1. Start with the Problem, Not the Model
AI is powerful, but it’s not a strategy. Before thinking about algorithms or prompts, step back:
- What is the actual user pain point?
- Where is the friction, inefficiency, or unmet need?
Product managers excel at problem-first thinking. Tools like Jobs to Be Done or problem framing canvases can guide AI discovery toward meaningful outcomes — not just novel tech.
2. Think in Experiments, Not Releases
Building AI features isn’t about long spec documents. It’s about hypotheses, fast feedback, and experiments.
Try:
Wizard-of-Oz tests (manual backends that simulate the AI)
Lo-fi Figma prototypes with simulated outputs
Small-scale GPT-based copilots to validate assumptions
This mindset accelerates learning before investing in ML pipelines.
3. Keep Humans in the Loop
AI without UX is noise. Product managers need to design trust, transparency, and control into every AI feature.
Examples:
Add confidence scores to predictions
Let users undo or correct the AI’s output
Use clear, explainable copy to describe model behaviour
This builds confidence and keeps the human as the decision-maker.
4. Build Feedback Loops Early
The real product spec isn’t static — it’s live data. Especially with AI, user interactions shape model performance and product usefulness.
Set up:
UX metrics: How often are AI suggestions accepted?
Model monitoring: Drift, error rates, confidence variance
Continuous feedback: In-product feedback or implicit signals
PMs should work with engineers and data scientists to design feedback into the loop.
5. Define Success Like a PM
Great AI doesn’t mean great product. Tie success to outcomes:
Are users completing tasks faster?
Is satisfaction or retention improving?
Are we solving the original problem?
KPIs to track:
Feature adoption rates
NPS / qualitative feedback on usefulness
Impact on activation or conversion funnels
This ties model success to business value.
Conclusion: Think Strategy, Not Just Tech
AI is not a magic bullet — but with the right product mindset, it becomes a powerful tool. As a PM (or PM-adjacent leader), your job is to balance user insight, technical feasibility, and strategic impact.
Acknowledgments
For More
Originally published at nuno.digital. Follow me on LinkedIn for more insights on AI strategy and innovation.
