IdeaPlan: what 1,200 product managers are actually doing with AI in 2026

IdeaPlan published this survey-based report in March 2026, drawing on responses from over 1,200 product managers. The article’s value is in its specificity: rather than describing what PMs should do with AI, it measures what they are actually doing, in what proportions, and with what results.

The headline figure is adoption depth. Seventy-three percent of respondents report using AI tools on a weekly or daily basis, a marked increase from scattered experimentation in 2024. The article does not treat this as a story about enthusiasm; it tracks how the work has changed as a result.

By task, adoption breaks down as follows. PRD writing and editing leads at 68%, reflecting that document-generation tasks were the first to become routine. Customer feedback analysis sits at 54%, which the article attributes to the clear input-output structure of the task — raw comments in, synthesized themes out. Competitive research is at 47%. User story and acceptance criteria generation is at 41%. Data analysis and SQL generation is at 38%. Roadmap narrative writing is at 31%. At the far end, only 14% of respondents are using AI for LLM evaluations on their own AI features, which the article flags as a gap given how central evaluation is to shipping reliable AI products.

The time savings numbers are consistent across use cases: PMs using AI weekly report reclaiming five to eight hours per week, mostly on documentation and research tasks. The article makes a specific point about where that time goes. Respondents redirect it toward customer discovery and strategic work — not meetings and administrative tasks. That redistribution matters because it aligns with the direction organizations are pushing PM roles.

Career implications are also measured. Sixty-one percent of PM job postings in 2026 mention AI experience, up from 12% in 2024. The salary premium for AI-fluent PMs is 15 to 20% across levels, with directors earning the highest premium at around 20%. The article frames AI fluency not as a differentiator but as an emerging baseline expectation.

The most practically useful observation is about how adoption has matured. The pattern that separates high-performing PM teams from average ones is not which tools they use but how they use them. Teams that have moved from ad hoc prompting to structured workflows with templatized prompt chains report more consistent results and shorter ramp-up time for new hires. Fluency, in this reading, means process design as much as tool familiarity.

Useful for PMs making the case internally for AI investment, or for leaders benchmarking their team’s adoption against peers.