Earlier this week, I joined seven other Product Management Ambassadors for a Gartner Peer Community discussion about AI and product management. Here we are smiling for the camera:
We spent the session tackling questions submitted by fellow product managers, including:
- How has the AI hype changed over the past year?
- Where have we needed to temper that hype with reality?
- What skills do product leaders need to effectively manage AI?
- How do you identify and prioritize practical AI use cases?
- How do you encourage experimentation while making sure you’re actually creating value?
That last question stuck with me.
It Got Me Thinking About Kites
One of the challenges with AI right now is that everyone wants to experiment with it. And they should.
The technology is moving incredibly fast. New capabilities seem to emerge daily. Product teams need room to explore them, understand what’s possible, and figure out where they might make a difference in our customers’ lives.
But AI, like a kite, needs a string.
Some people look at a kite string and see an unwanted constraint. Just cut the string and you give the kite complete freedom. No constraints! Go wild! Go wherever the wind takes you!
Except without the string to hold it, the kite can’t really fly anymore. Eventually, it spins, drifts, and crashes back to the ground.
For AI experimentation, that string is business value.
The string doesn’t stop the kite from flying. It gives you enough control to let it fly. The same is true of business value and AI capabilities: Tie it to a real outcome, then let it climb as high as it can.
“Add AI” Is Not A Product Strategy
One thing all the panelists agreed on was that adding AI might be a feature request but it’s not a strategy.
Some problems are particularly well suited to AI because they involve things like complex or repetitive tasks, pattern recognition, anomaly detection, or prediction. Others might be solved perfectly well without it. As you’re considering your approach, think about:
- Is there a real problem we’re trying to solve, or is this just something cool with no business value?
- Is the opportunity aligned with our strategy?
- What’s the benefit: reducing costs, generating revenue, improving efficiency, or creating a better customer experience?
- Is AI actually a good fit for the problem we’re trying to solve?
And then there’s the data. If you don’t have access to enough relevant, trustworthy data, even a great AI use case may not get very far.
Constraints Are Not The Enemy
Giving product teams some constraints increases the odds that they’re experimenting with something that really matters to customers, and that has a business case to take it further.
So test ideas. Learn what these technologies can do. Break a few things along the way.
Just don’t cut the string.
Bonus Content
Speaking of kites, check out this countdown of the Top 15 Kites from around the world. It’s a fantastic showcase for how advancements in Kite Technology have allowed designers to create huge, complex, and imaginative creatures including one kite that measures more than 10,000 square feet.
New Around Here?
- Connect on LinkedIn
- Follow me on X
- Schedule time to talk product
- Join the product management community on Gartner Peer Insights
