1. Solutions Engineer — The Storyteller

Optimizes for: credibility, clarity, and value translation
Core responsibilities:

• Conduct technical discovery sessions
• Map workflows and pain points to concrete AI use cases
• Build customized demos tied to customer context
• Construct ROI projections grounded in real data and workflows
• Address objections and de-risk the pre-sale conversation

Success metric: Demo quality + deal closure + realistic scope setting

2. AI Solutions Architect — The Blueprint Designer

Optimizes for: technical feasibility and architectural confidence
Core responsibilities:

• Design end-to-end AI architecture aligned with business goals
• Evaluate data readiness, quality, and integration complexity
• Select appropriate models, infrastructure, and orchestration layers
• Identify risks, constraints, and long-term implications
• Produce implementation roadmap and timeline

Success metric: Feasibility validated + architecture blueprint + realistic timeline

3. Customer Stakeholders — The Decision Makers

Includes: CTO, VP Engineering, Product leads, business owners, technical teams
What they need before they commit:

• Credibility
• ROI clarity
• Risk mitigation
• Clear timeline with accountable roles

This group determines whether the deal progresses to Phase 2.

The Discovery Workflow (4 Steps)

Step 1 — Initial Discovery

Led by the Solutions Engineer

• Stakeholder interviews
• Workflow mapping
• Use case identification
• Technical and environmental assessment

Output: Clear articulation of problems worth solving with AI.

Step 2 — Technical Validation

Led by the AI Solutions Architect

• Assess feasibility and constraints
• Evaluate data quality/availability
• Draft integration and architecture blueprint
• Identify blockers and dependencies

Output: A blueprint that proves “this is possible.”

Step 3 — Demo & Proof

Led by the Solutions Engineer

• Build contextualized demo
• Show value using customer data (if available)
• Explain technical approach
• Address executive concerns

Output: Evidence that the solution is credible, valuable, and low-risk.

Step 4 — Scope & Close

Led by the AI Solutions Architect (with SE support)

• Produce implementation plan
• Define success metrics, deliverables, and timeline
• Align stakeholders
• Close deal with technical clarity

Output: A signed, scoped, and realistic implementation commitment.

Critical Success Factors for Phase 1

1. SE–SA Alignment

The most common failure mode is misalignment:

• SE oversells use cases
• SA discovers feasibility gaps
• Result: distrust, friction, deal risk

Alignment ensures what is promised is deliverable.

2. Realistic Scoping

Avoid “pilot purgatory.”
Set boundaries so timelines and outcomes are achievable.

3. Multi-Stakeholder Buy-In

Deals die when:

• Business wants it but engineering blocks it
• Engineering wants it but business won’t fund it

Phase 1 must secure both.

4. Data Reality Check

Most AI projects fail because of data, not models.
The SA must validate data quality upfront — not after the contract is signed.

5. Executive Storytelling

Technical capability means nothing until reframed in:

• ROI
• Risk
• Operational impact
• Competitive advantage

The SE makes AI legible to executives.

6. Clear Handoff to Phase 2

Document exactly:

• What was promised
• What constraints apply
• What success looks like
• What the FDE team must deliver

This reduces downstream chaos and protects margins.

The Business Engineer Interpretation

What Phase 1 really is:

A risk transfer mechanism.

• The vendor transfers technical and architectural uncertainty into a structured, validated plan.
• The customer transfers budget and commitment based on evidence, not hype.

What Phase 1 must prevent:

• Overpromising on AI capability
• Underscoping engineering complexity
• Ambiguous data assumptions
• Misaligned expectations across stakeholders

What Phase 1 guarantees for the next phases:

• Clear boundaries for the FDE team
• Feasible architecture
• Realistic timeline
• Executable implementation plan

Phase 1 is the precondition for successful AI implementation — as explored in the growing gap between AI tools and AI strategy — .
Every deployment failure can be traced back to shortcuts taken here.