Almost every product organization is asking some version of the same question:
Where should we invest in AI?
And there's no shortage of opportunities. AI assistants. Intelligent automation. Predictive capabilities. New AI-powered products.
Product teams are getting increasingly good at generating ideas, exploring technical feasibility, and identifying promising use cases.
But identifying opportunities is only the beginning.
The harder question is deciding which opportunities are actually worth investing in.
Because every AI investment competes for the same limited resources: engineering capacity, capital, and leadership attention.
And the right decision depends on something many product teams don't examine deeply enough: What does the business actually need right now?
More revenue? Better retention? Improved margins? A stronger competitive position?
Different business priorities can justify very different AI investments. Let's look at an example.
Three very different investment decisions
Imagine you're leading product for a B2B SaaS company.
You have a team of engineers available for a significant initiative. Your leadership team is considering three options:
Option A: Build an AI assistant.
The promise is a differentiated customer experience. But the team will need to understand adoption, willingness to pay, inference costs, and whether customers truly value the capability.
Option B: Introduce AI-powered workflow automation.
The promise is measurable customer productivity and potentially new monetization. The team needs to evaluate implementation complexity, usage economics, and whether the automation solves an important enough problem.
Option C: Improve your existing product before investing heavily in AI.
The promise is potentially stronger near-term retention or profitability, without the complexity of launching a new AI capability.
Which is the right choice?
There isn't enough information yet.
And that's exactly the point.
If your company desperately needs revenue growth, one option might be more attractive.
If profitability is under pressure, another might make more sense.
If churn is threatening the business, you may need to reconsider whether AI is even the right investment right now.
The same three opportunities can produce completely different strategic recommendations depending on the company's priorities.
A strong product leader doesn't simply choose the most exciting AI feature.
They make the best investment recommendation for the business.
Four questions I would ask before recommending an AI investment
1. What business outcome are we optimizing for?
Revenue growth, profitability, retention, and strategic differentiation can justify different choices.
Before evaluating individual features, get clear on the business outcome that matters most.
2. What is the economic model?
What revenue or savings might the investment generate? What will it cost to build, operate, and support?
And critically for AI, how does usage affect contribution margin?
An AI feature can be popular with customers and still be a poor investment if the economics don't work.
3. What are we giving up?
Every strategic initiative consumes engineering capacity that cannot be invested elsewhere.
Choosing one investment means delaying or abandoning another.
Opportunity cost isn't a theoretical finance concept. It's one of the most important parts of product strategy.
4. What evidence would change our recommendation?
A strong strategy distinguishes what we know from what we assume.
It also identifies the signals that would justify continuing, adjusting, or stopping an investment.
You don't need certainty to make a strategic decision.
But you do need to understand the assumptions you're making and the evidence you'll use to evaluate them.
This is how the role of product leadership is changing
The ability to execute a roadmap remains important.
But executive product leadership requires something more: the ability to connect product decisions to financial outcomes, strategic trade-offs, and business priorities.
This becomes especially important in the AI era.
Companies can create new capabilities faster than they can necessarily demonstrate economic value.
And product executives are increasingly expected to help answer not just what can we build? but what should the business invest in, and why?
The product leaders who distinguish themselves won't simply know how to use AI.
They'll know where the business should invest in it—and where it shouldn't.
Learn the method with me
This Wednesday, November 4, I'm hosting a free Lightning Lesson on How to Build an AI Strategy for Your Existing Product.
We'll explore how to approach AI opportunities through a business lens and think more strategically about where to invest.
And if you're ready to apply these principles directly to your company, my Business-Minded Product Leader cohort is underway.
We work in a small group, applying business economics, AI investment thinking, strategic prioritization, and executive communication to real challenges.
Every student also receives a private 1:1 coaching session with me.
I'm considering evolving the program into a different format, so this may be among the final opportunities to experience this level of hands-on, individualized support.
Enrollment remains open through Sunday, November 8, at a special cohort price of $1,500 instead of $2,500 with code FALL1500 ($1,000 off).
Until next time,
Elena Leonova
Executive product & business-strategy leader