Fractional AI Product Manager · Phoenix, AZ

Hire an AI product manager in Phoenix.

A full-time AI product manager in Phoenix earns $130,000 to $160,000 per year. Add $15,000 to $20,000 in recruiting fees and a 3 to 4 month hiring cycle. You spend the first quarter of that hire's tenure watching them figure out what to work on.

A 3-month fractional AI PM engagement produces an AI product roadmap, three production-ready feature specs, vendor evaluation for two or three vendors, and an evaluation framework your team keeps. For Banner Health Digital Health, Change Healthcare, and Magellan Health, this is the fastest path to a clear AI product direction.

Fixed engagement, scoped to your needs. Price agreed before work starts.

How we work

Tell us about your AI product direction question.

The build vs. hire cost calculation.

Full-time AI PM in Phoenix, total first-year cost: $130,000 to $160,000 salary, $15,000 to $20,000 in recruiting fees, 90 days to hire, and another 30 days of onboarding before the hire is making independent decisions. You are 4 to 5 months away from having a productive AI PM, and you have spent $150,000 to $180,000.

A 3-month fractional engagement starts in 2 weeks and produces specific deliverables with defined completion criteria. The engagement ends with documents, specs, and a framework your team keeps and uses.

The two paths are not mutually exclusive. Many Phoenix companies run the fractional engagement while recruiting for the full-time hire. The full-time hire starts with an AI product roadmap, three production-ready specs, and a vendor evaluation framework already done. Their first 90 days are spent executing, not orienting.

For companies that are not yet ready for a full-time AI PM hire (the role is not yet clearly defined, the AI product direction is still unclear, or the board has not yet approved the headcount), the fractional engagement produces the clarity that makes the full-time hire decision possible.

What a 3-month engagement produces.

Four deliverables. Each one is a document or framework your team owns and uses after the engagement ends.

AI product roadmap (12 months)

Current quarter: scoped features with production-ready specs. Quarters 2–3: pipeline features with effort estimates and sequencing. Quarter 4 and beyond: directional bets tied to specific business outcomes. No roadmap items without a defined success metric.

3 production-ready feature specs

Each spec includes the task definition, success metric, failure mode handling, vendor or model selection, and go-live criteria. Engineering can start from these specs without a separate scoping phase.

Vendor evaluation for 2–3 vendors

Evaluation against a scoring rubric calibrated to your priorities: HIPAA compliance, accuracy on healthcare-specific benchmarks, integration capabilities, pricing model at your expected volume, and customer references from comparable companies.

Evaluation framework your team keeps

A scoring rubric, data sheet template, reference check script, and decision memo template. Your team uses this framework to evaluate the next vendor without starting from scratch.

Healthcare-specific deliverables.

Banner Health, Change Healthcare, and Magellan Health operate under HIPAA. The deliverables reflect that.

HIPAA compliance checklist for vendor evaluation

Business Associate Agreement coverage, data residency commitment, model training exclusion, and breach notification timeline. Applied to every vendor evaluated in the engagement.

Data privacy spec for AI features

What data the AI feature uses as input, where model inference runs, what the retention period for model inputs is, and who has access to AI outputs. Written before engineering starts.

Success metrics tied to revenue cycle or clinical outcomes

Not engagement metrics or model accuracy metrics in isolation. The success metric for a healthcare AI feature is the business outcome: denial rate change, documentation time reduction, or coding accuracy improvement on labeled claims.

How we work.

1
Discovery call (1 hour)
We map the current AI product situation: what AI features are in development or planned, what the business goals are, and what the recruiting timeline looks like. Fixed price quoted at the end of this call.
2
Roadmap and spec sprint (weeks 1–6)
We interview product, engineering, and clinical or operations stakeholders. We write the roadmap, produce three production-ready feature specs, and complete vendor evaluation for the first vendor.
3
Framework and handoff (weeks 7–12)
We complete the vendor evaluation for the remaining vendors, build the evaluation framework, and produce the handoff package. The engagement ends with your team fully able to proceed independently.

Questions from Phoenix product teams.

What does an AI product roadmap from a fractional engagement look like. How far out does it cover?: An AI product roadmap from a 3-month fractional engagement covers 12 months. It has three layers. The first layer is the current quarter: the specific AI features scoped for immediate development, with production-ready specs, vendor selections, and go-live criteria written. The second layer is quarters 2 and 3: the AI features in the pipeline, with enough scoping to estimate effort and sequence them against each other, but without full specs yet. The third layer is quarter 4 and beyond: directional bets on AI capability areas (computer vision, retrieval-augmented generation, voice AI) based on your product strategy. The roadmap does not include features that are not yet tied to a specific business outcome. At Banner Health Digital Health, a roadmap item needs a defined metric it is supposed to move. A roadmap full of AI capabilities with no success metrics is a research agenda, not a product roadmap.
What does vendor evaluation for a healthcare AI feature include. What specific questions do you ask?: Healthcare AI vendor evaluation covers six areas. First, HIPAA compliance documentation: does the vendor have a Business Associate Agreement, and what does it cover for model inputs and outputs? Second, data residency: where is the model inference run, and does the vendor commit to not training on your data? At Change Healthcare and Magellan Health, this is a contract requirement, not a preference. Third, model accuracy on healthcare-specific benchmarks: what published accuracy results exist for the specific clinical or administrative task, and on what dataset? A vendor with strong general benchmarks but no healthcare-specific evaluation data is a risk. Fourth, integration: what APIs exist, what the latency is on a 95th percentile basis, and what the SLA is. Fifth, pricing model: per-token, per-call, or per-seat, and how that scales with your expected volume. Sixth, customer references from healthcare companies with a similar use case. References from non-healthcare customers do not transfer to a HIPAA-regulated context.
How do you build an evaluation framework a healthcare company can use after the engagement ends?: An evaluation framework for continued AI vendor assessment has four components. First, a scoring rubric: the specific criteria the company evaluates (compliance documentation, accuracy benchmarks, integration capabilities, pricing model, references) with a weighting for each based on the company's priorities. The weights are calibrated to the company, not generic. Banner Health Digital Health weights compliance documentation more heavily than a commercial software company would. Second, a data sheet template: a structured document that captures the evaluation data for each vendor in a consistent format. This makes it possible to compare vendors across different evaluation cycles. Third, a reference check script: a set of questions specifically for reference calls with healthcare customers of the vendor. The script is designed to surface integration friction and HIPAA compliance issues, not general satisfaction. Fourth, a decision memo template: the document that records the vendor selection decision, the alternatives considered, and the rationale. This document matters when procurement asks why a specific vendor was chosen.
When does it make sense to do a fractional engagement first vs. hiring full-time directly?: A fractional engagement makes sense before a full-time hire when two conditions are true. First, the company does not yet know exactly what the AI PM role should own. If the job description says 'own all things AI product' without specifying which features, which vendors, and which stakeholders, the full-time hire will spend their first 90 days figuring out what to do. A fractional engagement produces a specific roadmap, vendor evaluations, and a set of feature specs. The full-time hire inherits a defined scope rather than a blank slate. Second, the company cannot wait 3–4 months to start. A 3-month fractional engagement at $6–8k produces a clear AI product direction while recruiting is running in parallel. The full-time hire starts with work already done, not from zero. At Banner Health, Change Healthcare, or Magellan Health, the AI product function can be materially further along after a fractional engagement than it would be if the company waited for a full-time hire to close first.

Get a clear AI product direction without a 6-month hiring cycle.

Tell us what AI features you are trying to build and where your product direction is unclear. We reply within one business day with a rough scope and price range.

Hire an AI product manager in Phoenix.

Fixed engagement, scoped to your needs. Price agreed before work starts.

The build vs. hire cost calculation.

What a 3-month engagement produces.

Four deliverables. Each one is a document or framework your team owns and uses after the engagement ends.

AI product roadmap (12 months)

3 production-ready feature specs

Vendor evaluation for 2–3 vendors

Evaluation framework your team keeps

A scoring rubric, data sheet template, reference check script, and decision memo template. Your team uses this framework to evaluate the next vendor without starting from scratch.

Healthcare-specific deliverables.

Banner Health, Change Healthcare, and Magellan Health operate under HIPAA. The deliverables reflect that.

HIPAA compliance checklist for vendor evaluation

Business Associate Agreement coverage, data residency commitment, model training exclusion, and breach notification timeline. Applied to every vendor evaluated in the engagement.

Data privacy spec for AI features

What data the AI feature uses as input, where model inference runs, what the retention period for model inputs is, and who has access to AI outputs. Written before engineering starts.

Success metrics tied to revenue cycle or clinical outcomes

How we work.

Discovery call (1 hour)

We map the current AI product situation: what AI features are in development or planned, what the business goals are, and what the recruiting timeline looks like. Fixed price quoted at the end of this call.

Roadmap and spec sprint (weeks 1–6)

We interview product, engineering, and clinical or operations stakeholders. We write the roadmap, produce three production-ready feature specs, and complete vendor evaluation for the first vendor.

Framework and handoff (weeks 7–12)

We complete the vendor evaluation for the remaining vendors, build the evaluation framework, and produce the handoff package. The engagement ends with your team fully able to proceed independently.

Questions from Phoenix product teams.

What does an AI product roadmap from a fractional engagement look like. How far out does it cover?

An AI product roadmap from a 3-month fractional engagement covers 12 months. It has three layers. The first layer is the current quarter: the specific AI features scoped for immediate development, with production-ready specs, vendor selections, and go-live criteria written. The second layer is quarters 2 and 3: the AI features in the pipeline, with enough scoping to estimate effort and sequence them against each other, but without full specs yet. The third layer is quarter 4 and beyond: directional bets on AI capability areas (computer vision, retrieval-augmented generation, voice AI) based on your product strategy. The roadmap does not include features that are not yet tied to a specific business outcome. At Banner Health Digital Health, a roadmap item needs a defined metric it is supposed to move. A roadmap full of AI capabilities with no success metrics is a research agenda, not a product roadmap.

What does vendor evaluation for a healthcare AI feature include. What specific questions do you ask?

Healthcare AI vendor evaluation covers six areas. First, HIPAA compliance documentation: does the vendor have a Business Associate Agreement, and what does it cover for model inputs and outputs? Second, data residency: where is the model inference run, and does the vendor commit to not training on your data? At Change Healthcare and Magellan Health, this is a contract requirement, not a preference. Third, model accuracy on healthcare-specific benchmarks: what published accuracy results exist for the specific clinical or administrative task, and on what dataset? A vendor with strong general benchmarks but no healthcare-specific evaluation data is a risk. Fourth, integration: what APIs exist, what the latency is on a 95th percentile basis, and what the SLA is. Fifth, pricing model: per-token, per-call, or per-seat, and how that scales with your expected volume. Sixth, customer references from healthcare companies with a similar use case. References from non-healthcare customers do not transfer to a HIPAA-regulated context.

How do you build an evaluation framework a healthcare company can use after the engagement ends?

An evaluation framework for continued AI vendor assessment has four components. First, a scoring rubric: the specific criteria the company evaluates (compliance documentation, accuracy benchmarks, integration capabilities, pricing model, references) with a weighting for each based on the company's priorities. The weights are calibrated to the company, not generic. Banner Health Digital Health weights compliance documentation more heavily than a commercial software company would. Second, a data sheet template: a structured document that captures the evaluation data for each vendor in a consistent format. This makes it possible to compare vendors across different evaluation cycles. Third, a reference check script: a set of questions specifically for reference calls with healthcare customers of the vendor. The script is designed to surface integration friction and HIPAA compliance issues, not general satisfaction. Fourth, a decision memo template: the document that records the vendor selection decision, the alternatives considered, and the rationale. This document matters when procurement asks why a specific vendor was chosen.

When does it make sense to do a fractional engagement first vs. hiring full-time directly?

A fractional engagement makes sense before a full-time hire when two conditions are true. First, the company does not yet know exactly what the AI PM role should own. If the job description says 'own all things AI product' without specifying which features, which vendors, and which stakeholders, the full-time hire will spend their first 90 days figuring out what to do. A fractional engagement produces a specific roadmap, vendor evaluations, and a set of feature specs. The full-time hire inherits a defined scope rather than a blank slate. Second, the company cannot wait 3–4 months to start. A 3-month fractional engagement at $6–8k produces a clear AI product direction while recruiting is running in parallel. The full-time hire starts with work already done, not from zero. At Banner Health, Change Healthcare, or Magellan Health, the AI product function can be materially further along after a fractional engagement than it would be if the company waited for a full-time hire to close first.

Get a clear AI product direction without a 6-month hiring cycle.

Tell us what AI features you are trying to build and where your product direction is unclear. We reply within one business day with a rough scope and price range.