40% productivity lift. 60% lower AI spend. In 90 days.
Ten months into their AI push. CS had ChatGPT. RevOps had Replit. Product was on Claude. Marketing had Lovable, Jasper, and three other tools nobody could name without checking the credit card statement. Every team had tools. Nobody had outcomes.
What we found in the first two weeks
We start with the Toil Audit — where time is actually being spent, where AI can take real work off the plate. Specific workflows. Specific hours. Specific people.
Tool sprawl was burning cash
Seven AI tools, three of them doing the same job. Two teams paying enterprise tier on tools they used twice a week.
The wrong tools were doing the wrong jobs
Engineers prompting ChatGPT for code reviews instead of using Claude Code. Marketers using Claude to generate images. CS using Replit to write emails.
Prompts were the real cost driver
Vague prompts and ten or fifteen iterations to get a usable answer. The dominant cost wasn’t prompt length — it was iteration count. A single CS workflow was burning 11× more tokens than it needed to.
Shadow AI was everywhere
23 internal apps catalogued. Six were active risks. Two had to be shut down the same day.
Adoption was a ghost
Two or three power users per team carrying the productivity numbers while everyone else logged in once a week and gave up.
The tools were fine. The people using them hadn’t been taught how.
The 90-day rebuild
Days 1–30 · Diagnose and stabilize
Toil Audit across every function. Inventory of every tool, every internal app, every prompt pattern in production. Risk triage on apps touching customer data. Immediate kills for redundant tools. Governance framework drafted with legal and IT.
Days 31–60 · Rebuild the high-ROI workflows
Three to five workflows per team, picked from the audit based on hours saved and revenue impact. We don’t prototype — we build them in production with the people who’ll own them. Each workflow comes with a playbook, a prompt library, and a clear ownership model.
Days 61–90 · Train the team to run it without us
The phase most consulting engagements skip. Cohort training, by function — not by tool. CS through renewals, escalations, QBR prep. RevOps through pipeline hygiene, forecast modeling, lead enrichment. Product through PRD drafting, research synthesis, spec validation. People learn faster when the practice material is their actual work.
Three skill levels, three tracks. The Prompt Quality Framework — our proprietary teaching tool — drove the biggest cost reduction on its own. Average iteration count per workflow dropped from 11 to 3 within two weeks of the cohort starting.
Training teaches the tool. Enablement makes the team self-sufficient.
What came out the other side
Numbers from the engagement, measured at day 90 and verified at day 180.
The CEO’s quarterly board update went from “we’re investing in AI” to a one-page memo with hours saved per function, dollar impact on retention, and the runway extension from cost reduction. That’s the difference between AI as a slide and AI as a system.
Illustrative composite based on common patterns we see, not a single named client. Numbers are representative.