AI Adoption Is Widespread, but Only One-Third of Enterprises Are Putting Agents to Work

Enterprise AI adoption has moved rapidly into the mainstream, but widespread experimentation is not yet translating into equally widespread improvements in project delivery.

New research from Tempo Software finds that 91% of organizations surveyed are piloting or actively using AI in project delivery. Yet only 33% have progressed to delegating real delivery work—including coding, documentation and quality assurance—to AI agents.

The 2026 State of AI in Portfolio Management report, based on a survey of 300 senior project, portfolio and PMO leaders, suggests that the more meaningful performance divide is emerging not between organizations that use AI and those that do not, but between companies experimenting with the technology and those putting agents into production.

Organizations with production agents reported that 71% of projects finish within six months, compared with 55% among organizations without deployed agents. The findings point to a shift in the enterprise AI conversation from adoption toward operational deployment, governance and measurable business outcomes.

AI Adoption Is Outpacing Operational Deployment

Although AI has become a common part of project delivery environments, its use remains concentrated in reporting and support functions rather than higher-level planning and decision-making.

According to Tempo, 47% of respondents actively use AI for reports and dashboards. By comparison, 26% use AI to prioritize or reprioritize work, while only 20% use it for scenario planning and modeling.

This gap suggests that many organizations remain in an intermediate stage of AI maturity: the technology is available to teams and embedded in workflows, but enterprises have not yet delegated significant operational responsibilities to it.

“The results show that using AI is not, by itself, an advantage,” said Vic Chynoweth, CEO of Tempo Software. “What matters is whether organizations can put agents to work, and govern that work alongside their people, investments and strategic priorities.”

For enterprise technology leaders, that distinction is becoming increasingly important. As AI capabilities move deeper into project management, ERP and other operational environments, organizations need to determine not only where AI can assist employees, but also which activities can safely be delegated to autonomous or semi-autonomous agents.

Production Agents Are Associated With Faster Project Delivery

Tempo’s findings indicate a notable performance difference among organizations that have moved agents into production.

At companies with production agents, 71% of projects finish within six months. Among organizations without deployed agents, the figure is 55%, even when teams are managing more than 50 projects simultaneously.

The research does not establish that AI agents alone cause faster delivery. However, it does indicate that organizations with more mature AI deployments are reporting different delivery outcomes from those still primarily using AI for pilots and productivity tools.

An even smaller group demonstrates how limited advanced AI maturity remains.

Only 18 of the 300 organizations surveyed have both production agents and all seven operational AI capabilities examined in the study. Those capabilities span resource management, planning and scenario modeling, forecasting, reports and dashboards, risk detection, work prioritization and autonomous project management.

For this advanced group, the typical project runs for approximately three and a half months, compared with nearly six months across the full sample.

Measuring AI ROI Remains a Major Enterprise Challenge

As organizations move from individual AI tools toward agentic workflows, measurement is emerging as a significant challenge.

Tempo found that 42% of leaders cannot tie AI spending to ROI. The problem extends beyond financial measurement to understanding exactly how work is being performed.

Some 39% of leaders cannot distinguish AI-produced work from human work in their current tools. Even among teams that have agents in production, 22% still cannot see AI ROI.

This creates a new governance requirement for enterprises. When AI primarily assists an employee with an isolated task, attribution may be relatively straightforward. As agents begin performing work across projects and systems, organizations need greater visibility into which activities were completed by people, which were performed by AI and how both contributed to business outcomes.

For ERP and portfolio management environments in particular, that attribution becomes relevant to broader questions around accountability, approvals, resource allocation and auditability.

Regional and Industry Differences Reveal an Uneven Transition

The move toward production AI agents is also progressing at different speeds across regions.

Tempo found that 40% of North American organizations surveyed have production agents, compared with 21% in Western Europe.

Differences between industries are even more pronounced. Software companies lead the organizations surveyed, with 47% having production agents, while the corresponding figure among construction and engineering companies is just 5%.

The contrast illustrates how enterprise AI maturity can depend heavily on operating environment. Digitally native organizations may have workflows and technology architectures that make it easier to introduce agentic capabilities, while industries combining physical operations, complex project structures and multiple enterprise systems can face additional implementation and governance requirements.

Tempo also notes that its sample excludes some highly regulated industries where security and compliance requirements have slowed AI deployment.

Enterprises Want AI to Move Beyond Alerts

The research also indicates that portfolio leaders increasingly expect AI to progress from describing what is happening to recommending what organizations should do next.

Only 30% of leaders surveyed have a tool that effectively connects strategic planning with work execution.

At the same time, according to the report announcement, 83% expect an AI-ready portfolio platform to recommend how to correct strategic drift rather than simply flagging the problem.

That expectation reflects a broader transition taking place across enterprise technology. Reporting systems have traditionally helped organizations understand past and current performance. AI introduces the possibility of systems continuously analyzing execution against strategic objectives and recommending—or eventually carrying out—corrective actions.

Tempo describes this emerging operating model as Intelligent Portfolio Orchestration, centered on the continuous coordination of strategy, investment and execution across a workforce comprising both people and AI agents.

The Next Phase of Enterprise AI Will Be About Governance

The 2026 State of AI in Portfolio Management study was conducted by independent market research firm Potloc in June 2026. It surveyed 300 senior project, portfolio and PMO leaders working at enterprises with between 200 and 4,999 employees and annual revenues ranging from $100 million to $5 billion.

Sixty percent of respondents were based in North America and 40% in Western Europe, and all respondents personally lead or directly influence how portfolio work is planned and delivered.

The findings suggest that measuring enterprise AI maturity simply by adoption is becoming less meaningful. With 91% of organizations surveyed already piloting or actively using AI, access to the technology is no longer the primary differentiator.

Instead, the next stage will be defined by how enterprises move AI into real operational workflows while maintaining visibility and control. That means establishing clear attribution between human and AI work, connecting AI investment to measurable outcomes, and determining how agents should operate alongside existing enterprise systems and governance structures.

For ERP, portfolio management and other core enterprise platforms, this represents an important evolution. The competitive question is increasingly shifting from whether an organization uses AI to how effectively it can govern AI as part of the operating model itself.