How leaders should respond to AI

The decisions that matter are not about tools. Buying the licences is the easy part and it is already done in most organisations; the hard part is deciding what you will not delegate, and who is answerable when the machine is wrong. The evidence supports an uncomfortable summary of where most leadership teams actually are. Adoption has run faster than the personal computer, measured effects on pay and hours are so far close to zero, and underneath that flat surface the structure of work is already being reorganised. In other words, the consequential choices are being made right now, mostly by default, by people well below the executive team, and the numbers that would tell you about it will not move for years. Leadership here is not about being enthusiastic or cautious about AI. It is about noticing which decisions are being settled without anyone deciding, and taking those back.

What the evidence shows

Start with the pace. Bick, Blandin and Deming found that by late 2024, nearly forty percent of US adults aged 18 to 64 used generative AI, twenty-three percent of employed respondents had used it for work in the previous week and nine percent used it every working day, with work adoption as rapid as the PC and overall adoption faster than the internet. And only one to five percent of total work hours were actually being assisted. Enormous reach, thin penetration into the hours: your people have the tool and your work has not been redesigned.

Then the outcomes. Humlum and Vestergaard linked adoption surveys to administrative labour records across roughly 25,000 Danish workers in 7,000 workplaces and eleven exposed occupations. Two years after ChatGPT launched they found precise null effects on earnings and hours, ruling out effects larger than two percent, while documenting substantial task reorganisation and new tasks in content generation, AI oversight and AI integration. Their phrase for it deserves to be read twice in a board meeting: technological change reshapes work well before it surfaces in earnings or hours.

On the design of oversight, the most important recent result is a warning against the reflex. Vaccaro, Almaatouq and Malone's 2024 meta-analysis of 106 experimental studies and 370 effect sizes found human-AI combinations performing significantly worse on average than the better of human or AI alone, with losses concentrated in decision-making and gains in content creation. Adding a person to a process is not a control. Dell'Acqua and colleagues showed the sharp edge of this with 758 consultants: outside the model's competence, those using GPT-4 did worse than those using none, because they trusted confident output they should have questioned.

On where value settles, Autor and Thompson analysed four decades of task data across 303 occupations and found that automation removing the less expert tasks raised wages, while automation removing the expert tasks lowered them. And on capability, Bastani and colleagues, in a 2025 PNAS field experiment, found that unrestricted access to a GPT-4 tutor left students performing seventeen percent worse than a control group once the tool was removed, while a version designed to give hints rather than answers largely eliminated the harm. Design of the tool, not presence of the tool, determined whether people developed. Meanwhile the World Economic Forum's 2025 Future of Jobs report names analytical thinking as the most valued core skill and skills gaps as the biggest barrier to transformation.

Where the evidence is uncertain

Denmark is a high-trust, heavily unionised labour market with strong employment protection, and two years is early for a general-purpose technology; null wage effects there do not settle the question elsewhere. The meta-analysis covers studies published between 2020 and mid-2023, so it predates the current model generation, which probably shifts more tasks into the category where human intervention subtracts rather than adds. The Autor and Thompson data runs to 2018, making it a lens rather than a forecast. And the Bastani experiment was school mathematics, not professional work, so it establishes that the design variable exists and matters without telling you what to build.

The largest uncertainty is unresolvable today: nobody has measured what a decade of AI-assisted work does to organisational capability, because a decade has not passed. Anyone selling you certainty in either direction is selling something.

The SuperSkills interpretation

Most organisations do not decide their way into their AI configuration. They arrive at it, through a thousand small choices nobody quite made, and then describe the result as a strategy. That is what I mean by drift versus design, and it is the single most useful lens I can offer a leadership team, because it moves the conversation off whether AI is good or bad and onto a question with an answer: which of these decisions did we actually make?

In the CEOWORLD piece where I set the framework out, I described four postures organisations take. The Sleepwalkers are moving fast with no design, mistaking activity for transformation. The Programmed have adopted someone else's design, usually a vendor's, and are executing it without having chosen it. The Stuck have seen the risk clearly enough to freeze, and are losing ground while they deliberate. The Designers have decided in advance where human judgement has to remain and are building towards it. The postures are not a maturity model and you do not graduate through them; most large organisations contain all four simultaneously, function by function, which is itself the finding.

The leadership failure I see most often is not recklessness or timidity. It is the substitution of a phrase for a decision. "We keep a human in the loop" is the most common example, and the meta-analysis is now the empirical case against it: a person placed at the end of a process, with no time budget, no stated basis on which they would disagree, no authority to stop it and no consequence for approving, does not produce oversight. They produce a signature, and they can make the system worse than either party alone. Governance that cannot name who, at what point, with what authority to say no, is not governance.

The second failure is slower and more expensive. Redesigning the doing without redesigning the learning produces the productivity gain and the damage at the same time, and only one of them is visible this year. That accumulation is capability debt, and its distinguishing feature is that outputs look fine throughout, right up to the decision the AI cannot make and nobody has been trained to make either.

Six decisions a leadership team should actually make

• Where human judgement must remain, in writing. A list of the decisions a person owns, with reasons. If it has never been written, it has been answered by default.
• Who is accountable for each AI-shaped decision. A named person, before the decision, with authority to stop it. Not a committee, not a process, not a policy.
• How people will still learn. For each role where AI has absorbed the junior work, name what now builds the judgement that work used to build. If the answer is nothing, you have a pipeline problem dated five to eight years out.
• Which way each role is moving. Once AI takes the automatable tasks, is what remains harder or easier? Harder means fewer, better and better paid. Easier means a role commoditising, with consequences for pay and retention.
• What you will measure that can fall. Adoption metrics only rise, which is why executives like them. Add one measure of capability without the tool, and one of how often humans genuinely disagree with machine output.
• What you will deliberately not automate. Some things should stay slow and human because of what they carry rather than what they cost. See what stays human.

What to stop doing

Stop reporting adoption as progress. Seat counts and prompt volumes measure activity, and people learn to perform whatever you measure. I call the result usage theatre.

Stop treating this as a technology decision with an HR appendix. The consequential choices are about work design, accountability and development, which means they belong to the executive team and the CHRO rather than to procurement. See the CHRO guide to AI.

Stop waiting for the productivity number. It is the slowest indicator available and it will move long after the positions have been taken.

Stop cutting the graduate intake on a productivity argument without pricing the pipeline. The saving is this year and visible. The cost is your senior population in seven years and invisible. See will AI replace entry-level jobs.

Development of the idea

The four postures and the drift versus design framework are set out in CEOWORLD, Drift versus design: why most companies mistake activity for transformation (9 July 2026), and developed in the Box of Amazing essay The Architecture of Drift. The accountability argument is in the European Business Review, Why the Real AI Risk is Not Automation, but Accountability Gaps in Leadership Decisions (21 August 2026). On measurement, Irish Tech News, You're not adopting AI. You're paying for it. (July 2026). The full framework is in SuperSkills (Kogan Page, 2026).

Key research and primary sources

• Humlum, A. and Vestergaard, E. (2025). Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI. NBER Working Paper 33777, revised March 2026.
• Vaccaro, M., Almaatouq, A. and Malone, T. (2024). When combinations of humans and AI are useful: a systematic review and meta-analysis. Nature Human Behaviour, 8, 2293-2303.
• Bick, A., Blandin, A. and Deming, D. J. (2024). The Rapid Adoption of Generative AI. NBER Working Paper 32966.
• Autor, D. and Thompson, N. (2025). Expertise. NBER Working Paper 33941; published in the Journal of the European Economic Association, 23(4).
• Bastani, H. et al. (2025). Generative AI Without Guardrails Can Harm Learning. Proceedings of the National Academy of Sciences, 122(26).
• Dell'Acqua, F. et al. (2023). Navigating the Jagged Technological Frontier. Harvard Business School and BCG working paper.
• World Economic Forum (2025). The Future of Jobs Report 2025.

Related SuperSkills research

On the organisational plan, AI workforce strategy and the AI readiness lie. On oversight design, human and AI decision making, Human at the Start and AI agents and human judgement. On the capability consequences, capability debt and how humans learn with AI.

About this research

Rahim Hirji is the author of SuperSkills: The Seven Human Skills for the Age of AI (Kogan Page, 2026) and the founder of The SuperSkills Intelligence Company. This work draws on research across more than 200 organisations in 30 countries over seven years. Findings are attributed to the studies that produced them and kept separate from the interpretation, which is the author's. Drift versus design, the four postures, capability debt and usage theatre are part of the SuperSkills lexicon; automation bias and cognitive offloading are established concepts from the research literature and are not his. This is a living reference, reviewed and updated as significant new evidence appears.