Individuals use AI tools personally: drafting, summarizing, searching. Value is real but private; it leaves no trace in your systems and walks out the door with the employee. There are no shared workflows, no evaluation, and usually no policy beyond an anxious memo.
The tell: usage is high in surveys but invisible in process metrics. If you cannot name a workflow whose cycle time changed, you are Literate, regardless of how many licenses you bought.
The move that advances you: pick three recurring workflows, instrument them, and rebuild each with AI inside and an owner accountable for the metric. Individual literacy becomes organizational capability only through owned, measured workflows.
Score each dimension honestly. Your stage is your lowest score, not your average.
AI is embedded in named workflows with before-and-after numbers. There is a working policy (what data can go where), a permission model, spend visibility, and someone who owns AI operations even if it is a fraction of their job. Evaluation exists: sampled review, regression cases, tripwires.
The tell: you can answer "where does AI make you money?" with line items instead of anecdotes.
The move that advances you: shift from retrofitting AI into human-shaped processes toward designing processes around AI capacity. That is an org-design exercise, not a tooling exercise, and it is where the operating model conversation starts in earnest.
Rate yourself 1 to 3 across six dimensions: workflows, policy, evaluation, spend visibility, ownership, skills. Your true maturity stage is your lowest score. Invest there, not in the dimension that demos best.
New processes are designed assuming the agent does the work and humans set direction and review output. Hiring plans, budgets, and org charts account for machine capacity. The company maintains evaluation infrastructure the way it maintains accounting: continuously, boringly, as a cost of doing business.
Almost nobody is fully here, including most companies claiming it. Treat AI First as a direction, not a destination: each workflow you redesign around machine capacity moves you along the gradient.
Score each dimension 1 to 3 (Literate, Enabled, First): workflows, policy, evaluation, spend visibility, ownership, and skills. Your stage is your lowest score, not your average, because the lowest dimension is the one that produces the incident or the stall.
Common result: a company with impressive pilots scores 3 on workflows and 1 on evaluation and policy. That is a Literate company with expensive hobbies, and it is one bad week away from a retrenchment.
Fear of replacement is rational; answer it with structure, not slogans. Publish which roles change and how. Fund training on work time. Convert your best internal users into paid champions with real mandates. Make the first automations remove drudgery visibly rather than headcount quietly. Adoption follows evidence that the company intends to redeploy people, not merely reduce them.
Three: AI Literate (individual tool use, no organizational capability), AI Enabled (AI embedded in owned, measured workflows with policy and evaluation), and AI First (processes designed around machine capacity with humans directing and reviewing).
With focus, one to two quarters: three instrumented workflows, a one-page policy, basic evaluation, and named ownership. Scale extends the timeline; complexity of ambition extends it more.
If you are Literate, you are with the majority, and the gap to Enabled is closable in a quarter of focused work. Behind is not the risk; staying unmeasured is.
Bring one AI question from this article. We will tell you what we would do, whether or not you hire us.
Book a 15-Min Chat →