine by Crono
Where sales teams and AI agents work side by side.
Accenture Launches Historic Microsoft 365 Copilot Rollout to 743,000 Employees
Accenture is deploying Microsoft 365 Copilot to its entire workforce, marking the largest enterprise rollout of the tool to date to enhance productivity.
‘AI deflation’ comes to India’s tech services giants and puts downward pressure on revenue
The rise of AI is impacting revenue for India's major tech services firms, though employment numbers remain stable.
AI-Powered Manifest OS Secures Historic Funding to Revolutionize Legal Billing
Manifest OS is replacing the billable-hour model with outcomes-based pricing, starting with business immigration, to enhance efficiency and transparency in the legal industry.
AI is changing who gets to be an expert. Are your colleagues ready to become ‘directors of intelligence’?
As AI transforms how knowledge is accessed and applied, it’s reshaping work, widening opportunity gaps, and elevating the human skills that matter most in a machine-driven world.
Female-founded French startup Cleo Labs raises €1.5 million to automate international product compliance
Cleo Labs, a Paris-based RegTech startup specialising in product regulatory compliance, has today announced a €1.5 million funding round to accelerate the development of its AI-powered platform for international product compliance. The round was led by Larry Berger, alongside Kima Ventures and Financière Saint-James, as well as several tech ecosystem figures, including Boris Paillard (Le […]
A Faceted Proposal for Transparent Attribution of AI-Assisted Text Production
arXiv:2604.25346v1 Announce Type: new Abstract: Artificial intelligence systems are increasingly integrated into writing processes, challenging traditional notions of authorship, responsibility, and intellectual contribution. Current disclosure practices usually indicate whether AI was used, but rarely explain how it was used, where it intervened, or how its output was reviewed. This paper proposes a faceted model for representing AI-assisted text production at the levels of documents, chapters, sections, and paragraphs. The proposal introduces a core model based on Form, Generation, and Evaluation, and an extended model that adds Intent, Control, and Traceability. The model is positioned as a minimal operational baseline with extensibility toward higher-fidelity representations. A worked example based on the production of this article demonstrates applicability.
The Stanford Economist Studying A.I.’s Jobs Impact Is ‘Mindfully Optimistic’
Economist Erik Brynjolfsson breaks down A.I.’s impact on early careers, the rise of vibe-coding and why human agency will define the future of work.
New Survey from Harvard Business Review Analytic Services Finds AI Adoption Remains High, Yet Value May Lag Without Modernization and Workflow Integration
/PRNewswire/ -- Most organizations have moved beyond experimenting with artificial intelligence, but few are realizing its full value. New research from...
Genpact and HFS Research: 92% of Executives Say Agentic AI Will Fundamentally Change Business Operations | Morningstar
The research, based on a survey ... that 92% of respondents believe agentic AI – systems that can autonomously coordinate tasks and make decisions – will fundamentally change how work is executed. Despite this, nearly 80% of organizations still operate these systems in supervised ...
Most firms still keep humans approving agentic AI as spending rises
Survey of 545 executives found firms expect to scale agentic AI in 17 months, but 33% cite unprepared processes as the top barrier.
A Merger of the Minds: The Role of Knowledge Management in AI Adoption - Troutman Pepper Locke
This article discusses Troutman Pepper Locke's integration of AI and knowledge management—especially in Private Equity—to drive efficient, consistent, and responsible client service.
US audit firms shift focus from AI adoption to oversight
US audit firms move from AI roll-outs to tighter oversight as Caseware-backed research shows stronger calls for validation and controls.
Connecting Agents to Decisions
Palantir argues that enterprises require an AI decision architecture built around data, logic, action, and security layers to effectively implement agentic AI.
People Using AI to Self-Represent Are Clogging The Courts
Basically, AI helps simplify complex legal pathways and explain them in a way regular people can understand.
Leveraging Real-Time Generative AI Coding for Accelerated Organizational Decision-Making
Integrating AI coding tools directly into team meetings can significantly compress project timelines and improve engagement. This approach shifts the focus from discussion to immediate, iterative prototyping during collaborative sessions.
The rise and ROI of the chief AI officer | IBM
Most companies now have a CAIO. While new data indicates it’s already paying off, the job’s mandate is still evolving.
Nearly 90 percent of companies believe people will determine AI success, but far fewer are investing in related people strategies, Inaugural Aon Study Finds
/PRNewswire/ -- Aon plc (NYSE: AON), a leading global professional services firm, today released its inaugural Human Capital Trends Study, revealing a critical...
ISG Europe ServiceNow report highlights AI, sovereignty | III Stock News
Audit-ready AI and location-bound data handling are shaping deployments in Europe. ISG assessed 40 providers across three ServiceNow categories.
‘AI deflation’ comes to India’s tech services giants and puts downward pressure on revenue
Headcounts, however, are mostly holding up AI is beginning to make a dent in the business models of India’s big four technology services giants…
CIOs move to reclaim value as AI shakes up outsourcing contracts
As AI reshapes outsourcing models, CIOs are renegotiating contracts, reworking governance, and rethinking risk management.
CFOs Funded the AI Revolution. Now They’re Joining It. | Bain & Company
CFOs championed enterprise AI investment while their own function lagged. That calculus is changing fast.
Towards Automated Ontology Generation from Unstructured Text: A Multi-Agent LLM Approach
arXiv:2604.23090v1 Announce Type: new Abstract: Automatically generating formal ontologies from unstructured natural language remains a central challenge in knowledge engineering. While large language models (LLMs) show promise, it remains unclear which architectural design choices drive generation quality and why current approaches fail. We present a controlled experimental study using domain-specific insurance contracts to investigate these questions. We first establish a single-agent LLM baseline, identifying key failure modes such as poor Ontology Design Pattern compliance, structural redundancy, and ineffective iterative repair. We then introduce a multi-agent architecture that decomposes ontology construction into four artifact-driven roles: Domain Expert, Manager, Coder, and Quality Assurer. We evaluate performance across architectural quality (via a panel of heterogeneous LLM judges) and functional usability (via competency question driven SPARQL evaluation with complementary retrieval augmented generation based assessment). Results show that the multi-agent approach significantly improves structural quality and modestly enhances queryability, with gains driven primarily by front-loaded planning. These findings highlight planning-first, artifact-driven generation as a promising and more auditable path toward scalable automated ontology engineering.
Why AI poses a unique ROI challenge for accounting firms | CFO Dive
AI is exposing the limits of ROI frameworks rooted in billable-hour models, writes Thomson Reuters accounting expert Elizabeth Beastrom.
Cognizant Launches Startup Investment Arm for AI | CTSH Stock News
Aimed at Global 2000 clients, the new unit will invest in AI, data, cybersecurity and cloud startups to turn emerging tech into enterprise products.
How SAP Concur Automates Expense Reporting with Agentic AI
SAP Concur is utilizing agentic AI to streamline expense reporting, specifically focusing on automating the completion of incomplete receipt data.
Ryan Vasquez - Actor on Broadway (Hamilton, Wicked ...
Organizations that can answer those questions soon are in a fundamentally different position than the ones that don't. That's why I built Provenance, an AI governance and ethics consulting practice working with organizations and individuals navigating the ethical and regulatory dimensions of emerging technology.
Measuring Successful Cooperation in Human-AI Teamwork: Development and Validation of the Perceived Cooperativity and Teaming Perception Scales
As human-AI cooperation becomes increasingly prevalent, reliable instruments for assessing the subjective quality of cooperative human-AI interaction are needed. We introduce two theoretically grounded scales: the Perceived Cooperativity Scale (PCS), grounded in joint activity theory, and the Teaming Perception Scale (TPS), grounded in evolutionary cooperation theory. The PCS captures an agent's perceived cooperative capability and practice within a single interaction sequence; the TPS captures ...
How To Talk To Non-Technical Stakeholders About AI
By the time you’re in the room, you’re not introducing AI – you’re already correcting what people think they know about it.
Accenture to Roll Out Copilot to All Employees
Accenture is rolling out Microsoft's Copilot 365 AI assistant to all of its roughly 743,000 employees.
2026 AI Impact Survey Report | Grant Thornton
Most organizations are scaling AI they cannot explain, measure or defend.
Tech Services Buyer Survey: Betting Big on AI and Resilience
This Bain survey summarizes enterprise expectations for AI, resilience, cybersecurity, and tech services spending in 2026.
Recognition Without Authorization: LLMs and the Moral Order of Online Advice
arXiv:2604.22143v1 Announce Type: new Abstract: Large language models are increasingly used to mediate everyday interpersonal dilemmas, yet how their advisory defaults interact with the concentrated moral orders of specific communities remains poorly understood. This article compares four assistant-style LLMs with community-endorsed advice on 11,565 posts from r/relationship_advice, using the subreddit as a concentrated, vote-ratified moral formation whose prescriptive clarity makes divergence measurable. Across models, LLMs identify many of the same dynamics as human commenters, but are markedly less likely to convert that recognition into directive authorization for action. The gap is sharpest where community consensus is strongest: on high-consensus posts involving abuse or safety threats, models recommend exit at roughly half the human rate while maintaining elevated levels of hedging, validation, and therapeutic framing. The article describes this pattern as recognition without authorization: the capacity to register harm while withholding socially ratified permission for consequential action. This divergence is not incidental but structural: a portable advisory style that remains validating, risk-averse, and weakly directive across contexts. Safety alignment is one plausible contributor to this pattern, alongside training-data averaging and broader assistant design. The article argues that model divergence can be reframed from a technical error to a way of seeing what standardized assistant norms flatten when they encounter situated moral worlds.
Indian IT Giants Thrive Amid AI Integration
India's IT giants, including Infosys and Tech Mahindra, are posting robust financial results driven by AI services that offset traditional IT declines. Despite macroeconomic uncertainties, AI-led solutions are reshaping revenue streams, with companies like TCS seeing AI contribute over 6% to their revenue.
How can modern professionals navigate security risks in 2026?
Yash Jain discusses how cybersecurity needs to be an institutional ‘fundamental component’, not a ‘compliance checkbox’. Read more: How can modern professionals navigate security risks in 2026?
Inside the AI Agents Conference 2026 – What industry leaders will reveal
How to measure ROI on agentic AI deployments, moving from "this demo looks impressive" to "this system creates measurable business value", requires evaluation frameworks and organisational structures that most enterprises haven't yet built.
Council Post: How AI Agents Can Help Small Businesses Compete
AI agents are more than an emerging trend to monitor; they offer operational advantages that can allow small teams to perform like large ones.
r/automation on Reddit: What are some automations that actually got 10x better due to advancements in AI?
Data Drive Content Creation & Repurposing : Our team has used AI tools like Frizerly to automate the process of coming up of a content strategy using our Google search data. It also spies on our competitors to identify keyword gaps. This is then used to automatically publish a blog on our website every day using our internal data like customer testimonials, case studies etc.
Read the Paper, Write the Code: Agentic Reproduction of Social-Science Results
arXiv:2604.21965v1 Announce Type: new Abstract: Recent work has used LLM agents to reproduce empirical social science results with access to both the data and code. We broaden this scope by asking: Can they reproduce results given only a paper's methods description and original data? We develop an agentic reproduction system that extracts structured methods descriptions from papers, runs reimplementations under strict information isolation -- agents never see the original code, results, or paper -- and enables deterministic, cell-level comparison of reproduced outputs to the original results. An error attribution step traces discrepancies through the system chain to identify root causes. Evaluating four agent scaffolds and four LLMs on 48 papers with human-verified reproducibility, we find that agents can largely recover published results, but performance varies substantially between models, scaffolds, and papers. Root cause analysis reveals that failures stem both from agent errors and from underspecification in the papers themselves.
How Supply Chain Dependencies Complicate Bias Measurement and Accountability Attribution in AI Hiring Applications
arXiv:2604.22679v1 Announce Type: new Abstract: The increasing adoption of AI systems in hiring has raised concerns about algorithmic bias and accountability, prompting regulatory responses including the EU AI Act, NYC Local Law 144, and Colorado's AI Act. While existing research examines bias through technical or regulatory lenses, both perspectives overlook a fundamental challenge: modern AI hiring systems operate within complex supply chains where responsibility fragments across data vendors, model developers, platform providers, and deploying organizations. This paper investigates how these dependency chains complicate bias evaluation and accountability attribution. Drawing on literature review and regulatory analysis, we demonstrate that fragmented responsibilities create two critical problems. First, bias emerges from component interactions rather than isolated elements, yet proprietary configurations prevent integrated evaluation. A resume parser may function without bias independently but contribute to discrimination when integrated with specific ranking algorithms and filtering thresholds. Second, information asymmetries mean deploying organizations bear legal responsibility without technical visibility into vendor-supplied algorithms, while vendors control implementations without meaningful disclosure requirements. Each stakeholder may believe they are compliant; nevertheless, the integrated system may produce biased outcomes. Analysis of implementation ambiguities reveals these challenges in practice. We propose multi-layered interventions including system-level audits, vendor guidelines, continuous monitoring mechanisms, and documentation across dependency chains. Our findings reveal that effective governance requires coordinated action across technical, organizational, and regulatory domains to establish meaningful accountability in distributed development environments.
Despite the hype, AI is not replacing the customer service workforce | HR Dive
The hype says “agentless” service is imminent, but data shows most teams are still staffing up while trying to make artificial intelligence actually function in real workflows.
"Capital is flowing into the market, but it is more selective, more money going to fewer companies" | CTech
Delia Pekelman, SVP Corporate and Growth at LeumiTech, spoke at Calcalist's Tech Independence 2026 event about how startups are dealing with the AI revolution: "More mature growth companies must integrate AI in a meaningful way. That often requires rethinking and rebuilding core technology ...
4TB of voice samples just stolen from 40k AI contractors at Mercor
A significant data breach at Mercor has resulted in the theft of 4TB of voice data belonging to 40,000 AI contractors.
Should your board appoint a bot?
New AI tools help chairs and directors with prep and research but are unlikely to be granted a vote
Ex-AWS Legend Explains AI Transformation
Ex-AWS legend explains what enterprises need to make AI actually work. AI transformation is about people and organization, not technology
From Skills to Talent: Organising Heterogeneous Agents as a Real-World Company
arXiv:2604.22446v1 Announce Type: new Abstract: Individual agent capabilities have advanced rapidly through modular skills and tool integrations, yet multi-agent systems remain constrained by fixed team structures, tightly coupled coordination logic, and session-bound learning. We argue that this reflects a deeper absence: a principled organisational layer that governs how a workforce of agents is assembled, governed, and improved over time, decoupled from what individual agents know. To fill this gap, we introduce \emph{OneManCompany (OMC)}, a framework that elevates multi-agent systems to the organisational level. OMC encapsulates skills, tools, and runtime configurations into portable agent identities called \emph{Talents}, orchestrated through typed organisational interfaces that abstract over heterogeneous backends. A community-driven \emph{Talent Market} enables on-demand recruitment, allowing the organisation to close capability gaps and reconfigure itself dynamically during execution. Organisational decision-making is operationalised through an \emph{Explore-Execute-Review} ($\text{E}^2$R) tree search, which unifies planning, execution, and evaluation in a single hierarchical loop: tasks are decomposed top-down into accountable units and execution outcomes are aggregated bottom-up to drive systematic review and refinement. This loop provides formal guarantees on termination and deadlock freedom while mirroring the feedback mechanisms of human enterprises. Together, these contributions transform multi-agent systems from static, pre-configured pipelines into self-organising and self-improving AI organisations capable of adapting to open-ended tasks across diverse domains. Empirical evaluation on PRDBench shows that OMC achieves an $84.67\%$ success rate, surpassing the state of the art by $15.48$ percentage points, with cross-domain case studies further demonstrating its generality.
Large UK companies in the dark about how their data is used overseas by AI
Survey of senior technology and data executives finds lack of understanding about how information is handled abroad
Help! Our newest client is an AI model
A behind-the-scenes look at the work of Rutherford Hall, critical communications strategist
How a chance encounter in Texas sparked a $1 billion Kleiner Perkins-backed AI startup
Avoca, founded by two MIT grads, brings Silicon Valley tech to trades like plumbing, roofing, and HVAC.
Recruiting Brainfood - Issue 498 - by Hung Lee
The democratisation of software production might be one of the most profound long term consequences of AI . We’re seeing evidence of it already in our own community, with examples like this becoming more common as recruiters begin vibe coding their own toolkit for custom purpose.
Professional Jurisdictional Competition as a Primary Outcome of AI-Driven Labor Market Disruption
AI-driven job displacement will likely trigger intense inter-professional competition for control over new, high-value task boundaries. This struggle will manifest through regulatory capture, credentialing requirements, and public advocacy rather than simple net job loss metrics.
AI synthetic audiences are already here and poised to upend the consulting industry
There is a war brewing between AI and consulting. Akin to an armies slow march towards the castle, a new technology is coming to dethrone the expert guessers of Mckinsey, Nielsen, Gartner, Publicis and the rest. Any consulting that involves analyzing people (think all of marketing, research, polling, etc.) will have to reckon with the technology of “synthetic audiences”. Synthetic audiences aim to generate digital versions of people that can then be surveyed almost instantly and affordably, but not as accurately. Think Tamagochi but with people. By prompting AI with information about a person, we ask AI to get in their shoes, simulate the thoughts, behaviors, priorities and decisions of real world humans. We can also invent non-specific placeholder people or personas and survey them as though they are real. Various firms have already fielded products in these domains, including startups Electric Twin, Artificial Societies, and Aaru, and even the century-old Dentsu. What used to take 4 months to survey people, plus two months to create a nice PowerPoint presentation of findings at a total cost of thousands or even tens of thousands, now takes two minutes and costs only a few dollars. It may seem like I’ve picked my winner. But in this war of tribes, I’m a Romeo, caught between the two warring houses. I work for a large incumbent in this space. From 2023-2025 while working at the London headquarters of WPP, I built similar tools for numerous Fortune 500’s and advised many New York University researchers on the subject. Companies like WPP with head counts and revenues that rival the populations and GDP’s of small European nations need startups for their speed and high margins, while startups need our distribution. My advice has always been for unity between these tribes. Considering WPP is partnering with numerous startups, is working tirelessly in building our own tools and building deep connections with hyper scalers, it’s possible I mislead you with the war analogy. This may be a love story after all. But destiny’s bottle of poison is in our hands. These next few years are pivotal and formative. The future will ultimately be determined by the buyers of these studies. Fortune 500’s, with the largest appetite for market research, often hesitate to include synthetic audiences in their diet. The first question I’m asked in any pitch is "will AI steal my data?" I find this question to be an emotional response. It seems to me like most AI fears are remnants of a 2022 LinkedIn post that burrowed itself into our collective consciousness. I generally respond to this question with another: “Do you use Microsoft Teams?” The answer is often "yes." Almost every enterprise stores sensitive data in a cloud service that Google Amazon or Microsoft provides. These are the same companies that provide enterprise AI services, which state in their terms and conditions that they won’t train models with your data. Now, believing this statement is optional, but for that matter believing is voluntary for all things. Criticisms of accuracy on the other hand, are harder to dispute. The famed venture capital firm Andreessen-Horowitz (a16z) titled its analysis of this budding tech scene as “Faster, smarter, cheaper”. As the hopeful mediator in this war, I agree synthetic research is faster and cheaper, but is it smarter? Not sure. A seminal paper from Stanford by Park et al. established a benchmark in 2024 proving that AI can simulate human responses to surveys with an average of 85% accuracy. In fact for certain portions of the general social survey, they replicated answers with more than 90% accuracy. When the model is provided relevant information and is given rich context (like a mini biography of the person) it can guess their actions and thoughts very accurately. But no prediction can be 100% accurate. A future where human propensities are modeled even better than humans can express their own desires is a possibility. Maybe we’ll live in a future where the movie Minority Report becomes reality. However, this future is too distant to warrant the attention of a business reader and is better suited for Tom Cruise and Steven Spielberg. What is more interesting to me is what this technology can do at lower accuracies. In my private tests, I’ve seen that with very simple information about a person, such as their age, neighborhood and gender, certain behaviors can be modeled with 72% accuracy. An argument can be made that these are easy-to-make predictions. Predicting whether a married person will have children is low stakes. This can’t completely replace the unique insight of a strategist. However, considering how elusive it is to understand and model people. A solution that’s better than random and so attainable poses to make an impact. Think about the immense scale. The human mind works with a small range of values. We understand when something is twice as fast but we can’t comprehend when something is 175,200 times faster. All of a sudden a journey that took several days becomes becomes several hours, bridges get built, gas stations, entire industries are started. When improvement isn’t marginal but exponential, it has positive externalities that are impossible to predict even by this article. What I suggest for all of us is to eat the popcorn and watch the show. No matter what happens, it’ll be fun.
AI Skills: How They Can Boost Your Salary In India - Rediff.com Business
Employees with artificial intelligence skills are likely to see better salary increments in the coming years, especially in technology, GCCs, and BFSI sectors, according to TeamLease Edtech.
AI Agents Revolutionize Research
AI agents are now capable of reconstructing complex academic papers independently, marking a significant shift in reproducibility and peer-review processes.
AI Skills: How They Impact Salary Increments In India - Rediff.com Business
Employees with artificial intelligence skills are likely to see better salary increments in the coming years, especially in technology, GCCs, and BFSI sectors, according to TeamLease Edtech.
Ex-AWS legend explains what enterprises need to make AI actually work
AI transformation is about people and organization, not technology Enterprise AI projects go off the rails when companies focus on the technology instead of the people.…
I lost my job to AI. Here’s why mass layoffs won’t transform your company
Pearl's Head of AI Operations Mark Quinn was displaced by the same technology he now deploys at scale. That experience taught him the difference between companies that shrink their way to efficiency and the ones that actually reinvent themselves.
AI Company ROI: The Ultimate Guide to 2026
In 2026, businesses are no longer asking whether AI is worth it—they are asking how to maximize returns from it. Every organization aiming to partner with the Best AI company is focused on measurable…
How Corporates Are Maximizing AI's Potential
Corporates are maximizing AI's potential by leveraging AI tools for businesses in regulated industries, according to a report by The Innovator's Radar.
Publicis Unveils AI Hub in Singapore
Publicis Groupe APAC has launched an AI Development Hub in Singapore, aiming to create scalable marketing technologies with the support of the Singapore Economic Development Board.
AI-powered performance management: The shift to continuous feedback | Robert Half
AI-powered performance management: The shift to continuous feedback | Robert Half # AI-powered performance management: The shift to continuous feedback By Rob Hosking, Executive Director for Administrative and Customer Support, Robert Half Annual performance reviews were designed for a workplace that no longer exists. Work moves faster. Roles evolve constantly. Teams operate across functions, departments and time zones. Yet many organizations still rely on a once-a-year checkpoint to evaluate contribution and plan development. That disconnect is becoming harder to ignore. Artificial intelligence helps close the gap. Used thoughtfully, AI allows HR leaders to support real-time coaching, personalized development and greater consistency in evaluation. When implemented with strong governance and human oversight, it transforms performance management from a compliance exercise into a continu
AI Agents: Why Most Projects Fail Not at the Model Layer, but at the Architecture Layer | by IVAN GUTNIK | Apr, 2026 | Medium
AI Agents: Why Most Projects Fail Not at the Model Layer, but at the Architecture Layer | by IVAN GUTNIK | Apr, 2026 | Medium Sign up Get app Sign up # AI Agents: Why Most Projects Fail Not at the Model Layer, but at the Architecture Layer 3 min read Just now -- Share Press enter or click to view image in full size The right agent architecture Most AI agents do not fail because the model is “weak.” They fail because teams try to force a business process on top of an LLM without a proper architecture. The market is talking a lot about AI agents right now, but in practice they are still often designed as “a chat with an LLM that will somehow figure things out on its own.” That is where the core mistake begins. An AI agent is not just a prompt + API + chat interface. An AI agent is a decision-making system embedded into a business process. I would describe a universal framewo
How Denis Brovarnyy is closing the AI Skills gap with real-world training
How Denis Brovarnyy is closing the AI Skills gap with real-world training The gap between finishing a course and actually being useful on a team, Denis Brovarnyy has seen it from both sides. And in an era where AI is reshaping every technical role, that gap is getting more expensive to ignore. Companies are no longer experimenting with AI. They are implementing it. And they need people who can contribute from day one, not after six months of onboarding. He spent years as a software engineer and then an engineering manager in Israel, building products and leading teams. He knew what it took to hire someone junior and get them productive fast. He also knew how rarely training programs produced that person. When he lost his job, he didn’t immediately look for the next one. He sat with a question most people skip: “Is there a better use of what I know?” The answer became AIT Technology Sch
The AI Labor Report — Friday, April 24, 2026
That gap between expectation and reality shapes the bind workers currently face. A survey of 2,400 knowledge workers across the U. S.