Incorporating AI Responsibly and Strategically

I’m a little late to this topic, and there are a lot of colleagues and friends that are far more directly experienced with this, but… I’ve observed enough so that I’m ready to share some of my thoughts on Artificial Intelligence (AI). It is no longer a futuristic aspiration. It’s here, it’s everywhere, and CEO’s and product leaders have been pressured to “do something with AI” for a while now.

Although the hype was (is?) loud, it is starting to plateau, and the path remains murky. Many product leaders feel caught between the fear of falling behind and the risk of jumping in blindly.

As a coach to product leaders and executives, I’ve heard the same questions on repeat:

• “How do we use AI without just chasing shiny objects?”

• “What’s our AI strategy beyond a prototype?”

• “How do we build trust with users and avoid ethical landmines?”

• "How much will this cost versus how much will it benefit the business?"

• "Will adding AI truly make things better for this use case?"

This post is for leaders navigating those very questions. Let’s talk about how to bring AI into your product strategically, responsibly, and credibly.

1. Resist the “AI for AI’s Sake” Trap

Just because you can add AI doesn’t mean you should.

I’ve seen product teams pour months into building AI-powered features that deliver little value, confuse users, or worse, break trust.

Start with a grounding question:

What real user or business problem would AI help us solve more effectively than traditional approaches?

AI isn’t a strategy. It’s a capability. Use it in service of your strategy, not as a substitute for one.

2. Understand Where AI Adds Value (and Where It Doesn’t)

Before diving into models and data pipelines, product leaders must understand the types of problems AI excels at:

• Classification: Tagging, flagging, sorting.

• Prediction: Forecasting churn, demand, or next best action.

• Generation: Summarizing, writing, creating imagery or speech.

• Clustering: Finding patterns in complex data sets.

Equally important: know where AI struggles: ambiguous ethics, logic-heavy decision-making, or use cases requiring high precision and explainability.

Use that knowledge to guide use case selection, set stakeholder expectations, and avoid overpromising.

3. Start Small, Learn Fast, and Build Iteratively

Many organizations stall by trying to craft the perfect “AI roadmap” up front.

A better approach? Find one small, high-impact use case and ship a controlled experiment.

Treat it like any new capability:

• Pilot in a narrow, low-risk context.

• Measure specific outcomes (speed, accuracy, cost reduction).

• Watch how users interact and adapt from there.

These early wins create internal confidence and external credibility while you develop deeper AI maturity.

Note: I've often done this myself with similar (non-AI) problems. Before asking engineers to code a complex solution to a problem, I would hire one (or more) temporary part-time employees to do the work manually to validate that customers actually wanted that behavior. Another popular example: Jeff Bezos did his own Amazon shipping himself before hiring (and ultimately investing in automation.) This truism remains regardless of the problem space...

4. Build Cross-Functional AI Literacy Early

AI isn’t just for data scientists anymore.

If you want to embed AI into your product responsibly, your entire team needs a shared understanding of:

• What AI can (and can’t) do

• What it costs: in time, data, and risk

• How to design for transparency, consent, and fallback paths

• Where bias and unintended consequences can emerge

That means:

• Training product managers, designers, and engineers in basic AI principles

• Partnering closely with legal, compliance, and customer support

• Having ethical conversations before the launch, not after

5. Prioritize Responsible AI from the Start

As stewards of the product, it’s on product leaders to ask the hard questions:

• Are we training on biased data?

• Can users understand, override, or question the AI?

• What are the failure modes, and how visible are they?

Trust is hard to earn and easy to lose. And in AI-powered experiences, the margin for error is slim.

Put policies in place around data privacy, transparency, and human oversight. Not as a blocker, but as a foundation.

A responsible AI strategy is a competitive advantage. Especially as regulation and public scrutiny increase.

6. Connect AI Efforts to Clear Product and Business Outcomes

Don’t let AI become an R&D island or a scattered set of prototypes.

Hold AI-powered features to the same standard as anything else in your roadmap:

• What user problem are we solving?

• How will we measure success?

• What behavior change or business result do we expect?

Translate technical breakthroughs into product impact:

“By using AI to summarize support tickets, we reduced first-response time by 40%.”“By predicting churn risk, we targeted interventions that improved retention by 8%.”

That’s how you turn AI from a buzzword into real value.

7. Evolve Your Role as a Product Leader

Leading AI initiatives isn’t just about the tech. It’s about navigating uncertainty, ethics, and organizational readiness.

That means:

• Advocating for responsible experimentation

• Building bridges between engineering, legal, and UX

• Helping stakeholders distinguish between hype and substance

• Thinking deeply about what kind of intelligence your product needs, and what kind of trust your users deserve

This is new terrain for many organizations. But it’s also a moment of opportunity for product leaders ready to step up.

Final Thought: Be Bold, But Be Deliberate

The best product leaders aren’t rushing to “AI-wash” their products. They’re choosing deliberate, meaningful ways to enhance user value—with clarity, care, and conviction.

You don’t need to have all the answers. But you do need to ask the right questions, bring the right people to the table, and lead with purpose.

Incorporating AI isn’t just a technical challenge, it’s a leadership one. And that’s where you make the difference.