Five tips for making and carrying out an AI-enabled product strategy that go beyond the ChatGPT hype

To build a product strategy that survives the hype cycle and delivers a real return on investment (ROI), product leaders must move from experimentation to execution. Here are five recommendations for formulating a robust, AI-enabled product strategy.

1. Solve the "Job to be Done," Not the "Prompt to be Written"

The most successful AI products make the technology invisible. Instead of forcing users to learn "prompt engineering," integrate AI into existing workflows.

• The Strategy: Map your user’s journey and identify "toil"—repetitive tasks like data entry, categorization, or initial drafting.

• The Goal: Shift from Generative AI (creating content) to Agentic AI (executing tasks). Your product shouldn't just tell the user what to do; it should do it for them.

2. Prioritize Data Propriety Over Model Size

With the democratization of Large Language Models (LLMs), the model itself is rarely a moat. Your competitive advantage lies in your proprietary data.

• Recommendation: Focus on "Data Hygiene" as a product feature. Ensure your data pipelines are clean, structured, and legally compliant.

• The Edge: Fine-tune smaller, open-source models on your specific industry data. This often results in higher accuracy and lower latency than using generic, massive models.

3. Build for "Explainability" to Earn Trust

In enterprise settings, "because the AI said so" is not an acceptable answer. Trust is the primary barrier to AI adoption.

• The Strategy: Implement glass-box AI. Provide citations for AI-generated claims and create "Human-in-the-Loop" (HITL) checkpoints for high-stakes decisions.

• The Goal: Transform the AI from a "black box" into a "collaborative assistant" that can explain its reasoning.

4. Adopt a "Modular" Architecture

The AI landscape moves too fast for rigid stacks. A model that is state-of-the-art today might be obsolete in six months.

• Recommendation: Build a model-agnostic infrastructure. Use orchestration layers that allow you to swap LLMs or vector databases without rebuilding your entire front end.

• The Impact: This future-proofs your product and allows you to optimize for cost and performance as newer, cheaper models emerge.

5. Measure "Time-to-Value," Not Just "Accuracy"

Standard AI metrics like BLEU or perplexity scores don't reflect business success.

• The Strategy: Align your AI OKRs with business outcomes. Measure Mean Time to Resolution (MTTR), reduction in manual clicks, or the "Stickiness" of AI-driven features.

• The Metric: If an AI feature doesn't measurably shorten the user's path to their goal, it's a gimmick, not a strategy.