Product managers shape what teams build and why it matters. They decide priorities, tradeoffs, and timelines. Yet a growing number of them are being asked to lead AI-driven products without the skills needed to do that work well. A recent report shows that only thirty-nine percent of product managers say they receive adequate training in AI. That gap is not small. It affects roadmaps, customer trust, and business results.

This blog breaks down what that number really means, why the gap exists, and what tech teams can do next.

What is the data saying?

The thirty-nine percent figure comes from a recent industry survey highlighted by Yahoo Finance. The study focused on product managers and their readiness to work with AI-based systems. Fewer than four out of ten felt confident about their AI training. Many shared that they learn on the job, often through trial and error.

That approach may work for basic features. It does not work well for generative AI product management or systems built on machine learning models. These products behave differently from rule-based software. They change over time, depend on data quality, and carry higher risk if misunderstood.

Why does this gap keep growing?

AI work has moved faster than product education. Most PM training still focuses on user stories, sprint planning, and feature prioritization. Those skills still matter, but AI adds new layers.

Many PMs now face questions like

• How does the model learn
• What data is being used
• How do we test model output
• What metrics define success

Without training in machine learning for product managers, these questions stay unanswered. Engineering teams fill the gap, often making product calls that should involve PM judgment.

Another reason is role confusion. Some leaders assume AI decisions belong only to data science teams. That mindset sidelines PMs instead of upskilling them.

The real cost of undertrained PMs

A lack of AI training creates visible problems.

One common issue is unclear product scope. A PM may promise features that a model cannot support or ignore edge cases tied to bias or drift.

Another issue is weak evaluation. AI products need clear AI model evaluation metrics. Accuracy alone is not enough. Precision, recall, latency, and human feedback loops matter. PMs who lack this knowledge struggle to guide testing plans.

Trust also suffers. Users notice inconsistent outputs. Internal teams lose confidence. Leaders begin to question AI investments.

McKinsey reports that many AI initiatives stall after pilot stages due to skill gaps across teams, not model quality.

Product leadership gaps play a large role in that outcome.

Generative AI raises the bar for PMs

Generative AI product management adds fresh challenges. Outputs are probabilistic. The same input may produce different results. That affects UX, support, and compliance.

PMs need to know how prompt design affects results. They must set boundaries on acceptable output. They should work with legal and ethics teams early.

Without structured learning, PMs rely on intuition. That leads to inconsistent decisions across products.

Gartner predicts that by twenty twenty-six, organizations that fail to train product leaders in AI will face slower product cycles and higher rework costs.

What do AI-ready product managers actually do?

AI-trained PMs work differently.

• They treat data as a core product input. They ask where it comes from, how often it updates, and who owns the quality checks.
• They define success using AI model evaluation metrics instead of feature completion alone.
• They collaborate closely with ML teams without handing over product ownership.
• They plan for monitoring after launch, knowing that model behavior shifts over time.

This is the core of AI product strategy. It blends business goals, technical limits, and user impact.

Why is self-learning not enough?

Many PMs try to fill gaps through blogs or short videos. That helps with awareness, not practice.

AI product work needs structured learning. PMs need case studies, hands-on examples, and shared language with engineers.

According to IBM research, teams with formal AI training programs report higher project confidence and better alignment between product and engineering.

What teams can do right now?

Tech leaders should start by auditing skills. Ask PMs how comfortable they feel with AI concepts tied to their products.

Next, update role expectations. AI literacy should sit alongside roadmap ownership.

Create shared reviews where PMs explain model behavior and tradeoffs to stakeholders. This builds trust and clarity.

Most of all, support learning paths that focus on AI product work, not generic AI theory.

Closing thoughts

The thirty-nine percent figure should concern every tech leader. AI products are already part of daily roadmaps. The question is whether PMs are ready to guide them.

Structured learning in generative AI product management, machine learning for product managers, AI product strategy, and AI model evaluation metrics helps close that gap.

For PMs who want a clear path forward, the AI Product Manager certification from AI CERTs offers focused training built for real product roles.

Download the program guide

It supports PMs who want to lead AI products with confidence, clarity, and accountability. Enroll Today

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