How to Maximize ROI From AI Investments in Software Engineering
AI tools are making developers faster. That speed is not landing as business value, and executives have started asking harder questions.
In this report, Gartner analysts lay out why productivity gains alone no longer differentiate an engineering organization, and how leaders can manage both sides of the ROI equation: the business outcomes AI enables and the AI spend required to get there.
Our key takeaways from the report:
- Which outcome metrics your C-suite uses to judge AI investments
- How to turn executive priorities into engineering OKRs and KPIs
- Where to redirect AI-freed capacity for the highest return
- How to govern AI spend so returns scale faster than costs
- The success measures worth tracking to prove value over time
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The metrics in this report only matter if you can connect them to what’s actually happening across your engineering organization..
Jellyfish brings AI usage and spend, engineering work, delivery data, and business priorities together in one view so you can measure where AI is being adopted, how it’s changing engineering productivity and capacity, what it costs, and whether those investments are translating into business outcomes.
Instead of asking whether AI is making developers faster, Jellyfish helps you answer the questions that matter to the business: Where is AI creating value? What is it costing us? And where should we invest next? See Jellyfish in Action
Gartner, How to Maximize ROI From AI Investments in Software Engineering, Miriam Colman, C.A. Swan, Neha Agarwal, Luke Parker, 22 April 2026. GARTNER is a trademark of Gartner, Inc. and/or its affiliates.