New DORA Report Claims Strong Engineering Foundations Drive AI Return on Investment

New DORA Report Claims Strong Engineering Foundations Drive AI Return on Investment

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New DORA Report Claims Strong Engineering Foundations Drive AI Return on Investment

May 11, 2026

5 min read

by Matt Saunders

Write for InfoQ

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Google Cloud's DORA

team has published an updated report, the ROI of AI-Assisted Software Development (2026.01)

offering a practical framework for calculating the financial return on AI investment in software development. The report sets out a structured model for translating engineering metrics into business value. It is a followup to the 2025 DORA State of AI-Assisted Software Development report

and was authored by a team from Google Cloud's DORA group and its delta innovation practice.

The report's central argument is that AI acts as an amplifier. "The greatest returns on AI investment come not from the tools themselves but from a strategic focus on the underlying organizational system: the quality of the internal platform, the clarity of workflows, and the alignment of teams," writes Nathen Harvey, the DORA team lead at Google Cloud. "Without this foundation, AI creates localized pockets of productivity that are often lost in downstream chaos." This framing directly echoes the 2025 DORA research, which found that AI magnifies the strengths of high-performing organisations and the dysfunctions of struggling ones.

A key idea in the report is the J-Curve of value realisation. The authors argue that most organisations will experience a temporary productivity dip before achieving long-term gains from AI adoption. This dip has three main causes: the learning curve as teams adapt their workflows, the verification tax imposed by reviewing AI-generated code, and the need to adapt downstream processes such as testing and change approval to handle increased code volumes. The report describes this period as "the tuition cost of transformation" and argues that leaders who misread it as failure risk pulling funding during the dip and losing the eventual return.

The report's methodology for computing ROI is built on a value model drawn from Google Cloud's Value Realisation practice

. Value flows from AI adoption through a set of seven capabilities; including a quality internal platform, version control practices, and AI-accessible internal data. This flows into improved DORA delivery metrics, then into non-financial outcomes such as developer experience and user experience, and finally into financial outcomes: cost savings and revenue growth. The ROI is calculated using the standard formula: value minus investment, divided by investment. Using illustrative figures for a 500-person engineering organisation with a fully loaded salary of $176,000 per head, the report models a first-year return of approximately $11.6 million against an investment of $8.4 million, yielding a 39% ROI and a payback period of around eight months.

The report is careful not to overstate these figures. "Treat these calculations as a high-uncertainty estimate meant to spark a conversation, rather than a rigid mathematical formula," the authors write. They note that inference costs for AI models have fallen dramatically; dropping by a factor of 280 between November 2022 and October 2024 according to the Stanford Artificial Intelligence Index. This means that the true financial burden of adoption has shifted to governance: managing the verification tax, adjusting workflows, and upskilling staff.

"We don't measure AI by the code it writes but by the bottlenecks it clears." - DORA team, Google Cloud --

DORA ROI of AI-Assisted Software Development report

The report also highlights the instability of AI adoption and the need for a structured approach to managing the verification tax and adjusting workflows. The authors argue that leaders should focus on building a strong engineering foundation and investing in the skills and processes needed to support AI adoption.

In conclusion, the report provides a valuable framework for calculating the financial return on AI investment in software development and highlights the importance of building a strong engineering foundation for achieving long-term gains from AI adoption.