maddaisy – cultivating innovation

Tag: ai-adoption

  • Shadow AI and the Year-Two Governance Challenge

    Over a third of employees have already used AI tools without their organisation’s knowledge or permission. As enforcement deadlines tighten, the gap between what staff are doing and what leadership has sanctioned is becoming one of enterprise AI’s most urgent — and most overlooked — governance problems.

    The term “shadow AI” describes AI tools adopted by employees outside official channels — free-tier large language models used to draft client communications, image generators repurposed for internal presentations, coding assistants plugged into development environments without security review. It is the AI equivalent of shadow IT, but with a critical difference: the data exposure risks are significantly higher and the speed of adoption is faster than anything IT departments have previously managed.

    According to the State of Information Security Report 2025 from ISMS.online, 37% of organisations surveyed said employees had already used generative AI tools without organisational permission or guidance. A further 34% identified shadow AI as a top emerging threat for the next 12 months. These are not projections about some distant risk — they describe what is happening now, in organisations that thought they had AI under control.

    The year-two reckoning

    The pattern is familiar to anyone who lived through the early cloud adoption cycle, but compressed into a fraction of the time. Year one — roughly 2024 into early 2025 — was characterised by experimentation. Departments trialled AI tools, leaders encouraged innovation, and governance was deferred in favour of speed. The implicit message in many organisations was: try things, move fast, we will sort out the rules later.

    Year two is when “later” arrives. And the numbers suggest most organisations are not ready for it. The same ISMS.online report found that 54% of respondents admitted their business had adopted AI technology too quickly and was now facing challenges in scaling it back or implementing it more responsibly. That figure represents a majority of enterprises acknowledging, in effect, that they have accumulated governance debt they do not yet know how to service.

    This aligns with a pattern maddaisy has been tracking across several dimensions. Deloitte’s 2026 State of AI report revealed that organisations report growing strategic confidence in AI but declining readiness on the operational foundations — infrastructure, data quality, risk management, and talent — needed to execute responsibly. Shadow AI is the ground-level expression of that paradox: strategy says “adopt AI,” but operations never built the guardrails to manage what adoption actually looks like across thousands of employees making independent tool choices every day.

    Why traditional IT governance falls short

    The instinct in many organisations is to treat shadow AI like shadow IT — block unsanctioned tools, enforce approved vendor lists, and route everything through procurement. That approach, while understandable, misses what makes AI different.

    When an employee uses an unapproved project management tool, the risk is primarily operational: data silos, integration headaches, wasted licences. When an employee pastes client data, financial projections, or intellectual property into a free-tier language model, the risk is fundamentally different. That data may be used for model training, stored in jurisdictions with different privacy regimes, or exposed through security vulnerabilities the organisation has no visibility into.

    As CIO.com recently noted, boards are increasingly recognising that AI is not waiting for permission — it is already shaping decisions through vendor systems, employee-adopted tools, and embedded algorithms that grow more powerful without explicit organisational consent. The question for governance teams is not whether employees are using unsanctioned AI. It is how much sensitive data has already left the building.

    The regulatory dimension

    Shadow AI also creates a specific compliance exposure that many organisations have not fully mapped. As maddaisy examined last week, the EU AI Act reaches its most consequential enforcement milestone in August 2026, with requirements for high-risk AI systems carrying penalties of up to €35 million or 7% of global annual turnover. Colorado’s AI Act takes effect in June 2026 with its own set of requirements around algorithmic discrimination and impact assessments.

    The compliance challenge with shadow AI is that an organisation cannot demonstrate responsible use of systems it does not know exist. If an employee in a hiring function uses an unsanctioned AI tool to screen CVs, or a financial analyst uses a free language model to generate risk assessments, the organisation may be deploying high-risk AI — as defined by regulators — without any of the documentation, monitoring, or impact assessment that compliance requires.

    This is not a theoretical concern. It is a direct consequence of the gap between adoption speed and governance maturity that maddaisy has documented across the enterprise AI landscape.

    What a pragmatic response looks like

    The organisations handling shadow AI most effectively are not the ones deploying the heaviest restrictions. They are the ones that recognised early that prohibition does not work when the tools are free, browser-based, and genuinely useful.

    A pragmatic governance approach has several components. First, visibility: understanding what AI tools employees are actually using, through network monitoring, surveys, and — critically — creating an environment where people feel safe disclosing their usage rather than hiding it. Second, clear usage policies that distinguish between acceptable and unacceptable use cases, specifying what data categories must never enter external AI systems regardless of the tool’s provenance.

    Third, an approved toolset that is genuinely competitive with the free alternatives. One of the most common drivers of shadow AI is that official enterprise AI tools are slower, more restricted, or simply worse than what employees can access on their own. If the sanctioned option requires a three-week procurement process while ChatGPT is a browser tab away, governance has already lost.

    Fourth, the frameworks exist. ISO 42001 provides a structured approach to establishing an AI management system, covering policy, roles, impact assessment, and continuous improvement through the familiar Plan-Do-Check-Act cycle. It is not a silver bullet, but it offers a starting point for organisations that currently have no systematic approach to AI governance beyond hoping the problem does not escalate.

    The window is narrowing

    The uncomfortable reality for many enterprises is that shadow AI has already created facts on the ground. Data has been shared with external models. Workflows have been built around unsanctioned tools. Employees have integrated AI into their daily routines in ways that would be disruptive to simply switch off.

    The year-two governance challenge is not about preventing AI adoption — that ship has sailed. It is about catching up with what has already happened, building the visibility and policy infrastructure to manage it going forward, and doing so before enforcement deadlines turn a governance gap into a compliance crisis.

    For consultants and practitioners advising organisations through this transition, the message is straightforward: audit first, policy second, technology third. The organisations that will navigate this best are not the ones with the most sophisticated AI strategies. They are the ones that know, concretely and completely, what AI is actually being used within their walls — and by whom.

  • Lloyds Banking Group’s £100 Million AI Bet: What the UK’s First Agentic Financial Assistant Means for Enterprise AI

    Lloyds Banking Group expects its artificial intelligence programme to deliver more than £100 million in value this year — double the £50 million it attributes to generative AI in 2025. The figures, disclosed alongside the group’s annual results in January 2026, represent one of the more concrete attempts by a major financial institution to attach a number to what AI is actually worth.

    That specificity matters. As maddaisy examined last week, PwC’s 2026 Global CEO Survey found that 56% of chief executives still cannot point to measurable revenue gains from their AI investments. Lloyds is not claiming to have solved the ROI puzzle entirely, but it is doing something most enterprises have not: publishing the numbers and tying them to specific operational improvements rather than vague promises of transformation.

    The financial assistant: what it actually does

    The headline initiative is a customer-facing AI financial assistant, which Lloyds describes as the first agentic AI tool of its kind offered by a UK bank. Announced in November 2025 and scheduled for public rollout in early 2026, the assistant sits within the Lloyds mobile app and is designed to help customers manage spending, savings, and investments through natural conversation.

    The system uses a combination of generative AI for its conversational interface and agentic AI to process requests and execute actions. In practical terms, a customer can query a payment, ask for a spending breakdown, or request guidance on savings options — and the assistant will interpret the request, plan the necessary steps, and carry them out. Where it reaches the limits of what automated support can handle, it refers users to human specialists.

    The scope is intended to expand. Lloyds has said the assistant will eventually cover its full product suite, from mortgages to car finance to protection products, serving its 28 million customer accounts across the Lloyds, Halifax, Bank of Scotland, and Scottish Widows brands.

    Testing at scale, not in a lab

    Before public launch, Lloyds tested the assistant with approximately 7,000 employees, who collectively completed around 12,000 trials. That is a meaningful pilot — large enough to surface edge cases and failure modes that a controlled lab environment would miss, and conducted with users who understand the bank’s products well enough to stress-test the system’s accuracy.

    The employee testing sits alongside a broader internal AI deployment that has already delivered measurable results. Athena, the group’s AI-powered internal search assistant, is used by 20,000 colleagues and has reduced information search times by 66%. GitHub Copilot, deployed to 5,000 engineers, has driven a 50% improvement in code conversion for legacy systems. An AI-powered HR assistant resolves 90% of queries correctly on the first attempt.

    These are not experimental pilots. They are production tools used at scale, and the fact that Lloyds is willing to attach specific performance metrics to each one distinguishes its approach from the many enterprises that describe AI impact in qualitative terms only.

    The ROI question: credible or convenient?

    The £100 million figure invites scrutiny, and it should. “Value” in corporate AI disclosures is notoriously slippery — it can mean cost savings, time savings converted to a monetary equivalent, revenue uplift, or some combination of all three. Lloyds has not published a detailed methodology for how it arrived at the £50 million figure for 2025 or how it projects the 2026 target.

    That said, the bank’s approach has features that lend it more credibility than many comparable claims. The internal tools have named user populations and specific performance benchmarks. The customer-facing assistant was tested with thousands of employees before launch, not unveiled as a concept. And the 2025 figure is presented as a delivered outcome, not a forecast — a distinction that matters when most enterprises are still struggling to prove any return at all.

    Lloyds also rose 12 places in the Evident AI Global Index last year — the strongest improvement of any UK bank — suggesting that external assessors see substance behind the claims.

    Agentic AI in financial services: the governance dimension

    The move to customer-facing agentic AI in banking raises governance questions that go beyond what internal productivity tools require. As maddaisy explored earlier this week, Deloitte’s 2026 AI report found that only one in five enterprises has a mature governance model for agentic systems. When those systems move from internal search assistants to customer-facing financial advice, the stakes escalate considerably.

    A banking AI that can execute transactions, provide savings guidance, and eventually handle mortgage queries operates in regulated territory. The Financial Conduct Authority’s expectations around suitability, fair treatment, and clear communication apply regardless of whether the advice comes from a human or an algorithm. Lloyds has acknowledged this by building in human referral pathways, but the real test will come at scale — when millions of customers interact with the system simultaneously, and edge cases multiply.

    Ron van Kemenade, the group’s chief operating officer, has framed the launch as “a pivotal step in our strategy as we continue to reimagine the Group for our customers and colleagues.” Ranil Boteju, chief data and analytics officer, has positioned it as a demonstration of responsible deployment, noting that the assistant “can understand and respond to specific, hyper-personalised customer requests and retains memory to offer a more holistic experience, ensuring the generated answer is safe to present to customers.”

    What this signals for the sector

    Lloyds is not the first bank to deploy AI, nor the first to make bold claims about its value. What distinguishes this move is the combination of a concrete financial baseline (£50 million delivered), a named and tested product (the financial assistant), a clear expansion roadmap (full product suite), and an institutional commitment to upskilling (a new AI Academy for its 67,000 employees).

    For practitioners watching the enterprise AI landscape, the Lloyds case offers a useful reference point against the prevailing narrative of deployment fatigue and unproven returns. It does not resolve the broader ROI question — one bank’s results do not establish an industry pattern — but it does suggest that organisations which invest in specific, measurable use cases and test rigorously before launch can move beyond the proof-of-concept purgatory that still traps most enterprises.

    The harder question is what happens next. An AI assistant that helps customers check spending patterns is useful. One that advises on mortgages and investment products enters a different category of risk and regulatory complexity. How Lloyds navigates that expansion — and whether the £100 million value target holds up under the scrutiny of real-world deployment — will be worth watching over the months ahead.

  • Capgemini Added 82,300 Offshore Workers Last Year. So Much for AI Replacing Everyone.

    If artificial intelligence is about to make IT services firms obsolete, someone forgot to tell Capgemini. The French consultancy ended 2025 with 423,400 employees — up 24% year-on-year — after adding 82,300 offshore workers in a single year. Its offshore workforce now stands at 279,200, a 42% increase, representing two-thirds of total headcount.

    At the same time, the company is planning €700 million in restructuring charges to reshape its workforce for AI. It is positioning itself as, in CEO Aiman Ezzat’s words, “the catalyst for enterprise-wide AI adoption.”

    The contradiction is only apparent. What Capgemini’s numbers actually reveal is the gap between the market’s AI narrative and the operational reality of large-scale enterprise transformation.

    The WNS factor

    The headline headcount surge requires context. The bulk of those 82,300 new offshore employees came from Capgemini’s acquisition of WNS, the India-headquartered business process services firm, completed in late 2025. Cloud4C, another acquisition, contributed further. This was not organic hiring — it was a deliberate strategic bet on scaling offshore delivery capacity at precisely the moment investors were questioning whether such capacity has a future.

    The acquisition arithmetic is revealing. Capgemini’s 2026 revenue growth guidance of 6.5% to 8.5% includes 4.5 to 5 percentage points from acquisitions, primarily WNS. Strip out the acquired growth, and organic expansion looks more like 2% to 3.5%. The company needed WNS to hit its targets.

    WNS brings something specific: expertise in AI-powered business process services, particularly in financial services, insurance, and healthcare. The company had already identified around 100 cross-selling opportunities and signed an intelligent operations contract worth more than €600 million. This is not a firm buying bodies for the sake of scale. It is buying the delivery infrastructure needed to operationalise AI at enterprise level.

    The restructuring counterweight

    The other side of the equation is the €700 million restructuring programme, most of which will land in 2026. Capgemini describes this as adapting “workforce and skills” to align with demand for AI-driven services. In plainer terms: some roles are being eliminated or relocated, while others — particularly those requiring AI engineering, data science, and agentic AI expertise — are being created or upskilled.

    As maddaisy noted when examining Capgemini’s full-year results last week, generative and agentic AI accounted for more than 10% of group bookings in Q4, up from around 5% earlier in the year. The company has trained 310,000 employees on generative AI and 194,000 on agentic AI. The restructuring is not a contradiction of the hiring — it is the other half of the same workforce transformation.

    The pattern is expand first, optimise second. Acquire the delivery capacity, then reshape it. It is a playbook that makes more commercial sense than the market’s preferred narrative of AI simply deleting headcount.

    What the market gets wrong

    Capgemini’s share price has fallen roughly 26% in 2026, driven largely by investor anxiety that AI will cannibalise the IT services business model. The logic runs: if AI can automate code generation, testing, and business process management, why would enterprises pay consultancies to do it?

    Ezzat addressed this directly in the post-earnings call. “I don’t think clients are thinking this way about reduction,” he told MarketWatch. “Clients are looking at how critical it is for them to adopt AI and where it can have an impact.”

    The distinction matters. Enterprises are not replacing their consulting relationships with AI tools. They are asking their consultants to help them implement AI — which, for now, requires more people, not fewer. The skills are different. The delivery models are changing. But the demand for hands-on expertise in making AI work within complex organisational environments is, if anything, increasing.

    This aligns with what Ezzat told Fortune separately: “AI is a business. It is not a technology. It cannot just be used to keep the house running.” His argument is that CEOs who treat AI as an efficiency tool for individual departments are missing the larger opportunity — and the larger threat — of enterprise-wide transformation.

    The offshore model evolves, but does not disappear

    The shift to 66% offshore headcount is not a return to the labour arbitrage model of the 2000s. Capgemini’s onshore workforce held steady at 144,200. What changed is the nature of offshore work. WNS’s strength is in intelligent operations — business process services enhanced by AI, automation, and analytics. These are not call centres or basic coding shops. They are delivery centres where AI tools augment human workers on complex processes.

    This is broadly consistent with what the wider consulting industry is signalling. McKinsey’s deployment of 25,000 AI agents across its workforce, as maddaisy reported this week, points in the same direction: AI as workforce augmentation, not replacement. The difference is that McKinsey is building internally, while Capgemini is acquiring externally. Both are betting that the future of professional services involves more AI and more people, deployed differently.

    What practitioners should watch

    For consultants and enterprise leaders, Capgemini’s workforce data offers a useful reality check against the AI replacement narrative. Three signals are worth tracking.

    First, the restructuring outcomes. If the €700 million programme results in meaningful upskilling rather than simple headcount reduction, it will validate the “expand and reshape” model. If it quietly becomes a cost-cutting exercise, the AI transformation story weakens.

    Second, the organic growth rate. With acquisitions contributing nearly five percentage points of 2026 growth, the underlying business needs to demonstrate it can grow on its own merits. The Q4 acceleration to over 10% AI bookings was promising, but one quarter does not make a trend.

    Third, the onshore-offshore ratio over time. If AI genuinely transforms delivery models, the 66% offshore share should eventually stabilise or even decline as automation reduces the need for large delivery teams. If it keeps rising, the industry is still in the scale-up phase, and the productivity gains from AI remain further away than the marketing suggests.

    Capgemini’s apparent paradox — adding 82,300 people while betting its future on AI — is not a paradox at all. It is the messy, expensive reality of what enterprise AI transformation actually looks like on the ground: more complexity before less, more people before fewer, more investment before returns. The firms that navigate this transition phase successfully will define the next era of professional services. The market just needs to be patient enough to let them.

  • The AI ROI Crisis: Why 56% of CEOs Still Cannot Prove Value From Their AI Investments

    More than half of the world’s chief executives cannot point to revenue gains from their AI investments. That is not a fringe finding from an alarmist report — it is the central conclusion of PwC’s 2026 Global CEO Survey, which polled 4,454 business leaders and was released at Davos in January.

    The figure — 56% reporting no measurable revenue uplift from AI — lands at a moment when enterprise AI spending continues to accelerate. Budgets are growing, headcounts in AI-adjacent roles are expanding, and the consulting industry’s order books are thick with transformation mandates. Yet the returns remain stubbornly elusive for the majority. Only 12% of CEOs surveyed reported achieving both revenue growth and cost reduction from their AI programmes.

    This is not, as some commentary has framed it, evidence that AI does not work. It is evidence that most organisations have not yet figured out how to make it work — a distinction that matters enormously for practitioners and consultants navigating the current landscape.

    The pattern maddaisy has been tracking

    PwC’s data confirms a dynamic that maddaisy has examined from several angles over the past fortnight. Deloitte’s 2026 State of AI report revealed that enterprises feel more strategically confident about AI than ever, yet less operationally ready — a paradox the report’s authors attributed to weak data infrastructure, insufficient talent, and immature governance. Research published in Harvard Business Review, which maddaisy covered last week, found that AI tools were making employees busier rather than more productive, with efficiency gains absorbed by task expansion rather than redirected toward higher-value work.

    The ROI gap is what happens when these operational failures compound. Organisations adopt AI tools without redesigning workflows. They measure deployment (how many teams have access) rather than outcomes (what changed as a result). They invest in the technology layer while underinvesting in the organisational layer — the process changes, role redesigns, and measurement frameworks that turn a pilot into a production capability.

    A measurement problem masquerading as a technology problem

    One of the more revealing aspects of the PwC data is what it implies about how enterprises are tracking AI value. CEO revenue confidence sits at a five-year low of 30%, and this pessimism correlates with the inability to demonstrate AI returns. But the question is whether the returns genuinely are not there, or whether organisations simply lack the instrumentation to detect them.

    The answer, for many enterprises, is likely both. Some AI deployments are genuinely failing to deliver — deployed in the wrong processes, aimed at the wrong problems, or undermined by poor data quality. But others may be generating real value that never surfaces in the metrics that CEOs review. Time saved in middle-office processes, reduced error rates in document handling, faster iteration cycles in product development — these are real gains, but they rarely appear on a revenue line unless someone has built the measurement architecture to capture them.

    This is a familiar pattern in enterprise technology adoption. The early years of cloud computing saw similar complaints: organisations spent heavily on migration but struggled to quantify the business impact beyond infrastructure cost reduction. The value was real — in agility, speed to market, and developer productivity — but it took years for finance teams to develop frameworks that could track it. AI is following the same trajectory, with the added complication that its benefits are often diffuse, spread across many small improvements rather than concentrated in a single, measurable outcome.

    What the 12% are doing differently

    PwC’s survey found that the minority of organisations achieving both revenue and cost benefits from AI share common characteristics. They have invested in data foundations — not just data lakes and pipelines, but governance structures that ensure data quality, accessibility, and appropriate use. They have moved beyond isolated pilots to embed AI into core business processes. And they treat AI adoption as an organisational change programme, not a technology deployment.

    None of this is conceptually new. Consultancies have been advising clients on change management, data governance, and process redesign for decades. What is new is the speed at which the gap between leaders and laggards is widening. The 12% who have cracked the ROI equation are pulling ahead, using AI-generated insights to inform strategy, AI-automated processes to reduce costs, and AI-enhanced products to capture new revenue. The 56% who have not are still running pilots, still debating governance frameworks, and still struggling to answer the board’s most basic question: what are we getting for this money?

    The consulting industry’s uncomfortable position

    For the consulting sector, PwC’s findings create an awkward tension. The firms advising enterprises on AI strategy are, in many cases, the same firms whose clients cannot demonstrate returns. This is not necessarily a reflection of poor advice — the operational barriers to AI value are genuine and deep — but it does raise questions about what consulting engagements are actually delivering.

    As maddaisy noted when examining Capgemini’s recent results, CEO Aiman Ezzat explicitly framed the company’s direction as a shift “from AI hype to AI realism.” Generative AI bookings exceeded 8% of Capgemini’s total for the year, but the company’s 2026 revenue guidance fell slightly below analyst expectations — a reminder that even firms positioning AI at the centre of their strategy face questions about whether the investment is translating into proportional growth.

    The risk for consultancies is that the ROI gap erodes client confidence in AI-related engagements. If more than half of CEOs see no revenue benefit, the appetite for further AI spending — and the advisory services that accompany it — may tighten. The counter-argument, which PwC’s own data supports, is that the solution to poor AI returns is not less AI but better AI implementation. That is a consulting engagement waiting to happen, provided firms can credibly demonstrate that they know how to close the gap.

    What practitioners should watch

    Three developments will shape whether the AI ROI picture improves or deteriorates over the coming quarters.

    First, the regulatory environment is tightening. As maddaisy recently examined, the EU AI Act’s high-risk system requirements become enforceable in August 2026, and state-level legislation in the US is creating a fragmented compliance landscape. Compliance costs will add to the total cost of AI ownership, making the ROI equation harder to balance for organisations that have not already built governance into their deployment model.

    Second, the rise of agentic AI — systems that plan and execute tasks with minimal human oversight — will test whether organisations can capture value from more autonomous AI without losing control. Deloitte’s data showed a nearly fivefold increase in planned agentic AI deployments over the next two years, but only one in five companies has a mature governance model for autonomous agents. The ROI potential is significant; so is the risk of expensive failures.

    Third, and perhaps most importantly, watch for a shift in how organisations measure AI value. The enterprises that move beyond “did revenue go up?” to more granular metrics — cycle time reduction, error rate improvement, employee capacity freed for strategic work — will be better positioned to demonstrate returns and justify continued investment. The measurement framework may matter as much as the technology itself.

    PwC’s 56% figure is striking, but it is a snapshot of a transition, not a verdict on AI’s potential. The technology is not the bottleneck. Execution is. And for consultants and practitioners, that distinction is where the real work — and the real opportunity — lies.

  • Deploy First, Fix Later: How Poor AI Rollouts Are Engineering Burnout Into the System

    Over the past week, maddaisy has examined two dimensions of the AI-burnout nexus: the work intensification mechanisms that make employees busier rather than better, and the organisational frameworks that might address the people side of the equation. But there is a third dimension that sits upstream of both: the technology deployment itself.

    Before burnout becomes a management problem, it is often an implementation problem. And a growing body of evidence suggests that the way organisations roll out AI tools — rushed timelines, inadequate capability-building, and a persistent belief that go-live equals readiness — is baking unsustainable working conditions into the system from day one.

    The capability gap that no one budgets for

    A detailed analysis published by CIO.com this month draws on workforce upskilling data from large-scale enterprise rollouts to make a blunt assessment: systems rarely fail because the technology does not work. They fail because the organisation has not built the infrastructure to support the people using them.

    The numbers are sobering. Organisations routinely experience productivity drops of 30 to 40% within the first 90 days of a major technology go-live when workforce capability has not been adequately addressed. Support tickets triple. Workaround behaviours — offline spreadsheets, manual reconciliations, shadow systems — proliferate as employees revert to what feels safe and controllable. Post-go-live support costs run 40 to 60% over budget. ROI timelines slip by six to 12 months.

    None of this is caused by employee resistance or technological failure. It is the predictable consequence of treating capability-building as a training event rather than a systemic requirement.

    The 10% problem

    Perhaps the most striking data point concerns training transfer. Research on technology implementation training suggests that only 10 to 20% of skills learned in formal programmes translate into sustained on-the-job performance. The issue is not poor course design. It is the assumption that a three-day training session two weeks before go-live can substitute for the repeated practice, pattern recognition, and feedback loops that genuine capability requires.

    This matters directly for the burnout conversation. When employees lack the capability to operate new systems confidently, every minor issue becomes a support ticket. Every exception becomes an escalation. The cognitive load of navigating unfamiliar tools while maintaining output expectations creates exactly the kind of sustained pressure that Harvard Business Review’s recent research identified as driving work intensification. The tools are not the problem — the deployment is.

    Where AI deployments differ from traditional IT rollouts

    Enterprise technology deployments have always carried capability risks. But AI introduces specific complications that amplify them.

    First, AI tools change the scope of work, not just the process. As maddaisy’s earlier analysis noted, employees using AI do not simply do the same tasks faster — they absorb new responsibilities, blur role boundaries, and take on work that previously justified additional headcount. A traditional ERP deployment changes how someone does their job. An AI deployment can change what their job is. Upskilling programmes designed around process training cannot address a shift that is fundamentally about role redesign.

    Second, AI output is probabilistic, not deterministic. An ERP system produces the same result given the same inputs. An AI tool might produce different outputs each time, requiring users to exercise judgement about quality, accuracy, and appropriateness. That judgement cannot be trained in a classroom — it is built through experience, and it demands a kind of cognitive engagement that is qualitatively different from following a process manual.

    Third, AI raises the visibility of individual output. When an AI tool enables someone to produce a first draft in minutes rather than hours, the speed becomes the new baseline expectation. Employees who are still building capability with the tool face pressure to match output norms set by early adopters or by the tool’s theoretical capacity. The result is the self-reinforcing acceleration cycle that researchers have now documented repeatedly.

    Governance as deployment infrastructure

    The CIO.com analysis proposes treating upskilling as a governance concern rather than a training administration task — embedding capability-building into the transformation workstream with the same rigour as data migration or integration testing. This means defining capability in behavioural terms tied to business processes, starting practice in sandbox environments as soon as process designs stabilise, and measuring performance data rather than training completion rates.

    One practical recommendation stands out: establishing a “performance council” distinct from the training team. This group — composed of process owners, frontline managers, and high-performing end users — meets weekly to review whether people are performing reliably in live operations. They examine error rates, support ticket patterns, workaround behaviours, and time-to-competency. Critically, they have the authority to pause rollouts when the data shows capability is not sticking.

    This is not a radical proposal. It is the kind of operational discipline that well-run technology programmes have always applied to infrastructure and integration. The fact that it needs to be articulated separately for workforce capability suggests how often organisations skip the people side of deployment planning.

    Connecting the implementation layer to the burnout evidence

    The burnout data that has emerged over recent weeks tells a consistent story. DHR Global reports that 83% of workers experience some degree of burnout, with engagement dropping 24 percentage points in a single year. Only 34% of employees say their organisation has communicated AI’s workplace impact clearly. Among entry-level staff, that figure falls to 12%.

    These are not just management failures. They are deployment failures. When organisations ship AI tools without building the capability infrastructure to support them — without adequate training transfer, without performance monitoring, without role redesign — they are not just creating a change management problem. They are creating a technical debt of human capability that compounds over time, manifesting as burnout, disengagement, and quiet resistance.

    As Deloitte’s 2026 AI report made clear, the gap between strategic confidence and operational readiness remains the defining feature of enterprise AI. The implementation evidence suggests that closing this gap requires treating workforce capability not as a soft skill or an HR deliverable, but as core deployment infrastructure — as essential as the data pipeline and as measurable as system uptime.

    What this means for the next phase

    The organisations that avoid engineering burnout into their AI deployments will share a common trait: they will treat go-live as the beginning of capability-building, not the end. They will budget for the 90-day productivity dip and plan for it rather than being surprised by it. They will measure whether people can perform reliably at scale under real business pressure, not whether they completed a training module.

    Most importantly, they will recognise that the fastest way to undermine an AI investment is not a technical failure — it is deploying capable technology to an unprepared workforce and then measuring success by adoption rates alone. The tools work. The question, as it has always been with technology transformations, is whether the organisation around them does too.

  • The Missing Half of AI Adoption: Why Organisations Are Failing the People Side of the Equation

    The evidence that AI tools can intensify work rather than lighten it has been building steadily. As maddaisy examined earlier this week, UC Berkeley researchers found that employees using AI worked faster but not better — absorbing more tasks, blurring work-life boundaries, and entering a self-reinforcing cycle of acceleration. The diagnosis is now well-documented. The question that remains largely unanswered is what organisations should actually do about it.

    New data suggests that most are doing very little — and that the gap between leadership confidence and frontline reality is wider than many boards realise.

    The engagement crisis hiding behind adoption metrics

    A 2026 workforce trends report from DHR Global paints a stark picture. Some 83% of workers report experiencing at least some degree of burnout, with the technology sector among the worst affected at 58%. That figure has held roughly steady since 2025 — but what has changed is burnout’s impact on engagement. More than half of employees (52%) now say burnout actively reduces their engagement at work, up from 34% just a year earlier.

    Meanwhile, overall engagement has dropped sharply. Only 64% of workers describe themselves as very or extremely engaged, down from 88% in 2025. That 24-percentage-point decline should concern any organisation that has been measuring AI success purely by deployment velocity.

    The report also reveals a telling asymmetry in how AI communication is handled. Only 34% of employees say their organisation has communicated AI’s workplace impact “very clearly.” Among entry-level staff, that figure falls to just 12%. Among C-suite leaders, it rises to 69%. The people making decisions about AI adoption and the people living with its consequences occupy different informational worlds.

    Resistance is not the problem — silence is

    A common framing in boardrooms is that employees resist AI because they fear change. The reality, according to recent research published in Harvard Business Review, is more nuanced. Workers’ responses to AI map onto three core psychological needs: competence (feeling effective), autonomy (feeling in control), and relatedness (maintaining meaningful connections with colleagues).

    When AI deployment satisfies these needs — by removing drudgery and enabling more skilled work — adoption tends to follow. When it frustrates them — by making expertise feel disposable, reducing worker agency, or replacing human collaboration with human-machine interaction — the response is not always visible protest. It is quiet disengagement. Some 31% of knowledge workers admit to actively working against their company’s AI initiatives. Among Gen Z workers, that figure rises to 41%.

    Perhaps more telling: 54% of workers say they would use unapproved AI tools, and 32% hide their AI use from employers entirely. The adoption numbers that leadership teams celebrate may obscure a more complicated picture of how AI is actually being used — and resisted — on the ground.

    From diagnosis to framework

    If the problem is well-documented, the response toolkit is still emerging. Two frameworks have gained traction in the consulting and HR strategy space this year, and both share a common starting point: organisations need to treat AI adoption as a change management challenge, not a technology deployment exercise.

    The first, proposed by Ranganathan and Ye in their HBR study on work intensification, is the concept of “AI practice” — deliberate organisational norms around how AI is used. This includes structured pauses before major decisions (to counteract the speed bias that AI creates), sequencing work to reduce context-switching, and protecting time for human connection. The researchers argue that without intentional guardrails, the default trajectory is escalation: faster output, rising expectations, and eventual burnout.

    The second is the AWARE framework, developed by researchers studying psychological resistance to AI. It stands for: Acknowledge employee concerns proactively; Watch for both adaptive and maladaptive coping behaviours; Align support systems with the psychological needs that AI may be disrupting; Redesign workflows around human-AI complementarities rather than simple substitution; and Empower workers through transparency and genuine participation in implementation decisions.

    Neither framework is complicated. Both amount to structured versions of what good management has always looked like: listen to your people, involve them in decisions that affect their work, and pay attention to unintended consequences. The fact that these principles need to be formalised into acronyms and published in academic journals suggests how far many organisations have drifted from applying them during AI rollouts.

    The consulting opportunity — and obligation

    For consultancies advising on AI transformation, this data represents both a market opportunity and a professional obligation. The firms that positioned AI adoption as primarily a technical challenge — choose the right model, integrate the right data pipeline, deploy at scale — are leaving the harder, more valuable work on the table.

    The harder work is organisational. It involves job redesign, not just process automation. It requires mapping which tasks benefit from AI augmentation and which need to remain human — not as a one-off exercise, but as an ongoing practice. It means building managerial capability in areas that technology deployments rarely prioritise: emotional intelligence, coaching, and the ability to recognise cognitive overload before it becomes a retention problem.

    Only 44% of business leaders currently involve workers in AI implementation decisions. That figure alone explains much of the resistance and burnout data. People who have no say in how their work changes will inevitably feel that change is happening to them rather than with them — regardless of whether the technology itself is genuinely useful.

    What comes next

    The most thoughtful organisations are beginning to reframe AI not as a productivity multiplier but as a capacity management tool. The question shifts from “how can AI help people do more?” to “how can AI help people think more clearly, recover more effectively, and focus on what actually matters?” It is a subtle but significant distinction — one that moves the conversation from output volume to work quality and sustainability.

    As maddaisy noted in its analysis of Deloitte’s 2026 AI report, the gap between strategic confidence and operational readiness remains one of the defining features of enterprise AI. The burnout and engagement data suggests that this gap is not just a technology integration problem. It is, at its core, a people management problem — and one that will not be solved by deploying more tools faster.

    The organisations that get this right will not be the ones with the most AI models in production. They will be the ones that treated adoption as a human process from the beginning.

  • The Productivity Trap: Why AI Tools Are Making Employees Busier, Not Better

    The promise of AI in the workplace has always rested on a simple equation: automate the routine, free up humans for higher-value work. It is the pitch that has launched a thousand consulting engagements and underpinned billions in enterprise software investment. But new research suggests the equation may be running in reverse.

    A study published in Harvard Business Review this month, based on eight months of ethnographic research at a US technology company with roughly 200 employees, found that AI tools did not reduce workload. They intensified it. Employees worked faster, took on more tasks, and felt busier than before — despite the efficiency gains that AI was supposed to deliver.

    The researchers, Aruna Ranganathan and Xingqi Maggie Ye from UC Berkeley’s Haas School of Business, identified three distinct mechanisms through which AI was quietly ratcheting up the pressure.

    Task expansion: doing more with less becomes doing more with the same

    The first mechanism was task expansion. When AI tools made certain tasks faster, employees did not use the freed-up time for strategic thinking or creative work. Instead, they absorbed responsibilities that had previously belonged to other roles. Product managers and designers started writing code. Researchers took on engineering tasks. Workers assumed duties that, in the researchers’ words, “might previously have justified additional help.”

    This is a pattern that will be familiar to anyone who has watched organisations respond to efficiency gains over the past two decades. The spreadsheet did not eliminate accounting departments — it gave accountants more to do. Email did not reduce communication overhead — it multiplied it. AI appears to be following the same trajectory, with one critical difference: the speed at which task expansion occurs is considerably faster.

    The study also identified a secondary burden. Engineers found themselves spending additional time reviewing their colleagues’ AI-assisted work, a form of quality assurance that had not existed before because the work itself had not existed before.

    The vanishing boundary between work and rest

    The second mechanism was the blurring of work-life boundaries. Employees began incorporating work into what had previously been downtime — lunch breaks, gaps between meetings, even waiting for files to load. Because interacting with an AI tool felt, as one participant put it, “closer to chatting than to undertaking a formal task,” the psychological barrier to picking up work during off moments effectively disappeared.

    This is a subtler form of intensification, and arguably a more dangerous one. The physical cues that traditionally separated work from rest — closing a document, leaving a desk, switching off a screen — lose their power when work can be initiated through a conversational prompt on any device. The distinction between being productive and being available collapses.

    The multitasking illusion

    The third mechanism was increased multitasking. With AI handling parts of each task, employees managed multiple active threads simultaneously, creating what the researchers described as “continual switching of attention.” Worse, because AI made fast output visible to colleagues, it raised speed expectations across teams. Employees felt pressure not just to work with AI, but to keep pace with the output norms that AI made possible.

    The result was a self-reinforcing cycle: AI accelerated certain tasks, which raised expectations for speed, which made workers more reliant on AI, which widened the scope of what they attempted. As one engineer told the researchers, they felt “busier than before” despite the supposed time savings.

    Not new, but newly documented

    It is worth noting that the HBR findings are not entirely without precedent. Research from the University of Chicago and the University of Copenhagen published last year found that AI chatbots saved workers only about an hour per week — and that the tools created enough new tasks to largely nullify even that modest gain. What Ranganathan and Ye have added is the ethnographic depth: eight months of direct observation, 40 interviews, and a detailed account of the mechanisms through which intensification occurs.

    For consulting practitioners, this distinction matters. The earlier studies quantified the problem. This one explains the pathways. And that makes it actionable.

    Connecting the dots: from strategy gap to people gap

    The timing of this research is significant. As maddaisy noted last week, Deloitte’s 2026 State of AI in the Enterprise report revealed a widening gap between strategic confidence and operational readiness. Forty-two per cent of companies consider their AI strategy highly prepared, yet fewer feel equipped to execute it.

    The burnout research suggests one reason that gap persists: organisations are measuring AI adoption by deployment metrics — tools rolled out, processes automated, tasks per hour — while ignoring the human cost of that adoption. The operational readiness problem is not just about data pipelines and integration architecture. It is about whether the people using these tools can sustain the pace that the tools enable.

    This also has implications for the AI governance frameworks now coming into force across Europe and beyond. Most governance discussions focus on algorithmic bias, data privacy, and transparency. Workforce wellbeing — whether AI deployment is creating sustainable working conditions — barely features. As enforcement mechanisms sharpen, that gap may become harder to defend.

    What practitioners should watch

    The HBR researchers recommend what they call “AI practice” — a set of organisational disciplines designed to counteract intensification. These include intentional pauses (structured breaks for assessment), sequencing (deliberate pacing rather than continuous output), and human grounding (protected time for dialogue and connection).

    These are not revolutionary ideas. They are, in essence, good management practices adapted for an AI-augmented workplace. But the fact that they need to be articulated at all tells a story about how many organisations are deploying AI tools without thinking through the second-order effects on their people.

    For consultancies advising on AI transformation, this research is a prompt to broaden the conversation. Deployment is not the finish line. If the tools make employees faster but not better — busier but not more effective — then the productivity gains that justified the investment may prove temporary, eroded by turnover, cognitive fatigue, and declining work quality.

    The question, as the researchers put it, is not whether AI will change work, but whether organisations will actively shape that change — or let it quietly shape them.