maddaisy – cultivating innovation

Category: Technology

  • 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.

  • The Non-AI Agenda: What CIOs Are Actually Prioritising Beyond Artificial Intelligence in 2026

    Global IT spending will hit $6.15 trillion in 2026, a 10.8 per cent rise on the previous year. But dig beneath that headline and the distribution is strikingly lopsided. Gartner’s latest forecast projects AI spending to surge 80.8 per cent and data centre outlays to climb 31.7 per cent, while communications services and device budgets limp along at low single digits. The message is clear: AI is swallowing the budget. The question is what happens to everything else.

    That question matters because, as maddaisy has documented over recent weeks, the AI investment thesis is under strain. PwC’s 2026 CEO Survey found that 56 per cent of executives cannot point to measurable revenue gains from their AI programmes, and governance frameworks are lagging well behind deployment ambitions. If the technology absorbing most of the budget has yet to prove its return, the priorities being squeezed to fund it deserve closer scrutiny.

    The squeeze is real — and deliberate

    John-David Lovelock, a vice president analyst at Gartner, describes a dynamic in which runaway AI spending is forcing CIOs to find savings elsewhere — and IT services providers are bearing the brunt. The logic is straightforward: buyers expect their vendors to be using AI internally, and they want those efficiency gains reflected in lower fees.

    “CIOs need to find somewhere that they have control of their budget, and they can pick on the services companies because they’re using AI,” Lovelock told CIO.com.

    But not every non-AI line item can be trimmed without consequences. Several priorities are growing more urgent precisely because of AI adoption, not despite it.

    Cybersecurity: AI’s expanding attack surface

    The most immediate non-AI priority is, paradoxically, driven by AI itself. Dmitry Nazarevich, CTO at software firm Innowise, notes that his company’s security spending increase “is directly related to the increase in exposure and risk to data associated with the increased attack surface resulting from the introduction of generative AI.”

    This is not a theoretical concern. Every new AI model integrated into enterprise workflows introduces new vectors — data exfiltration through prompt injection, model poisoning through compromised training data, and the simple reality that agentic systems with write access to business processes can do real damage if they malfunction or are manipulated. As enterprises move from experimental AI pilots to production deployments, security spending is not optional — it is the prerequisite.

    Data foundations: the unsexy precondition

    Salesforce CIO Dan Shmitt offered a telling anecdote about an AI agent on the company’s help site that surfaced two conflicting answers to the same question. “Our first reaction was to assume the model was wrong,” he said. “The truth was that our data and content needed more consistency.”

    This pattern — blaming the model when the real problem is the data — is remarkably common. Capgemini’s TechnoVision 2026 framework places “thriving on data” as one of its nine foundational technology domains, emphasising data sharing, AI-driven insights, and sustainable data practices. The message is consistent across vendors and analysts: AI systems are only as reliable as the data infrastructure beneath them.

    For CIOs who have spent two years funding AI pilots, investing in data quality, master data management, and integration architecture may feel like a step backwards. It is not. It is the work that determines whether those pilots ever graduate to production.

    Technical debt and modernisation

    Legacy systems are not merely an annoyance in an AI-first world — they are a bottleneck. Nazarevich highlights that increased modernisation spending at Innowise is “partly a result of the fact that legacy or outdated systems limit the effectiveness of AI technology and delay deployment schedules.”

    This creates a compounding problem. Organisations that deferred modernisation to fund AI experiments now find that their AI initiatives are underperforming because the underlying platforms cannot support them. The CIO.com analysis of strategic imperatives for 2026 makes the point explicitly: if eight out of 10 strategic priorities relate to AI, “you’re likely missing some critical emerging technologies and trends.”

    Edge computing, digital twins, and platform modernisation may not generate the boardroom excitement of a generative AI demo, but they are the infrastructure on which AI capabilities depend.

    FinOps: managing the unpredictable bill

    AI workloads have introduced a new kind of cost unpredictability into IT budgets. Unlike traditional cloud computing, where usage patterns are relatively stable and forecastable, AI inference costs can spike with demand in ways that are difficult to model in advance. Innowise reports increased FinOps spending specifically because of “the unpredictability of computing bills created by AI workloads.”

    FinOps — the practice of bringing financial accountability to cloud spending — is no longer a niche discipline for cloud-native firms. For any organisation running AI at scale, it has become an essential management capability. Without it, the 80 per cent surge in AI spending that Gartner forecasts could easily overshoot, consuming budget earmarked for the very modernisation and security initiatives that AI requires to succeed.

    Workforce fluency: the persistent gap

    Technology investment alone solves nothing if the people using it are not equipped to do so effectively. Rebecca Gasser, global CIO at FGS Global, frames this as a literacy challenge: building digital and AI fluency across the organisation so that workers “can be more agile and adaptable to the ongoing changes.”

    Pat Lawicki, CIO of TruStage, puts it more directly: “We’re committed to balancing innovation with humanity: leveraging digital tools where they add real value while preserving the human connection that defines trust and empathy.”

    This is consistent with the pattern maddaisy has tracked in recent coverage of AI-driven burnout and the organisational failures behind poor AI rollouts. The technology works best when employees understand it, trust it, and can exercise judgement about when to rely on it and when to intervene. That requires sustained investment in training, change management, and communication — none of which appear in an AI spending forecast but all of which determine whether the forecast delivers value.

    The convergence argument

    The most sophisticated framing of the CIO’s 2026 agenda comes not from treating AI and non-AI priorities as competitors for budget, but from recognising their interdependence. Capgemini’s TechnoVision 2026 describes a shift toward “synchronicity at scale” — the idea that boundaries between digital, physical, and biological innovation are dissolving, and that the CIO’s job is to orchestrate across all of them simultaneously.

    In practice, this means cybersecurity investment protects AI deployments. Data foundation work makes AI outputs reliable. Modernisation enables AI to reach production. FinOps keeps AI costs sustainable. Workforce fluency ensures AI adoption sticks.

    The CIOs who treat 2026 as an AI-only year will likely find themselves explaining, 12 months from now, why their AI investments still are not delivering returns. The ones who invest in the full stack — the security, the data, the infrastructure, the people — are building the conditions under which AI can actually work.

    That is not a story about choosing between AI and everything else. It is a story about understanding that AI does not operate in isolation, and neither should the budget that funds it.

  • SAP’s “Break Glass” Cloud Plan Exposes the Limits of European Digital Sovereignty

    SAP, Microsoft, Capgemini, and Orange have announced a joint contingency plan for European cloud services — a “break glass” option in case US hyperscalers are legally blocked from operating in Europe. The partnership, routed through SAP’s German subsidiary Delos Cloud and the French entity Bleu (co-owned by Capgemini and Orange), promises business continuity in crisis scenarios ranging from sanctions to military conflict.

    It is a notable development, and it connects directly to the sovereignty narrative maddaisy.com has been tracking. But before treating it as a solution, it is worth examining what the plan actually offers — and what analysts say it cannot.

    The deal in context

    When maddaisy examined Capgemini’s sovereignty strategy earlier this month, the picture was clear: European digital sovereignty is converging on a pragmatic middle ground. Rather than building independent infrastructure from scratch, European firms are positioning themselves as trusted operators running workloads on American hyperscaler platforms — sovereign in governance and operations, reliant on US technology underneath.

    The SAP-Microsoft-Capgemini-Orange agreement is the logical extension of that approach. SAP’s announcement describes a mutual assistance framework where Delos Cloud and Bleu would cooperate on cross-border crisis response, including “early detection, analysis, defence, and remediation of cyber incidents.” Separately, Delos Cloud and Microsoft signed a business continuity agreement allowing Delos to access source code and maintain operations if sanctions restrict Microsoft’s European services.

    In other words: if the worst happens, European operators would run a local copy of Azure, disconnected from Microsoft’s global network.

    The wildcard is Washington, not Brussels

    Analysts are broadly aligned on one point: the EU itself is highly unlikely to block American cloud providers. Some 75% of the European cloud market sits with US hyperscalers, according to Forrester senior analyst Dario Maisto. Cutting off that access would amount to economic self-harm on a significant scale.

    The real concern is the reverse scenario — the US government using its leverage over hyperscalers to pressure European governments. As Maisto put it to CIO: “What if the US administration pulls the kill switch? It would be the weaponisation of IT, because the US knows about this dependency.”

    Danilo Kirschner, managing director at European cloud consulting firm Zoi, was blunter: “There have been non-logical, nonsensical decisions in the past year. From a European perspective, we need to prepare for anything.”

    The likelihood of such a scenario remains low. But the fact that SAP and Microsoft are publicly planning for it signals that enterprise customers are asking uncomfortable questions — and expect answers.

    A lifeboat, not a luxury liner

    The technical reality is where the plan runs into difficulty. Running a severed version of Azure in a European data centre sounds feasible in a press release. In practice, as Kirschner explained, Azure is millions of lines of code updated daily. Disconnected from Microsoft’s global security intelligence, engineering updates, and optimisation pipelines, a local copy would degrade rapidly.

    “This is a lifeboat, not a luxury liner,” Kirschner said. “Your disaster recovery plans must account for the fact that a sovereign cloud in crisis mode will likely be a static, maintenance-only environment.”

    The hardware question compounds the problem. Azure runs on proprietary, custom-designed server infrastructure. If geopolitical tensions are severe enough to block software access, sourcing replacement hardware under the same sanctions regime becomes equally difficult. And if a crisis lasts months rather than weeks, the global Azure platform will have evolved while the European fork remains frozen — creating what Kirschner described as “a technological dead end that requires a total rebuild to reconnect.”

    Even the legal framework is untested. “This agreement will have to be tested in court once the problem happens, when it could be too late,” Maisto noted. “This is not compliance as much as risk management.”

    The sovereignty paradox deepens

    There is an irony at the heart of this deal that Kirschner identified clearly: by offering a break-glass option for European sovereignty, Microsoft has paradoxically strengthened its own position. The single biggest political risk to using American hyperscalers in the European public sector — the theoretical possibility of a forced disconnection — has been partially neutralised. European governments and enterprises can now point to a contingency plan, however imperfect, and continue building on US infrastructure.

    As maddaisy’s earlier analysis of Capgemini’s sovereignty framework noted, Capgemini CEO Aiman Ezzat has been candid that “there is no such thing as absolute sovereignty” because no entity controls the entire value chain. The SAP deal underscores that position. Europe is not building an alternative to American cloud infrastructure. It is building contingency plans that assume American cloud infrastructure remains the default.

    For hardliners in France and elsewhere who want European-built alternatives at the highest sovereign classification levels, this approach will be unsatisfying. But the practical question — what is the alternative? — remains unanswered. The European Cybersecurity Certification Scheme continues to evolve, yet the gap between regulatory ambition and infrastructure reality shows no sign of closing.

    What practitioners should take from this

    For enterprise architects and CIOs managing European workloads, the SAP-Microsoft-Capgemini deal changes the conversation without changing the underlying calculus. It provides a political answer to a political risk — a contingency plan that reassures procurement committees and satisfies sovereignty checkboxes. It does not, however, solve the fundamental dependency.

    The practical takeaway is threefold. First, organisations should treat this as risk management, not a guarantee — the plan’s viability in a real crisis remains unproven and potentially short-lived. Second, workload portability and multi-cloud strategies become more important, not less, in a world where even the contingency plans assume degraded service. Third, the sovereignty requirements that Capgemini estimated would feature in over 50% of European service contracts by 2029 are becoming structurally embedded in how deals are structured — and this agreement is part of that shift.

    Europe’s cloud sovereignty story is not moving toward independence. It is moving toward managed dependency, with increasingly elaborate safety nets. Whether those nets would hold under real stress is a question no one can answer yet — and the honest participants in this deal are not pretending otherwise.

  • From Stablecoins to Smart Settlement: How Distributed Ledger Technology Is Quietly Rebuilding Financial Plumbing

    For years, distributed ledger technology sat in the awkward space between genuine innovation and inflated expectations. Blockchain was going to revolutionise everything from supply chains to voting systems, or so the pitch decks claimed. Most of those promises went nowhere. But while the hype cycle burned itself out, something quieter and more consequential was happening: major financial institutions started rebuilding their core infrastructure around the technology, and regulators began writing the rules to let them do it at scale.

    The result, now visible across multiple industry reports and institutional moves in early 2026, is that distributed ledger technology has become financial plumbing — not a speculative asset class, not a retail phenomenon, but the infrastructure layer that processes, settles, and records transactions between institutions. The shift is less dramatic than the headlines of 2021 suggested, but arguably more significant.

    The Regulatory Unlock

    The single biggest catalyst has been regulatory clarity. In the United States, the GENIUS Act, signed into law in July 2025, established the first comprehensive framework for stablecoins. It requires permitted payment stablecoin issuers to maintain 100% reserve backing with liquid assets, submit to federal or state supervision, and implement anti-money laundering programmes equivalent to those of traditional banks.

    The effect was immediate. According to BDO’s 2026 fintech predictions, stablecoin transaction volumes surged from $6 billion in February 2025 to $10 billion by August, a direct consequence of regulatory certainty reducing institutional hesitancy. In Europe, the Markets in Crypto-Assets regulation (MiCA) added a parallel framework, creating a passportable licensing regime for crypto-asset service providers across the EU.

    This matters because the absence of clear rules was the primary barrier to institutional adoption. Banks and asset managers were not sceptical of the technology itself — they were sceptical of deploying it without knowing which regulations would apply. That uncertainty is now largely resolved in the two largest financial markets.

    Institutional Moves, Not Startup Promises

    The institutions acting on this clarity are not fintech startups chasing venture capital. They are some of the largest names in global finance.

    JPMorgan Chase launched its JPMD deposit token on Coinbase’s Base blockchain in 2025, representing US dollar deposits on a distributed ledger — a significant step toward tokenised banking. Visa piloted stablecoin payouts through Visa Direct, enabling businesses to send payments directly to stablecoin wallets. Mastercard has been investing in crypto transaction processing infrastructure, including its reported interest in acquiring Zero Hash.

    These are not experiments. They are strategic commitments backed by significant capital. When a bank the size of JPMorgan puts deposit tokens on a public blockchain, it signals that the technology has passed internal risk assessments, compliance reviews, and board-level scrutiny. The pilot phase, for these institutions at least, is over.

    What the Technology Actually Changes

    The practical impact centres on three areas: settlement speed, asset accessibility, and cross-border payments.

    Settlement is the most consequential. Traditional securities settlement operates on a T+1 or T+2 cycle — transactions take one to two business days to finalise. Distributed ledger technology enables near-instant settlement, reducing counterparty risk and freeing up capital that would otherwise be locked during the settlement window. For large institutions managing billions in daily transactions, even marginal improvements in settlement efficiency translate to meaningful cost savings.

    Tokenisation is extending access to asset classes that were previously illiquid or inaccessible to smaller investors. Real estate, private equity, commodities, and corporate bonds are being represented as digital tokens on distributed ledgers, enabling fractional ownership. BDO notes that analysts estimate more than $30 billion of assets are now tokenised globally, with Standard Chartered projecting a multi-trillion-dollar market as the technology matures. The practical effect is that an investor can now purchase a fraction of a commercial property or a private equity fund for as little as $1,000, something that was structurally impossible under traditional ownership models.

    Cross-border payments stand to gain the most from stablecoin infrastructure. Transfers that previously took days and incurred significant fees through correspondent banking networks can now settle in seconds at a fraction of the cost. For businesses operating across multiple jurisdictions, particularly in emerging markets with currency volatility, this is a material operational improvement.

    The Barriers That Remain

    None of this means the transition is complete or without friction. Several significant challenges persist.

    Accenture’s 2026 banking trends report estimates that $13 trillion in transaction value could shift to alternative payment methods by 2030, putting approximately $13 billion in payment fees at risk for traditional banks. That creates a powerful incentive for incumbents to adopt the technology, but also a defensive posture that can slow genuine transformation. Seventy-six per cent of financial institutions surveyed reported they still have work to do to enable smart money capabilities.

    Regulatory fragmentation is another concern. While the US and EU have made progress, global alignment is far from complete. Different jurisdictions impose different AML and KYC requirements, creating compliance complexity for institutions operating across borders. BDO’s analysis highlights that sponsor banks are now demanding detailed AML compliance specifications from fintech partners before deals can proceed — a sign that the industry is maturing, but also that the compliance burden is substantial.

    Cybersecurity risk is also evolving alongside the technology. As more value moves onto distributed ledgers, the attack surface expands. Financial services already accounts for 33% of all AI-powered cyberattacks, and blockchain systems introduce additional vectors around consensus protocols and smart contract vulnerabilities. The security infrastructure needs to mature at the same pace as the financial infrastructure.

    What Practitioners Should Watch

    For consultants and technology leaders advising financial services clients, the key shift is one of framing. Distributed ledger technology is no longer a conversation about cryptocurrency adoption. It is a conversation about infrastructure modernisation — how institutions settle trades, manage assets, process payments, and comply with regulation.

    The practical questions for 2026 are specific: Should a bank position itself as a stablecoin issuer, custodian, or facilitator? How does tokenisation change the custody and compliance requirements for an asset management firm? What does near-instant settlement mean for treasury operations and capital allocation?

    These are not speculative questions. They are operational ones, driven by technology that is already deployed and regulation that is already in force. The organisations that treat distributed ledger technology as infrastructure — rather than innovation theatre — will be the ones that capture the efficiency gains and competitive advantages it offers. The rest will find themselves paying someone else’s transaction fees.

  • 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.