The Framework for Strategic AI Product Leadership
Here’s a framework I’ve developed for AI product managers — a practical guide to navigating the AI product development process with purpose and clarity. It’s called the Strategic AI Enablement Framework (SAEF).
At its core, SAEF is more than a process — it’s a reminder of a fundamental truth: AI strategies often fail when treated as purely technical initiatives. Many organizations unknowingly set themselves up for failure by misplacing ownership of their AI efforts, handing them off exclusively to technical or data science teams without product leadership at the table.
Within SAEF, you’ll find four critical stages:
Strategic Intelligence
Operational Alignment
AI Technology Enablement
Tinkering
What’s important to note is that three out of these four stages are inherently business- and product-driven. Yet, in many companies, AI projects are siloed within technical departments, bypassing the product function altogether — and this is where things go wrong.
Product leaders bring critical perspectives to AI development:
Deep customer insights
Business context
A value-first mindset
When AI is treated purely as a technical project, it often results in:
Solutions looking for problems
Poor integration into business workflows
Low adoption due to lack of user-centric design
KPIs that don’t translate into real business value
Before rushing to embed AI or machine learning into every product or feature, pause and ask:
What is the intelligence we’re trying to enable in our application? What business outcome are we aiming to drive? What problem are we solving, and why does it matter?
At the heart of SAEF is the principle of starting with clarity. That means identifying the right problem first — not choosing a technology and looking for where to apply it. It’s about mapping the process that needs improvement, then asking how AI can meaningfully enhance it.
Stage 1: Strategic Intelligence
As you explore the problem space, consider these essential questions to determine whether AI is the right solution — and how to design for real impact:
Is this a real need or a perceived want for the end user?
How will you assess user behavior?
What existing solutions will you benchmark against?
What will you do if users don’t perceive the problem the same way you do?
The answers to these questions will shape not only your design decisions but also whether the AI solution should exist at all.
And once you’ve clearly identified the problem or uncovered the customer’s pain point, your next step is to define clear, measurable key performance indicators (KPIs). These KPIs should capture both user impact and business outcomes — ensuring that your AI solution drives meaningful, measurable value.
Looking to apply SAEF to your next AI initiative or craft a product strategy that connects intelligence to real business impact? Let’s connect and explore how to make it work for your team.
Stay tuned for the next blog, where I’ll dive into the remaining three stages of AI product design and share practical insights to help you lead with clarity and impact.
