SAP Told Its Customers Which AI to Use. Three Per Cent Listened.

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By February 2026, DSAG’s own Investment Report had already surfaced the gap SAP would spend the rest of the year trying to close by other means. Only 3 per cent of surveyed companies ran their production AI use cases on SAP’s own tools, while 77 per cent ran theirs on non-SAP solutions instead, Microsoft Copilot chief among them. Two months later, SAP rewrote its API policy. Section 2.2.2 of the new terms blocks external AI agents from calling SAP systems directly, prohibiting what it calls independent scheduling or execution of API calls. Every agentic use case now has to route through Joule, SAP’s own assistant, adding a second layer of inference and cost to anything a customer’s AI tools want to do inside SAP.

 

Two Competitors Bet the Opposite Way

Salesforce and ServiceNow read the same gap differently. Salesforce launched Headless 360 in April 2026, giving external agents direct access through REST, Model Context Protocol tools and the command line, no mandatory detour through a proprietary assistant required. ServiceNow’s Action Fabric, announced at Knowledge 2026, opened two decades of workflows and business rules to any agent through the same open standards. Neither company built a legal wall around its intelligence layer. Both are betting that customers will choose their AI because it is good, not because the contract requires it.

 

The Mandate Had a Structural Problem Too

Joule is only available to customers on RISE or GROW with SAP cloud contracts, which shuts out the thousands of organisations still running on-premise ECC. Support for ECC 6.0 ends in 2027, so over ten thousand customers are being asked to migrate their core system and adopt a mandated AI layer on the same timeline, set by the vendor rather than the customer. SAP’s own chief customer officer has disputed the DSAG survey’s reach, noting that fewer than 100 organisations responded, which is a fair challenge to the precision of the number. It does not explain why SAP is separately funding a hundred-million-euro partner investment programme to accelerate adoption of a product it says the market already wants.

 

The Hedge That Followed the Mandate

At Sapphire 2026, a few months after the API policy took effect, SAP quietly hedged its own bet. Alongside Joule Studio 2.0, it introduced an AI Agent Hub, described as vendor-agnostic governance for SAP and third-party agents alike, bundled into the platform at no extra charge. A company confident that mandating Joule had closed the gap DSAG measured in February would not need to build the open door it spent the spring telling customers they did not need.

 

The Question Every PMO Should Be Asking Now

None of this is really about SAP. Every PMO running a platform strategy right now is making the same call in miniature, whether to route AI agents through one sanctioned tool or leave the door open for whichever tool the business actually adopts. SAP had the market power to write the mandate into a contract, and it wrote that mandate only after its own user group had already published the adoption numbers it was meant to fix. Assume your organisation has less leverage than SAP did, and plan the exit clause before you write the mandate, not after the adoption numbers come back.

Your AI Steering Committee Meets Monthly. Your AI Ships Weekly.

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Most AI governance committees meet on a monthly or quarterly cadence. Most AI systems do not wait for them.

Anthropic’s own release notes tell the story in a single scroll. Opus 5 launched in late July 2026, a step-change release over Opus 4.8, which was barely two months old at the time. Sonnet 5 landed a month before that, at the end of June. Opus 4.8 before that, in late May. Opus 4.7 before that, in April, with breaking API changes attached. Opus 4.5 in November 2025. Sonnet 4.5 in September. Six major model versions inside ten months, with incremental API and feature changes logged in between on a near-weekly basis. That is one vendor’s changelog. Most enterprises are running several.

 

The Governance Side Has Not Kept Pace

Deloitte’s 2026 State of AI in the Enterprise survey, covering 3,235 business and IT leaders across 24 countries, found that only 21% of organisations have a mature governance model in place for agentic AI. Meanwhile, 74% of the same leaders expect their organisation to be using AI agents at least moderately by 2027, with 23% expecting extensive use and 5% expecting full integration.

That is not a small gap closing gradually. It is deployment intent running well ahead of the governance capable of managing it, at the exact moment agentic systems are gaining the authority to act rather than just draft.

 

People Have Already Stopped Waiting

The gap does not sit quietly while committees catch up. A Cybernews survey of more than 1,000 US employees found that 59% use AI tools their employer never approved. Among executives and senior managers specifically, the same survey found 93% do the same. The people who would sit on the steering committee approving AI use are, by a wide margin, the people going around it.

That is the quiet part of this problem. The people with the authority to slow things down are the ones setting the pace of the tools rather than the pace of the committee.

 

What Happens When the Gap Surfaces

Gartner’s prediction for next year is specific: 40% of enterprises will have their autonomous AI efforts partly derailed by governance gaps discovered only after a production incident, not before one. Sanchit Vir Gogia of Greyhound Research, commenting on the same data, put the mechanism plainly: “the real governance problem is not model intelligence. It is delegated operational authority moving across trust boundaries faster than enterprises can instrument, constrain, or audit it.” His own warning, in his words: “Do not scale agents faster than you can govern their authority. A small number of well-governed agents will create more enterprise value than a sprawling estate of clever, fragile, over-permissioned digital apprentices.”

 

What Governance Cadence Actually Needs to Look Like

The fix is not more meetings. A committee that reviews AI deployment quarterly cannot govern a system that changes weekly, no matter how thorough each quarterly review is. What changes the equation is moving from periodic review to continuous, tiered oversight: automatic flags for any new agent or capability change above an agreed risk threshold, standing authority delegated to a smaller operational group empowered to act between full committee meetings, and a real audit trail that lets the full committee review what was approved at pace, after the fact, rather than approving everything before the fact and creating the exact bottleneck this problem describes.

Keeping oversight in place does not mean keeping the quarterly cadence that was designed for a technology that no longer exists.

 

The Question Worth Asking Before the Next Steering Committee Meeting

Ask what actually changed in your AI environment since the last meeting, specifically, not generally. If nobody in the room can answer that with real detail, the governance structure is reviewing a snapshot that was already out of date the moment the meeting started.

The organisations that get this right are not the ones meeting more often. They are the ones who redesigned what needs a meeting at all, and gave someone standing authority to handle everything else in between.

EU Cyber Resilience Act – Reporting Duty Begins on an Unfinished Platform

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On 11 September 2026, the European Union’s Cyber Resilience Act began requiring manufacturers of products with digital elements to report actively exploited vulnerabilities and severe incidents within 24 hours of becoming aware of them. The duty runs through the EU Agency for Cybersecurity’s new Single Reporting Platform, which ENISA switched on the same day while describing it as having reached only “initial operating capability.” That the deadline and its supporting infrastructure arrived on the identical date is itself the story, and it comes with a genuine credit: a manufacturer selling into the EU can now file one exploited-vulnerability report and have it reach every relevant national authority, rather than notifying each member state separately.

This Pre-Mortem asks the questions a post-mortem would ask, before failure is possible: what is being bet on, what single assumption could break it, what got decided before the safeguards existed, who carries the pager when it fails, and what proof would settle whether it worked. It is the diligence a compressed rollout deserves before its first real test, not after.

 

The Bet

The EU is betting that centralising exploited-vulnerability and severe-incident reporting through one platform gives ENISA and national CSIRTs (Computer Security Incident Response Teams) EU-wide visibility into what is actually being attacked, and that manufacturers will treat a 24-hour clock as workable even while the tool underneath it is still being built out. The bet favours momentum over completeness. The European Commission’s own guidance on the CRA’s reporting obligations confirms the platform was already operational on 11 September 2026, the same day the 24-hour duty took effect, rather than waiting until it was finished. It also asks industry to build compliance discipline around a tool still being assembled, in the same quarter that discipline is tested.

 

The Assumption

The load-bearing belief is that a “single” platform stays meaningfully single even with core pieces missing. As the specialist tracker cyberresilienceact.eu reported on launch day itself, voluntary reporting under Article 15, a programming interface, and a field recording exactly when a manufacturer became aware of an incident all “did not arrive with it,” and access runs only through an Assigned Representative role via EU Login multi-factor authentication. If that gap persists, manufacturers with products across many jurisdictions and limited access routes could end up filing manually into individual CSIRTs anyway, the exact fragmentation Article 16 was written to end.

 

The Sequence

The Commission did not settle what counts as a manufacturer becoming “aware” of a reportable event, the trigger that starts every clock in the regime, until its guidance published on 27 July 2026, six weeks before the duty began. For most of the preceding compliance-planning year, manufacturers had no published definition of the one moment that decides whether a 24-hour window has even opened. ENISA’s own Assigned Representative registration guidance still carried dated updates in launch week itself, on 9 and 10 September 2026, the guidance for actually using the platform arriving alongside it rather than well ahead of it.

 

The Pager

ENISA’s Executive Director, Juhan Lepassaar, put his name to the launch, framing it as a step toward “a more resilient Digital Single Market,” and carries the operational pager for the platform itself. Above him sits European Commission Executive Vice-President for Tech Sovereignty, Security and Democracy Henna Virkkunen, who holds the political brief for EU cybersecurity policy, having worked on the Cyber Resilience Act dossier in Parliament. Beneath both, national market surveillance authorities enforce the penalty tier attached to Article 14 failures: up to €15 million, or 2.5 per cent of global turnover. No individual has been named as accountable for the missing programming interface and voluntary-reporting channel, only an agency-level pledge to keep improving.

 

The Proof

No public dashboard, uptime figure, or notification-volume metric has been attached to the platform’s launch, and ENISA’s own pledge to keep improving it carries no completion date. The measure that would actually settle the question, whether a report filed with one coordinating CSIRT reaches every other relevant CSIRT and ENISA at the speed the platform promises, has not been made public in any testable form. That will only become visible once a real, cross-border, multi-product incident runs through the system at volume, rather than from the demonstration traffic of launch week, and eighteen months allows roughly two CRA reporting cycles for that test to arrive.

 

Verdict

If ENISA closes the Single Reporting Platform’s programming-interface and voluntary-reporting gaps, and attaches a public date to doing so, before the platform faces its first genuinely cross-border, multi-CSIRT incident, then 11 September 2026 will read as a pragmatic phased launch that got the hardest deadline live on time. If those gaps are still open when that incident arrives, manufacturers already working to a 24-hour clock will discover that the single reporting platform was, in practice, still several platforms wearing one name.

The Difference Between an AI Pilot and an AI Programme Is Who Gets Blamed When It Fails.

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A pilot has no name attached to its failure. A programme does. That is the entire distinction, and almost nobody treats it as the one that matters.

Ask most organisations why their AI initiative is still called a pilot eighteen months after launch and the answer usually involves budget, integration complexity or waiting for the model to mature. The real answer is simpler and less comfortable. Nobody has agreed who owns the outcome if it goes wrong, and a pilot is the one structure where that question never has to be answered.

 

The Pilot Was Never Built to Answer This Question

MIT’s Project NANDA found that 95% of generative AI pilots fail to deliver a measurable return, based on 150 leadership interviews, a survey of 350 employees and analysis of 300 public deployments. The report itself points to a different explanation: tools that never adapt to how the organisation actually works, budgets aimed at sales and marketing while the real return sits in back-office automation, and internal builds that consistently underperform specialist vendor partnerships.

Underneath all three is the same gap. A pilot is built to prove a capability exists. Whether anyone is answerable for what happens once that capability touches real customers, real decisions and real money is a different question, one a pilot was never built to answer. Most organisations discover this the hard way, months into a pilot that technically works and still cannot get budget to go further, because nobody signed up to own what happens next.

 

Ownership Is the Line, Not Scale or Budget

Grant Thornton’s 2026 AI Impact Survey of 950 senior leaders found 78% lack strong confidence they could pass an independent AI governance audit within 90 days. Among organisations still piloting, that confidence drops further, to just 7%. The gap shows up directly in results: organisations with fully integrated AI are close to four times more likely to report AI-driven revenue growth than those still piloting, 58% against 15%.

Grant Thornton’s Tom Puthiyamadam put the underlying issue plainly: organisations that have invested in governance move faster precisely because they have the confidence to scale, while the ones without it are one incident away from a far harder conversation.

MIT’s own findings point the same way: among the factors separating pilots that scale from the ones that stall, the report names empowering line managers, not just centralised AI labs, to drive adoption. A central lab can build a working model, but naming who answers for what that model does inside someone else’s process is a separate task entirely, one a programme takes on and a pilot leaves undone.

 

What a Programme Actually Commits To

Turning a pilot into a programme is not a budget decision. It is naming, before the next phase starts, who is accountable if the thing fails in production, what failing actually means in that specific context, and what happens in the following week if it does. None of that requires new technology. It requires a decision most organisations postpone precisely because a pilot lets them.

Just 38% of organisations have a formal, comprehensive AI policy in place, up from 28% the year before, according to ISACA’s 2026 research covering more than 3,400 digital trust professionals globally, which means most of what currently passes for AI governance gets improvised the first time something breaks, rather than designed before it ships. An owner named after an incident is not accountability. It is damage control wearing accountability’s name.

The organisations closing the gap treat this as day-one work, not late-stage paperwork. A named business owner, not a technical one, accountable for the outcome. And a clear definition of what failure looks like for that specific use case, agreed before launch rather than improvised during the post-mortem.

 

Whose Name Is on This When It Breaks

Before the next AI initiative gets called a programme instead of a pilot, ask one thing in the room where budget gets approved: if this fails next month, whose name is on the outcome, and did they agree to that before it happened or only after.

Most organisations cannot answer that today. That gap explains why so many pilots never leave the lab.

A Tenfold Attack Surge in London, a Governance Mandate in Abu Dhabi

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In January to May 2026, UK healthcare providers logged 264,000 attack events, against 27,000 for the whole of 2025. That is a tenfold jump in five months, according to SonicWall’s telemetry across NHS-linked sensors, and it occurred on a sector where the gap between what boards think they know and what is actually protected has never been wider.

 

The Long Tail of a Single Attack

The clearest illustration of what that gap costs sits two years in the past and is still unresolved. The June 2024 ransomware attack on Synnovis, the pathology provider serving South East London hospitals, exposed data from roughly a million NHS patients and forced more than 10,000 outpatient appointments and 1,700 elective procedures to be postponed. As of April 2026, South London and Maudsley NHS Foundation Trust was still processing pathology results without fully restored systems, and the incident has been linked to a patient death at King’s College Hospital. Nearly two years is not a recovery timeline any board signed off on. It is what happens when governance treats cyber resilience as an IT programme rather than a clinical safety issue.

 

Reactive Versus Mandated: Two Governance Models

The UK’s surge sits inside a largely reactive governance model, where boards respond to incidents and regulators tighten guidance after the fact. Abu Dhabi has taken the opposite route. Its Healthcare Information and Cyber Security standard, now in its second version, is built around six pillars, and governance is listed first, ahead of resilience, capability, partnerships, maturity and innovation. Every hospital, insurer and medical device maker operating in the emirate has to comply, and the standard explicitly frames cyber security as an organisation-wide responsibility covering people and process rather than a narrow technology control bolted onto IT. It is a mandated structure built before the incident, rather than a review commissioned after one.

 

Why the Stakes Keep Rising

The economics make the governance question harder to defer. Healthcare ransom demands have climbed sharply, with one widely cited industry analysis putting the average demand at $16.9 million, up from $577,800 the prior quarter, and healthcare remains one of the most targeted sectors globally, with 77 per cent of organisations reporting a ransomware attempt in the past twelve months, because attackers know disrupted care creates leverage no other industry carries. A board that treats a green status on a cyber dashboard as sufficient assurance is trusting a number it has usually never interrogated.

 

What Boards Should Actually Be Asking

Two different regulatory paths, one shared conclusion. Cyber governance in healthcare cannot sit exclusively with a CISO or an IT director reporting up through a technical channel. It needs a named board-level owner who can answer, in plain terms, three questions: which clinical services stop if this system fails, how long the organisation can run on manual process before patient safety is affected, and when that assumption was last tested rather than assumed. The Synnovis timeline suggests most boards do not currently have confident answers. The Abu Dhabi model suggests what building toward one actually looks like, governance treated as the first pillar of resilience rather than the last line of a post-incident report.

Pre-Mortem: The UN’s Global Dialogue on AI Governance

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On 6 July 2026, the United Nations opened its first General Assembly-mandated forum on AI governance in Geneva. All 193 member states attended, the first time every country, developing and developed alike, has held a formal seat at an AI governance table. Secretary-General António Guterres named four priorities: common safety standards, human-rights red lines, capacity-building for developing nations, and environmental transparency. After two days, the forum closed with a co-chair summary. Not a treaty. Not an enforcement mechanism. The document records what governments agreed in the room. It does not bind any of them to act on it.

A pre-mortem applies five fixed questions to a public commitment before the outcome is known: what is being bet on, what single assumption underpins it, what was decided before governance existed, who carries it if it fails, and what would prove it worked. This piece applies that format to the public record of the first UN-mandated global AI governance dialogue, convened in Geneva on 6 and 7 July 2026.

 

The Bet

The UN’s bet is that convening all 193 member states repeatedly, Geneva first, New York in May 2027, produces a governance architecture capable of managing AI at global scale before the harms it is designed to prevent have already arrived. The mechanism is norm-setting: shared principles, common language, multilateral dialogue, with no enforcement powers and no treaty obligations. The independent scientific panel, co-chaired by Yoshua Bengio and Maria Ressa, published its first report on 1 July 2026, before the forum opened. Guterres proposed a Global Fund for AI and an AI Child Safety Pledge. Those are specific commitments, publicly made. The bet is that naming four priorities in a room of 193 governments changes what those governments do next.

 

The Assumption

The Dialogue’s credibility turns on a single calculation: that member states, with vastly different AI capabilities, legal systems, and strategic interests, will treat non-binding shared principles as a meaningful constraint on sovereign AI deployment decisions. The Dialogue produces a co-chair summary, not a resolution or treaty. A state that attends, agrees on human-rights red lines, and then deploys AI in ways that cross them faces no published consequence. At the Dialogue itself, the United States delegation argued that voluntary cooperation between government and industry, not binding rules, is the only approach agile enough to govern AI, a preference for exactly the model the Dialogue is testing. The architecture assumes that participation changes behaviour.

 

The Sequence

In 2017, Guterres called for global AI governance. In September 2024, the General Assembly adopted a Global Digital Compact that included AI provisions. In August 2025, the General Assembly established the Global Dialogue by resolution. The first session convened in July 2026. In the same period, the largest frontier AI models were trained, deployed at scale, and embedded in healthcare, finance, law enforcement, and defence, across countries that attended Geneva and agreed that safety is a priority. The Bengio-Ressa panel stated plainly that science currently cannot guarantee AI will not cause catastrophic harm as capabilities increase. The governance structure followed the deployment decisions. That is the sequence.

 

The Pager

Ambassador Egriselda López of El Salvador and Ambassador Rein Tammsaar of Estonia co-chaired the first session. Amandeep Singh Gill, UN Special Envoy for Digital and Emerging Technologies, coordinates the process. When a member state deploys an AI system that crosses the Dialogue’s own human-rights red lines, no document names the consequence. The co-chair summary records what was agreed. It is not a mechanism. The next session is May 2027 in New York. The question of who carries accountability between now and then has no published answer.

 

The Proof

Guterres named four priorities at Geneva. The measure that would prove any of them produced outcomes by May 2027 is at least one of these: a common safety standard that member states have adopted, a documented case where a human-rights red line prevented a harmful deployment, a committed capacity-building fund with named recipients, or an AI environmental reporting mechanism with verified data. The co-chair summary is the output the Dialogue committed to producing. An outcome requires a different commitment entirely.

 

Verdict

If the second session in New York, May 2027, produces a named accountability mechanism for at least one of Guterres’s four priorities, with a state or institution publicly committed to carrying it, the Geneva forum will stand as the first step of something with teeth. Without that, it stands as the moment 193 governments agreed that AI is the most consequential technology of the era, and scheduled a follow-up.

Patience Is Not Passive. It Is the Hardest Active Choice Most People Never Make

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Patience gets treated as the absence of action: the thing you’re doing while you wait for something else to happen. The clearest evidence says that’s backwards, not from child-development research, but from what happens when trained professionals are given a straight choice between waiting and acting. Patience is usually the harder, more active choice, and impulsive action is often the easier one dressed up as decisiveness.

 

Retiring the Marshmallow Test

Any conversation about patience eventually reaches for the marshmallow test, the classic finding that children who delayed eating one marshmallow to get two later did better in life. A 2024 replication study in Child Development, using far larger and more representative data than the original, found the test doesn’t reliably predict adult outcomes at all: nearly every regression-adjusted relationship between childhood marshmallow performance and adult achievement, health, or behaviour came back statistically insignificant. The honest version of patience research doesn’t lean on a famous but shaky finding about children. It looks at what patience actually costs adults making real decisions.

 

The Bias That Makes Patience Feel Wrong

Behavioural economists have a name for the instinct patience has to fight: action bias, the tendency to act even when the evidence says waiting is the better choice, because acting feels like doing the job and waiting feels like doing nothing. A widely cited study of professional goalkeepers facing 286 penalty kicks found this bias in its purest form: goalkeepers dive left or right the overwhelming majority of the time, even though staying in the centre of the goal is statistically the better strategy, since kicks go there roughly 29% of the time. Goalkeepers dive anyway, because conceding a goal while standing still feels worse, and looks worse, than conceding one while visibly trying.

Medical research on “intervention bias” finds the identical mechanism in physicians: doctors reliably feel more satisfied recommending a treatment than recommending watchful waiting, “giving a sense of greater activism in their patients’ care,” even when the evidence supports doing nothing. Neither of these is really about competence. Both are about how much easier it feels to have visibly acted, regardless of whether acting was actually correct.

 

What Patient Leadership Actually Looks Like

A qualitative study of leaders who are known for patience found something specific underneath the trait: patience functions as a decision-making framework in its own right, not an absence of one, guiding a distinct process for weighing a situation before committing to act on it. A separate survey of 578 working professionals found leaders rated as more patient saw their teams’ self-reported creativity and collaboration rise by an average of 16%, and productivity by 13%.

A six-year study of more than 20 pairs of executives working in genuinely volatile markets coined the useful term for what this looks like in practice: active waiting. Not paralysis, and not indecision. Deliberate preparation during a lull, so that when a real opportunity actually opens, the leader can act decisively rather than reactively.

 

Why the Harder Choice Rarely Gets Made

None of the research above suggests patience is passivity’s better name. It suggests the opposite: patience requires resisting a bias that’s actively working against it in the moment, on a soccer pitch, in an exam room, or in a boardroom under pressure to be seen doing something. The impulsive decision is usually the one that requires less discipline, not more, because it removes the discomfort of visibly not acting while everyone is watching.

 

The Actual Choice Worth Making

The next time waiting is the objectively better move and doing something still feels necessary, the honest question isn’t whether patience is the right call. The research says it usually is. The question is whether you can tolerate looking like you’re doing nothing for exactly as long as doing nothing is correct.

Governance Fatigue Is Real, and It’s Killing the Governance That Matters

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Every governance failure gets the same response: add a committee. Nobody ever asks which of the five committees already in the room should be deleted.

I have watched this play out on the same programme, twice, thirteen months apart. An incident happens. A review is commissioned. The review recommends a new gate, a new sign-off, a new board with a name that sounds important. Nobody asks whether the five existing boards had already covered this ground and simply were not being used properly. The new layer gets built. The old layers stay exactly where they were, because retiring a control is a much harder conversation than adding one, and nobody wants to be the person who removed the safeguard right before something went wrong.

Multiply that pattern across a few years of incidents, mergers, audits and regulatory nudges, and you get an organisation with more governance than anyone can actually operate.

 

The sprawl nobody planned and everybody built

Governance frameworks get bloated one reasonable-sounding addition at a time, not in a single decision. A near-miss produces a new checkpoint. An audit finding produces a new form. A departing executive leaves behind a committee that made sense under their sponsorship and none under anyone else’s. Each addition was defensible in isolation. Nobody ever sat down and asked what the whole structure looked like once you added them all together.

The organisations most proud of their governance maturity are often the ones carrying the heaviest version of this problem. More boards. More gates. More documented sign-offs. It reads as rigour on an org chart and feels like wading through treacle to anyone actually trying to get something delivered.

 

Fatigue looks like silence, not rebellion

The expensive part is what people do once the meetings stop making sense to them, not the extra meetings. They do not object. They do not escalate the absurdity of an ninth sign-off. They quietly learn which boxes can be ticked without real scrutiny, which approvals are theatre, and which route gets something through fastest regardless of whether it is the correct one.

That is governance fatigue, and it is far more dangerous than having too little governance in the first place. An organisation with no controls at least knows it is exposed. An organisation with too many controls believes it is protected, right up until the one decision that actually mattered slipped through a gate everyone had stopped taking seriously.

 

Clarity beats volume, every time

The research on decision rights backs this up more directly than most governance debates acknowledge. Itonics’s analysis of partner and programme governance found that organisations using a properly maintained RACI framework report 70 per cent fewer “who decides” disputes and 25 per cent faster decision cycle times. RACI works by replacing ambiguity with a single, shared answer to a question that used to require a meeting to resolve, not by adding another layer. The gain comes from governance that is unambiguous enough that people stop needing to ask, rather than from adding more of it.

That is the distinction most organisations miss when they respond to a failure by adding structure. The problem was usually oversight so diffuse that nobody could say, without checking three separate documents, who actually held the decision.

 

The discipline of taking something away

Fixing this requires a habit most organisations have never built: retiring governance on purpose. Every new control should come with an audit of what it is replacing, not just what it is adding. Every steering board should have to justify its existence against a simple test: if this group disappeared tomorrow, what decision would genuinely not get made anywhere else? If the answer is “nothing, it would just move up a level,” that board is inertia with a calendar invite, not governance.

The organisations that manage this well treat their governance structure the way a good engineer treats a system under load, asking what is carrying weight it no longer needs to carry and taking it out, rather than just adding capacity when something breaks.

Governance was never supposed to be heavy. It was supposed to be clear. Somewhere along the way, most organisations mistook the two for the same thing.

 

What We Found When We Measured Adoption, Not Just Deployment.

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A go live report is easy to write. Every site is on the new system, every licence is issued, every training session delivered on schedule. Three months later, the usage dashboard tells a different story, and it is the dashboard nobody puts in front of the steering committee.

 

The Metric Everyone Reports

Deployment is countable in a way adoption never is. Percentage of sites migrated, number of licences activated, hours of training delivered, these are the figures that go into a programme status report because they can be measured on the day the rollout finishes. None of them says whether anyone is still using the system a quarter later, or whether they have quietly gone back to the spreadsheet it was meant to replace.

 

The Number Nobody Puts in the Steering Deck

IBM’s 2026 Global CEO Study, based on more than 2,000 chief executives surveyed worldwide by the IBM Institute for Business Value, found that only 25 per cent of workers use AI regularly in their jobs, even though 86 per cent of CEOs believe their workforce already has the skills to do so. Eighty three per cent of the same CEOs said AI’s success depends more on people’s adoption than on the technology itself. The rollout finished on schedule. The adoption did not follow.

 

What Poor Adoption Actually Costs

A Forrester Consulting study commissioned by Whatfix, surveying 335 senior decision makers at large organisations across North America, Europe, APAC and India, put a figure on what that gap costs a mid-sized enterprise, $10.9 million a year, plus 728 hours lost per employee navigating systems that were rolled out but never properly embedded. That is not a training budget line. It is the ongoing cost of a deployment nobody followed up on. The same research found a wide gap between organisations with adoption maturity and those without, 53 per cent of mature adopters reported improved user experience against 28 per cent of the rest, and 56 per cent reported stronger return on investment against 28 per cent.

 

Why Deployment Metrics Miss This

Programme reporting is built around milestones a PMO can close off: go live achieved, training complete, licences distributed. Adoption behaves more like a curve than a milestone, one that keeps moving long after the project has been marked complete and the team has moved on to the next initiative. By the time low usage shows up in a satisfaction survey or a renewal conversation, the people who owned the rollout are three programmes further down the roadmap.

Part of the reason adoption rarely gets measured is that almost nothing in a typical programme is set up to reward it. Vendor contracts are frequently structured around go live milestones rather than usage thresholds, so the commercial incentive to keep measuring stops the day the system switches on. Programme teams are resourced to deliver a rollout, not to own what happens to it afterwards, and by the time adoption data would be available, the team has usually been reallocated to the next initiative. None of this is deliberate. It simply reflects a reporting structure and an ownership structure that both end at the same milestone.

 

What We Started Measuring Instead

On some programmes I have run, the fix had less to do with better software and more to do with what the steering committee agreed to look at. We started tracking active usage at 30, 60 and 90 days after go live, alongside a simple drop off rate, how many people who logged in during week one had stopped logging in by week twelve. We also moved benefits realisation sign off away from the go live date and tied it to a usage threshold instead, so a project could not be closed as successful until people were actually using what had been built. It is a small governance change, and it surfaces problems a deployment report never will.

Rollout dates and licence counts still matter, deployment discipline was never the issue. What most programmes are missing is a second dashboard sitting next to the first one, what deployment made possible, and what adoption is showing three months after anyone stopped watching. The programmes that quietly fail are rarely the ones that missed a go live date. They are the ones that hit it, reported it as a win, and never checked what happened next.

Your Reputation Travels Faster Than You Do. Act Accordingly.

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Most executives manage their reputation like a local matter: how you’re seen in this room, on this team, in this market. That’s the wrong frame. Reputation moves through networks faster and further than any individual career move, and it arrives in the next room before you do.

 

The Research Behind Why Word Travels

A 2022 study in Science, based on five years of randomised experiments across 20 million LinkedIn users, 2 billion new connections, and 70 million job applications, found that professional information travels most efficiently through weak ties, not close friends. The loose, wide network of people who know you a little, rather than the small circle who know you well, is what actually carries information about you into rooms you haven’t entered yet.

That mechanism cuts both ways. It is exactly how good work gets you noticed somewhere new. It is also exactly how a reputation for cutting corners, mistreating people, or leaving a mess behind you gets there first.

 

What Happens to Reputation That Travels Badly

The clearest, most rigorously measured evidence of this comes not from executive search literature, which is surprisingly thin on hard numbers, but from corporate governance research on company directors. A 2005 study in the Journal of Accounting Research tracked 409 US firms that restated their earnings between 1997 and 2001. Directors of those firms lost roughly a quarter of their positions on other, unrelated companies’ boards afterward, not just the one where the restatement happened. A related 2007 study in the Journal of Financial Economics found that outside directors named in shareholder fraud lawsuits saw a measurable decline in how many other directorships they held, even at companies with no connection to the original case, at an estimated cost of roughly $1 million per lost seat.

That is reputation travelling, quantified: conduct in one boardroom measurably closing doors in boardrooms that had nothing to do with it.

 

Real Cases, Not Hypotheticals

Steve Wynn resigned from Wynn Resorts in 2018 following sexual misconduct allegations. The consequences did not stay in Nevada. Massachusetts gaming regulators, investigating a market he had never previously operated in, fined the company $35 million and forced it to strip his name from its brand-new $2.6 billion property before it opened, renaming Wynn Boston Harbor to Encore Boston Harbor specifically to distance the business from him. He personally paid $10 million in 2023 to permanently exit the Nevada gaming industry. A reputation formed in one state travelled into a state where he had never done business, and shaped how a market he’d never worked in treated him before he arrived.

Travis Kalanick’s departure from Uber followed him into an entirely new, unrelated venture years later. Coverage of his food-delivery startup CloudKitchens traces his Uber exit “amid a firestorm of privacy concerns, allegations of widespread sexual harassment and gender discrimination,” then quotes a former CloudKitchens executive calling it “the most toxic place I’ve ever seen or experienced,” and an operator who said the company “tried to destroy” the brand he had built there. The new business was never assessed purely on its own merits. It was read through the lens of the one he had just left.

Not every case ends the same way. Andreessen Horowitz invested $350 million in Adam Neumann’s new venture Flow in 2022, valuing it above $1 billion before it had launched, despite WeWork’s collapse from a $47 billion to an $8 billion valuation under his leadership. Marc Andreessen’s public justification leaned on second chances: “we love seeing repeat-founders build on past successes by growing from lessons learned.” Reputation still shaped every headline, every term, and every question asked about the deal, even though it never blocked the capital.

 

Acting Accordingly

One caveat is worth stating directly: nobody has produced a clean statistic for how much weight boards or recruiters place on informal, back-channel reputation versus formal references. That data mostly doesn’t exist, and anyone who claims otherwise is making it up. The mechanism, though, is well documented: wide, weak professional networks carry information fast, and reputational damage in one role measurably reduces opportunity in entirely unrelated ones.

The practical implication isn’t paranoia. It’s that the version of you that shows up in a room you’ve never been in was written by people you may not remember meeting, months or years before you walked in. Act like the story is already there, because it usually is.