maddaisy – cultivating innovation

Category: Technology

  • Open Protocols Promise AI Flexibility, But Platform Partnerships Define Delivery Advantage

    Enterprise AI orchestration faces a fundamental tension between technological flexibility and delivery complexity. While EY’s Canvas platform now processes 1.4 trillion lines of audit data annually and Model Context Protocol deployment spans 10,000+ enterprise servers, the promise of vendor flexibility through open standards is colliding with a harder reality: successful AI delivery depends on platform partnerships, not protocol freedom.

    As we reported in June, consulting firms that embed in outcome-validated platforms capture the most strategic margin. The April 2026 data now reveals why: Adobe’s formal partnerships with nine system integrators and seven AI model providers signal that vendors view professional services firms as mandatory delivery channels, not optional implementers.

    From Tool Selection to Strategic Partnership

    The shift is structural, not incremental. Where enterprises once procured AI tools through IT departments, they now select strategic partners whose vendor relationships define delivery capability. Choosing an agentic AI vendor in 2026 is fundamentally different, according to AI practitioner Kai Waehner. “Unlike a CRM or an ERP, an AI vendor is not just a tool you deploy. It is a strategic partner whose safety culture, governance model, and long-term ambitions will directly influence the reliability and trustworthiness of your most critical business processes.”

    Adobe’s April announcement exemplifies this shift. The company has established formal partnerships with Accenture, Capgemini, Cognizant, Deloitte Digital, EY, IBM, Infosys, PwC, and TCS – creating an ecosystem where consulting firms become the primary go-to-market channel for enterprise AI adoption. The partnership structure positions consulting firms as the implementation layer that handles change management, governance architecture, and cross-functional coordination required for enterprise deployment.

    The Open Standards Paradox

    Model Context Protocol (MCP) and Agent-to-Agent (A2A) standards are being deployed across thousands of enterprise environments, promising interoperability between model providers like OpenAI, Anthropic, and Google. The technical flexibility is real – enterprises can now orchestrate multiple AI models without rebuilding integration layers for each vendor switch.

    But this flexibility introduces governance complexity that few organisations are prepared to manage. HCLTech’s survey of 467 senior executives reveals that 43% of major AI initiatives are expected to fail, driven not by model quality or tool access, but by “governance gaps, technical debt, integration pitfalls, or vendor lock-in” management failures.

    Open protocols solve the vendor selection problem but create an architecture management problem. Enterprises now need specialists who can design multi-vendor governance frameworks, manage the integration dependencies, and navigate the trust implications of distributing critical workflows across multiple AI providers. This requirement favours consulting firms with proven platform delivery experience over those offering point-solution deployment.

    Platform-Centric Delivery Wins

    The evidence from production deployments confirms platform-centric delivery as the competitive advantage. Salesforce Agentforce deployments achieve 84% case resolution improvements in production environments, while EY’s Canvas platform supports 130,000 professionals across 160,000 global engagements. These outcomes reflect integrated platform approaches that bundle orchestration, governance, and change management into a single delivery framework.

    The time-to-value pressure is intensifying this platform preference. HCLTech’s research shows that nearly 50% of enterprise leaders expect measurable value from AI investments within 18 months. Custom integrations of multiple point tools rarely meet this timeline, while platform-based approaches with proven governance models can deliver results within enterprise expectations.

    For professional services firms, this creates a clear strategic choice: build delivery practices around proven platforms with established vendor partnerships, or compete on custom integration capability in an increasingly commoditised market.

    Governance as Competitive Differentiator

    As we noted in May, regulatory compliance is forcing governance onto the critical path of AI delivery. The April 2026 production data shows this creating a deeper structural shift: governance capability is becoming the primary differentiator between consulting firms that capture strategic margin and those competing on implementation hours.

    Waehner’s framework positions vendor selection around two dimensions: trust in the vendor’s AI capabilities and tolerance for vendor lock-in. But this analysis misses the third dimension that consulting firms must navigate: governance architecture across multi-vendor environments. As Capgemini’s Mark Roberts notes, 2026 represents a shift “from innovation theatre to a more mature focus on real, practical deployment” where “integration rather than invention” defines success.

    Professional services firms that position themselves as governance and integration specialists rather than tool deployers capture the highest-value engagements. They architect for compliance, design for multi-vendor orchestration, and manage the organisational change required to sustain agentic AI at scale.

    What This Means for Professional Services

    The implications centre on three shifts evident in the production data and vendor partnership announcements. First, vendor partnerships have become part of go-to-market value propositions. The formal ecosystem partnerships announced by Adobe, Microsoft, Google, and AWS create delivery channels that differentiate consulting firms’ ability to scale AI implementations.

    Second, as Waehner’s vendor selection framework demonstrates, technical interoperability through open protocols does not reduce complexity – it redistributes it. MCP and A2A standards enable flexibility if governance and integration architecture can support it, but the 43% failure rate for major AI initiatives suggests most enterprises cannot yet manage multi-vendor orchestration effectively.

    Third, the data suggests that execution gaps rather than tool limitations drive the high failure rates for AI initiatives. Professional services firms that focus on change management, stakeholder alignment, and governance maturity rather than tool sophistication are better positioned to deliver within enterprise timelines and capture strategic consulting margin.

    The promise of open standards eliminating vendor lock-in reflects a structural trade-off rather than a pure benefit. This points to a shift from vendor dependency to architectural complexity and integration risk. But for consulting firms with the governance and platform delivery capability to manage that complexity, this shift creates sustainable competitive differentiation in an increasingly crowded market.

  • Enterprise AI Finds Its Middle Ground: How Pega Blueprint Solves the Governance Gap

    Enterprise software development has a governance problem. Teams want the speed of AI-powered “vibes coding” – where natural language commands generate working applications – but enterprise requirements demand the control and predictability that casual AI tools cannot provide. Pegasystems’ latest update to its Pega Blueprint platform offers a glimpse of how this tension might resolve.

    The March 2026 Blueprint update transforms what was originally a linear design-to-handoff tool into a continuous conversational interface. Users can now modify enterprise applications through natural language – via text or speech – while maintaining the governance standards that large organisations require. It is a practical solution to what consultancies increasingly encounter: clients who want AI development speed but cannot sacrifice compliance and control.

    From Static Design to Living Conversation

    When Pega Blueprint launched in February 2024, it addressed a specific pain point: the slow, expensive upfront process of designing enterprise applications. The original version followed a conventional pattern – users described their application idea, Blueprint’s AI generated a structured starting point, then handed it to developers.

    The conversational update represents a different approach entirely. Rather than a one-time design exercise, Blueprint becomes what the company calls a “continuous copilot” – an interface that allows ongoing modification and refinement through natural language while preserving enterprise-grade security and governance requirements.

    “Organizations can create workflows more quickly, improve data and logic, and preserve control and predictability across mission-critical applications,” according to the company’s announcement. This combination – speed with governance – addresses what has become a fundamental challenge for enterprise AI adoption.

    The Consulting Opportunity  

    For consultancies, this development signals both opportunity and competitive pressure. The broader market context supports this view: recent Capgemini research shows that 85% of corporate clients plan to engage with non-bank providers within the next year, while only 23% believe traditional banks meet current expectations.

    The data reveals a broader pattern: organisations are seeking more agile, technology-forward partners. Traditional providers struggle – 82% of banking executives report no revenue gains from new products, and 51% see no expected cost reductions from innovation initiatives. Only 29% of IT budgets are directed toward transformative technologies.

    This creates space for consultancies that can effectively bridge the gap between AI capabilities and enterprise requirements. The challenge is not just technical – it is organisational. As Fortune reported, AI companies have discovered they need consultants to help sell their AI agents, as effective AI implementation requires significant organisational transformation: cleaning up data, redesigning workflows, and strategic thinking about competitive advantage.

    The Implementation Reality

    The consulting industry’s relationship with AI has evolved in an unexpected direction. Rather than eliminating consulting roles, AI complexity has created new demand for implementation services. OpenAI employs approximately 70 “forward deployed engineers” for customer implementation, and Anthropic maintains a similar number of implementation specialists.

    “AI still suffers from a trust deficit – most boards would still rather put their faith in advice from McKinsey or BCG than ChatGPT,” the Fortune analysis noted. This trust gap creates opportunities for consultancies that can position themselves as essential partners in AI implementation.

    The Pega Blueprint evolution illustrates this dynamic clearly. The platform promises that completed blueprints can be deployed as working workflows “in minutes,” but the enterprise governance layer – security, compliance, audit trails, role-based permissions – requires careful implementation and ongoing management.

    Market Signals

    Financial markets are taking a measured view of these developments. Citigroup raised its price target for PEGA stock to £75 from £73, citing “stable Q4 software results” and the company’s position in “defensive end markets.” The modest adjustment and “stable” language suggest measured progress rather than breakthrough momentum.

    This reflects a broader pattern in enterprise AI adoption: incremental evolution rather than revolutionary transformation. Conversational interfaces for development are not new, but solving the enterprise governance problem while maintaining development speed represents meaningful progress.

    What This Means for Consultancies

    The Blueprint update signals several trends worth monitoring. First, expect similar conversational interfaces to appear across enterprise platforms. The pattern – natural language interaction with robust governance – addresses a real market need.

    Second, the consulting market appears to be splitting between firms that can deliver AI-assisted solutions with enterprise governance and those constrained by traditional delivery models. The 85% of corporate clients planning to engage non-traditional providers suggests demand for more agile implementation partners.

    Third, the governance gap creates a consulting opportunity. Organisations need partners who understand both AI capabilities and enterprise requirements – not just one or the other. This requires a different skill set from traditional systems integration: understanding AI model behaviour, data governance for machine learning, and the organisational change required for AI-assisted workflows.

    The Pega Blueprint evolution represents enterprise AI finding its practical middle ground. For consultancies, the question is whether they can navigate this balance effectively – delivering AI-powered innovation without sacrificing the control and predictability that enterprise clients require. The market opportunity appears significant, but it demands a more sophisticated approach than either pure AI enthusiasm or traditional enterprise delivery.

  • 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.

  • 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.

  • 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.

  • Vibe Coding Enters the Enterprise. The Governance Question Follows It In.

    When Andrej Karpathy coined the term “vibe coding” in early 2025, he was describing something informal — a developer giving in to the flow of conversation with an AI assistant, accepting whatever code it generated, and iterating by feel rather than by specification. It was a shorthand for a new way of working that felt more like directing than engineering.

    Fourteen months later, the term has migrated from developer Twitter into enterprise press releases. Pegasystems announced this week that its Blueprint platform now offers an “end-to-end vibe coding experience” for designing mission-critical workflow applications. Salesforce has embedded similar capabilities into Agentforce. Gartner, in a May 2025 report titled Why Vibe Coding Needs to Be Taken Seriously, predicted that 40 per cent of new enterprise production software will be created using vibe coding techniques by 2028. What started as a solo developer’s guilty pleasure is being repackaged as an enterprise strategy.

    The question is whether the repackaging addresses the risks, or merely relabels them.

    From Slang to Sales Pitch

    The appeal of vibe coding in an enterprise context is straightforward. Natural language replaces formal specification. Business users can describe what they want in conversational terms — a workflow, an approval chain, a customer-facing process — and an AI assistant translates that intent into a working application. Development cycles that previously took months collapse into days or hours. Stakeholder alignment happens at the prototype stage rather than after months of requirements gathering.

    Pega’s implementation illustrates the model. Users converse with an AI assistant using text or speech to design applications, refine workflows, define data models, and build interfaces. They can switch between conversational input and traditional drag-and-drop modelling at any point. Completed designs deploy directly into Pega’s platform as live, governed workflows. The company’s chief product officer, Kerim Akgonul, framed it as “the excitement and speed of vibe coding” combined with “enterprise-grade governance, security, and predictability.”

    That framing is telling. Enterprise vendors are not adopting vibe coding wholesale — they are domesticating it. The original concept involved a developer accepting AI-generated code on trust, with minimal review. The enterprise version keeps the conversational interface but routes the output through structured frameworks, predefined best practices, and platform-level guardrails. Whether that still qualifies as vibe coding or is simply a new marketing label for low-code development with an AI front end is an open question.

    The Numbers Behind the Hype

    Gartner’s 40 per cent prediction is eye-catching, but it deserves scrutiny. The firm also projects that 90 per cent of enterprise software engineers will use AI coding assistants by 2028, up from under 14 per cent in early 2024. These are not niche forecasts — they describe a wholesale transformation of how software gets built.

    The market signals support the direction. Y Combinator reported that a quarter of its Winter 2025 startup cohort had codebases that were 95 per cent AI-generated. AI-native SaaS companies are achieving 100 per cent year-on-year growth rates compared with 23 per cent for traditional SaaS. Pega’s own Q4 2025 results showed 17 per cent annual contract value growth and a 33 per cent surge in cloud revenue, with management attributing much of the acceleration to Blueprint adoption.

    But there is a less comfortable set of numbers. A Veracode report from 2025 found that nearly 45 per cent of AI-generated code introduced at least one security vulnerability. Linus Torvalds, creator of Linux, publicly cautioned that vibe coding “may be a horrible idea from a maintenance standpoint” for production systems requiring long-term support. And Gartner’s own research acknowledges that only six per cent of organisations implementing AI become “high performers” achieving significant financial returns.

    The Shadow Already Has a Name

    For regular readers of maddaisy, these risks will sound familiar. When we examined shadow AI in February, the data showed 37 per cent of employees had already used AI tools without organisational permission — including coding assistants plugged into development environments without security review. Vibe coding, in its original ungoverned form, is essentially shadow AI with a better name.

    The enterprise vendors’ pitch — governed vibe coding, with guardrails — is a direct response to this problem. Rather than fighting the tide of developers and business users reaching for AI-assisted tools, platforms like Pega and Salesforce are channelling that energy through controlled environments. It is the same pattern that played out with cloud computing a decade ago: shadow IT became sanctioned cloud adoption once the governance frameworks caught up.

    The difference this time is speed. Cloud adoption played out over years. Vibe coding is moving in months. And as maddaisy’s coverage of agentic AI drift highlighted, AI-generated systems do not fail suddenly — they degrade gradually, in ways that are harder to detect than traditional software failures. An application built through conversational prompts, where the development team may not fully understand the underlying logic, amplifies that risk considerably.

    The Governance Gap Is the Real Story

    The enterprise vibe coding pitch rests on a critical assumption: that platform-level guardrails can substitute for developer-level understanding. In regulated industries — financial services, healthcare, government — this assumption will be tested quickly and publicly.

    The immediate challenge is not whether vibe coding works in a demo. It clearly does. The challenge is what happens six months into production, when the original conversational prompts have been refined dozens of times, the underlying models have been updated, and the people who designed the workflows have moved on. That is the maintenance problem Torvalds flagged, and it maps directly onto the agentic drift pattern: small, individually reasonable changes accumulating into a system whose behaviour no longer matches its original intent.

    Consultants and technology leaders evaluating vibe coding platforms should be asking three questions. First, can you audit the reasoning chain — not just the output, but why the system built what it built? Second, what happens when the AI model underneath is updated — does the application need to be revalidated? Third, who owns the maintenance burden when the person who “vibe coded” the application is no longer available?

    What to Watch

    Enterprise vibe coding is not a fad. The productivity gains are real, the vendor investment is substantial, and the Gartner forecasts — even if directionally approximate — point to a genuine shift in how software gets built. PegaWorld 2026, scheduled for June in Las Vegas, will likely showcase dozens of enterprise vibe coding implementations.

    But the narrative developing around it echoes the early days of every enterprise technology wave: speed first, governance second. The organisations that get this right will be those that treat vibe coding as a development interface, not a development shortcut — using the conversational speed to accelerate design while maintaining the engineering discipline to ensure what gets built can be understood, audited, and maintained over time.

    The vibes are entering the enterprise. The question is whether the rigour follows them in.

  • AI Inference Is the Enterprise Security Risk Most Organisations Are Not Addressing

    Most enterprise AI security conversations still focus on training — how models are built, what data goes in, how to prevent poisoning. But the greater operational exposure sits elsewhere: in inference, the moment a trained model processes a live query and produces an output. That is where proprietary logic, sensitive prompts, and business strategy become visible to anyone watching the traffic.

    A recent panel hosted by The Quantum Insider, featuring leaders from BMO, CGI, and 01Quantum, put the point bluntly: inference is AI working, and AI working is where risk accumulates. Nearly half of the audience polled during the session admitted they lack confidence that their AI systems meet anticipated 2026 security standards. That number is consistent with broader industry data: a Cloud Security Alliance survey found that only 27 per cent of organisations feel confident they can secure AI used in core business operations.

    This is not an abstract concern. It is the practical, operational end of the governance conversation maddaisy has been tracking for weeks.

    Why inference, not training, is the exposure point

    Training happens once (or periodically). Inference happens continuously — every API call, every chatbot interaction, every agentic workflow execution. As Tyson Macaulay of 01Quantum explained during the panel, inference models often contain the distilled intellectual property of an organisation. In expert systems, the model itself reflects proprietary training data, domain knowledge, and internal logic. Reverse engineering an inference endpoint can reveal insights about what the organisation knows and how it thinks.

    But the exposure runs in both directions. Prompts themselves reveal information — about individuals, strategy, and operational priorities. A medical query reveals personal health data. A corporate query may signal product development direction. The question, in other words, can be as sensitive as the model.

    When maddaisy examined CIOs’ non-AI priorities in February, cybersecurity topped the list — precisely because AI adoption was expanding the attack surface. Dmitry Nazarevich, CTO at Innowise, described security spending increases as “directly related to the increase in exposure and risk to data associated with the increased attack surface resulting from the introduction of generative AI.” Inference security is where that expanding surface is most exposed — and most neglected.

    The shadow AI dimension

    The problem is compounded by what organisations cannot see. Research suggests that roughly 70 per cent of organisations have shadow AI in use — employees running unauthorised tools outside IT oversight. Every unsanctioned ChatGPT or Claude query involving company data is an unmonitored inference event, pushing proprietary information through systems the organisation does not control.

    JetStream Security, a startup founded by veterans of CrowdStrike and SentinelOne, raised $34 million in seed funding last week to address precisely this gap. The company’s product, AI Blueprints, maps AI activity in real time — which agents are running, which models they use, what data they access. The premise is straightforward: you cannot secure what you cannot see.

    When maddaisy covered shadow AI in February, the focus was on governance and policy. Inference security adds a harder technical dimension. It is not enough to write policies about acceptable AI use if the organisation has no visibility into what models are being queried, by whom, and with what data.

    Real-world vulnerabilities are already surfacing

    The risks are not hypothetical. In February, LayerX Security published a report describing a critical vulnerability in Anthropic’s Claude Desktop Extensions — a malicious calendar invite could silently execute arbitrary code with full system privileges. The issue stemmed from an architectural choice: extensions ran unsandboxed with direct file system access, enabling tools to chain actions autonomously without user consent.

    The debate that followed was instructive. Anthropic argued the onus was on users to configure permissions properly. Security researchers countered that competitors like OpenAI and Microsoft restricted similar capabilities through sandboxing and permission gates. The real lesson for enterprises is that inference-layer vulnerabilities are architectural, not incidental — and they require controls before deployment, not after.

    As Rock Lambros of RockCyber put it: “Every enterprise deploying agents right now needs to answer — did we restrict tool chaining privileges before activation, or did we hand the intern the master key and go to lunch?”

    The governance gap has a security-shaped hole

    Maddaisy has covered the emerging agentic AI governance playbook extensively — the frameworks from regulators, the principles converging around least-privilege access and real-time monitoring. But frameworks are policy instruments. Inference security is the engineering layer that makes those policies enforceable.

    The numbers illustrate the disconnect. According to the latest governance statistics compiled from major 2025-26 surveys, 75 per cent of organisations report having a dedicated AI governance process — but only 26 per cent have comprehensive AI security policies. Fewer than one in 10 UK enterprises integrate AI risk reviews directly into development pipelines. Governance without security controls is aspiration without implementation.

    The financial services sector offers a partial model. Kristin Milchanowski, Chief AI and Data Officer at BMO, described her bank’s approach during the Quantum Insider panel: bringing large language models in-house where possible, ensuring that additional training on proprietary data remains contained, and treating responsible AI as a board-level cultural priority rather than a compliance exercise. But BMO operates under some of the strictest regulatory regimes globally. Most enterprises do not face equivalent pressure — yet.

    What practitioners should be doing now

    The practical agenda emerging from this convergence of research is specific and actionable:

    Audit inference endpoints. Map every production AI system, including shadow deployments. The JetStream model — real-time visibility into which models are running, what data they touch, and who is responsible — is becoming table stakes.

    Apply least-privilege to AI agents. The agentic governance frameworks maddaisy covered last week prescribe this. At the inference layer, it means restricting tool chaining, sandboxing execution environments, and requiring explicit permission gates for cross-system actions.

    Build cryptographic agility into procurement. The Quantum Insider panel raised a forward-looking point: “harvest now, decrypt later” attacks — where encrypted inference traffic is collected today for decryption once quantum computing matures — are overtaking model drift as the top digital trust concern among infrastructure leaders. Embedding post-quantum cryptography expectations into vendor contracts now is practical and low-cost.

    Treat inference security as infrastructure. Not as a feature, not as an add-on. As the panel concluded: critical infrastructure must be secured before it is tested by failure.

    The operational layer matters most

    The governance conversation has matured rapidly. Frameworks exist. Principles are converging. Regulation is arriving. But between the policy layer and the production environment sits inference — the operational layer where AI actually works, where data flows through models, where prompts reveal strategy, and where the absence of controls creates the exposure that governance documents are supposed to prevent.

    Gartner projects spending on AI governance platforms will reach $492 million this year and surpass $1 billion by 2030. That money will be wasted if it funds policies without the engineering to enforce them. The organisations pulling ahead will be those that treat inference security not as a technical detail for the security team, but as the operational foundation on which their entire AI strategy depends.

  • Insurtech’s AI-Fuelled Five Billion Dollar Comeback — And the Question the Industry Has Not Answered

    Global insurtech funding reached $5.08 billion in 2025, up 19.5% from $4.25 billion the year before. It is the first annual increase since 2021 — and, according to Gallagher Re’s latest quarterly report, it marks a fundamentally different kind of recovery from the one the sector last enjoyed.

    The 2021 boom was driven by venture capital chasing consumer-facing disruptors. The 2025 comeback is driven by insurers and reinsurers themselves investing in operational AI. That distinction matters far more than the headline number.

    The money is coming from inside the house

    In 2025, insurers and reinsurers made 162 private technology investments into insurtechs — more than in any prior year on record. This is not outside capital speculating on disruption. It is the industry itself funding its own modernisation, a shift Gallagher Re describes as a “changing of the guard” in the insurtech investor community.

    The fourth quarter was particularly striking. Funding hit $1.68 billion — a 66.8% increase over Q3 and the strongest quarterly figure since mid-2022. More than 100 insurtechs raised capital for the first time since early 2024, and mega-rounds (deals exceeding $100 million) returned in force, with 11 such rounds totalling $1.43 billion for the full year, up from six in 2024.

    Property and casualty insurtech funding rebounded 34.9% to $3.49 billion, driven by companies like CyberCube, ICEYE, Creditas, Federato, and Nirvana, which collectively secured $663 million in Q4 alone. Life and health insurtech, by contrast, declined slightly — a 4.6% dip that underlines where the industry sees its most pressing operational gaps.

    Two-thirds of the money follows AI

    The most telling statistic in the report is this: two-thirds of all insurtech funding in 2025 — $3.35 billion across 227 deals — went to AI-focused firms. By Q4, that share had climbed to 78%.

    Andrew Johnston, Gallagher Re’s global head of insurtech, frames this as convergence rather than a trend: “Over time, we see AI becoming so integrated into insurtech that the two may well become synonymous — in much the same way as we could already argue that ‘insurtech’ is itself a meaningless label, because all insurers are technology businesses now.”

    That trajectory is visible in the deals themselves. mea, an AI-native insurtech, raised $50 million from growth equity firm SEP in February — its first external capital after years of profitable organic growth. The company’s platform, already processing more than $400 billion in gross written premium across 21 countries, automates end-to-end operations for carriers, brokers, and managing general agents. mea claims its AI can cut operating costs by up to 60%, targeting the roughly $2 trillion in annual industry operating expenses where manual workflows persist.

    At the seed stage, General Magic raised $7.2 million for AI agents that automate administrative tasks for insurance teams — reducing quote generation time from approximately 30 minutes to under three in early deployments with major insurers.

    Profitability, not just growth

    What separates the 2025 wave from the 2021 boom is that several insurtechs are now proving they can make money, not just raise it.

    Kin Insurance, which focuses on high-catastrophe-risk regions, reported $201.6 million in revenue for 2025 — a 29% increase — with a 49% operating margin and a 20.7% adjusted loss ratio. Hippo, another property-focused insurtech, reversed its 2024 net loss with $58 million in net income, driven by improved underwriting and a deliberate shift away from homeowners insurance toward more profitable lines.

    These are not unicorn-valuation stories. They are companies demonstrating operational discipline — the kind of results that explain why insurers and reinsurers, rather than venture capitalists, are now leading the investment.

    The B2B shift

    Gallagher Re’s data reveals another structural change worth watching. Nearly 60% of property and casualty deals in 2025 went to business-to-business insurtechs — a 12 percentage point increase from 2021’s funding boom. Meanwhile, the deal share for lead generators, brokers, and managing general agents fell to 35%, the lowest on record.

    The implication is clear: capital is flowing toward technology that improves how existing insurers operate, not toward new entrants trying to replace them. The disruptor narrative of the early 2020s has given way to something more pragmatic — and, arguably, more durable.

    This parallels a pattern visible across financial services. As maddaisy noted when examining Lloyds Banking Group’s AI programme, established institutions are increasingly treating AI not as an innovation experiment but as core operational infrastructure — and measuring it accordingly.

    The question the industry has not answered

    For all the funding momentum, Johnston raises a challenge that the sector has yet to confront seriously: the “so what” problem.

    “As the implementation of AI starts to deliver efficiency gains, it is imperative that the industry works out how to best use all of this newly freed up time and resource,” he writes.

    This is not a hypothetical. If mea can genuinely reduce operating costs by 60% for a carrier, that frees up a substantial portion of the 14 percentage points of combined ratio currently consumed by operations. The question is whether that freed capacity translates into better underwriting, deeper risk analysis, and improved customer outcomes — or whether it simply gets absorbed into margin without changing how insurance fundamentally works.

    The broker market is already feeling the tension. In February, insurance broker stocks dropped roughly 9% after OpenAI approved the first AI-powered insurance apps on ChatGPT, enabling consumers to receive quotes and purchase policies within the conversation. Most analysts called the selloff overdone — commercial broking remains complex enough to resist near-term disintermediation — but the episode illustrated how quickly market sentiment can shift when AI moves from back-office tooling to customer-facing distribution.

    What to watch

    The $5 billion figure is a milestone, but the real signal is in its composition. Insurtech funding is no longer a venture capital bet on disruption. It is the insurance industry’s own investment in operational AI — led by incumbents, focused on B2B infrastructure, and increasingly backed by profitability rather than just promise.

    Whether that investment translates into genuinely better insurance — not just cheaper operations — depends on how the industry answers Johnston’s question. The money is flowing. The efficiency gains are materialising. What the sector does with them will determine whether this comeback is a lasting structural shift or just the next chapter of doing the same things with fewer people.

  • PwC Built an AI That Can Actually Read Enterprise Spreadsheets. Here Is Why That Matters.

    Most enterprise AI demonstrations involve chatbots, code generation, or image synthesis — capabilities that are impressive but often disconnected from the workflows where organisations actually make decisions. PwC has taken a different approach. On 19 February, the firm announced a frontier AI agent that can reliably reason across complex, multi-sheet enterprise spreadsheets — the kind of messy, formula-dense workbooks that underpin deals, risk assessments, and financial modelling across virtually every large organisation.

    The announcement would be easy to dismiss as incremental. It is, in fact, one of the more practically significant AI developments of the year so far.

    The Spreadsheet Problem No One Talks About

    AI has made rapid progress with text, images, and code. But enterprise spreadsheets have remained stubbornly resistant. The reason is structural: a typical enterprise workbook is not a neatly formatted data table. It is a sprawling, multi-sheet artefact containing hundreds of thousands of rows, cross-sheet formulas, hidden dependencies, embedded charts, and formatting inconsistencies accumulated over years of manual editing by multiple authors.

    Conventional AI systems — including the most advanced large language models — struggle with this complexity. They can process a clean CSV file or answer questions about a simple table. But ask them to trace a formula chain across five sheets in a workbook with 200,000 rows and inconsistent column headers, and accuracy collapses. For regulated industries where precision is non-negotiable — auditing, tax, financial due diligence — this limitation has kept spreadsheet analysis firmly in the domain of human practitioners.

    PwC’s agent addresses this directly. Combining multimodal pattern recognition with a retrieval-augmented architecture, the system can process up to 30 workbooks containing nearly four million cells. In internal benchmarks, it achieved roughly three times the accuracy of previously published methods while using 50% fewer computational tokens — a meaningful efficiency gain that reduces both cost and energy consumption.

    How It Works, Without the Hype

    The technical approach mirrors how experienced analysts actually work. Rather than attempting to ingest an entire workbook at once — a strategy that overwhelms even million-token context windows — the agent scans, indexes, and selectively retrieves relevant sections. It can jump across tabs, trace logic through formula chains, integrate visual elements like charts, and explain its reasoning with what PwC describes as “defensible precision.”

    Two internal use cases illustrate the practical impact. In engagement documentation, PwC teams work with large, nominally standardised workbooks that document business processes and controls. In practice, these files vary significantly — column names shift, fields appear in different orders, structures change between engagements. The agent handles this in two stages: first mapping the workbook’s structure, then extracting specific details using targeted retrieval rather than brute-force ingestion.

    In risk assessment, the agent replaces what was previously weeks of custom development work. Each new set of files could break existing programmatic approaches due to formatting variations. The agent indexes and extracts directly, regardless of these inconsistencies. PwC reports that what previously required weeks of configuration can now be completed in hours.

    The ROI Connection

    The timing of this announcement is worth noting. Earlier this month, maddaisy examined PwC’s own 2026 Global CEO Survey, which found that 56% of chief executives could not point to measurable revenue gains from their AI investments. Only 12% reported achieving both revenue growth and cost reduction from AI programmes.

    The spreadsheet agent is, in a sense, PwC’s answer to its own data. Rather than pursuing the kind of ambitious, organisation-wide AI transformation that the survey suggests most companies are failing at, this tool targets a specific, bounded problem: making AI useful where decisions actually get made. Spreadsheets are unglamorous, but they remain the substrate of enterprise decision-making across every industry. If AI cannot work reliably with them, the ROI gap that PwC’s own research documented will persist.

    Matt Wood, PwC’s Commercial Technology and Innovation Officer, was notably direct about the origin: “This didn’t start as a research project. It started because our teams were spending weeks manually tracing logic through workbooks that no existing tool could handle.”

    A Broader Pattern: Consulting Firms as Technology Builders

    This development fits a pattern that maddaisy has been tracking across the consulting industry. Firms are not merely advising clients on AI — they are building proprietary capabilities that change the economics of their own delivery. McKinsey’s 25,000 AI agents. Accenture’s ongoing automation of delivery operations. Now PwC, with a tool that converts weeks of manual work into hours.

    The competitive implications are significant. A firm that can process complex financial workbooks in hours rather than weeks can bid more aggressively on engagements, take on more work with the same headcount, and offer the outcome-based pricing models that clients increasingly prefer. The spreadsheet agent is not just a productivity tool — it is a structural advantage in the shifting economics of professional services.

    What Practitioners Should Watch

    For consultants and enterprise leaders, the PwC announcement carries a practical message: the AI value gap may start closing not through headline-grabbing deployments, but through targeted tools that tackle specific bottlenecks in existing workflows.

    The broader FP&A landscape is moving in the same direction. IBM’s 2026 analysis of financial planning trends highlights that 69% of CFOs now consider AI integral to their finance transformation strategy, with the primary applications centring on data ingestion, budget analysis, and narrative generation — precisely the kind of spreadsheet-adjacent work that PwC’s agent addresses.

    The question is no longer whether AI can handle enterprise data complexity. It is whether organisations will deploy these capabilities against the right problems — the mundane, time-intensive, precision-critical workflows where the return on investment is most measurable and most immediate.

    PwC appears to have started there. Given the firm’s own data on the AI ROI crisis, that is arguably the most credible place to begin.

  • Europe’s Cloud Sovereignty Rush Meets Its Regulatory Reality Check

    European sovereign cloud spending is set to nearly double in 2026, from $6.9 billion to $12.6 billion according to Gartner’s latest forecast. Every major US hyperscaler now has a European sovereignty answer. AWS launched its European Sovereign Cloud from Germany in January, backed by a €7.8 billion investment. Google operates through S3NS, a French joint venture with Thales that holds SecNumCloud certification. Microsoft has Delos Cloud in Germany and Bleu in France.

    Yet beneath the flood of partnership announcements and sovereign cloud launches sits a less comfortable truth: the regulatory framework driving all of this activity is still incomplete, sometimes contradictory, and in certain critical areas, stalled entirely. For organisations trying to build disaster plans around Europe’s digital infrastructure, the ground has not stopped moving.

    The regulatory pile-up

    Three major pieces of European regulation now intersect on questions of cloud resilience and digital sovereignty — and none of them align neatly.

    The Digital Operational Resilience Act (DORA), enforceable since January 2025, requires financial institutions to implement comprehensive ICT risk management frameworks, including detailed third-party risk assessments for cloud providers. DORA is specific, prescriptive, and already creating compliance pressure across European banking and insurance.

    The NIS2 directive, enforceable since October 2024, extends similar resilience requirements to a much broader set of critical infrastructure operators — energy, transport, health, and digital infrastructure itself. Where DORA targets financial services, NIS2 casts a wider net but leaves more room for national interpretation, creating an uneven patchwork across EU member states.

    Then there is the European Cybersecurity Certification Scheme for Cloud Services (EUCS), which was supposed to provide a unified standard for assessing cloud security across the EU — including, controversially, sovereignty requirements that would have effectively barred non-EU cloud providers from the highest certification tier. That sovereignty clause was stripped from the latest drafts under intense lobbying pressure. The scheme itself remains unadopted. In January 2026, the European Commission proposed a revised Cybersecurity Act that would overhaul the entire certification framework — effectively resetting the process while organisations wait for clarity that may not arrive before 2027.

    Disaster planning in a regulatory fog

    The practical consequence for enterprises is an uncomfortable paradox. Regulations now require detailed disaster recovery and business continuity plans for cloud-dependent operations. But the certification framework that would define what “sovereign” or “resilient” actually means in practice remains unfinished.

    As maddaisy examined last week, the SAP-Microsoft “break glass” contingency plan illustrates the tension. It offers a theoretical failover for European Azure workloads in a crisis scenario, but analysts questioned whether a disconnected copy of Azure could remain operationally viable beyond a few weeks. The plan satisfies a political need — demonstrating that contingency planning exists — without resolving the deeper technical question of what happens when a severed cloud stops receiving updates.

    Capgemini’s CEO Aiman Ezzat has framed this pragmatically, arguing that Europe has meaningful sovereignty over data, operations, and regulation — but not over the underlying technology stack. The four-layer model he has described reflects the reality most enterprises face: sovereign in governance, dependent on US technology, and now required by law to plan for scenarios where that dependency becomes a liability.

    The hyperscaler response: sovereignty as a service

    The US cloud providers have responded to the regulatory and political pressure with significant investment. AWS’s European Sovereign Cloud, operating from Brandenburg, is architecturally separated from other AWS Regions — a genuine sovereign partition with EU-resident leadership and local operational control. AWS CEO Matt Garman called it a “big bet”, with expansion planned for Belgium, the Netherlands, and Portugal.

    Google’s approach in France, through S3NS (a joint venture where Google holds a minority stake under French law), has achieved SecNumCloud 3.2 qualification — the most demanding sovereignty standard currently in force in Europe. Microsoft’s structure routes through nationally controlled entities: Delos Cloud in Germany and Bleu (co-owned by Capgemini and Orange) in France.

    The pattern across all three is consistent: legal and operational separation, EU-resident personnel, local data residency, and contingency plans for geopolitical disruption. What differs is the depth of that separation. A fully air-gapped partition like Google’s Distributed Cloud offering for defence clients sits at one end of the spectrum. A contractual failover arrangement like the SAP-Microsoft deal sits at the other. Most enterprise workloads will land somewhere in between — and DORA and NIS2 require organisations to understand precisely where.

    What practitioners need to do now

    For consultants and technology leaders navigating this landscape, three priorities stand out.

    First, classify workloads by sovereignty sensitivity before choosing infrastructure. Not every application needs the highest tier of sovereign protection. DORA’s third-party risk requirements are prescriptive but risk-proportionate — a core banking system and an internal collaboration tool do not demand the same level of contingency planning. The trap is treating sovereignty as a binary choice rather than a spectrum.

    Second, build disaster plans around regulatory timelines, not vendor announcements. DORA enforcement is live. NIS2 implementation varies by member state but is progressing. The EUCS framework is stalled, but the underlying requirements it was meant to codify — around data residency, operational control, and access restrictions — are already being enforced through sector-specific regulation and national certification schemes like France’s SecNumCloud. Waiting for a pan-European standard before acting is not a viable compliance strategy.

    Third, pressure-test vendor contingency claims. The proliferation of sovereign cloud offerings and disaster recovery partnerships creates an illusion of completeness. But as Forrester analyst Dario Maisto noted of the SAP-Microsoft plan, many of these arrangements remain untested and legally unproven. “This is not compliance as much as risk management,” he said. Organisations should ask pointed questions about update cycles, hardware dependencies, and the operational lifespan of any disconnected cloud environment.

    The long view

    European digital sovereignty has moved from policy aspiration to market reality faster than the regulatory framework can keep pace. The investment figures are significant — AWS alone is committing €7.8 billion. The compliance deadlines are real. The contingency plans exist, at least on paper.

    But the gap between what regulations require and what certification frameworks define remains open. For organisations building disaster plans today, the most honest assessment is that they are planning against a moving target, using vendor solutions that have never been tested in the crisis scenarios they are designed for. That is not a reason to delay — DORA and NIS2 make delay legally untenable. It is a reason to plan with humility, build in flexibility, and avoid treating any single vendor’s sovereignty narrative as a finished answer.