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Digital Law & Regulation.
Adobe report reveals major agentic AI readiness gap
Adobe’s latest AI research shows organizations lag in agentic AI readiness, data foundations, governance, and ROI measurement.
India Inc deepens AI adoption to stay ahead of legal curve - The Economic Times
Major Indian corporations are swiftly integrating artificial intelligence into their legal departments. This approach is enhancing the efficiency of contract assessments, monitoring compliance and automating routine legal tasks. Firms such as the Aditya Birla Group and Adani Group are harnessing ...
Narrative Violation: In B2B Customer Support, AI is a Copilot, Not a Replacement
Data from B2B customer support shows that AI functions as a triage layer rather than a full replacement, as complex, high-stakes tickets still require human intervention.
Dublin’s AI TaxTech startup Fonoa raises €94.4 million Series C and buys PwC’s tax platform
Fonoa, a Dublin-based AI tax operating system for global businesses, has raised €94.4 million ($110 million) in Series C funding and acquired Indirect Tax Edge (Edge) from PricewaterhouseCoopers (PwC). The funding round was led by Headline and included participation from new investors Eurazeo and Forestay Capital, alongside existing investors Index Ventures, OMERS, Coatue, and Dawn […]
Intellectia
Impact of AI on Employment: Paulson mentioned that corporate boards face pressure to translate AI efficiencies into cost savings, with some believing AI may slow hiring rather than trigger layoffs, indicating differing views on the future labor market dynamics.
What Three New Studies On AI, Work And Jobs Tell Us
The BCG study emphasizes the importance ... the organization on upskilling and reskilling, shaping new pathways for careers and fostering a new level of human-AI collaboration. It also stresses the importance of clear leadership communication that encourages the workforce to embrace ...
AI job search tools impact offer rates - Baltimore Business Journal
AI is changing the game for how job applications are submitted and for how successful the submissions are.
The AI agent bottleneck isn't model performance — it's permissions
Enterprise AI agents are stalling — not because of model performance, but because of permissioning. Every agentic workflow eventually hits the same wall: what is this agent allowed to touch, on whose behalf, and how does the system know? Workday's answer is to make its existing system of record the governance layer for agents. Gerrit Kazmaier, the company's president for product and technology, to
Law firm Kirkland to spend $500 million developing its own AI platform | Reuters
Law firm leaders told Reuters in recent interviews that there is increasing demand for custom-designed AI programs to assist with specific business and legal tasks, and an ongoing debate over how much to develop internally.
Offloading Score: Measuring AI Reliance Through Counterfactual Workflows
arXiv:2605.29392v1 Announce Type: cross Abstract: AI tools are increasingly integrated into real-world workflows. However, existing measures of reliance on these tools focus on AI output adoption or on self-reported indicators, rather than how task effort is distributed between users and tools. Here, we introduce offloading score, a measure of reliance that quantifies the fraction of cognitive effort offloaded to an AI tool. Offloading Score is simulation-based -- we construct a counterfactual workflow by estimating how the user would have completed the task without the tool, and then computing the fraction of steps saved by using the tool. We validate offloading score through intrinsic evaluations of metric validity, and a controlled user study ($n=40$) with developers performing programming tasks using AI tools. We vary time pressure to test whether reliance measures capture the known increase in reliance under time pressure. We show that offloading score detects significantly higher reliance in time-constrained settings ($+43\%$, $p=0.018$), while usage-based and self-reported baseline measures of reliance do not distinguish the conditions. We complement this with descriptive insights showing that higher reliance manifests as greater delegation of subtasks to the tool and more direct reuse of AI outputs. Finally, we demonstrate an approach of using offloading score in combination with target outcomes of a task (e.g., code understanding) to identify when reliance may be (in)appropriate. Our framework offers two contributions: an instrument users can apply to measure and reflect on their own reliance, and a quantitative signal that agent designers can utilize to mitigate overreliance.
The New Pro Se: Generative AI and the Surge in Federal Civil Self-Representation
arXiv:2605.29493v1 Announce Type: new Abstract: Since public access to generative AI tools became widespread, federal civil litigation has seen a marked increase in pro se (self-represented) plaintiffs. This paper analyzes that shift using ~2.8 million filings, asking whether the post-GenAI period is associated not only with more pro se filings, but also with detectable changes in complaint text,
Democratizing Generative AI for Sustainable Competitive Advantage
arXiv:2605.27398v1 Announce Type: new Abstract: As generative artificial intelligence (GenAI) diffuses across industries and becomes broadly accessible, the locus of sustainable competitive advantage shifts from technology ownership toward the quality of employee-level adoption and use. This paper develops a cross-level conceptual framework linking firm-level GenAI investment and governance to individual-level AI democratization, defined as the extent to which employees meaningfully, responsibly, and effectively use GenAI in their daily work. We argue that individual-level AI democratization, grounded in three micro foundations (AI usefulness, ease of use, and AI literacy), mediates the relationship between organizational GenAI investments and sustainable competitive advantage. Drawing on the technology acceptance model, resource-based theory, and emerging empirical evidence on AI productivity effects, we advance six propositions linking perceived usefulness, ease of use, AI literacy, responsible use, and innovation outcomes to organizational transformation and sustained relative performance. The framework provides a measurement scaffold for empirical research and offers managerial guidance on treating GenAI as augmentation infrastructure rather than solely as automation. We conclude by outlining future research directions, including longitudinal and cross-cultural investigations of literacy, governance, and transformation dynamics.
Kirkland & Ellis to spend $500mn building its own AI technology
World’s highest-grossing law firm plans to put the ‘collective intelligence’ of its lawyers into a tech platform
Agentic Literacy Debt: A Structural Problem the AI Literacy Field Has Not Yet Named
arXiv:2605.27396v1 Announce Type: new Abstract: Autonomous AI agents now plan, decide, and act on behalf of users across healthcare, financial services, and workplace contexts, often without step-by-step human approval. Existing AI literacy frameworks were built for a world in which humans evaluate AI outputs and decide whether to act; they have no vocabulary for the user who has delegated decision-making authority to an agent whose actions may not be observable, reversible, or controllable. This paper names the resulting problem agentic literacy debt: the accumulating societal deficit that grows when agentic AI systems are deployed at scale without corresponding literacy infrastructure. The debt compounds through three reinforcing channels (normalization of opaque delegation, multi-agent ecosystem complexity, and institutional path dependence), and it is incurred by the organizations that deploy agents but paid by the users, patients, and citizens on whose behalf the agents act. Evidence from healthcare, financial fraud, and global equity contexts suggests the gap is already consequential. The problem is structural, not a temporary lag that curriculum reform will close. It demands a reframing of AI literacy as a governance capability, not an evaluative one.
Shanghai unveils AI push for services sector in new policy directive
Shanghai plans to accelerate AI-powered industrial software development and promote industry-specific large models to boost its services sector by 2030.
The Services Budget Is AI's Biggest Prize | PYMNTS.com
Enterprise software budgets are large. Enterprise services budgets are larger. A company might spend $10,000 licensing accounting software and 10 times
Human-AI Collaboration for Estimating Scientific Replicability
arXiv:2605.27394v1 Announce Type: new Abstract: Determining whether published scientific findings can successfully be replicated is a long-standing challenge in the empirical sciences. Existing approaches for replicability assessment typically rely either on human judgment, i.e., creative assembly of human experts, or on machine learning models trained on paper content metadata. While both approaches have demonstrated value, each also has important limitations. Human forecasts can be influenced by cognitive biases and narrow exposure to the research literature, while automated assessments often struggle to capture contextual cues and subtle signals of credibility. In this paper, we examine a hybrid approach. Specifically, we introduce a hybrid prediction market in which algorithmic agents trade alongside human participants to jointly estimate the likelihood that a published scientific finding will be corroborated via the outcome of a controlled replication study. Agents are trained on outcomes from hundreds of prior replication studies while human participants contribute domain knowledge through real-time trading. We evaluate this hybrid approach through multiple live experiments involving participants from different academic disciplines and compare its performance to artificial-only and human-only baselines. Our results show that, except for a few cases, hybrid markets match or outperform artificial prediction markets, producing more accurate and reliable replication forecasts.
AI is threatening the giants of consulting | The Straits Times
The technology opens the door for smaller players to challenge the Big Four. Read more at straitstimes.com.
Is Your AI ROI Getting Stuck In a Logjam?
Individual AI implementation is driving execution at warp speed while approval cycles struggle to keep up. The result? Instead of ROI, you're stuck with an AI logjam.
India accounting leaders say AI won't kill offshore talent - Outsource Accelerator
Not a single India-based leader at major United States accounting firms believes AI will eliminate the offshore accounting model.
Hong Kong flags cross-border data hurdles for Greater Bay Area legal AI model
Differing data-governance rules across Guangdong, Hong Kong and Macao are creating obstacles to developing an AI foundation model for legal services, according to Deputy Secretary for Justice Cheung Kwok-kwan.
Building Self-Improving Tax Agents with Codex
OpenAI and Thrive Holdings developed a tax-preparation automation tool using Codex, practitioner feedback, and evaluation loops to create a self-improving agent.
Council Post: Your AI Is Making Million-Dollar Decisions Based On Data Nobody Understands
The organizations that will derive the most value from AI over the next several years will not necessarily be the ones with the largest models or the most experimental pilots. They will be the organizations that build architectures capable of preserving meaning as data moves across increasingly ...
Maja Voje's Post - LinkedIn
I spent 45 minutes trying to break an AI sales rep on a live call. The most interesting part was what it refused to do. Nigel is 1mind's Ride-Along 𝗦𝘂𝗽𝗲𝗿𝗵𝘂𝗺𝗮𝗻. Joins the Zoom as a named… | Maja Voje | 19 comments Agree & Join LinkedIn By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy. # Maja Voje’s Post Bestselling Author | Bringing My Go-To-Market Method to 10K Orgs | B2B AI GTM Consultant | ATM: Loving Claude Code, Context & GTM Engineering | 83K LinkedIn | 33K Newsletter 1h I spent 45 minutes trying to break an AI sales rep on a live call. The most interesting part was what it refused to do. Nigel is 1mind's Ride-Along 𝗦𝘂𝗽𝗲𝗿𝗵𝘂𝗺𝗮𝗻. Joins the Zoom as a named participant. Answers buyer questions in real time. I asked the questions a real buyer would ask. Top objections. Pricing. Integrations. Case
Phoenix Built an Empire of Cubicle Jobs. AI Is Coming to Tear It Down.
# Phoenix Built an Empire of Cubicle Jobs. AI Is Coming to Tear It Down. Published: 2026-05-27T01:00:00+00:00 Source: wsj.com (wsj.com) Language: en ## Story PHOENIX—All around this desert city’s sprawling metro area, low-rise office parks with tinted windows and vast parking lots stretch to the horizon. This is America’s back office. Abundant land and cheap labor made Phoenix a premier place for
4 AI Strategy Questions Every Executive Needs To Drive ROI
Despite record AI spending, a KPMG study of 237 US executives reveals why ROI remains elusive — and what organizations getting it right do differently.
How AI Is Changing Decision Making In Organizations
AI isn't making decisions for organizations, but reshaping how they get made. Here's why over-automating may be as costly as not automating at all.
D&B's Database of 642 Million Businesses Was Built for Humans, Not AI Agents. So They Rebuilt It.
D&B rebuilt its commercial data graph so AI agents can query and act on business records. Companies must rebuild data foundations and semantic layers before AI automation can scale.
Cork employee experience firm Poppulo bags France’s Sociabble
Poppulo’s AI platform aims to help organisations deliver relevant and measurable employee communication. Read more: Cork employee experience firm Poppulo bags France’s Sociabble
KPMG scouts Silicon Valley start-ups in bid to head off AI disruption
Firm looks to partner or invest in groups that could otherwise undermine its business model
How AI threatens the giants of consulting
The technology opens the door for smaller, well-funded challengers to take market share from the Big Four and others
Capgemini Aims to Harness AI Surge in 2028 Strategic Plan
# Capgemini Aims to Harness AI Surge in 2028 Strategic Plan Published: 2026-05-27T06:07:00+00:00 Source: wsj.com (wsj.com) Language: en ## Story May 27, 2026 2:07 am ET Capgemini CAP 0.05 %increase; green up pointing triangle set out a plan through to 2028, expecting revenue to grow as it continues to tap into artificial intelligence to help scale businesses. The French consulting and technology group said it expects a compounded annual growth rate of 5.5% to 7.5% at constant currency between 2025 and 2028.
AI, privilege, and discovery in view of 'Heppner' and 'Morgan'
# AI, privilege, and discovery in view of 'Heppner' and 'Morgan' Published: 2026-05-27T17:17:19.608000+00:00 Source: reuters.com (reuters.com) Language: en ## Story May 27, 2026 - As parties and attorneys increasingly use generative artificial intelligence for litigation preparation, federal courts are beginning to wrestle with the significant practical impacts to litigation discovery. Two recent decisions, United States v. Heppner, No. 25 CR. 503 (JSR), 2026 WL 436479 (S.D.N.Y. Feb. 17, 2026) and Morgan v. V2X, Inc., No. 25–CV–01991–SKC–MDB, 2026 WL 864223, (D. Colo. Mar. 30, 2026), offer a study in contrasts, revealing that the discovery landscape around AI-generated materials depends in large part on the facts and context of the case. For legal counsel managing AI use across an organization, these cases merit close attention. Heppner arose in a criminal prosecution in the Southe
Workday likely must face employment discrimination claims on AI tools, US judge says
A federal judge issued a tentative ruling that Workday must face claims of employment discrimination under California law regarding its AI-driven hiring screening tools.
Workday likely must defend AI hiring-discrimination allegations, US judge says
A federal judge in San Francisco indicated in a tentative opinion that Workday will likely have to defend claims that it violated California's Fair Employment and Housing Act.
Council Post: HR Tech’s Next Shift Isn’t About AI Features; It’s About Action
The systems organizations choose will increasingly shape how work gets coordinated, how decisions get made and how demand capability gets built. Second, capability is becoming the real constraint. From what I see, most organizations are investing faster in AI tools than in helping managers and ...
The Most AI-Fluent People Are Being Filtered Out Before They Even Land a Job
The Most AI-Fluent People Are Being Filtered Out Before They Even Land a Job Armand Burger The future of brands gets decided here. Join the industry’s top marketers at Brandweek for the ideas, insights, and connections shaping what’s next. Get your ticket. Twenty-three years ago, I walked off a stage with a master’s degree and into an economy that was rewriting itself. Roles that had barely existed before suddenly roared into life: interactive designer or information architect. As a young consultant doing change management, I taught people how to use new technology and redefine their jobs around it. Though some jobs were eliminated. During that time, curious leaders hired curious people because curiosity was the only credential that kept up with the pace of change. This year, my cousin turns 23, the same age I was when I walked off that stage. He graduated last year and luckily sta
What AI Still Can’t Do for Leaders
As leaders weave generative AI tools like ChatGPT and Claude into their daily workflows, where does the output fall short? Moreover, where are leaders falling short for their organizations by giving away too much agency to artificial intelligence? MIT Sloan School of Management professors Deborah Ancona and Katherine W. Isaacs have thought deeply about the […]
From AI insight to business outcomes: What enterprises need to move beyond the “Chat Phase” | TechRadar
Moving enterprise AI from conversations to coordinated execution
One in five workers say AI has replaced parts of their job
Staff are changing how work is done with artificial intelligence tools, often outside company systems and without clear oversight.
UBS’ Khan Says AI Will Impact Jobs While Aiding Productivity - Bloomberg
UBS Group AG’s Asia Pacific President Iqbal Khan said artificial intelligence will free up capacity and improve productivity but also have an impact on jobs.
Job Outlook: Employers Prioritize Sales, Customer Experience and AI Skills - CPA Practice Advisor
In a tougher economic environment, organizations are prioritizing the capabilities that drive revenue, strengthen resilience and help manage risk.
The AI Cohesion Paradox: Why AI-Empowered Teams Cooperate Less — And What Leaders Can Do About It
The AI Cohesion Paradox: Why AI-Empowered Teams Cooperate Less — And What Leaders Can Do About It # Sovereign Agentic AI (Volodymyrs View) SubscribeSign in Sovereign Agentic AI (Volodymyrs View) Podcast The AI Cohesion Paradox: Why AI-Empowered Teams Cooperate Less — And What Leaders Can Do About It 1 0:00 -14:59 Audio playback is not supported on your browser. Please upgrade. ## The AI Cohesion Paradox: Why AI-Empowered Teams Cooperate Less — And What Leaders Can Do About It May 27, 2026 1 Share Transcript # The AI Cohesion Paradox ## Why AI-Empowered Teams Cooperate Less — And What Leaders Can Do About It Volodymyr Pavlyshyn | Sovereign Agentic AI --- “The human is not the island.” We repeated that for decades. It turns out we were wrong — or at least, AI is proving we can be. Something quietly changed in the organizations that moved earliest and fastest on AI. Their
Global firms rethink GCC hiring in India as AI shifts skill demand
As artificial intelligence reshapes job roles, global companies in India are becoming increasingly selective in their hiring processes, emphasizing the need for advanced tech skills and adaptability. A growing skills gap poses challenges for workforce readiness.
Opinion: AI transforming how tenders are written but not how they’re evaluated
AI is changing how tenders are written but not how they're evaluated in Ireland. That gap is becoming a problem, says BidReview.ai founder Tony Corrigan. Read more: Opinion: AI transforming how tenders are written but not how they’re evaluated
UK law firm Pinsent Masons reprimanded by court over AI error
Judge Mark Mullen warns lawyers against outsourcing legal research or reasoning
Why Your Engineers' Favorite AI Tools Are Wrecking Your 2026 Budget
Microsoft and Uber both blew past 2026 AI coding budgets in months. Token-based pricing turns engineer-loved tools into runaway costs. Three controls every CFO needs.
New Report Highlights How Successful Staffing Companies Are Evolving Amid AI Boom
/PRNewswire/ -- The staffing industry is undergoing significant transformation, as persistent labor shortages across critical sectors collide with rapid AI...
AI in the Enterprise: How People Use M365 Copilot Chat
arXiv:2605.23958v1 Announce Type: cross Abstract: M365 Copilot is used every week by millions of people across more than a million companies around the world as part of their workflows. Uniquely positioned in the AI landscape given its near-exclusive use for work purposes, M365 Copilot can offer a clear picture of how people use AI for work and where that usage may expand next. This paper charact
Rethinking organizational design in the age of agentic AI
Amid rapidly growing adoption of enterprise-level AI agents, there’s a disconnect emerging between ambition and execution. Although 85% of organizations say they want to be agentic within the next three years, 76% say their current operations and infrastructure can’t support that change. They cite a lack of readiness across people, processes, and workflows. The sticky…
Council Post: The Resurgence Of Change Management: How To Survive The AI Transition
Organizational change management is the critical foundation for envisioning new operating models and reinventing business processes to achieve success in the AI era.
The Rise of Ethical AI & What it Means for Corporate Governance | IntelligentHQ
Ethical AI has moved from a niche discussion to a board-level priority in a very short timeframe. With organisations adopting automation and machine The Rise of Ethical AI & What it Means for Corporate Governance
PAIRED: A Process-Anchored Framework for Transparent Reporting of AI Contributions in Scientific Research
arXiv:2605.24325v1 Announce Type: new Abstract: The rapid integration of generative AI into scientific research has exposed a critical gap in academic disclosure practice. Existing frameworks for reporting AI contributions are uniformly output-oriented -- they document what AI produced, not how the research unfolded. As a result, researchers who wish to report their AI collaboration honestly lack the tools to do so: no current framework can distinguish between a researcher who originated a research direction and one who adopted a direction proposed by AI, or between a researcher who critically evaluated AI-generated alternatives and one who accepted AI output without independent assessment. This gap is not a matter of compliance detail; it is a failure to capture the cognitive dynamics that determine what kind of intellectual contribution a paper actually represents. We propose PAIRED -- Process-Anchored Interaction Reporting for AI-Enabled Discovery -- a dual-facing framework that addresses this gap through four design principles: process orientation, which takes the decision point rather than the research product as the fundamental unit of documentation; dual-facing output, which derives a structured publisher disclosure from a prospective author log without double work; decision-point granularity, which operates between session-level coarseness and message-level impracticality; and artifact-triggered logging, which provides an auditable rule against selective omission. We demonstrate PAIRED through worked examples, discuss its limitations openly, and propose a model-assisted adoption pathway that embeds the framework's logging discipline directly into AI research platforms.
How To Prove AI ROI In 90 Days, Without Gaming Metrics
AI ROI is not proven by AI activity. It is proven when one important workflow decision improves relative to a clear baseline, while counter-metrics did not get worse.
AI tools lead to ‘clear racial disparities’ in job hiring
New Stanford-led study finds candidates that fail AI-hiring tests face ‘systemic rejection’ across companies
Why Is Digital Risk Protection Becoming a Business Priority in the AI Era? - Market Research - Global Risk Community
Organizations today operate in an environment where cyber threats extend far beyond traditional networks. Brand impersonation, phishing campaigns, data leaks, exposed credentials, and malicious activity on the dark web can damage business operations and customer trust within hours.
Redrawing the AI Map: A Theory of Accountability Boundaries in Agentic Ecosystems
arXiv:2605.23179v1 Announce Type: new Abstract: Agentic AI orchestrators reduce the interface and assembly costs of composing information systems capabilities across organizational boundaries, seemingly accelerating modularization and organizational disaggregation. Yet AI-enabled capabilities whose outputs require evidence, review, signoff, or assignable responsibility may retain integrated accou
In-Depth Analysis: Automatic Data Processing Versus Competitors In Professional Services Industry - Autom - Benzinga
In this article, we will undertake an in-depth industry comparison, assessing Automatic Data Processing (NASDAQ:ADP) alongside its primary competitors in the Professional Services industry. By meticulously examining crucial financial indicators, market positioning, and growth potential, we aim to ...
Why AI governance starts with rethinking your people, processes, and technology
Poor AI oversight can magnify workflow errors, expose firms to regulation and erode trust if CIOs do not redesign controls and roles.
EY, Microsoft launch AI push with USD $1 billion plan
Organisations will get a single team to deploy AI across core functions, as EY and Microsoft commit more than USD $1 billion over five years.
Cognitive offloading and the speedup illusion in human-AI interaction
arXiv:2605.23177v1 Announce Type: new Abstract: Large language models (LLMs) have the potential to boost human productivity by speeding up task completion -- provided users know when to offload cognitive work to them. But we do not know if users are well-calibrated in estimating these potential time savings. We conducted a preregistered large-scale behavioral study (N = 1237) to characterize mism
This week's Claude OS update: The Agentic Expansion Cascade - FourWeekMBA
While business leaders debate AI ... and AI strategy — timelines, Anthropic’s latest Claude OS update signals something more immediate: the systematic transformation of how enterprises will structure operations within months, not years. The infrastructure for autonomous business processes isn’t coming—it’s here. The Business Engineer’s latest analysis introduces the “Agentic Expansion ...
AI, Automation, and Robotics Are Reshaping Value Creation for Private Equity
Across my portfolio company clients, AI has stopped being a slide in the strategic plan and started being a line item. Back office automation, forecasting, supply chain optimization, customer service deflection, code generation inside engineering.
RMA: an Agentic System for Research-Level Mathematical Problems
arXiv:2605.22875v1 Announce Type: new Abstract: We present $\textbf{Research Math Agents (RMA)}$, an agentic framework for automated reasoning on research-level mathematical problems. Unlike prior studies centered on competition mathematics or formal theorem proving, RMA targets research-level mathematical problems that require long-horizon reasoning, literature grounding, and iterative proof refinement. RMA decomposes research-level proof solving into specialized modules for problem analysis, literature search and understanding, fair comparison, knowledge-bank construction, and proof verification, all coordinated by initializer, proposer, and verifier agents through a shared structured memory. Within this unified framework, these agents operate in a multi-role, multi-round workflow, collaboratively generating, refining, and verifying candidate proofs through iterative feedback. We evaluate RMA on the First Proof benchmark, which consists of ten research-level problems contributed by expert mathematicians across diverse domains. Through comprehensive expert evaluation, RMA outperforms strong baselines on the First Proof benchmark, including GPT-5.2R and Aletheia, solving eight out of ten research problems and producing more logically sound and readable proofs. Our comprehensive ablation studies further show that performance gains arise from the interaction of structured reasoning modules, iterative refinement, and verifier-based feedback, rather than any single component. Our solutions and implementations will be made publicly available upon acceptance.
Why Enterprise AI Strategy and ROI fails? - Articles
Master Enterprise AI Strategy and ROI. Learn how to bridge the implementation gap, evolve developer skills, and drive measurable business impact today.
[BPO Insights] Enterprise Buyers Are Adding AI Clauses to BPO Contracts -- Here's What They Say
Enterprise procurement teams are embedding AI-specific requirements directly into BPO contracts -- from automation minimums and audit trails to hallucination liability and model governance. The new contract language reveals exactly what enterprise buyers expect and which BPOs will survive renewal ...
Roles and Responsibilities: Threshold Questions in Enterprise AI Adoption
May 18,2026 Roles and Responsibilities: Threshold Questions in Enterprise AI Adoption As companies rapidly move artificial intelligence out of the pilot sandbox and into their ordinary operating architecture, boards and executives must confront new questions about the roles AI may assume in ...
India's AI ambitions hinge on workforce re-skilling, IBM India head says | Reuters
India will need a coordinated push across government, companies and academia on skilling and policy if it wants to become an AI powerhouse, an IBM executive said, as the technology threatens the country's position as a global services hub.
How AI is forcing McKinsey and its peers to rethink pricing
Clients are questioning the value of advice while getting more used to fees based on successful task completion
AI for Service Businesses: The Practical 2026 Guide
AI for service businesses in 2026 looks different from AI for ecommerce. Here's the practical guide for consultants, coaches, agencies. Real workflows, real numbers, no fluff.
D&B's database of 642 million businesses was built for humans, not AI agents. So they rebuilt it.
Dun & Bradstreet has spent over 180 years building a comprehensive commercial database. Its Commercial Graph, covering 642 million businesses and their relationships, corporate hierarchies and risk profiles, was designed for people. Credit analysts, risk managers and sales professionals who could wait for query results and work through ambiguous entity matches. AI agents cannot do any of those thi
AI Skills Shortage Hits 45% of Indian Organisations: Report
Nearly 45% of Indian firms cite AI skills as top workforce constraint. SHRM India Report reveals 54% show low urgency on AI investment despite looming disruption.
Council Post: AI Agents And The Future Of Work: How Early Adopters Are Building Competitive Advantage
The intelligent era is here. The question is whether your organization will shape it or be shaped by it.
Council Post: How CFOs Can Turn Early AI Adoption Into A Future-Ready Advantage
CFOs who act now and embed AI thoughtfully will be best positioned to lead.
Forum: Professional services sector must seize the moment amid AI disruption | The Straits Times
The professional services sector ... 230,000 professionals across finance and accounting, consulting, legal and engineering services. For decades, firms in this sector built their business models around expertise, manpower and billable hours. Today, that model is being challenged faster than expected. AI tools can ...
Workday wants AI to punch in instead of having to hire new recruits
CEO eyes margin gains by keeping headcount flat – bold for a company selling HR software to employers
Personality Engineering with AI Agents: A New Methodology for Negotiation Research
arXiv:2605.20554v1 Announce Type: new Abstract: According to canonical negotiation theory, people's success in a negotiation depends on how well they balance competing demands--empathizing and asserting, demonstrating concern for other and concern for self, being soft on the people and hard on the problem. Yet people struggle to manage these tensions, so researchers have lacked the ability to rigorously test the field's prescriptions under controlled conditions. AI agents do not face the same limitations, and their precision, repertoire, consistency, and scalability enable a new class of experiments to contribute to negotiation theory. In this article, we introduce personality engineering: a methodology that uses AI agents to precisely parameterize, manipulate, and evaluate negotiator personality. We propose using the interpersonal circumplex--and its two core dimensions of warmth and dominance--as a foundational coordinate system for the field. This approach offers both a rigorous methodology for testing classic negotiation theories and a practical guide for designing the personalities of AI negotiation agents.
EY and Microsoft to invest over $1bn in enterprise AI transformation
EY and Microsoft have expanded their partnership, pledging more than $1bn over five years to help organisations scale AI and achieve enterprise-wide results.
Autonomous AI Agents: Complete Enterprise Guide 2026
Here’s the uncomfortable truth most enterprise leaders already know but rarely say out loud: the way work gets done inside large organizations hasn’t fundamentally changed. What’s changed is the volume. More data, more systems, more decisions, but the same human-dependent workflows underneath it all. Skilled people are spending their days on tasks that shouldn’t require their skills in the first place. Traditional automation gave us speed on simple, repetitive work. AI ...
How can leaders win with agentic AI?
Agentic AI requires a business transformation approach rather than just a technology rollout. Leaders should focus on practical moves to drive value beyond incremental productivity.
Dublin document automation start-up Better Futures raises €600,000
The start-up is building towards a larger VC-led investment round, said CEO Anthony Mc Loughlin. Read more: Dublin document automation start-up Better Futures raises €600,000
GLOBAL STUDY FINDS WIDENING GAP BETWEEN AI AMBITION AND WORKFORCE READINESS
/PRNewswire/ -- A global Adecco Group study of 2,000 c-suite executives across 13 countries finds that organizations are accelerating AI adoption, but many...
Governance by Design: Architecting Agentic AI for Organizational Learning and Scalable Autonomy
arXiv:2605.20210v1 Announce Type: new Abstract: Agentic AI systems - systems that can pursue goals through multi-step planning and tool-mediated action with limited direct supervision - are moving from experimental prototypes to enterprise deployments. This transition introduces tensions in implementation, scaling, and governance: organizations seek scalable autonomy for knowledge and coordinatio
EY and Microsoft announce global initiative to help clients scale AI enterprisewide value creation and move beyond experimentation - Source
Janet Truncale, EY Global Chair ... of AI at scale. With access to a single, integrated team, clients will have at their disposal both Microsoft’s market-leading engineering depth, alongside EY teams’ deep industry knowledge and change management capabilities. By combining people and innovation in this next phase of the Alliance, clients will be empowered to realize the transformative power of agentic AI within ...
Employers told to prepare internal teams for emerging AI roles | Human Resources Director
Gartner says AI will start creating new roles in 2028
'Workforce rebalancing' comes for Kyndryl, and delivery teams are in the firing line
Kyndryl is targeting up to $500 million in savings through workforce rebalancing and the adoption of agentic AI.
Workers say reliance on AI is eroding skills and judgment
A new GoTo study finds workers increasingly depend on AI tools, raising concerns about misuse, poor judgment and declining skills.
AI Is Reshaping Early Career Hiring Expectations, New ICIMS Data Reveals
/PRNewswire/ -- ICIMS, a leading enterprise talent acquisition platform, released the ICIMS Insights May 2026 Workforce Report, revealing a growing imbalance...
Visma Dinero halts Danish rollout of new AI assistant to preserve competition
Visma Dinero stopped the release of its AI assistant in Denmark after antitrust authorities warned that the tool could facilitate anticompetitive information exchange between rivals.
Ex-Facebook exec Sheryl Sandberg says the 10-year career plan is dead thanks to AI: ‘Don’t script your career when the future is uncertain,’ she warns Gen Z
The former Meta says rigid career plans will backfire: "If I had one, I would have missed the internet," Sheryl Sandberg warned Gen Z.
Agents, Human Agency, and the Opportunity for Every Organization
Microsoft outlines how AI agents can expand high-value work and transform organizations into learning systems, aiding leaders in managing Copilot adoption and organizational redesign.
Why business process reinvention is needed for agentic AI workflows | Computer Weekly
Business processes were developed before AI, which makes them legacy. Camunda offers an agentic AI platform provider aiming to tackle this legacy.
Harvey's Winston Weinberg: Why AI will force lawyers to change their fee structure
The co-founder of the legal start-up talks about how AI could shake up law firms’ business models and how he plans to stay ahead of rivals
Learning to Hand Off: Provably Convergent Workflow Learning under Interface Constraints
arXiv:2605.19140v1 Announce Type: new Abstract: We study workflow learning in a setting where specialized agents hand off control through a shared artifact, each agent observes only a local function of that artifact and its own private state, and no centralized learner accesses joint trajectories -- the operating regime of multi-agent LLM pipelines that span organizational, vendor, or trust boundaries. We formalize this regime as an interface-constrained semi-Markov decision process (IC-SMDP), whose decision epochs occur at handoff times, and design IC-$Q$, an asynchronous decentralized $Q$-learning algorithm in which cross-agent coordination at every handoff is exactly one scalar. Our main result is a finite-sample bound for neural IC-$Q$ that decomposes into three independently controllable error sources: neural function-approximation error, interface representation gap, and a mixing-time residual, under the random option-duration discount. Establishing this bound requires lifting the approximate information state (AIS) framework from single-agent primitive-step MDPs to multi-agent SMDPs and controlling Markovian noise under random duration, neither of which has been done in prior work. To our knowledge this is the first finite-sample guarantee for neural $Q$-learning under decentralized partial observability. Four experiments: a controlled synthetic IC-SMDP that validates the bound term-by-term, multi-LLM mathematical reasoning, multi-agent routing, and multi-agent CPU programming, show that IC-$Q$ matches a centralized oracle without any agent observing joint trajectories, with each of the three error sources scaling along its corresponding axis as the bound predicts.
RobustiPy: An efficient next generation multiversal library with model selection, averaging, resampling, and explainable artificial intelligence
arXiv:2506.19958v4 Announce Type: replace-cross Abstract: Scientific inference is often undermined by the vast but rarely explored "multiverse" of defensible modelling choices, which can generate results as variable as the phenomena under study. We introduce RobustiPy, an open-source Python library that systematizes multiverse analysis and model-uncertainty quantification at scale. RobustiPy unifies bootstrap-based inference, combinatorial specification search, model selection and averaging, joint-inference routines, and explainable AI methods within a modular, reproducible framework. Beyond exhaustive specification curves, it supports rigorous out-of-sample validation and quantifies the marginal contribution of each covariate. We demonstrate its utility across five simulation designs and ten empirical case studies spanning economics, sociology, psychology, and medicine, including a re-analysis of widely cited findings with documented discrepancies. Benchmarking on ~672 million simulated regressions shows that RobustiPy delivers state-of-the-art computational efficiency while expanding transparency in empirical research. By standardizing and accelerating robustness analysis, RobustiPy transforms how researchers interrogate sensitivity across the analytical multiverse, offering a practical foundation for more reproducible and interpretable computational science.
India’s AI first GCCs are reshaping global enterprise strategy: Nasscom’s Rajesh Nambiar - BusinessToday
While many global AI models continue to be developed by hyperscalers and frontier AI labs, Nambiar argued that India is increasingly becoming the application and deployment layer where enterprise AI gets operationalised at scale.
Operationalizing Document AI: A Microservice Architecture for OCR and LLM Pipelines in Production
arXiv:2605.18818v1 Announce Type: new Abstract: Academic research tends to focus on new models for document understanding creating a wide gap in the literature between model definition and running models at production scale. To close that gap, we present a microservice architecture that encapsulates pipelines of multiple models for classification, optical character recognition (OCR), and large language model structured field extraction as well as our experience running this pipeline on thousands of multi-page documents per hour. We describe our primary design decisions, including a hybrid classification, separation of GPU-bound inference from CPU-bound orchestration, use of asynchronous processing for the many IO-bound operations in the pipeline, and an independent, horizontal scaling strategy. Using batch profiling, we identified two surprising qualitative findings that shape production deployments: OCR, not language-model parsing, dominates end-to-end latency, and the system saturates at a concurrency determined by shared GPU-inference capacity rather than worker count. Our goal is to provide practitioners with concrete architectural patterns for building document understanding systems that work beyond the benchmark; effectively operationalizing models in production.
Towards Zero Trust Architecture: A Pilot Study on Information Systems Security Readiness amongst Small and Medium Enterprises
arXiv:2605.18901v1 Announce Type: cross Abstract: Small and medium enterprises (SMEs) face growing cyber threats but often lack the resources and expertise needed to adopt Zero Trust Architecture (ZTA). This pilot study examines the drivers and barriers shaping SME perceptions of ZTA necessity and proposes an exploratory staged adoption path. Survey data from 64 IT and security professionals in the Asia-Pacific region show that ZTA familiarity and cloud-computing needs are the strongest positive correlates of perceived necessity, whereas accumulated barriers show only a weak negative association. Identity and access management complexity and scalability emerge as the main implementation hurdles. Based on these findings, we propose a three-stage route for SMEs: strengthening identity governance, segmenting high-value assets, and introducing targeted monitoring in line with operational capacity. The study offers early evidence for more realistic Zero Trust transitions in resource-constrained firms.
AI oversight creates fresh governance pressure for directors and officers | Insurance Business
Boards face growing disclosure, compliance, and litigation risks as AI adoption accelerates
FinancialContent - 85% of Law Firms Say Clients Are Driving AI Investment Decisions, New Litera Survey Finds
85% of Law Firms Say Clients Are Driving AI Investment Decisions, New Litera Survey Finds
“Poisoning the well:” EY retracts cyber report packed with AI slop | Cybernews
Consultancy group Ernst & Young (EY) has withdrawn a cybersecurity report after an investigation by GPTZero found that 70% of the citations within it were either fabricated or broken.
Enhancing Metacognitive AI: Knowledge-Graph Population with Graph-Theoretic LLM Enrichment
arXiv:2605.16676v1 Announce Type: new Abstract: Metacognition-the ability to monitor one's own knowledge state, spot gaps, and autonomously fill them--remains largely absent from modern AI. Here, we present MetaKGEnrich, a fully automated pipeline that endows large language model (LLM) applications with self-directed knowledge repair. The system (i) builds knowledge graphs from a seed query, (ii) detects sparse regions via seven graph metrics, (iii) has GPT-4o generate targeted questions, (iv) retrieves web evidence with Tavily and ingests it into Neo4j, and (v) re-answers the query with GraphRAG for GPT-4 to evaluate improvement. Tested on 30 queries from each of three widely-used datasets: Google Research Natural Questions, MS MARCO, and Hot-potQA. MetaKGEnrich improved answer quality in 80% of HotpotQA questions, 87% of Google Research Natural Questions and 83% of MS MARCO questions, while preserving well-supported regions. This proof of concept demonstrates how topological self-diagnosis plus targeted retrieval can advance AI toward humanlike metacognitive learning.
CEO of AI-powered performance review firm says annual evaluations weren’t designed for the AI era: ‘The practice just hasn’t kept up’
15Five CEO David Hassell told Fortune that companies must stop relying on the annual performance review cadence.
Shadow AI invades the workplace, up 4x in the last year
Employers increasingly blind to unauthorized AI use and where their staff are sending proprietary files
Irish business leaders place higher value on empathy in AI – report
Expleo’s research explored European business mindsets on the adoption of AI. Read more: Irish business leaders place higher value on empathy in AI – report
Cost to implement AI-powered SEO tools
This means SEO strategies must also become AI-aligned to remain competitive. Businesses that fail to adopt AI SEO tools risk falling behind in: ... When businesses evaluate the cost to implement AI-powered SEO tools, they often look only at subscription pricing. However, modern AI SEO ecosystems operate under multiple pricing structures that directly affect total investment. These pricing models determine how scalable and ...
SAP customers warned AI agents could put costs on autopilot
Billing will be based on 'actions,' whatever those are, leaving enterprises to wonder how fast the meter might run
SAP's AI strategy: Come for the openness, stay because you have to
Joule Studio 2.0 waves the flag of interoperability, API policy tells enterprises who's really in charge
Big Four post more job ads for AI specialists than auditors
Increase comes as world’s largest accounting firms rush to adapt to technological disruption
Counterparty Modeling is Not Strategy: The Limits of LLM Negotiators
arXiv:2605.16575v1 Announce Type: new Abstract: Negotiation requires more than inferring what the other side wants: it requires using that information to make advantageous offers and counteroffers over multiple turns. We study whether large language model (LLM) agents do this in a controlled multi-attribute bargaining environment. We find that current LLM agents can model a counterparty's preferences, but do not reliably turn that knowledge into strategic bargaining. When given negotiating partner preference information, agents model it accurately and early in their reasoning traces, yet this does not reliably improve outcomes for the informed side. Turn-level analyses show why: agents often respond to what they believe the counterparty values, but do not consistently pair those moves with gains on their own high-value attributes. Sellers are more accommodating overall, and in asymmetric-information conditions, the informed side often makes the more weakly compensated concessions. Because agents fail to leverage this underlying utility structure for strategic advantage, their final agreements are heavily dictated by surface-level opening anchors rather than actual utility weights. Finally, requiring agents to explicitly state concession-for-reciprocity trades before making an offer makes individual turns look more strategic, but ultimately fails to improve the efficiency of the final agreements.
Companies Don’t Have to Slash Jobs Because of AI
Harry Haysom/Ikon Images | Carolyn Geason-Beissel “If AI is going to destroy all the jobs, why don’t we just stop?” That was the rhetorical question my college-age son asked after we talked about the possibility of drastic changes to career paths and society thanks to AI (technically, generative AI). It was in line with what […]
New research reveals deep public scepticism over AI benefits for jobs
King's College London research reveals extent of unease among public over advent of AI and distrust of employers when it comes to jobs.
'Office jobs will be heavily automated in next 18 months': Microsoft AI Chief Mustafa Suleyman’s big warning - Technology News | The Financial Express
Microsoft AI Chief Mustafa Suleyman, Mustafa Suleyman warning, office jobs automation, white-collar jobs automation, AI replacing jobs, Microsoft AI news, automation in workplaces, future of office jobs, AI impact on employment, AI and white-collar workers, artificial intelligence jobs threat, ...
Workday to keep expanding Indian workforce, deepen AI investments
Workday would sustain the pace of workforce expansion in India, a top executive said on Tuesday
How Agentic AI is Transforming HR, Finance, Healthcare & Retail
Real-world Agentic AI use cases across HR, finance, healthcare, retail and more. Understand how autonomous AI agents improve efficiency and decision-making.
Council Post: The Context Gap: Why Your Agentic AI Investment Isn't Paying Off (Yet)
Without the right context, you may still get an output. But at what cost and in what time frame?
Corporate Insourcing Trends Driven by AI-Enabled Productivity Gains
Large enterprises are increasingly shifting toward insourcing talent to capture AI-driven productivity gains internally rather than relying on external vendors. This strategic pivot allows firms to retain proprietary advantages and reduce long-term dependency on third-party service providers.
Opinion | AI chatbots should be allowed in the courtroom - The Washington Post
The white-shoe law firm got caught red-handed. Last month, Sullivan & Cromwell apologized for submitting a filing in federal bankruptcy court riddled with AI -generated errors. The mistake was embarrassing, but the use of the technology wasn’t. Far from an indictment of large language models, the ordeal revealed that even the country’s most prestigious attorneys rely on such services.
A Need for Nuance: The Economist’s Andrew Palmer
On today’s episode of the Me, Myself, and AI podcast, Andrew Palmer, senior editor at The Economist, describes how organizations can experiment with generative AI while balancing speed, quality, and risk. At his own organization, Andrew and others test artificial intelligence with human oversight to develop editing and publishing efficiencies. As the host of The […]
Opinion | What jobs will AI destroy? Exhibit A shouldn’t be on the list.
Pity the lawyers: Whenever people want to dramatize the impact of artificial intelligence on white-collar work, they tend to reach for the legal profession as Exhibit A. Mustafa Suleyman, head of Microsoft AI, opined in February that legal tasks ...
Is AI really improving productivity and job outcomes? - HR Leader
New research reveals that workers are adopting AI in the workplace at an extremely rapid rate. While this is the case, many people remain uncertain about whether it is actually improving productivity within their jobs or improving the outcomes of the jobs being done.
AI made marketers faster, but organizations stayed the same | MarTech
AI can generate solid content in ... faster organizations. Most marketing teams spent the last two years doing exactly what they were incentivized to do. Each specialist figured out how to make AI useful within their own workflow. The content specialist drafts newsletter snippets in ChatGPT. The designer generates brand-compliant graphics in Firefly. The email marketer built a QA workflow that saves hours every week. Managers use ChatGPT ...
I was laid off from eBay. Now I run a business with 27 AI agents — these are the human parts I still handle myself.
After being laid off from eBay, Linara Bozieva launched an AI-powered marketing agency which she runs with 27 custom AI agents.
Understanding AI Project Execution Pricing Models: A Guide to ai project cost analysis
When you start an AI project, one of the first questions you ask is: How much will this cost? Understanding the pricing models behind AI project execution is crucial. It helps you plan your budget, set realistic expectations, and avoid surprises. In this post, I will walk you through the key ...
I’m an AI ethicist accused of AI plagiarism. Now what?
Automated screeners can mistake style, rhythm and consistency for machine-made prose, putting writers at risk, a community news leader writes.
Beyond the Chatbot: Why Your Business Needs Agentic AI for Voice Automation
For years, the promise of voice automation in the enterprise has been tantalizingly close. We’ve seen consumer technology like Google Home and Alexa normalize voice commands, and early-generation chatbots have handled simple, repetitive customer queries. But for complex, high-value business ...
More AI, less productive? New global research reveals the paradox hitting workplaces
ADP's People at Work 2026 report reveals a paradox that every SME owner managing a team needs to understand before their next AI investment.
Berlin-based LawX raises €7.5 million to build an AI-powered operating system for notaries and law firms
LawX, a Berlin-based LegalTech startup, has raised €7.5 million in a Seed funding round to develop the first holistic AI-powered operating system for law firms and notaries. The round was led by Motive Partners, with additional investors such as WENVEST Capital, xdeck, and SIVentures. In addition to institutional investors, several prominent angel investors from the […]
SDOF: Taming the Alignment Tax in Multi-Agent Orchestration with State-Constrained Dispatch
arXiv:2605.15204v1 Announce Type: new Abstract: Multi-agent orchestration frameworks such as LangChain, LangGraph, and CrewAI route tasks through graph-based pipelines but do not enforce the stage constraints that govern real business processes. We present SDOF, a framework that treats multi-agent execution as a constrained state machine. SDOF operates through two primary defensive layers, implemented by three components: (1) an Online-RLHF Specialized Intent Router trained via Generative Reward Modeling (GRPO) and (2) a StateAwareDispatcher with GoalStage finite-automaton checks and precondition/postcondition SkillRegistry validation for auditable execution control. On a recruitment system backed by the Beisen iTalent platform (6000+ enterprises), 185 expert-curated scenarios trigger 1671 live API calls. Our GSPO-aligned 7B Intent Router achieves higher joint accuracy than zero-shot GPT-4o on this FSM-constrained adversarial routing benchmark (80.9% versus 48.9%). In end-to-end execution, SDOF reaches 86.5% task completion (95% confidence interval 80.8 to 90.7) and blocks all 22 operations in the injection, illegal HR subset. Under a broader message-level blocking audit, SDOF attains precision 100% and recall 88%, expert agreement kappa=0.94. A separate evaluation on 960 SGD-derived dialogues spanning 8 service domains surfaces 201 stage-order conflicts under our FSM mapping, 41 of which arise in the normal split. This arXiv version reports the current validated scope; extended multi-seed training comparisons and deeper workflow evaluations will be released in a subsequent update.
How Eightfold Uses Agentic AI To Make Opportunity More Inclusive
Eightfold AI details how it used agentic AI workflows to automate accessibility compliance, reducing a six-month remediation process to two months.
DeepSlide: From Artifacts to Presentation Delivery
arXiv:2605.15202v1 Announce Type: new Abstract: Presentations are a primary medium for scholarly communication, yet most AI slide generators optimize the artifact (a visually plausible deck) while under-optimizing the delivery process (pacing, narrative, and presentation preparation). We present DeepSlide, a human-in-the-loop multi-agent system that supports preparing the full presentation process, from requirement elicitation and time-budgeted narrative planning, to evidence-grounded slide--script generation, attention augmentation, and rehearsal support. DeepSlide integrates (i) a controllable logical-chain planner with per-node time budgets, (ii) a lightweight content-tree retriever for grounding, (iii) Markov-style sequential rendering with style inheritance, and (iv) sandboxed execution with minimal repair to ensure renderability. We further introduce a dual-scoreboard benchmark that cleanly separates static artifact quality from dynamic delivery excellence. Across 20 domains and diverse audience profiles, DeepSlide matches strong baselines on artifact quality while consistently achieving larger gains on delivery metrics, improving narrative flow, pacing precision, and slide--script synergy with clearer attention guidance.
Gen AI Could Fix Performance Reviews—or Make Them Even Worse
This article explores how generative AI can assist managers in creating more comprehensive, data-driven employee performance evaluations.
Publicis to buy US data company in $2.2bn deal as it deepens AI marketing push
French advertising group to purchase LiveRamp and enhance its focus on disruptive technology
AI hiring trends 2026: Skills over job titles and degrees, says LinkedIn report - India Today
LinkedIn Talent Velocity Report 2026 shows hiring is changing fast. Human skills are rising even as AI grows, job titles are fading, and companies are rethinking hiring due to skill gaps and rising AI costs. The result is a major shift in how students and professionals build careers.
Decoding ROI from AI in Africa
# Decoding ROI from AI in Africa Published: 2026-05-15T16:20:27+00:00 Author: PricewaterhouseCoopers ## Summary PwC's AI performance study revealed that companies with higher AI fitness are making bold decisions using AI to drive growth and create new value creation. The study surveyed 1,217 large companies globally and found that the top 20% capture 74% of AI-driven financial returns. This could potentially widen the gap in market leadership and profitability over time. ## Story Decoding ROI from AI in Africa Menu Industries Menu Industries Menu Industries Menu Industries Menu Industries Menu Industries Menu Industries Menu Industries Menu Industries Menu Industries Menu Industries Menu Industries Featured PwC's annual power and utilities roundtable Menu Services Menu Services Menu Services Menu Services Menu Services Menu Services Menu Services Menu About us Menu About us Menu Abo
AI Adoption: Top‑down or Bottom‑up? | by Raymond Ng, Certified Prompt Engineer, MSc (KM) | AI Trek | May, 2026 | Medium
AI Adoption: Top‑down or Bottom‑up? | by Raymond Ng, Certified Prompt Engineer, MSc (KM) | AI Trek | May, 2026 | Medium Sign up Get app Sign up ## AI Trek Empowering humanity with AI Member-only story # AI Adoption: Top‑down or Bottom‑up? Raymond Ng, Certified Prompt Engineer, MSc (KM) 2 min read Just now -- Share Press enter or click to view image in full size Recently, I spoke with a lawyer about her experience with AI in a previous firm. She explained that the firm had no firm‑approved AI tools. Instead, individual lawyers simply adopted whatever tools they found useful. That’s a bottom‑up approach: innovation driven by staff, sometimes guided by policy — and sometimes not. It can spark creativity, but it also risks inconsistency and exposure to unmanaged risks. By contrast, a top‑down approach means firm‑wide governance, approved tools, and clear guardrails. This miti
How CBA, BHP, PwC, Freehills, and Telstra are using AI to change their operations
How CBA, BHP, PwC, Freehills, and Telstra are using AI to change their operations Skip to navigationSkip to contentSkip to footer Help using this website - Accessibility statement Series # The AI Dividend: Lessons from CBA, PwC, BHP, Telstra, and Freehills For this special series, we spoke to six high-profile companies about how they are putting artificial intelligence to work in their operations. 6 stories Save Log in or Subscribe to save article Share Copy link Copied Copy link Copied Share via... ### Freehills uses AI to slash contract delivery from 28 days to six Using AI tools such as Legora, the law firm culled 200,000 documents down to 18,500, achieving an 80 per cent time saving over manual review. - May 1, 2026 - Janek Drevikovsky ### Inside the CBA war room where AI bots fight an army of fraudsters CBA’s “Pollen” team uses AI honeypots to trap scammers in auto
Agentic AI for Global Capability Centers: A Practical Guide for Modern Enterprises | by Inductus Tech | May, 2026 | Medium
Agentic AI for Global Capability Centers: A Practical Guide for Modern Enterprises | by Inductus Tech | May, 2026 | Medium Sign up Get app Sign up # Agentic AI for Global Capability Centers: A Practical Guide for Modern Enterprises 6 min read 15 hours ago -- Share ## Introduction Agentic AI for global capability centers is becoming one of the most important shifts in enterprise operations. GCCs are no longer just delivery hubs; they are turning into innovation engines that can design, test, and scale intelligent automation across business functions. For business leaders, the appeal is clear. Agentic AI can help GCCs move from task automation to goal-driven execution, where systems can reason, act, and learn across workflows with human oversight. This article explains what it means, why it matters, how it works, and how organizations can adopt it in a practical way. ## What Ag
Why a ‘two-speed’ AI strategy can help your enterprise achieve ROI goals - Data and Analytics - iTnews Asia
Why a ‘two-speed’ AI strategy can help your enterprise achieve ROI goals - Data and Analytics - iTnews Asia # Why a ‘two-speed’ AI strategy can help your enterprise achieve ROI goals Image Credit: Oracle ## The success of AI depends on strong data foundations, workflow integration and measurable KPIs over “moonshot” pilots. By Abbinaya Kuzhanthaivel on May 15, 2026 3:34PM Artificial intelligence investments continue to accelerate across enterprises, but many organisations are still struggling to convert pilots into measurable business returns. While proof-of-concept projects often generate excitement internally, scaling them into production environments has proven far more difficult. Speaking with iTnews Asia, Srikant Gokulnatha, senior vice president of AI and Analytics at Oracle shares his perspective on why some AI initiatives succeed while others remain trapped in proof-of-conc
The growth of ‘build-your-own’ legal AI tools
Law firms are developing their own systems, sometimes with an eye to selling them to clients
50 AI Automation Use Cases for Enterprise [2026 Guide]
50 real AI automation use cases for CTOs, CIOs, and operations leaders with ROI estimates, complexity ratings, and a prioritization framework for 2026.
Building an AI Business Case Your CFO Will Actually Approve
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IBM rolls out Forward Deployed Units for AI deployment
IBM has introduced FDUs, a new delivery model that pairs small, senior teams with AI agents to speed up enterprise AI deployments.
PwC, Anthropic boost alliance to scale enterprise AI adoption
PwC, Anthropic boost alliance to scale enterprise AI adoption PwC’s leaders have also begun practical adoption following training sessions at recent internal events. Credit: Bendix M/Shutterstock.com. PwC and Anthropic have announced an expansion of their alliance, aiming to increase the use of Anthropic’s Claude AI technology within PwC’s services and operations. The two firms are deploying Claude across several key business areas, including technology development, transaction execution, and enterprise operational models, with an emphasis on production-level implementation at scale. #### Access deeper industry intelligence Experience unmatched clarity with a single platform that combines unique data, AI, and human expertise. The collaboration centres on three main initiatives: accelerated software development using Claude Code, integration of AI agents into deal-making processes,
The End of One-Size-Fits-All Enterprise Software
The End of One-Size-Fits-All Enterprise Software # Leadership Library SubscribeSign in # The End of One-Size-Fits-All Enterprise Software ### by Deep Nishar and Nitin Nohria May 15, 2026 2 Share Generative AI is dissolving the economic logic that made standardized enterprise software the only practical choice for most companies. What replaces it will be shaped not just by the rapidly evolving capabilities of this new technology, but by leaders willing to ask a harder question: Which workflows do we actually need to own? Until recently, organizations adapted their workflows to standardized software solutions. The workflow conventions of a vendor, such as Salesforce’s customer relationship management (CRM), SAP’s enterprise resource planning (ERP), Workday’s human capital management (HCM), or Epic’s electronic health record (EHR), became, by default, the workflow conventions of yo
Gartner warns firms without AI talent strategy risk losing half of key staff by 2027
Gartner warns firms without AI talent strategy risk losing half of key staff by 2027 Gartner. [Photo: Shutterstock] IT market research firm Gartner on May 13 announced findings from a study on AI talent strategies and warned that by 2027, half of companies that fail to establish a comprehensive AI talent strategy will lose key AI personnel to rivals. Gartner said talent will gravitate to companies that prioritise strengthening workforce capabilities rather than simply adopting AI. Gartner conducted a "Global Labor Market Survey" in the first quarter of 2026 to measure AI’s impact on work, employee psychology and workforce capabilities, surveying 12,004 employees and managers at companies in 40 countries. Swagatham Basu (스와가탐 바수), a senior director analyst in Gartner’s HR practice, said, "Most leaders are mistaking basic approaches to AI or adoption indicators for true transformation
The Workforce Skills Gap That AI Can't Solve for Itself
Tech skills alone won't cut it in the AI era. IDC's Human Skills Framework for Agentic AI identifies 8 trainable capability clusters your workforce needs now.
McKinsey cuts partner cash share in post-AI pay revamp
Consultancy tells senior staff their remuneration will comprise a greater proportion of equity
EY retracts study after researchers discover AI hallucinations
Incident is latest example of professional services firm being led astray by new technology
A Two-Dimensional Framework for AI Agent Design Patterns: Cognitive Function and Execution Topology
arXiv:2605.13850v1 Announce Type: new Abstract: Existing frameworks for LLM-based agent architectures describe systems from a single perspective: industry guides (Anthropic, Google, LangChain) focus on execution topology -- how data flows -- while cognitive science surveys focus on cognitive function -- what the agent does. Neither axis alone disambiguates architecturally distinct systems: the same Orchestrator-Workers topology can implement Plan-and-Execute, Hierarchical Delegation, or Adversarial Verification -- three patterns with fundamentally different failure modes and design trade-offs. We propose a two-dimensional classification that combines (1) a Cognitive Function axis with seven categories (Context Engineering, Memory, Reasoning, Action, Reflection, Collaboration, Governance) and (2) an Execution Topology axis with six structural archetypes (Chain, Route, Parallel, Orchestrate, Loop, Hierarchy). The resulting 7x6 matrix identifies 27 named patterns, 13 with original names. We demonstrate orthogonality through systematic cross-axis analysis, define eight representative patterns in detail, and validate descriptive coverage across four real-world domains (financial lending, legal due diligence, network operations, healthcare triage). Cross-domain analysis yields five empirical laws of pattern selection governing the relationship between environmental constraints (time pressure, action authority, failure cost asymmetry, volume) and architectural choices. The framework provides a principled, framework-neutral, and model-agnostic vocabulary for AI agent architecture design.
Australian law firms are taking a lead on navigating best use of AI
Leaders are focusing on how their business models will change. Plus: the top 30 innovative law firms ranked
In-house legal teams step up on AI strategies
Good foundations are essential in embracing new tech opportunities. Plus, case studies highlighting the most innovative work achieved by in-house teams in Asia-Pacific
Valencia-based Fresh People raises €2.6 million to scale Booster, its AI leadership copilot
Fresh People, a Valencia-based technology company specialised in leadership and team management, has closed a €2.6 million round to accelerate the launch of Booster, a new AI-powered leadership system and prepare its international expansion. The round was led by Inveready, with participation from Archipiélago Next, Successful Fund, Paloma Tejada, a former BBVA executive, among others. […]
From Descriptive to Prescriptive: Uncover the Social Value Alignment of LLM-based Agents
arXiv:2605.14034v1 Announce Type: new Abstract: Wide applications of LLM-based agents require strong alignment with human social values. However, current works still exhibit deficiencies in self-cognition and dilemma decision, as well as self-emotions. To remedy this, we propose a novel value-based framework that employs GraphRAG to convert principles into value-based instructions and steer the agent to behave as expected by retrieving the suitable instruction upon a specific conversation context. To evaluate the ratio of expected behaviors, we define the expected behaviors from two famous theories, Maslow's Hierarchy of Needs and Plutchik's Wheel of Emotion. By experimenting with our method on the benchmark of DAILYDILEMMAS, our method exhibits significant performance gains compared to prompt-based baselines, including ECoT, Plan-and-Solve, and Metacognitive prompting. Our method provides a basis for the emergence of self-emotion in AI systems.
Bridging Legal Interpretation and Formal Logic: Faithfulness, Assumption, and the Future of AI Legal Reasoning
arXiv:2605.14049v1 Announce Type: new Abstract: The growing adoption of large language models in legal practice brings both significant promise and serious risk. Legal professionals stand to benefit from AI that can reason over contracts, draft documents, and analyze sources at scale, yet the high-stakes nature of legal work demands a level of rigor that current AI systems do not provide. The central problem is not simply that LLMs hallucinate facts and references; it is that they systematically draw inferences that go beyond what the source text actually supports, presenting assumption-laden conclusions as if they were logically grounded. This proposal presents a neuro-symbolic approach to legal AI that combines the expressive power of large language models with the rigor of formal verification, aiming to make AI-assisted legal reasoning both capable and trustworthy, thus reducing the burden of manual verification without sacrificing the accountability that legal practice demands.
AI Alignment Amplifies the Role of Race, Gender, and Disability in Hiring Decisions
arXiv:2605.13866v1 Announce Type: cross Abstract: Humans increasingly delegate decisions to language models, yet whether these systems reproduce or reshape human patterns of discrimination remains unclear. Here we run a large-scale study to analyse whether language models use demographic information in hiring decisions. We show, across 27 models and 177 occupations, that language models give fema
EY pulls report that contained AI hallucinations
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Council Post: Why AI Transformation Is Really A Human Coordination Challenge
AI performs best in structured environments, but real-world operations are rarely structured.
Consulting’s Partnership Model Is Shifting with AI - Strat-Bridge
McKinsey’s reported partner compensation overhaul reveals a deeper shift in consulting economics as AI, outcome-based pricing, and capital investment reshape the traditional partnership model.
Council Post: Why Traditional Outplacement Programs Are Failing AI-Era Layoffs
But one decision still belongs to you in a meaningful way: What support do the people leaving actually receive?
AI-driven layoffs aren’t making business sense | CIO
Most large enterprises lay off workers after launching AI projects, but industry research says job cuts have no correlation to return on AI investment. Experts offer better routes to ROI.
AI-driven layoffs aren’t making business sense | CIO
Most large enterprises lay off workers after launching AI projects, but industry research says job cuts have no correlation to return on AI investment. Experts offer better routes to ROI.
Learning Transferable Latent User Preferences for Human-Aligned Decision Making
arXiv:2605.12682v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as reasoning modules in many applications. While they are efficient in certain tasks, LLMs often struggle to produce human-aligned solutions. Human-aligned decision making requires accounting for both explicitly stated goals and latent user preferences that shape how ambiguous situations should be resolved. Existing approaches to incorporating such preferences either rely on extensive and repeated user interactions or fail to generalize latent preferences across tasks and contexts, limiting their practical applicability. We consider a setting in which an LLM is used for high-level reasoning and is responsible for inferring latent user preferences from limited interactions, which guides downstream decision making. We introduce CLIPR (Conversational Learning for Inferring Preferences and Reasoning), a framework that learns actionable, transferable natural language rules that represent latent user preferences from minimal conversational input. These rules are iteratively refined through adaptive feedback and applied to both in-distribution and out-of-distribution ambiguous tasks across multiple environments. Evaluations on three datasets and a user study show that CLIPR consistently outperforms existing methods in improving alignment and reducing inference costs.
Career Mobility of Planning Alumni in the United States: Evidence from Professional Profile Data using Large Language Models
arXiv:2605.12618v1 Announce Type: new Abstract: Problem, Research Strategy, and Findings: Planning professions in the United States navigate complex and dynamic career landscapes under rapid urban changes, yet comprehensive evidence regarding their career trajectories, advancement patterns, and the influence of social, spatial, organizational, and educational factors remains limited. This study draws on boundaryless career theory, social capital theory, and spatial opportunity models to analyze career mobility among more than 130,000 planning alumni. Using large language models to extract structured information from LinkedIn profiles, our results reveal that planning alumni who adopt boundaryless career patterns, specifically multisector experience or lateral and industry-switching trajectories, achieve significantly higher upward mobility. While technical competencies provide a foundational entry-level signal, soft skills leveraged through strategic lateral moves become increasingly decisive as planners reach senior stages. Geographic mobility and employment in larger, diverse metropolitan labor markets are both associated with advancement, though the latter provides modest benefits. Larger professional networks and greater organizational engagement are consistently associated with upward career transitions, while AI-related skills, now commonplace, present limited additional advantage. Limitations include reliance on LinkedIn data, which may underrepresent alumni without online profiles, and an individual-level focus that omits organizational factors.
AI investments surge, ROI doesn't: Orgvue - The Business Journals
Business leaders continue to invest in artificial intelligence, but the return on those investments (or lack thereof) is drawing more attention.
Evaluating the Business Case for AI in Patent Practice
Legal organizations that treat AI as a capital allocation, workflow design, and practice-management discipline will be positioned to generate real advantage.
How AI is reshaping the future of hiring
Hiring decisions have traditionally relied on degrees, resumes, and institutional reputation to evaluate candidates. While these credentials offer insight into a person’s academic background, they do...
Council Post: Your AI Investment May Be Making Your Leadership Team Worse At Their Jobs
Delegating mental effort to AI can be an advantage. However, used habitually, it erodes the skills that make people valuable: critical thinking.
How Indeed Doubled Productivity With AI
Also in the Forbes CIO newsletter: The space race gets a new AI dimension; vibe coding requires less time to write, but more time to verify.
GrowthLoop Unveils 2026 AI and Marketing Performance Index, Highlighting that Data Issues Significantly Slow Marketing Cycles, Experimentation, and Personalization
/PRNewswire/ -- GrowthLoop today released its 2026 AI and Marketing Performance Index, a survey of more than 300 marketers and data leaders across the U.S. and...
Four ways to create a lasting cost advantage from AI | Fortune
A recent BCG analysis identifies what sets AI winners apart.
Dissatisfied: Three-fourths of AI customer service rollouts are a letdown
AI rollback rates hit 81% at firms with mature guardrails, suggesting enterprises are struggling to manage the systems in production, says Sinch
Anthropic expands Claude's AI tools for law firms, lawyers | Reuters
WASHINGTON, May 12 (Reuters) - Artificial intelligence company Anthropic on Tuesday released an expanded suite of features for lawyers using its Claude AI assistant, including tools for specialized legal topics and access within Claude to other legal research and AI products.
Beyond Verification — What Responsible AI Really Demands of Human Experts | MIT Sloan Management Review
Can responsible AI exist without input from humans?
White-collar workers report growing feelings of ‘AI brain fry’
Workers are reporting feeling overwhelmed by the new technology
Imposter syndrome used to be a lie. AI made it true
For decades, psychologists told us self-doubt at work was a distortion. Then AI came along and made the gap real — for everyone, at once.
Homebuyers remain sceptical about AI replacing estate agents
A new survey suggests that most UK homebuyers and sellers still prefer speaking to an actual estate agent rather than using AI during key stages of the property transaction process. Research from Moneypenny, based on responses from 2,000 adults, found that 83% would rather deal with a human when booking a valuation or making an […]
Auditing African Content Moderators' Working Conditions by Using the European General Data Protection Regulation (GDPR)
arXiv:2605.11699v1 Announce Type: new Abstract: In this article, we audit the working conditions of content moderators in Kenya and Nigeria employed by business process outsourcing (BPO) companies by using the European General Data Protection Regulation (GDPR). We demonstrate its extraterritorial scope for gaining access to elements such as employment contracts and NDAs that have never been provided to the workers concerned. The results of this approach provide legally grounded evidence of the structural disadvantages faced by content moderators in the Global South, whose exploitative working conditions violate workers' rights. Our work also highlights the benefits of legislation aimed at protecting individuals' data rights as a counterweight to the tech industry's discourse of exceptionalism, which obscures its dependence on BPOs to externalise labour costs and accountability, whilst claiming that its products, business models, and methods of resource extraction are unprecedented and fall outside any existing legal framework.
Joanna Stern: An AI Immersion for 365 Days
She has 3 major rules in her assessment of AI : (1) Ruthless testing; (2) Benchmark vs human; and (3) Costs, which include “compute cost” that we discussed.
AI Responsibility and Transparency Act: Key Workplace Impacts » CBIA
The legislation is a wide-ranging “online safety” and AI bill with several provisions that directly affect hiring and employers.
Jobs lost to AI could reappear elsewhere — and solidify AI-focused roles
The arrival of AI has upended the business world, especially the job market. But positions lost to AI now could morph into new opportunities elsewhere, analysts say.
Frontier AI models don't just delete document content — they rewrite it, and the errors are nearly impossible to catch
As large language models become more capable, users are tempted to delegate knowledge tasks where models process documents on their behalf and provide the finished results. But how far can you trust the model to stay faithful to the content of your documents when it has to iterate over them across multiple rounds? A new study by researchers at Microsoft shows that large language models silently co
The new layoff may not look like a layoff: How AI is reshaping workforce stability
As AI changes how organisations operate, the bigger shift is not just job cuts. It is the quiet redesign of roles, workflows, expectations, and trust itself.
Agentic AI Powers the Future of Customer Experience
Agentic AI is reshaping CX—uniting Genesys and ServiceNow with Capgemini to automate service, orchestrate workflows, and deliver faster outcomes.
Benjamin Yuille - Enterprise Sales Leader | B2B SaaS ...
AI revtech is impressive. It works really well… Just not in true enterprise. It thrives in low ACV, transactional sales.
Spatial Priming Outperforms Semantic Prompting: A Grid-Based Approach to Improving LLM Accuracy on Chart Data Extraction
arXiv:2605.08220v1 Announce Type: new Abstract: The automated extraction of data from scientific charts is a critical task for large-scale literature analysis. While multimodal Large Language Models (LLMs) show promise, their accuracy on non-standardized charts remains a challenge. This raises a key research question: what is the most effective strategy to improve model performance (high-level semantic priming) or low-level spatial priming? This paper presents a comparative investigation into these two distinct strategies. We describe our exploratory experiments with semantic methods, such as a two-stage metadata-first framework and Chain-of-Thought, which failed to produce a statistically significant improvement. In contrast, we present a simple but highly effective spatial priming method: overlaying a coordinate grid onto the chart image before analysis. Our quantitative experiment on a synthetic dataset demonstrates that this grid-based approach provides a statistically significant reduction in data extraction error (SMAPE reduced from 25.5% to 19.5%, p < 0.05) compared to a baseline. We conclude that for the current generation of multimodal models, providing explicit spatial context is a more effective and reliable strategy than high-level semantic guidance for this class of tasks.
Is your enterprise adaptive to AI?
Presented by EdgeVerve For most enterprises, AI adoption began with a straightforward ambition: automate work faster, cheaper, and at scale. Chatbots replaced basic service requests, machine‑learning models optimized forecasts, and analytics dashboards promised sharper insights. Yet many organizations are now discovering that deploying individual AI solutions does not automatically translate into enterprise‑level impact. Pilots proliferate, but value plateaus. The next phase of AI maturity is no longer about deploying more models. It is about adapting AI continuously to changing business objectives, regulatory expectations, operating conditions, and customer contexts. This shift is particularly critical for complex, globally distributed organizations such as Global Business Services (GBS), where outcomes depend on orchestrating work across functions, regions, systems, and stakeholders. From automation to adaptation AI can no longer be treated as a standalone tool to accelerate discrete tasks. To remain competitive, enterprises must move from isolated, single‑purpose models toward systems that can sense context, coordinate actions, and evolve over time. This is where adaptive AI ecosystems come into play. An adaptive AI ecosystem is a network of interoperable AI agents, models, data sources, and decision services that work together dynamically. These ecosystems integrate capabilities such as natural language processing, computer vision, predictive analytics, and autonomous decision‑making, while remaining grounded in human oversight and enterprise governance. For GBS organizations, the relevance is clear. GBS operates at the intersection of scale, standardization, and variation, managing high‑volume processes across markets that differ in regulation, customer behavior, and operational constraints. Static automation struggles in such environments. Adaptive AI, by contrast, allows GBS teams to orchestrate end‑to‑end processes, intelligently route work, and continuously improve outcomes based on real‑time signals. Why enterprise AI deployments stall Despite strong intent, scaling AI remains a challenge. Research consistently shows that while many organizations invest in generative and agentic AI initiatives, far fewer succeed in operationalizing them across workflows and business units. The issue is rarely ambition; it is fragmentation. SSON Research highlights several persistent barriers to generative AI adoption in GBS, including poor data quality, lack of specialized skills, data privacy concerns, unclear ROI, and budget constraints. Beneath these symptoms lies a common root cause: siloed environments. Data is fragmented, ownership is unclear, and AI initiatives are driven locally rather than through a shared enterprise strategy. As a result, enterprises accumulate AI solutions that cannot easily work together. Models lack shared context, decisions are hard to explain, and governance becomes an afterthought rather than a design principle. Adaptive AI ecosystems and platforms: Clarifying the relationship An adaptive AI ecosystem describes the enterprise‑wide outcome for how AI capabilities collaborate across the organization. An adaptive AI platform is the foundation that makes this possible. The platform provides common services and guardrails that allow AI agents and models to: access harmonized, trusted data orchestrate end‑to‑end processes enable intelligent agent handoffs between systems and humans interoperate with both agentic and legacy applications through out‑of‑the‑box connectors operate within defined security, compliance, and ethical boundaries Without this platform layer, adaptive ecosystems remain theoretical. With it, AI becomes composable, governable, and scalable. What an adaptive AI platform must enable To meet the demands of modern enterprises, and especially GBS organizations, an adaptive AI platform must deliver a set of core capabilities. Real‑time data harmonization is foundational. Adaptive decisions require access to both structured and unstructured data across functions and regions. Platforms must provide a unified data foundation, with observability built in, so AI systems understand not just the data itself but its quality, lineage, and relevance. Edge‑to‑cloud architectures play a role here, ensuring insights are available where decisions occur whether at the point of interaction or within a centralized decision engine. Adaptive process orchestration is equally critical. GBS organizations increasingly rely on AI platforms that can orchestrate workflows dynamically across business units and systems. This includes coordinating multiple AI agents, enabling seamless agent‑to‑agent and human‑in‑the‑loop handoffs, and adjusting process paths in response to real‑time conditions. Cognitive automation with governance moves beyond rule‑based automation. AI systems must be able to make context‑aware decisions with minimal human intervention, while still providing explainability, confidence indicators, and ethical constraints. The goal is not to remove humans from the loop, but to elevate their role from manual execution to oversight and judgment. Decision governance and observability tie these capabilities together. Enterprises must be able to trace how decisions are made, understand which models contributed, and audit outcomes across markets. As regulatory expectations around AI risk management, data protection, and accountability increase globally, embedding governance into the platform becomes essential rather than optional. Establishing trust at scale Trust is the foundation of scalable AI. Enterprises that lack confidence in their AI systems across data integrity, model behavior, and regulatory compliance will struggle to move beyond experimentation into sustained adoption. Building this trust requires deliberate investment. Organizations must ensure explainable AI, so decision logic is transparent to business and risk stakeholders, alongside privacy‑ and security‑by‑design principles that protect sensitive data from the outset. Continuous bias detection, model reliability, performance management, and clearly defined responsible AI guardrails are critical to maintaining consistent and ethical outcomes. Equally important is a clear Target Operating Model. This model defines ownership across the AI lifecycle, clarifies roles and escalation paths, and aligns accountability from frontline teams to executive leadership. In GBS environments where AI‑driven decisions often span functions, geographies, and regulatory regimes these trust mechanisms are not optional. They are essential. The road ahead Enterprises that continue to rely on fragmented AI deployments and siloed operating models will find it increasingly difficult to keep pace. The future belongs to organizations that adopt a platform‑based approach — one that enables them to move from incremental efficiency gains to transformational, enterprise‑wide impact. Success will not be defined by a single model or use case. It will be defined by adaptive AI ecosystems built on strong agent architectures, interoperable connectors across agentic and legacy landscapes, and shared foundations for data, orchestration, and governance. For GBS organizations in particular, this approach provides a clear path to scale AI responsibly delivering agility, trust, and sustained value in an increasingly complex world. In an era where change is constant and scrutiny is rising; the real question is no longer whether enterprises use AI but whether they are truly adaptive to it. N. Shashidar is SVP & Global Head, Product Management at EdgeVerve. Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.
Why compliance admin is driving SME AI adoption
For many business owners, compliance admin remains a source of friction.
AI saddles CIOs with new make-or-break expectations | CIO
IT resiliency and business results are not enough. Today’s IT leaders must build AI teams and guide their organizations through sweeping workflow changes.
Cost-of-Ethics Crisis: Beliefs, Decisions, and Justifications in the Job Searches of Computer Science Students in Canada and the United States
arXiv:2605.09680v1 Announce Type: new Abstract: Workplace norms in computer science have received growing attention due to a series of recent ethical scandals. One type of response has been a push to improve the ethics education provided to computer science students. Evidence for the effectiveness of ethics education remains mixed; some evidence suggests that norms are changing, while gaps between stated values and practice remain. Our focus here is on whether students, who have received some contemporary CS ethics education, are able to effectively apply ethical reasoning to their own decision-making in what is typically the first significant ethical decision of their careers: their job search. Our study examines the ethical decision making of 129 computer science students and recent graduates during their job searches. We find that most students prioritize factors like compensation, location, and workplace culture over ethical and social issues. Even when expressing ethical concerns, respondents often justify taking actions contradicting their moral views through commonly-shared explanations such as desire to make money or the perceived inability to avoid unethical workplaces. This work sheds light on the disconnect between ethics education and real-world CS graduate decision making. We offer insights for evolving curricula to better address practical ethical dilemmas, with implications for educators and industry.
ISO 42001 AI Management System Requirements: What Organisations Building Agentic Employees Need to Know | Flowtivity
Complete guide to ISO 42001 AI Management System requirements. Covers all 10 clauses, 39 Annex A controls, and practical implementation guidance for organisations deploying AI agents as digital employees.
PLACO: A Multi-Stage Framework for Cost-Effective Performance in Human-AI Teams
arXiv:2605.08388v1 Announce Type: new Abstract: Human-AI teams play a pivotal role in improving overall system performance when neither the human nor the model can achieve such performance on their own. With the advent of powerful and accessible Generative AI models, several mundane tasks have morphed into Human-AI team tasks. From writing essays to developing advanced algorithms, humans have found that using AI assistance has led to an accelerated work pace like never before. In classification tasks, where the final output is a single hard label, it is crucial to address the combination of human and model output. Prior work elegantly solves this problem using Bayes rule, using the assumption that human and model output are conditionally independent given the ground truth. Specifically, it discusses a combination method to combine a single deterministic labeler (the human) and a probabilistic labeler (the classifier model) using the model's instance-level and the human's class-level calibrated probabilities.
Human Learning about AI
arXiv:2406.05408v3 Announce Type: replace Abstract: We study \emph{Human Projection} (HP): people's tendency to evaluate AI using the same frameworks they use for humans -- treating features such as task difficulty and the reasonableness of mistakes as diagnostic of overall ability. We formalize HP and its consequences for equilibrium adoption, testing its predictions experimentally. First, people project human difficulty onto AI, overestimating performance on human-easy tasks, underestimating it on human-hard ones, and over-updating after easy failures and hard successes -- leading to systematic misspecification when AI performance is jagged rather than human-ordered. Second, HP interprets observed performance through a single ability index, inducing all-or-nothing adoption even when AI outperforms humans on only some tasks; experimentally stripping AI of human-like cues weakens cross-task generalization and reduces over-adoption. Finally, a field experiment with a parenting-advice chatbot shows that less humanly reasonable mistakes cause larger drops in trust and future engagement. Anthropomorphic AI design can amplify HP, misaligning beliefs and distorting adoption.
Corporate Functions of the Future Won't Look Like Functions at All
Generative AI is expected to reshape corporate functions by compressing layers and rebuilding workflows, though the transition faces significant challenges like implementation costs and political resistance.
Turning AI cost spikes into strategic growth opportunities
Presented by Apptio, an IBM company AI spending is surging, but the full impact often remains an open question. Closing the gap requires clear answers to how AI is governed, measured, and tied to business outcomes. ROI uncertainty isn’t unique to AI: In the Apptio 2026 Technology Investment Management Report, 90% of technology leaders surveyed said that ROI uncertainty has a moderate or major impact on overall tech investment decisions, a 5-percentage point year-over-year increase. In other words, tech leaders are increasing their reliance on ROI – even if they don’t fully know how to measure it. And AI economics involves new and unpredictable costs, further complicating ROI calculations. Faced with increasing uncertainty and increasing budgets, technology leaders need a clear, reliable framework for evaluating AI ROI. Organizations increasingly expect scaled AI to pay its own way, at least partially. According to Apptio’s technology investment management report, 45% of organizations surveyed intend to fund innovation by reinvesting savings from AI-driven efficiencies. That model assumes that such savings are both achievable and quantifiable. Meanwhile, the two-thirds of organizations planning to reallocate existing budget capital to AI will need clarity on the trade-offs involved. Much like the early days of public cloud, AI costs and returns are difficult to predict. Pricing varies widely across providers and continues to evolve, while consumption is unpredictable. The pressure to adopt quickly is also formidable as organizations navigate the threat of disruption by more agile competitors. The new math of AI ROI Considering the many variables, tech leaders should view AI ROI as a matter of optimization. At a high level, the implementation of AI initiatives is inevitable. The question is how to achieve the greatest possible returns — both financial and organizational. Start with the business problem. There are many ways AI can deliver positive impact, but organizational resources and focus may be limited. Make sure you’re prioritizing the right initiatives by basing your AI investment strategy on quantifiable goals tied to real business outcomes. Are you trying to improve decision-making speed? Increase throughput or capacity? Or chasing cool edge cases with high potential returns but minimal strategic relevance? Determine what success looks like. AI can introduce a new capability or augment an existing one. For new capabilities, articulate the possibilities you’d like to unlock, such as new revenue opportunities, workflows, or decision-making processes. For augmentations, establish baseline performance and the expected lift you aim to achieve with AI. Consider how finances will influence your evaluation. Some use cases may show minimal results in the near-term but drive significant value in the long-term. What’s your timeframe for return? On the other hand, more successful rollouts with rapid adoption can generate unexpectedly high inference bills. Would that mean pulling the plug — or leaning in further? What should your cost and return curve look like over the years? As you map your timeline, establish clear thresholds to determine whether you’ll proceed, pause, stop, or accelerate your investment. Identify the right KPIs. The returns on an AI investment can be even more difficult to evaluate than the costs. Usage, efficiency, and financial impact all matter. But AI success metrics won’t always be straightforward. There may be new usage patterns you don’t yet have a way to measure. Your technology environment may experience follow-on shifts that call for further evaluation. Will you be able to lessen your reliance on other tools, such as reducing seats in your data analytics platform? How will you factor in cross-tool pricing comparisons for multiple AI providers with shifting rates? To gain full context and insight, you must also take into account the alignment of the initiative with your broader strategy and consider the opportunity cost of the investments you might otherwise have made. Remember that you’re not evaluating AI business value in isolation; you’re deciding whether it's the best use of finite capital across all your investments. These decisions will call for a level of insight far exceeding what was needed to justify traditional purchases like network infrastructure or enterprise software. Tech leaders navigating the complexities of AI economics should consider a new framework for data-driven decision-making. Making AI investment sustainable with TBM Technology business management (TBM) helps make ROI more concrete and measurable, so it can be relevant to the business. By bringing together IT Financial Management (ITFM), AI FinOps (cloud financial management for AI workloads), and Strategic Portfolio Management (SPM), a TBM framework connects financial, operational, and business data across the enterprise.This makes it possible to account for AI value and cost across a wide array of dimensions — and translate hypothetical innovation into board presentations and budget justifications that hold up under scrutiny. TBM can help leaders build a trustworthy cost foundation that captures AI spend across labor, infrastructure, inference, storage, and applications. As AI workloads shift dynamically, TBM provides visibility into how that spend is distributed across on-premises systems and cloud environments — both of which require different capacity planning for specialized skill sets. The framework also connects investments to business outcomes, aligning AI initiatives with strategic priorities and measurable results. With increased visibility, you’re able to identify issues and make decisions fast, such as catching cost spikes early. Early detection can help to determine if the usage shift merits shifting funding. This unified view of financial and operational data helps leaders scale what’s working and reassess what isn’t as adoption increases. TBM provides essential visibility and context across the entire AI spend management conversation. Even as pricing evolves, tooling changes, and workflows shift, you can apply the same analytical approach and understand what’s actually working and demonstrate ROI. Leaders who operationalize AI within a TBM framework can: Evaluate ROI at both project and portfolio levels Spot unexpected cost spikes Compare multiple AI tools Understand ripple effects across run-the-business systems Defend investment decisions with confidence Understand and manage total costs and usage across the AI investment lifecycle From theory to practice Organizations are moving beyond AI experiments, and we’re past the point where these investments can be funded on optimism alone. Amid heightened uncertainty and cost sensitivity, boards are asking more strategic questions and finance wants trustworthy data. Enterprise leaders who treat AI as a managed investment, rather than a bet on innovation, are those who will scale it successfully. To fund AI responsibly, leaders must establish clarity around scope, outcomes, cost drivers, and readiness. A TBM-driven approach provides the data foundation, visibility, and accountability to make those decisions. Learn more here about how Apptio TBM transforms IT spend management in the AI era. Ajay Patel is General Manager at Apptio, an IBM 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.
Clear gap between AI expectations and preparedness, finds report
Accenture finds employees increasingly believe reskilling is unavoidable, yet many are being asked to use new technologies without the required training. Read more: Clear gap between AI expectations and preparedness, finds report
AI Could Hollow Out the Next Generation of Workers - Business Insider
Companies embracing AI too quickly risk hollowing out the pipeline that trains future professionals, investment manager Tom Slater said.
The end of typing? Why workers are suddenly ditching their keyboards
Employees are now whispering to AI voice dictation tools rather than clacking the keys. Will ‘voicepilling’ make everyone more productive – or just more annoying? Name: Voicepilled. Age: Reid Hoffman first declared himself “voicepilled” in the autumn of last year. Continue reading...
Redesigning Your Marketing Organization for the Agentic Age
This HBR article argues that marketing organizations must redesign around agentic AI, focusing on shared brand knowledge and human-agent collaboration.