Tag: product strategy

  • Every knowledge worker is a manager now

    Every knowledge worker is a manager now

    Every knowledge worker is a manager now. Agentic AI has turned individual contributors into managers of AI agents, and first-line managers into leaders of managers of agents. The job descriptions have not caught up yet. The operating models have not caught up yet. The reskilling plans have not caught up yet. All of that is lagging the capability frontier by twelve to eighteen months — and the organizations that close that gap first will operate at a structurally different throughput than the ones still writing job descriptions for the jobs that existed in 2023.

    The shift: agentic AI crosses the line from tool to colleague

    For the first year and a half after ChatGPT, the thing called “AI” in most organizations was a better search box. A more patient editor. A faster rough-draft generator. Useful, but still a single-interaction tool. You asked, it answered, you moved on. The job of the knowledge worker did not fundamentally change — they just had a slightly sharper pencil.

    What changed in the eighteen months leading into 2026 is the arrival of agentic models. The word “agent” in that context is not marketing. An agent is a system that can do a sequence of things, hold state across those steps, make decisions about what to do next, use tools, and come back with a completed multi-step task. That is a categorically different interaction than “ask question, get answer.” It is closer to “give a junior colleague an outcome to produce and trust them to produce it.” The commercial consequence of that shift is the subject of this post.

    Knowledge-worker image candidate K02-HC-pipeline: HC2 — INPUT-AGENT-OUTPUT-JUDGE-SHIP pipeline with human at JUDGE
    Input → agent → output → judge → ship. The human stays at the judgment node.

    The role change: ICs become managers of agents

    The individual contributor job has silently changed. Writing short summaries of long content — once a junior-to-mid task — is now an agent task. The human role is to specify the outcome, check the output, and decide what to do with it. Meeting preparation — the pre-meeting brief of background, context, attendees, prior touchpoints — is now an agent task. The human role is to feed the context, review the brief, and adjust the framing. Drafting a first pass of almost any structured document — a proposal, a plan, an analysis — is now an agent task. The human role is the editor, not the author of the first draft.

    The common thread is that the IC’s job has shifted from doing to specifying outcomes and judging output. Those are management skills. Not in the metaphorical sense — in the literal sense. Framing a task clearly enough that someone (or something) else can execute it. Evaluating whether the execution meets the specification. Deciding when to iterate and when to ship. These are exactly the skills that used to distinguish a first-line manager from a senior IC, and they have become baseline requirements for an IC working with agents.

    Knowledge-worker image candidate K03-HC-editor: HC3 — colleagues editing agent outputs + overlay text
    The new role for the IC: editor of agent output.

    The org change: first-line managers become leaders of managers of agents

    If every IC is now a manager of agents, then every first-line manager is now a leader of managers of agents. Their job is no longer to supervise execution — the agent is doing the execution. Their job is to coach the humans on their team in how to specify outcomes, how to judge output, how to know when an agent is producing garbage, and how to scale their orchestration over time. That is a completely different job than the first-line management job of three years ago, and it requires a different skill set.

    Two structural consequences follow. First, the middle management layer compresses because a first-line manager leading managers-of-agents can reach further than one managing direct executors — the coordination overhead per report drops when the reports are themselves operating on a multiplier. Second, the definition of “span of control” stretches, but not infinitely: the Dunbar layers still govern the number of humans a manager can hold relationships with, even if each of those humans is now operating agents underneath them. The org chart can get flatter. It cannot get unbounded.

    Knowledge-worker image candidate K05-WILD-conductor: WILD — human conductor directs an orchestra of AI agents
    One human, many agents — the conductor metaphor for first-line management at scale.

    The strategic consequence: orchestration is now a baseline skill, not an advanced one

    The skill that used to distinguish senior managers from junior ones — the ability to frame work so someone else can execute it and judge whether their execution is good — is now a baseline IC capability. Orchestration is the new baseline. Writing is the new baseline. Judgment about output quality is the new baseline. The organizations that will operate at structurally higher throughput over the next five years are the ones that reskill their IC population around these baseline orchestration skills, rather than hiring more specialists who each do one thing well.

    Talent leverage, not headcount, becomes the scoreboard. A commercial organization that operates at 300 humans with strong orchestration capability can outproduce a commercial organization that operates at 600 humans with legacy IC job descriptions. The difference is not about working harder. It is about operating model. The 300-human organization has fewer Dunbar breakpoints, shorter decision loops, less cross-functional friction, and a higher per-seat agent-multiplier. All of that is the consequence of a single structural decision made at the job-description layer.

    So what boards should do

    Three actions sit on the CEO agenda over the next two quarters. First, rewrite the IC job descriptions for every knowledge-worker role in the organization so that orchestration and output judgment are explicit baseline capabilities, not bonus ones. Second, rewrite the first-line management job description so that coaching for orchestration is the core of the role, not supervision of execution. Third, audit the reskilling plan against the assumption that every knowledge worker in the organization is now a manager and needs to be trained as one — because the capability frontier has already shipped and the only question is whether the organization catches up in quarters or in years.

    Boards that do not require a reskilling plan at this scope are budgeting against an operating model that does not exist anymore. The plan does not need to be perfect. It needs to exist. The gap between organizations that have this plan and organizations that do not is the structural competitive advantage of the next five years, and it is already being measured — in throughput, in decision velocity, in the quiet retention of the top performers who can see the gap coming.

  • When 88% of organizations have adopted AI, adoption stops being the question

    When 88% of organizations have adopted AI, adoption stops being the question

    What was announced

    The Stanford HAI 2026 AI Index landed in mid-January with a set of numbers that close out a debate. Organizational AI adoption reached 88% globally. Global corporate AI investment more than doubled in 2025 to $581.7 billion. Generative AI hit 53% population adoption within three years — faster than the personal computer or the internet. Four out of five university students now use generative AI as part of their coursework.

    What it means

    When adoption crosses the 80% line, the question of “should we adopt” becomes structurally uninteresting. Every relevant comparison group has already answered it. What remains is differentiation — and differentiation in a world of universal access is harder, not easier, than in a world of selective access. The strategic margin moves from access to integration depth, from licenses to workflow penetration, and from procurement decisions to operating-model decisions.

    The investment number is the more telling signal. $581.7 billion of corporate AI investment in a single year is a capital allocation that prices in a specific belief: that AI capability will compound at a rate that makes today’s spending the cheap option in retrospect. That belief either turns out to be correct, in which case the laggards face a permanent gap, or it overshoots, in which case the survivors of the correction still own infrastructure and skills the laggards do not.

    Andreas’s view

    My read on this: the AI Index numbers are not a celebration of momentum, they are a notice of obsolescence. Adoption was the entry-level metric — the one that let companies say “we are doing AI” without committing to anything that mattered. With 88% adoption, that metric is exhausted. The companies that conflate “we have AI deployed” with “we have an AI strategy” will be the ones surprised in 18 months when peers with the same headline adoption rate are operating at a fundamentally different unit-economics base.

    I don’t think the next two years will be about adopting more. They will be about routing work differently — deciding which functions become AI-native, which roles get redesigned, which middle-management layers compress, and which workflows get rebuilt from the ground up rather than augmented. The companies treating this as a tooling question will keep the org chart they had in 2024 and bolt assistants onto it. The companies treating it as a structural question will redesign for AI-native operations and harvest a different cost base.

    My expectation is that boards still reporting on adoption rates are measuring the wrong thing entirely. The number that matters is the percentage of work routed through AI-native processes versus AI-augmented legacy processes. Those are two different cost structures and two different competitive positions. The first is a step change. The second is a feature.

    Three things I’m watching

    1. I’ll be watching whether companies move away from adoption KPIs toward integration-depth KPIs — specifically, the percentage of revenue-generating workflows that are AI-native, not just AI-touched.
    2. The companies that stand out to me will be the ones that build the comparison the AI Index doesn’t make for them: how their spend per FTE on AI infrastructure and tooling stacks up against the 90th-percentile peer in their sector. If that number isn’t visible to leadership, it isn’t informing strategy.
    3. I’ll be watching whether organizations use the next 12 months as a workflow-redesign window rather than a tooling-procurement window. The structural opportunity narrows the moment competitors finish their redesign.

    References and related signals