maddaisy – cultivating innovation

Tag: enterprise-ai

  • Brussels Blinked: The EU AI Act’s High-Risk Deadline Just Moved, but the Compliance Clock Has Not Stopped

    When maddaisy examined the shift from AI principles to penalties in February, the EU AI Act’s August 2026 deadline for high-risk AI systems sat at the centre of the analysis. That date — 2 August 2026 — was the moment when compliance stopped being theoretical and started carrying fines of up to seven per cent of global turnover.

    Four weeks later, Brussels blinked.

    On 13 March, the EU Council agreed its position on the Digital Omnibus package, pushing back the application of high-risk AI rules to December 2027 for standalone systems and August 2028 for those embedded in products. The proposal still requires negotiation with the European Parliament, but the direction is clear: the EU’s own regulatory infrastructure was not ready for its own deadline.

    What Actually Changed

    The delay is narrower than the headlines suggest. The EU AI Act’s prohibited practices — social scoring, manipulative AI targeting vulnerable groups, unauthorised real-time biometric surveillance — have been in force since February 2025 and remain untouched. Obligations for general-purpose AI model providers, including transparency and copyright requirements, still apply from August 2025. The Code of Practice requiring machine-readable detection techniques for AI-generated content is already published.

    What shifted is specifically the high-risk classification regime: the rules governing AI systems used in employment decisions, credit scoring, healthcare, education, law enforcement, and critical infrastructure. These are the provisions that demand conformity assessments, technical documentation, human oversight mechanisms, and registration in the EU database. They are also the provisions that most enterprises have been scrambling to prepare for.

    The Council’s rationale is pragmatic rather than political. The European Commission missed its own February 2026 deadline for publishing the guidance and harmonised standards that enterprises need to demonstrate compliance. Without those standards, companies were being asked to hit a target that the regulator had not yet fully defined. As the Cypriot presidency put it, the goal is “greater legal certainty” and “more proportionate” implementation — diplomatic language for acknowledging that the implementation machinery was not keeping pace with the legislative ambition.

    The Compliance Paradox

    For enterprises that have spent the past 18 months building AI governance programmes, risk inventories, and compliance frameworks, the delay creates an awkward question: should they slow down?

    The short answer is no — and the reasoning matters more than the conclusion.

    First, the delay is conditional. The Council’s position sets fixed dates — December 2027 and August 2028 — but the Commission retains the ability to confirm earlier application if standards become available sooner. Organisations that pause their compliance programmes risk finding themselves back under pressure with less runway than they had before.

    Second, the regulatory landscape extends well beyond Brussels. As maddaisy has previously examined, the United States is building its own patchwork of state-level AI laws. Colorado’s AI Act takes effect in June 2026. California’s transparency requirements are already live. The EU delay does not change these timelines. An enterprise operating across both markets still faces near-term obligations.

    Third, and perhaps most importantly, the governance work itself has value beyond regulatory compliance. Organisations that have inventoried their AI systems, established accountability structures, and implemented monitoring processes are better positioned to manage operational risk, regardless of when a specific regulation takes effect. As the Ethyca governance framework notes, the shift from policy documentation to continuous operational evidence is happening independently of any single regulatory deadline.

    What the Delay Reveals

    The Digital Omnibus is not just a timeline adjustment. It is a signal about the structural challenges of regulating AI at the pace the technology is evolving.

    The EU built the world’s most comprehensive AI regulation. It classified systems by risk tier, defined obligations for providers and deployers, established penalties that exceed GDPR maximums, and applied the rules extraterritorially. What it did not build quickly enough was the operational layer: the harmonised standards, the conformity assessment procedures, the guidance documents that translate legal text into practical compliance steps.

    This mirrors a pattern maddaisy has observed across multiple regulatory domains. Europe’s cloud sovereignty push encountered similar friction — ambitious policy goals meeting incomplete implementation frameworks. The gap between legislative intent and operational readiness is becoming a recurring theme in European technology regulation.

    The Council’s position does include substantive additions alongside the delay. A new prohibition on AI-generated non-consensual intimate content and child sexual abuse material was introduced. Regulatory exemptions previously limited to SMEs were extended to small mid-cap companies. The AI Office’s enforcement powers were reinforced. These are not trivial changes — they show that the regulation is still being actively shaped even as its core provisions await full application.

    The ISO 42001 Factor

    One development running parallel to the regulatory delay is the accelerating adoption of ISO/IEC 42001, the international standard for AI management systems. Enterprise buyers are increasingly adding it to vendor procurement requirements, and AI liability insurers are beginning to factor governance certifications into risk assessments.

    For organisations uncertain about how to structure their compliance programmes during the delay, ISO 42001 offers a practical framework. It maps to the EU AI Act’s requirements without being dependent on them, meaning that compliance work done under the standard retains its value regardless of how regulatory timelines shift. Pega’s recent certification is one example of vendors using the standard to demonstrate governance readiness to enterprise clients.

    What Practitioners Should Do Now

    The EU AI Act delay changes timelines, not trajectories. The practical recommendations remain consistent with what maddaisy outlined in February, with one important addition:

    • Continue AI system inventories. Understanding what AI is deployed, where, and at what risk level is foundational work that no regulatory timeline change invalidates.
    • Monitor the Parliament negotiations. The Council position must be reconciled with the European Parliament before becoming final. The dates could shift again — in either direction.
    • Use the extra time for standards alignment. With harmonised standards still being developed, organisations now have an opportunity to align with ISO 42001 or the NIST AI Risk Management Framework before mandatory compliance begins.
    • Do not treat the delay as permission to deprioritise. Colorado, California, and other US state deadlines remain unchanged. Enterprise clients and procurement teams are not waiting for regulators — they are setting their own governance expectations now.

    The EU built the most ambitious AI regulation in the world, then discovered that ambition requires infrastructure. The delay is a concession to reality, not a retreat from intent. For enterprises, the message is straightforward: the destination has not changed, only the speed limit on the road getting there.

  • CTOs and CHROs Are Climbing the Pay Table. That Tells You Where Boards Think Risk Lives Now.

    For decades, the path to being among a public company’s five highest-paid executives ran through operations, sales, or running a business unit. Technology leaders and HR chiefs were important, certainly — but they were support functions, not the positions that commanded top-tier compensation.

    That hierarchy is shifting. New research from The Conference Board, published this week, tracks the named executive officers (NEOs) — the five highest-paid executives — across the Russell 3000 from 2021 to 2025. The findings are striking: Chief Technology Officers appearing as NEOs increased by 61%, while Chief Human Resources Officers rose by 55%. In the same period, business unit leaders — historically the dominant non-CEO, non-CFO category — declined by 15%.

    The numbers are not subtle. They represent a structural revaluation of which functions boards consider most critical to company performance and risk.

    From support functions to enterprise risk owners

    The explanation is not difficult to locate. Two forces are converging: the AI boom is making technology capability an existential strategic question, and a persistent talent war is making the ability to attract, retain, and reskill workers a board-level concern rather than an HR department problem.

    “Growth in CHRO and CTO roles signals that talent, culture, and digital capability are now viewed as enterprise risks, not support functions,” said Andrew Jones, Principal Researcher at The Conference Board. “Boards are prioritising leaders who shape resilience and transformation across the organisation.”

    This tracks with what maddaisy has been reporting for months. The consulting pyramid piece in early March documented how firms are reshuffling roles rather than eliminating them — cutting some positions while creating others in AI engineering and data science. Accenture’s decision to track AI tool usage for promotions was another signal: when a firm ties career progression to technology adoption, the executive overseeing that technology becomes strategically indispensable.

    The specific numbers tell the story

    CTO NEO disclosures rose from 155 to 249 in the Russell 3000 between 2021 and 2025. CHRO disclosures went from 148 to 230. These are not marginal shifts — they represent boards deciding, through the bluntest mechanism available (compensation), that these roles belong at the top table.

    Meanwhile, business unit leaders fell from 1,734 to 1,475 disclosures. The decline suggests that boards are placing less emphasis on divisional performance and more on enterprise-wide capabilities: technology infrastructure, talent strategy, and the legal and regulatory architecture that governs both.

    That last point matters. Legal roles — including chief legal officers, corporate secretaries, and general counsels — saw the largest increase of any non-mandatory NEO category, rising 21% over the same period. As AI governance, data regulation, and compliance pressures mount, the lawyers are moving closer to the centre of power too.

    What this means for the talent market

    The compensation data carries implications well beyond boardroom politics. When CTOs and CHROs move into the highest-paid tier, it reshapes the talent pipeline for those roles. More ambitious executives will target those paths. Boards will demand different skill sets — not just technical competence for CTOs, but strategic vision for how AI and digital infrastructure create competitive advantage. Not just process management for CHROs, but the ability to navigate workforce transformation at scale.

    The Conference Board’s data also reveals an interesting gender dimension. Among S&P 500 NEOs, women CEOs earned 11% more than men in 2025. But male NEOs overall still earned 8% more in the S&P 500 and 12% more in the Russell 3000. The gap, as researcher Paul Hodgson noted, “largely reflects who holds which roles” — men remain more prevalent in higher-paid operational and commercial positions, with longer average tenure and concentration at larger firms.

    The COO plateau and what it signals

    One quieter finding deserves attention: COO representation rose just 6% over the period and has actually declined from a 2023 peak. The chief operating officer — once the natural second-in-command — appears to be losing ground to more specialised enterprise-wide roles. In an era where the critical operational questions are “how do we deploy AI safely” and “how do we retain the people who know how to do it,” a generalist operations mandate may no longer be enough to justify top-tier compensation.

    The board’s revealed preferences

    Compensation data has always been the most reliable indicator of what organisations actually value, as opposed to what they claim to value. Press releases can announce “people-first cultures” and “digital-first strategies” without consequence. Paying the CHRO and CTO as much as the head of your largest business unit is a commitment that shows up in proxy statements.

    The Conference Board’s findings confirm a pattern that has been building for several years: boards are redefining which risks are existential and which executives own them. The AI boom has made technology leadership a strategic imperative. The talent war — intensified by the very AI transformation companies are pursuing — has elevated workforce strategy from an administrative function to an enterprise risk.

    For consultants and practitioners, the practical implication is clear. The organisations they advise are restructuring their leadership hierarchies around technology and talent. Advisory work that once centred on operational efficiency and market strategy increasingly requires fluency in AI deployment, workforce transformation, and the regulatory landscape that governs both. The C-suite is telling you where the priorities are. The pay data just makes it impossible to ignore.

  • The Three-Tool Threshold: BCG Research Reveals Where AI Productivity Gains Turn Into Cognitive Overload

    For months, the evidence that AI tools are intensifying work rather than simplifying it has been accumulating. maddaisy has tracked this story from the UC Berkeley research showing employees absorbing more tasks under AI, through the organisational failures that leave workers unsupported, to the implementation problems that bake burnout into the system from day one. What was missing was a specific threshold — a number that tells enterprises where the gains end and the damage begins.

    Boston Consulting Group has now supplied one. In a study published in Harvard Business Review this month, researchers surveyed 1,488 full-time US workers and found a clean break point: employees using three or fewer AI tools reported genuine productivity gains. Those using four or more reported the opposite — declining productivity, increased mental fatigue, and higher error rates. BCG calls the phenomenon “AI brain fry.”

    The finding is not just academic. Among workers reporting brain fry, 34% expressed active intention to leave their employer, compared with 25% of those who did not. For a workforce already under pressure from rapid technology deployment, that nine-percentage-point gap represents a tangible retention risk.

    The cognitive cost no one budgeted for

    The BCG research puts numbers to something the UC Berkeley study identified in qualitative terms earlier this year. When AI tools require high levels of oversight — reading, interpreting, and verifying LLM-generated content rather than simply delegating administrative tasks — workers expend 14% more mental effort. They experience 12% greater mental fatigue and 19% more information overload.

    Many respondents described a “fog” or “buzzing” sensation that forced them to step away from their screens. Others reported an increase in small mistakes — exactly the kind of errors that compound in professional services, financial analysis, and other high-stakes environments.

    “People were using the tool and getting a lot more done, but also feeling like they were reaching the limits of their brain power,” Julie Bedard, the study’s lead author and a managing director at BCG, told Fortune. “Things were moving too fast, and they didn’t have the cognitive ability to process all the information and make all the decisions.”

    This aligns with what maddaisy has previously described as the task expansion pattern: when AI makes certain tasks faster, employees do not use the freed-up time for strategic thinking. They absorb more work. The BCG data now suggests the breaking point arrives sooner than most organisations assume — at the fourth tool, not the tenth.

    The macro picture is equally sobering

    The three-tool threshold sits against a broader backdrop of underwhelming AI productivity data at scale. A Goldman Sachs analysis published this month found “no meaningful relationship between productivity and AI adoption at the economy-wide level,” with measurable gains confined to just two domains: customer service and software development.

    Separately, a survey of 6,000 C-suite executives found that 90% saw no evidence of AI impacting productivity or employment in their workplaces over the past three years. Their median forecast: a 1.4% productivity increase over the next three. That is hardly the transformation narrative that justified billions in enterprise AI spending.

    These findings do not mean AI is useless. The Federal Reserve Bank of St. Louis estimated a 33% hourly productivity boost for workers during the specific hours they use generative AI. The problem is that this micro-level gain does not scale linearly. Adding more tools, more prompts, and more AI-generated outputs does not multiply the benefit — it multiplies the cognitive overhead.

    What the threshold means for enterprises

    The practical implications are straightforward, even if they run against the instincts of most technology procurement processes.

    First, fewer tools, better deployed. The BCG data suggests that organisations would get better results from consolidating around two or three well-integrated AI tools than from giving every team access to every available platform. This runs counter to the current market dynamic, where vendors push specialised AI tools for every function — writing, coding, data analysis, scheduling, customer interaction — and enterprises buy them all to avoid falling behind.

    Second, oversight design matters as much as tool selection. The highest cognitive costs were associated with tasks requiring workers to interpret and verify AI output, not with AI performing autonomous background work. Enterprises that can shift more AI usage toward the latter — automated workflows, pre-verified data processing, agent-completed administrative tasks — will impose less cognitive strain on their people.

    Third, training needs to include when not to use AI. As maddaisy has previously noted, most organisations treat AI capability-building as a deployment event rather than a sustained practice. The BCG researchers found that when managers provided ongoing training and support, brain-fry symptoms decreased. The Berkeley team suggested batching AI-intensive work into specific time blocks rather than leaving it on all day — a scheduling discipline that few organisations currently enforce.

    The next chapter in a familiar story

    The AI-productivity narrative is following a pattern that technology historians will recognise. Early adopters see real gains. Organisations rush to scale. The gains plateau or reverse as implementation complexity outpaces human capacity to manage it. Eventually, a more measured approach emerges — not abandoning the technology, but deploying it with greater discipline.

    The BCG three-tool threshold may turn out to be an early data point rather than a universal law. But it offers something that has been missing from the AI-adoption conversation: a concrete starting point for right-sizing the technology stack to what human cognition can actually sustain.

    For consultants advising on AI transformation, that is a message worth delivering — even when it runs counter to the vendor pitch deck.

  • The Venture Subsidy Era for AI Is Ending. Enterprise Budgets Are Not Ready.

    For the past three years, enterprises have been building their AI strategies on pricing that does not reflect reality. The era of venture-subsidised AI — where a ChatGPT query costs pennies despite burning roughly ten times the energy of a Google search — is approaching its expiry date. The question is not whether prices will rise. It is whether organisations have budgeted for what comes next.

    The subsidy model, laid bare

    The numbers tell the story clearly enough. OpenAI’s own internal projections show $14 billion in losses for 2026, against roughly $13 billion in revenue. Total spending is expected to reach approximately $22 billion this year. Across the 2023–2028 period, the company expects to lose $44 billion before turning cash-flow positive sometime around 2029 or 2030.

    Anthropic’s trajectory looks different but carries the same structural tension. The company hit $19 billion in annualised revenue by March 2026, growing more than tenfold annually. But its gross margins sit at around 40% — a long way from the 77% it needs to justify its $380 billion valuation. That gap has to close, and it will not close through efficiency gains alone.

    Both companies have raised staggering sums to sustain the current pricing. OpenAI’s $110 billion round in February valued it at $730 billion. Anthropic’s $30 billion Series G came from a coalition including GIC, Microsoft, and Nvidia. This is venture capital on a scale that makes the ride-hailing subsidy wars look modest — and, like those wars, it is designed to capture market share before the real pricing arrives.

    The millennial lifestyle subsidy, enterprise edition

    The pattern is familiar. Uber and DoorDash used investor capital to underwrite artificially cheap services, building habits and dependencies before gradually raising prices toward sustainable levels. AI providers are running the same playbook, but the stakes are larger. When Uber raised fares, consumers grumbled and occasionally took the bus. When AI API costs increase threefold — which industry analysts suggest may be the minimum adjustment needed for sustainable economics — enterprises will face a different kind of reckoning.

    The reckoning is already starting at the platform level. Microsoft will raise commercial pricing across its entire 365 suite from July 2026, with increases of 8–17% depending on the tier. The company attributed the rises to AI capabilities such as Copilot Chat being embedded into standard subscriptions. Its new $99-per-user E7 tier bundles Copilot, identity management, and agent orchestration tools — positioning AI not as an optional add-on but as a cost baked into the platform itself.

    The broader enterprise software market is following the same trajectory. Gartner forecasts enterprise software spend rising at least 40% by 2027, with generative AI as the primary accelerant. Average annual SaaS price increases now range from 8–12%, with aggressive movers implementing hikes of 15–25% at renewal.

    The budget gap nobody is discussing

    The disconnect between AI ambition and AI economics is widening. Organisations now spend an average of $7,900 per employee annually on SaaS tools — a 27% increase over two years. AI-native application spend has surged 108% year-on-year, reaching an average of $1.2 million per organisation. And these figures reflect the subsidised era.

    As Axios reported this week, the unusually low cost of many AI services will not survive the transition from venture-funded growth to public-market accountability. As OpenAI and Anthropic pursue potential IPOs, investors will demand the margins that current pricing cannot deliver. Subscription prices and usage-based costs are expected to rise across the industry.

    For enterprises that have been scaling AI adoption on the assumption that current costs are permanent, this represents a planning failure in the making. A consumer application with $2 in AI costs per user per month looks viable. The same application at $10 per user does not. High-volume automation workflows — precisely the use cases enterprises are most excited about — are the most vulnerable to cost increases.

    The pacing argument gains new weight

    This pricing trajectory adds a new dimension to arguments maddaisy has previously explored around pacing AI investment. When Capgemini’s CEO Aiman Ezzat cautioned against getting “too ahead of the learning curve,” his concern was primarily about deploying capabilities ahead of organisational readiness. The pricing question strengthens that case. Organisations that rush to embed AI across every workflow at subsidised rates may find themselves locked into architectures whose economics no longer work when the real costs arrive.

    Similarly, the enterprise scaling gap reported last week — where two-thirds of organisations cannot move AI past pilot stage — takes on a different character when viewed through an economic lens. The skills shortage and governance deficits that constrain scaling today may prove less urgent than the budget constraints that arrive tomorrow. Organisations struggling to scale AI at subsidised prices will find it considerably harder at market rates.

    What prudent organisations should do now

    The adjustment does not need to be dramatic, but it does need to start. Three measures stand out.

    First, stress-test AI budgets against realistic pricing. If API costs tripled tomorrow, which workflows would still deliver positive returns? The answer reveals which AI investments are genuinely valuable and which are artefacts of artificially cheap compute.

    Second, build multi-provider flexibility into the architecture. Vendor lock-in has always been a risk in enterprise technology. In AI, where pricing models are still evolving and open-source alternatives like Llama and Mistral are improving rapidly, flexibility is not just prudent — it is a hedge against the cost increases that are coming.

    Third, watch the open-source floor. The existence of capable open models creates a price ceiling that limits how aggressively commercial providers can raise rates. Organisations that invest in the capability to run open models on their own infrastructure — or through commodity inference services — will have negotiating leverage that others will not.

    The correction, not the crisis

    None of this means AI is overvalued or that enterprise adoption will stall. The technology works. The productivity gains are real. But the current pricing does not reflect the true cost of delivering those gains, and the correction will arrive gradually over the next two to four years as the industry’s largest players transition from growth-at-all-costs to sustainable economics.

    The organisations best positioned for that transition will be those that treated the subsidy era as a window for experimentation — learning which AI applications genuinely transform their operations — rather than a permanent baseline for their technology budgets. The window is closing. The question is whether the planning has already begun.

  • Digital Experiences Now Have Two Audiences. Most Enterprises Are Only Designing for One.

    For as long as digital products have existed, experience design has asked a single question: what does the user want? The user browses, clicks, hesitates, backtracks, and eventually converts — or does not. Every interface decision, from navigation hierarchy to button placement, has been optimised around that human journey.

    In 2026, a second audience has arrived. AI agents now browse websites, interpret content, summarise product pages, compare services, and make purchasing recommendations — often before a human ever sees the interface. Search engines have done this quietly for years. But the new generation of autonomous agents does it actively, making decisions and taking actions on behalf of the people they serve.

    The implication for enterprises is straightforward and largely unaddressed: digital experiences must now be designed for two interpreters simultaneously, and they do not read the same way.

    The dual-interpreter problem

    Humans and machines process digital experiences through fundamentally different lenses. A human visitor might scan a page loosely, drawn by visual hierarchy, tone of voice, and emotional cues. They browse without clarity, explore without urgency, and change their minds mid-session. That inconsistency is not a flaw — it is how people navigate complex decisions.

    Machines, by contrast, prefer structure. They infer meaning from hierarchy, repetition, semantic markup, and patterns. They classify, compress, and summarise. When an AI agent visits a product page, it does not feel reassured by a warm brand photograph. It parses structured data, identifies key claims, and decides — in milliseconds — what that page is about, what matters, and what to report back to the user who sent it.

    As Composite Global noted in a recent analysis, experience design has shifted from being about flow to being about interpretation. The question is no longer just “how will a person navigate this?” but “how will an agent read this — and will it get the right answer?”

    Where the gap shows up

    The consequences of ignoring machine intent are already visible. When AI agents summarise a company’s offerings inaccurately, the problem is rarely that the agent is broken. More often, the page was never designed to be machine-readable in any meaningful way. The content was written for humans — rich in nuance, light on structure — and the agent did its best with what it found.

    Research from TBlocks found that 71 per cent of users now expect digital experiences to adapt to their intent, while 76 per cent notice and feel frustrated when that adaptation fails. Those expectations increasingly extend to agent-mediated experiences. If a user asks an AI assistant to compare three consulting firms’ service offerings, and the agent returns a garbled summary because one firm’s website relies on unstructured prose and JavaScript-rendered content, the brand loses — not the agent.

    The practical failures tend to cluster around a few recurring problems: content hierarchies that make sense visually but not semantically; messaging that requires context an agent cannot infer; calls to action that depend on emotional persuasion rather than clear structure; and pages that load dynamically in ways that agents cannot reliably parse.

    This is not SEO by another name

    It would be tempting to treat this as an extension of search engine optimisation. After all, making content machine-readable has been a concern since the early days of Google. But the agent-readability challenge goes further than search ranking.

    Search engines index pages and rank them. AI agents interpret pages and act on them. An agent does not return a list of blue links — it makes a recommendation, completes a task, or rules out an option entirely. The stakes are different. A page that ranks poorly in search results is still findable. A page that an AI agent misinterprets may never surface at all, or worse, may surface with the wrong message attached.

    This distinction matters for how enterprises invest. SEO focuses on keywords, metadata, and backlinks. Agent-readability requires structured data, semantic clarity, explicit labelling, and content architectures that hold meaning when stripped of their visual presentation. The overlap exists, but the disciplines are not the same.

    What maddaisy’s coverage has been pointing toward

    Readers of maddaisy’s recent coverage will recognise the broader pattern here. When this publication examined the governance challenges of AI agents, the focus was on how enterprises monitor and control autonomous systems. When it covered OpenAI’s Frontier Alliance, the story was about agents disrupting enterprise software by sitting above it. And when it explored vibe coding’s enterprise arrival, the thread was about how AI is reshaping how software gets built.

    The digital experience question is downstream of all three. If agents are going to interact with enterprise digital products — browsing service pages, interpreting pricing structures, summarising capabilities for prospective clients — then those products need to be designed with agents in mind. Not instead of humans. Alongside them.

    Designing for clarity across interpreters

    The emerging discipline — sometimes called “dual-intent design” — requires thinking in layers. Composite Global’s framework identifies three dimensions of intent that designers must now map simultaneously: explicit intent (what a user directly communicates), behavioural intent (what systems infer from interaction patterns), and emotional context (the confidence, uncertainty, or curiosity a human brings to the interaction).

    The first two are measurable. The third is where human judgment lives — and where machines consistently fall short. Strong experience design ensures that machine interpretation reinforces human meaning rather than distorting it. In practice, that means clear content hierarchies so agents classify correctly, structured data so machines parse quickly, explicit labelling so summaries remain accurate, and focused messaging so automated recommendations do not flatten a brand’s positioning.

    CoreMedia’s analysis of 2026 customer experience trends puts it bluntly: AI has become “a powerful new intermediary stepping between brand and customer.” The brands that treat that intermediary as an afterthought will find their message distorted in transit.

    The practical question for enterprises

    For most organisations, the immediate question is not whether to redesign everything. It is whether their existing digital properties communicate clearly to both audiences. A simple audit reveals the answer quickly: take a key product or service page, strip away the visual design, and read only the structured content. Does it still make sense? Would an agent, parsing that structure, draw the right conclusions?

    If the answer is no — and for most enterprise websites built in the pre-agent era, it will be — the remediation is less about redesign than about augmentation. Adding structured data, clarifying semantic hierarchy, making content modular rather than monolithic, and ensuring that key claims do not depend on visual context for meaning.

    None of this requires abandoning human-centred design. The point is not to optimise for machines at the expense of people. It is to build clarity that holds up under both interpretations — a standard that, arguably, should have been the goal all along.

    The enterprises that get this right will not just rank well or convert well. They will be accurately represented by the AI systems that increasingly mediate how their customers discover, evaluate, and choose them. In a market where agents are becoming the first point of contact, being misunderstood by a machine may prove more costly than being overlooked by a human.

  • The Governance Frameworks for AI Agents Exist. The Hard Part Is Making Them Work.

    The governance playbook for autonomous AI agents is no longer a blank page. Regulatory bodies have published frameworks. Law firms have issued guidance. Industry coalitions have identified priorities. The principles – least privilege, human checkpoints, real-time monitoring, value-chain accountability – are converging across jurisdictions. And yet, Gartner predicts that 40 per cent of agentic AI projects will be cancelled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls.

    The problem is not that enterprises lack governance policies. It is that they lack governance infrastructure – the operational machinery to translate principles into practice across live, autonomous systems operating at scale.

    When maddaisy examined the emerging governance playbook last week, the direction of travel was clear: regulators and advisors were converging on what good governance should look like. The question that follows is more difficult. What does it take to actually run that governance, day after day, across agents that plan, execute, and adapt autonomously?

    The Gap Between Policy and Operations

    The most revealing data point in recent weeks comes not from a governance report but from Logicalis’s 2026 CIO Report. Among 1,000 chief information officers surveyed globally, 89 per cent described their AI governance approach as “learning as we go.” That is not experimentation. That is the absence of operational governance.

    The skills gap compounds the problem. Nearly nine in 10 organisations cite a lack of internal technical capability as their primary constraint on AI deployment. For governance specifically, the deficit is acute. Monitoring agent behaviour in production, auditing multi-step reasoning chains, and interpreting regulatory requirements across jurisdictions all demand expertise that most enterprises have not yet hired for – and in many cases, cannot find.

    A PwC survey found that 79 per cent of companies have adopted agents in some capacity. But when enterprise search firm Lucidworks assessed over 1,100 organisations, only 6 per cent had deployed more than one agentic solution. The implication is significant: most enterprises are governing a single, contained pilot. The governance challenge changes materially when agents multiply, interact, and share data across business functions.

    Regulations Are Arriving – Unevenly

    The regulatory landscape is not waiting for enterprises to catch up. The EU AI Act’s obligations on high-risk and general-purpose AI systems take effect from August 2026, applying globally to any organisation whose systems affect EU residents. In the United States, the picture is more fragmented. President Trump’s December 2025 Executive Order signalled federal intent to consolidate AI oversight, but as legal analysis from Gunderson Dettmer makes clear, it does not preempt existing state laws.

    California, Colorado, and Texas have each enacted comprehensive AI governance statutes with distinct requirements for high-risk systems. New York’s RAISE Act imposes transparency obligations that do not apply elsewhere. For multinational enterprises deploying autonomous agents, the compliance surface is not one framework – it is dozens, with different definitions of high-risk, different disclosure requirements, and different enforcement timelines.

    This is where governance-as-policy meets governance-as-operations. A well-crafted internal policy cannot resolve the question of whether an agent deployed in London, which processes data from a New York customer and executes a transaction through a Singapore-based system, complies with three different regulatory regimes simultaneously. That requires technical infrastructure: jurisdictional routing, dynamic compliance rules, and audit trails that satisfy multiple authorities.

    What Operational Governance Actually Requires

    Several CIOs interviewed by CIO.com this month offered a consistent message: governance cannot be separated from workflow design.

    Don Schuerman, CTO at Pega, put it directly: the expectation that thousands of agents can be deployed randomly across a business and left to operate is a myth. Successful deployments anchor agents in well-defined business processes with prescribed steps, high predictability, and clear audit requirements. The governance is not a layer added afterwards – it is embedded in how the agent’s workflow is designed.

    IBM CIO Matt Lyteson echoed the point, stressing that organisations need to understand the outcomes they are targeting, the data agents will require, and the controls needed to manage them before deployment – not after. Salesforce CIO Dan Shmitt added that without high-quality data and a unified governance model, agents produce unreliable results regardless of the policy framework around them.

    The emerging consensus among practitioners, distinct from the framework-level guidance, centres on three operational requirements.

    First, governance must be embedded in agent design, not bolted on. Decision boundaries, escalation rules, and compliance checks need to be part of the agent’s workflow architecture. Retrofitting governance onto an agent already in production is significantly harder and more expensive.

    Second, observability infrastructure is non-negotiable. As maddaisy has previously reported on agentic drift, agents that pass review at launch can behave differently months later. Continuous monitoring of reasoning chains, action sequences, and decision outcomes is the minimum viable governance stack – not periodic audits.

    Third, governance requires dedicated roles, not committees. The Mayer Brown framework identified four governance functions: policy-setters, product teams, cybersecurity integration, and frontline escalation. Most enterprises have distributed these responsibilities informally. As agents scale beyond pilot stage, informal arrangements become liabilities.

    The Trajectory Ahead

    The governance conversation has moved faster than most observers expected. Twelve months ago, agentic AI governance was a theoretical concern. Today, it has dedicated regulatory guidance, published legal frameworks, and named positions on practitioners’ organisational charts. That is genuine progress.

    But the distance between knowing what governance should look like and operating it reliably is where the next phase of difficulty lies. The 40 per cent cancellation rate Gartner projects is not primarily a technology failure – it is a governance and operational maturity failure. The organisations that succeed with autonomous agents will not be those with the most sophisticated AI models. They will be the ones that built the operational infrastructure to govern them before they scaled.

    For consultants advising enterprise clients on agentic AI, the message has shifted. The question is no longer whether governance frameworks exist. It is whether the organisation has the skills, tooling, and organisational design to make those frameworks operational. That is a harder conversation, but it is now the one that matters.

  • Nearly Every Enterprise Wants AI. Two-Thirds Cannot Scale It Past a Pilot.

    The Appetite Is Not the Problem

    Nearly every large organisation wants AI. The technology works. The budgets are approved. The pilots are running. And yet, when it comes to deploying AI at scale – moving from a successful proof of concept to a production capability that changes how the business operates – two-thirds of enterprises are stuck.

    That is the central finding of Logicalis’s 2026 CIO Report, which surveyed more than 1,000 chief information officers globally. The numbers paint a stark picture of an industry that has solved the belief problem but not the execution one: 94% of CIOs report growing organisational appetite for AI. Over a third have accelerated AI initiatives based on early proof-of-concept results. But two-thirds say they cannot scale AI beyond those initial deployments.

    The gap between wanting AI and running AI at enterprise scale has become the defining challenge of 2026. And the constraints holding organisations back are not the ones most boardrooms are discussing.

    Skills, Not Budgets, Are the Bottleneck

    The most striking finding in the Logicalis data is what CIOs identify as their primary constraint. It is not funding. It is not technology. It is skills.

    A lack of internal technical capability is holding back AI ambitions in nearly nine out of ten organisations. This is not a shortage of data scientists or machine learning engineers in the abstract – it is a shortage of people who understand how to integrate AI into existing business processes, manage its outputs, and govern its behaviour in production environments.

    The skills gap becomes more consequential as AI moves from experimentation to operation. A pilot needs a small team of enthusiasts and a sandbox. A production deployment needs data engineers, integration specialists, change managers, and – critically – people who understand both the technology and the business domain well enough to know when the AI is wrong.

    This connects directly to what Harvard Business Review reported in February: 88% of companies now report regular AI use, yet adoption is stalling because employees experiment with tools without integrating them into how work actually gets done. The tools are present. The capability to use them well is not.

    Governance Is Being Compromised, Not Solved

    If the skills gap explains why organisations cannot scale, the governance gap explains why scaling carries risk. The Logicalis report found that 62% of CIOs have compromised on AI governance due to limited knowledge, and only 44% say they fully grasp the risks of AI adoption. Meanwhile, 76% describe unchecked AI as a serious concern.

    This is not a theoretical problem. As maddaisy.com has reported extensively, the governance question follows AI into every new domain – from agentic systems that drift in production to vibe coding tools that generate enterprise software without conventional oversight. Organisations are deploying AI faster than they can build the frameworks to manage it, and most acknowledge this openly. An overwhelming 89% of CIOs in the Logicalis survey describe their current approach as “learning as we go.”

    That phrase deserves attention. It means that the majority of enterprise AI programmes are operating without mature risk management, without clear accountability structures, and without the monitoring infrastructure needed to catch problems before they compound. For regulated industries – financial services, healthcare, defence – this is not a growing pain. It is an exposure.

    The Pattern maddaisy.com Has Been Tracking

    The Logicalis data does not exist in isolation. It quantifies a pattern that has been building across multiple data points this year.

    In February, maddaisy.com reported on PwC’s Global CEO Survey, which found that 56% of chief executives could not point to measurable revenue gains from their AI investments. The diagnosis then was a measurement problem as much as a technology one – organisations deploying AI without redesigning workflows or building the instrumentation to track outcomes.

    Earlier this month, Capgemini’s CEO Aiman Ezzat made the case for pacing AI investment, arguing that companies are deploying capabilities ahead of their organisation’s ability to absorb them. EY research cited in that analysis showed organisations failing to capture up to 40% of potential AI benefits – not because the technology underperformed, but because the surrounding processes, skills, and culture were not ready.

    And in maddaisy.com’s analysis of the consulting pyramid, Eden McCallum research revealed that 95% of AI pilots have failed to deliver returns – a figure that aligns precisely with the Logicalis finding that two-thirds of organisations cannot move past the pilot stage.

    These are not isolated reports reaching coincidentally similar conclusions. They are different measurements of the same underlying problem: the bottleneck in enterprise AI has moved from technology capability to organisational readiness.

    The Managed Services Pivot

    One of the more telling details in the Logicalis report is that 94% of CIOs plan to lean on managed service providers over the next two to three years to help navigate AI governance, scaling, and sustainability. This is a quiet but significant shift in how enterprises relate to their technology infrastructure.

    It suggests that many CIOs have concluded they cannot build the required skills and governance frameworks internally – at least not at the pace the technology demands. Rather than owning and operating AI capabilities directly, they are moving toward orchestrating a network of external providers. The CIO role, in this model, becomes less about technology ownership and more about vendor management, risk oversight, and strategic coordination.

    For consulting and technology services firms, this creates a substantial market opportunity. But it also raises a question that the industry has not yet answered convincingly: if the clients cannot scale AI internally because they lack the skills and governance frameworks, and they outsource to service providers who are themselves still working out how to embed AI into their own operations, where does the actual expertise reside?

    What Practitioners Should Watch

    The adoption gap is unlikely to close quickly. Skills take time to develop. Governance frameworks take time to mature. Organisational change – the kind that turns a pilot into a production capability – is measured in quarters and years, not weeks.

    Three things are worth tracking. First, whether the 89% “learning as we go” figure starts to decline in subsequent surveys – that will be the clearest signal that enterprises are moving from experimentation to operational maturity. Second, whether the managed services pivot produces measurable outcomes or simply moves the scaling problem from one organisation to another. And third, whether the 67% of CIOs who expressed concern about an “AI bubble” translate that concern into more disciplined investment, or whether competitive pressure continues to override caution.

    The technology has arrived. The appetite is not in question. What remains unresolved is whether organisations can build the human and structural foundations fast enough to use what they have already bought.

  • OpenAI’s Frontier Alliance Is Not Just About Consulting. It Is a Bet Against the Enterprise Software Stack.

    When maddaisy examined OpenAI’s Frontier Alliance in February, the focus was on what it meant for the consulting firms — McKinsey, BCG, Accenture, and Capgemini — and the admission that AI vendors cannot scale enterprise deployments alone. That story was about the consulting industry. This one is about the companies the alliance is quietly aimed at: the enterprise software vendors that have built trillion-dollar businesses on per-seat licensing.

    The per-seat model under pressure

    OpenAI’s Frontier platform, launched in early February, is designed as an enterprise operating layer — a unified system where AI agents can log into applications, execute workflows, and make decisions across an organisation’s entire technology stack. CRM systems, HR platforms, ticketing tools, internal databases. The ambition is not to replace any single application but to sit above all of them.

    The threat to SaaS vendors is structural, not incremental. If AI agents execute the tasks that human employees currently perform inside Salesforce, ServiceNow, or Workday, the justification for per-seat licensing weakens. Fewer human users logging in means fewer seats to sell. And if agents can orchestrate workflows across multiple systems from a single platform, the case for buying specialised point solutions — each with its own subscription — becomes harder to make.

    The market has not waited for proof. Investors wiped roughly $2 trillion in market value from technology stocks in a single week over AI displacement concerns. ServiceNow shares fell more than 20% year-to-date by mid-February. IBM suffered its largest single-day decline in 25 years after Anthropic’s Claude demonstrated competency with legacy COBOL systems — the very maintenance work that underpins a significant portion of IBM’s consulting revenue.

    The consulting conduit

    What makes the Frontier Alliance specifically dangerous for SaaS incumbents is not the technology. It is the distribution channel.

    McKinsey, BCG, Accenture, and Capgemini are not just consulting firms. They are the primary implementation partners for the very software companies that Frontier could displace. When a Fortune 500 company deploys Salesforce, it typically hires one of these firms to manage the rollout. When it migrates to ServiceNow’s IT service management platform, the same consulting firms handle the integration. The relationships are deep, multi-year, and built on trust.

    OpenAI has effectively enlisted those relationships as a distribution network. Each of the four firms has established dedicated OpenAI practice groups, certified their teams on Frontier, and committed to multi-year alliances. OpenAI’s own forward-deployed engineers will sit alongside consulting teams in client engagements — a model borrowed from Palantir’s playbook for embedding in enterprise accounts.

    The result is a direct-to-enterprise pipeline that does not need SaaS vendors as intermediaries. A consulting firm advising a client on AI strategy can now recommend Frontier agents that orchestrate existing systems, rather than recommending new SaaS products that require their own implementation projects. The consulting firm earns either way. The SaaS vendor may not.

    SaaS is not dying. But the economics are shifting.

    The counterarguments deserve a hearing. Fortune 500 companies will not abandon decades of enterprise software investment overnight. Compliance requirements, audit trails, data sovereignty obligations, and the sheer operational complexity of large organisations create friction that no AI platform can simply wave away. As one analyst put it, “we are simply not going to see a complete unwinding of the past 50 years of enterprise software development.”

    The incumbents are also adapting. Salesforce has pioneered what it calls the “Agentic Enterprise Licence Agreement” — a fixed-price, consumption-based model designed to decouple revenue from headcount. ServiceNow and Microsoft are shifting toward outcome-based pricing. These moves acknowledge the threat and attempt to neutralise it by changing the unit of value from the human user to the business outcome.

    But adaptation comes at a cost. Per-seat licensing has been the engine of SaaS margins for two decades. Moving to consumption or outcome-based models compresses revenue predictability and margins in the short term, even if it preserves relevance in the long term. The transition is not painless, and investors know it.

    Where this connects

    This development sits at the intersection of several threads maddaisy has been tracking. Capgemini’s CEO, Aiman Ezzat, argued last week that organisations are deploying AI capabilities ahead of their ability to absorb them. He is right — but that does not mean the structural pressure on SaaS pricing will wait for organisations to catch up. The market reprices on expectations, not on deployment maturity.

    And the consulting pyramid piece from yesterday noted that the industry’s base is being reshaped as AI compresses the need for junior analytical work. A similar compression is now visible in enterprise software: the middle layer of the stack — the specialised tools that automate individual workflows — faces pressure from platforms that automate across workflows.

    The question for enterprise software vendors is not whether AI agents will change their businesses. It is whether they can shift their pricing, their value propositions, and their competitive moats fast enough to remain the platform of choice — rather than becoming the legacy infrastructure that sits beneath someone else’s agent layer.

    For practitioners evaluating their own technology stacks, the practical implication is this: the next software audit should not just ask what each tool costs per seat. It should ask what happens to that cost when half the seats belong to agents that do not need a licence.

  • The Consulting Pyramid Is Not Collapsing. It Is Being Quietly Redesigned.

    The consulting industry’s foundational staffing model — a small number of partners supported by large cohorts of junior analysts — is facing its most serious structural challenge in decades. But the narrative that AI is “dismantling” the pyramid overstates the pace and understates the complexity of what is actually happening.

    Nick Pye, managing partner at Mangrove Consulting, argued in Consultancy.uk this month that the traditional pyramid is “becoming increasingly difficult to justify.” His core thesis is straightforward: AI can now perform the analytical heavy lifting — research, financial modelling, scenario analysis — that once required rooms of graduates billing at premium rates. Clients are noticing. They want senior judgement, not junior analysis. And they increasingly have their own data capabilities, reducing dependence on external advisers for the work that used to fill the pyramid’s base.

    The argument is sound in principle. Where it risks overreach is in assuming the transition is further along than the evidence suggests.

    The data tells a more complicated story

    If AI were genuinely dismantling the consulting pyramid, one would expect to see mass reductions in headcount at the bottom. The picture is more mixed than that.

    As maddaisy reported in February, Capgemini ended 2025 with 423,400 employees — up 24% year-on-year — after adding 82,300 offshore workers in a single year. The company simultaneously announced €700 million in restructuring charges. It is not shrinking. It is reshuffling: eliminating some roles while creating others, primarily in AI engineering, data science, and agentic AI delivery.

    McKinsey, meanwhile, has begun testing new recruits on AI capabilities, and more than half of graduate roles now reportedly require AI skills. The Big Four have reduced student intakes, but the UK consulting market’s difficulties in 2025 — its worst year since lockdown — owe as much to broader demand softness as to structural AI disruption.

    And the technology itself is not yet delivering at the scale the hype implies. Eden McCallum research published this year found that while excitement for generative AI remains high, revenue impact remains minimal. Ninety-five per cent of AI pilots have failed to deliver returns, according to industry data cited by Consultancy.uk. That is not a technology that has already displaced the analyst class.

    What is genuinely changing

    None of this means the pyramid is safe. The direction of travel is clear, even if the pace is slower than the most breathless accounts suggest.

    Three shifts are converging. First, clients increasingly own and understand their own data. The analytical monopoly that consulting firms once held — gathering, processing, and synthesising information that clients could not access themselves — has eroded as organisations have built internal data teams and deployed their own AI tools.

    Second, the economics of the base are deteriorating. When an AI system can produce a comparable market analysis in minutes rather than weeks, it becomes progressively harder to justify billing rates for the same work performed by junior staff. This does not eliminate the need for human analysis, but it compresses the time and headcount required.

    Third, client expectations have shifted from deliverables to outcomes. As Pye puts it: clients want “decisions and performance,” not “decks and processes.” That shift favours experienced practitioners who can navigate organisational politics and drive implementation — not the analysts who assemble the slides.

    The diamond, the inverted pyramid, and the graduate question

    The replacement models being discussed are instructive. The most conservative is the “diamond” — wider in the middle, thinner at the base, with fewer entry-level analysts but more mid-level orchestration roles. It preserves hierarchy while acknowledging that the bottom of the pyramid has less to do.

    The more radical option is what Pye calls “flipping the pyramid”: small teams of senior and mid-level consultants tackling specific challenges, supported by AI systems rather than junior staff. Boutique consultancies have operated variations of this model for years. What AI changes is the scale at which it becomes viable.

    But neither model addresses the question that should concern the industry most: if junior roles contract, where do future senior consultants come from?

    The traditional pyramid functioned as a training pipeline. Graduates entered, learned the craft through years of analytical work, and developed into the experienced practitioners clients now prize. Close that entry point, and the industry faces a slow-motion skills crisis — a generation of senior consultants with no successors trained in the discipline.

    Pye’s answer is that consulting firms will increasingly recruit mid-career professionals who have already developed sector expertise elsewhere. The career path inverts: specialise first in an industry, then move into consulting.

    This is plausible but raises its own problems. The consulting skill set — structured problem solving, client management, the ability to diagnose organisational dysfunction — is not the same as industry expertise. A decade in financial services does not automatically produce someone who can run a transformation programme. The two capabilities overlap, but they are not identical.

    The real risk is not speed — it is the talent pipeline

    The firms that are moving fastest on AI are not necessarily the ones best positioned for the long term. Accenture is tracking AI logins for promotion decisions. OpenAI has formed alliances with McKinsey, BCG, Accenture, and Capgemini to deploy its enterprise AI platform. Capgemini’s CEO is counselling patience, arguing that deploying AI ahead of organisational readiness wastes both money and credibility.

    Each response reflects a different bet on how quickly the pyramid will change — and how to navigate the transition without breaking the firm’s ability to develop talent.

    The consulting industry is not being dismantled by AI. It is being redesigned, unevenly, firm by firm, with no consensus on the target operating model. The firms that get the balance right — reducing the base without severing the pipeline that produces tomorrow’s senior partners — will define what the industry looks like in a decade. The ones that treat this as a simple cost-cutting exercise will find, in five years, that they have cut too deep in exactly the wrong place.

  • Capgemini’s CEO Makes the Unfashionable Case for Pacing Your AI Investment

    There is a particular kind of courage in telling a room full of executives to slow down. Aiman Ezzat, CEO of Capgemini, has been doing exactly that – and his reasoning deserves more attention than the typical “move fast or die” narrative that dominates AI strategy discussions.

    “You don’t want to be too ahead of the learning curve,” Ezzat told Fortune in February. “If you are, you’re investing and building capabilities that nobody wants.”

    Coming from the head of a €22.5 billion consultancy that has trained 310,000 employees on generative AI and is actively building labs for quantum computing, 6G, and robotics, this is not a counsel of inaction. It is a strategic position on pacing – one that puts Ezzat at odds with much of the technology industry’s current mood.

    The FOMO problem

    The fear of missing out on AI has become a boardroom affliction. Boston Consulting Group reports that half of CEOs now believe their job is at risk if AI investments fail to deliver returns. That pressure creates a predictable dynamic: spend big, move fast, worry about outcomes later.

    The data suggests the worry-later approach is not working. EY research shows that while 88% of employees report using AI at work, organisations are failing to capture up to 40% of the potential benefits. In the UK, only 21% of workers felt confident using AI as of January 2026. The tools are arriving faster than the capacity to use them well.

    Ezzat’s argument is that this gap is not a technology problem. It is a pacing problem. Companies are deploying AI capabilities ahead of their organisation’s ability to absorb them – and ahead of genuine customer demand for the outcomes those capabilities promise.

    AI is a business, not a technology

    The more substantive part of Ezzat’s case is about framing. Too many leadership teams, he argues, treat AI as “a black box that’s being managed separately” – a technology initiative bolted onto the existing business rather than a force reshaping how the business operates.

    “The question you have to focus on is: ‘How can your business be significantly disrupted by AI?’” Ezzat says. “Not ‘How is your finance team going to become more efficient?’ I’m sure your CFO will deal with that at the end of the day.”

    The distinction matters. Departmental efficiency projects – automating invoice processing, summarising meeting notes, generating marketing copy – are the low-hanging fruit that most enterprises are picking right now. They deliver incremental gains but rarely transform a business model. The harder question, the one Ezzat wants CEOs to sit with, is whether AI fundamentally changes what a company sells, how it competes, or what its customers expect.

    That question takes time to answer well. Rushing it produces expensive experiments that solve the wrong problems.

    The trust deficit

    Perhaps the most underexplored part of Ezzat’s argument is about human trust. “How do you get humans to trust the agent?” he asks. “The agent can trust the human, but the human doesn’t really trust the agent.”

    This cuts to a practical reality that technology roadmaps tend to gloss over. Agentic AI – systems designed to take autonomous actions rather than simply generate content – is the next wave of enterprise deployment. As maddaisy.com noted when covering Capgemini’s role in the OpenAI Frontier Alliance, the gap between a capable AI platform and a working enterprise deployment remains stubbornly wide. Trust is a significant part of why.

    Employees who do not trust AI agents will find ways to work around them. Managers who cannot explain AI-driven decisions to clients will revert to manual processes. Organisations that deploy autonomous systems faster than their culture can absorb them will create friction, not efficiency.

    Ezzat draws an analogy to ergonomics – the mid-twentieth century discipline of designing tools for humans rather than forcing humans to adapt to tools. “Bad chairs lead to bad backs,” he observes. “Bad AI is likely to be far more consequential.”

    Consistent with Capgemini’s own playbook

    What makes Ezzat’s position credible is that Capgemini’s recent actions align with it. The company’s approach has been to invest broadly but scale selectively.

    As maddaisy.com’s analysis of the company’s 2025 results highlighted, generative AI bookings rose above 10% of total bookings in Q4 – meaningful but not yet dominant. The company maintains labs for emerging technologies including quantum and 6G, keeping a foot in multiple possible futures without betting the firm on any single one.

    Meanwhile, the company added 82,300 offshore workers in 2025 – largely through the WNS acquisition – while simultaneously earmarking €700 million for workforce restructuring. The message is clear: AI changes the shape of the workforce, but it does not eliminate the need for one. Building the human infrastructure to deliver AI at scale takes as much investment as the technology itself.

    The metaverse lesson

    Ezzat’s most pointed comparison is to the metaverse – a technology that commanded billions in corporate investment before the market concluded that customer demand had been dramatically overstated. Capgemini itself experimented with a metaverse lab. Mark Zuckerberg renamed his company around it. Now, as Ezzat puts it, “like air fryers, its time may now have passed.”

    The parallel is not that AI will follow the metaverse into irrelevance – the use cases are far more concrete, and the enterprise adoption data is already stronger. The point is about the cost of overcommitment. Companies that invested heavily in metaverse capabilities before the market was ready wrote off those investments. The same risk exists with AI, particularly in areas like agentic systems where the technology’s capability is advancing faster than organisational readiness to use it.

    Ezzat’s prescription is agility over ambition: small pilots, constant monitoring, and the willingness to scale rapidly when adoption genuinely accelerates. “We have to be investing – but not too much – to be able to be aware of the technology, following at the speed to make sure that we are ready to scale when the adoption starts to accelerate.”

    What this means for practitioners

    For consultants advising clients on AI strategy, Ezzat’s framework offers a useful counterweight to the prevailing urgency. The question is not whether to invest in AI – that debate is settled. The question is how to pace that investment so that capability, demand, and organisational readiness move roughly in step.

    Companies that get the pacing right will avoid the twin traps of overinvestment (building capabilities nobody wants) and underinvestment (being caught flat-footed when adoption accelerates). In a market where half of CEOs fear for their jobs over AI outcomes, the discipline to move at the right speed – rather than the fastest speed – may prove to be the more valuable skill.