40% → 58%
Support tickets resolved without a human. Roughly 18% of the queue stopped reaching agents, so the team absorbed growth without adding headcount.
Composite case · Fractional
The AI lead quit. The pilots were drifting. We triaged, shipped, and hired the owner.
A healthcare SaaS company whose AI lead quit gets its stalled pilots triaged, fixed, and shipped, plus a permanent owner hired during the engagement.
Composite · IllustrativeComposite engagement. The operational shape is drawn from real patterns; the company is not a real named client and the numbers are illustrative. For work that is real and checkable, see baxie.
30% → 84%
Reps actually using the tool. Below 30% the license spend was hard to defend at renewal. Above 80% the call notes are reliable enough to plan off.
~31%
Claims routed without a human triage step, with an audit trail on every decision. The adjudication team spends its day on the exceptions instead of the sorting.
During engagement
A full-time replacement signed before handoff, so the portfolio never went unowned a second time.
01
Client context
This composite is a $45M healthcare SaaS company with about 180 employees. The senior AI hire left after nine months, leaving three pilots with no clear owner and no accountability chart, so AI work now lived nowhere. A support copilot was deflecting tickets with no instrumented metric, sales summarization sat at low adoption, and the real high-value bet, claims-adjudication assist, was a slide deck with no prototype. The CTO was overwhelmed and reluctant to hire another senior person who would quit, with a board AI update due in 90 days.
02
The problem
- 01 The senior AI hire left after 9 months. Three pilots had no clear owner and no accountability chart, so AI work now lived nowhere.
- 02 A customer-support copilot was running at about 40% ticket deflection, but no one had defined or instrumented the success metric.
- 03 Sales-call summarization sat at 30% rep adoption. It was built to fully automate, reps didn't trust it, and the draft-then-edit pattern was never built.
- 04 The actual high-value bet, claims-adjudication assist, was a slide deck with no prototype and no owner.
- 05 The CTO was overwhelmed and reluctant to hire another senior person who would quit, with a board AI update due in 90 days.
03
GAT's approach
Diagnose (2 weeks)
Triage the portfolio, name the owners.
Ran stakeholder interviews and delivered pilot triage: kill, scale, or re-scope, each with a rationale. Stood up an AI accountability chart distributing ownership across function leads, plus a board-ready quarterly update template the CTO could defend without me in the room.
Build (full Fractional shape)
Operate across the org for several months.
Killed the orphaned tool that had no real demand. Re-scoped sales summarization to draft-then-edit so reps stayed in control. Scaled the support copilot with proper measurement so the deflection number meant something.
Ship the high-value bet
Claims-adjudication assist, Phase 1.
Shipped a multi-agent workflow: intake parse, code suggestion, escalation routing, with an audit trail the compliance team could read. It reclaimed hours per week per adjudicator instead of replacing the human call.
Stand up the shared knowledge base
One layer the team and the agents both read.
Built a domain glossary, customer archetypes, decision logs, and scoped automation patterns. The team writes to it, the agents read from it, and nobody is guessing what the canon is.
Hire the permanent owner
Replace the role, then hand off warm.
Wrote the JD, screened candidates, advanced finalists to the CTO, and signed the full-time hire during the engagement. Ran a warm handoff with office hours and regression triage so the new owner inherited a running system, not a reset.
In their words
Our AI lead quit and three pilots were drifting. Inside a few months one was killed, two were live and measured, the real bet shipped, and we'd hired the person to own it. The CTO got a day a week back.
VP of Operations, composite client
04
Measurable outcomes
40% → 58%
Support ticket deflection, instrumented and prompt-optimized. Roughly 18% of the queue stopped reaching a human, which is how the team absorbed growth without hiring (illustrative).
30% → 84%
Sales summarization adoption, re-scoped to draft-then-edit. Adoption is what makes the license defensible at renewal and the call notes trustworthy enough to plan off (illustrative).
~31%
Claims adjudication auto-routed, a Phase 1 multi-agent workflow with an audit trail on every decision. The adjudication team moved from sorting to exceptions (illustrative).
During engagement
Permanent AI owner hired, a full-time replacement signed before handoff.
Ready to talk?
Stalled pilots and no owner? Start here.
If your AI work lives nowhere and the board update is coming, the Fractional engagement triages what you have, ships the real bet, and hires the person to own it after.
Questions
What buyers ask about this one.
Our head of AI quit and the pilots are stranded. What happens first?
Triage before anything else. Every stalled piece gets classified as continue, fix, or kill, and each survivor gets a named owner inside your team. Most stranded portfolios have one or two things worth saving and several worth stopping, and nobody has been empowered to say so.
Can we hire a permanent head of AI while a fractional one is embedded?
That is the intended path. I work the search alongside your recruiter, help write the job description against what we learn in the seat, support panel interviews, and hand off when the right person signs. Most of these engagements end inside nine months for exactly that reason.
What does a fractional head of AI actually do week to week?
A weekly executive sync, daily availability on high-leverage calls, hands-on contribution to the highest-stakes build, and training running in parallel. One to three days a week. The standing rule is that every workflow shipped names a human accountable for its output.
Who should own AI at a 50-person company with no CTO?
Someone on the executive team who can stop projects, not just start them. The common failure is handing AI to whoever is most enthusiastic about it, which produces a champion with no authority. If nobody there can kill a project, the ownership is not real.
What happens to the work when the engagement ends?
Your team runs it. The portfolio, the vendor decisions and their reasoning, the standards, and the training all stay. If your team cannot take it at handoff, the engagement is not finished.