Nine in ten product engineering leaders plan to increase AI investment--but most favor modest growth of 1-25%, reveals new MIT Technology Review Insights report
Sustainability and product quality outrank cost reduction as top measurable AI outcomes in physical systems.
CAMBRIDGE, Mass., March 16, 2026 /PRNewswire/ -- A new report by MIT Technology Review Insights finds that product engineering leaders are scaling artificial intelligence cautiously, prioritizing verification, measurable outcomes, and first-time-right performance over rapid transformation.
The report, "Pragmatic by design: Engineering AI for the real world," is produced in partnership with L&T Technology Services (LTTS) and is based on a survey of 300 product development, engineering, and technology leaders conducted in December 2025 and January 2026. All respondents are based in the United States and represent organizations with annual revenue of $500 million or more across 16 industries. The research also incorporates in-depth interviews with senior executives and industry experts.
Speaking at the launch of the report, Amit Chadha, CEO and managing director for L&T Technology Services, observed, "AI is moving beyond experimentation, becoming an integral part of how next-gen products are designed, engineered, and validated. Our current collaboration with MIT Technology Review Insights highlights how global engineering leaders across industries are adopting AI pragmatically – prioritizing reliability, driving measurable outcomes, and ensuring 'first-time-right' performance in physical systems. We see this shift accelerating as organizations continue to embed AI in the product lifecycle for enhanced quality, sustainability, and innovation across complex engineering environments."
The key findings from the report are as follows:
- Verification, governance, and explicit human accountability are mandatory in an environment where the outputs are physical—and the risk high. Where product engineers are using AI to directly inform physical designs, embedded systems, and manufacturing decisions that are fixed at release, product failures can lead to real-world risks that cannot be rolled back. Product engineers are therefore adopting layered AI systems with distinct trust thresholds.
- Predictive analytics and AI-powered simulation lead near-term priorities. These capabilities—selected by a majority of survey respondents—offer clear feedback loops, allowing companies to audit performance, attain regulatory approval, and prove return on investment (ROI). Building gradual trust in AI tools is imperative.
- Nine in ten product engineering leaders plan to increase investment in AI in the next one to two years, but the growth is modest. The highest proportion of respondents (45%) plan to increase investment by up to 25%, while nearly a third favor a 26% to 50% boost. And just 15% plan a bigger step change—between 51% and 100%. The focus for product engineers is on optimization over innovation, with scalable proof points and near-term ROI the dominant approach to AI adoption, as opposed to 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 bottom.
- Scaling requires refocusing the engineering workforce, forging strategic partnerships with third-party experts, and embedding trust early. With 73% of leaders expecting AI to take on routine engineering work, in-house expertise is shifting toward architecture and strategic judgment. As organizations increasingly rely on third-party ecosystems for execution, ownership of core intelligence becomes a decisive source of strategic control.
"AI adoption in product engineering follows a different logic than in purely digital environments," says Laurel Ruma, global director of custom content for MIT Technology Review Insights. "Where outputs shape physical systems and cannot be rolled back, leaders are prioritizing reliability, governance, and measurable outcomes. The organizations that scale successfully will be those that embed trust early and treat governance as an enabler of performance."
To download the report, click here.
For more information please contact:
Natasha Conteh
Head of Communications
MIT Technology Review Insights
natasha.conteh@technologyreview.com
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About MIT Technology Review Insights
MIT Technology Review Insights is the custom publishing division of MIT Technology Review, the world's longest-running technology magazine, backed by the world's foremost technology institution—producing live events and research on the leading technology and business challenges of the day. Insights conducts qualitative and quantitative research and analysis in the U.S. and abroad and publishes a wide variety of content, including articles, reports, infographics, videos, and podcasts.
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