Research

Seven years. Two hundred organisations. One question.

Since 2019 I have been asking organisations across thirty countries the same thing: are you using AI, or has it started using you? The research runs through interviews, advisory engagements and surveys with leaders in banking, logistics, media, law, professional services, technology and education. It is where the SuperSkills framework comes from, and it is why the book carries evidence rather than opinion.

Here for the research on AI and human capability? Start with the evidence, or go straight to the full index of every page.

The Drift Index

An annual reading of organisational judgement.

Everything below this line is synthesis: what the published evidence establishes, how strong it is, and what it does not support. Read together it is also an argument for something that does not exist yet. No validated instrument measures whether an organisation is losing capability, which is why this research lists capability debt as a framework rather than a finding. The Drift Index is the attempt to close that gap: an annual reading of organisational judgement, where it is holding and where it has moved without anyone deciding. First edition December 2026.

The first edition is compiling now. If you would like your organisation included, get in touch. Until it publishes, the honest position is the one set out in what we actually know: the mechanism is well evidenced, the organisational measurement is not.

The drift versus design matrix A two-by-two matrix. Vertical axis: agency, low to high. Horizontal axis: awareness, low to high. Low agency and low awareness: the Sleepwalkers, who do not notice the drift. Low agency and high awareness: the Stuck, who see the patterns but cannot break them. High agency and low awareness: the Programmed, who optimise without questioning. High agency and high awareness: the Designers, who see the systems and shape them. AGENCY High Low AWARENESS Low High The Sleepwalkers They do not noticethe drift. The Stuck They see the patternsbut cannot break them. The Programmed They optimisewithout questioning. The Designers They see the systemsand shape them.
The drift versus design matrix. Where an organisation sits depends on two things: whether it can act on what shapes its choices, and whether it can see those forces at all. From SuperSkills (Kogan Page, 2026).
Start with a question

The questions this research answers.

The hubs below are organised by idea. This is the same body of work organised by the problem you arrived with. Each links to the main answer.

Does AI weaken human judgement?Not on its own. It reduces the demand for judgement, and demand is what builds it. Does AI weaken critical thinking?It does not disappear. It migrates to verification, and thins unless you design against it. What human skills become more valuable as AI improves?Not the ones on the reassuring list. The ones that raise the expertise of the work that remains. What happens when AI removes junior work?The wrong worry is graduate unemployment. The right one is where senior people come from. How should humans and AI make decisions together?Not by adding a person at the end. Across 106 studies, undesigned pairing underperformed both. How can organisations prevent AI-driven deskilling?By redesigning how people learn at the same pace as they redesign how work gets done. How do I use AI without becoming dependent on it?Form the view before you prompt, and keep one unaided rep of whatever carries your value. What should leaders keep human?Accountability, recognition, and the origination of the question. The task list will keep moving.

All 616 questions about humans and AI →

Questions about the research

How the work is done.

How was the SuperSkills research conducted?

Through interviews, advisory engagements and surveys with leaders across more than 200 organisations in over 30 countries, from 2019 to 2026. The sectors span banking, logistics, media, law, professional services, technology and education. The research base is where the seven SuperSkills framework comes from.

What is the Drift Index?

The Drift Index is an annual reading of organisational judgement: where it is holding, and where it has moved without anyone deciding. It draws one senior respondent per organisation and publishes each December, with the first edition in December 2026.

Can my organisation take part in the Drift Index?

Yes. The first edition is compiling now, with one senior respondent per organisation. Get in touch through the contact page and mention the Index.

Is the research peer-reviewed?

No, and it does not claim to be. It is practitioner research: systematic, longitudinal and cross-sector, conducted through direct engagement rather than academic publication. Where the book cites external studies, those citations go to the primary sources.

Can I use Rahim's frameworks in my own work?

Yes, with attribution. The drift versus design matrix, synthetic seniority and the other frameworks are published and citable: name the source and link to the original. For commercial licensing, training use or reproduction in publications, get in touch.

How this research works, in full →

The work

Start here.

Six ways in. The map of the questions, what the evidence actually supports, the chronology of the writing that moved the argument, the position this research will defend, the board version, and the tool. Everything else is in the full index below.

The questions

The public map of the territory. 275 real questions about what increasingly capable AI does to humans, grouped into 15 areas and marked answered, partial or open, and being built towards 500. The unanswered ones stay visible on purpose.

Enter →

What We Actually Know

The state of the evidence. Nineteen claims separated into strong evidence, emerging evidence, and what remains unknown, each graded and linked to its primary source. Including the questions this research cannot yet answer. Reviewed quarterly.

Enter →

The Best Writing on AI

A chronology from March 2023 to August 2026, because the order is the argument. What actually moved the conversation, why the founding estimates are still quoted over the measurements that complicate them, and how the question changed underneath everyone.

Enter →

Human in the Loop Is Not a Safeguard

A position page. 370 effect sizes across 106 experiments say the arrangement most organisations have installed makes results worse, not better. With the strongest objections given in their strongest form, and what would change the position stated rather than withheld.

Enter →

What a Board Should Ask

Twelve questions, each paired with the answer that should worry you. Not an agenda item: ask two or three in passing and listen for hesitation. The twelfth does more work than the other eleven combined.

Enter →

The Delegation Boundary Map

"Keep a human in the loop" does not say which human, at which point, or on what grounds. Nine stages of work, and for each: what the human keeps, what the machine may do, who verifies, and who is accountable. With a downloadable working grid.

Enter →

Everything, in one place

The full index.

Every research page on this site, grouped. Generated from the estate rather than maintained by hand, so nothing can be published and then be impossible to find.

Start here

The seven SuperSkills

The named concepts

Judgement, oversight and accountability

  • What board oversight of AI actually looks likeWhat the frameworks actually require and what belongs on a quarterly agenda. Article 14 treats the capability to override as the content of oversight rather than the presence of a person. NIST expects reversal thresholds before they are needed. Ayinde (2025) made the verification duty non-delegable and upward-travelling. And the capability question sits in none of the three lines of defence.
  • Does AI weaken human judgement?The flagship review. 106 experiments where human and AI pairs did worse than either alone, endoscopists losing six points of unassisted detection, students scoring lower once the tool was removed, and where AI demonstrably improves decisions instead.
  • Human and AI decision making
  • Decision Quality in the AI Era
  • AI agents and human judgementWhen AI can plan and act, the human job becomes deciding what to delegate and owning the result. Agents, human at the start, and who is on the hook.
  • Should I let an AI agent act on my behalf?Not a trust question. A system that answers gives you something to reject; a system that acts has already done it. Every oversight model assumes a pause that agents remove.
  • Human in the loop is not a safeguard
  • What is meaningful human oversight?Article 14 of the EU AI Act came into force on 2 August 2026 and names automation bias in the legislation. The five things an overseer must be enabled to do, why most arrangements fail the test, and why it is a capability problem wearing a governance costume.
  • Who owns verification when AI does the work?In most organisations, nobody. It is not in a job description, a budget line or an org chart. The capability test that settles whether verification is real, and four ownership models with their actual costs.
  • Who supervises work they cannot do themselves?A person three years into a career reviews AI-generated work of a kind they have never produced, and signs it off. The organisation records a control as satisfied. Nothing has been checked.
  • When should I override AI?
  • How do I know when AI is wrong?
  • How do you audit an AI-assisted decision?
  • What should a board ask about AI?
  • The Delegation Boundary Map
  • Who can override an AI system?Oversight without authority is ceremony. What Article 14 actually requires, the one paragraph that names competence, training and authority together, and why overriding costs something while deferring is free.
  • Does explaining an AI's reasoning help?NIST separates explanation accuracy from decision accuracy: a true account of how a system reached a wrong answer is an ordinary outcome. Why a reason can substitute for a check, and the three things an explanation is genuinely good for.
  • How should AI decision rights be allocated?Doing the work of deciding and holding the right to decide were always different. What the regulation already allocates between provider and deployer, and why an allocation arrived at by drift cannot be reviewed.
  • Which decisions should become slower because of AI?Cognitive forcing cut overreliance in a 199-participant experiment, and the designs that worked best were the ones people rated worst. Article 14(5) already mandates a two-person check for one category. Four conditions that justify putting the friction back.
  • When agents become part of the workforce, who manages them?Article 26 already names the person: competence, training and authority, plus the power to suspend. The gap sits underneath it. The FAccT visibility paper states we lack methods for determining when an agent has created a sub-agent, which breaks every span-of-control assumption in management.
  • How do you design a stop button people will actually use?Article 14(4)(e) requires the button and decides nothing about whether it gets pressed. One ICU study annotated 12,671 arrhythmia alarms and found 88.8 per cent false. A regulator has on record that stop-work authority went unused for fear of reprisal. Five properties that make a stop control real.
  • How should an AI agent communicate uncertainty to a human?Two problems, usually confused. GPT-3's verbalised confidence carries an expected calibration error of 0.52, and readers put "very likely" at 62 per cent where the guidelines mean above 90. Giving readers a translation table made them worse. First-person hedging raised accuracy from 63.9 to 72.8 per cent and lowered intention to use.

Thinking, learning and capability

Work, careers and the labour market

Organisations and leadership

  • The shape of the organisation after AIAcemoglu puts ten-year productivity gains under 0.66 per cent, Danish records find precise nulls on pay two years in, and US payroll data rules out widespread displacement. What the evidence supports about redesign and fragility, and why middle management is the layer nobody has counted.
  • What AI does to a teamEveryone improves and the room converges. Doshi and Hauser on individual creativity rising while collective diversity falls, Dell'Acqua on AI flattening the difference between an engineer and a marketer, and the sycophancy evidence on what happens when challenge moves from colleagues to a model that affirms half again as often.
  • Who should own AI strategy in an organisation?Ownership is decided by inheritance rather than argument, and it silently answers a bigger question: augmentation or replacement. Autor and Thompson on why which tasks you automate matters more than how many, what each placement is blind to, and the role missing from most implementation teams.
  • AI workforce strategy
  • The CHRO guide to AI
  • How leaders should respond to AI
  • What does AI literacy mean for leaders?A legal obligation since February 2025, enforced since this month, at every risk tier. What Article 4 requires, the clause almost every programme misses, and why a completed training module is not evidence of capability.
  • How do you write an AI use policy that works?Most are unenforceable and everyone involved knows it. Six elements that survive every model release, what EU law now makes auditable, and the one-afternoon test that beats legal review.
  • How do you measure AI adoption properly?
  • Why reskilling programmes mostly failA position page against the near-universal institutional answer to AI. Five structural reasons, the evidence stated honestly including where it is indirect, and the published data that would change the position.
  • AI Transformation Is Not a Change-Management Problem
  • Common AI Transformation Challenges
  • Should AI attend my meetings?
  • The Third Way
  • How do you keep expertise in an organisation?Documentation preserves what experts can say, and most of what they know is not that. Polanyi on the tacit part, Lave and Wenger on how it transfers, and why the work juniors learned from is the work most easily automated.
  • What happens to institutional memory?Retrieval is not retention. An organisation can get better at finding things while getting worse at knowing them, because the reasons, the rejected options and the trust calibration were never in the archive.
  • What happens when AI removes the visible work?Bainbridge, 1983: taking away the easy parts of a task can make the difficult parts harder. Why time saved prices only what was removed, why task productivity and workload are different variables, and what responsible de-automation requires.
  • What is a capability audit?A proposed method, labelled as one. What people can still do unaided, where expertise actually sits, what has no redundancy and what would be hard to rebuild. Why confidence and output are both broken instruments.
  • When should an organisation reverse an AI deployment?NIST's five conditions for deactivating a system, Perrow on why adding a safeguard can reduce safety, and why a fallback that exists only as a document is not a fallback.
  • How do you preserve capability across vendors?Prahalad and Hamel's 1990 warning, applied. Keep enough to specify the work, judge it, handle exceptions and replace the supplier. Three things that are genuinely new, and one that is not new at all.
  • If every competitor has the same AI, where does the advantage come from?A licence every rival can buy at list price passes one of Barney's four tests. US Census data puts firm use at 18 per cent with 57 per cent of adopters in three or fewer functions, so parity has not arrived, and the assets that pass the test are the ones the invoice does not include.
  • How long should we give an AI investment before deciding whether it worked?The J-curve says an early read understates and a late one overstates, for the same reason. Danish administrative records found precise nulls on pay two years in, next to heavy task reorganisation. Three questions, three clocks, and most business cases wind only one.
  • What becomes more valuable in a business as AI gets cheaper?Three randomised trials found the same compression: the tool raises the floor far more than the ceiling. So the capability losing value fastest is being unusually good at exactly the work the model does well. Four complements that appreciate, chosen by standing rather than by capability.
  • What happens to work whose purpose was moving information around?Garicano's model says a layer exists because matching problems to knowledge is costly. Resume data on 3,100 firms finds hierarchies flattening after AI adoption, on tests the authors call under-powered. What the reporting job was doing that the report never showed.
  • Which AI investments should we stop?Between 30 and 40 per cent of information systems projects show some degree of escalation, and the founding case study of that literature was an expert system that ran for a decade. There is no credible non-vendor base rate for AI programme failure: the 80 per cent figure traces to a press article. Three questions that do not need a counterfactual.
  • How do humans and agents divide work across a process?Task-level allocation has been on the wall since 1951 and has never worked, because every assignment creates new work for the other party. The unanswered part is the boundary. Medicine calls those discontinuities gaps, the best multi-agent failure data puts 36.9 per cent of failure at the joins, and Article 14 says nothing about transitions.

Professions and sectors

  • Which professions face the greatest deskilling risk?
  • How will AI change medicine?
  • How will AI change law?
  • How will AI change consulting?
  • How will AI change accounting and audit?The oversight precedent everybody cites was replaced in April 2026, and its replacement puts generative AI expressly out of scope. Meanwhile the UK audit regulator found the six largest firms had not measured what their tools do to audit quality.
  • How will AI change journalism?Twenty-two broadcasters, 14 languages, 2,709 graded answers. Sourcing failed at 31 per cent against 20 per cent for accuracy, and the reputational cost arrives at the masthead that was cited rather than the assistant that cited it.
  • How will AI change customer service?The best-evidenced occupation there is, and the study watched the tool break. Gains ran to novices, the best agents got slightly worse, and the learning survived an outage only for the workers who had engaged with the suggestions.
  • How will AI change teaching?A school-randomised trial in England cut lesson planning time 31 per cent with no quality change a blinded panel could see. English teachers plan with it at 35 per cent and mark with it at 5 per cent, which is the profession quietly putting the tool where the risk is lowest.
  • How will AI change the public sector?Both flagship UK figures, 26 minutes a day and 19, are self-reported, and one was calculated from tick-box midpoints with its top tail capped by the analysts. The sector's deepest precedent is not a productivity study, it is the presumption that the computer is right.
  • How will AI change human resources?Retrieval models favoured White-associated names in 85.1 per cent of comparisons. The first algorithmic hiring audit law in the world produced published reports from 18 of 391 employers checked. And the function advising on capability is the one most exposed.

Everyday life

International

  • AI and work, country by country
  • AI and work in AsiaSingapore has written the loss of entry-level training grounds into a national AI framework. Hong Kong measures augmented reality adoption and does not ask about AI at all. Japan, Korea and the adoption gap, all read at source.
  • AI and work in the GulfSaudi Arabia, the UAE and Qatar read at the issuing body's own pages. The one Gulf instrument that names over-reliance, the region's only official AI adoption statistic, and the widely quoted claims that could not be verified.
  • AI and work in JapanThe country with the strongest imaginable economic case for adopting AI, an 11 million worker shortfall by 2040, and adoption running at roughly 18 per cent. The control condition this debate never had.

Definitions

Reference and record

  • The most-quoted AI statistics, checked
  • The AI reports worth reading
  • The official guidance on AI in educationUNESCO, UNICEF, the UK, the EU, Australia, the US and MIT, read at source and labelled by what each document is. Mostly schools: no government here has issued guidance for universities. Five primary-education mandates checked, one of which has already been reversed.
  • Does the brain mature at 25?It does not, and the number came from a 2004 magazine interview rather than a finding. Traced to source, checked against a 3,802-person 2025 study that puts the end of the adolescent epoch nearer 32, and reframed on the question that can actually be answered.
  • The evidence on AI and human capabilityA curated, dated reference to the best research in the field, from the World Economic Forum, PwC, MIT, the NBER, Harvard and BCG and the key academic studies.
  • Essential works on AI and human capabilityEssential works on human capability in the age of AI: hard data, experimental evidence, serious interpretation and the intellectual foundations most current writing rediscovers without attribution. Classified, and read rather than listed.
  • The timelineWeekly since January 2017, five years and ten months before ChatGPT. When each idea first surfaced, what form it took, and how it developed. The early years described honestly as curation rather than thesis.
  • The predictions recordThe dated provenance record since 2017, plus the reference layer: the annual predictions, the glossary, the people who shape AI, and the reading strategy.
  • The SuperSkills Glossary
  • AI People
  • The AI Reading Strategy
  • Accuracy and correctionsA public log of what this research got wrong: the original claim, what was found, what changed. Nothing is removed, including the corrections that weaken an argument made here.
Writing

Essays and columns.

Writing on AI, human capability and the future of work, in my own words, every week since 2017. Box of Amazing began in January 2017 and moved to Substack later; the live archive there runs from the move onward.

Essays · Box of Amazing

A woman crossing a city street at dusk, eyes closed and arms outstretched, following lit arrows painted on the road

The Architecture of Drift

How AI became the most sophisticated drift machine ever built, and how to use it without being used by it.

Read → An airliner flight deck at dawn, instruments lit, the aircraft flying itself

Are you flying, or being flown?

Algorithmic drift: the slow process by which systems start making your choices so smoothly you stop noticing.

Read → An open drawer of tangled cables, adapters and discarded instruction leaflets

We’ve been the AI all along

What changes when millions of people can build any software they can imagine.

Read → An archivist seated on the floor of a manuscript room by lamplight, reaching for a bound volume

The Crossing

Hope is not a feeling. It is a skill, and it can be practised.

Read → The words Synthetic Seniority set in heavy type, the letters misprinted and slipping out of register

Solving Synthetic Seniority

Why AI can make people look senior without the reps that make them senior, and what to do about it.

Read → The Box of Amazing logo

The full archive

Essays and curation since 2017, a new one every Sunday. Free.

Browse all →

Where these essays became research: The Architecture of Drift → Drift versus Design, Solving Synthetic Seniority → Synthetic Seniority, We’ve been the AI all along → Staying Valuable.

Published elsewhere

An illustration of a hand holding a pair of balanced scalesNew York Observer · Jul 2026

The future of AI: power, judgment and trust

On drift, design and staying the author of your own judgement as AI takes over more decisions.

Read → Two people silhouetted against a wall of illuminated AI dashboards and chartsCEOWORLD Magazine · Jul 2026

Drift versus design

Why most companies mistake activity for transformation, and how to design change on purpose.

Read → Two people shaking hands over an open notebook and a financial reportThe Bookseller · 2026

Artificial intelligence: the chain of trust

What publishing, and every knowledge industry, stands to protect or lose as AI reshapes trust.

Read → A close-up of a pick-and-mix of brightly coloured boiled sweetsIrish Tech News · 2026

You’re not adopting AI. You’re paying for it.

Why most AI transformation spend fails to change how people actually work.

Read → A hand touching a glowing network of illuminated person iconsEntrepreneur · 2026

AI amplifies human decisions, it doesn’t go rogue

The real risk is not runaway machines. It is the human judgement we hand over without noticing.

Read → A presenter addressing a group seated around a boardroom table with laptops openTechRound · 2026

Are universities teaching the skills employers actually need?

World Youth Skills Day 2026: the widening gap between what is taught and what the AI-era workplace demands.

Read → The European Business Review cover image for Leaders in the AI Age, a group around a table marked Artificial IntelligenceThe European Business Review · 2026

The real AI risk is accountability, not automation

The gaps open in leadership decisions, not in the machines. What accountable AI adoption actually requires.

Read → A robotic hand and a human hand fitting two jigsaw pieces togetherWorldatWork · 2026

From skills management to capability design

Why HR’s next job is designing capability, not managing skills, as AI resets what expertise means.

Read →
"The goal isn't more technology. It's more capable humans."
Rahim Hirji
Box of Amazing

New thinking, every Sunday.

A weekly essay and curation on AI, human behaviour and the future of work, written every week since 2017 and read by more than 25,000 leaders. Prefer less often? Choose the monthly roundup instead. Free either way.

Or open Box of Amazing →

Put it to work

The ideas are the start. The value is in the room.

Reading is the backup. The change happens when Rahim is on your stage or working with your leaders. Bring this work into your organisation through a keynote or an advisory engagement.

Keynotes Advisory & coaching Book a call