Your AI Tool Needs an Adoption SLO

A renewal decision is on the table: the internal AI assistant’s licenses come due, the dashboard shows seats activated and weekly actives climbing, and a champion has testimonials. None of that can distinguish a tool that changed how work happens from a tool people opened twice and routed around. Pilots die in the gap between working and being used ; the diffusion essay’s composite claims team had licenses, a sponsor, and usage in the low twenties eight months in. What that essay’s argument was missing is the instrument, so here it is: an adoption SLO. The actual machinery, not the metaphor, because the machinery is what makes SLOs resistant to wishful reading.

One service-level indicator, defined before the data arrives: successful governed completions divided by eligible workflow instances, over a rolling 28-day window. Every term is load-bearing. Completions, not logins: instances of the target workflow finished through the governed path . Eligible instances, not enrolled users: the denominator comes from an eligibility ledger of cases the tool should have handled, which forces the instrumentation that makes the whole thing decidable. That means a shared case ID across the tool and the legacy path, and mutually exclusive terminal states per case: completed-governed, abandoned (with a timeout rule), manual-fallback, exception. If you can’t produce that event model, you don’t have an adoption problem yet; you have a measurement problem, and it’s cheaper.

Around the SLI, guardrails and diagnostics, not co-equal targets. User reach, meaning eligible users with at least one opportunity who used the tool, guards against coverage carried by three enthusiasts. Repeat use, measured only among users with eligible opportunities in both periods and windowed to the workflow’s natural frequency, separates habit from novelty; a fixed week-five cohort is arbitrary for a monthly workflow. Abandonment rate says where to investigate, as in, the tool wins the opening and loses a specific step, without claiming to say why. And the quality set (error rate, rework, cycle time, exceptions) holds the line the adoption number must never be allowed to buy.

Which is the objection that deserves the most respect: put adoption on someone’s review and recommend deleting the old button, and you’ve written the recipe for Goodhart. Two rules keep the SLO from being gamed. The target belongs to the tool’s owner, never to the users; adjusters are scored on claims quality, not assistant usage, so the owner can only move the number by making the governed path genuinely cheaper. And coercive defaults, such as removing the legacy button for covered cases, are permitted only while every quality guardrail holds; the moment rework or exceptions rise, the default reverts and the breach conversation is about the tool. Under those rules, persistently low adoption becomes what it sometimes is: the users correctly rejecting a tool that isn’t good enough, now visible as such instead of relaunched annually.

Then the part that makes it an SLO rather than an OKR: the breach protocol, agreed in writing at rollout. A floor, say 60% coverage with guardrails green (your numbers will differ), and a budget: two consecutive windows below the floor triggers the review, no discretion. The review has two exits. Exit one is a time-boxed remediation hypothesis: a named change to the workflow, defaults, or tool, an owner, a budget, a checkpoint date, guardrails unchanged. Exit two, taken automatically if the checkpoint misses, is decommissioning. Licenses lapse, the workflow reverts, the ledger closes. What breach can never buy is the third exit every failing tool currently takes: a relaunch email and another training session. Training is a legitimate remediation hypothesis exactly once, with a checkpoint like any other. Metrics without a breach condition are decoration.

The renewal meeting this was built for then gets its finance line: spend per accepted business outcome on the governed path, against the same figure for the legacy path. Not spend per completion, which a worthless completion satisfies. With McKinsey’s August survey putting about a fifth of organizations at cost-constrained AI use, the renewals that survive should be the ones that win that comparison with the breach protocol attached. Adoption is necessary for renewal, never sufficient. The tools that clear the bar are capabilities. The rest were licenses.