Fractional Head of AI - Senior AI Leadership on Retainer
A fractional Head of AI is a senior operator who runs your AI function part-time, usually one to three days a week, against a fixed monthly retainer. The role exists because most companies between 50 and 500 employees have shipped one or two AI features, blown past their inference budget, and discovered nobody owns model risk, vendor selection, or the roadmap.
Hiring a full-time Head of AI costs $400K to $750K loaded and takes nine months to recruit. A fractional gets governance, cost discipline, and a 12-month roadmap in place in 90 days for $8K to $20K a month, then either converts to full-time or hands the function back to the CTO.
What a Fractional Head of AI Actually Does Week to Week
The job is 70% executive judgment, 20% governance plumbing, 10% hands-on review. If they spend more than 30% in Jupyter notebooks, you have hired the wrong person.
- Daily 15-minute model-health stand-up, attended only on threshold breach (latency, hallucination rate, cost spike)
- Weekly 45-minute experiment review with a binary pass/fail/kill decision on every active pilot
- Monthly 60-minute cost-and-risk summit with the CFO and head of engineering covering inference spend, GPU utilization, vendor sprawl
- Quarterly 15-minute board slot on AI risk register, ROI, and roadmap progress
- Vendor and contract negotiation (model providers, GPU reservations, observability tooling) - typically pays for the retainer
- Owns risk-tier classification, release gates, and red-team protocols against NIST AI RMF or EU AI Act
- Coaches the internal ML lead so they can chair the weekly review independently by month five
How It Differs From Fractional CTO, CAIO, and AI Consultant
The titles overlap in marketing but split cleanly on accountability.
- Fractional CTO owns all technology: infrastructure, hiring, security, delivery cadence, DORA metrics. AI is one of ten priorities
- Fractional CAIO and Fractional Head of AI are effectively the same role; CAIO is the title at mid-market non-tech companies, Head of AI at venture-backed startups
- AI consultant produces a deck and leaves; a fractional Head of AI signs off on production releases, holds vendor contracts, and is named in the AI governance charter
- CAIO is accountable to the CEO and board for AI ROI and risk; the consultant is accountable to a project sponsor for a deliverable
- If your CTO has real ML bandwidth and interest, you do not need a separate fractional Head of AI yet. If the CTO is already stretched across infra, security, product, you do
- Fractional Head of AI is complementary to the CTO; the two roles co-sign architecture decisions involving model serving, data pipelines, inference budgets
When You Actually Need This Role
Hire when two or more of these quantitative triggers persist for a full sprint, not on hype.
- Inference spend exceeds 15% of gross margin or grows faster than revenue
- Two or more AI features live in production with real customer traffic
- Experiment-to-production lead time exceeds 90 days despite having two or more ML engineers on staff
- P95 latency over 800ms on customer-facing LLM features, trending up
- Hallucination or error rate over 2% in live traffic
- Three or more unsanctioned LLM vendors with no unified cost dashboard
- Enterprise procurement is now asking governance questions and you have no signed model-risk policy
- Annual AI budget has crossed roughly $250K, or jumped from $120K to $900K year over year
- Series C fundraise with AI-focused investors within 90 days, or IPO readiness 12-18 months out
Engagement Shape, Hours, and Contract Length
The market has converged on three tiers. Below the entry tier you are buying advice, above the premium tier you should hire interim at a day rate.
- Entry: $4K-$5K per month, around 12 hours, fits early-stage with one or two use cases
- Core sweet spot: $8K-$12K per month, 16-24 hours, fits 50-250 employees with 2-4 production use cases
- Premium: $15K-$20K per month, 24-32 hours, fits pre-hiring evaluation periods and mid-market companies with regulated workloads
- Umbrex playbook benchmark: $15K-$30K per month with a 32-48 hour cap, overages pre-approved
- Contract length: 90-day minimum standard, 6-month renewals typical, 12-month annual contracts shave 10-15% off cycle pricing
- Cash retainer only; equity is rare for fractional, and a fractional asking for equity is usually positioning to convert to full-time
- Most operators serve three to five clients simultaneously, which is how the math works at this price point
First 30, 60, 90 Day Deliverables
A credible engagement produces written artifacts on a fixed schedule. Verbal updates do not count.
- Days 1-15: Portfolio diagnostic identifying GPU burn, latency, hallucination, vendor sprawl, plus one symbolic quick win (e.g. 20% inference cost cut via prompt optimization)
- Day 30: Current-state memo, AI maturity scorecard, data readiness assessment, ROI-ranked use case inventory, draft policy framework
- Day 60: AI governance charter signed by CEO and CTO, 12-month roadmap, vendor and model recommendations, risk register, FinOps dashboard live showing cost per 1k tokens
- Day 90: First one or two use cases shipped at 50% live traffic with measured ROI, automated governance gates in pipeline, substantive board update, decision memo on full-time conversion vs continuation
- Days 91-180 if retained: audit-readiness dress rehearsal, sub-30-minute incident response drills, Stay-Scale-Sunset memo quantifying savings and revenue lift
- Inference cost per user dropping another 20-25% via quantization, LoRA, or model swap is a common Phase 2 win
What Good Looks Like vs What Bad Looks Like
The signal is in the artifacts and the budget line, not in the Slack presence.
- Good: kills stalled experiments without sentiment, produces signed governance docs, renegotiates vendor spend enough to cover their own retainer
- Good: inference spend stabilizes or drops while feature count grows, experiment cycle time shrinks from 12 weeks to 6, internal ML lead chairs weekly reviews by month five
- Good: hallucination rate under 2% in live traffic, GPU utilization above 70% in business hours, governance charter enforced at the pipeline gate
- Bad: drowning in notebooks, no live feature shipped by day 120, governance charter unsigned, cost-per-token metric absent or worse after 60 days
- Bad: routinely blowing past contracted hours without renegotiation, no documented decision authority, invisible between monthly touchpoints
- Bad: produces a strategy deck on day 90 with no shipped use case behind it - that is a consultant, not a Head of AI
Pricing Benchmarks and Comparison to Full-Time
Fractional runs 20-40% of fully loaded full-time cost and skips the 6-9 month recruiting cycle.
- Fractional total: $60K-$240K per year on a $5K-$20K monthly retainer, no equity, no benefits, no recruiting fee, terminable on 30 days
- Full-time Head of AI base: $250K-$400K, plus 15-25% bonus ($40K-$100K), plus equity ($50K-$150K+), plus benefits ($30K-$60K), plus recruiter fee ($25K-$50K) = $400K-$750K loaded year one
- Published market reference points: chiefaiofficer.com Embedded Fractional CAIO at $180K/year or $54K per 90-day cycle; Strategic Oversight at $145K/year; AI Rapid Response Blocks at $8K per 10-hour block
- Hourly equivalents land at $300-$600 for senior US operators with multiple exits or named industry credibility
- Day rates of $2,500-$5,000 common for interim or embedded weeks above 32 hours
- Honest break-even: if you need 30+ hours a week of AI leadership for 12+ months, hire full-time. Otherwise fractional wins on cost, speed-to-impact, and optionality
FAQ
How is a fractional Head of AI different from a fractional CAIO?
Functionally they are the same role with different audience labels. Head of AI is the title venture-backed software companies use. Chief AI Officer is the title mid-market and non-tech companies use because it signals C-suite parity with the CFO and COO. Same retainer ranges, same deliverables, same governance work.
How many hours per month should I expect?
16 to 24 hours is the core sweet spot for companies with 2-4 production use cases. 12 hours works for early-stage with one use case. Above 32 hours, restructure as interim at a day rate because the operator cannot serve other clients at that load.
Should I offer equity instead of cash?
No. Fractional engagements are cash retainers. An operator asking for equity is signaling they want to convert to full-time, which is a separate conversation. If you want the optionality, write a 90-day fractional with a defined conversion path and salary band at day 90.
How long should the initial contract be?
90 days minimum, because nothing meaningful ships in 60. Most engagements then roll into 6-month renewals or 12-month annual contracts, with the annual saving 10-15% versus monthly. Build in a 30-day termination clause both ways.
When is it too early to hire a fractional Head of AI?
If you only have a single chatbot or GPT wrapper in production, no executive can quantify expected AI savings or revenue, and your CTO has genuine ML interest, you are not ready. Spend the $10K on an ROI Blueprint engagement instead and revisit in two quarters.
When is it too late, meaning I should just hire full-time?
When AI leadership work consistently exceeds 30 hours per week, when AI is more than 25% of engineering spend, or when you are 12-18 months from an IPO S-1 and need a named executive in the prospectus.
Will the fractional code or just advise?
They will review architecture, sign off on model and vendor choices, and occasionally pair with the ML lead on a thorny decision. They will not own a sprint backlog or write production code. If your engagement turns into them coding, you have hired a senior ML engineer at executive rates.
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