Drawing 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’s limiting broader adoption, and which specific capabilities are shaping adoption today and, in the long run, with actual or potential measurable outcomes.
Key findings from the research include:
Verification, governance, and explicit human accountability are mandatory in an environment where the outputs are physical—and the chance high. Where product engineers are using AI to directly inform physical designs, embedded systems, and manufacturing decisions which might be fixed at release, product failures can result in real-world risks that can’t be rolled back. Product engineers are due to this fact adopting layered AI systems with distinct trust thresholds as an alternative of general-purpose deployments.
Predictive analytics and AI-powered simulation and validation are the highest near-term investment priorities for product engineering leaders. These capabilities—chosen by a majority of survey respondents—offer clear feedback loops, allowing firms to audit performance, attain regulatory approval, and prove return on investment (ROI). Constructing gradual trust in AI tools is imperative.
Nine in ten product engineering leaders plan to extend investment in AI in the following one to 2 years, but the expansion is modest. The best proportion of respondents (45%) plan to extend investment by as much as 25%, while nearly a 3rd favor a 26% to 50% boost. And just 15% plan a much bigger step change—between 51% and 100%. The main target for product engineers is on optimization over innovation, with scalable proof points and near-term ROI the dominant approach to AI adoption, versus multi-year transformation.
Sustainability and product quality are top measurable outcomes for AI in product engineering. These outcomes, visible to customers, regulators, and investors, are prioritized over competitive metrics like time to-market and innovation—rated of medium importance—and internal operational gains like cost reduction and workforce satisfaction, at the underside. What matters most are real-world signals like defect rates and emissions profiles reasonably than internal engineering dashboards.
