Forward Deployed Product Manager
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- Find the real enterprise workflow problem and define measurable AI-agent value
- Own the path from scoped deployment to secure production and evidence-driven iteration
- Scale reliability, customer adoption and field-to-platform product judgment
Benchmark: Scale AI + Cresta forward-deployed product roles (current Sep 2026) Market refresh: own enterprise AI outcomes from discovery through production; agent product fluency; integration and security constraints; evals and experiments; executive trust; adoption and expansion; convert field evidence into reusable platform signal.
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Enrichment
Find the real enterprise workflow problem and define measurable AI-agent value
Customer discovery, workflow mapping + outcome economics
Coverage: Product Discovery / Customer Research / KPIs & Product Metrics
AI-agent product fluency: tools, context + failure modes
Coverage: Generative AI / AI Agents / MCP / Prompt & Context Engineering
Own the path from scoped deployment to secure production and evidence-driven iteration
Enterprise integration, security boundaries + delivery
Coverage: APIs / Distributed Systems / Security / Technical Program Delivery
Evals, experiments + product feedback loops
Coverage: AI Evals & Monitoring / Product Analytics / Experimentation
Scale reliability, customer adoption and field-to-platform product judgment
Production quality, SLOs + operational optimization
Coverage: AI Reliability / Observability & SRE / Production Readiness
Executive trust, adoption + reusable product signal
Coverage: Executive Communication / Change Adoption / Product Strategy
Role-Readiness Proof / Capstone
Own a real or simulated enterprise AI deployment from first workshop through expansion decision. Build a workflow map, baseline economics and success criteria; write an agent/product spec; map data, tool, identity and integration boundaries; document security and change constraints; create representative evals plus an A/B or staged experiment; launch with SLO, quality, latency, cost, escalation and rollback gates; run an executive steering cadence; instrument adoption and workflow impact; optimize post-launch; then write a productization memo classifying each need as platform, configuration, integration or one-off and justify any expansion only from demonstrated value.
Proof Rubric
- Prove product judgment in the field: identify the actual workflow problem rather than transcribing requests
- quantify value and success criteria
- demonstrate credible AI/system fluency
- resolve integration, security and change constraints
- use evals and experiments to improve quality
- defend production reliability, cost and rollback choices
- earn senior stakeholder trust
- measure adoption and workflow impact
- distinguish reusable platform signal from bespoke work
- and ground expansion in evidence