The State of Engineering Management in 2026: New survey data from more than 600 engineering leaders shows how AI is driving real productivity gains

AI in engineering has officially moved beyond experimentation. What was once a question of if teams should adopt AI is now a question of how effectively they’re using it – and what that means for productivity, growth, and competitive advantage.

Jellyfish’s 2026 State of Engineering Management report reveals a clear trend: AI is no longer optional, and the organizations that are embracing it most aggressively are already pulling ahead.

The data is compelling. Nearly two-thirds (64%) of engineering teams report achieving at least a 25% increase in developer velocity and productivity using AI. That’s not marginal improvement – it’s a meaningful shift in how software gets built. And importantly, only a negligible number of respondents report being slowed down by AI.

But the story doesn’t stop at productivity gains. The real divide is emerging between organizations that have fully embraced AI and those still treating it as an experiment.

The AI Adoption Gap Is Growing

One of the most important findings in this year’s report is that AI adopters are pulling away. Teams with very high AI adoption consistently report higher job satisfaction, stronger efficiency gains, and a more optimistic outlook on company growth.

In fact, 92% of respondents at high-adoption organizations say their company’s growth outlook has improved year over year, compared to just 69% of those with low adoption.

This gap isn’t just about tools – it’s about outcomes. High-adoption teams are shipping faster, aligning better with business goals, and delivering more impact with the same resources.

And that’s exactly why engineering productivity has become a top priority across the business.

Engineering Productivity Is Now a Strategic Imperative

Engineering is no longer a back-office function. Today, it sits at the center of business strategy.

84% of SEMR respondents say engineering productivity is a top management concern, and 75% say it’s a strategic concern for the business as a whole.

That shift is reflected in how organizations view their engineering teams:

• 89% say engineering helps the business operate more efficiently
• 87% say it drives business growth
• 88% say it informs overall business strategy

The implication is clear: improving engineering productivity isn’t just about writing code faster – it’s about unlocking business value. And AI is quickly becoming the most powerful lever to do that.

AI Is Delivering Real, Measurable Impact

Across the board, engineering teams are seeing tangible benefits from AI adoption:

• 80% say AI improves team productivity
• 68% say it enhances efficiency
• 57% say it increases job satisfaction

Even more telling: 75% of respondents say AI frees up time to focus on higher-value work. That shift – from maintenance to innovation – has long been a goal for engineering leaders. AI is starting to make it a reality.

We’re also seeing changes in how engineers spend their time. Nearly half (46%) expect to spend more time on roadmap work as AI takes on routine tasks, with that number rising to 60% among high-adoption teams.

This is a fundamental transformation of the engineering role – from executing tasks to driving innovation.

Adoption Still Lags Behind Potential

Despite the strong results, most organizations are still early in their AI journey.

The most common scenario? AI tools are available – but not fully enabled. Only 10% of teams report strong enablement and high adoption across their organization.

That means the majority of companies are leaving value on the table.

Why? The challenges are both technical and cultural:

• Rising costs of AI tools (42%)
• Resistance from senior engineers (36%)
• Tool fragmentation and decision fatigue (31%)

But the most significant barrier is enablement. A small group of power users is driving most of the impact, while the rest of the organization lags behind.

This creates uneven outcomes – and limits the overall return on AI investment.

The Next Phase of AI in Engineering

As AI continues to evolve, the focus is shifting from adoption to optimization.

Leading organizations are moving beyond simply providing tools. They’re investing in:

• Clear metrics to measure AI impact
• Structured enablement and training programs
• Continuous experimentation with new tools
• Alignment between engineering output and business outcomes

Data plays a critical role here. 60% of respondents say they need better data to understand and improve engineering productivity, and organizations using Software Engineering Intelligence platforms are significantly more confident in their metrics.

In the AI era, intuition isn’t enough. Measurement is what separates progress from noise.

What Comes Next

The next 12 months will define the leaders and laggards in AI-driven engineering.

The companies that win won’t just adopt AI – they’ll integrate it deeply into how their teams work, how they measure success, and how they align engineering with business outcomes.

AI is already changing the way software is built. The question now is whether organizations can adapt fast enough to capture its full potential.

Because one thing is certain: the gap between those who do and those who don’t is only going to widen.