── ── AI & agents
AI Pilot to Scale
Most AI pilots do not fail on the model. They fail on the organization: the tool sits outside the systems people already use, nobody on the business side owns the result, and the success metric is chosen after the fact — so any outcome can be narrated as progress and the pilot is extended indefinitely. That state has a name…
Run AI Pilot to Scale on a real problem
Bring something you're actually deciding — free, in the browser.
How it works
Output: Pilot Charter + Scale/Kill Decision Record
1. Pick one narrow workflow. One workflow, one team, high frequency, value measurable — prefer the bottleneck. Gate: scope spans more than 2 departments → narrow it. 2. Measure the baseline. At least 2 weeks of real data: cycle time, cost, error rate, throughput. Gate: no baseline → do not launch. 3. Write success criteria in advance. Hard numbers and a date — e.g. "handling time down 40%, error rate not up, verdict at week 8." Write the kill conditions at the same time. Gate: the business owner signs the criteria; they cannot be changed after launch. 4. Name the owner. One business owner (not IT, not the vendor) is accountable for the result and has authority to change the workflow. 5. Integrate into existing systems. Run it inside the tools people already use (CRM, inbox, ticketing), not in a new window. Gate: staff must copy-paste between tools → build the integration first. 6. Grant autonomy in stages. L1 suggest → L2 approve-then-act → L3 act-then-report. Each step up requires hitting a pre-set error threshold. 7. Decide on the date. Against the written criteria, pick one: Scale (replicate to an adjacent workflow/team) · Revise (allowed once, time-boxed) · Kill (record why). 8. Stop-rule: criteria missed at the deadline and no concrete fix identified → kill; move the resources to the next workflow.
Output template: Workflow · Baseline (time/cost/error) · Success criteria + kill conditions · Owner · Integration point · Autonomy stage + threshold · Decision date · Verdict (scale/revise/kill) + evidence
When to use it
- user says 'our AI pilot is done but nobody uses it', 'the demo was great, how do we roll it out?', 'how do we get this POC into daily operations?', 'should we scale or kill this pilot?', 'the pilot keeps getting extended', or is about to launch an AI/agent pilot
When not to use it
When the decision is routine and reversible, applying a formal method costs more than it returns.
Worked example
Generative AI at Work — A Staggered Support-Desk Rollout (2020–2021)
The best-documented pilot-to-scale path for a generative AI tool in a real business is the one studied by Erik Brynjolfsson, Danielle Li and Lindsey Raymond in Generative AI at Work (NBER Working Paper 31161, 2023; published in the Quarterly Journal of Economics, 2025). The authors had access to the deployment data of a GPT-based assistant at a Fortune 500 enterprise software company's chat-based technical support operation. The firm is not named in the paper; everything below is taken from the paper itself.
Install this skill (free, MIT)
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