Small Talk Is Not Wasted Time. It Is the Only Rehearsal for Big Trust.

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Most executives treat small talk as the tax you pay before the real conversation starts. Research on negotiation and workplace behaviour suggests it’s closer to the opposite: the low-stakes rehearsal that determines whether the real conversation goes anywhere at all.

 

What the Research Actually Shows

A frequently repeated claim holds that people who make small talk before negotiating are four times more likely to reach agreement. That number doesn’t survive a check against the study it’s supposedly drawn from. The actual 2002 research behind it, published in Group Dynamics, found something more modest but still real: negotiators who “schmoozed” beforehand reported significantly higher rapport than those who didn’t, and reached an impasse less often, 40.6% of the time versus 60.7%, though that gap was only marginally significant. The finding itself is that small talk measurably raises rapport and modestly improves outcomes. It is not some four-times multiplier, and repeating the inflated number would undercut exactly the kind of precision this argument needs to be taken seriously.

 

The Mechanism, Confirmed More Recently

A 2021 study in the Academy of Management Journal tracked 100 employees across 978 daily workplace observations over three weeks and found small talk works through a specific, two-sided mechanism: it “enhanced employees’ daily positive social emotions at work,” which increased helpful, cooperative behaviour toward colleagues, while simultaneously disrupting people’s ability to concentrate on their actual tasks in the moment. Both things are true at once. Small talk builds the social capital that makes cooperation possible later, and it costs a small amount of focus right now. A 2024 qualitative study of 35 B2B professionals found the same rapport-building mechanism operating specifically in negotiation contexts, identifying genuine curiosity, active listening, and respect for boundaries as the actual ingredients, not just friendly chatter for its own sake.

 

The Counterargument Worth Taking Seriously

Not everyone in this field agrees, and the disagreement is worth taking seriously rather than editing out. Kim Scott, whose Radical Candor framework has shaped how a generation of executives think about direct feedback, has argued the opposite case directly: real trust with employees comes from substantive one-on-ones and working relationships, not casual chat, and treating small talk as the relationship-building mechanism risks substituting a comfortable habit for the harder work of actually knowing someone. That critique lands hardest in ongoing management relationships. The negotiation and cross-cultural research above is mostly about a different situation: the first few minutes with someone you don’t yet have a working relationship with, where there’s no substantive history to draw on yet, and small talk is the only tool available to establish enough trust for the real conversation to start at all.

 

Why This Matters More in Some Rooms Than Others

Research on Arab business negotiators found relationship-building carries even more weight in that context, with negotiators leaning on personal networks and trust-building as central to how deals actually get made, rather than an optional warm-up. The mechanism isn’t unique to any one culture. It’s just more visibly load-bearing in markets where trust is built through recurring personal contact rather than through contracts alone.

 

What This Means in Practice

Skipping small talk to “get to the point faster” isn’t efficient. It’s removing the only low-stakes moment where two people calibrate whether they trust each other, before the stakes get high enough that a miscalibration actually costs something. The application is genuine curiosity about the person in front of you, delivered before you need anything from them, rather than performed friendliness, so that when you do need something, the trust required to ask for it is already there.

Pre-Mortem: The Liability Chain Medicare’s AI Prior Auth Model Has Not Drawn

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On 1 January 2026, the Centers for Medicare and Medicaid Services in USA launched the WISeR model in six states, introducing prior authorisation to procedures that traditional Medicare had always provided without it. Contracted companies now assess medical necessity using AI. Human clinicians are required to sign off on any denial. The Senate voted 46-50 in July 2026 to keep the programme running. One question has not been answered.

This is the twelfth piece in the Pre-Mortem series. Five questions, applied to the public record, before a programme has had the chance to succeed or fail.

 

The Bet

CMS is wagering that AI-assisted prior authorisation reduces unnecessary Medicare spend without producing the patient-safety incident that forces a political reversal. If WISeR delivers measurable waste reduction without a documented causal chain from AI denial to patient harm, it becomes the template for prior authorisation across Medicare nationally. If it produces that chain, a documented line from AI recommendation to denial to patient harm, it does not just end WISeR. It becomes the reference point that makes AI prior auth politically untouchable in federal health programmes for a generation.

 

The Assumption

CMS has answered every operational question about WISeR except this one:

When an AI recommendation leads a contracted clinician to deny care and a patient is harmed as a result, where does liability sit?

The model design places a human clinician between the AI output and the denial decision. That establishes a paper trail. It does not establish a liability framework. Contractors earn between 10 and 20 per cent of the savings generated by denials and lose that payment when a denial is overturned on appeal. That is a commercial penalty, not a clinical one. The Federal Tort Claims Act does not cover contracted entities. No federal court has tested whether a contracted clinician reviewing AI recommendations at volume carries the same duty of care as a treating physician making an independent clinical judgement.

The assumption doing all the work in this model is that the human review layer is accountability enough. That assumption has not been tested.

 

The Sequence

1 July 2025. CMS published the WISeR notice in the Federal Register and did not submit it to Congress under the Congressional Review Act. That omission would matter later.

1 January 2026. WISeR launched in New Jersey, Ohio, Oklahoma, Texas, Arizona, and Washington.

17 March 2026. The Washington Post published an exclusive: Medicare’s new AI gatekeeper was delaying care for seniors. The University of Washington’s medical system had nearly 100 patients waiting for epidural injections. In Arizona, Phoenix pain specialist Dr Matthew Crooks told Medscape that every epidural injection submitted in the first three months had been denied and described the system as completely nonfunctional and unsustainable. In Texas, initial AI approval rates ran at 62 per cent, against a 92 per cent national approval rate across Medicare Advantage.

25 March 2026. The Electronic Frontier Foundation filed a FOIA lawsuit against CMS in federal court in California, seeking records on WISeR’s AI algorithms, training data, bias safeguards, and the financial incentives paid to contractors. The suit confirmed that CMS had not made its AI methodology or vendor compensation structure publicly available seven weeks after launch.

6 April 2026. CMS published a Federal Register notice delaying prior authorisation implementation for certain services within the model to allow additional time for operational readiness. CMS also issued a corrective action order against one of its AI contractors. Both confirmed that the model’s operational design had not performed as intended in the first quarter.

12 May 2026. The Government Accountability Office issued its determination: WISeR met the Administrative Procedure Act definition of a rule and was subject to the Congressional Review Act. CMS had not made the required submission to Congress before the model took effect.

20 May 2026. Senator Ron Wyden and Representatives Suzan DelBene and Greg Landsman introduced resolutions of disapproval in both chambers, seeking to repeal WISeR under the CRA.

6 July 2026. Gold carding launched in Washington state. Providers achieving a 90 per cent affirmation rate across a minimum of ten prior authorisation requests become exempt from further review for covered services. Quarterly rollout to the remaining five states is planned.

16 July 2026. The Senate voted 46-50 against advancing the disapproval resolution. Party line. WISeR survived. The liability question the GAO had exposed survived with it.

The Pager

Dr Mehmet Oz, Administrator of the Centers for Medicare and Medicaid Services.

The message: WISeR’s accountability chain has not been drawn. The model places a contracted clinician between an AI denial recommendation and a Medicare beneficiary, but no published document establishes where negligence sits when a patient is harmed following an AI-assisted denial. The Federal Tort Claims Act does not cover contractors. Contractors point to the human clinician. Clinicians are reviewing AI output under volume pressure with no published duty-of-care standard for that specific context. When the first federal lawsuit tests this configuration, and one will, CMS will need a published framework, not a contract clause. That framework is easier to write before litigation than after.

 

The Proof

Gold carding is the model’s self-correction mechanism. If quarterly rollout reaches all six states and the 90 per cent affirmation threshold functions as a genuine quality signal, the AI layer contracts over time as trust is established. Proven providers exit prior auth. New entrants face the review. The model becomes calibrated rather than blanket.

If gold carding stalls or rollout criteria are applied inconsistently across jurisdictions, the AI layer expands without a release valve. Prior auth burden accumulates regardless of provider track record. The model becomes a cost-reduction instrument with no exit for providers who have earned one.

The proof of the bet is not the aggregate savings figure. It is whether WISeR, by the end of 2026, has published a liability framework and delivered gold carding in all six states. Without both, the model is running on the same untested assumption it started with.

 

Verdict

If CMS publishes a liability framework for AI-assisted denials before a federal case forces the question, and gold carding delivers consistent rollout across all six states, WISeR will be the strongest government evidence yet that AI-assisted utilisation review can reduce Medicare waste without a patient-safety crisis. The accountability design would become the reference for every federal health programme that follows.

Without the liability framework, WISeR accumulates its risk quietly. Not through a single dramatic incident, but through the gap between AI recommendation volume and human review capacity, compounded by an accountability vacuum no published document has yet closed. That gap does not stay open indefinitely.

Pre-Mortem: The US Government’s 3,611 AI Use Cases

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On 3 April 2025, the White House issued OMB Memorandum M-25-21, directing every major federal agency to appoint a Chief AI Officer, expand the use of artificial intelligence across government operations, and manage risk proportional to each system’s impact on citizens. Twelve months later, the Federal Agency AI Use Case Inventory records 3,611 AI use cases across 56 agencies, more than double the prior year’s total. A May 2026 survey of more than 200 technology executives across civilian and defence agencies found 53% are actively planning agentic AI pilots. Only 8% of those agencies have incident response frameworks in place.

This is the eleventh piece in the Pre-Mortem series. Five questions, applied to the public record, before a programme has had the chance to succeed or fail.

 

The Bet

The US government is betting that embedding AI across 3,611 federal workflows covering benefits decisions, immigration adjudications, healthcare determinations, and law enforcement, will make government faster and more efficient before the accountability architecture governing those decisions is clarified. OMB M-25-21 requires Chief AI Officers, public AI inventories, and risk management proportional to impact. The hard compliance deadline for role-specific AI training arrives in September 2026. If that architecture catches up to the deployment before a consequential wrong decision reaches a citizen with no named relief, the bet holds.

 

The Assumption

The expansion’s credibility turns on one unanswered question: whether the Federal Tort Claims Act, designed to govern negligent acts by human federal employees, applies without amendment to decisions made by AI agents running inside federal systems. The same May 2026 survey found only 44% of agencies include vendor liability clauses in AI contracts, and only 29% have documented kill-switch procedures. The legal architecture governing accountability in federal government was designed for humans acting on behalf of the state. No court has ruled on whether it extends to the agents they built.

 

The Sequence

In 2024, federal agencies reported 1,757 AI use cases. By 2025, that figure had grown to 3,611. In March 2026, the Department of Veterans Affairs expanded AI use in claims processing, with 215 of its 367 AI systems classified as high-impact, covering benefit eligibility, healthcare access, and fraud detection. In May 2026, the majority of agencies were planning agentic pilots, with only 20% having defined pre-deployment testing policies. The AI reached citizens before the accountability reached the AI.

 

The Pager

Russell Vought, Director of the Office of Management and Budget, carries the M-25-21 mandate at the centre of the federal AI expansion. Every covered agency has designated a Chief AI Officer responsible for inventory, risk management, and AI governance at agency level. The VA alone runs 215 high-impact AI systems. No single published document names what relief is available to a veteran whose claim was influenced by one of those systems, which official carries accountability for that decision, or whether the Federal Tort Claims Act applies when the acting party is software, not a civil servant.

 

The Proof

The measure that would settle this is a published legal standard: a named accountability chain clarifying who carries liability when a federal AI agent makes a consequential wrong decision, whether government, vendor, or joint, and whether the Federal Tort Claims Act applies or new legislation is required. No such standard has been published. OMB M-25-21 requires risk management proportional to impact. It does not name the relief available to a citizen when that risk management fails, nor the date by which that question must be answered.

 

Verdict

If OMB publishes, before the September 2026 training compliance deadline, a named accountability standard for AI-driven decisions in high-impact federal systems, covering who carries liability when the AI is wrong and what legal remedy a citizen holds, the expansion will stand as the most deliberate attempt the US federal government has made to govern AI before it reaches citizens at scale. Without that, the US government has put AI into 3,611 workflows and left the question of who carries the call when the AI gets it wrong to be answered in court, by accident, or not at all.

Regional Leadership Roles Reward a Different Kind of Patience Than Western Corporate Careers Teach.

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Most Western executives arrive in a regional leadership role with the instincts that got them promoted at home: move fast, show visible wins early, let the quarterly numbers do the talking. Those instincts do not transfer. They actively work against you.

Western corporate careers train impatience, and reward it. Promotion cycles run on twelve to eighteen month windows. Performance reviews measure the quarter just closed. The fastest route up is a visible individual win, attached cleanly to your name, delivered before the next review cycle starts. None of that is wrong inside the system that built it. It is a system optimised for institutional and contractual trust, built on the assumption that the relationship survives even if the individual leaves tomorrow.

 

What Regional Leadership Rewards

Gulf leadership runs on a different clock, and the data on how regional leaders actually spend their time backs that up. PwC’s most recent Middle East CEO survey found Saudi Arabian chief executives dedicate 27% of their time to planning five years or more ahead, compared with 16% globally, and GCC chief executives overall spend meaningfully more of their schedule on long-term strategic planning than their global counterparts. That time allocation is a rational response to a business environment where relationships carry the weight of a deal through its inevitable rough patches.

The mechanism underneath that patience has a name: wasta, the use of personal relationships and trust networks to get things done. Research summarised by Northeastern University’s D’Amore-McKim School of Business puts it plainly: “in the Arab context, trust at the interpersonal level needs to be established before any business relationship can unfold.” That is not a bureaucratic delay. It is the actual mechanism through which decisions get made, and skipping it does not speed anything up. It just means the decision never fully lands.

 

The Failure Rate Everyone Quotes Does Not Hold Up

Ask any expat executive circle and you will hear it: 20 to 40% of international assignments fail. It is one of the most repeated statistics in expatriate management, and it is very likely wrong. Anne-Wil Harzing’s 1995 analysis traced the figure back to a small number of frequently misquoted articles, only one of which actually contained solid empirical evidence, and that evidence showed failure rates to be low. The myth persisted for three decades anyway, because it made a better opening line for a consulting pitch than the more boring truth.

The more useful question is why the ones who do fail actually fail. The qualitative research on that is far less contested: misjudging the local pace of trust-building rather than any shortage of competence or effort.

 

What Patience Looks Like in Practice

Cross-cultural consultancy Commisceo Global puts the practical advice plainly: do not expect deals to be completed in two visits to the region, because the relationship has to be nurtured before the commercial conversation can move forward at all. That advice sounds soft to an executive trained to treat a second meeting without a signed term sheet as a stalled deal. It is a description of how decisions actually get authorised in a hierarchy where personal trust in the room outweighs the org chart on paper.

Twenty years running programmes across markets that operate on this clock, the pattern I have seen most often has nothing to do with understanding the concept. Most executives can recite the advice back accurately in an interview. Where they struggle is stopping themselves from behaving as though the Western clock is still running underneath it: checking for the win they would expect by month three, reading the absence of one as a signal that the relationship has stalled, when it has not.

 

Three Things That Actually Help

Building trust deliberately, rather than waiting for it to accumulate on its own, takes specific behaviour. Spend real time in the room before asking for the decision. Learn who actually holds influence in a given relationship, because it is rarely only the most senior title at the table. And resist manufacturing a visible early win for the story it tells back home, because the fastest way to damage the trust being built is to be seen optimising for your own next review cycle instead of the relationship in front of you.

 

What This Requires

The patience regional leadership rewards has nothing passive about it. Ninety-three per cent of CEOs across the GCC expect domestic momentum to keep strengthening, against 55% of their global peers. These are not leaders coasting on a slow market. The patience is aimed at a specific target: building the trust that makes fast execution possible later, instead of chasing a fast, visible win the relationship cannot yet support.

That is a genuinely different skill to the one a Western corporate career trains for, and it rarely shows up on the resume that got someone the role in the first place. The executives who actually succeed in regional leadership rarely arrive already patient. They learn to notice, quickly, that the clock they were trained on is the wrong clock, and recalibrate before the org chart tells them they have run out of time to.

Two-Thirds See AI Working. The CFO Still Can’t Prove It.

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Two-thirds of organisations say AI is delivering real productivity gains. Ask the CFO whether that translates into a return they can defend, and the answer changes completely.

 

Two Surveys, Same Landscape, Different Question

Deloitte’s 2026 State of AI in the Enterprise report, surveying 3,235 senior leaders across 24 countries, found 66% of organisations reporting real productivity and efficiency gains from AI, and 53% reporting genuinely better insights and decision-making. That is not a marginal result. Two-thirds of a very large sample are seeing the operational benefit show up in how the work actually gets done.

EY’s 2026 Global DNA of the CFO Survey, covering 1,610 CFOs, finance directors, and heads of finance at organisations with over a billion dollars in revenue, was fielded in the same window and asked a different question: not whether AI is helping, but whether finance can prove it in the language capital allocation actually requires. The answer is uncomfortable. Just 12% of CFOs say their finance transformation outcomes exceeded expectations over the past two years. Only 21% describe their function’s AI readiness as leading or advanced. And 71% say plainly that traditional metrics are not enough to evaluate initiatives that combine people and technology.

 

This Is Not Two Surveys Disagreeing

Read carelessly, that looks like a contradiction: one report says AI is working, the other says it isn’t. It isn’t a contradiction. It is two different functions answering two different questions, and the gap between the answers is the actual story.

The operational teams reporting gains in the Deloitte data are measuring what they can see directly: faster cycle times, fewer manual steps, sharper analysis. None of that is fabricated, and none of it is trivial. But productivity gain and return on invested capital are not the same measurement, and the EY data shows finance has not built the bridge between them. Sixty-one per cent of CFOs cite data quality and bias as their top challenge in securing further AI investment, which is a polite way of saying the numbers underneath the business case are not yet reliable enough to defend in a capital allocation meeting.

A separate July 2026 survey of 1,505 senior finance leaders across the US, UK, Australia, and India found the same pattern from a slightly different angle: 92% feel active pressure to prove AI investment is paying off, while only half report their AI agents have actually achieved a measurable return. The pressure to prove value is running well ahead of the organisation’s actual ability to measure it.

 

The Real Governance Gap

None of this is a technology problem. The Deloitte numbers show the technology is doing what it was bought to do, in a majority of cases, across a very large sample. It is not really a capital problem either. Boards are still funding AI investment at pace, and EY’s own data shows CFOs largely expect that to continue.

It is a measurement problem, and measurement problems are governance problems wearing a data costume. Somebody has to own translating “the team says this is working” into “here is the return, measured the way capital allocation actually requires it to be measured,” before the investment decision, not after it, using metrics that were agreed while everyone could still agree on them.

Most organisations have not assigned that ownership to anyone specific. It sits, by default, somewhere between IT, who built the thing, and finance, who has to defend the number nobody built the measurement framework to produce.

 

What Actually Closes the Gap

Fixing this does not start with better AI. It starts with defining, before the next AI investment gets approved, exactly what “return” means for that specific initiative, in terms finance and the operational team both sign off on before deployment, not after. It means putting one named owner against that measurement framework, not a committee, and not IT by default because they happened to build the system. And it means accepting that a lot of the value AI is already producing is real but currently invisible in the metric finance is required to report against, which is a reason to fix the metric, not to distrust the value.

 

The Question Worth Asking Before the Next AI Business Case

The next time someone asks whether an AI investment delivered a return, the sharper question is whether anyone defined, in advance, what return was actually supposed to look like, and in whose language it would need to be proven. Most organisations running significant AI programmes right now cannot answer that question. That is the real gap the numbers are describing, and it is entirely fixable, starting with the next business case, not the last one.

Transformation Fatigue Is Not About the Number of Changes. It Is About the Absence of a Finish Line.

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Most organisations diagnose change fatigue as a volume problem: too many initiatives running at once, too many change requests hitting the same team. The volume is real, but it isn’t the actual mechanism.

 

What the Volume Framing Gets Right, and Wrong

The average employee experienced ten planned enterprise changes in 2022, up from two in 2016, according to Gartner research reported in Harvard Business Review. Prosci’s long-running change-saturation research puts 73% of organisations near, at, or past the point where employees are running out of capacity to absorb more.

Those numbers are real, and the standard academic definition of change fatigue treats volume as the cause: “a perception that too much change is taking place.” A 2021 peer-reviewed study in Public Money & Management tested this directly across repeated public-sector reorganisations and found the number of prior changes predicted fatigue, mediated by uncertainty and workload.

That is the correct diagnosis for some organisations. It is an incomplete one for most.

 

The Mechanism That Volume Alone Doesn’t Explain

Deloitte’s 2026 Global Human Capital Trends research, covering 9,000 leaders across 76 countries, names the actual shift more precisely than a raw change count can. One-third of surveyed workers experienced 15 major changes in the past year alone, but only 27% of leaders say their organisation actually manages change well, and just 8% found their change and learning efforts highly effective. Deloitte’s own recommendation is a reframe, not a volume reduction: move from episodic “change management,” which assumes a start and a finish, toward continuous “changefulness,” because the assumption of a finish line no longer holds.

It is not just that there is more change. It is that employees no longer get the stabilisation phase that used to follow each change, the period where a new way of working became the way of working, before the next initiative started.

 

Why Closure Isn’t Optional

Kurt Lewin’s original change model, still the theoretical foundation for most modern frameworks, has three stages: unfreeze, change, refreeze. Refreeze is the stage where a new behaviour actually solidifies into habit, where people stop consciously managing the transition and start simply working the new way. Skip refreeze consistently enough, running one unfreeze-change cycle straight into the next, and nothing ever solidifies. Every process, every system, every way of working stays permanently provisional.

Psychologist Pauline Boss’s concept of ambiguous loss, a loss that is real but never gets acknowledgement or closure, was developed for grief, not transformation programmes. But the mechanism translates directly: what exhausts people is not the change itself, it is never getting to grieve the old way and fully arrive in the new one before being asked to leave that behind too.

 

What This Actually Means for Leaders Running Transformation

BCG’s own research on transformation puts a number on the stakes: only about one in four transformations succeeds in capturing both short-term and long-term value, while people-centred change management, done properly, increases the odds of sustained improvement by up to 90%. Most of that people-centred work gets spent managing the volume of change. Almost none of it gets spent deliberately building in stabilisation points, the moments where a team is told, explicitly, that this phase is genuinely done.

The fix is not necessarily fewer initiatives. Some organisations doing fine with a high change volume have simply built real closure into the cadence: a defined point where the previous change is declared stable, celebrated as finished, and left alone for long enough that people stop bracing for the next disruption to hit the same process again.

 

The Question Worth Asking Before the Next Initiative Launches

Before adding another initiative to the roadmap, the honest question is not whether the organisation can absorb one more change. It is whether the last one was ever actually declared finished, or whether it’s still technically in flight, quietly compounding on top of whatever comes next.

Most transformation fatigue comes down to a single sentence leaders rarely say out loud: this is done, and it is going to stay this way for a while.

The PMO Evolves From Reporting to Enablement Engine

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Most PMOs are still optimised for a question that stopped mattering years ago: is the project on time.

That question still matters. It has just stopped being the question that decides whether the PMO survives.

Clarkston Consulting’s 2026 programme management research puts the shift plainly: leading PMOs are moving beyond reporting to serve as enterprise enablement engines, connecting strategy to execution, building readiness into delivery from the start, and treating benefits realisation as a core discipline with clear ownership and early indicators of whether value is actually being created. A better dashboard will not get you there. This is a different function entirely.

 

The Reporting Function Was Never the Point

A PMO built around status reporting produces a specific kind of artefact: a red, amber, or green rating, a variance against plan, a risk log updated on schedule. Useful, in a narrow sense. None of it tells an executive whether the portfolio is creating value, whether the organisation’s capacity to change is being spent well, or whether the thing being delivered still matches the strategy that funded it eighteen months ago.

Wellingtone’s PMO research this year makes a related but sharper point: the inconsistency that plagues portfolio reporting, different teams defining “green” differently, decisions made on opinion rather than evidence, is a data problem before it is a reporting problem. Standardise the definitions and data model underneath the reporting, and AI can act as an assistant that takes a defined goal such as producing this week’s portfolio report or re-planning a delayed project, and coordinates the steps across your tools, removing low-value work rather than just colouring in a status field faster.

That distinction matters because AI is already acting as that assistant. It is already running in PMOs now.

 

Where the Real Shift Is Happening

House of PMO’s 2026 trends names the structural change underneath the tooling change: PMOs are increasingly being embedded into business areas rather than operating as a distant central function. This proximity builds trust, improves understanding, and allows PMOs to influence decisions earlier, where they can actually make a difference. The PMO becomes a connector between strategy and delivery, between different delivery models, and between governance and pace, rather than a function three steps removed from where the decisions get made.

Put those two shifts together: the data foundation improving enough for AI to generate real insight, and the PMO physically and organisationally closer to where decisions happen. The reporting function stops being the PMO’s reason to exist. It becomes infrastructure, while the enablement work, the strategic conversation, the early warning that changes a decision before it becomes a recovery programme, is where the value actually sits.

 

The Question Every Sponsor Should Be Asking

For anyone accountable for a large programme or portfolio, there is one question worth asking about the PMO function right now: has it been designed for accountability, or for assurance.

An assurance PMO produces reports that document what happened. It is useful for the audit trail and largely irrelevant to the decisions that mattered, because by the time the report lands, the decision has already been made without it. That is not a project management decision. It is a leadership decision about what the function is actually for, and most organisations have never asked it directly. They inherited a PMO structure built around reporting cadence and have never revisited whether reporting cadence was ever the point.

 

Why the Clock Is Actually Running

The reason this stops being an optional repositioning is straightforward. AI is already absorbing the administrative core of traditional PMO work: status compilation, report drafting, risk-log maintenance, the tasks that used to justify a PMO analyst’s headcount. A PMO whose value proposition is still “we produce the reports” is competing against a capability that produces the same reports faster, more consistently, and without a salary.

Gartner expects more than 40% of agentic AI projects to be cancelled by the end of 2027, mostly because organisations deployed the technology without redesigning the governance and decision rights around it. That is precisely the work a repositioned PMO is suited to do, and precisely the work a reporting-only PMO has no mandate to touch.

 

What This Looks Like in Practice

The organisations getting this right are not adding an AI tool to the existing PMO operating model. They are redesigning what the PMO is accountable for first, then deciding what gets automated underneath that redesign. That means fewer metrics, chosen because they connect directly to financial outcomes, risk reduction, or strategic alignment, not because they are easy to collect. It means a PMO embedded close enough to the business to have the conversation before the decision, not after the variance report. And it means a governance model built to keep pace with AI and agentic deployments, not one still calibrated for a world where the only automation was a spreadsheet macro.

That happens by deciding, at leadership level, what the PMO is actually for, not by upgrading the reporting tool.

My own view is that this repositioning does not stop at a PMO with better metrics. Within a couple of years, the distinction between the PMO and whatever function owns strategy execution will start to disappear in the organisations doing this well, because once a function is genuinely accountable for whether strategy translates into delivered value, calling it a project management office undersells what it has become.

The PMOs still measuring themselves on whether the project shipped on time are answering a question that stopped being the one that mattered. The ones worth funding are the ones that can already tell you whether it was the right project.

Pre-Mortem: The UK’s Critical Third Party Regime

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On 13 July 2026, Amazon Web Services, Google Cloud, Microsoft Azure, and Oracle became the first companies formally designated as Critical Third Parties to the UK financial system. The Bank of England, the Prudential Regulation Authority, and the Financial Conduct Authority now hold powers to gather information, assess resilience, and make enforceable rules against the four providers for the services they supply to the financial sector. A 2024 Bank of England and FCA survey found the top three cloud providers accounted for 73% of all cloud providers named by respondents across the UK financial sector. The designation names the risk. It does not resolve it.

This is the tenth piece in the Pre-Mortem series. Five questions, applied to the public record, before a programme has had the chance to succeed or fail.

 

The Bet

The UK is betting that direct regulatory oversight of four technology providers, applied specifically to their financial-sector services, will reduce the systemic risk from having most of the sector’s cloud infrastructure concentrated in three companies. The Financial Services and Markets Act 2023, which created the CTP regime, gives the Bank of England, PRA, and FCA powers to assess resilience and enforce CTP-specific rules. The designation is a supervisory relationship, not a structural remedy. If that supervisory relationship produces documented, published improvements in resilience before the first major cloud incident in UK financial services, the bet holds.

 

The Assumption

The regime’s credibility turns on one scoping decision: that overseeing four providers for the services they supply to the UK financial sector is sufficient to contain risks generated by four companies whose infrastructure decisions are made globally, across legal jurisdictions and customer bases far larger than the UK financial system. Microsoft Ireland Operations Limited is the designated entity. Its architecture decisions are made in Redmond. The supervisory perimeter covers the financial-sector slice. The concentration risk does not stop there.

 

The Sequence

The concentration risk pre-dated the regime by years. The Financial Services and Markets Act 2023 established the legislative basis for the CTP framework. A 2024 Bank of England and FCA survey confirmed the scale: three providers controlling the majority of UK financial-sector cloud infrastructure. HM Treasury announced the first four designations on 10 July 2026, effective 13 July. The sequence is legislation, then evidence, then designation. The risk was present throughout.

 

The Pager

Rachel Blake MP, Economic Secretary to the Treasury and City Minister, made the designation announcement. The Bank of England, PRA, and FCA share oversight of the four providers under the regime. Three regulators. Three separate mandates. No published document names which of the three leads incident coordination when a designated provider’s outage affects UK financial services. The CTP framework assigns supervisory responsibility. It does not assign the call.

 

The Proof

The measure that would settle this regime’s effectiveness is a published resilience outcome: a before-and-after comparison of systemic vulnerability at a named date after the CTP rules take effect. No such commitment has been published. The three regulators hold powers to gather information from the four providers. No public document names what information will be published, in what form, and by when. The first formal review cycle has no published date.

 

Verdict

If the three regulators jointly publish a named lead for CTP incident coordination and commit to a quantified resilience outcome before the first formal review cycle, the designation will stand as the most substantive step the UK has taken to address cloud concentration risk in its financial sector. Without that, four of the world’s most powerful technology companies have been formally named, and the framework that names them has not yet named who is in charge when one of them goes down.

Healthcare AI Enters Its Accountability Phase

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Healthcare AI has stopped being an experiment. Holland & Knight, the US law firm, put it plainly in its mid-2026 healthcare report: the sector has entered a “recalibration phase,” where capital discipline and demonstrable return on investment have replaced the growth-first logic that funded the last five years of digital health.

That is a legal and investment framing, not a clinical one. But the clinical evidence backing it up is now specific enough to name.

Kaiser Permanente’s Permanente Medical Group rolled out ambient AI scribing to 7,260 physicians across more than 2.5 million patient encounters between October 2023 and December 2024. The result, confirmed by Kaiser’s own Division of Research: nearly 16,000 clinician-hours of documentation time saved. Not a pilot cohort. Not a vendor’s projection. A production deployment, measured after the fact, across a workforce large enough that the number means something.

Ambient documentation is also the part of healthcare AI with the least room left to argue about. A 2026 survey of 120 US health systems, run by the healthcare research firm Eliciting Insights, found clinical note-taking and ambient listening tools now sit at 68% adoption, up 62% year on year. Among the health systems able to quantify results, 61% report at least a 2x return specifically from ambient listening tools.

 

The $3.20 Figure Is Real, and Older Than It Looks

The oft-quoted “$3.20 return for every $1 invested in healthcare AI” is genuine, but it is worth knowing where it actually comes from before repeating it in a board pack. It traces to a Microsoft-sponsored IDC study published in late 2023 and reported in early 2024, not a fresh 2026 finding. It has simply become the industry’s standing benchmark figure, cited so often across 2025 and 2026 coverage that it now reads as current data. It is not wrong. It is just two years old and vendor-commissioned, which matters if you are the one deciding how much weight to put on it.

The Kaiser and adoption figures matter more, precisely because they are recent, specific, and independently reported rather than recycled.

 

What Actually Produced the Return

This ROI happened because of a specific programme design, not because someone bought a good tool, one that most other sectors experimenting with AI have not adopted.

Kaiser did not deploy ambient scribing and then discover the workflow around it. Clinical documentation workflow got redesigned first, and the AI tool was the mechanism, not the starting point. Accountability for the outcome, hours saved, adoption sustained, clinician trust maintained, was established before rollout, not retrofitted afterwards to justify the spend. And the whole exercise operated under exactly the capital discipline Holland & Knight describes: prove the return, or the funding does not continue.

That sequence, workflow redesign first, accountability from day one, capital discipline over growth optimism, is the actual explanation for why healthcare produced verifiable ROI while most other sectors are still producing pilot decks.

 

Healthcare Is Now the Benchmark, Not the Exception

Treat healthcare’s result as evidence that AI works and you will draw the wrong lesson. The technology was never really in question. What was in question, and what most other sectors are still failing to answer, is whether the organisation deploying it redesigned anything before switching it on.

Healthcare had no choice but to answer that question properly. Clinical documentation errors have consequences that show up in patient outcomes and malpractice exposure, not just quarterly numbers, so the sector could not afford the deploy-first governance-later approach that has quietly become normal everywhere else.

That is what other sectors should actually be benchmarking against: not whether their AI produces a return, but whether their programme was ever designed to make one provable.

 

The Question Worth Asking Before the Next AI Business Case

Before signing off the next AI investment, the question is not whether AI delivers value. Healthcare has already answered that question, under specific and now well-documented conditions.

The real question is whether your programme has been designed to match those conditions, workflow redesign before deployment, accountability defined from the outset, capital discipline over growth optimism, or whether it has been designed the way most digital health investment was designed before 2026: fund it, hope the outcomes show up eventually, and find out later whether anyone was ever going to check.

Healthcare already found out. That is the whole difference.

The Programme Succeeded. That Was the Problem.

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Most digital transformation programmes are designed to end.

That is the actual design flaw. Not the technology chosen, not the budget allocated, not even the ambition behind the initiative. The programme itself is structured around a finish line, a go-live date, a steering committee sign-off, a moment when the work is declared complete and the team disbands.

The problem is that the market, the technology, and the customer never agreed to stop moving at that point.

 

The Project Mindset Is the Actual Liability

Transformation programmes are built like construction projects. Define the scope, execute the plan, hand over the keys, move on to the next thing. That structure works well for building a bridge. It works badly for building an organisation’s capacity to keep adapting, because the moment the programme ends, so does the organisation’s active attention to the problem it was meant to solve.

A May 2026 Forbes Business Council analysis puts it plainly: treating digital transformation as a project sets the expectation that there is a finish line to cross. There is not. Markets keep moving. Customer expectations shift faster than any single programme can track. Data environments and operating models change shape well after the sign-off. A transformation programme with a defined end date is optimised for a world that stopped changing the day the programme closed, which is not the world any organisation actually operates in.

The Forbes analysis draws a comparison that holds up well: digital transformation works like fitness. When you stop, you atrophy. Nobody who has kept fit for a decade did it with a single twelve-week programme and then stopped. They built a habit that never formally ends.

 

What Continuous Capability Actually Looks Like

Tesla is the clearest large-scale example of what this looks like in practice. Tesla ships software improvements to vehicles already on the road through over-the-air updates, rather than treating the car’s capability as fixed at the point of sale. Autopilot and Full Self-Driving features are refined through frequent releases, often tested on a small subset of vehicles before wider rollout, rather than waiting for a full model cycle to bundle every improvement together. The car’s capability keeps changing for as long as the vehicle is on the road, never declared finished at any single point.

Most organisations do not need to ship software to a fleet of vehicles. But the underlying pattern is the same: small changes shipped continuously and tested before wide release, rather than large changes bundled into an infrequent big-bang release. That pattern is exactly what separates organisations still adapting years after their transformation programme closed from the ones still running the same processes the programme was meant to replace.

 

The Five Things That Actually Change

Shifting from a transformation mindset to a continuous one is not about abandoning structure. It requires deciding to do a small number of things differently, and doing them consistently.

Start with the conversation itself. The goal is not to convince stakeholders that transformation was wrong. It is to convince them that the current approach stops too early. Frame the shift as doing transformation properly, not as replacing it with something else.

Build modular, not monolithic. Large, all-or-nothing platform overhauls are exactly the kind of investment that locks an organisation into a single technology decision for a decade. Modular, scalable components can be replaced or upgraded individually as needs change, without requiring another multi-year programme to do it.

Treat learning as infrastructure, not an event. A single training push before go-live does not build a capability. Continuous training, embedded guidance, and space to experiment safely are what actually let people keep pace with a system that keeps changing.

Change what gets measured. Tracking project completion tells you the programme finished. It tells you nothing about whether the organisation can still adapt six months later. Track agility, the rate of continuous improvement, and customer outcomes instead, because those are the metrics that actually describe ongoing capability.

Build the feedback loop permanently. Regular input from employees and customers is not a phase of the programme. It is the mechanism that tells the organisation when the next adjustment is needed, and it only works if it never switches off.

 

The Question Worth Asking Before the Next Transformation Sign-Off

Before the next transformation programme gets a steering committee sign-off and a closing date, the honest question is whether the organisation’s capacity to keep adapting exists independently of the programme that is about to close, not whether the scope was delivered.

If the answer is no, the programme did not fail to transform the organisation. It succeeded at exactly what it was designed to do, and the design was the problem.