While the vast majority (98 percent) of product managers now use AI at work, only 39 percent have received comprehensive, job-specific training, according to a report from tech education company General Assembly.
The survey collected responses from 117 product managers working in the United States, United Kingdom, Canada, and Singapore at companies with at least 100 employees
The findings show that AI has become a daily fixture in product management. Product managers reported using AI an average of eleven times per day, with nearly half saying they learned these tools on their own.
Summary
How are Product Managers Using AI?
More than three quarters (78 percent) of respondents said they use AI agents, while 31 percent reported creating or adapting custom language models, specialized agents, or domain-specific GPTs.
The report notes that most PMs rely on AI for tasks such as managing product development cycles, coordinating cross-functional teams, building roadmaps, running customer interviews, and analyzing feedback data.
Despite widespread adoption, the survey identified significant skill gaps, with 47 percent of respondents saying they want to learn “vibe coding,” the process of prototyping and validating product concepts without engineering support, but only 38 percent currently do so.
Beatrice Partain, director of product marketing at General Assembly, says the ability to iterate faster and ground ideas in user feedback is incredibly valuable in product management, and vibe coding essentially unlocks this for people who don’t write code themselves.
“It’s helping you get to the prototype stage faster. In the past, you might have drawn out an idea on paper or a whiteboard,” she says.
When AI can be used to build a prototype that people can look at and interact with, it can help with instant feedback before tapping engineering resources.
Partain says when developing AI skills like vibe coding, it’s helpful to start with experimentation.
- Explore no-code or AI prototyping platforms to understand their basic functions and capabilities.
- Once familiar, analyze how each tool works and learn how to evaluate the quality of its outputs.
- Establish a personal framework for how you’ll use these tools — for a product manager, this could mean defining a repeatable process for setting up a prototype.
- Engage in peer learning by connecting with other PMs through group training sessions, workshops, or informal knowledge-sharing discussions.
What Impact is AI Having on Product Teams?
Most respondents said AI has had a clear positive effect on their teams, for example 97 percent said it helps their departments make faster decisions, and 98 percent said it improves their product lifecycle.
Two-thirds said AI has improved productivity without reducing headcount, while more than a quarter reported that their teams have grown since adopting AI. Only one percent said their teams have shrunk.
Even with these gains, many product managers remain uncertain about AI’s long-term implications. Roughly a quarter (26 percent) said their biggest concern is that AI could eventually replace their roles, and another 25 percent said they were worried it may make it harder for entry-level PMs to develop foundational skills--22 percent fear it could lead to broader workforce displacement among their peers.
Vishal Sood, chief product officer at Typeface, says the best PMs he sees have embraced a hands-on approach.
“They're not waiting for engineering to validate their ideas, they're using prompt engineering and no-code tools to create compelling proof-of-concepts that get stakeholders excited and customers engaged,” he explains.
By the time they hand something off to engineering, they've already de-risked the core assumptions and built organizational buy-in around a proven concept.
How Can PMs Close the AI Skills Gap?
The survey found strong demand for structured training and ongoing learning, with nearly two-thirds of product managers saying they want regular training updates as AI tools evolve, and just over half said they value peer learning sessions.
Nearly half of respondents said they favor self-paced training programs with product-specific examples, while others cited the importance of access to technical support and hands-on workshops focused on practical use cases.
Partain says before you can begin addressing the AI fluency gap, your organization or team needs to have clear expectations and goals set around AI use cases.
From her perspective, saying “we want everyone to use AI” is useless if you don’t have outcomes that you’re hoping to achieve.
“One thing we have found is that generic AI upskilling is largely ineffective,” she says. “People need to learn how they can use AI as part of their specific workflows and responsibilities.”
Offering targeted programs that address specific PM workflows, incorporate peer learning opportunities, and continuously update as technology advances is going to go a long way toward driving day-to-day usage that supports business goals, not just individual productivity.
How Can PMs Maintain Governance?
Sood points out governance absolutely matters, but it should be solved through platforms and configurations, not rulebooks.
“Embed AI into the systems people already use rather than hoping they'll follow compliance guidelines,” he explains.
The right platform choices handle governance at the infrastructure level, preventing the productivity islands that emerge when everyone adopts their own random tools.
Partain says she agrees it’s important to have an AI usage policy and governance around how people are using AI.
“Once you have clear goals and guardrails for AI, you can map training to those outcomes,” she says.
Ryan Gialames, principal product manager, innovation at Western Governors University, says leaders can balance innovation and governance by giving PMs safe spaces to experiment through sandbox environments and a clear set of Gen AI Design Principles.
“Providing approved, internal agentic tools that are pre-wired for security and data governance allows teams to innovate boldly while ensuring data provenance, compliance, and a seamless, user-centered experience,” he says.
What Skills Should PMs Develop?
Sood counts speed and iteration among the most important skills for a PM today, noting the PMs hired at Typeface know how to move fast, break down roadmaps and turn ideas into experiments with AI.
“Experience with prompt engineering, rapid prototyping, and integrating AI into broader product and marketing ecosystems really stands out,” he says. “We also want to see impact.”
The best candidates have projects where they shipped MVPs faster, automated grunt work, and drove measurable results with AI.
“Being able to walk through portfolio examples or case studies can help bring these stories to life,” he says.
Partain says companies are looking for strong PMs who deliver business impact.
“Demonstrating that you have curiosity around AI and an eagerness to learn new skills is important, but it’s less about specific AI skills and more about how you use AI to do your job better,” she says.
Instead of focusing on specific AI tools you know how to use during an interview, focus on the outcomes you have driven–what value did you create for the business, or end users? If you used AI strategically to help you get there faster or achieve a better outcome, that can give you a competitive edge.
“It’s ultimately all about the outcomes you achieved, regardless of what tools or technology you used to do it,” Partain says.