The Handoff Tax

Ask around any engineering floor and people will tell you the tools made them faster. Ask the CFO whether the income statement noticed and the answer gets quieter. McKinsey’s August survey puts numbers on both halves: 80% of respondents report individual productivity gains from AI, while 37% attribute any effect to enterprise EBIT, a share unchanged from last year. Four in five people say they’re personally faster; the share of companies whose income statement can tell stayed flat.

Self-reports both, so start with the explanations that require no one to be wrong. Measurement lag: EBIT is a long way from task speed, and enterprise technology historically takes a year or two to show up there. Absorption: the gains are real but spent, on adoption costs, on quality, on scope growth, or competed away in pricing. Inflation: people overestimate their own speedup. All three are live, and a survey can’t separate them. What a survey can do is tell you where to point instruments, and the instruments can separate them, because delayed conversion, spent gains, and destroyed gains leave different marks on a workflow’s queues.

Here is the mechanism worth instrumenting for, named so it can be falsified. Call it the handoff tax: an individual speedup converts to enterprise throughput only if the downstream stage absorbs the extra flow, and where it doesn’t, the gain converts to queue instead. The signature is specific. Task time down at one stage, end-to-end throughput flat, and waiting time or rework rising at a nameable transition. A developer produces changes twice as fast into a review process sized for the old arrival rate. A team drafts three proposals in the time one took, into a decision meeting that still meets weekly, and decision latency was a P&L variable before the arrivals tripled. Output volume rises into fixed verification capacity, so either backlog grows or scrutiny quietly falls. If the tax is your problem, those queues exist and are growing. If task time fell and no downstream queue grew and throughput still didn’t move, the tax is not your problem, and this diagnosis is wrong for your org. That’s the test, and it cuts both ways.

The survey offers one more clue, to be handled with care: 47% of mid-level managers and individual contributors report negative strain from AI-related change, against 31% of executives. That is not evidence that the middle layer causes the gap; strain could equally measure proximity to rollout churn or distance from insulation. Treat it as a searchlight, not a verdict. The layer reporting the most friction is the layer where handoffs, reviews, and exceptions live, which is reason to put the instruments there first, and no reason to blame the people improvising unpaid pipeline rebalancing.

The instrument itself is one workflow, traced. Pick a single customer-valued output. Timestamp five events per item: intake, draft-ready, review-start, exception, done. Four weeks of data gives you arrival rate, service rate, queue age, and first-pass yield per stage, before-and-after if you have history. The tax, if present, is legible in one picture: the stage where AI landed shows falling task time, and some downstream transition shows rising queue age. Then run the confirming experiment. Add capacity or change policy at that one transition, whether review staffing, the WIP limit , or the decision cadence, and watch end-to-end throughput. If it moves, you found where the 80% was leaking. If it doesn’t, reject the diagnosis and go test measurement lag, which is the serious competitor.

What makes this a leadership post rather than a queueing tutorial is who owns the fix. Rebalancing a pipeline after the tools changed its arrival rates is a management act: deciding where freed capacity goes, which is currently in nobody’s job description and therefore happens by default, and the default is queue. Unallocated speedup defaults to queue. So allocate it, into the constraint stage, into verification capacity that buys trustable autonomy , into fewer things finishing sooner. The decision rule for the next QBR fits in one sentence: individual gains are inventory until the next stage absorbs them, and inventory doesn’t show up in EBIT. Queues do.