Based on data from a survey of 300 respondents and in-depth interviews with senior technology executives and other experts, this report examines how product engineering teams are scaling AI, what is limiting broader adoption, and what specific capabilities are shaping adoption today and in the future, with actual or potential measurable results.
Key findings of the research include:
Validation, governance, and clear human accountability are imperative in environments where outcomes are physical—and risk is high. While product engineers are using AI to directly inform physical designs, embedded systems, and manufacturing decisions that are made at release, product failure can lead to real-world risks that cannot be undone. So product engineers are adopting layered AI systems rather than general-purpose deployments that have distinct confidence limits.
Predictive analytics and AI-powered simulation and validation are near-term investment priorities for product engineering leaders. Selected by the majority of survey respondents, these capabilities offer clear feedback loops, allowing companies to audit performance, obtain regulatory approval and prove return on investment (ROI). Gradually building trust in AI tools is inevitable.
Nine out of ten product engineering leaders plan to increase investment in AI over the next one to two years, but growth is modest. The highest proportion of respondents (45%) intend to increase investment by 25%, while almost a third favor an increase of 26% to 50%. And only 15% plan a major change—between 51% and 100%. Product engineers are focused on optimization over innovation, with scalable proof points and near-term ROI being the dominant approach to AI adoption, as opposed to a multi-year transformation.
Sustainability and product quality are the most measurable outcomes for AI in product engineering. These results, visible to consumers, regulators, and investors, are prioritized over competitive metrics such as time-to-market and innovation—ranked medium importance—and internal operational benefits such as cost reduction and workforce satisfaction, at the bottom. It’s real-world signals like defect rates and emissions profiles that matter most, rather than internal engineering dashboards.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by the MIT Technology Review editorial staff. It was researched, designed and written by human writers, editors, analysts and illustrators. This includes survey writing and data collection for the survey. AI tools that might have been used were limited to secondary production processes that underwent thorough human review.