Thank you AllThingsOpen.ai for having me speak at this great event. The title of my talk—"You Don’t Need an AI Strategy, But You Do Need to Be Strategic About AI"—might sound provocative, but that’s intentional. Below you'll find the slides, citations, resources and I'll add the video if it gets released.
Too many companies are getting caught in "strategy overload," layering AI strategies on top of data strategies, marketing strategies, and tech strategies. The result? Confusion, competing priorities, and a whole lot of meetings with no clear direction.
So, let’s reset. This talk is about how to integrate AI into your business strategy—without letting it become a distraction.
Key Takeaways from the AI Strategy Talk
Strategy Overload Is Real
Raise your hand if your company has an AI strategy. Now keep it up if it actually aligns with your business strategy.
If your AI efforts don’t support your overall business goals, you’re just chasing hype.
AI Isn’t Magic—It’s a Tool
AI can automate, analyze, personalize, and recommend—but not all AI projects are good investments.
A bad AI use case is one that could be solved with traditional software or doesn’t have the right data to train a model.
Thinking Like a Strategist
Instead of asking, “Do we need an AI strategy?” use these questions from Ethan Mollick.
What’s valuable today that might become obsolete?
What was impossible before but is now feasible?
What can we democratize or personalize with AI?
A strong AI use case checks these boxes:
✅ Clear value for both business and customers.
✅ Uses unique data that gives you an advantage.
✅ Has a baseline for comparison.
✅ AI is actually necessary—not just for the sake of AI.
Bad AI use cases?
❌ Could be done with traditional software.
❌ No clear patterns in the data.
❌ Requires absolute precision (e.g., medical diagnosis).
❌ The cost of implementation outweighs the benefits.
Slides
AI Use Case Worksheet
Citations
Advice for finding AI use cases (Cassie Kozyrkov)
AI Transformation Playbook (Andrew Ng)
Working Backwards: Insights, Stories, and Secrets from Inside Amazon (Colin Bryar and Bill Car)
The Flywheel Effect (Jim Collins)
Tech at Work: What GenAI Means for Companies Right Now (HBR IdeaCast featuring Ethan Mollick)
Resources
Using the term ‘artificial intelligence’ in product descriptions reduces purchase intentions (WSU Insider)
Introduction to ML and AI - MFML Part 1 (Cassie Kozyrkov)
7 Reasons Why Most AI Projects Never Make It to Production (Jan Van Looy)
