For Teams

Use Cases by Role

Practical guidance for AI adoption that survives scrutiny. Each role has different leverage points and pitfalls.

Innovation PM

When AI helps

+Drafting discovery briefs and experiment hypotheses
+Synthesizing user research into actionable themes
+Generating stakeholder update variations
+Creating first-draft PRDs and acceptance criteria

When to be careful

!Strategic prioritization (AI lacks business context)
!Stakeholder negotiations (nuance matters)
!Risk assessments (AI underweights tail risks)
!Novel market positioning (AI extrapolates from existing)

Typical Delta & TLX patterns

Delta: Delta +3 to +8 on brief drafts; Delta +12 on strategy docs

TLX: TLX 35-50 on synthesis tasks; TLX 65+ on novel positioning

Start with

1.Ideation review: Use AI to generate 10 variants, pick 2
2.Brief drafting: AI first draft, you add context and judgment
3.Vendor questions: AI generates evaluation criteria

Discovery brief in half the time

A product lead ran a Fair Trial on discovery briefs. Manual took 3.5 hours. AI-assisted took 1.5 hours—but required 45 minutes of context injection the AI missed. Net gain: 90 minutes. The key insight: AI drafts faster, but PMs still own the "why" and stakeholder framing. The team now uses AI for structure, then adds judgment layers. Delta dropped from +9 to +4 after two calibration cycles.

Run the 3-minute demo Open quickstart

Software Engineer

When AI helps

+Boilerplate generation and scaffolding
+Test case creation from specifications
+Documentation refactoring and updates
+Code review assistance (pattern matching)

When to be careful

!Security-critical code (AI misses context)
!Performance-sensitive sections (AI defaults to clarity over speed)
!Legacy system modifications (AI lacks institutional knowledge)
!Architectural decisions (AI optimizes locally, not globally)

Typical Delta & TLX patterns

Delta: Delta +2 to +5 on tests; Delta +10 on architecture reviews

TLX: TLX 30-45 on boilerplate; TLX 70+ on debugging AI-generated code

Start with

1.Code review rubric: AI flags patterns, you assess severity
2.Test generation: AI writes cases, you verify coverage
3.Doc refactoring: AI restructures, you validate accuracy

Test coverage up, review time down

An engineering team ran Fair Trials on test generation. AI produced tests in 20 minutes vs 90 minutes manual. But 30% of AI tests had incorrect assertions—caught in review. After adding a "verify assertions" step to their workflow, net time savings held at 45 minutes per test suite. TLX stayed below 50 because the review step was predictable. Key: AI generates quantity, engineers ensure correctness.

Run the 3-minute demo Open quickstart

Next Steps

Run the 3-minute demo Methodology Evidence

Ready to measure your AI impact? Start with a quick demo to see your Overestimation Δ and cognitive load metrics.