From Pain to Impact: How I’d Enable AI in a Product
AI isn’t the strategy — it’s the enabler. The real impact comes from starting with a user pain, identifying where AI adds unique value, and designing experiences that feel natural, not forced. Here’s how I’d approach enabling AI in a product end-to-end.
When I think about enabling AI in a product, I don’t start with models, APIs, or fancy demos. I start with a pain point worth solving. Technology follows; it doesn’t lead.
Take Stripe as an example. Many merchants lose revenue from failed payments or fraud. Rules-based retries already exist, but they’re limited — they can’t learn or adapt. AI, on the other hand, can predict which transactions are likely to failand optimize retries in real time, helping recover revenue seamlessly.
This is how I break it down:
1. Start with the Problem, Not the Tech
The foundation of any great AI product is a clearly defined user pain.
Clarify: What problem are we solving? Who feels it most?
Example: Failed payments → lost revenue for merchants.
Without a real, valuable problem, AI becomes a gimmick.
2. Identify Where AI Adds Unique Value
Not every problem needs AI. I ask: Why AI and not rules or automation?
AI shines in three areas:
Pattern Recognition: Finding signals in complex data (e.g., fraud detection, personalization).
Prediction: Forecasting likely outcomes (e.g., churn, payment success).
Generation: Creating content or experiences (e.g., personalized recommendations, chatbots).
In this case, AI predicts failed transactions better than static rules.
3. Define the User Experience (AI in the Flow)
The magic isn’t in the model — it’s in how AI shows up in the user journey.
Invisible AI: Works quietly in the background, like fraud scoring or smart retries.
Visible AI: Interactive, like chat assistants or recommendations.
For merchants, I’d keep the AI invisible initially: it silently boosts payment success rates. Later, I’d layer in insights like “Stripe recovered $X in failed payments this month.” — making impact visible, not complexity.
4. Data & Training Considerations
AI’s effectiveness depends on data quality and responsibility. I’d evaluate:
Data Availability: Do we have clean, labeled, unbiased data?
Privacy & Compliance: Are we handling sensitive data like payments or PII correctly?
Cold Start: For new merchants with no history, pair baseline rules with AI for a hybrid approach.
5. Build with Iteration & Guardrails
I’d resist the temptation to “boil the ocean” on day one.
Start with a minimum viable model + baseline rules.
Use human-in-the-loop to review edge cases and build trust.
Instrument for errors: monitor false positives, drift, bias, and explainability.
6. Define Success Metrics
Clear metrics make the value of AI tangible.
User Value: Faster decisions, fewer failures, less manual work.
Business Value: Increased GMV, reduced fraud, higher retention.
Trust: High accuracy, transparency, low false positive rates.
For Stripe, that might mean reduced failed payments and incremental revenue recovered.
7. Anticipate Risks & Mitigations
AI introduces new risks — and managing them builds trust.
Over-reliance: Avoid blind trust by showing clear, explainable outputs.
Bias: Train on diverse datasets and continuously monitor.
Complexity: Keep UX simple; don’t overwhelm users with model details.
Closing Thought:
AI is most powerful when it’s not the headline, but the engine behind real user value. I’d enable AI where it solves a clear pain, design the experience to be intuitive and trust-enhancing, and measure success through business impact and user delight.
In other words: start with the fog, end with focus and impact.