# What Leaders Need to Know About AI *Source: https://steadman.ai/newsletters/david/what-leaders-need-to-know-about-ai.html* *Published: 5th April 2026* *A logical argument for leaders navigating AI, built from axioms to recommendations.* Each claim in this framework is tagged with its epistemic role: - **Axiom**: a statement accepted as true, serving as a starting point for reasoning. - **Implication**: a conclusion that follows logically from something already accepted. - **Your Call**: a decision, especially one that rests with a single person. - **Consequence**: a result or effect of an action or condition. - **Recommendation**: a suggestion or proposal as to the best course of action. --- ## I. The Case — what's true today The opportunity builds from four facts. ### Axiom 01 — Language models read, think, and write well. Three core skills: **comprehension** (reading text and images), **synthesis** (reasoning, analysis, connecting ideas), and **writing** (including code, and therefore tool use). "Well" matters. The output is already useful, often good, sometimes excellent, today. ### Axiom 02 — They loop to handle complex work. A model reads, thinks, writes. Then reads its own output, thinks further, writes again. Research, apps, analytics, computer control: all are read-think-write loops applied to different domains. ### Implication 01 — Good first drafts of almost anything, quickly and cheaply. "Almost anything" because the loop covers most knowledge work. "Quickly" because compute is fast. "Cheaply" because the marginal cost is negligible versus a human doing the same task. "First draft" because human judgement still owns the final output. The CEO principle: Check, Edit, Own. ### Implication 02 — This changes everything. Not a slogan. A logical consequence. If you can get a good first draft of almost any knowledge task, at near-zero marginal cost, then the economics of every knowledge-work function are being rewritten. This is true even if capability froze tomorrow. --- ## II. Only the leader can set this direction. ### Your Call 01 — What do you want to do with AI? Everything downstream depends on it: which roles change, which processes to redesign, what to build. And the decision requires genuine knowledge of what AI makes possible, which is why we began by laying out the facts. Two paths (or a blend): - **Path 1 — Same things, done better, quicker and happier.** Become more efficient and effective at what you already do. Do you get smaller, stay the same size at higher quality, or grow within existing markets? - **Path 2 — Different things, now viable.** AI makes some products, services, and markets economically feasible that weren't before. Too hard, too expensive, too complex. A growth path into new territory. --- ## III. What You'll Encounter — four consequences, regardless of the path ### Consequence 01 — Roles shift: from doing, to setting up and judging. If AI produces good first drafts, the bottleneck moves from production to direction and evaluation. Everyone becomes an orchestrator of AI output: senior leaders, the middle, junior recruits in their first week. We are all managers now. ### Consequence 02 — Judgement is still learned, but learned differently. Traditionally, judgement came slowly, from years of doing the work yourself. In an AI-augmented world, it is learned more quickly through setting up and evaluating, not through doing. A different acquisition path, not a loss. ### Consequence 03 — Roles, teams and processes must change to capture the benefits. Without structural adjustment, productivity gains get absorbed. People do the same work to a higher standard, but the organisation does not capture the value. This is an organisational design problem, not a technology problem. If you don't reorganise roles, teams, and processes, the bottleneck just shifts. Speed up one step and the constraint moves elsewhere: to review queues, to approval chains, to the people making decisions, to the hand-offs between teams. ### Consequence 04 — Getting organisational results is hard and complex. The honest counterweight. AI touches every function, requires new ways of working, and demands change across multiple dimensions simultaneously. Realism, not pessimism. --- ## IV. The Urgency — and it is getting harder ### Axiom 03 — Capabilities are expanding. The frontier keeps moving, rapidly. This is not about current AI being inadequate. It is already good enough to change everything. The point is that models become more capable on multiple fronts every month, and costs continue to fall. The two compound. The viable unit of work is shifting from task to process. What once required a single prompt-and-check can now run as an extended, multi-step workflow. This shift will continue. - The opportunity is growing. What is possible tomorrow exceeds what is possible today. - The competitive pressure is growing. Competitors, clients, and markets are adapting. - Plans must be adaptive. A static view of AI capability is already wrong by the time you act. - The window for advantage (or not falling behind!) narrows the longer you wait. --- ## V. The How — everyone on the journey, in sequence We could write an entire guide on how to approach AI transformation, and we are happy to do so if that would be useful (email david@steadman.ai and ask). For now, two high-level implications we see people commonly missing. ### Recommendation 01 — Everyone needs to come on the journey. Not just a technology team. Not just early adopters. AI touches every knowledge-work role, so adoption must be organisation-wide. This is a people challenge as much as a technology one. Once you have given people the chance, the encouragement, training, and the support, tough conversations are necessary for people who are not adopting AI. ### Recommendation 02 — Sequence: people, teams, organisation, new products and services. Build foundational capability in individuals first. Then embed it in team workflows. Then redesign organisational processes. Only then reach for the genuinely new. Each step depends on the ones before it. --- *Steadman helps organisations use language models to work better, quicker and happier.* *Feedback, ideas or questions? Email david@steadman.ai* *Interactive walkthrough: https://steadman.ai/newsletters/david/what-leaders-need-to-know-about-ai.html*