Category Archives: Management

Healthcare’s Algorithm Is Working. That Is the Problem

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Somewhere in American hospital records, there is a pattern that should not exist.

Diagnoses of acute posthemorrhagic anaemia, a serious blood-loss condition that requires transfusion, have risen sharply at facilities that adopted AI billing tools. Blood transfusions have not. A condition is being recorded. The standard treatment for that condition is not being given. According to a Blue Cross Blue Shield Association analysis, the discrepancy is not a rounding error. It is a signature.

This is not a story about a medical error. No patient was misdiagnosed. No physician made a wrong call. What happened is more systemic and more troubling. An AI system trained to identify billable conditions found one. It coded it. The hospital billed for it. Nobody questioned whether the diagnosis reflected care that was actually delivered.

This is what AI looks like when there is no governance around it.

What the Bill Says About the Chart

The Blue Cross Blue Shield analysis examined what happened to hospital billing after AI coding tools arrived at scale. The numbers are not ambiguous. Inpatient spending attributable to AI coding practices reached an estimated $663 million. Outpatient spending tied to the same pattern reached $1.67 billion. One facility’s case complexity rating, the metric that determines how much a hospital can charge, rose 6.7 per cent in the year after adopting an AI billing tool. The average rise at comparable facilities in the same state was 0.9 per cent.

The practice is called upcoding: coding a patient as sicker, or their treatment as more complex, than the clinical record supports. It has existed in healthcare administration for decades. What AI has done is industrialise it. According to a federal data brief from the Office of the National Coordinator for Health Information Technology, 71 per cent of US hospitals were using predictive AI by 2024. AI use for billing specifically rose 25 percentage points in a single year, from 36 per cent of hospitals in 2023 to 61 per cent in 2024. The speed of that adoption has outrun every oversight mechanism that existed to check it.

The tool is not complicated. What was built around it is the problem. AI coding tools scan patient records and flag conditions that could legitimately be billed. In the right environment, with clinical oversight and audit processes, that is a useful capability. In the environment most hospitals actually built, which is one without meaningful governance, they become a revenue maximisation engine. The algorithm does what it was trained to do. Nobody verifies whether the conditions it codes for were actually treated. The bills go out.

The Insurer’s Algorithm Has a Different Objective

At the same time hospitals are using AI to add conditions to bills, health insurers are using AI to remove approvals from treatment requests.

Prior authorisation, the process by which insurers must approve procedures before they happen, has become a primary deployment zone for AI-driven decision-making. The American Medical Association surveyed physicians and found that 61 per cent reported health plan use of AI is increasing prior authorisation denials. A US Senate Permanent Subcommittee on Investigations report found that denial rates at UnitedHealthcare, CVS, and Humana’s Medicare Advantage plans rose as each insurer increased AI deployment in its review process.

The governance picture on the insurer side is no better than on the hospital side. A January 2026 study in Health Affairs by researchers at Stanford Health Care, drawing on a survey of 93 large health insurers, found that more than one-quarter of insurers do not document the accuracy of their AI models or test them for bias, around 40 per cent have no accountability practices in place for AI tools used in prior authorisation and claims decisions, and fewer than one-quarter even tell providers when AI was involved in a determination.

The result is a healthcare system in which AI is simultaneously inflating what hospitals charge and compressing what insurers approve. Patients sit between the two. The treatment they need may be denied before it is given and billed for a complication they were never treated for.

Arizona, Maryland, Nebraska, and Texas all passed legislation in 2025 requiring human oversight before AI can be used to deny a prior authorisation request, prohibiting it as the sole basis for medical necessity determinations. From 2026, the Centers for Medicare and Medicaid Services (CMS) will require payers to provide a specific reason for every AI-assisted denial and to publish aggregate approval data. That regulatory response confirms the scale of what is happening. Legislators do not write laws against things that are not happening.

Nobody Has Had to Answer for This

The question that neither the hospital nor the insurer has been required to answer is a straightforward one: who is responsible for what the algorithm decides?

A 2025 survey of 182 US hospital leaders by Black Book Research found that only 22 per cent are confident they could produce a complete AI audit trail within 30 days if asked. Only 29 per cent have implemented and enforced policies covering AI model inventory and accountability sign-offs. Forty-one per cent identified limited vendor documentation, the model cards and drift reports that explain how a system behaves over time, as their top barrier to audit readiness. The median share of IT and quality budgets allocated to AI governance is 4.2 per cent.

These are not numbers that describe an industry taking AI risk seriously. They describe an industry that deployed the technology and deferred the governance question for later.

The procurement happened fast. The governance never followed. Across billing departments and claims operations, AI has been handed consequential authority over patient finances and care access by organisations that did not build the structures that authority demands. The tools were procured. The governance was not.

The Wrong Diagnosis

Every time this gets written about as an AI problem, the real fix gets deferred.

If the algorithm is the villain, the solution is a better algorithm. A more accurate one. A less biased one. Another procurement cycle, another vendor, another pilot. That framing lets every decision-maker who signed the purchase order, approved the deployment, and chose not to build the oversight infrastructure step back from the frame. The machine did it. The machine was wrong.

In healthcare, the machine is doing exactly what it was built to do. It finds billable codes and it finds reasons to deny claims. It operates at the scale and speed that human reviewers cannot match. And it does all of this inside organisations that did not build the governance structures, the audit processes, the accountability frameworks, or the appeals mechanisms that consequential decisions at that scale require.

The United States is where this data exists. It is not where the problem stops.

That is not an AI failure. It is an organisational one. And unlike a broken algorithm, it cannot be fixed with a software update.

 

Governance Is Not a Committee. It Is a Decision Architecture

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A technology programme was delivered on time. The steering committee signed it off. The system went live on schedule and within budget. Twelve months later, usage across the organisation sat at eleven percent. The project had been a success by every measure the governance structure tracked. It had failed by the only measure that mattered.

Nobody was accountable for the eleven percent. The named owner had moved to a different role. The steering committee was dissolved at go-live. The vendor had fulfilled its contract. The organisation had built something that worked perfectly and was used by almost nobody, and no single person in the building could explain why.

That is not a delivery failure. It is a governance failure. And it is far more common than any organisation publicly admits.

 

What Governance Actually Is

Governance is one of those words that everyone uses and nobody defines. In most organisations, it has come to mean a structure: a committee, a framework document, an approval process, a risk register. Something you have rather than something you do. You have a governance framework. The governance is in place. The committee meets quarterly.

This version of governance is useless.

Governance is not a structure. It is a decision architecture. It is the infrastructure that determines how decisions are made, who makes them, what they are accountable for, and how fast the organisation can act when circumstances change.

Every organisation has a governance architecture, whether it has designed one or not. The informal version is still a governance architecture: decisions made by whoever is most senior in the room, accountability absorbed by whoever is most junior when something goes wrong, escalation triggered whenever someone is uncomfortable. It is simply a poor one. The difference between organisations that move well and organisations that stall is rarely capability. It is usually the quality of the decision infrastructure underneath the capability.

 

Governance Theatre

The most dangerous governance is the kind that looks correct from the outside.

Most large organisations have built governance that performs the appearance of oversight without the function. The risk register is meticulously maintained and never acted upon. The steering committee meets monthly and has not once paused a programme. The policy required six weeks of approval and is read by nobody after signing. The assurance review always concludes the project is on track.

This is more harmful than no governance, for one reason: it generates confidence without protection. The board believes the oversight is in place. The programme team believes the risks are managed. The organisation proceeds as if the architecture exists, while operating without it. When the failure arrives, it arrives at scale, having been invisible to every structure designed to catch it.

The question is not whether your organisation has governance. The question is whether your governance is real.

 

What Good Governance Looks Like

Good governance has five characteristics that distinguish it from the committee-and-checkpoint version most organisations have built.

The first is named ownership. Every material decision, every significant deployment, every consequential process has a single individual accountable for the outcome. Not a committee. Not a function. A person. The committee can advise. The function can review. One name sits against each thing that matters, and that person knows it and accepts it.

The second is authority that matches accountability. The most common governance failure is asking someone to be accountable for an outcome they cannot influence. If the named owner cannot pause a deployment, redirect a budget, or override a recommendation, their accountability is nominal. If you cannot identify what the accountable person can stop, you have not given them accountability. You have given them exposure.

The third is pre-agreed frameworks. Good governance does not require full escalation for every decision. It requires that boundaries are agreed in advance, so decisions within those boundaries can be made quickly, and decisions outside them trigger a defined path. The approval gate model creates queues. The framework model reserves escalation for the decisions that genuinely need it. Speed and governance are not a trade-off. They are a design choice.

The fourth is transparency of reasoning. Material decisions need a record. Not for audit purposes, but because the organisations that navigate change well are the ones where future leaders can understand not just what was decided, but why, what alternatives were considered, and what conditions would prompt a different outcome. This is not bureaucracy. It is institutional memory, and its absence is one of the most expensive losses any organisation experiences.

The fifth is a culture that supports use. The best governance architecture fails if the organisation punishes the people who use it correctly. The programme manager who escalates a risk that delays a milestone. The engineer who flags a model limitation that complicates a launch. The analyst who says the data is not fit for purpose. If those people are sidelined or not listened to, the framework is decorative. Governance is architecture and behaviour. Building the architecture without addressing the behaviour is half the work.

 

Governance Debt

There is a cost to governance failure that does not appear on any balance sheet until it is too late to address cheaply.

Every decision made without proper governance accumulates what might be called governance debt. The decision is made, the programme moves forward, the system is deployed. The cost is not visible immediately. It appears two years later, when the person who made the original choice has moved on, when nobody can explain why the architecture was designed the way it was, when the organisation needs to change a system it no longer fully understands and cannot safely modify.

Like financial debt, governance debt compounds. Small omissions early in a programme create disproportionately large costs at the point of change. The organisations that experience the most expensive transformations are rarely those that started with the hardest problems. They are those that accumulated governance debt in the early stages and discovered the interest charge when conditions changed.

 

The Speed Paradox

The dominant assumption about governance is that it slows things down. The evidence says otherwise.

Financial services is among the most heavily governed sectors in the world. It is also, by measurable data, among the fastest at moving AI from experimentation to production. Databricks’ analysis of enterprise AI adoption found that financial services improved its experimental-to-production ratio from 29:1 to 10:1 in under eighteen months, the sharpest improvement of any sector measured. The governance culture that financial services built under regulatory compulsion became, in practice, a deployment accelerant.

The reason is straightforward. When governance is architecture rather than checkpoint, when boundaries are pre-agreed and ownership is named, decisions within the framework do not require escalation. The work that in a poorly governed organisation requires a committee review happens at team level, within agreed parameters, without delay. The governance does not add a stage to the process. It is the process.

The organisations that move slowly under governance are the ones with checkpoints. The ones that move fast under governance are the ones with architecture.

 

Why AI Makes This Urgent

AI does not create governance problems. It amplifies the ones that already exist.

Every organisation deploying AI is making decisions at scale and at speed in ways that are not always visible to the people accountable for outcomes. When a model influences hiring, lending, clinical treatment, or procurement, the decision architecture governing that model matters as much as the architecture governing any senior leader. In some respects more.

Three risks are specific to AI. The first is accountability diffusion. When a decision is made by a model, who is accountable is rarely defined in practice. The model carries no accountability. The vendor carries it within narrow contractual limits. The organisation must deliberately assign it or it defaults to nobody, which is where most organisations currently sit.

The second is scale of error. A human decision-maker with a blind spot makes that error incrementally. A model with the same blind spot can make it thousands of times before the pattern is identified. The governance that catches a human error at ten instances must catch a model error at ten thousand. Most governance frameworks were not designed for that volume.

The third is the deployment and use gap. AI systems are deployed for a defined purpose in a defined context. They are then used in contexts their designers did not anticipate, by people not trained on their limitations, for decisions the governance framework never considered. Governance must follow the system into use, not stop at the deployment gate.

One additional risk is specific to the current moment. In most organisations, AI governance covers the official deployments. It has no visibility of, and no authority over, the AI already in use through personal accounts, consumer tools, and unapproved models. The governance gap that will produce the first visible failures is not in the formal AI programme. It is in the tools already running beneath the governance architecture’s line of sight.

For boards, this is a specific accountability question. Most are receiving AI updates without the frameworks to evaluate them. The question is not whether the organisation has an AI strategy. It is whether the board can answer four things: who is accountable for each material AI deployment, what authority they hold, what the escalation path looks like when something goes wrong, and whether the governance covers the AI that is actually in use rather than only the AI that was formally approved.

 

Three Questions That Will Tell You More Than Any Framework Audit

Name the person accountable for your most significant AI deployment. Not the team. Not the function. One person. If you cannot name them in under ten seconds, you do not have governance. You have the appearance of it.

When did your governance last stop something? Not delay it, not document a risk against it. Stop it. If the answer is never, your governance is not functioning as risk infrastructure. It is functioning as a record-keeping exercise.

If the three people who made your most significant programme decisions in the last two years left tomorrow, what would the organisation know about why those decisions were made? If the answer is not much, you are accumulating governance debt at a rate your future leaders will pay.

Governance is not a committee. It is not a document. It is the infrastructure through which an organisation makes consequential decisions, learns from them, and remains able to change course when it needs to.

Most organisations have not built that infrastructure. AI has not created that problem. It has simply made the cost of not solving it impossible to ignore.

Tokens Don’t Run Transformation Programmes

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Somewhere right now, a CFO is presenting a slide that frames it as: tokens or headcount. Allocate to AI infrastructure, reduce salary costs, reinvest in capability. The maths is clean. The logic looks compelling. The slide is wrong.

The phrase “tokens or humans” has entered the corporate vocabulary fast. CNBC ran it as a headline in May 2026 and they were right to, because it captures something real: organisations are now making explicit choices between paying for people and paying for AI. But the framing treats it as a resource allocation problem. It isn’t. It’s a transformation governance problem, and most organisations are making the call before they understand what they are trading away.

 

The Numbers Look Better Than They Are

More than 142,000 tech jobs have been cut in 2026 already. Amazon, Meta, Salesforce, Block, Cloudflare. Executives are public about the logic: AI agents handle what humans used to, smaller teams move faster, capital gets redirected to infrastructure. The numbers are real.

So are these: over 80% of companies using AI showed no productivity benefit in a February 2026 study. Uber burned through its entire annual AI coding budget in four months. Microsoft cancelled a large tranche of Claude Code licences after six months. Productivity gains in controlled studies can be significant. In most real-world settings, the gains are a fraction of what those studies suggest, if they materialise at all.

Token prices are falling, yes. Gartner projects a 90% reduction by 2030. But Goldman Sachs projects a 24-fold increase in enterprise token consumption over the same period. The unit cost goes down; the total bill goes up. Companies reporting their AI budgets exhausted in one or two months are not outliers. They are the pattern.

The trade-off that looks like a saving is, in many cases, a substitution of one cost for a more volatile, harder-to-govern one.

 

You’re Cutting the Wrong People

Here is the part executives are not discussing on those slides.

When organisations reduce headcount to fund AI infrastructure, they do not cut at random. They cut operational staff, programme delivery roles, change management functions, middle management layers. These are the roles that look like friction. In a spreadsheet, they are the easiest cost to justify removing.

In a transformation, they are the load-bearing walls.

The tacit knowledge that keeps a complex programme on track does not live in a document or a prompt. It lives in the people who have navigated the politics three times before, who know which stakeholders will quietly block a decision, who understand why the last attempt failed. AI does not have that context. More importantly, it cannot build it. It can only work with what you give it.

When transformation programmes stall, which they do with regularity, the most common cause is not a lack of technology. It is a lack of people who know how to move organisations through change. Cutting those people to fund AI tools that have not yet delivered consistent productivity returns is not a strategy. It is a bet. And it is a bet being made with institutional knowledge that cannot be easily rebuilt.

 

The Governance Question Nobody Is Asking

Most boardroom conversations about tokens versus humans are efficiency conversations. They should be risk conversations.

Specifically: what is the reversibility of this decision? Hiring back experienced programme delivery professionals, change managers, and technology integrators in a tighter labour market is slow and expensive. The talent you let go walks straight into competitor organisations or into consulting. You do not get it back on demand.

Meanwhile, the AI infrastructure you are funding with those savings is subject to vendor pricing changes, model deprecation cycles, and adoption curves that are far less predictable than a salary line. The White House’s own March 2026 AI governance framework acknowledged the workforce transition risk. State lawmakers introduced hundreds of AI-related bills in 2025. Political and regulatory pressure is accelerating.

Boards approving headcount reductions to fund AI should be asking: what is our recovery plan if the productivity gains do not arrive on the timeline assumed? Few are.

 

What Good Decision-Making Looks Like Here

The organisations getting this right are not choosing between tokens and humans. They are sequencing the decisions differently.

They are deploying AI where the productivity case is proven and measurable: customer-facing automation, code assistance, data analysis, routine administrative work. And they are preserving the human capability needed to execute the transformation that makes AI integration actually work.

They are building governance frameworks around AI spend with the same discipline applied to capital programmes: defined outcomes, stage gates, budget controls, and exit criteria if results do not materialise. They are not treating AI infrastructure as a guaranteed return.

They are also being honest internally about what is driving the headcount decisions. If cost pressure is the real driver and AI adoption is the justification, that is worth naming clearly. Obscuring the actual motivation behind a technology narrative creates cultural damage that outlasts the short-term saving.

 

The Slide Does Not Run the Programme

The “tokens or humans” framing will stick around because it captures something real about the economics of 2026. But it is a simplification that is costing organisations more than they realise.

The numbers are not the decision. The decision is how you get from where you are to where you need to be. That still requires people who know what they are doing.

Your AI Isn’t the Problem. Your Organisation Is.

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The technology isn’t the problem. It never was.

CEOs have finally said what transformation leaders have known for years. According to CIO.com‘s 2026 digital transformation analysis, a growing view at board level is this: AI adoption is failing because of workforce dysfunction and management failure, not because the tools aren’t good enough. The tools are excellent. The organisations deploying them are not ready.

That sounds like progress. It is not, entirely. Because the honest follow-on question, the one almost nobody is asking out loud, is this: what does it actually cost to fix an organisation that isn’t ready? And more to the point, who is being straight about that number?

 

The Comfortable Diagnosis

Acknowledging a workforce problem is easier than solving one. I have seen this pattern many times. The conversation shifts, the language changes, and suddenly the organisation is talking about upskilling programmes, change management workshops, and appointing a Chief AI Officer. Comfortable. Budgeted. Deliverable. Launch event confirmed.

Also insufficient.

What CEOs are actually describing is a change architecture challenge. Not a training programme. Not a comms plan. How do you get a workforce to reconfigure around fundamentally different ways of working, without losing the institutional knowledge and relationships that make the business worth anything? That takes years. In my experience, the failure rate is high, and rarely discussed honestly before the programme starts. And it requires a very different kind of leadership than deploying technology does.

 

What Boards Have Not Priced In

Technology investment decisions follow a familiar pattern. The vendor presents the business case. The pilots show strong results. The board approves the budget. The programme launches.

What nobody puts on that slide is the organisational cost of change. Not the cost of the technology. The cost of the human system that has to absorb it. The management bandwidth consumed. The productivity drop during transition. The cultural resistance that does not show up in workshops but absolutely shows up in usage data six months after go-live. The governance rework needed before AI-assisted decisions can actually be trusted.

Boards have been pricing in technology risk. They have not been pricing in change architecture risk. Those are different categories, and conflating them is precisely how organisations end up with expensive tools and thin results. The numbers bear it out. CIO.com‘s analysis of AI misconceptions found that 42% of companies abandoned most AI initiatives in the past year, up from 17% the year before. That is not a technology failure rate. That is an organisational one.

 

The Consultancy Pivot Is Real, and Worth Watching

The market is starting to notice. As Florian Douetteau, CEO of Dataiku, put it: “Instead of selling cloud migrations and data platforms, consultants will start selling organisational rewiring to prepare for AI-run operations.”

He is right. And executives need to tell the difference between genuine expertise and repackaged change management with AI branding.

The signal is specificity. Anyone selling organisational rewiring should be able to answer three questions: What does the post-rewired organisation look like, and how is it materially different from today? How do you measure progress at the midpoint, not just the end? And what happens when it does not go to plan?

Vague answers are a warning sign. If the firm cannot describe the failure modes honestly, they are probably not equipped to help you navigate them.

 

The Transformation Leader’s New Mandate

The transformation leader’s remit has shifted. It is no longer primarily about technology deployment. It is about change architecture: the sequencing, the governance, the capability-building, the stakeholder management that lets an organisation absorb new ways of working without destabilising what already works.

Harder to sell on a slide. Harder to put an end date on. Harder to celebrate in a press release. But it is the actual work, and anyone who has run a transformation programme at scale knows it.

The practical implication: if you are accountable for AI adoption and spending more time managing technology vendors than managing your leadership team’s readiness to change, you are working on the wrong problem.

 

Three Things Worth Doing Now

Start with an honest capability audit, not of your technology stack, but of your management layer. Which leaders have the resilience to sustain adoption pressure? Which ones will quietly resist in ways that never surface in a steering group but absolutely show up in usage data? You need to know before you scale.

Re-examine your success metrics. If the primary measures are deployment milestones and licence utilisation, you are measuring the technology, not the adoption. Add behavioural indicators: how are decisions being made differently, how has workflow changed, what are managers doing that they were not doing before?

And build the longer timeline into the plan, not as a caveat but as a structural reality. If your board believes this is an eighteen-month programme and you privately know it is a four-year change effort, that gap will surface. Better now, through a direct conversation, than in a programme review where the numbers no longer make sense.

 

The Gap Is the Risk

The AI is ready. Most organisations are not. The risk is not the gap. The risk is the pretence that it is smaller than it is, approving investment on that basis, and finding out the real cost when there is no runway left to correct it.

Honesty about the gap is not pessimism. It is the foundation of a credible plan.

AI Deployment Without Governance Is Not Transformation

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AI deployment without governance is not transformation. It is expensive experimentation.

Most organisations know this. And most organisations are doing it anyway.

The pressure to deploy is real. Boards are asking about it. Competitors are announcing it. Technology vendors are selling it with a conviction that borders on evangelical. And so CIOs, CTOs, and transformation directors are buying, piloting, integrating, and announcing. The pace of activity is impressive. The demonstrable results, when you look past the press releases and the internal communications, are considerably less so.

The problem is not the technology. The tools are genuinely capable, some remarkably so. The problem is what has been skipped in the rush to deploy: the governance infrastructure that determines whether AI investment creates accountable, measurable, sustainable value, or simply generates activity that resembles transformation while the underlying risks accumulate, unmanaged and unmeasured.

 

What Ungoverned AI Actually Looks Like

Every organisation that has rushed deployment without the infrastructure to support it shows the same patterns.

Proliferation without accountability. AI tools appear across departments, purchased by individual teams, integrated into workflows, processing sensitive data, producing outputs that influence decisions. Nobody owns it. Nobody monitors it. Nobody is accountable when something goes wrong. And something will go wrong.

Measurement without meaning. Leaders can tell you how many tools have been deployed, how many users are active, how many hours have been saved. What they cannot tell you is whether those savings translate to outcomes that matter, or whether the metrics being tracked were chosen because they were easy to collect rather than because they were meaningful. The reporting looks credible. The underlying picture is opaque.

Risk without recognition. AI systems inherit the biases in the data they are trained on. They produce errors in ways that are not always visible. They embed themselves in decision-making processes in ways that are difficult to unpick. Without governance structures that surface and manage these risks, organisations are running exposures they have not modelled and cannot quantify. This matters in every sector. In healthcare and financial services, it is potentially catastrophic.

Adoption without sustainability. Most AI deployments stall not because the technology fails, but because the human system around it was never properly designed. People use the tool when it is mandated. They stop when the mandate loosens. The promised transformation does not materialise because the operational disciplines required to embed new ways of working were never built. The pilot looked like a success. The programme was not.

 

Why Governance Gets Skipped

Because it is slower than deployment. Because it requires difficult conversations about accountability that nobody wants to have in a climate of enthusiasm and competitive anxiety. Because governance sounds like bureaucracy to people who have come to associate progress with pace.

The irony is that skipping governance does not make things faster. It makes the eventual reckoning slower, more expensive, and considerably more painful. An AI system embedded across an organisation’s core processes without proper oversight is not an asset. It is a liability with a very good PR strategy.

The organisations that have moved most decisively into AI without governance infrastructure are not ahead. They are exposed. They have made commitments they cannot sustain, taken risks they cannot quantify, and created dependencies they cannot easily exit. That is not a position of strength. It is a position of fragility that has not yet been tested.

 

What AI Governance Actually Means

Not a committee. Not a policy document on an intranet page that nobody reads. Not a risk register reviewed quarterly and then filed. Those are the bureaucratic imitations of governance. The real thing is different.

Real AI governance means someone is accountable for every deployed AI system, with a clear mandate, clear authority, and clear consequences when standards are not met. It means data quality is a precondition for deployment, not an afterthought. It means risk frameworks are designed before tools go live, not retrofitted after something fails. It means adoption is planned around outcomes, not headcount or activity metrics.

It also means the organisation has an honest view of its own readiness. Not every process is ready for AI. Not every dataset is clean enough. Not every team has the change capability to absorb a significant operational shift. Good governance makes that assessment before investment is committed. Not after.

There is also a strategic dimension that is frequently missed. AI governance is not just a risk management function. It is a value protection function. Organisations that govern well can identify what is working, scale it deliberately, and stop what is not working before it becomes costly. Organisations that do not govern well discover problems at the worst possible time: through failures that are visible, expensive, and in the current regulatory environment, increasingly public.

 

Three Questions Worth Asking Before the Next Deployment

Who is accountable for the outcomes of this AI system, defined by the results it produces, not the tool it deploys?

How will we know if this is working, measured by the things that actually matter, not the metrics that are easy to count?

What are the risks we have not fully modelled, and who owns them?

If the answers are unclear, the organisation is not ready to deploy. It is ready to experiment. And experimentation, at the scale and pace of current AI investment, is not a cost most organisations have properly accounted for.

The organisations that will extract durable value from AI are not the ones moving fastest. They are the ones that have built the infrastructure to know what is working, why it is working, what the risks are, and what to do when things go wrong.

That infrastructure is governance. And without it, transformation is not what you are doing.

The Most Important Leadership Skill in 2026 is Knowing What NOT to Automate

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Every company now has access to the same AI tools. The same large language models. The same automated workflows. Efficiency has moved from competitive advantage to baseline expectation. If your edge is speed and scale, it is an edge almost everyone has.

The leaders who are winning are not the ones who automated the most. They are the ones with the discipline to stay manual where it actually matters.

 

The efficiency trap

The logic is seductive. If a machine can do it 90% as well at a fraction of the cost, the decision seems obvious. So you automate the feedback loop. You automate the check-in with a direct report. You automate the client thank-you.

But when you automate a human connection, you do not save time. You delete the value. If a process is designed to build trust and you remove the person from it, you have removed the trust.

Efficiency is the right lens for a workflow. It is the wrong lens for a relationship.

 

Three things you should never automate

1. Contextual mentorship

An AI can give a junior team member the best-practice answer. It cannot tell them how that answer sits inside the specific, messy history of your organisation, why a certain stakeholder is sensitive about a particular decision, or what is actually at stake in the conversation they are about to have. Leadership provides the why. The model provides the what. Those are not the same job.

2. Hard conversations

Gallup’s 2025 State of the Global Workplace report records global employee engagement at its lowest level since 2020. The cost: $10 trillion in lost productivity. The primary driver of that collapse is not strategy, pay, or economic uncertainty. It is managers. Managers account for 70% of the variance in team engagement. And manager engagement itself has fallen nine points since 2022, the sharpest sustained decline in years. The quality of the manager’s hardest interactions determines the majority of how engaged a team is. The performance conversation. The difficult feedback. The call that someone is not right for the role.

Automated performance reviews. AI-generated feedback. Algorithmically produced bad news. These are not efficiency gains. They are abdications. If you are not willing to sit with someone through a difficult conversation, you have not earned the right to lead them. Automation of conflict is one of the fastest ways to destroy a culture, and it tends to do it quietly, one avoided interaction at a time.

3. Visionary intuition

Algorithms are retrospective. They look at what has happened to predict what might happen. Leadership is prospective. It requires the willingness to take a risk the data does not yet support, to make a call before the pattern is clear, to back a direction the model would not have recommended.

A BCG and Harvard Business School study of 758 consultants using GPT-4 found that when tasks fell outside the model’s capability, consultants using AI were 19 percentage points less likely to produce correct solutions than those working without it. AI makes experienced people wrong more often when the problem requires genuine judgment. That is precisely where leadership is most needed.

If the algorithm is making your strategic pivots, you are not leading. You are following a script.

 

The question to ask before you automate anything

Stop asking whether something can be automated. Most things can. Start asking:

If the recipient knew this was automated, would they feel less valued?

If the answer is yes, keep it manual. That is the test. Not cost. Not speed. Not capacity. Whether removing the person removes the point.

 

Presence is the advantage now

Gallup’s 2025 report is plain about what the solution is not. More software will not reverse a decline caused by managers becoming less human. More meaningful human connection will.

In a landscape where everything is optimised, the things that are not optimised stand out. The intentional choice to spend time where it is not scalable is increasingly rare. That rarity is the advantage.

Efficiency gets you into the room. Presence is what keeps you there.

Stop trying to be a more efficient machine. Start being a more present human.

You Didn’t Transform. You Digitised

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Most organisations that have spent the last five years claiming digital transformation have not transformed anything. They have taken broken processes, outdated thinking, and dysfunctional ways of working, and moved them online. That is not transformation. That is digitisation with a better slide deck. And the reason it keeps happening is not technology. It is not budget. It is not even capability. It is the fact that real transformation is genuinely uncomfortable, and most leaders are not willing to do what it actually requires.

 

The Lie We Have Been Telling Ourselves

Somewhere along the way, the industry decided that transformation meant deploying new platforms. Move to the cloud. Implement the ERP. Launch the patient portal. Go live by Q3. And when the system went live, someone in the boardroom called it a success.

McKinsey’s research, tracking digital transformation outcomes across more than 1,500 executives globally, found that fewer than 30% of digital transformation programmes achieve their stated goals. When the definition of success is tightened to organisations that both improved performance and sustained those improvements over time, the figure drops to 16%. The transformation was declared. The programme was closed. The leadership team moved on. And the results did not follow.

The same pattern is now playing out in artificial intelligence investment. Organisations are deploying AI tools at pace, adding automation to existing workflows, and calling the outcome transformation. The underlying question, whether the organisation has genuinely changed how it thinks, decides, and operates, goes unasked. The tools change. The organisation does not.

What actually happened on the ground: the same approval bottlenecks that existed in the paper process existed in the digital one. The same data quality problems that plagued the spreadsheet now plagued the database. The same people who did not trust each other before the system launched still did not trust each other after it. The technology arrived. The transformation did not. Because transformation was never on the project plan.

 

What You Actually Did

MIT researchers studying digital capability across more than 400 global organisations identified four categories of digital maturity. At the top: Digital Masters. High investment in technology, high investment in leadership and operating model transformation. Consistent outperformers.

At the bottom of the performance curve: what the researchers called “digital fashionistas.” High technology investment. Low operating model and leadership change. They look like digital leaders. They have the tools, the platforms, the dashboards, and the announcements. They consistently underperform the organisations that did both. The research, published in Leading Digital (Westerman, Bonnet and McAfee, Harvard Business Review Press, 2014), found that what separates genuine digital leaders from organisations that merely digitise is not technology investment. It is the depth of change to operating model and leadership capability that sits alongside it.

The fashionista is not a reckless organisation. It is a capable one that solved the easier half of the problem. Technology procurement has clear timelines, visible outputs, and measurable spend. You can point to it in a board presentation. Changing how an organisation makes decisions, how it tolerates uncertainty, how it deploys talent, how it responds to what customers actually do rather than what the strategy assumed they would do, that work is slower, harder, and less photogenic. So the easy half gets done. The hard half gets deferred. And the deferral becomes permanent.

Digitisation has real value. I am not dismissing it. But it does not change what is possible. It does not challenge why a process exists in the first place. It does not ask whether the workflow serving the organisation in 2010 should still be serving it today.

I have walked into healthcare systems where clinicians were still duplicating data entry across three platforms because no one had the political will to consolidate them. I have seen government programmes where the digital portal replicated a form-filling exercise that should have been eliminated entirely. I have watched organisations spend eight figures on enterprise systems and then rebuild their old spreadsheet workarounds alongside them, because the system did not fit how people actually worked, and no one was willing to change how people actually worked. New technology. Old thinking. Zero transformation.

 

Why Real Transformation Is Harder Than Anyone Admits

Consulting firm BCG surveyed 825 senior executives on their digital transformation experience. Approximately 70% reported falling short of the value they expected. The consistent pattern in that data, and in the broader body of research on transformation failure, is not a technology shortfall. The technology largely worked. What did not work was the organisational and cultural infrastructure around it. Organisations deployed new capability into old structures. New tools into old decision-making patterns. New data into organisations that did not know how to act on it.

Genuine transformation requires something that technology cannot deliver and no vendor will sell. It requires leaders to look at the way their organisation functions and be honest about what is not working, not just inefficient, but fundamentally wrong. Wrong structures. Wrong incentives. Wrong assumptions baked into processes that have never been questioned because they have been there too long for anyone to remember why.

That conversation is threatening. It implicates decisions made by people still in the room. It requires dismantling things that gave people power, status, or comfort. It means telling parts of the organisation that the way they have worked for a decade is the problem, not the solution. Most leaders are not willing to have that conversation. So instead, they commission a technology programme and call it transformation. It feels like action. It produces visible outputs. And it avoids the harder truth entirely. The technology becomes the distraction from the real work.

 

The Questions That Would Actually Change Something

Real transformation starts before any platform is selected, any vendor is appointed, or any project plan is written.

It starts with questions most organisations never ask.

Why does this process exist? Not how does it work, but why does it exist? What problem was it designed to solve, and is that still the problem we have?

Who benefits from keeping this the way it is? Because in every organisation, there are people whose influence depends on information asymmetry, manual steps, or processes that only they understand. Digital transformation threatens that. And those people will, consciously or not, find ways to make sure it does not fully land.

What behaviour needs to change, not just what system needs to be replaced? Because if that question cannot be answered before go-live, the transformation will fail after it.

What are we willing to stop doing? Every genuine transformation requires eliminating something. A process, a role, a way of making decisions. If nothing has been stopped, nothing has been transformed.

 

The Leader’s Role Nobody Talks About

This is where most transformation discourse goes quiet.

Because the answer to why transformation fails is almost always leadership. Not IT leadership. Not programme leadership. Senior organisational leadership.

The leaders who delegated transformation to a project team and checked in quarterly. The ones who approved the technology investment but never showed up to the change management conversation. The ones who said they needed to transform in the all-hands and then protected every structural thing that made transformation impossible.

Transformation cannot be delegated. Implementation can. But the decisions that actually change an organisation, who has authority, how work flows, what gets measured, what behaviour gets rewarded, those decisions sit at the top. When leadership avoids them, the project team delivers what they can. They go live. They hit their milestones. And the organisation looks digitised, not transformed.

 

What Transformation Actually Looks Like

I have seen it done well. Not often, but I have seen it.

It looks like a leader standing in front of their organisation and naming the real problem, not the technology gap, but the cultural or structural one underneath it. It looks like decisions being made that upset people, because those people were benefiting from the dysfunction. It looks like processes being eliminated, not just automated. It looks like the technology arriving last, after the hard thinking has already been done, as an enabler of a new way of working, not a substitute for designing one.

It is slower than digitisation. It is harder to measure. It produces fewer milestone celebrations. But two years later, the organisation actually works differently. Not just faster. Differently.

 

The Uncomfortable Question

If you are sitting with a transformation programme right now, in progress, recently completed, or about to start, ask one question.

What have we changed about how this organisation thinks, decides, and operates? Not what have we deployed. What have we changed?

If the honest answer is “not much,” you have not transformed. You have digitised.

And until someone is willing to say that out loud, the investment in transformation programmes will keep delivering digitisation results, and the question of why the return never arrived will keep going unanswered.

The technology was never the problem. It was always the thinking.

Empathy Is Not a Perk. It Is the Mechanism That Makes Transformation Work

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Fifty-nine percent of CEOs now view empathy as a perk or a nice to have. That figure comes from Businessolver’s 2025 State of Workplace Empathy report, the largest study of its kind, drawing on data from over 26,000 participants across ten years. What makes it striking is not the number itself. It is the direction of travel. That figure is up twelve points in a single year. Executives, as a group, are actively moving away from empathy as a leadership priority, and they are doing so at precisely the moment when the evidence against that position has never been stronger.

This is not an abstract concern about culture or kindness. It is a delivery risk. And it deserves to be treated as one.

 

The Conditions That Produced This Retreat

There is a logic to the shift, even if the conclusion is wrong. The past two years have delivered economic pressure, AI-driven disruption, and a wave of organisational restructuring that has pushed leaders toward harder, more measurable stances. Accountability culture, in many organisations, has become a proxy for toughness. Empathy, by contrast, has been quietly reframed as a luxury, something appropriate for stable times rather than periods of intense change.

The same Businessolver data shows that only 55% of CEOs now say empathy is undervalued in their organisation, down 28 points year-over-year. That is not a marginal shift. That is an entire segment of executive leadership changing its position within twelve months. The message being sent, intentionally or not, is that empathy had its moment and that moment has passed.

The problem is that the evidence says the opposite.

 

What the Data Actually Shows

When 27% of employees view their organisation as unempathetic, they become 1.5 times more likely to leave within six months. Across US organisations, Businessolver estimates that compounds into an annual attrition risk of 180 billion dollars. That is not a culture metric. That is a cost of goods figure that should sit on the CFO’s desk alongside every other delivery risk in a transformation programme.

The same employees in unempathetic organisations report three times higher workplace toxicity and 1.3 times more mental health issues. Those are not soft indicators. Toxic teams do not collaborate. People managing mental health crises do not adopt new systems, embrace new processes, or commit to new ways of working. They survive. And surviving is the opposite of transforming.

The leader who reads these numbers and still concludes that empathy is optional has misread the risk register.

 

Empathy Is Not About Feelings. It Is the Engine of Behaviour Change.

Here is the harder point, and the one that gets lost in the debate. Empathy is not a leadership style choice between being tough and being kind. It is the mechanism through which people change their behaviour. And behaviour change is the only thing that makes transformation real.

Technology does not transform organisations. People do. Systems get deployed, processes get redesigned, roadmaps get approved, and then the actual work of transformation happens in the minds and habits of the people who have to do things differently, every day. That work requires trust. Trust requires people to feel that they are understood, not just managed. And the leader who has stripped empathy from their approach has also, whether they intended to or not, stripped the conditions under which that trust can form.

BCG’s 2025 research on AI transformation makes this explicit. Their finding is that successful AI transformation is 70% people and processes, 10% algorithms, and 20% technology and data. In a programme driven by the most technically sophisticated tools in the history of business, the primary variable is still human. The technology is the smallest part of the challenge. The people transformation that runs alongside it is where programmes are won or lost. That is not a comforting aspiration. That is BCG’s operational conclusion from studying what separates AI transformations that deliver from those that do not.

Against that backdrop, the decision to treat empathy as a perk is not cautious. It is expensive.

 

The False Trade-Off Leaders Keep Making

The framing that puts empathy and accountability in opposition is one of the most persistent and damaging myths in transformation leadership. The assumption is that softer human approaches reduce rigour, that spending time on how people feel comes at the cost of delivery discipline. Executives who have internalised this framing tend to reach for control when things get hard, tightening governance, escalating pressure, increasing reporting frequency, as though the problem is visibility rather than engagement.

It rarely is.

The 70% transformation failure rate, driven by employee resistance and lack of management support, is not a governance failure. It is a people failure that governance cannot fix. You can have every RAG status in the programme green and still be six months from collapse if the people who need to change their behaviour have stopped believing that the organisation cares whether they succeed or fail.

Accountability and empathy are not alternatives. They are complements. The most effective transformation leaders hold both simultaneously. They are direct about what is required. They are also genuinely interested in whether the people doing the work have what they need to deliver it. That combination is not soft. It is operationally serious.

 

Tough Choices Require Human Leadership

Organisations under pressure will face genuinely difficult decisions in the months ahead. Restructuring, reprioritisation, AI adoption at scale. None of that gets easier by removing human connection from the programme. It gets harder, because the people who need to carry the change through are the same people who are watching how the organisation treats them under pressure.

The organisations deprioritising empathy right now are not being tough. They are being wrong about what produces outcomes. And the cost of that mistake will show up, reliably, in the delivery data.

The Leader Who Doesn’t Know They’re Feared

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There is a specific kind of dangerous leader that nobody talks about in boardrooms.

Not the one who shouts. Not the one who publicly humiliates people or rules through naked aggression. That leader is visible. People name them. Organisations deal with them eventually.

The dangerous one is the leader who has created a culture of fear without ever raising their voice. The one whose team has learned, quietly and carefully, that honesty is a liability. The one who genuinely believes they are approachable, open, and trusted, while the people around them have become experts at managing what they say, how they say it, and what they keep entirely to themselves.

This leader does not know they are feared.

And that is exactly what makes them so costly.

 

The Gap Between Who You Think You Are and Who They Experience

Most leaders who create fear do not intend to. That matters, and it also does not matter at all.

Intent does not determine impact. Culture does not care about your intentions. Your team’s behaviour is shaped entirely by what they have learned happens when they take a risk with you, when they bring bad news, push back on a decision, admit a mistake, or say the thing nobody else is saying.

The research on this is striking. Organisational psychologist Tasha Eurich conducted a multi-year study involving thousands of participants across multiple studies and found that while 95% of people believe they are self-aware, only 10 to 15% actually meet the criteria for genuine self-awareness. The finding most relevant here is this: internal self-awareness, how we believe we come across, and external self-awareness, how we actually come across to others, are essentially uncorrelated. A leader can be deeply reflective and still have almost no accurate sense of how they are experienced by their team. This is not an arrogance problem. It is a structural blind spot.

If those moments of honesty have gone badly, even once, even subtly, people remember. They adjust. They start self-censoring before the words leave their mouth. They develop what looks like professionalism but is actually armour.

And from your side of the table, everything looks fine. People are engaged in meetings. Nobody is causing problems. The reports are clean. The team seems to be functioning.

What you are actually seeing is a highly guarded team performing composure.

 

The Behaviours That Build Feared Leaders

None of what follows requires malicious intent. Most of it comes from competence, pressure, or simply the unchecked habits of someone who has always been the fastest, most decisive person in the room. The mechanism is consistent: a behaviour that felt like leadership produced a reaction in others that went unnoticed. And that reaction, over time, became the culture.

You respond to bad news with visible frustration. You do not think it is a big deal. You move on quickly. But the person who delivered that news felt the temperature drop the moment the words left their mouth. They filed it away. Next time, they will soften it. The time after that, they will delay telling you. Eventually, you will be the last to know.

You solve problems before people finish explaining them. You are fast and you are good and your instinct is usually right. But what the other person experiences is that their perspective does not matter. That the conversation is a formality. That you have already decided. So they stop bringing you half-formed problems. They only come when they have the answer, which means you lose access to the problems at the stage when you could actually help.

You confuse directness with dismissal. You believe you give clear, honest feedback. And you do. But there is a difference between feedback that challenges someone’s thinking and feedback that makes them feel their thinking is worthless. The words can be almost identical. The delivery is everything.

You reward the people who agree with you. Not deliberately. But your energy lifts when someone validates your thinking. Your attention sharpens. And the people in that room are excellent at reading energy. They learn the correlation. Agreement gets warmth. Challenge gets friction. The lesson lands fast.

You are always the most certain person in the room. Certainty from a leader is reassuring up to a point. Beyond that point, it signals that doubt is unwelcome. And if doubt is unwelcome, so is the information that generates it. Your team starts protecting you from complexity, which means they start protecting you from reality.

You have never once said: I got that wrong. Or if you have, it was so rare that people remember it. When a leader never admits error, the message to the team is clear: mistakes are not safe here. And a team that cannot make mistakes safely cannot take risks, cannot innovate, and cannot tell you the truth when the truth involves something going wrong.

 

 

What a Guarded Team Looks Like

The tragedy is that guarded teams can look like high-performing teams from a distance.

They are efficient. They deliver. They do not create noise. Meetings run smoothly because nobody says anything that might create friction. Reports look clean because the real information has been edited out. Problems get solved at the level below you because bringing them upward feels more dangerous than managing them quietly.

What you are missing is everything below the surface.

The risk that has been on someone’s mind for three weeks but has not made the register because the person managing it does not want to be seen as struggling. The team member who is six weeks from walking out the door because they have felt invisible for months. The supplier relationship that is quietly deteriorating because your account manager is telling you it is fine rather than managing the conversation you would need to have.

The gap between the reported reality and the lived reality grows, week by week, in proportion to how safe people feel telling you the truth.

And at some point, the gap becomes a crisis. And everyone knew except you.

 

Why the Leaders Who Need This Most Will Not Recognise Themselves

This is the most difficult feature of the whole dynamic.

Research by Jack Zenger and Joseph Folkman of leadership development firm Zenger|Folkman, drawing on one of the largest 360-degree feedback databases in existence, consistently finds that senior leaders show the widest gap between how they rate themselves and how their subordinates rate them, particularly on dimensions like listening, approachability, and empathy. The people with the most influence over organisational culture are, on average, the least accurate judges of their own impact on it.

The behaviours that create fear are almost always the same behaviours that made someone successful. The pace. The decisiveness. The high standards. The intolerance for mediocrity. These are not bad qualities. They are, in many environments, exactly what drove results.

But at a certain level of leadership, those same qualities, unchecked and unexamined, become the thing that limits the people around you. And because they drove your success, you do not interrogate them. You double down on them.

And the people around you adapt. They become mirrors. They reflect back what you want to see. You walk into rooms and the room agrees with you. You raise concerns and the concerns get managed. You ask if everything is on track and the answer is always, essentially, yes.

You look successful. The organisation is slowly becoming fragile.

The leaders who most need to hear this will read it and think of someone else. That is not a criticism. It is the nature of the blind spot. You cannot see what the system around you has been carefully constructed to hide from you.

 

The Only Way Through

It starts with an uncomfortable question, not asked of yourself, but asked of someone who will tell you the truth if the environment is safe enough.

What is it like to work with me when things are going wrong?

Not in general. Specifically when things are going wrong. When the pressure is highest. When the news is bad. When someone has made a mistake. What happens in that room?

If you do not know the answer, if you genuinely cannot predict what your team would say, that is the information.

The standard prescription at this point is 360-degree feedback. It can help. But the research on its effectiveness is mixed, and the most common pattern is familiar: leaders receive the results, rationalise the parts that are uncomfortable, and make short-term adjustments that do not hold. The tool is not the problem. The willingness to sit with what it reveals, and to keep sitting with it, is.

Because the leaders who have done the work know exactly how that room feels. They have been told. They have asked enough times, and created enough safety, that people have told them. Not through a survey. Through the kind of environment where someone can walk into your office and say: I need to tell you something you are not going to enjoy hearing.

The ones who have not done the work are always slightly surprised when good people leave. Slightly confused when the programme that looked fine on paper collapsed in execution. Slightly unsure why the energy in the room feels managed rather than genuine.

They are not bad leaders. They are unexamined ones.

And in the long run, the cost is exactly the same.

The Most Dangerous Status Report Is the One Everyone Is Comfortable With

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The governance is running. The reports are flowing. The steering committee met on time, every question got a confident answer, and the pack looked clean.

And the programme is in more trouble than anyone in that room is prepared to say.

This is not an unusual situation. It is not a sign of dysfunction or dishonesty. It is, in my experience, the most common information environment in large-scale programme delivery. The data supports that reading. Research by Milliken, Morrison and Hewlin, published in the Journal of Management Studies, found that 85% of employees have withheld important information from their manager because they feared the consequences of speaking up. The fear is not of formal punishment. It is relational: the fear of being seen negatively, of damaging a relationship, of being labelled someone who creates problems rather than solves them.

This is not a minority behaviour. It is the default.

The question is not whether a filter exists on your programme. The question is how thick it has become, and whether you would know.

 

Nobody Decides to Build the Filter

The pattern I have watched play out more times than I can count begins with a capable, experienced leader who genuinely means what they say. They have told the team, in kick-offs and town halls and one-to-ones, that they want to hear the bad news early. They are not performing openness. They believe it.

But then someone raises a concern in a steering committee and the leader’s body language shifts before the words are out. A risk gets flagged and the first question is why it was not caught earlier rather than what needs to happen now. A project manager delivers a difficult update and spends the following week under a level of scrutiny that has nothing to do with fixing the problem.

Nobody announces a new policy. Nobody says: do not bring me bad news.

But the room notices. Every single time.

And slowly, without anyone deciding to do it, the filter gets built. The team learns which concerns land well and which ones create friction. They learn how to frame things to reduce the emotional temperature in the room. They learn the difference between the truth and the version of the truth that keeps the meeting moving and their professional standing intact.

The updates keep arriving. The reports keep flowing. The governance keeps running.

But the signal has been stripped out. What remains is noise dressed up as information.

 

You Do Not Build This Through Negligence

This is the part that most leadership development will not tell you directly. You do not build a closed information environment through negligence. You build it through a series of entirely human, entirely understandable responses to difficult moments.

A flash of impatience when a problem arrived at the wrong time. A habit of moving to solutions before the problem is fully understood. A preference, however subtle, for the reassuring narrative over the complicated one.

These are not character flaws. They are instincts under pressure.

But at leadership level they are not private. The CIPD’s 2024 evidence review on psychological safety identifies leader and manager behaviour as the most critical driver of whether people feel safe to speak up, and specifically notes that what matters is not what leaders say about wanting honesty, but what they demonstrate through their actions when honesty arrives. The research is unambiguous: psychological safety is fragile. A single punitive response to good-faith feedback can damage trust that took months to build.

Every reaction is observed, interpreted, and factored into how safe it feels to tell you the truth next time. The leader who says they want honesty but visibly struggles to receive it is not running an open culture. They are running an organisation that has learned to give them what they can handle rather than what they need.

The most dangerous status report is not the one with red items on it.

It is the one everyone is comfortable with.

 

Four Practical Moves

The filter is not permanent. It is a learned behaviour, and learned behaviours can be unlearned. But reversing it requires something more specific than an open-door policy.

Stop asking questions that invite the managed answer. “How are things going?” will get you the curated version every time. Try instead: what is the one thing you would not put in a status report but think I should know? If this programme were going to fail, what would the early sign look like? What are we not talking about that we probably should be? Those questions signal that you are interested in the reality, not the performance of it.

Go to where the real work is happening. Not to inspect. To listen. The people closest to delivery carry an understanding of programme health that rarely makes it into formal reporting. A single honest conversation with a delivery lead or a technical team that has been carrying a quiet problem for weeks will tell you more than three months of steering committee updates.

Create a visible moment where surfacing difficulty is rewarded rather than merely tolerated. When someone raises something uncomfortable and your public response is genuine appreciation followed by a real conversation about what to do next, the entire room recalibrates what is safe to say. One moment like that shifts the culture more than any open-door policy ever will. The inverse is equally true: one moment where the messenger suffers sets the filter back months.

Learn to read silence as data. The steering committee where every question gets a confident answer. The risk log that has not changed in three weeks. The team that delivers polished updates but never raises anything unexpected. These things can mean a programme is running well. They can also mean the filter is fully operational and the real conversation is happening somewhere else entirely. If nobody is telling you anything that surprises you, that is not necessarily a sign that everything is on track. It may be a sign that you have stopped being the kind of leader people bring hard news to.

 

The One Question That Cuts Through

There is a question I now use when a programme looks clean but feels wrong.

I find someone close to the real work. Someone who has been there long enough to know where the bodies are buried. And I ask them one thing.

What does everyone here know that nobody is saying out loud?

The answer to that question is almost always where the programme actually is. The gap between that answer and what appears in the formal reporting is almost always where the real leadership work needs to happen.

 

Comfortable Information Is Borrowed Time

PMI’s research on complex programme delivery is consistent on this point: early warning signals are frequently present and frequently ignored, causing problems to compound in severity before they are addressed. The pattern is not exceptional. It is systematic.

Every week a real problem stays hidden is a week where the options for addressing it narrow. Manageable risks become serious ones. Recoverable situations become critical ones. And the longer the filter operates, the more the team’s trust erodes, because people who know the truth and watch it go unacknowledged eventually stop believing that leadership is operating in good faith.

When the programme finally tells you the truth, and it always does eventually, the question is rarely how to get back on track.

It is whether getting back on track is still possible.

 

The Leaders Who Get This Right

The leaders who consistently deliver in high-stakes environments are not always the most experienced or the most technically skilled.

But they share something that is harder to develop than either of those things. They have learned to want the truth more than they want to be comfortable. They have built the self-awareness to notice when they are receiving a managed version of reality, and the discipline to go looking for the unmanaged one. They have created environments where people bring problems early because they have learned, through consistent experience, that doing so leads somewhere useful.

That is not a natural state for most leaders. It requires sustained effort, genuine self-awareness, and a willingness to sit with difficult information and resist every instinct to make it someone else’s problem.

But the alternative is a programme that looks healthy until it does not. A team that has learned to give you what you can handle. A steering committee that runs on time and misses everything that matters.

When your team tells you how things are going, are they telling you what is happening?

Or are they telling you what they have learned you can live with?

The gap between those two answers is where most programmes are won or lost.