About the Role
MIT Technology Review Insights
2026-04-14 14:00:00
www.technologyreview.com
This report, which is based on a survey of 300 engineering and technology executives, finds that software engineering teams are seeing the potential in agentic AI and are beginning to put it to use, but so far in a mainly limited fashion. Their ambitions for it are high, but most realize it will take time and effort to reduce the barriers to its full diffusion in software operations. As with DevOps and agile, reaping the full benefits of agentic AI in engineering will require sometimes difficult organizational and process change to accompany technology adoption. But the gains to be won in speed, efficiency, and quality promise to make any such pain well worthwhile.
Key findings include the following:
Adoption momentum is building. While half of organizations deem agentic AI a top investment priority for software engineering today, it will be a leading investment for over four-fifths in two years. That spending is driving accelerated adoption. Agentic AI is in (mostly limited) use by 51% of software teams today, and 45% have plans to adopt it within the next 12 months.
Early gains will be incremental. It will take time for software teams’ investments in agentic AI to start bearing fruit. Over the next two years, most expect the improvements from agent use to be slight (14%) or at best moderate (52%). But around one-third (32%) have higher expectations, and 9% think the improvements will be game changing.
Agents will accelerate time-to-market. The chief gains from agentic AI use over that two-year time frame will come from greater speed. Nearly all respondents (98%) expect their teams’ delivery of software projects from pilot to production to accelerate, with the anticipated increase in speed averaging 37% across the group.
The goal for most is full agentic lifecycle management. Teams’ ambitions for scaling agentic AI are high. Most aim for AI agents to be managing the product development and software development lifecycles (PDLC and SDLC) end to end relatively quickly. At 41% of organizations, teams aim to achieve this for most or all products in 18 months. That figure will rise to 72% two years from now, if expectations are met.
Compute costs and integration pose key early challenges. For all survey respondents—but especially in early-adopter verticals such as media and entertainment and technology hardware—integrating agents with existing applications and the cost of computing resources are the main challenges they face with agentic AI in software engineering. The experts we interviewed, meanwhile, emphasize the bigger change management difficulties teams will face in changing workflows.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
The Ledger Nano X Crypto Wallet Signer takes security even further by keeping your private keys isolated from any connected system. With 8,725 global ratings and a 4.3 out of 5 star rating, users value its ease of use and powerful security features. 400+ bought in the past month shows strong demand. Available now with a 20% discount for $79.00.
Support Techcratic
Independent. Uninfluenced. Performance-focused.
Whether it’s Tech Flix, 30 Second Tech, App Zone, or deep
Techcratic analysis, every piece is built for readers who want clarity over noise.
If you value that, you can support Techcratic below. Thank you.
Bitcoin Support
Scan the QR code with your crypto wallet app
or copy and paste the below address:
bc1qlszw7elx2qahjwvaryh0tkgg8y68enw30gpvge
Please read the Privacy and Security Disclaimer.
Disclaimer: As an Amazon Associate, Techcratic may earn from qualifying purchases.