Timeline: 3–9 months
Objective: Convert a validated concept into a working AI system with measurable business value.
Strategic Purpose: Replace prototype theatrics with production reality, using embedded engineering to bridge organizational, technical, and operational gaps.

The Linchpin Role: Forward-Deployed Engineer (FDE)

The Special Forces of AI Implementation
The FDE is the decisive factor separating pilot purgatory from production success.

What Makes FDEs Different

• Embedded at customer site, not HQ
• Write production-grade code on customer infrastructure
• Operate under maximum ambiguity
• Ship through rapid iteration, not theoretical planning
• Own outcomes, not feature delivery
• Act as the human integration layer between customer and product team

Why this matters: AI systems fail at the interfaces between teams, workflows, and contexts. The FDE sits exactly at those interfaces.

FDE Core Responsibilities

• Build custom integrations and workflows
• Resolve ambiguous implementation challenges
• Translate customer domain complexity into AI capability
• Rapid-prototype, iterate, harden, deploy
• Document implementation patterns for productization
• Train customer teams to adopt and operate the system

This is the real moat: implementation knowledge, not models.

Success Signals

Operational indicators that Phase 2 is working:

• Time-to-first-value under four weeks
• Running production system
• Customer team uses and trusts the system
• Quantifiable business impact
• Expansion requests
• Field insights feeding product roadmap

These are economic signals, not engineering artifacts.

The Supporting Cast

AI/ML Engineer — The Model Optimizer

Focus Areas:

• Develop and train ML models for customer use cases
• Optimize model latency, performance, and cost
• Build data processing and training pipelines
• Implement monitoring and retraining systems
• Support FDEs on production integration

Purpose: Turn raw capability into stable, predictable behavior in production.

AI Product Manager — The Prioritizer

Focus Areas:

• Define product strategy informed by real-world deployments
• Prioritize features based on customer value, not intuition
• Convert FDE insights into product primitives
• Track adoption and business impact
• Balance customization with scalability

Role: Prevent the system from devolving into bespoke consulting.

Customer Technical Team — The Implementation Partners

Focus Areas:

• Collaborate daily with FDEs
• Provide domain workflows and edge cases
• Test and validate integrations
• Build competence for long-term ownership
• Champion adoption inside the organization

Why they matter: No AI system scales without internal operators who trust and understand it.

Strategic Insight

Phase 2 is where AI companies take on COGS risk, but also where they create the deepest moat.
FDEs are not a luxury; they are the only mechanism that closes the gap between foundational models and business outcomes.