Monthly Archives: August 2026

The $5.5 Trillion Bill for Doing Nothing About AI Skills

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IDC put a number on the cost of enterprises not knowing what to do about AI skills: $5.5 trillion. Not by 2030. By the end of 2026.

That is not a distant workforce-planning problem. It is the price tag on decisions organisations are making, or more often deferring, this quarter.

The number comes from IDC’s survey of enterprise IT leaders across the US and Canada. It measures a specific kind of pain: product delays, lost competitiveness, and business walking out the door because the people needed to build and run AI capability were not there when the work needed them.

An IDC Spotlight Paper distributed via workforce-skills platform Workera applies the same figure directly to the AI skills conversation, and the application largely holds up. Most of what IDC’s original survey describes as a broader tech talent shortage is, in practice, an AI capability shortage wearing a wider label.

Three other figures make the same point from different angles. The World Economic Forum’s Future of Jobs Report 2025, surveying over a thousand employers and 14 million workers across 55 economies, found that 59% of the global workforce will need reskilling or upskilling by 2030, and that 11% of that group are unlikely to receive it. Indeed’s hiring data shows the share of job postings with “AI” in the title has more than tripled since 2022, from 2.6% to 8.3%. And PwC’s 2026 Global AI Jobs Barometer, built on more than a billion job advertisements, found that AI-skilled workers now command a 62% wage premium over comparable peers, up from 57% the year before and roughly 25% two report cycles before that.

These numbers describe a market that has already repriced itself, not a future state, while most enterprise workforce plans are still budgeted as though it hasn’t.

 

The Premium Is the Market Telling You Something

Wages move slowly almost everywhere except where genuine scarcity exists. A skill premium that has climbed from roughly 25% to 62% across three consecutive PwC survey cycles is not a normal labour-market signal. When a specific skill commands 62% more pay than the equivalent role without it, and that gap is still widening year over year, that is the market pricing in a shortage faster than most HR functions can respond to it, not a talent management curiosity.

Second Talent’s 2026 research puts a shape on that shortage: roughly 1.6 million open AI-related roles globally against around 518,000 candidates qualified to fill them, a demand-to-supply ratio a little over three to one. Every organisation competing for AI capability right now is competing inside that gap, and every quarter spent treating reskilling as a training-budget line item rather than a capital allocation decision is a quarter spent losing that competition to whoever moved first.

 

Why This Belongs in the Risk Register, Not the Learning and Development Plan

Most organisations still route AI reskilling through the same governance as any other training initiative: an L&D budget line, a completion metric, a once-a-year review. That treatment made sense when the skills in question were incremental. It does not hold up against a $5.5 trillion cost estimate and a wage market moving by double digits year over year.

A capital risk gets tracked differently to a training initiative. It gets a named owner, a quantified exposure, and a review cadence tied to the business calendar rather than the HR calendar. Few organisations apply that discipline to AI skills, because the function historically responsible for skills, HR, was never built to run risk registers, and the function that runs risk registers, finance and the PMO, was never asked to own workforce capability.

That gap in ownership is the reason the $5.5 trillion figure keeps compounding instead of shrinking, not a technicality.

 

What Actually Changes the Trajectory

Closing this gap requires three specific shifts most organisations have not made, not a bigger training budget.

Put a named executive owner on AI capability risk, distinct from whoever owns general L&D, with the same reporting rigour as any other material risk on the register. Measure the capability gap in the terms the wage market already uses: roles you cannot fill, roles you are overpaying to fill, and work you are declining because you lack the people to do it, not completion percentages on a training platform. And treat the reskilling decision as time-sensitive capital allocation, where every quarter of delay is a quarter in which the 62% premium, and the competitors already paying it, get further ahead.

 

The Window Is a Cost Curve, Not a Deadline

There is no single date after which the AI skills gap becomes unrecoverable. What exists instead is a cost curve that gets steeper the longer it is ignored, priced daily by a labour market that has already decided what AI capability is worth.

The organisations that treat this as a 2027 problem will be paying 2026 prices for it well into the decade. The ones already moving are the ones setting the price.

The Governance Training Nobody Budgets For

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Nobody has ever failed a project because they didn’t know how to build a Gantt chart. However some failed because nobody taught them who was allowed to say no.

I have sat in quite a number of programme inductions, and every one of them covers the same ground. Scheduling. Budgeting. Risk logs. RAID templates. Reporting cadence. Then, somewhere around the second afternoon, someone puts up a slide about governance and the room’s attention visibly leaves the building. It is treated as the compliance module, the thing you sit through before you get to the real work. Nobody walks out able to say, with any confidence, who actually owns the call when a decision does not fit neatly on a template.

That gap is not an oversight. It is a choice organisations keep making, year after year, without ever naming it as one.

 

The curriculum has a hole in it

Ask a newly promoted programme manager to explain their RAID log and they will do it fluently. Ask them who has the authority to accept a risk above a certain threshold without escalating it, and watch the pause. That pause is the sound of someone realising they were never actually taught the answer, only ever expected to absorb it by watching more senior people long enough.

A 2026 AI Governance Gap Report surveying more than 500 HR professionals found that only 45 per cent of organisations provide AI literacy training to all employees, one concrete, measurable instance of the broader governance-literacy gap this piece is about. Two-thirds of HR teams are already using AI to shape compliance and policy decisions, and the same report found fewer than half have given employees even that AI-specific literacy training to keep pace. The gap shows up well beyond HR too, in every function that has ever built a decision-rights framework, filed it in a folder, and assumed the document itself did the teaching.

Documents do not teach. People do, and usually only the ones who were already senior enough to have picked it up somewhere else.

 

Decision rights are treated like folklore

Most organisations do not lack a governance framework. They have one, usually a good one, sitting in a policy library that almost nobody outside the PMO has opened. What they lack is a mechanism for turning that document into instinct.

The result is a workforce that learns decision rights the hard way: by guessing wrong in front of a steering committee, by escalating something trivial and being quietly told off for wasting everyone’s time, or by not escalating something serious and finding out only when it has become a crisis. Every one of those is an expensive way to teach a lesson that could have been taught in an afternoon.

This is where the “knowing-doing” research cited by Harvard Business Review becomes uncomfortable reading for anyone who runs a training budget. Two out of three managers say they are still uncomfortable having accountability conversations with their own people, despite most of them having sat through the leadership training designed to prepare them for exactly that. The problem was never a shortage of content. Knowing a framework exists and being able to act on it under pressure are two entirely different skills, and organisations keep training the first while assuming it produces the second.

Governance training suffers from the same fault line. Knowing there is an escalation policy is not the same as recognising, in the middle of a stressful Tuesday, that the decision in front of you is the one the policy was written for.

 

The training everyone skips because it looks obvious

There is a reason this particular gap survives budget reviews when almost nothing else does. Governance training looks like it should be simple, so nobody prioritises building it properly. Everyone assumes the framework document is self-explanatory, right up until the moment someone makes the wrong call and the post-incident review discovers that three different people had three different understandings of who was supposed to decide.

I have run those reviews. The finding is almost never “the framework was wrong.” Almost always, nobody had ever been walked through what the framework meant in a live situation, so everyone applied their own version of common sense, and common sense is not actually common.

 

What actually needs teaching

A longer policy document will not fix this. Longer documents get read less, not more. The fix is teaching people to recognise a decision point before it arrives, not after.

That means running people through real scenarios instead of abstract categories, trading “what is your escalation threshold” for “here is a supplier problem that looks small and is not, what do you do in the next ten minutes.” It means naming, out loud and often, the handful of decisions in your organisation that carry disproportionate weight, so people learn to feel the shape of one before it is labelled for them. And it means treating governance literacy the way you would treat safety training: refreshed, tested, and taken seriously enough that senior leaders visibly participate in it themselves, rather than left as a one-off induction module.

The organisations that get this right build fewer, better decision-makers rather than thicker governance frameworks: people at every level who can spot a genuine decision point on instinct, the same way an experienced engineer can hear an engine fault before the dashboard lights up.

You cannot budget for the crisis a decision creates and then refuse to budget for teaching people to see it coming.

 

 

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

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

 

What the Research Actually Shows

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

 

The Mechanism, Confirmed More Recently

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

 

The Counterargument Worth Taking Seriously

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

 

Why This Matters More in Some Rooms Than Others

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

 

What This Means in Practice

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

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

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

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

 

The Bet

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

 

The Assumption

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

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

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

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

 

The Sequence

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

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

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

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

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

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

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

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

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

The Pager

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

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

 

The Proof

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

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

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

 

Verdict

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

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