Category Archives: AI

The Problem With Your AI Strategy Is Not the AI

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The boardroom conversation has shifted. Not to whether AI matters. That debate is over.

The new question is what to actually do with it. And the 2025 data suggests most organisations are answering it badly.

BCG’s research found that 60% of companies globally are generating no material value from AI despite significant investment. McKinsey’s 2025 State of AI survey found that nearly 80% of organisations are regularly using generative AI in at least one function, yet only around 5% are seeing substantial financial returns. The gap between AI as a boardroom announcement and AI as a functioning operational capability is the defining management challenge.

 

The pressure is real. The direction is not.

Executives are asking about AI. Vendors are selling it. Competitors are announcing it. Boards are expecting it.

And in the middle of all that noise, organisations are launching AI initiatives without defining what success looks like. That is not ambition. It is drift with a budget.

The fear of being left behind is understandable. But speed without clarity produces activity, not results. And activity without outcomes is expensive.

RAND Corporation’s 2025 research into AI project failure found that 80.3% of initiatives failed to deliver their intended business value. A third were abandoned before reaching production. A further 28% reached completion and still failed to deliver. The problem is not a lack of investment or effort. It is a lack of clarity about what the investment is actually for.

 

Most organisations are starting in the wrong place

The most common mistake in AI adoption is starting with the tool rather than the problem.

The conversation usually begins with: “What AI platform should we procure?” It should begin with: “What is broken and where?”

AI is not a business objective. It is a capability. And capabilities only create value when they are pointed at something real.

BCG’s research found something that cuts against the instinct of most organisations. The organisations generating real value from AI average 3.5 use cases. Those generating no value average 6.1. More is producing less.

The organisations getting results are not asking which platform to buy. They are asking where their people are losing time to work that should not exist. Where good decisions are being slowed down by fragmented information. Where a process that should take hours takes weeks because nobody has questioned whether it needed to be that way.

That is the right starting point. Not a procurement conversation. A diagnostic one.

 

AI exposes what you were already avoiding

Many leaders believe AI will fix their inefficiency problem. It will not. It will make it harder to ignore.

Unclear processes do not get fixed by AI. They get amplified by it. Poor data quality does not improve because a model has been deployed on top of it. Weak governance does not disappear. Siloed departments do not start collaborating because there is an AI tool in the mix.

The evidence on data readiness is striking. Cloudera and Harvard Business Review Analytic Services, surveying enterprise organisations in early 2026, found that only 7% said their data was completely ready for AI. More than a quarter said it was not ready at all. Gartner estimates that 60% of AI projects unsupported by AI-ready data will be abandoned through 2026.

The obstacle in most AI projects is not the technology. It is the organisation the technology is being asked to work inside. Not the model. Not the platform. Not the vendor. The organisation itself.

 

Fewer tools. More focus. Better results.

Buying advanced technology does not automatically create change. It creates the expectation of it.

Many organisations are deploying AI on top of environments defined by disconnected systems, inconsistent processes and poor data. They are not implementing AI. They are automating the same dysfunction at scale, with a larger budget attached to it.

The organisations getting real value are not the ones running the most pilots. They have chosen a small number of problems that matter, built the right foundations to address them, and stayed focused long enough to see results. They do not announce it. They build it.

They also understand something most organisations have not yet accepted. AI should work in service of a clear business direction. If that direction is unclear, AI will not provide it.

 

Stop performing AI and start implementing it

A significant portion of what organisations call an AI strategy is AI theatre. Presentations. Innovation labs. Pilot programmes. Strategy papers. Announcements. And very little operational change.

IDC’s 2025 research found that for every 33 AI proofs of concept an enterprise starts, only four reach production. A March 2026 survey of 650 enterprise technology leaders found that 78% of enterprises have AI agent pilots running but fewer than 15% have reached production deployment. The pilots do not fail because the technology is immature. They fail because the hard work of creating the conditions for success was never done.

That hard work is not glamorous. It is redesigning how work flows. Making difficult calls about what to stop doing. Getting data into a state a model can actually use. Bringing people along rather than announcing change at them. None of it photographs well for a board update.

But that is what transformation actually looks like. The rest is performance.

If your AI strategy looks like a series of well-designed slides with no corresponding change in how work gets done, you do not have an AI strategy. You have a communication exercise.

 

The question every organisation eventually has to answer

Every organisation reaches the same moment. The excitement fades. The pressure increases. The questions get harder. And someone in that room finally asks: “What exactly is this improving?”

The organisations with a clear answer will accelerate. The ones without one will keep funding a future they never properly defined.

The question is not whether your organisation is using AI. Most are. The question is whether you can name, in a single sentence, the operational problem your AI investment is solving, and how you will know when it is solved.

If you cannot, the technology is not the problem.

Already Building: Epic Agent Factory and the Governance Gap

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The pre-mortem on Epic Agent Factory asked who would answer when a health-system-built agent made a clinically significant error. It published on 9 June. I have since learned of a Becker’s Hospital Review report from 30 March confirming that one of America’s largest health systems had already been building those agents for weeks before the question was published.

It confirms the pre-mortem’s central argument. Neither the research nor the article surfaced how quickly the sequence had already begun.

 

The Deployment That Was Already In Motion

Advocate Health had already tapped Epic’s Agent Factory, becoming one of the first health systems to build and deploy agents through the platform. Andy Crowder, Advocate Health’s SVP and Chief Digital and AI Officer, described the direction in a LinkedIn post on 26 March: “By combining Epic’s Agent Factory Platform capabilities with Advocate Health’s scale, clinical insight, and commitment to innovation, we’re translating AI from promise into practice.” He pointed to a three-day Epic immersion at The Pearl innovation district in Charlotte, focused on speeding up pharmacy verification for complex medications and cutting infusion chart preparation time for pharmacists and nurses. Four working prototypes emerged, scheduled to go live in July 2026.

Crowder added: “Together, we’re advancing responsible, practical AI that fits naturally into clinical workflows, reduces friction, and gives clinicians back time to focus on what matters most.” It is a considered statement, and the commitment is genuine. But it is not a governance document. And Advocate Health is not unusual here. They are representative. They moved first because the platform enabled it, the commercial pressure to reduce administrative burden was real, and nothing in the regulatory landscape said stop.

This is the sequence the pre-mortem described. Capability arrived. Deployment followed. The governance architecture to surround it had not been ratified.

 

The Workflows That Come Next

Pharmacy verification and infusion chart preparation are not, in themselves, clinical decision-making. They reduce documentation burden and carry genuine operational value. But they are the entry point, not the ceiling.

Epic’s own Penny agent already handles prior authorisation for thousands of health systems. Agent Factory is the platform through which health systems build their own versions of exactly those capabilities. Prior authorisation sits at the intersection of clinical judgment and payer approval. An AI-generated argument that misrepresents a contraindication, omits a relevant diagnosis, or positions a clinical case in a way that leads a payer to deny appropriate care causes harm that is downstream and deniable. The agent did not make the clinical decision. But the agent shaped the argument that influenced it.

The pre-mortem’s central question, who owns the error, was always pointed at this trajectory. The agent is built by the health system, on Epic’s platform, using Curiosity’s foundation models, in a regulatory environment where no one has yet specified how liability is allocated between vendor and deployer. Advocate Health’s prototypes are the first step of a sequence that leads directly to that question.

 

Colorado Tried to Build the Rails

While health systems were building, legislators in Colorado were attempting to create the governance scaffolding that the platform lacks at a federal level. Three separate AI-related healthcare laws had been passed by June 2026, each addressing a different dimension of the problem, and each confirming the same underlying gap.

Colorado’s original AI Act, SB 24-205, was scrapped before it ever took effect. A legal challenge from X.AI in April 2026, supported by federal intervention from the DOJ, led to enforcement being suspended and the legislature repealing the law entirely. Its replacement, SB 26-189, was signed on 14 May. It is a narrower law, retaining consumer notice requirements and the right to meaningful human review following adverse outcomes, but dropping the duty-of-care standard and mandatory impact assessments that had made the original controversial. It takes effect January 1, 2027.

HB 26-1139, signed on 2 June, constrains how payers use AI in coverage determinations. It requires that AI-driven decisions be based on the patient’s individual medical and clinical history rather than group data, and that any denial or delay of coverage based on medical necessity receive review by a licensed clinician. It too takes effect January 1, 2027.

Together, SB 26-189 and HB 26-1139 create obligations on both sides of the prior authorisation workflow. Neither specifies who bears the cost when an agent-generated output leads to the wrong clinical outcome. Three laws confirming the gap exists is not the same as closing it.

 

The Sequence Is Not a Prediction. It Is a Pattern.

On 1 June 2026, eight days before the pre-mortem was published, the Joint Commission launched its first voluntary AI certification programme for healthcare organisations. Built on the initial guidance published with the Coalition for Health AI in September 2025, the certification covers governance, data management, risk and bias reduction, and monitoring. It is a meaningful step forward. But the certification recognises organisations, not individual tools. It does not validate or certify individual AI products. It contains no discussion of liability allocation. It is a framework for responsible intent, not a mechanism for accountability when something goes wrong.

Epic has not published a liability framework specifying what a health system owns when a self-built Agent Factory agent produces a clinical error. No Epic contract language or public terms of service document does so. No federal regulatory body has published guidance specifically addressing liability allocation for agentic AI operating within EHR environments. The FDA has authorised more than 1,400 AI-enabled devices and issued no specific enforcement guidance for agentic AI in EHR environments.

The pre-mortem’s conclusion was that if Epic published a clear liability framework and paired it with a safety review mechanism, Agent Factory could become the defining infrastructure layer of hospital AI over the next decade. That conclusion stands. What the evidence now confirms is that the clock is not running from some future launch date.

It was already running.

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.

AI Is Eating Theory. Companies Are Firing the People With Judgement.

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AI is replacing the work that used to define the first decade of a career. At the same moment, organisations are quietly thinning out the people whose work AI cannot do.

That pairing sits somewhere between obvious and uncomfortable, depending on which part of the workforce you sit in. The data behind it is no longer disputed. The talent decision being made in response to it is almost universally backwards.

 

What AI is actually replacing

The popular framing of AI in the workplace is that it threatens knowledge workers broadly. The 2025 data tells a sharper story.

Stanford’s Digital Economy Lab released a study in November 2025 with the deliberately unsettling title Canaries in the Coal Mine. It tracked employment in occupations highly exposed to AI from late 2022 onwards. Workers aged 22 to 25 in those roles saw employment fall by roughly 13%. Workers in their forties, fifties and sixties in the same occupations continued to grow.

The mechanism is not redundancy. It is non-replacement. Entry-level vacancies are quietly not being backfilled. The career ladder is losing its bottom rungs.

The Stanford authors are unusually direct about why. AI is replacing codified knowledge, the part of expertise that can be written down, while complementing the experiential wisdom that only comes from years on the job. Other 2025 work in customer support and software development tells the same story. AI lifts the bottom of the distribution faster than the top. Two-month-experience workers using AI now match six-month-experience workers without it. The work AI does best is the kind of standardised, learn-from-a-book task that used to define the first few rungs of a career.

 

The thing AI cannot replicate

There is a second half to this story that gets less coverage.

Boston Consulting Group ran a study with Harvard Business School using 758 of its own consultants and GPT-4. On standard tasks, AI users completed 12% more work, 25% faster, with 40% better quality. The finding that rarely makes the press summary: when the same study tested tasks designed to fall outside the model’s actual capability, consultants using AI were 19 percentage points less likely to produce correct solutions. AI made experts wrong more often when the problem required judgement AI lacked.

The capability to know when to trust an AI answer and when to override it is itself a function of experience. It is built from a personal library of cases, situations and outcomes that no model has been trained on.

Decades of research in naturalistic decision-making, the field Gary Klein founded by watching firefighters and military commanders make calls under uncertainty, describes the same mechanism. Experts under pressure do not deliberate between options. They pattern-match against situations they have seen before. The library is built by exposure, not by reading frameworks.

This is what is meant by judgement. It is the residual human advantage in the AI era, and it has a clear demographic profile.

 

The talent decision being made backwards

Put the two findings beside each other.

AI is removing the codified, junior-level work fastest. The cohort whose work AI is actually complementing is the experienced one. The economic logic of an organisation in 2026 should be to lean into that experienced layer, because it is the part of the workforce AI cannot reproduce and which increasingly determines the quality of any AI-augmented output.

What organisations are doing instead is the exact opposite. The over-50 cohort is being quietly thinned through restructures, voluntary exit programmes, redundancy schemes, and the slow erosion of roles experienced workers tend to hold. It is rarely a stated policy. It is almost everywhere a pattern.

The talent decision is being made backwards. The cohort being pushed out is the one most worth keeping. The cohort being squeezed at the bottom is the one whose work AI is already doing. The organisation ends up with no future and no memory.

 

The cost of forgetting

There is an institutional dimension to this that gets ignored because it does not show up in the next quarterly report.

Roughly 42% of an organisation’s working knowledge sits in the heads of individual employees and nowhere else. Industry estimates put the cost of knowledge loss from rapid organisational change at tens of billions of dollars a year across Fortune 500 firms. The direction is consistent even where the precise figure varies. Restructures remove people, and the people take the unwritten knowledge with them. A newly arrived CEO who clears out the over-50 cohort does not just lose those individuals. They lose the only group who remembers why the last three transformations failed and what is different about this one.

That is not a fairness argument. It is a structural one. The organisation is paying a real cost. It will appear on the books eighteen months later, in the form of mistakes the experienced layer would have caught.

 

What we are not calling it

Age discrimination is unlawful in most major jurisdictions. It is also one of the most reliably under-reported categories of workplace harm, because it is rarely framed as discrimination by the people doing it.

ProPublica’s multi-year investigation into IBM found the company eliminated more than 20,000 workers aged 40 and over from 2013 onwards. The US Equal Employment Opportunity Commission concluded in 2020 that the layoffs had a clear adverse impact on older workers. More than 85% of those targeted for layoff in that period were older workers, even when rated as high performers. In 2023, former IBM HR professionals filed suit alleging termination linked to age and explicit plans to replace them with AI.

Almost no one running these processes describes them as age discrimination. They are called “right-sizing,” “talent refresh,” “succession planning,” “rebalancing the pyramid.” The language and the outcome have been routinely diverging for at least a decade. What is new in 2025 is that AI has made the underlying decision economically illiterate as well as legally exposed.

 

Where this leaves you

If you run an organisation that has quietly pushed out the experienced cohort, you have spent real money to remove the layer of your workforce AI cannot replicate, while leaving in place the layer whose work AI is doing without you noticing.

The question is not whether you can afford to keep experienced staff. It is whether you can afford to lose them at exactly the moment they became the most valuable people on your payroll.

Smarter, Faster, More Dangerous: How Hackers Are Using AI to Target You

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Cyberattacks used to take time.
A convincing phishing email required effort. A fake website needed a designer. Voice impersonation meant hours of editing.

Not anymore.

Thanks to generative AI and widely available tools, today’s hackers can launch highly convincing, targeted attacks at scale, and they’re getting much better by the day.
The days of poorly written scam emails and generic threats are long gone. What we’re now seeing is a new era of intelligent, adaptive, and believable cybercrime.

And all that isn’t the scary part.
It’s not just corporations being targeted. It’s you.

What’s Changed?
AI has lowered the barrier to entry for cybercriminals.
What once required technical skills can now be done with simple prompts, pre-built tools, and large language models. Hackers no longer need to be code-savvy, they just need to know what to ask AI to do.

Some of the most common and dangerous tactics include:

1. AI-Enhanced Phishing Emails
You know the old tell-tale signs of a scam email, bad grammar, odd formatting, suspicious links.

But now?
AI models can craft flawless, natural-sounding messages that mimic corporate tone, structure, and urgency. Some are even personalised using information scraped from social media or public platforms.
A Harvard Business Review article warns that AI is not only increasing the volume of phishing scams, it’s making them dramatically more believable, eroding the traditional red flags people rely on.

Examples:

  • “Your HR document has been flagged for review.”
  • “Unusual login activity detected. Please confirm access.”

These messages look like they came from your IT department. They’re often convincing enough to trick even experienced professionals.

2. Instantly Generated Fake Websites
Previously, creating a fake login page or payment portal took time. Now, AI can generate realistic website templates in seconds, complete with company logos, branding, and believable copy.

According to Axios, a security firm found that attackers used generative AI to spin up over 130 phishing sites mimicking Okta’s login pages in under 30 seconds, faster than most organisations can detect them.

Hackers use these sites to:

  • Steal login credentials
  • Collect payment details
  • Harvest personal information

And with AI image tools, they can even generate realistic “employee photos” and fake testimonials to make it all look legitimate.

3. Deepfake Audio and Voice Cloning
Voice imitation isn’t science fiction anymore, it’s a real and rising threat.

With just a few seconds of audio (often taken from videos, podcasts, or voice notes), AI can clone someone’s voice and generate new speech that sounds eerily accurate.

This threat has already gone mainstream. The Wall Street Journal reported a rise in deepfake CEO scams, where criminals impersonated executives to trick employees into making large financial transfers. In one case, a UK engineering firm, Arup, lost $25 million to a realistic deepfake video of its CFO during a fraudulent video call.

Scenarios include:

  • A “CEO” calling an employee requesting an urgent wire transfer
  • A loved one’s voice asking for help while travelling
  • A “bank representative” confirming personal details

As AP News points out, even 30 seconds of audio is enough to train a convincing voice clone.

4. AI Chatbots and Social Engineering
Hackers are deploying AI-powered chatbots on fake websites, posing as support agents or HR reps.

These bots:

  • Engage victims in believable conversations
  • Ask probing questions
  • Capture sensitive information over time

And they learn quickly. The more people interact, the better they become at deception.

5. Highly Targeted Attacks (Spear Phishing 2.0)

With access to LinkedIn profiles, public emails, and personal posts, AI can generate customised attacks that feel personal.

You might receive an email from a “colleague” referencing a recent project. Or a text that uses your child’s name.

This hyper-targeted approach increases trust, and increases the chance you’ll click.

Even Government Sites Are Being Faked

Hackers aren’t just targeting companies and individuals, they’re now cloning government websites with alarming accuracy.

A recent TechRadar report revealed that attackers are using AI to build replicas of official government portals, tricking citizens into submitting tax details, bank info, or ID documents.

Why This Should Concern Everyone

Cybercrime is clearly no longer just a corporate risk.

It’s personal, scalable, and increasingly indistinguishable from real communication.

And the tools hackers use are getting faster, cheaper, and smarter.

Even careful individuals are falling for scams that, five years ago, wouldn’t have passed the sniff test.

As the Economist notes, we’re entering an era where AI-enabled cybercrime may outpace traditional digital defences, causing massive financial and societal damage.

So, What Can You Do?

1. Stay Sceptical, Even When It Sounds Right
Don’t trust by default. Even if a message or voice seems legitimate, double-check independently.

 

2. Verify URLs and Sender Addresses
Look closely at email addresses, links, and domain names. AI-generated scams often use domains that look almost right.

 

3. Avoid Clicking, Go Direct Instead

If you receive a message from your bank, employer, or supplier, visit their website directly rather than clicking a link.

4. Use Multi-Factor Authentication
It adds a second layer of protection even if your login details are compromised.

5. Talk About It
The more we educate each other, family, colleagues, employees, the harder it becomes for scams to succeed.

Takeaways That Matter

AI is a powerful tool, but it’s not neutral.
The same technologies that help us write, code, and communicate are being used to deceive, manipulate, and exploit.

This is more to do with awareness rather fear.

Because in a world where anyone can fake anything, critical thinking becomes your first line of defence.

The best protection you have is to stay informed, stay alert, and stay a step ahead.

AI-Powered PMOs: What You Need to Know

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The Future of PMOs is Not Just Smarter – It’s Transformational

What if AI could redefine the role of the Project Management Office (PMO) entirely?
For years, PMOs have been the backbone of organisational efficiency, but the rapid evolution of artificial intelligence is not just streamlining project oversight, it is transforming how projects are planned, executed, and evaluated.

AI-powered PMOs are much more than an operational upgrade. Leaders who understand and embrace this shift will drive efficiency, enhance decision-making, and position their organisations ahead of the competition.

 

Why AI is Reshaping PMOs

Traditional PMOs face persistent challenges:

  • Overwhelming Data – Managing multiple projects generates vast amounts of information, making it difficult to extract actionable insights.
  • Inefficiencies in Resource Allocation – Manual planning often leads to overworked teams or underutilised talent.
  • Limited Foresight – Without predictive analytics, PMOs struggle to anticipate risks and proactively address them.

AI is addressing these challenges by automating workflows, improving forecasting, and enabling data-driven decision-making.

 

How AI is Transforming PMO Operations

  1. Data-Driven Decision-MakingAI can analyse vast datasets in seconds, identifying patterns, trends, and risks that would take humans weeks to uncover. Predictive analytics enable teams to make smarter decisions and mitigate challenges before they escalate.
  2. Optimised Resource ManagementAI-powered scheduling and task allocation ensure that the right resources are assigned to the right projects at the right time, maximising efficiency while reducing delays.
  3. Proactive Risk MitigationBy leveraging machine learning, AI tools can predict potential project risks, whether budget overruns, schedule delays, or stakeholder misalignment, allowing teams to take corrective action before issues arise.
  4. Automated Reporting and Real-Time InsightsAI eliminates the need for manual reporting by generating dynamic dashboards with real-time project performance data. Leaders gain instant visibility into project health without waiting for periodic updates.
  5. Process Optimisation and Continuous ImprovementAI-powered insights reveal inefficiencies in workflows, helping PMOs refine processes, eliminate redundancies, and improve project execution over time.

The Impact on Organisational Performance

An AI-powered PMO is not just about automation, it is about delivering measurable business value:

  • Faster project completion with fewer bottlenecks.
  • Improved resource utilisation and workload distribution.
  • Greater alignment between projects and business objectives.
  • More informed decision-making with data-driven insights.

Organisations that integrate AI into their PMO functions will not only enhance operational efficiency but will also gain a competitive edge in an increasingly complex business environment.

 

How to Get Started

To integrate AI into your PMO successfully, consider these steps:

  1. Pilot AI Solutions – Start with a small-scale implementation, such as AI-driven predictive scheduling or automated reporting tools, to assess their impact before wider adoption.
  2. Upskill Your Team – Ensure project managers and PMO staff are trained in AI-driven project management tools to maximise their effectiveness.
  3. Define Clear KPIs – Establish measurable goals, such as reduced project timelines, improved resource utilisation, and enhanced risk mitigation, to track AI’s impact.

Final Thoughts

AI is not replacing the PMO, it is elevating it. Organisations that embrace AI-powered project management will optimise efficiency and also redefine their approach to strategic execution.

Project Management Will Never Be the Same: Are You Ready for What’s Coming?

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What if your project management tools could predict problems before they arise, adapt to your workflows seamlessly, and enable collaboration across time zones as if everyone were in the same room?

This isn’t a future aspiration, it’s where project management is heading in the next five years.

PM tools are set to evolve rapidly, powered by AI, VR, blockchain, and IoT. These technologies won’t just improve how we manage tasks, they will redefine collaboration, accountability, and decision-making.

This isn’t business as usual. It will be a complete shift in how we operate. Here’s how these advancements will shape the future and what it means for project managers.

AI: Predict, Automate, Optimise
AI is turning PM tools into powerful, decision-driving engines.

  • Anticipate Risks: AI will analyse data patterns to identify potential bottlenecks, resource shortages, and delays before they impact delivery.
  • Actionable Insights: AI won’t just flag problems, it will recommend solutions, helping PMs make smarter, faster decisions.
  • Focus on Strategy: Administrative tasks like updating schedules and generating reports will be fully automated, freeing project managers to focus on high-value activities.

Why You Should Care: AI will turn PMs into proactive leaders rather than reactive managers. The ability to predict and prevent issues before they happen will completely change the role of a PM.

VR and AR: Collaboration, Visioning, and Risk Analysis
Technologies like Virtual and Augmented Reality are not only breaking collaboration barriers but also becoming indispensable tools for visioning, testing, and mitigating risks in project management.

  • Virtual Collaboration Without Borders: Teams will be able to step into a shared virtual workspace, interacting with 3D project models as if they were in the same room.
  • Stakeholder Buy-In, Simplified: VR will allow stakeholders to see, explore, and interact with project plans before execution, improving clarity and reducing costly misunderstandings.
  • Visioning for Big-Picture Goals: Teams will experience the finished project before it even begins, ensuring better alignment and expectation-setting.
  • Testing in a Risk-Free Environment: PMs will be able to simulate real-world conditions to stress-test projects, identify weak points, and refine plans.
  • Advanced Risk Analysis: Virtual models will allow for early detection of potential risks, so mitigation strategies can be tested and adjusted before they impact execution.
  • Immersive Training & Onboarding: AR-driven training will help upskill teams faster, ensuring everyone is fully prepared before they start working on a project.

Why You Should Care: From visioning and testing to collaboration and risk analysis, VR and AR provide project managers with tools to anticipate challenges and deliver precision, innovation, and confidence at every stage of the project lifecycle.

Blockchain: Building Trust Through Transparency
Blockchain is redefining accountability and security in project management.

  • Immutable Records: Every decision, contract, and milestone will be securely recorded, creating a tamper-proof trail of accountability.
  • Smart Contracts: Automated milestone-based payments will reduce disputes and ensure compliance.
  • Global Collaboration: Decentralised platforms will enable secure partnerships across borders while maintaining data privacy.

Why You Should Care: Trust becomes effortless when transparency is built into the system.

IoT: Real-Time Visibility
IoT is bringing unprecedented visibility to projects involving physical assets.

  • Dynamic Monitoring: IoT devices will provide live data on equipment, resource utilisation, and environmental conditions, enabling agile responses.
  • Proactive Risk Mitigation: Real-time insights will allow PMs to address logistical and safety risks before they escalate.

Why You Should Care: Real-time data means real-time decisions, keeping projects on track and under control.

Personalisation: Tools That Adapt to You
The future of PM tools is all about user-centric design.

  • Tailored Interfaces: Dashboards will adapt dynamically to individual roles and priorities, presenting only the most relevant information.
  • AI-Powered Learning: Embedded AI will offer real-time guidance on tool features, ensuring teams can unlock their full potential with minimal effort.

Why You Should Care: When tools work for you, not the other way around, productivity thrives.

Upskilling: The Key to Thriving in This New Era
As these technologies reshape project management, the role of the project manager must evolve too.

  • Learn to Leverage Technology: PMs must understand how to apply AI, VR, blockchain, and IoT to enhance project outcomes.
  • Embrace Continuous Learning: Staying ahead will require ongoing education and training to adapt to emerging tools and methodologies.
  • Lead Through Change: Beyond mastering technology, PMs must guide their teams through this transformation with vision and clarity.

Why You Should Care: The tools are only as effective as the people who wield them. Upskilled PMs will be the driving force behind successful projects in this new era.

Preparing for the Future
To lead this transformation, organisations need to act now:

  1. Adopt Early: Start experimenting with emerging technologies on smaller projects to build expertise.
  2. Upskill Your Teams: Invest in training programs that empower PMs to harness the full potential of new tools.
  3. Redefine Processes: Align workflows and methodologies with the capabilities of next-generation PM tools.

Are You Ready to Lead the Way?
This is much more than just a technological shift, it’s a leadership moment. The next five years will redefine how projects are managed, and the PMO’s and organisations that embrace this change will set the standard for innovation and success.

The question now isn’t whether these technologies will impact you, it’s whether you’ll be ready to lead with them.

Have you started preparing for the transformation of project management?

How AI Has Transformed Analytics and Data Science

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Artificial intelligence has brought about one of the most significant transformations in the history of analytics and data science. Once primarily reliant on manual processes and painstaking statistical methods, the field now moves at a pace and scale previously thought impossible. As organizations harness the ever-expanding volumes of data at their disposal, AI not only changes how we analyze and interpret information but also redefines the role of data professionals and the possibilities for innovation.

In this article we will delve into how AI has revolutionized data science, and what it means for the future.

From Manual Processes to Unprecedented Speed and Scale
Not long ago, data scientists spent the majority of their time on tedious, labor-intensive tasks: scrubbing raw data, performing exploratory analyses, and running repetitive scripts just to grasp the meaning of their data. It was necessary groundwork, but it consumed valuable time that could have been spent solving complex problems or generating forward-looking insights.

AI has changed all of that. With machine learning algorithms that can handle data preparation, pattern recognition, and feature selection, the time to insight has drastically shortened. Automated machine learning (AutoML) platforms now allow organizations to produce predictive models without extensive human intervention, accelerating the entire analytical workflow. Data professionals, instead of slogging through hours of preprocessing, can direct their efforts toward high-level strategy, interpretation, and innovation. The result is a step-change in productivity, and in the quality of decisions that follow.

Real-Time Decision-Making: The New Standard
Beyond speed, AI introduces a fundamentally new capability: real-time analytics. Historically, organizations made decisions based on what had already happened. They reviewed past performance, identified trends, and adjusted their strategies accordingly, an inherently reactive approach.

Today, AI-powered analytics allows companies to stay ahead of the curve. Streaming data sources, such as IoT sensors, social media feeds, or live transactional systems, can be analyzed as events unfold. This enables businesses to detect anomalies, predict future demand, and respond to market shifts the moment they occur. In industries like healthcare, financial services, and retail, real-time analytics is a competitive necessity. Companies that can identify trends and act in the moment are poised to outpace their competition, reduce risks, and seize opportunities at lightning speed.

Empowering Every Professional: The Democratization of Data Science
AI’s impact isn’t confined to data scientists. One of its most powerful effects has been making advanced analytics accessible to a much broader audience. Non-technical users, product managers, marketers, financial analysts, can now leverage AI-driven tools to extract insights and build models without needing deep programming expertise. This democratization has transformed how organizations think about data, embedding analytical capabilities across entire teams and departments.

What’s more, this shift means that data science is no longer a niche skillset. By equipping more professionals with AI-powered platforms, companies foster a culture where data-driven decision-making becomes the default rather than the exception. Teams are empowered to experiment, innovate, and test ideas faster than ever before, driving better outcomes and unlocking new growth opportunities.

Evolving the Role of the Data Scientist
Paradoxically, as AI takes over many of the traditional responsibilities of data scientists, the value of these professionals has only grown. Far from being replaced, data scientists are now expected to bring greater creativity, ethical judgment, and strategic vision to their work. They’re increasingly involved in designing AI systems that are fair, transparent, and accountable, ensuring that the insights delivered by machines are both accurate and actionable.

This shift has also sparked a more strategic approach to data science careers. Today’s professionals must not only understand the technical intricacies of machine learning but also excel in communication, storytelling, and business alignment. As AI handles the heavy lifting, data scientists have more time to focus on innovation, governance, and using data to answer big, forward-looking questions.

Navigating New Ethical Challenges
The power of AI also comes with responsibility. The ability to process enormous datasets, run complex algorithms, and produce actionable insights at scale has amplified the importance of ethical data practices. Organizations are grappling with questions about bias in AI models, data privacy, and the long-term implications of AI-driven decisions.

For data scientists and business leaders alike, this means reevaluating not only how data is used, but how it is collected, shared, and governed. Ethical AI is becoming a key differentiator in earning trust from customers, regulators, and society at large. Building transparency, accountability, and fairness into AI systems is a moral imperative.

A Catalyst for Continuous Innovation
At its core, AI’s greatest contribution to analytics and data science is the way it enables continuous innovation. Every industry, from manufacturing to healthcare to education, is finding new ways to leverage AI-powered insights to enhance efficiency, improve customer experiences, and create entirely new value propositions.

Consider healthcare, where AI is helping to detect diseases earlier, personalize treatments, and predict patient outcomes. Or retail, where AI-driven recommendation engines are reshaping how consumers interact with brands. Across the board, AI is empowering organizations to move beyond incremental improvements and think boldly about what’s possible.

As AI continues to mature, the opportunities will only grow. From uncovering untapped markets to solving global challenges like climate change and public health, the potential applications of AI-driven analytics are boundless.

In Closing
AI has not merely improved the field of analytics and data science, it has fundamentally changed it. By automating routine tasks, delivering real-time insights, and democratizing access to sophisticated tools, AI has turned data into one of the most powerful assets a business can have. But this revolution is about more than technology. It’s about the human ingenuity behind the models, the ethical responsibility to use data wisely, and the courage to innovate and lead.

As we look to the future, it’s clear that AI will be a partner in shaping the decisions, strategies, and breakthroughs that will define the next era of business and society.