How PMs Can Talk AI with Engineers Without Being Too Technical

One of the fears I hear most often from PMs is: “What if I sound clueless when talking to my engineers about AI?

It is a fair concern. AI has its own jargon, equations, and research papers. But here is the good news: you do not need to be an expert in algorithms to earn respect. What matters is knowing how to bridge the gap between user needs and technical implementation.

Focus on the Problem, Not the Math

Engineers and data scientists want clarity about what they are solving. You do not need to explain transformers or neural networks. You need to explain the user pain point, the outcome you want, and the metric that proves success.

• Instead of: “We should use an LLM to handle this.”

• Try: “Users spend too much time searching for answers. We want to cut that by 50 percent.”

Learn the Core Concepts

You do not need a PhD, but you should know the basics. Terms like training data, fine tuning, embeddings, or hallucination come up daily. Being fluent in this vocabulary shows respect for the team and keeps discussions efficient.

Think of it like learning enough local language when you travel—not to sound like a native, but to get around confidently.

Translate Business Goals into Technical Needs

Your role is not to dictate the algorithm but to connect business outcomes to technical goals.

• Business: “We want to reduce average support response time from 48 hours to 12.”

• Engineering: “Which models, data, or pipelines can achieve that?”

This shift in framing allows engineers to choose the best approach without you overstepping.

Use Metrics That Everyone Understands

Accuracy, latency, trust, adoption—these are metrics both PMs and engineers can rally around. Avoid hiding behind vanity KPIs. The best way to align is to define measurable outcomes that matter to users and the business.

Be Honest About What You Don’t Know

You will not have all the answers, and that is fine. Engineers will respect you more if you say, “I don’t fully understand this part, can you walk me through it?” than if you fake expertise. Curiosity builds credibility.

Final Thought

As a PM, your value is not in writing better code than your engineers. Your value is in keeping the team focused on solving the right problem, aligning around clear success metrics, and making sure the AI delivers value to real users. That is how you speak the language of AI without being too technical.

💡 This post is part of my ongoing series on AI Product Management.

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