AI ROI Fails to Outpace Spend for 57% of Enterprises, Unchanged Since 2025, Even as 93% Now Report Improved Production

New research also identifies the last-mile gap between AI models in production and the business users who need to unlock their value

SAN FRANCISCO, July 21, 2026 /PRNewswire/ -- Domino Data Lab, provider of the unified platform to build, scale, and govern AI-powered applications for the most-regulated enterprises, today released the Fifth Annual Domino Enterprise AI Report, a new global study finding that the share of enterprises whose ROI fails to outpace their investment has held at 57% since 2025, even as enterprise AI production capability continues to climb. The survey of 639 senior enterprise AI leaders found that 93% report improved production capability in 2026, up from 88% in 2025.

This is now a two-year plateau. In 2025, Domino's research first identified that enterprises were struggling to generate ROI on their AI investments. Independent research from MIT NANDA[1] reached a parallel conclusion just months later, finding that 95% of organizations running AI pilots saw no measurable profit and loss impact, with only 5% of integrated pilots generating meaningful financial value. The 2026 findings confirm the plateau has persisted, and identify a second, related finding: a last-mile gap between AI models in production and the business users who need to unlock its value.

"Getting a model into production used to be the milestone that mattered," said Thomas Robinson, COO at Domino Data Lab. "Our research shows that's not enough anymore. The real milestone is the moment a business user can act on what the model found, and for too many enterprises, that moment still isn't happening at the pace or scale of business, and with the governance regulated industry requires."

The Last-Mile Gap: Production Isn't Delivery

Deploying a model and delivering its value to the people who need it are not the same thing, and for most enterprises, a wide gap separates the two. Asked how business users actually access AI-generated insights today, the responses reveal a fragmented, largely mediated picture:

• 34% of organizations report a mix of AI access methods that varies by business unit, the single most common answer, reflecting a lack of any consistent delivery layer or standard experience across the business.
• 40% still rely on at least one mediated access method entirely: a scheduled report from a data science team, or a request submitted to an analyst who runs the analysis and returns results.

Agentic AI tops enterprise priorities, but governance lacks universal adoption

Expanding agentic AI use ranks as the top organizational priority for enterprise AI leaders in 2026, tied with upskilling business users, both 38.5%, and leads every other AI investment type, including the governance infrastructure meant to manage agents.

A closer look at agentic governance shows almost as many organizations are running agentic AI without governance as are running it with governance in place:

• 43% of organizations have agentic AI running in governed production.
• 41% are piloting (12%) or scaling (29%) agentic AI today without the governance to manage it, with those actively scaling outnumbering those merely piloting by more than two to one.

Governance maturity is the clearest dividing line between the two groups:

• Among organizations whose governance is fully keeping pace with their AI activity, 67.5% have agentic AI running in governed production.
• Among organizations where governance is only partially keeping pace, that figure drops to 17.2%, making fully governed organizations 3.9 times as likely to have reached governed agentic deployment.
• Among organizations with fully integrated AI governance, 75% report significantly improved AI delivery velocity, more than three times the rate among organizations where governance is falling behind (23%).

Financial services, banking, and insurance organizations, among the most heavily regulated in the survey, lead every vertical measured on both governance maturity and production velocity. The enterprises succeeding here built governance infrastructure first, then scaled.

A Global Pattern, With Regional Differences

The findings hold across geographies, though the size of the gaps differs by region. The 2026 survey expanded to include 148 UK respondents alongside 397 in North America and 94 in continental Europe.

ROI tells a sharp regional story. North American organizations are markedly less likely to report ROI stuck at the same level as investment or lower:

• 51.1% in North America
• 66.9% in the UK
• 67.0% in Europe

Business user access shows the opposite pattern. North American organizations are far more likely to report no direct access to AI-generated insights at all:

• 12.8% in North America
• 1.4% in the UK
• 6.4% in Europe

The agentic governance gap is most acute in Europe. European organizations report the lowest rate of fully integrated governance of the three regions, at 42.6%, compared to roughly 51% in North America and the UK. That shortfall shows up directly in agentic deployment: nearly half of European organizations are piloting or scaling agentic AI without the governance to manage it, compared to 40% in North America and 38% in the UK.

"This year's data shows two things clearly," said Shawn Rogers, CEO, BARC US. "A significant share of enterprises still can't get AI-generated insights to the people who need them, and the ones with governance built in from the start are far more likely to get agentic AI to that point safely. Put those together, and the takeaway is clear: govern early, and build the applications that turn AI into something business users can actually use."

A Platform for Building and Governing AI Applications at Scale

Domino closes AI's last-mile gap by giving data science teams the tools to build and publish governed AI-powered applications directly from their existing AI infrastructure, without a separate engineering handoff, and without retrofitting governance after agentic systems are already live. Business users get purpose-built interfaces. Data science teams retain control over the models that power applications throughout development, deployment, and monitoring. Domino also provides the governance layer agentic deployment requires, treating agents as managed, auditable entities within the same platform data science teams already use for AI development.

Methodology

The Fifth Annual Domino Enterprise AI Report is based on a global survey of 639 senior enterprise AI leaders, Director level and above, at organizations with annual revenues of $100 million or greater. Respondents were surveyed across North America, the United Kingdom, and continental Europe in April 2026, and represent financial services and insurance, life sciences, and public sector organizations. The survey was conducted independently by BARC Research on behalf of Domino Data Lab.

Additional Resources

• Download The Fifth Annual Domino Enterprise AI Report for the full findings on the AI ROI plateau and the last-mile gap holding back business value: https://domino.ai/enterprise-ai-report
• Learn more about the latest innovations in the Domino Enterprise AI Platform.
• Read the Domino Blog for customer successes and industry insights.
• Follow Domino on LinkedIn and X.

[1] The GenAI Divide: State of AI in Business 2025, July 2025. Authors: Challapally, Pease, Raskar, Chari.

About Domino Data Lab

Domino Data Lab partners with the world's most regulated enterprises as they build, scale, and govern AI-powered applications. From statistical computing to agentic AI, organizations use Domino's AI platform to develop new therapies, protect citizens, and secure financial markets — under the most complex regulatory requirements. Founded in 2013, Domino is backed by Sequoia Capital, Coatue Management, NVIDIA, Snowflake, UBS, and other leading investors. Learn more at domino.ai.

SOURCE Domino Data Lab