Decision frameworks for product managers evaluating, scoping, and shipping AI features in enterprise industrial software.
These frameworks are built from the perspective of a PM working in complex, high-stakes operational software — where AI being wrong has real consequences (a misrouted package, a false maintenance alarm, a conveyor that shuts down unnecessarily).
| Framework | When to Use |
|---|---|
| AI Feature Decision Matrix | Should this feature be AI-powered at all? |
| AI Sequencing Model | How do you phase AI from shadow mode to autonomous? |
| Operator Trust Ladder | What level of AI authority is appropriate for this use case? |
| AI Acceptance Criteria Template | What does "good enough to ship" look like for an AI feature? |
| Build vs Buy vs Partner for AI | Which path makes sense for this capability? |
When to use: You have an idea for an AI feature and need to decide whether to build it.
Score each dimension 1–3:
| Dimension | 1 (Low) | 2 (Medium) | 3 (High) |
|---|---|---|---|
| Rule-based ceiling: How badly does the current rule-based approach fail? | Rules work well | Rules work but are brittle | Rules fundamentally can't solve this |
| Data availability: Is the required training data already captured? | No data exists | Partial data | Clean, labeled data available |
| Error tolerance: How bad is it when the AI is wrong? | Catastrophic / hard to reverse | Recoverable with effort | Easily detected and corrected |
| Volume: Does this operate at scale where AI efficiency matters? | Low volume, manual OK | Medium volume | High volume, automation critical |
| Differentiation: Is AI here a competitive differentiator? | Table stakes / parity | Nice to have | Meaningful moat |
Scoring:
- 12–15: Strong candidate. Prioritize for roadmap.
- 8–11: Conditional. Resolve data or error tolerance gaps first.
- 5–7: Wait. Address foundational gaps before committing to AI.
- <5: No-go for now. The problem may not be AI-appropriate.
When to use: You've decided to build an AI feature. This is how you phase it.
Phase 0 · INSTRUMENT
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Goal: Ensure the data pipeline exists.
Ship: Logging, event capture, labeling hooks.
Don't call this AI yet.
Phase 1 · SHADOW MODE
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Goal: Run AI in parallel without customer impact.
Ship: AI makes predictions; humans still decide.
Measure: Accuracy vs. human decisions (or rule-based baseline).
Gate: >X% accuracy on held-out data before advancing.
Phase 2 · AI RECOMMENDATIONS
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Goal: AI advises; human confirms.
Ship: "AI suggests: Route to Lane 4 [Accept / Override]"
Measure: Override rate, outcome quality when AI is accepted vs. overridden.
Gate: Override rate <Y% AND outcome quality meets threshold.
Phase 3 · AI WITH HUMAN OVERRIDE
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Goal: AI decides; human can intervene.
Ship: AI acts autonomously by default; override always available.
Measure: Override rate, exception escalation rate.
Gate: Appropriate for use cases where speed > perfect accuracy.
Phase 4 · FULL AUTONOMY
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Goal: AI decides with no human in the loop.
Ship: Only when error tolerance is very high.
Appropriate for: Low-stakes, high-frequency, easily-reversible decisions.
Key principle: Never skip phases. The data from each phase is required to safely advance to the next.
When to use: Designing the UX for an AI feature in an operational environment.
Level 1 · INFORM
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AI provides information. No recommendation.
Example: "Sorter efficiency is 12% below baseline this shift."
Use when: Operators are experts; surfacing data is enough.
Level 2 · SUGGEST
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AI makes a recommendation. Human decides.
Example: "Consider rerouting high-priority orders to Lane 3."
Use when: AI accuracy is moderate; stakes are medium.
Level 3 · RECOMMEND WITH CONFIDENCE
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AI makes a specific recommendation with confidence signal.
Example: "Route to Lane 3 (High confidence). Tap to apply."
Use when: AI accuracy is high; operators need fast decisions.
Level 4 · ACT WITH NOTIFICATION
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AI takes action; operator is notified and can undo.
Example: "AI rerouted 847 units to Lane 3. [Undo]"
Use when: Speed is critical; error is reversible; trust is established.
Level 5 · FULLY AUTONOMOUS
───────────────────────────
AI acts; no human notification unless exception.
Use when: Decision frequency is too high for human involvement; error impact is minimal.
Design rule: Start every AI feature at Level 1 or 2. Earn the right to advance levels through production data.
When to use: Writing the definition of done for an AI feature.
## AI Feature Acceptance Criteria
### Accuracy Thresholds
- [ ] Precision on held-out test set: ≥ [X]%
- [ ] Recall on held-out test set: ≥ [X]%
- [ ] Performance does not degrade >5% on data from a new customer site
### Latency
- [ ] P95 inference time: ≤ [X] ms (must not block the real-time control loop)
### Failure Modes
- [ ] System falls back to rule-based behavior gracefully when model confidence < [threshold]
- [ ] Failure mode is logged and observable in monitoring
- [ ] No silent failures — all AI decisions are auditable
### Human Override
- [ ] Operator can override any AI decision within [X] seconds
- [ ] Override is logged with timestamp and operator ID
- [ ] Override rate is tracked in product analytics
### Explainability (if applicable)
- [ ] For each AI decision, a human-readable reason is surfaced in the UI
- [ ] Reason is accurate (not post-hoc rationalization)
### Monitoring
- [ ] Model performance dashboard exists with: accuracy over time, override rate, confidence distribution
- [ ] Alerting configured for: accuracy drop >10%, inference latency spike, override rate spikeWhen to use: Deciding how to acquire an AI capability.
| Dimension | Build | Buy (vendor/platform) | Partner |
|---|---|---|---|
| Core to your product? | Yes | No | Depends |
| Proprietary data advantage? | Yes | No | Sometimes |
| Time to production? | Long (6–18 mo) | Fast (weeks) | Medium (3–9 mo) |
| Customization needed? | High | Low | Medium |
| Internal ML capability? | Required | Not required | Helpful |
| IP ownership? | Full | None | Negotiated |
| Cost structure? | CapEx heavy | OpEx/per-seat | Hybrid |
Heuristics:
- If the AI capability is your primary differentiator: Build
- If it's infrastructure/commodity: Buy
- If you need speed + customization + don't have ML talent: Partner
- If you don't know yet: Start with Buy or Partner, migrate to Build once you have data and confidence
These frameworks are opinionated — they reflect experience in enterprise, industrial, and supply chain software. PRs with additions, counter-examples, or calibration data welcome.
Maintained by @ravishgm