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 USA – English India – English
Publish Date: 2026-03-16 09:00:00
Source Domain: www.prnewswire.com
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…
