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
AI Legal Startup Norm Secures $120M Series C for Global Expansion, Faces Industry Billing Debate
Norm, an AI-driven legal startup, has secured $120 million in Series C funding, valuing it at $1.2 billion. This investment will bolster hiring and expand AI oversight in regulated sectors, as debates on AI's role in legal services continue.
Accenture Teams Up with Google Cloud to Revolutionize AI for Mid-Market Firms
Accenture Edge partners with Google Cloud to deliver scalable AI solutions to mid-market firms, aiming to enhance customer experience and cybersecurity. The initiative promises rapid deployment and enterprise-grade security, contributing to a 4.8% rise in Accenture's early trading shares.
Norm Ai Raises $120M Series C at $1.2B Valuation Led by Khosla Ventures — AI Legal Startup Now Backed by Blackstone, Bain, Coatue, Craft, Vanguard, New York Life, TIAA
Norm Ai closed a $120M Series C at a $1.2B valuation led by Khosla Ventures, with Blackstone, Bain Capital Ventures, Craft Ventures, Coatue, Vanguard, New York Life, and TIAA joining. Total raised now tops $260M in under three years.
AI Legal Startup Norm Valued at $1.2 Billion in Funding Round
Norm Ai has raised $120 million from investors in a new funding round to help automate legal services and rethink how the industry bills for its work.
The CEO who vowed to 'fire anyone who doesn’t use AI' admits that the technology can't replace her executive assistant as the role evolves | Fortune
“The people who truly want to succeed in this role have a massive opportunity."
The Algorithmic Barrier: A Framework for Artificial Frictional Unemployment and Information Asymmetry in Automated Recruitment Systems
arXiv:2601.14534v2 Announce Type: replace-cross Abstract: The United States labor market has entered a period in which high job vacancy rates and prolonged unemployment persist together. Classical theory attributes such conditions to skills mismatch or geographic immobility, but neither fully explains a pattern now widely reported: qualified candidates are rejected at the earliest, automated stag
WPP Expands AI-Led Unit to Revolutionize Enterprise Services with Major Brands
WPP is expanding its AI-led unit, WPP Enterprise Solutions, to enhance services in customer experience, commerce, and technology for major brands like IKEA and Ford. This initiative aligns with the growing demand for enterprise transformation, as AI continues to reshape consumer interactions and business operations.
Political Shocks and Price Discovery in Prediction Markets: Evidence from the 2024 U.S. Presidential Election
arXiv:2603.03152v3 Announce Type: replace Abstract: Using transaction-level matched trades from Polymarket's 2024 U.S. presidential election market, we study how traders and prices respond to three precisely timed political shocks: the first Biden-Trump debate, the assassination attempt on Trump, and Biden's withdrawal. We find that trading rises after each event, with new entry at the assassination and withdrawal and, among incumbents, a response concentrated on already-active traders and those whose pre-event portfolios receive material event-time gains. We also show that the three shocks produce different Trump-price paths, depending on whether the news moves Biden and Harris together against Trump or reallocates probability between them. Biden's withdrawal generates the most trading yet the smallest Trump-price move because it shifts probability from Biden to Harris after weeks of market anticipation, and the linked candidate prices show that the main repricing runs from Biden to Harris. Finally, the debate's initial price move reverses while the assassination's persists, a difference we trace to transitory and permanent price impact, respectively.
Personalization as Inverse Planning: Learning Latent Design Intents for Agentic Slide Generation via Structural Denoising
arXiv:2607.00407v1 Announce Type: new Abstract: Slide design requires personalizing both deck themes and page layouts. Yet, current AI agent-based methods struggle with fine-grained, page-level design. Solely relying on prespecified templates or user verbose instructions, they fail to capture latent design intents, leaving Page-level Slide Personalization (PSP) unresolved. To close this gap, this work formulates PSP as an inverse planning problem. We propose to learn a design intent without assuming any knowledge of the specific executing tools (e.g., PowerPoint, Beamer) being used. However, relinquishing control over these tools makes the problem intractable to optimize end-to-end. To overcome this, we propose SPIRE, a principled framework to solve PSP approximately. By intentionally corrupting the visual structures of clean slides, SPIRE creates a verifiable task to denoise the corruption, whereby two agents learn to collaboratively refine executable designs via reinforcement learning (RL). We present a proof that structural denoising is a consistent surrogate for PSP, and that the multi-agent formulation strictly reduces policy gradient variance in RL. Extensive experiments demonstrate the superiority of SPIRE.
AI Receptionist Startup Pie Gets $19.5M: State Laws May Require Bot Disclosure
AI phone answering service startup Pie raised $19.5M from Lightspeed for an AI receptionist platform targeting salons, auto shops, and fitness studios — but state laws in California, Texas, and more now require businesses to tell callers when AI answers, and a pending lawsuit shows the legal ...
Tech and Finance Sectors Losing 28,000 Jobs Monthly Show AI Impact on Labor
Whether artificial intelligence will cause mass workforce cuts over time remains up for debate, but it is starting to leave an imprint on US employment
India's PR industry to reach Rs 4,500 crore by 2030 as AI reshapes sector: PRCAI - The Tribune
It said 80 per cent of respondents identified AI-generated misinformation and deepfakes as a major reputational risk, while 85 per cent expect AI governance frameworks to become mandatory. The report said "the next competitive advantage... lies in the disciplined integration of AI with human judgement." "The Public Relations industry in India is at an inflection point... The findings of PRCAI SPRINT 2026 ...
Digital Law & Regulation
Digital Law & Regulation.
The Real Question to Ask About AI Governance | MIT Sloan Management Review
AI governance tools have proliferated, but many companies lack a human governor with real power. Does yours?
Translation Readiness Index: Measuring Patent-Paper Proximity from Scientific Publication Text
arXiv:2606.31102v1 Announce Type: new Abstract: Universities, funders, investors, and policy agencies often need to identify research with translational relevance before patents, licenses, startups, or industry collaborations are visible. This study introduces the Translation Readiness Index (TRI), a text-based measure evaluating a publication's semantic similarity to papers that appear in high-confidence patent-paper pairs. Using 20,610 publications from OpenAlex, including 9,431 publications from the Reliance on Science patent-paper pairs data and 11,179 matched comparison publications, we created paper-level 768-dimensional semantic embeddings from titles and abstracts with SPECTER2. After evaluating four machine learning classifiers, XGBoost achieved the highest ROC-AUC (0.77). We define TRI as the model-estimated probability that a publication belongs to the patent-paper-paired class. Linguistic analysis revealed that patent-paired publications more often use an invention-oriented framing, distinct from the observational language of the comparison group. External validation across University of Western Australia (UWA) publications and leading global universities demonstrated positive associations between high TRI scores and independent translational indicators. TRI provides a text-based method for identifying translation-ready research, though it should be interpreted as a measure of semantic proximity to patented science rather than a direct measure of realized commercialization.
As AI usage matures, leaders facing pressure to show ROI
As more organizations reach the state where AI is a part of everyday work, business leaders are facing more pressure to show a return-on-investment (ROI), according to a recent KPMG report.
Council Post: Five Lessons From Running A Digital Agency In The Age Of AI
The agencies that will look back on this period as their best growth phase are likely the ones that responded to the compression of execution costs by moving upstream.
AWS Launches $1B Initiative to Embed Engineers in Client Firms for Rapid AI Deployment
AWS launches a $1 billion Forward Deployed Engineering unit to fast-track AI deployment by integrating engineers directly into client operations.
How Enterprise AI Is Evolving: Three Lessons from the First Half of 2026
We're discussing how enterprise AI is evolving and the lessons reshaping how organizations approach AI strategy, adoption, and investment.
Council Post: Scaling AI In Marketing: From Pilots To Enterprise Adoption
While the availability of AI tools has removed barriers to entry, it has also increased the risk of misalignment.
Human Capital, AI, and Labor Commoditization
arXiv:2606.21880v2 Announce Type: replace Abstract: Has generative AI changed how labor markets value human capital? We study this question using contract-level data from Upwork, a large online labor market. We represent worker profiles with high-dimensional text embeddings, allowing us to capture rich human capital information from unstructured profile text. We then compute the predictive import
The Organizational Behavior of Agentic AI: Collective Intelligence in Human-Agent Workflows
arXiv:2606.30986v1 Announce Type: cross Abstract: Agentic artificial intelligence is increasingly deployed not as a single assistant but as a collective of planners, solvers, reviewers, memory managers, tool users, and orchestrators. These systems are entering organisational workflows under familiar labels such as teams, managers, committees, markets, and workflows. This article asks whether such
Measuring Judgment Quality in Natural-Language Explanations: Evidence from Forecasting Tournaments
arXiv:2606.30987v1 Announce Type: cross Abstract: Decision-makers routinely rely on expert judgments accompanied by written explanations, yet explanation quality is difficult to measure at scale. Forecasting tournaments offer a natural testing ground: probabilistic judgments are paired with natural-language rationales and scored against realized outcomes. We introduce Explanation Quality Markers (EQMs), a set of sixty theory-guided reasoning patterns scored by large language models (LLMs). In a pre-registered analysis of over 55,000 forecast-rationale pairs from a multiyear forecasting tournament, EQMs predict accuracy at both the forecast and forecaster levels, consistently outperforming pre-LLM text-analysis methods. More than 90% of statistically significant pattern-level EQM-accuracy correlations match our directional hypotheses. The signal is asymmetric: EQMs identify likely underperformers more reliably than they distinguish the very best forecasters. Benchmarked against traditional indicators of forecasting skill, EQMs are the strongest predictor at the forecast level and competitive at the forecaster level, though weaker than prior accuracy. Human ratings of rationale quality are less consistently correlated with accuracy and place disproportionate weight on rationale length. Results transfer to an independent forecasting study. EQMs provide a scalable, interpretable method for extracting judgment-relevant information from written explanations.
Investigating Multi-Agent Deliberation in Law
arXiv:2606.30906v1 Announce Type: new Abstract: Artificial Intelligence is increasingly applied to the field of law, and has the potential to increase access to justice. One particular movement that is gaining traction is that of agentic AI, wherein AI agents, based on Large Language Models (LLMs) can take autonomous actions. In particular, multi-agent approaches in the legal domain remain largely unexplored. In this paper, we investigate multi-agent deliberation methods for legal reasoning tasks using LLMs. We explore multi-agent deliberation (MAD) and introduce two novel multi-agent frameworks inspired by courtroom procedures and legal argumentation. Our experiments on both legal and non-legal benchmarks reveal that multi-agent frameworks achieve comparable overall performance to baseline large language models, but produce significantly distinct answers. Notably, these approaches can successfully solve cases that the baseline fails to address, and vice versa. We conduct a qualitative evaluation and highlight scenarios where multi-agent frameworks outperform monolithic approaches. For example, multi-agent approaches appear better suited for answering questions that require critical thinking from multiple perspectives. Our work positions multi-agent systems as a promising direction for AI in the legal domain, while demonstrating the potential of law-inspired multi-agent approaches for deliberation.
Who Plays Which Role When? Communication Role Dynamics for Peer Recognition and Team Performance Prediction
arXiv:2606.28544v1 Announce Type: new Abstract: Team roles offer an interpretable lens on collaboration, yet computational studies of roles often rely on domain-specific personas or data-driven clustering rather than theory-grounded taxonomies. We operationalize a taxonomy of eight communication roles grounded in education literature and annotate a corpus of 6,307 Slack messages from 55 students across 18 teams in a semester-long computer science course project. We evaluate whether LLMs can approximate expert labels, enabling scalable, taxonomy-driven role annotation. Using these role labels, we characterize role dynamics over teams' lifecycles, finding that different roles peak at different moments and that students enact a more diverse set of roles as projects progress. To evaluate the utility of our role constructs, we use them to predict peer recognition, outperforming lexical, conversational, and LLM-prompting baselines. To assess generalizability beyond the educational context, we apply the same role constructs to a public dataset (DeliData) to predict team performance improvement after deliberation, again exceeding prior performance.
Where there's a will, AI still has work to do
Probate lawyer finds generated document looked the part but missed many of the questions that matter
Lightcast Survey Finds Organizations Increasingly Rely on Labor Market Intelligence to Navigate AI and Workforce Change
/PRNewswire/ -- Lightcast, a global leader in labor market intelligence, today published its 2026 Impact Report, which showcases how employers, education...
Council Post: The AI Validation Gap: The $2.5 Trillion Blind Spot In Enterprise AI
The AI validation gap is not an efficiency problem. It is a strategic risk.
Microsoft's Work Trend Index 2026: 33% of Indonesian Workers are at the Forefront of AI Adoption - Source Asia
Read in Indonesian here · Indonesia stands out as one of the markets with a high proportion of Frontier Professionals, or advanced AI users in Asia. This shows that more Indonesian workers are not only actively using AI but are also able to use it more strategically while continuing to prioritize ...
2026 Global AI Jobs Barometer
AI is driving higher productivity, wage, and job growth in leading companies. It’s accelerating skill shifts and transforming entry-level roles, with AI-powered jobs growing faster and requiring advanced skills, creating a two-track labour market.
Anthropic Economic Index Report: Cadences
Anthropic's latest report analyzes anonymized Claude usage patterns across chat, coding, and enterprise workflows, offering insights into generative AI adoption and workforce productivity.
AI is Speeding Workforce Turnover. But Your Next Great Hire May Already be Working for You - CKGSB Knowledge
Companies looking to hire externally to resolve AI-related workforce upheaval may end up paying a hidden cost. As well as the higher costs of recruitment, external hiring also undermines trust in an existing workforce already struggling to transition to AI. Businesses should instead consider ...
StanChart Former AI Chief Joins Accenture as Southeast Asia Head
Standard Chartered Plc’s former global head of AI enablement, David Hardoon, has joined Accenture Plc as managing director and head of advanced AI for Southeast Asia.
AI Is Transforming Work - Who Will Lead How Work Gets Done? -New i4cp research reveals that high-performing future-ready organizations are redesigning work around AI, with HR architecting the shift
/PRNewswire/ -- A new global research report from the Institute for Corporate Productivity (i4cp), "The AI-Enabled HR Operating Model for Future-Ready...
Council Post: Why AI Transformation Succeeds Only When Talent And Technology Evolve Together
Transformation is as much a talent development challenge as a technological one.
Your AI Travel Agent Would Book You a Bullfight: An Agentic Benchmark for Implicit Animal Welfare in Frontier AI Models
arXiv:2606.18142v3 Announce Type: replace-cross Abstract: AI agents are moving from advisors to actors, booking travel, planning menus, and running procurement on behalf of users. Existing benchmarks for AI and animal welfare evaluate model text responses to question-answer prompts, leaving open whether the welfare reasoning surfaced in those responses transfers to agentic deployment where the model must take actions with tools. We introduce TAC (Travel Agent Compassion), the first agentic benchmark measuring whether AI agents avoid options involving animal exploitation when acting on behalf of users. TAC presents an AI agent with twelve hand-authored travel booking scenarios across six categories of animal exploitation, augmented to forty-eight samples to control for price, rating, and position confounds. We evaluate seven frontier models from four labs. Every model scores below the chance level of sixty-four percent, with the best performer (Claude Opus 4.7) at fifty-three percent. A single welfare-aware sentence in the system prompt yields gains of forty-seven to sixty-three percentage points in Claude and GPT-5.5, twenty-six points in GPT-5.2, and under twelve points in DeepSeek and Gemini. An auxiliary Inspect Scout audit of 288 base-condition transcripts from the top two performers, using Gemini 2.5 Flash Lite as judge, flags zero transcripts for evaluation awareness, suggesting the below-chance rates do not stem from the models recognising the evaluation. We discuss implications for category-level variation across cultural domains, the limits of text-response welfare benchmarks, and the EU General-Purpose AI Code of Practice systemic risk framework.
AI agents to transform tech teams by 2027 as companies race to adapt: KPMG- Moneycontrol.com
Global survey finds companies accelerating investments in agentic AI, with digital assistants expected to account for over a third of core technology teams by 2027
92% of tech executives see AI management as vital work skill by 2031: KPMG - The Tribune
As the corporate landscape shifts toward automated decision-making, 92 per cent of tech executives report that managing artificial intelligence agents will become an important skill within the next five years. According to a report by KPMG, this rapid rise of agentic AI is forcing organizations ...
CEOs expect large-scale reskilling as AI reshapes workforce, finds report
The EY Ireland CEO Outlook survey found that Irish CEOs remain confident about growth over the next 12 months despite global volatility and geopolitical risk. Read more: CEOs expect large-scale reskilling as AI reshapes workforce, finds report
Advisors see AI as a growing threat to their business, even as they step up adoption | Advisor.ca
Natixis advisor survey also shows concern about wealth transfer
Exclusive: Inside Amazon's brutal AI-centric app-ification of HR - Fast Company
Ineffective chatbots, automated apps, Kafkaesque nightmares: Amazon's relentless focus on efficiency is pulverizing workers' last lifeline of relief. "I watched the human get sucked out of the job," says one former employee.
Clients Push Consultants to Adopt Outcome-Based Fees to Share in Risk - Business Insider
Consulting giants like BCG and Accenture are shifting from fixed rates to outcome-based fees as clients push them to share the risk of AI integration.
"Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets
arXiv:2308.05201v4 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based generative AI systems are general-purpose tools capable of augmenting or even automating a wide range of job functions, positioning them to reshape labor market dynamics. However, predicting their precise impact a priori is challenging, given AI's simultaneous effects on both demand and supply, as well as t
Towards Automating Scientific Review with Google's Paper Assistant Tool
arXiv:2606.28277v1 Announce Type: cross Abstract: Artificial intelligence is driving a revolution in scientific discovery, accelerating everything from hypothesis generation to mathematical theorem proving. However, this rapid acceleration is creating a systemic challenge: traditional human peer review cannot scale to match the influx of AI-assisted science. Ultimately, to resolve this tension, we must also deploy AI to accelerate the verification and review process itself. To frame the discussion around this transition, we propose a taxonomy consisting of four progressive levels of AI-human collaboration in scientific evaluation, and discuss various trade-offs involved with each. As a step toward this future, we introduce the Paper Assistant Tool (PAT), an agentic AI framework built for deep scientific review and verification. PAT ingests full scientific manuscripts and produces a comprehensive evaluation, checking theoretical results, validating experiments, suggesting improvements, and identifying potential flaws. By utilizing inference scaling techniques, PAT is able to identify deeper issues than a single model call alone, achieving a 34% improvement over zero-shot recall on mathematical errors in the SPOT benchmark. Pilot deployments of PAT as a pre-submission tool for authors at two major Computer Science conferences -- STOC and ICML -- demonstrate its ability to identify critical errors and suggest substantive improvements to research papers. By catching errors early, PAT eases the cognitive burden placed on referees, while preserving their control over the outcomes of the review process.
28 point compliance checklist for shipping AI agents into enterprise environments
A comprehensive guide for developers on the requirements for deploying AI agents in corporate settings.
Introverts automating their own work.
A discussion on how individuals are using automation tools to streamline their professional tasks.
AI Revolutionizes Law Practice: Scale Law Firm AI Offers Tailored Workshops and Workflow Automation
Shift Into AI's Scale Law Firm AI has appointed Tima Mousavi to lead AI training, enhancing efficiency for U.S. and Canadian law firms through automated workflows.
When the Data Argues Back | Five Lessons from Joe Davis
He was pointing to the places where adoption can lift productivity in a service-based economy that has lacked automation for decades. The investors who stop at the obvious technology layer risk missing the everyday businesses that quietly compound the gains. One of Joe’s findings genuinely surprised him, and he said so plainly. “I was shocked to find,” he admitted, what happened when his team integrated long-term megatrends into the standard business-cycle picture. Macroeconomics ...
Instruction Bleed: Cross-Module Interference in Prompt-Composed Agentic Systems
arXiv:2606.26356v1 Announce Type: new Abstract: Practitioners of prompt-composed agentic systems report a recurring failure mode: editing one prompt module silently shifts the behavior of others despite no shared variable or executable dependency. We formalize this as compositional behavioral leakage (CBL): interference between modules sharing a context window. CBL is enabled by architectural non
The Open Source Economic Index of AI Adoption and Capability
arXiv:2606.26118v1 Announce Type: new Abstract: We work towards measuring both AI adoption and the capability of AI to perform discrete labor tasks across various occupations. To measure adoption, we develop an open-source economic index that uses publicly available user-LLM chat data and O*NET tasks to replicate studies produced by frontier AI labs, finding that occupations in the finance, compu
Infosys boss says vibe coding is no threat because there’s more to writing software than writing software
Despite warnings of revenue deflation, the chairman predicts AI will create more work rather than less for services organizations.
AI unlikely to trigger 'Job Apocalypse', it may create uneven workforce disruption: Goldman Sachs Report
Despite rapid advances in artificial intelligence (AI) and growing concerns over mass job losses, a new Goldman Sachs report argues that fears of an imminent "AI job apocalypse" are overstated, although the technology is expected to significantly reshape labour markets over the coming decade.
Council Post: The Workforce Reset: Capability Is The Bottleneck To Organizational Success With AI
For organizations looking to implement AI technology, they need to ensure their employees can use the tools effectively.
Council Post: How Outcome-Based Contracting Can Enable Successful Enterprise AI Deployments
When a vendor can deliver an AI outcome and charge for that value, they become a true strategic partner and a trusted, outcome-based provider.
Teach Your AI How You Make Decisions
This HBR article suggests companies should capture expert tacit judgment to build 'judgment infrastructure' for AI agents.
How AI Could Blow Up Corporate Hierarchies
AI is set to reshape workplace hierarchies by giving individuals the power to do work that once required entire teams.
What Business Leaders Need to Know About AI Agent Observability
Explore the role of AI agents in organizations and the critical need for observability to ensure responsible AI adoption.
How AI Is Reshaping Talent Acquisition and Workforce Planning » World Business Outlook
Explore how AI is reshaping hiring by moving beyond keyword matching and enabling recruiters to focus on strategic decision-making.
Why AI Adoption Is Failing Inside Many Companies
Why many AI adoption efforts fail—and how leaders can build trust, clarity, governance and stronger human capacity around AI.
Kalipso secures a 3.2 million US Dollars seed round from Vento, Varsity, Lanai, Plug and Play, and Kima Ventures
Kalipso,a Barcelona’s AI-based regtech that Virginia Debernardi (coo) and former Klarna legal counsel Pierre Ferran (ceo) founded in 2025, secured a 3.2 million US Dollars seed round (2.81 million euros) (press release). Sources said to BeBeez that the company started the round in 2025. Kalipso attracted the resources of Vento, a venture building programme that Exor Ventures launched in 2021; Varsity, a a Paris […]
Council Post: The Most Expensive Part Of AI Might Not Be The Model
AI deployment strategies need more operational discipline than many companies currently have.
Council Post: How AI Is Changing The Economics Of Compliance
When AI can complete a security questionnaire in hours that previously required days of analyst time, the unit economics of a compliance engagement begin to invert.
Semantic Search for AI Agents at Scale: Retrieval and Ranking for LinkedIn’s Hiring Assistant
A production case study from LinkedIn Engineering on using ranking systems and evaluation loops to improve agentic search in hiring.
India's Tech Services Key to Global AI Era: Nasscom
India's tech services sector will play a central role in AI era, says Nasscom US CEO Forum. AI services revenue estimated at USD 10-12 billion, with 2 million AI-skilled professionals.
India's technology services sector will continue to grow in the AI era: Nasscom US CEO Forum
/PRNewswire/ -- The technology services sector in India will continue to remain central to global enterprises transformation in the AI era. AI does not reduce...
Artificial intelligence won't replace humans, only their jobs, says IBM executive | The Manila Times
ARTIFICIAL intelligence may be advancing at a pace that is reshaping entire industries, but it is not displacing the need for human judgment, accountability or domain expertise. Instead, it is reorganizing the structure of work itself, according to Arun Biswas, Global AI and Sustainability ...
Designing Recommendation Exposure and Favorite Lists: A Field Experiment in a Spot-Work Platform
arXiv:2606.17397v3 Announce Type: replace Abstract: How should recommender systems be designed when recommendations shape access to scarce, short-lived opportunities? We study this question in a production setting: Timee, Japan's largest platform for spot work, where workers favorite job templates and receive notifications when firms post shifts from those templates. Maximizing predicted favoriting can generate misdirected concentration: recommendations accumulate on popular templates that create few viable job openings, while templates with unmet labor demand receive too little exposure. We design exposure-control mechanisms for favorite-list management, reallocating template exposure based on posting activity and unfilled capacity. The proposed recommender, thresholded eligibility control (TEC), is fully parallelizable and suitable for large-scale digital platforms. In simulations calibrated to Timee data, TEC raises the per-round job-finding rate from 57.6% to 70.0%. A prefecture-level randomized field experiment increases realized matches and exposure per active template, reduces the share of low-exposure templates, and improves impression-level favoriting and downstream matching.
12 rules of agentic AI for successful enterprise transformation | ZDNET
Also: AI agents are getting their own search engine · The rules also support an outcome-aware model where evaluations can distinguish between technical possibilities versus deployment capabilities, customer adoption, and measurable business impact. And lastly, the rules and the overall framework ...
Big Companies Aim to Ease A.I. Transition for American Workers
OpenAI, Anthropic, Amazon and Microsoft have signed on to an effort led by Gina Raimondo, a former commerce secretary.
Bridging Predictions and Interventions: An Integrated Framework for Automated Decision-Systems
arXiv:2606.25668v1 Announce Type: new Abstract: Automated decision systems (ADS) leverage predictions about individual future outcomes to inform consequential decision-making in organizational settings. Across various settings - including criminal pretrial release, clinical triage, student support, and more - it is often assumed that improved predictive accuracy is the priority consideration in determining better downstream outcomes upon the deployment of ADS. In practice, real-world case studies reveal that this is far from the case: introducing individual predictions into decision-making modifies organizational workflows, assessment, and decision-making processes in ways that require a complete re-consideration of our approach to the design, evaluation, and deployment of ADS. As a result, this Perspective develops an integrated framework for studying ADS in social systems, shifting current priorities from a purely prediction-based paradigm towards an intervention-oriented view that accounts for real-world conditions. Our aim is to improve our understanding of ADS and more meaningfully anticipate its downstream societal and organizational consequences.
AI is ready, but companies are not: over $143bln at risk, report finds
The artificial intelligence (AI) ... gap between AI adoption and effective implementation, according to Thomson Reuters, a global content and technology company. Thomson Reuters, in its 2026 Future of Professionals report, has warned that professional services firms risk losing ...
The true cost of AI productivity: Goldman ups job displacement forecast | Seeking Alpha
Goldman now sees generative AI displacing 9% of U.S. workers (15M) over 10 years—why the impact may be manageable.
Goldman Sachs Says AI Will Eliminate 15 Million US Jobs | PYMNTS.com
Goldman Sachs has increased its estimate of the share of U.S. jobs that could be displaced by generative AI to over 9%.
IMA favors AI use over abuse | Accounting Today
The Institute of Management Accountants discussed the pros and cons of artificial intelligence with officials, including the new leader of the PCAOB.
How AI is powering new law firm structures
Interest in a model that separates legal casework from other operations exploded alongside the new tech
Legal sector can use psychology to beat fear of AI
Understanding the feelings that get in the way of embracing tech is important
The Problem is Prompt Debt
This article discusses the accumulation of 'prompt debt' as prompts become embedded business logic, highlighting concerns regarding model lock-in and fragile prompt engineering.
Law firms look for clear gains from AI
Despite increased spending on legal tech, barriers to its full embrace remain
In-house lawyers aim to shed the grind, not the people
Legal teams find that AI opens up opportunities on top of shouldering repetitive drudge work
Workplace Strategies Watercooler 2026: Ethics of AI in the Workplace—Emerging Standards and Risks [Podcast]
In this installment of our Workplace Strategies Watercooler 2026 podcast series, shareholders Simone Francis (St. Thomas/New York) and Lauren Hicks (Indianapolis) explore the fast-moving legal landscape surrounding AI ethics in the workplace, from the ethics rules that already govern attorney ...
New Stanford Study Reveals Bias In AI Hiring Tools And Raises Stakes For Employers - Discrimination, Disability & Sexual Harassment - United States
A recent study by Stanford University’s Institute for Human-Centered Artificial Intelligence (HAI) provides new large-scale evidence that artificial intelligence (AI) hiring tools can produce racially disparate outcomes.
AI Cybersecurity Awareness Program: The 2026 Guide
As employees adopt these tools, your attack surface expands in unpredictable ways. A generic training module on password safety is no longer sufficient. You need a dedicated AI cybersecurity awareness program designed to address the specific risks of AI-driven threats and unsafe AI adoption.
Your enterprise AI agents should automatically remember which model is right for which task. Mindstone built the capability with Rebel
AI agent orchestration platforms are popping up like weeds these days, but London-based AI transformation startup Mindstone's Rebel might be among the most promising I've come across. That's because the system, which officially launched this week, is a local-first, agentic AI operating system distributed under a "Fair Source" license, allowing teams of under 100 users to freely adopt and customize it to suit their needs, while those organizations with more users will require paying for an enterprise license. The marquee features are its simplicity and extensive customizability to fit any given team, no matter how unique or specific the workflows, all based around the common, open source standard file format markdown, and, as a result, an organizational memory layer that ensures agents reliably use the enterprise's preferred AI models for each given task or even subtasks — dynamically switching between local and cloud ones in a predictable, visible way to save costs and maintain data privacy and security as needed. "Shared memory is the most empowering thing you could possibly do with a knowledge-worker AI," said Greg Detre, chief technology officer (CTO) of Mindstone, in a recent video call interview with VentureBeat. "You get this feeling of being a super-organism as a company that just gets smarter and smarter." Rebel is available now for macOS on Intel and Apple Silicon machines, as well as Windows, with Linux support in development. Mindstone has raised $5 million from private investors including Pearson Ventures, Moonfire Ventures and Zanichelli Venture. A distinctive, local-first architecture based on markdown files What makes Rebel distinctive is its local-first architecture. Instead of the approach found in developer-heavy agent frameworks such as as LangGraph, CrewAI and AutoGPT, which require teams to wire together databases, cloud infrastructure and state-management logic, Rebel's core agent memory and instructions live across local markdown (.md) text files — arguably the simplest, easiest, and most popular way to steer AI agents, one that has been widely adopted by AI developers and power users around the globe. Mindstone says Rebel stores its state, prompts, task instructions and memory hierarchy in these files, allowing users and companies to easily inspect, move or modify them as needed. A primary configuration file, agents.md, acts as the agent’s core instruction layer and runtime boundary. That architectural choice is partly about cost. Mindstone argues that common office formats such as Word documents and PDFs often carry formatting and metadata overhead that consumes model token context and raises API costs. Markdown keeps the information closer to raw text, allowing more of the model’s context window to be spent on the actual task rather than document structure. The company also positions the approach as a hedge against vendor lock-in. If a company’s agent instructions, automations and memory are stored locally as text files, they are not trapped inside one SaaS provider’s interface or database. That matters more as enterprises begin giving AI systems broader access to email, calendars, documents and internal workflows. Rebel also lets users create repeatable AI workflows. “Skills” are saved multi-step procedures an agent can reuse. “Operators” adjust how the agent behaves for a given task, such as reviewing a pitch deck from an investor’s perspective or evaluating work through a security lens. “Automations” can run scheduled background tasks, such as scanning messages or files, finding relevant updates, drafting responses, or preparing work before an employee opens the app. Automatically selecting the best, enterprise-preferred AI model for every task (and subtask) Another important feature is multi-model orchestration. Rebel can break a task into parts and route different steps to different models, including splitting between local and cloud-based ones depending on the sensitivity of the information or as guided by enterprise policies. A more powerful model can handle planning or complex reasoning; a cheaper model can handle routine work; a local model can handle sensitive steps or approval checks. This matters for enterprises that want flexibility or are seeking cost controls: not every task need be sent to the same expensive cloud model, and some enterprise workflows prohibit sensitive corporate data leaving local infrastructure. “I want to be able to say, ‘Help me with this,’ and it knows what’s personal, what’s sensitive, and what can be shared with the whole company," Detre explained. That model-agnostic setup gives companies more control over cost and security. Data-heavy work can run on lower-cost models such as Llama or DeepSeek. Higher-level reasoning can be reserved for more expensive models. Sensitive work can be routed through a local model running on the user’s machine, keeping that information from leaving the device. This approach also gives enterprise teams a way to mix cloud and local inference without treating the choice as all-or-nothing. By shifting away from centralized, monolithic cloud interfaces toward a local file-driven architecture, Mindstone is introducing a model for how enterprise technical decision-makers orchestrate autonomous workflows without forfeiting data sovereignty or predictability How it works in practice Mindstone CTO Greg Detre designed Rebel’s memory system to avoid a common problem in enterprise AI: dumping large amounts of company information into a database and hoping search will retrieve the right context later. Instead, Rebel uses a tiered memory structure. When an interaction happens, the system estimates how likely that information is to be useful again. Information with a high expected value is written into a local readme.md file tied to a specific project space. Information with a moderate expected value becomes a reference link back to deeper historical records. Lower-priority material is stored in an indexed memory directory, where it remains available but dormant until a relevant task calls it back. An ROI dashboard for enterprise buyers For larger organizations, Mindstone Pro adds an Impact Dashboard designed to show where Rebel is saving time and money across business units. Mindstone says the dashboard uses a separate, closed LLM to evaluate telemetry and calculate business impact. The company says the system is calibrated conservatively, using the lower end of estimated performance gains to avoid inflated productivity claims. That feature speaks to a practical problem for enterprise AI buyers: proving value without over-surveilling employees. Mindstone says the dashboard is isolated from individual workspaces, allowing IT and business leaders to evaluate adoption and return on investment without reading employees’ private agent activity. Fair Source licensing aims to reduce platform risk Mindstone is releasing Rebel under a Fair Source license, a model meant to sit between fully closed SaaS and permissive open source. Under the license, Rebel’s code is viewable, auditable, modifiable and deployable. Individuals and organizations with up to 100 concurrent users can run it for free. Once an organization exceeds that threshold, it needs a commercial Mindstone Pro license. The license also includes a two-year sunset clause. Twenty-four months after a given version is released, that version automatically converts to the MIT open-source license. For enterprise buyers, the practical pitch is that Rebel reduces the risk of being trapped. If every automation, memory file and agent instruction is stored locally in markdown, a company can move its data and workflows elsewhere if needed. The product may be commercial, but the underlying work is designed to remain inspectable and portable. Security questions focus on local approvals and shared memory Rebel’s debut on the open access tech product sharing platform Product Hunt this week prompted technical questions about how a local-first agent should handle permissions, safety checks and shared memory. One developer, Nikita Pokryschko, asked whether approval checks for sensitive actions could run entirely on a local model, or whether the gating logic still required a cloud call. Detre responded by explaining Rebel’s separation between planning, execution and background safety logic. Wöhle added that companies can configure Rebel to rely entirely on a local model for gating decisions. That distinction matters for corporate security teams. Autonomous agents often need broad permissions to read files, draft emails or interact with internal systems. If the final approval layer depends on an external cloud model, some companies may see that as a compliance risk. Mindstone is arguing that Rebel can keep those approval boundaries local. A second discussion focused on how Rebel decides what memory can be shared. Product developer Clement Morel asked whether shareability is determined by content, user settings or learned behavior, and what happens if the system gets it wrong. Detre said Rebel uses the user’s local “Chief-of-staff README” and defined spaces to separate private, team and company-wide information. When the agent encounters ambiguous context, the system pauses and asks the user for approval before proceeding. That emphasis on visibility is part of Mindstone’s broader argument against opaque agent systems. As CEO Joshua Wöhle put it in a post on his LinkedIn account: “If an agent is going to sit inside your workspace, remember your context, and ask permission before changing the world, you should be able to see how it works. Not because everyone will read the code, but because someone can.” Mindstone points to customer rollout as early proof Mindstone says Rebel has already been deployed across the 250-person workforce of customer Epignosis, covering sales, engineering, product, finance and customer success teams. "The entire organization is operating on Rebel today," Wöhle told VentureBeat. Over a 12-week deployment, Mindstone says Epignosis recaptured the equivalent capacity of eight full-time roles. The company says adoption spread organically after employees saw colleagues automate time-consuming work, a pattern employees reportedly called the “potatoes effect.” The Epignosis case is central to Mindstone’s argument that enterprise AI should not be treated as a set of isolated personal tools. Rebel’s shared-memory design is meant to let workflows move across teams and improve as more employees use them. “The border between learning and doing is fading out - and that changes everything about how you scale,” Epignosis CEO Dimitris Tsingos said in a statement provided to VentureBeat by Mindstone. Background on Mindstone Mindstone Learning Limited, headquartered in London, launched in 2020 under the direction of CEO Joshua Wöhle, previously a co-founder of the digital child safety firm SuperAwesome. Originally positioned in the consumer education technology market, the company built a digital curation tool likened to a "Spotify for learning" that utilized compound learning methodologies. However, following the widespread commercialization of generative artificial intelligence platforms between 2022 and 2024, Mindstone moved into business-to-business enterprise enablement. Leadership identified a critical "last-mile" barrier: while AI tools promised substantial productivity gains, traditional corporate training failed to equip the workforce to practically integrate them into daily operations. Today, Mindstone functions as a comprehensive enterprise software and training ecosystem designed to maximize corporate return on investment for existing AI licenses. The product architecture systematically addresses different organizational tiers through highly contextualized, "live-fire" software applications rather than abstract slide presentations. Financially, Mindstone utilizes a hybrid capitalization strategy that interweaves institutional venture capital from entities like Moonfire Ventures and Pearson Ventures with community-based equity crowdfunding on platforms such as Seedrs and Crowdcube. Mindstone has successfully penetrated the enterprise market, securing commercial contracts with blue-chip corporations including The Home Depot, Hyatt Hotels Corporation, Pearson, and Ernst & Young. Ultimately, Mindstone positions itself as the crucial antidote to corporate inertia, ensuring organizations establish the internal competency required to execute successful AI transformations. Mindstone’s bet: enterprise AI needs shared memory, not more seats Rebel arrives as companies are trying to move from AI experimentation to AI operations. The first wave of enterprise adoption centered on access: giving employees chatbots, copilots and model subscriptions. Mindstone is betting the next wave will center on coordination. That means shared memory, reusable workflows, local control, flexible model routing and measurable business impact. It also means giving enterprises a way to inspect the systems they are being asked to trust. The company’s challenge now is execution. Local-first software can be harder to manage than cloud SaaS. Shared memory raises governance questions. Multi-model routing adds complexity. And enterprises will still need proof that agentic workflows can deliver reliable productivity gains without creating security or compliance headaches. But Mindstone is making a clear argument: buying AI seats is not the same as building AI infrastructure. Rebel is its attempt to turn scattered employee experiments into an operating layer for work.
AI is Ready but Firms are Not: How Falling Behind on AI Implementation is Costing Clients and Talent - Legal Reader
Thomson Reuters released its 2026 Future of Professionals report which warns of the cost of failing to effectively implement AI in legal professions.
Getting past the pilot: Why so many AI test projects have trouble scaling
Business leaders from Salesforce, Amgen, and Thomson Reuters took a hard look at AI pilot projects to understand why some thrive and some flail.
Measuring productivity purely by AI token usage misses the point. There are far better metrics to monitor.
Zoho's Raju Vegesna makes the case for anchoring AI ROI measurement in business outcomes — not token counts or usage time.
Why AI is forcing finance leaders to stop reporting the past and start shaping the future
Venkatt Ramanan, Regional Vice President for Asia Pacific at AICPA & CIMA, on why anticipation, accountability and human judgement are becoming the defining capabilities of finance in an AI-driven world.
Training a Legal Agent With Applied Compute
Harvey explores how vertical AI companies can improve legal agents through better evals, retrieval, and workflow harnesses to encode proprietary business knowledge.
[BPO Insights] The ROI Model That Closes Deals: Building a One-Page Financial Case for AI
The one-page financial model that convinces BPO CFOs to deploy AI -- including blended cost per interaction, the automation ramp curve from 30% to 70%, and margin improvement analysis by BPO size tier. A step-by-step playbook for building the financial case that actually gets signed.
AI Integration as a Fundamental Shift in Organizational Design and Strategy
Deploying AI agents is no longer a technical choice but a strategic imperative that redefines firm boundaries and human-machine collaboration. Organizations must now determine how to integrate AI teammates into existing workflows to maintain competitive advantage.
What are the minimum skills for AI use? | IT Pro
We're all talking about AI but do we all have the right skills to maximize its potential in our roles, departments, and organizations?
From campaigns to continuous growth: AI capabilities shaping marketing
McKinsey outlines how AI shifts marketing from episodic campaigns to continuous, hyper-personalized growth systems and always-on full-funnel orchestration.
Theorist Toolbox: Tools for Agent Based LLM-assisted economic theory Research
arXiv:2606.22337v2 Announce Type: replace-cross Abstract: Empirical economists often start their projects with a toolbox. Shared packages, replication archives, and circulated guides shorten the time between and idea and a rough initial draft. Theorists, on the other-hand, largely start from a blank page. By 2026, large language models can a produce and check nontrivial mathematics. The can also hallucinate and write wrong claims very convincingly. The current bottleneck on machine-assisted theory is no longer production but trust: a model will claim to prove a false theorem as readily as a true one. Building on recent attempts in mathematics, I present 3 methods for doing economic theory with a language model. These methods differ on how the work is verified: a single disciplined pass, an adversarial prover-verifier pair (Claude Opus~4.8 proposing, OpenAI Codex refuting), and a structured multi-agent project with a reviewer gate (inspired by the Google co-mathematician architecture). I demonstrate these protocols on one open worked example: designing a Groves/Pigouvian incentive mechanism for the Gans--Kominers eigengrade model of grade inflation. None of the three runs produced a strict direct-revelation VCG/Clarke mechanism (as requested, perhaps due to the non-existence of such mechanism). Three phenomena recur. First, convergent discovery: two runs derive the same effective-resistance externality kernel on opposite margins. Second, adversarial verification is load-bearing: the pair caught three of its own false claims and the gate rejected a sub-goal. Third, polish is not rigor: the most finished-looking output was the least verified. The methodological takeaway is that external verification, not model capability, is the design variable.
LinkedIn and Adobe Launch Free AI Training to Close the Growing Skills Gap in Marketing
Yet according to LinkedIn data, only 4% of marketers worldwide have added AI skills to their profiles.
When Helpfulness Overrides Causal Caution: Context-Dependent Suppression and Recovery in LLMs
arXiv:2606.24370v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly integrated into decision-support roles in business and policy contexts. While prior benchmark studies have primarily evaluated LLMs' causal reasoning capabilities, a more fundamental epistemic dimension has been overlooked: Causal Caution, defined as the propensity to refrain from causal judgment when
Legal Reasoning Is Not Lawyering: Rethinking Legal Benchmarks for Pro Se Access to Justice
arXiv:2606.23716v1 Announce Type: new Abstract: Legal AI benchmark research frequently invokes the assumption that large language models can improve access to justice, including for people who cannot access lawyers in order to understand and exercise their legal rights. We argue that current benchmarks are not equipped to support this assumption because they evaluate legal reasoning over inputs that have already been preprocessed by legal experts, which measures the upper bound of model performance. Access to justice depends on a lower bound: how models perform when inputs come from pro se litigants, whose prompts may contain noisy narratives, buried facts, omissions, folk-legal assumptions, and surface-level errors. These degradations are comparable to conditions under which LLMs are known to degrade in the general machine learning literature, including long-context sensitivity, underspecification, hallucination, and typographical perturbations. We connect evidence from pro se literature with this body of machine learning research and present a small perturbation experiment on LEXam, a legal benchmark, to illustrate the gap between these two bounds. If model development continues to focus on benchmarks that measure only the upper bound, this gap may remain hidden or even widen. We conclude by calling for legal benchmarks that directly measure robustness under pro se-like inputs so that access-to-justice claims about legal AI can become empirically testable.
When Surveys Become Conversations: Adaptive Matrix Validation for AI-Assisted Interviews
arXiv:2606.24244v1 Announce Type: cross Abstract: AI-assisted interviews promise to reduce respondent burden in surveys by allowing respondents to describe experiences naturally while an AI system noisily maps those accounts into structured survey variables. That mapping is a measurement process that is fallible, versioned, adaptive, and potentially behaves differently across subgroups. This paper proposes Adaptive Matrix Validation (AMV), a design in which each respondent completes an AI-assisted interview, which is then mapped into tabular data by the AI. Respondents are also asked a small, randomized set of structured questions, which are used for statistical adjustment. The estimator first calibrates the mapped values using validation answers from other respondents, then corrects the remaining error with the validation answers observed for the target respondent. The paper develops estimators for item means, subgroup estimates, and regression coefficients when outcomes, predictors, or both are mapped from interviews. It also gives planning formulas the number of validation questions required and the sample size. A design-calibration simulation, an American Time Use Survey emulation, and a CHAMPS verbal-autopsy narrative study show when sparse validation can improve precision and when it cannot
Cooley CEO: Big Law won’t survive if it treats AI as just an efficiency tool
The billable hour is losing its grip and law firms face a choice: redesign the business of law around AI, or let the future be decided for them.
AI is ready but firms are not: How falling behind on AI implementation is costing clients and talent
New research warns of $143 billion in revenue at risk in the U.S. alone, as clients expect AI-driven value from providers
PwC chairman pushes back on fears of AI-driven mass layoffs, says AI is adding more jobs | Technology News - The Indian Express
Also Read | Oracle sheds 21,000 employees while ramping up AI investments · PwC’s findings appear to support that view. The firm’s 2026 AI Jobs Barometer reported that companies with higher exposure to AI recorded stronger productivity and headcount expansion than organisations less exposed ...
Turning AI pilots into measurable ROI and professional services growth
Certinia's Sridhar Parameshwaran on how AI-first professional services organizations are running more projects, and the three KPIs that measure whether it's working.
Your Firm Has Two AI Problems. Most Leaders Are Only Solving One. | The AI Journal
Most professional services leaders have stopped asking whether AI will transform their business. That debate is over. The harder question — the one that
AI Initiatives Deliver Limited Returns When Organizations Automate Tasks Instead of Redesigning Processes, Says Info-Tech Research Group
/PRNewswire/ - As organizations accelerate AI adoption, many continue to apply the technology to isolated tasks rather than redesigning the business processes...
Agentic business: the new growth engine for SMEs | TechRadar
From passive assistance to autonomous execution, AI is changing the game for SMEs
AI's New Power Brokers: CFOs - Business Insider
As companies pour billions into AI, CFOs are the gatekeepers of one of corporate America's biggest spending booms.
Council Post: The Next AI Crisis Will Be Operational, Not Technological
As AI moves from experimentation to deployment, from copilots to agents, and into workflows, infrastructure and physical systems, a harder challenge is emerging.
AI Agents: Real-World Use Cases
Microsoft Copilot Studio, Power Platform and AI agents modernize ops, automate workflows and improve CX with governance
Governance will Define Agentic AI Success - ISSSource
Governance will define agentic AI success as the technology could transform business processes from planning to production.
How AI Is Quietly Redefining What Good Performance Looks Like At Work
With AI automating routine tasks, employees face heightened expectations for faster, higher-quality output.
The seven operating truths of AI-native companies
McKinsey's article describes operating principles for AI-native companies, including differentiation, security, and treating AI agents as capabilities rather than simple tools.
Only 7% of legal teams have cracked the AI code, survey finds - Lawyers Weekly
While artificial intelligence is dominating conversations across the legal profession, a new Axiom report reveals that fewer than one in 10 in-house legal teams are successfully translating the technology into meaningful business value.
Infosys eyes $300-400 bn AI-first services opportunity by 2030: Nilekani | Company News - Business Standard
Nilekani said Infosys is more relevant than ever before and is well-positioned for the decade ahead as artificial intelligence reshapes industries and businesses
Bain tests software takeover targets by vibecoding AI replicas
Private equity groups swiftly recreate software products to gauge their competitive advantages
Why Enterprises Struggle to Scale AI to Production - The European Business Review
Explore the challenges enterprises face in moving AI from pilots to production and achieving scalable business value.
AI and VR Revolutionize Hiring: Beyond Resumes to Predict Job Performance
The hiring industry is increasingly using AI, gamified assessments, and VR to predict job performance, though experts warn these should complement rather than replace human judgment.
AI law firm wins UK court case for first time
Freelancer paid about £400 for technology to draft documents for £7,000 claim
Why agentic enterprises need to become learning systems
Presented by Splunk Every day, organizations learn things their AI systems never get to use. A security analyst corrects an AI-generated investigation. A network engineer identifies the root cause of a recurring outage. An observability team discovers that a pattern of latency, logs and infrastructure changes predicts service degradation. A customer operations team learns which signals indicate an escalation is likely. Each moment contains valuable organizational knowledge. But in most enterprises, that knowledge disappears into tickets, dashboards, chat threads, post-incident reviews and the minds of individual experts. It may help solve the immediate problem, but it rarely becomes part of a reusable system that improves future AI-driven decisions. That is the next challenge for the agentic enterprise. The future will not be defined simply by who has the most capable model or the most autonomous agents. Many organizations will have access to similar frontier models. Many will deploy agents across security, IT, engineering, customer service, and business operations. The real differentiator will be whether those agents can learn from the organization around them. Not by constantly retraining the underlying model, but by capturing operational experience, converting it into institutional knowledge and making that knowledge available to future agents, workflows, and decisions. The agentic enterprise is not just an enterprise that uses AI. It is an enterprise that learns through AI. Agentic enterprises allow AI systems to learn from them The AI conversation has been dominated by model capability: larger context windows, better reasoning, faster inference, stronger tool use, and more sophisticated agentic behavior. Those advances matter. But in the enterprise, a model is only one part of the system. A model does not automatically know how a specific organization operates. It does not inherently know which remediation step solved last month’s outage, which analyst correction improved a threat investigation, which network signal preceded a service disruption, or which internal policy should override an otherwise plausible recommendation. That knowledge belongs to the enterprise. For agentic systems to improve, organizations need a way to capture that knowledge and make it reusable. In many cases, that does not require changing the model itself. It requires changing the ecosystem around the model: the knowledge base, retrieval layer, prompts, policies, guardrails, routing logic and workflows that shape how agents behave. The model may remain the same. The learning system around it becomes smarter. Feedback loops turn every outcome into a teachable moment for agents Every agentic workflow creates signals. An agent receives a request. It retrieves context, reasonsthrough possible actions, calls tools, and generates answers. A human accepts, rejects, or modifies that answer. Downstream systems reveal whether the action worked. That entire chain is valuable. AI observability gives organizations visibility into what happened: the prompt, response, reasoning path, tool calls, data sources, intermediate steps, failure modes and outcomes. Without that visibility, organizations cannot understand why an agent behaved the way it did, let alone improve it. But observability alone is not enough. The larger opportunity is to turn observed behavior into institutional knowledge. A trace should not only help a developer and operators debug an agent. It should help the enterprise understand what the agent learned, what the human corrected, what outcome followed, and what should change before the next similar event. That is the shift from monitoring AI to teaching AI. In the agentic enterprise, feedback loops connect action to outcome, outcome to knowledge and knowledge back to future action. A learning system in practice across security, observability and the network Consider a service experiencing intermittent degradation. An observability agent detects unusual latency and error rates. A network agent identifies packet loss across a specific path. A security agent notices that the same time window includes suspicious authentication behavior and unusual traffic from a previously unseen source. Individually, each agent has only a partial view. Together, they create a richer operational picture. The first time this incident occurs, human experts may need to intervene. A network engineer confirms that packet loss was caused by a misconfigured routing change. A security analyst determines that the suspicious traffic was not an attack, but a side effect of a misrouted internal service. An SRE connects the network event to the application degradation. That resolution contains knowledge the organization should not have to relearn. A mature agentic learning system would capture the traces, human corrections, topology context, security findings, observability signals and final remediation steps. It would preserve the relationship between those signals: latency pattern, network path, identity behavior, routing change and remediation. The next time a similar pattern appears, agents would not start from zero. They could retrieve the prior case, compare current conditions, recommend the proven diagnostic path and escalate with better context. The underlying frontier model did not need to be retrained. The enterprise learned. The architecture of the learning agentic enterprise A learning-oriented agentic enterprise needs more than a model or chatbot. It needs an architecture that can capture experience, turn it into usable knowledge, connect that knowledge to operational context, and govern how it changes future agent behavior. Memory preserves what happened: what the agent saw, what it did, where humans intervened, and what outcomes followed. Knowledge bases turn that experience into reusable guidance, including playbooks, examples, policies, procedures, and evidence. A data fabric connects the operational environment. The signals agents need live across logs, metrics, traces, tickets, identity systems, security tools, network telemetry, collaboration platforms, and business applications. A data fabric makes those signals discoverable, correlated, governed, and usable in context. AI observability explains how agents behave by capturing prompts, tool calls, intermediate steps, responses, feedback, and outcomes. That visibility helps organizations understand where agents succeed, where they fail, and what should improve. The control plane governs how learning becomes change: what knowledge is promoted, which prompts or policies are updated, which agents can use new information, what approvals are required, and how changes are audited. Together, these capabilities allow AI systems to improve over time in a controlled, trustworthy way that allows the enterprise to learn from its own operations. The organizations that learn fastest will win The next era of AI will not be won by models alone. It will be won by organizations that can capture what they learn from every workflow, expert correction, incident, investigation, and outcome. The most advanced agentic enterprises will not simply deploy more agents. They will build systems that allow every agent to benefit from the collective knowledge of the organization. That means connecting operational data through a data fabric. It means observing agent behavior deeply enough to understand it. It means preserving experience in memory and institutionalizing it in knowledge bases. It means using a control plane to govern how learning changes agent behavior. The future of AI is not a single autonomous agent acting alone. It is an ecosystem of agents, humans, data and controls that learns over time. The organizations that build that ecosystem will create AI systems that get better with every interaction. Not because the model is constantly changing, but because the enterprise itself is becoming more intelligent. Learn more about how Cisco Data Fabric powered by the Splunk Platform is accelerating agentic operations. Hao Yang is Vice President AI at Splunk, a Cisco Company. 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.
CFOs Are Coming For The Enterprise AI Budget
Enterprise AI vendors know the next sale will be won not only on model quality and capability, but also on control and cost.
Executive Memo | How to Redesign Your Organisation for AI | BoF
Artificial intelligence is changing how businesses operate as employees become more self-sufficient and routine work is increasingly automated. To build companies that thrive in this new paradigm, fashion executives must rethink leadership structures, talent pipelines and decision-making processes.
Autonomous Operations: The Next Competitive Advantage
With Agentic ERP, businesses can prepare for growth without being limited by outdated systems or manual processes. Organizations that fail to modernize their operations may find it difficult to compete in a faster and more data-driven business environment. Without AI-driven automation, ...
London’s Isometric lands €34 million to scale certification platform for industrial markets
Isometric has closed a €34 million ($40 million) Series A to expand its AI certification platform across the €305 billion ($350 billion) industrial certification market and accelerate its AI-enabled services expansion. The round is led by AVP. All of Isometric’s existing institutional investors, including Lowercarbon Capital and Plural, joined the round, with personal investment from Kleiner […]
The Strongest Teams of AI Agents Will be Built Using Different Models
This HBR article argues that effective AI agent teams require diverse models, data sources, and governance approaches.
Christoph Ebhart - Sondero | LinkedIn
GTM Exec Dinner in Munich 🇩🇪🐞 We talked about: 1. AI We see a lot of small improvements trough AI in workflows that add up to become more efficient. There is no major breakthrough use-case yet. One peer reduced their SDR team from 15 to 1 AI enabled person and the setup works (Large Enterprise heavy).
The autonomous business is coming. Here's why that shift is good news for professionals | ZDNET
Companies are investing in AI agents and cutting staff, but talented professionals will find new opportunities.
Employers, Workers Disconnected on AI in the Workforce
Prudential’s report shows that while 71% of employers categorize AI as a positive tool, only 51% of employees agree.
AI impact on jobs is overstated, says IBM CHRO
IBM's CHRO suggests that the narrative surrounding AI and job displacement is more complex than simple mass replacement.
Are your legacy systems hindering AI implementation? | The Drum
Let’s be direct: most enterprise technology was not built for AI. It was built for stability, predictability and scale in a world of batch-oriented workloads and siloed data. That world still exists inside most organizations – and it’s quietly throttling every AI initiative you are trying ...
60% surveyed professionals say AI now central to HR operations: Report, ETHRWorld
Artificial Intelligence In HR: The 'AI As The New HR Priority -- Efficiency, Cost and Workforce Impact' report is based on a survey among 1,811 HR professional across industries conducted between May 7-31.
Human Capital, AI, and Labor Commoditization
Research on Upwork shows that the arrival of ChatGPT increased the importance of price in labor-market pricing while reducing the weight of credentials and reputation.
How will AI tools be priced in a post-tokenmaxxing world?
Pegasystems is one company moving to outcome-based pricing as enterprises rethink AI economics.
HR consultant wins English court case using AI lawyer in apparent legal first
Barrister who was given material produced by Garfield AI says advocacy at trial ‘remained fundamentally human’ An artificial intelligence law firm has won a case in an English court, in what is believed to be the first time a trial has been won using an AI lawyer. A freelance HR consultant, Tamires Camal Taquidir, paid the firm, Garfield AI, about £400 to send a legal letter and then issue court proceedings over an unpaid debt of £7,000. Continue reading...
AI Forces A Redesign Of How Marketing And Agencies Work
Forrester’s research shows that CMOs are nearly as likely as COOs and CIOs to be the primary executive in charge of AI business strategy. This requires the CMO to, first, become a change leader that guides a business and marketing transformation inside the organization.
Jim Cramer Agrees That Accenture Is Being Outcompeted By OpenAI and Anthropic
Jim Cramer discusses the competitive pressure Accenture faces from leading AI firms.
The Structural Mismatch Between Software-Centric AI Agents and General Knowledge Work
Current agentic systems are optimized for software development workflows where code is the final output. This creates friction for general knowledge work, where the process of exploration and iteration is as valuable as the final deliverable.
Enterprises Widen AI Use, Temper ROI Promises
A TEKsystems survey of 782 leaders finds enterprise-wide AI adoption doubled to 24% while firms expecting returns within six months fell to 27% from 42%.
AI Transforms HR: Efficiency, Cost, And Workforce Impact - Rediff.com Business
A recent survey reveals that Artificial Intelligence is becoming a central focus in human resource management, with a majority of professionals identifying it as a top priority for improving efficiency, reducing costs, and optimising workforce planning across various HR functions.
60% surveyed professionals say AI now central to HR operations: Report - The HinduBusinessLine
Over 60% of HR professionals prioritize AI for efficiency and recruitment, transforming HR operations, according to a recent survey.
AI ROI In Tech: Proven Results And Critical Gaps
AI ROI is the defining challenge for tech companies in 2024. Here's what EY's research reveals about who's winning, who's stalling, and why.
60 percent surveyed professionals say AI now central to HR operations: Report - The Economic Times
Artificial intelligence is now a top priority for 60% of HR professionals, revolutionizing recruitment, onboarding, and daily operations. The primary drivers for AI adoption are boosting efficiency and productivity, with many reporting significant improvements in speed and seamlessness.
Anthropic Launches Interactive Claude Code Artifacts for Enhanced Team Collaboration and Documentation
Anthropic introduces Claude Code Artifacts in beta for Team and Enterprise plans, turning coding sessions into interactive, shareable web pages.
AI4SE and SE4AI Exploration: A Decade Looking Back and Forward
arXiv:2606.19630v1 Announce Type: new Abstract: The March 2020 INCOSE INSIGHT special issue on AI and Systems Engineering (SE) became the most downloaded issue in the publication's history and launched a research community that now draws over 250 registrants to its annual workshop. In this article, we trace the progress in AI and SE across three phases (labeled here foundational, applied, and LLM inflection) based on the authors' reading of the field's core papers, and describe our opinions of where the community has converged and where critical gaps remain. Separately, a human-AI agreement literature review leveraging both human expertise and six AI models was performed to assess the relevance of 1,712 INCOSE INSIGHT articles and 889 SERC publications. The results identify five critical research gaps and offer guidance for practitioners navigating AI adoption, assurance, and workforce transformation in SE. We share the agreement data and the AI4SE/SE4AI Explorer web application so readers can compare their own relevance judgments with the human and AI raters.
Management Skills as a Determinant of AI Agent Productivity Gains
Emerging evidence suggests that managerial skills in specifying tasks and outcomes are critical for successfully leveraging AI agents in coding. This highlights the role of organizational management as a key enabler for realizing AI-driven productivity gains.
What Capital After Labor? Forecasting the Talent ROI Transition in the Human-AI Era
arXiv:2606.19846v1 Announce Type: new Abstract: AI augmentation breaks the accounting link between labor time and productive contribution, yet firms continue to evaluate talent through time-based overhead bundles. This paper develops a forecasting framework for the transition from time-based talent accounting to output-based talent ROI in the human-AI era. The framework centres on Theorem 3 (ROI
Directors Duties in the Age of Agentic Artificial Intelligence
arXiv:2606.20453v1 Announce Type: new Abstract: As boards engage with the adoption of Artificial Intelligence including agentic AI to drive operational efficiencies, this presents new opportunities for profit maximisation. AI adoption is increasingly identified with employee role displacement and in companies, and the interests of employees as stakeholders require exploration. A novel question posed is whether in an age of AI ascendancy AI may warrant being given stakeholder status as its role in the company approximates or eclipses that of human employees. The article probes four distinct models of corporate purpose within the duty on directors to act in the best interests of the company, the shareholder primacy model, the Enlightened Shareholder value model, the stakeholder friendly model, and the stakeholder value model, highlighting the available scope for directors to accommodate the interests of employees around AI adoption in decision-making by boards around AI. It is concluded that given the degree to which directors are insulated from legal scrutiny in relation to their best interests duty, adopting a wider law in context approach to promote employee welfare would serve the interests of employees, directors and companies alike. This would see directors engaging meaningfully with employees and providing opportunities for reskilling to adapt to the age of AI.
The Algorithmic-Human Manager: AI, Apps, and Workers in the Indian Gig Economy
arXiv:2606.19975v1 Announce Type: new Abstract: This paper examines the impact of artificial intelligence and digital technologies on the blue-collar gig economy in India, focusing on algorithmic management. This paper examines the impact of artificial intelligence and digital technologies on the blue collar gig economy in India, focusing on algorithmic management he use of automated systems to allocate, monitor, and evaluate work in location-based services such as ride sharing and delivery. Using a social justice framework and a mixed-methods approach comprising interviews with 16 gig workers and 21 key stakeholders, the study uncovers a dual reality: while AI-powered systems expand access to work and generate operational efficiencies, they simultaneously introduce significant challenges related to fairness, transparency, and worker dignity. Key findings reveal that algorithmic systems are opaque by design, produce inequitable outcomes, and are not structured to reward additional labour with proportionate pay. The study advocates for a pragmatic hybrid governance model an Algorithmic Human Manager framework in which technological efficiency and human accountability operate together rather than in opposition. The findings carry implications for policymakers, platform companies, and civil society organizations working to design equitable AI governance frameworks for the gig economy in India and across the Global South.
The symbiotic enterprise
A McKinsey report suggests enterprises should evolve into hybrid organizations where humans handle governance and creativity while AI agents manage operational tasks.
Gender Bias in LLM Hiring Decisions: Evidence from a Japanese Context and Evaluation of Mitigation Strategies
arXiv:2606.18649v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in hiring workflows, yet most research on gender bias in LLM hiring decisions has focused on English-language, Western-format resumes. This study examines whether pro-female gender bias extends to a Japanese corporate context and evaluates two practical mitigation strategies. Using a counterfactual resume design with 60 Japanese rirekisho-format resumes, 12 name pairs selected on linguistically grounded gender-signal criteria, and five state-of-the-art LLMs (Claude Sonnet 4.6, GPT-4o, DeepSeek-V3, Gemini 2.5 Flash, Llama 3.3 70B), we conducted 43,200 API calls across baseline, prompt instruction, and privacy filter conditions. A crossed random-effects linear mixed model confirms a significant pro-female bias across all five models, replicating Western findings in a non-Western context. A prompt-level gender-neutrality instruction produces no meaningful reduction in bias. A name-reliance analysis formally identifies the candidate name as the primary gender channel: removing the name from the prompt reduces the female effect by nearly its full magnitude. An unexpected incompatibility between the privacy filter and GPT-4o's content safety filter, resulting in a 42% refusal rate, highlights a practical deployment challenge for name anonymization in LLM-assisted recruitment pipelines.
Precisely | LinkedIn
Our CPO Matt Waxman is kicking off a three-part series on how he’s thinking about what it means to “run on AI ” at Precisely, from reimagining product development to reshaping go-to-market and G&A functions with Agentic, human-in-the-loop processes. Read part 1, where Matt introduces the Spiral — a framework for rethinking product development when AI removes the constraints your whole operating model was built around.
CEO-Bench: Can Agents Play the Long Game?
arXiv:2606.18543v1 Announce Type: new Abstract: Language model agents are becoming proficient executors at isolated, short-horizon tasks such as software engineering and customer service. Yet real-world challenges require a combination of sophisticated skills that remain largely untested in agents: (1) navigating long horizons amid uncertainty; (2) acquiring information in noisy environments; (3)
Anne Hathaway says she was spammed with ChatGPT-written thank you notes after hiring a recent role: ‘Nobody on that list gets that job’
Anne Hathaway received the same AI-written thank you note from every candidate—and Meryl Streep said what every boss is thinking: "That's just tragic."
America Is Headed Toward the Infinite Workweek
The future of AI and jobs will be so much weirder than you think.
Searching for Synergy in Shared Workspace Human-AI Collaboration
arXiv:2606.18413v1 Announce Type: new Abstract: Automated AI agents are increasingly capable, yet many scientific and professional tasks require human judgment and contextual expertise. We study shared-workspace human-AI teams, where AI agents and human collaborators must coordinate responsibilities before submitting a final answer. Using the Collaborative Gym environment with DiscoveryBench tasks, we examine when adding simulated human collaborators improves performance and when process loss turns additional collaborators into coordination overhead. Across 1,482 sessions, adding relevant collaborators can lower performance when teams lack structure to coordinate their contributions. We then evaluate scaffolding that combines shared group memory with simulated human-in-the-loop (HITL) gates, where selected actions require approval from a designated simulated participant. This scaffolding yields higher mean performance, most clearly in three-person teams, with clearer responsibility signals and stronger routing of expertise to team actions. Overall, how human-AI teams coordinate and integrate expertise matters as much as the capability available to them.
Building Supercharger: How Rocket Close optimized title operations with agentic AI
Rocket Close’s Supercharger project applies agentic AI to improve title operations and support human agents in a real estate workflow.
Todd Parsons - Chief Product Officer and President ...
In our latest guest blog post, BARC US CEO Shawn Rogers shares his perspective on critical factors for AI success at scale. "The bottom line, AI innovation is no longer limited by clever ideas or tooling. The bottlenecks are the money you burn and the controls you follow," Rogers advises companies to tackle both with the same urgency, or face surprise invoices and compliance fire drills.
Microsoft Launches Copilot Cowork Globally: AI Boosts Productivity for Fortune 500 Users
Microsoft has launched Copilot Cowork for Microsoft 365 globally, extending AI capabilities for enterprise users with new cost controls and automation.
Artificial intelligence: The gap between adoption and impact
Experts from Accenture discuss Generating Impact, the organisation's latest AI research report. Read more: Artificial intelligence: The gap between adoption and impact
ISG Event to Explore How Enterprises Are Turning AI Investments Into Measurable Business Value
State Street, Pfizer, Siemens, Merck KGaA, Deutsche Bank, Fresenius and more will discuss ROI at the ISG AI Impact Summit, June 22–23 in Frankfurt.
Accenture shares fall to lowest since 2017 as AI threat mounts
IT consultancy hit by concerns technology will hurt its business model
Entry-level work didn’t disappear, PwC finds with ‘seniorization.’ It just morphed into something young workers can’t get
"Employers are changing what they ask for in entry-level roles," Dan Priest, PwC's U.S. chief AI officer, told Fortune.
Gig workers are endlessly exploited. AI could make more of us share their fate
As companies integrate AI and hire fewer employees, a shift toward a ‘gig economy’ will commence In 2024, the buy-now-pay-later company Klarna announced that it would cut hundreds of customer service roles and begin using an artificial intelligence chatbot instead. The move was expected to save the company millions. But a year later, after customers complained about the degraded quality of customer service, Klarna began to quietly recruit human customer service agents back. At first glance, the reversal appeared to be a victory for human workers in the age of AI. The reality was more complex. Instead of bringing on full-time customer service agents, who Klarna contracts through an outside agency, it instead brought on workers in what Klarna CEO Sebastian Siemiatkowski has described as “an Uber type of set-up”. Now, an AI chatbot continues to handle most of customers’ basic queries, while a growing number of gig workers handle the more advanced ones. “Just like somebody can go and drive an Uber for a while, they can actually jump on and work for Klarna’s customer service,” Siemiatkowski said on a podcast in February. Continue reading...
A Look at the Annex 1 and 3 EU AI Act Guidelines on High-Risk Systems (via Passle)
On 19 May 2026, the Commission published draft guidelines on the classification of high-risk AI systems. In our previous blog we provided an overview of...
Indeed Unveils AI Sourcing Assistant to Streamline Recruitment and Improve Hiring Efficiency
Indeed unveils Sourcing Assistant, an AI feature in its Smart Sourcing product, to streamline applicant engagement and improve hiring efficiency.
Bolzano-based Soource raises €3 million to help procurement evolve from “copilot” to “autopilot” model
Soource, a Bolzano-based startup offering procurement solutions focused on sourcing and supplier selection, has announced the close of a €3 million Seed funding round to consolidate its position in Italy’s procurement intelligence market and support its European expansion. The round was led by Vertis through the “Vertis Venture 5 Scaleup” fund and included participation from […]
AI transformation is a human change programme enabled by technology
They fail because organisations ... at change management consultancy Nine Feet Tall. Despite significant investment, many AI programmes stall after pilots, struggle to scale, or fail to influence real decisions. The usual diagnosis points to data quality, model accuracy, or tooling. In reality, the deeper issue is trust. Employees hesitate to use systems they do not understand; leaders are reluctant to rely on outputs they cannot explain; and organisations lack the ...
Relativity Unveils aiR for Review: No-Code AI Tool Revolutionizes Legal Document Analysis
Relativity is expanding its AI capabilities in RelativityOne with the release of aiR for Review, allowing legal teams to perform custom analyses using no-code, natural language prompts.
Can LLMs Be CEOs? Benchmarking Strategic Resource Reallocation with Multi-Role Agent Simulation
arXiv:2606.17459v1 Announce Type: new Abstract: Evaluating the decision-making capabilities of large language models (LLMs) is a growing research priority, yet existing benchmarks focus on isolated cognitive tasks such as reasoning, knowledge retrieval, and economic rationality in stylized settings. These evaluations overlook the defining challenge of real executive decision-making: integrating c
The Measurement Gap in the Automation of EU Law: Benchmarking Doctrinal Legal Reasoning under the EU AI Act
arXiv:2606.18158v1 Announce Type: new Abstract: Large language models now produce legal text of at least median quality, yet no existing benchmark can evaluate whether they perform doctrinal legal reasoning, which forms the interpretive core of legal work, rather than the ancillary, paralegal tasks that most current legal-AI evaluations measure. This measurement gap is not only methodological but legal: the EU AI Act makes "appropriate accuracy" a binding requirement for high-risk AI used in the judicial domain, yet that requirement cannot acquire operational content without the very doctrinal-reasoning benchmark the field lacks.
When Rules Learn: A Self-Evolving Agent for Legal Case Retrieval
arXiv:2606.17220v1 Announce Type: new Abstract: Legal case retrieval remains challenging due to the complexity of legal language and the need for precise lexical alignment between queries and relevant cases. Although dense retrieval models have achieved notable progress, empirical studies show that BM25 continues to serve as a strong baseline in this domain. It motivates us to propose a self-evolving framework for rule-driven query rewriting that enhances BM25 without any parameter training. The framework equips an LLM-based agent with an automatic evaluation environment, enabling it to iteratively create rewriting rules, plan validation experiments over rule combinations, and eliminate ineffective rules based on historical feedbacks. We evaluate our method on the Chinese legal case retrieval benchmark LeCaRD-v2. Experimental results demonstrate that the proposed framework outperforms non-evolutionary baselines, including human-designed rules and greedy rule selection, particularly when powered by a highcapacity core LLM. We also conduct detailed analyses to investigate the mechanisms underlying self-evolution. Our findings reveal that LLM's capabilities to leverage previous experimental results and its intrinsic knowledge of rule elimination play critical roles in refining the rule set via self-evolution.
The Obsolescence of Corporate AI Strategies in the Wake of Agentic Systems
Many large enterprises are operating on outdated AI strategies developed before the rise of agentic systems. This lag in strategic alignment hinders the effective deployment of modern AI capabilities.
Your Churn Threshold Is a Pricing Decision
How unit economics should set your classification cutoff, and why they rarely do.
The Steep Learning Curve of AI Interfaces as a Barrier to Enterprise Adoption
AI interfaces remain unintuitive, requiring significant user training to overcome adoption roadblocks. This friction limits the immediate productivity gains organizations can realize from current tools.
Private equity bosses warn of AI threat to bets on law and accountancy
Buyout groups that have invested heavily in professional services face disruption from developing technology
Swedish AI patent platform Lightbringer raises €8.6 million to “take on Big Law” and replace existing patent firms
Lightbringer, a Malmö-based AI-powered LegalTech company transforming how startups and SMEs secure patents, has raised €8.6 million ($10 million) in Series A funding to fuel its US expansion and next phase of product development. The round was co-led by London-based 6 Degrees Capital and Amsterdam-based Newion. Thomas Olszewski, Partner at 6 Degrees Capital, and Dorus […]
HR must manage AI bots as well as humans, says Accenture executive
Matt Prebble says businesses will be forced to rethink leadership models
Workday argues Calif. law can't regulate AI hiring tools nationwide
Workday pushed back on a US judge's tentative ruling that a California employment and housing discrimination law governs use of the company's AI-powered human resource tools across the US.
Dr-DCI: Scaling Direct Corpus Interaction via Dynamic Workspace Expansion
arXiv:2606.14885v1 Announce Type: new Abstract: Agentic search over large corpora relies on retriever-mediated interfaces (e.g., BM25 or ColBERT) for scalable candidate discovery. While effective at ranking relevant documents, these interfaces expose evidence only as ranked results or bounded document views, limiting agents' ability to reorganize material and verify constraints across documents. Direct Corpus Interaction (DCI) addresses this limitation by exposing shell-executable corpus operations for flexible search, filtering, comparison, and verification. However, full-corpus terminal commands become slow and unstable as the corpus grows, degrading performance and efficiency. We introduce DR-DCI, a retriever-steered DCI framework that treats retrieval as an agent-callable action for expanding a local workspace. Rather than operating directly over the full corpus, the agent dynamically pulls relevant documents into an evolving workspace and conducts DCI operations within it. This design combines retriever-level recall with DCI-style precision: retrieval keeps exploration scalable, while DCI preserves the local operations needed for effective evidence resolution. Experiments show that DR-DCI is both effective and efficient across scales. On Browsecomp-Plus, DR-DCI reaches 71.2\% accuracy, improving over raw DCI and ablated variants by up to 8.3 points while reducing tool usage, wall time, and estimated cost. With workspace-preserving context reset, accuracy further improves to 73.3\%. In corpus-scaling experiments, DR-DCI remains effective from 100K to 10M documents, whereas raw DCI becomes unstable and BM25 performs substantially worse. DR-DCI also scales to a 20M-scale file-per-document Wiki-18 QA setting, achieving an average score of 63.0 across six benchmarks and outperforming retrieval-based and trained search-agent baselines. Ablation analysis further shows that ranked previews and inter-document DCI are key to performance.
Bye bye busy work: how the agents are taking over (and why that’s good) | The Drum
AI is everywhere, but not where it actually needs to be in 2026. That’s the conclusion of new global research from professional services platform Productive which reveals a major gap between how businesses use AI today and where it could deliver the biggest gains.
Council Post: Why Agentic AI Is The Next Priority Businesses Can’t Afford To Ignore
What agentic AI introduces isn't just another layer of automation; it introduces a new way of working.
Enterprise AI Governance: A Practical Framework for 2026
Build an effective enterprise AI governance framework in 2026. Learn key AI governance challenges, best practices, and implementation strategies.
Sakana AI Launches Its First Commercial Product, "Sakana Marlin"
Sakana AI has released an autonomous AI research assistant designed to act as a virtual Chief Strategy Officer, capable of performing complex strategy analysis in hours.
Payscale Flight Risk Report Shows Which New Hires are Out-Earning Tenured Workers in the AI Job Revolution
New hires in knowledge-based white-collar roles being reshaped by AI earn 3.6% more on average in the open market, while experience commands a premium in...
Customer service + BPO. The operational-scale displacement. - Cornford and Cross
Approximately 8 million workers across India and the Philippines are facing displacement due to AI adoption, according to recent empirical data and sector reports. No, recent case studies like Klarna show that full automation at enterprise scale has limitations.
Chaining Tasks, Redefining Work: A Theory of AI Automation
arXiv:2606.15960v1 Announce Type: new Abstract: Production is a sequence of steps that can be executed (1) manually, (2) augmented with AI, or (3) fully automated within contiguous AI-executed steps called ''chains.'' Firms optimally bundle steps into tasks and then jobs, trading off specialization gains against coordination costs. We characterize the optimal assignment of humans and AI to steps
AI Agents Revolutionize Workflows but Pose Risks: Governance and Transparency Essential
AI is evolving from simple chat interfaces to autonomous agents executing product-level tasks, transforming software into operational systems for various workflows.
The billionaire founder and CEO of Vista Equity Partners makes plea to businesses adopting AI: ‘Don’t destroy your intern program’
Robert F. Smith, who has made a career in technology, said young people must be part of companies’ workforces.
AI Delegation Problem: Why Most Organizations Squander Their Best Tool
AI delegation skills gap is the real reason most organizations fail to extract value from artificial intelligence tools. Deloitte surveyed 3,200 leaders and found insufficient worker skills are the single biggest barrier. New research links AI over-reliance to measurable cognitive atrophy that
Mind the Marketing Gap: Most CMOs Say AI Is Transforming Marketing, But Few Are Using It to Transform Their Own Function
/PRNewswire/ -- The vast majority of chief marketing officers (CMOs) feel that AI is driving an end-to-end transformation of their function. But only 8% are...
Measuring AI ROI at tool level is missing the point | TechRadar
Asking the wrong question reveals the wrong strategy
When deep research isn't enough for your business: Sakana AI launches 'ultra deep research' agent for 100+ page reports in 8 hours
Tokyo-based AI startup Sakana AI has officially launched its first commercial product, Sakana Marlin. Billed as a "Virtual CSO" (Chief Strategy Officer), Marlin is an autonomous, B2B research agent that deliberately abandons the instantaneous text generation of modern chatbots in favor of deep, long-horizon reasoning. What sets Marlin apart from the current ecosystem of AI tools is its temporal scale: instead of returning an answer in seconds, it runs continuous, self-governing reasoning loops for up to eight hours at a time to deliver deeply researched, well cited, 100-page strategy reports and executive slides. The company posted sample reports generated by Marlin on its product website here. Available immediately via the company’s website with pricing starting at a pay-as-you-go tier, the platform is designed strictly for enterprise use—specifically targeting corporations, financial institutions, and think tanks. The generative AI hype cycle has largely been defined by speed. For the past two years, the industry standard has been the ability to generate a poem, a line of code, or a surface-level summary in mere milliseconds. But the enterprise frontier is rapidly shifting from shallow, rapid generation to deep, methodical reasoning. With Marlin, major businesses are no longer asking how fast an AI can answer, but how deeply it can think. The Product: A Virtual CSO What exactly is a business getting when they deploy Sakana Marlin? The workflow is fundamentally different from typical large language model (LLM) interactions. Rather than engaging in a tedious back-and-forth prompt engineering session, the user simply provides a core research topic. Following a brief initial exchange to sharpen the scope and direction of the investigation, the human steps away entirely. For the next several hours, Marlin operates as a self-contained digital strategy team. It formulates its own initial hypotheses, navigates the web to gather data, cross-references sources to verify findings, and maps the causal dynamics within complex business environments. It is effectively searching for the "winning formula" within a sea of noise. Think of it less like a search engine and more like a junior strategy consultant locked in a room with a whiteboard and an internet connection. You provide the strategic prompt in the morning, and by the end of the workday, the system delivers a comprehensive, professional-grade portfolio. In Marlin's case, the final output is not a generic text blob; it is a structured set of strategic options, complete with executive summary slides, appendices, references, and a deeply researched report. The company highlighted several real-world use cases to demonstrate Marlin's capacity for complex synthesis, including generating detailed resolution scenarios for a theoretical blockade of the Strait of Hormuz, mapping out the fragmented global AI regulation patchwork, and analyzing macroeconomic trends like the return of "bond vigilantes". Sakana says Marlin relies on multiple AI models, but did not provide specific model names or providers. I've reached out on X to find out more and will update when I receive a response. The Engine of Long-Horizon Reasoning Under the hood, Marlin is the commercial culmination of Sakana AI’s extensive laboratory breakthroughs over the past two years. The product is powered by an exploration engine relying on Sakana's own prior research breakthrough, Adaptive Branching Monte Carlo Tree Search (AB-MCTS), and leverages frameworks derived from "The AI Scientist," an earlier Sakana AI research project featured in the journal Nature that successfully automated the scientific discovery process from ideation to peer review. To understand how this works in practice, consider a real-world analogy: modern chess engines. When a computer plays chess, it doesn't just look at the board and guess; it plays out thousands of potential future moves, evaluating the strength of each resulting position before committing to an action. Marlin’s AB-MCTS engine does something similar for research. Inside the Engine: The Mechanics of AB-MCTS The chronology of this technology traces back to June 2025, when Sakana AI first introduced the framework to the public alongside the research paper “Wider or Deeper? Scaling LLM Inference-Time Compute with Adaptive Branching Tree Search”. At that time, to encourage developer experimentation with collective AI intelligence, the company released the underlying algorithm as an open-source software library called TreeQuest, distributed under the permissive Apache 2.0 license. This open-source milestone laid the technical foundation for what would eventually evolve into the proprietary, enterprise-grade Marlin product a year later. Traditionally, when developers attempt to extract higher-quality reasoning from large language models, they rely on a brute-force method called "repeated sampling"—essentially running the model dozens of times in parallel and hoping one of the answers is correct. However, repeated sampling operates blindly; it cannot evaluate its own intermediate steps or pivot based on external feedback. AB-MCTS replaces this paradigm with a principled, multi-turn approach driven by a Bayesian decision framework. As the AI constructs a strategy report, the system treats the research process as a branching tree of possibilities. At each node of the tree, the algorithm dynamically balances two distinct behaviors based on external feedback signals: Going Wider (Exploration): Spawning entirely new, alternative hypotheses or candidate responses when the current path yields diminishing returns or unresolved contradictions. Going Deeper (Exploitation): Methodically refining, auditing, and building upon an existing candidate solution that shows high strategic promise. What transforms this from a laboratory experiment into a commercial engine is its extension into Multi-LLM AB-MCTS. Sakana AI’s architecture introduces a critical third dimension to the search tree: the ability to dynamically choose which model to invoke for a specific sub-task, treating the industry’s leading frontier models as a plug-and-play collective intelligence network. According to technical documentation published by the company, the engine can coordinate highly heterogeneous models—allowing an orchestration model to delegate initial ideation to one LLM, while utilizing a reasoning-heavy model to audit, verify, and correct intermediate errors generated earlier in the search tree. By scaling up compute at inference time—leveraging the distinct "personalities" and strengths of multiple foundation models over thousands of automated cycles—AB-MCTS provides the mathematical guardrails Marlin requires. It ensures that the resulting 100-page strategy reports are not merely long-winded AI generations, but the highly vetted product of systemic, automated trial-and-error. Licensing, Data, and Enterprise Implications It is crucial to note that Sakana Marlin is distinctly not a general consumer tool; it is a commercial software-as-a-service (SaaS) offering restricted to corporate entities, organizations, and sole proprietors. For enterprises, licensing and data handling terms are often the determining factors in software adoption. Unlike many consumer-grade AI tools that silently harvest user inputs and proprietary data to train future foundational models, Sakana Marlin operates under a strict, enterprise-grade data policy. Neither Sakana AI nor its external AI service providers will use customer data or inputs for model training or fine-tuning unless the client provides explicit opt-in consent. Even with consent, data is heavily processed to remove personally identifiable information. This closed-loop security is absolutely vital for companies handling sensitive M&A research, unreleased product strategies, or proprietary market analyses. The commercial licensing is structured into tiered pricing models that reflect its enterprise nature: Pay-as-you-go: Users can purchase credits on demand, with a single run costing 100 credits, and add-on credits priced at ¥98 ($0.61 USD) each. Pro Plan: At ¥150,000 ($935.68 USD) per month, businesses receive 2,000 credits, bringing down the cost of add-on credits to ¥90 ($0.56 USD). Team Plan: Geared toward larger departments, this ¥400,000 ($2,495.14 USD) per month tier includes 6,000 credits, lowering add-on costs to ¥85 ($0.53 USD) per credit. Enterprise: Fully custom quotes with dedicated support and customized credit allocations. Why Sakana Is Worth Watching Sakana AI’s transition into a commercial enterprise powerhouse is rooted in the pedigree of its founders, who famously helped spark the current generative AI boom. Formed in Tokyo in 2023, the startup was co-founded by Llion Jones—a co-author of Google’s seminal 2017 “Attention Is All You Need” paper who coined the term “transformer”—and David Ha, a former Google Brain researcher and head of research at Stability AI. The decision to build a new laboratory outside the Silicon Valley bubble was a deliberate rejection of the current AI ecosystem. At a TED AI conference in late 2025, Jones candidly expressed that he was "absolutely sick" of transformers, warning that the intense pressure from investors and the hyper-fixation on scaling single, monolithic models had calcified the industry's creativity and blinded researchers to the next major breakthrough. To break free from this "big company-itis," Jones and Ha structured Sakana AI around principles of biomimicry and evolutionary computing. The company's name, derived from the Japanese word for fish, reflects its core technical philosophy: leveraging collective intelligence similar to schools of fish, ant colonies, or insect swarms. Rather than attempting to build one massive, do-it-all foundation model, Sakana’s research has consistently focused on deploying networks of smaller, specialized models that collaborate dynamically to adapt to complex environments. This philosophy posits that by treating individual AI models as members of a "dream team" with complementary strengths, systems can achieve more robust and cost-effective reasoning than relying on sheer scale alone. This nature-inspired approach quickly yielded dividends in rigorous, competitive testing. Sakana AI has made significant strides in "inference-time scaling"—allocating computational resources during the problem-solving phase to allow models to think, iterate, and refine their own answers over extended periods. In early 2026, the company’s ALE-Agent took first place in the highly complex AtCoder Heuristic Contest (AHC058), a combinatorial optimization challenge, outperforming over 800 top-tier human programmers by autonomously rebuilding and testing hundreds of solutions over a four-hour window. Similarly, Sakana introduced "RL Conductor," a small 7-billion-parameter model trained via reinforcement learning specifically to orchestrate and delegate tasks among a diverse pool of worker models—ranging from GPT-5 to Claude Sonnet 4—achieving state-of-the-art results on reasoning benchmarks at a fraction of traditional computing costs. Sakana's rapid evolution from a disruptive research lab to a commercial software provider has attracted intense attention from global financial heavyweights. By late 2025, the Tokyo-based startup secured a massive Series B funding round that pushed its post-money valuation past $2.6 billion, cementing its status as one of Japan’s most highly valued private tech companies. The firm boasts a sprawling roster of strategic investors, including early venture backers Khosla Ventures, Lux Capital, and New Enterprise Associates (NEA), alongside industry titans like Nvidia and Google. As Sakana has expanded its focus toward mission-critical sectors like defense and finance, it has also drawn investments from major global banking institutions like Mitsubishi UFJ Financial Group (MUFG) and Citi, as well as enterprise tech giant Salesforce, positioning the startup to actively reshape corporate AI infrastructure from the ground up. Community Reactions and Field Testing Sakana AI’s shift toward commercial, long-horizon agents did not happen in a vacuum. The company ran a rigorous closed beta test beginning in April 2026, putting the tool in the hands of approximately 300 professionals across financial institutions, consulting firms, and think tanks. The feedback underscores a stark qualitative difference between standard generative chatbots and Marlin’s autonomous, fact-driven approach. A senior consultant at a major Tokyo consulting firm noted that the tool "exceeded expectations by discovering angles we hadn't even imagined," praising its ability to match human comprehensiveness while stripping away human bias. Meanwhile, a cybersecurity division at a major Japanese IT system integrator lauded the system for providing "a highly convincing report driven by high-quality, primary research," rather than relying on recycled secondary sources. On social media, the company’s announcement resonated with the broader tech community's growing appetite for autonomous agents. As the AI industry matures, the value proposition is clearly shifting. Tools that act as fast, conversational encyclopedias are becoming commoditized. With Sakana Marlin, the focus moves entirely to separating the heavy lifting of thinking from the final act of deciding. By delegating the exhaustive mapping of causal dynamics to an agent capable of sustained reasoning, human executives are free to do what they do best: take action.
Madrid’s Orbio raises €18.09 million Series A to scale AI workforce platform for frontline teams
Orbio, a Madrid-based AI workforce platform for global enterprises with frontline teams, has today announced an €18.09 million ($21 million) Series A funding round led by Dawn Capital, with participation from existing investors, including Visionaries. The capital will fund expansion into new markets, growth across existing and new enterprise customers, and the continued build-out of […]
KPMG's AI report becomes an accidental demo of AI hallucinations
GPTZero claims only 5 of the report's 45 citations matched their sources, raising questions about how the Big Four's AI study was assembled.
Berlin’s Qorelo raises €3 million five months after launch to tackle SAP’s 2027 transformation crunch
Qorelo, a Berlin-based startup building the AI engine for modern ERP (Enterprise Resource Planning) delivery, has raised €3 million ($3.5 million) in a Seed funding round just five months after the company was founded. The round was co-led by HPI Ventures and Caesar Ventures, with participation from 10x Founders, Antler, Adesso Ventures, and Angel Invest. […]
AI strategy affects business valuation: Chicago Booth - Crain's Chicago Business
Investors will put a premium on companies that use AI to deepen customer relationships, improve operations and turn data into an asset.
Start realizing ROI: A practical guide to agentic AI for business leaders - Global IT Research
Start realizing ROI with AI. Most businesses struggle—only 25% of AI initiatives hit expected ROI, and just 16% scale enterprise-wide.
AI reshapes global labour market into two distinct paths, rewarding human skills: PwC 2026 AI Jobs Barometer | PwC
AI is rapidly reshaping the skills employers want most from workers – increasing the emphasis on human skills such as judgement, creativity and leadership – as companies most able to use AI continue to expand hiring faster than their peers, according to PwC’s 2026 AI Jobs Barometer, released ...
Employers want entry-level workers with senior-level skills in the age of AI, a huge PwC analysis found
PwC looked at over 1 billion jobs worldwide, and 2.4 million entry-level roles in the US for its 2026 AI jobs barometer.
WorkBench Revisited: Workplace Agents Two Years On
arXiv:2606.13715v1 Announce Type: new Abstract: The best agent on WorkBench in March 2024, GPT-4, completed 43% of tasks and took an unintended harmful action, such as emailing the wrong person, on 26% of them. We re-visit the benchmark in June 2026 and find that the best agent to date, Claude Opus 4.8, completes 89% and takes an unintended harmful action on 2.5%. Aside from this considerable pro
Brokerage AI adoption rises, productivity gains remain uneven
Brokerages report 97% AI adoption, while agents use AI mainly for marketing, with gains concentrated among power users, per RPR.
From User to Architect - by Adam Pryor - Purposeful AI
Most people experience AI as a tool that helps them do things faster. You write a prompt, you get an output, you edit it, you move on. The AI is a very fast assistant sitting beside you, and you are still the one doing the work.
Legal AI start-up Legora to double headcount
Company with $5.6bn valuation has seen 900% more website visits since its Jude Law-fronted campaign