Case study
Running a 40-engagement AI app-build studio across three continents
Global delivery leadership for Builder.ai’s Studio Store — turning a productized, AI-assisted app-build engine into a portfolio that held 85%+ renewals across the US, UK, and APAC, and recovered the accounts everyone else had written off.
- 40+Concurrent enterprise engagements led
- 85%+Renewal rate across the portfolio
- 3Regions on one operating model — US · UK · APAC
- 9 of 11At-risk accounts recovered & retained
- ~30%Faster build cycle via AI-assisted assembly
- 95%+On-time milestone delivery
The challenge
What I walked into.
- A productized promise meeting bespoke reality. Studio Store sold software assembly at platform speed, but enterprise buyers arrived with bespoke integrations, compliance constraints, and edge cases the catalog never anticipated — every engagement pulled toward custom work the model wasn’t priced for.
- Forty-plus engagements, one set of hands. The portfolio ran concurrently across US, UK, and APAC time zones, each with its own pod, sponsor, and delivery clock. Status lived in people’s heads and a dozen disconnected trackers; nobody could see portfolio health in one view.
- At-risk accounts inherited mid-flight. I took ownership of accounts already slipping — missed milestones, eroded sponsor trust, churn notices drafted. Several were weeks from cancellation when they landed on my desk.
- AI in the build loop, before the playbook existed. The platform generated and assembled large parts of each app. Speed was real, but so was variance — review discipline, human ownership of the last mile, and a clear quality bar had to be built around the AI, not assumed.
- Renewals as the only honest scoreboard. A one-time build is a transaction; a renewal is a verdict. The business needed the portfolio to renew, not just ship — which meant delivery quality had to translate into outcomes a sponsor would re-sign for.
The approach
I treated the studio as one portfolio with one operating model, not forty engagements with forty improvisations. First, visibility: a single delivery-health view across every active build — milestone status, risk rating, sponsor sentiment, the two or three signals that actually predicted a slip — so I spent attention where it changed outcomes. Second, a shared definition of done that held across US, UK, and APAC pods: what “delivered” meant, who owned the last mile, where AI-assembled output entered human review, and how an engagement escalated before it became a recovery. Third, I ran renewals backward from delivery — wiring client outcomes into the plan from kickoff rather than treating them as an account-management afterthought. The recovery work got its own discipline: a fast, unsentimental read on each red account, a re-baselined plan the sponsor could believe, and a tight loop until trust was rebuilt. Throughout, AI was leverage with guardrails — it compressed cycle time on assembly, but humans owned architecture, integration, and the quality bar a renewal depended on.
The systems
How I built it.
Portfolio delivery operating model
One delivery rhythm across three regions: a single health view spanning all 40+ engagements, weekly portfolio reviews keyed to risk rather than headcount, and a definition of done that meant the same thing in San Francisco, London, and Singapore. Pods kept autonomy on execution; the standard governed milestones, escalation, and the quality gate.
At-risk account recovery playbook
A repeatable path for accounts that arrived red. Diagnose fast and honestly: what slipped, why, and whether the original scope was ever real. Re-baseline a plan the sponsor could stake credibility on. Then run a tight loop with visible progress until trust came back. Nine of eleven inherited at-risk accounts recovered and renewed; the two that didn’t were called early enough to wind down cleanly.
AI-assisted build with a human last mile
The platform assembled large portions of each app — roughly a third off average build time on catalog-aligned work. The discipline around it: humans owned architecture, integration, and the final quality bar; AI output entered a defined review gate, never production unreviewed; the variance AI introduced was caught before a sponsor ever saw it.
Renewals-backward delivery
Treating the renewal as the real deliverable. Client outcomes were wired into the delivery plan at kickoff and tracked alongside milestones. Delivery and account health were read together, so a build that was technically on-time but missing the outcome got flagged while there was still time to fix it. That alignment carried the portfolio to 85%+ renewals.
The outcomes
Measured.
What it taught me
- A productized model survives contact with enterprise buyers only if someone owns the gap between the catalog and the edge case. That ownership is the delivery leader’s real job.
- Recovery is a discipline, not a rescue. An honest diagnosis and a re-baselined plan the sponsor believes beats a last-minute rescue — and most red accounts are recoverable if you read them early and tell the truth.
- In a studio business, the renewal is the only honest scoreboard. A build that ships but doesn’t renew is a delivery failure wearing a green status.
- AI compresses cycle time and introduces variance in the same motion. The leverage only converts to retained revenue when humans own the last mile.
- Forty engagements don’t need forty plans — they need one operating model and a health view sharp enough to tell you where to spend attention.