Enterprise benchmarks in AI and agentic orchestration 2026
How ownership, integration and governance are shaping the next phase of AI adoption
Contents
About this research
This report draws on a survey of 300 senior enterprise technology, risk and AI leaders across the United States, Canada and the United Kingdom. Respondents span more than a dozen industries, including software, retail, manufacturing and finance. Nearly half hold C-suite titles, and 69.7% represent organizations above $1 billion in annual revenue, with 30.4% above $5 billion. Every respondent has hands-on involvement in designing, implementing, deploying, governing or evaluating AI initiatives.
Executive snapshot
AI has become central to enterprise digital transformation. Adoption is widespread, but many still lack the connective layer to run it safely at scale.
AI is everywhere, but maturity is not universal
Most organizations have moved past experimentation. Far fewer have reached full maturity.
Where enterprises sit on the AI maturity curve
Scaling means having multiple AI use cases fully deployed across teams; mature means AI is embedded enterprise-wide across core processes.
Between “scaling” and “mature” is where AI adoption gets harder. It’s also where pressures like integration, ownership and governance start to surface.
Agentic AI is accelerating
Agentic AI is moving fast, although it’s earlier in its maturity curve than overall AI. Deployment grew at 89.4% of organizations in the past 12 months.
54% describe their agentic AI adoption as scaling or mature, well behind the 77.6% who say the same of their AI adoption overall.
The balance between traditional automation and agentic AI is shifting
Today, 45% run mostly traditional automation with some agentic AI, and only 4.7% are almost entirely agentic. Within two years, enterprises expect that to shift substantially: for the share running mostly or entirely agentic AI to shift from 20.4% today to 30% in two years.
Agentic AI is moving fast enough that gaps in ownership, integration, governance and trust will not stay theoretical for long.
Nobody fully owns agentic AI in the enterprise
No single function has taken full ownership of agentic AI:
When responsibility is split, integration and governance decisions inherit that same fragmentation.
The approach to building agentic AI is just as divided:
Neither decision, who owns it or how it’s built, has converged on a default. Most organizations are running a mix of bought and custom agents, with no single function accountable for how they work together.
Legacy and siloed systems are the load-bearing walls
While organizations know what they want to do with AI, they’re running into major barriers. With no single team accountable end to end, the standout technical challenge — connecting AI to the systems that already run the business — has gone unsolved.
Top barriers to scaling AI, ranked
When asked why AI initiatives stall before reaching everyday use, three reasons stood above the rest:
Proving ROI is still hard, even where adoption is strong
Even where AI is technically connected well enough to run, proving what it’s worth is a separate, unresolved problem.
Confidence in measuring the ROI of AI is split almost exactly down the middle:
The most common ways organizations measure ROI?
There’s real doubt about whether AI spend is worth it.
As far as why some think they’re overspending, it’s not price or vendor choice. They point to AI usage scaling faster than cost controls, followed by limited cost visibility.
Governance hasn’t kept pace with deployment
AI governance policies are common, but enforcement is where it gets tricky.
58.3% have a formal AI governance policy that is consistently enforced; 31.7% have one, but it’s inconsistently enforced.
What ungoverned AI agents have cost organizations
CIOs report more types of ungoverned AI consequences than any other role, and VPs/Directors of IT most commonly point to unexpected or excessive cloud costs.
Just 18.7% of all respondents (not just heavy agentic AI users) report no consequences at all.
By the seat: How this differs by role
Trust breaks down where stakes are highest
Governance failures explain why trust isn’t uniform.
Trust in AI agents to operate autonomously
Observability and stronger guardrails are nearly tied as the top two changes that would increase willingness to deploy AI agents autonomously on business-critical processes, cited by 49.3% and 48.3%, respectively.
Enterprise leaders also note observability and the ability to integrate with legacy systems as the two factors that would most increase their confidence to expand agentic AI use overall, cited by 62% and 58% respectively. Both point toward true orchestration as the way forward, before adding more AI.
By the seat: How this differs by role
The next milestone: Orchestration
Self-reported AI readiness looks strong on its face, but the specific capabilities orchestration depends on are still missing at roughly half of organizations.
What would increase confidence to expand agentic AI use?
What sets the most prepared organizations apart
Most enterprises have the ambition, adoption and early results to show AI is working. What’s still missing is the layer that connects AI agents to the systems, guardrails and audit trails that make autonomy safe to expand.
They didn’t wait for autonomy to outpace their infrastructure. Instead, they’re treating orchestration, integration, ownership and governance as prerequisites for scaling AI.
Explore what agentic AI readiness looks like in practice.
All data represented in this report was gathered by Redwood Software and Leger Opinion, a third party whose panel was used in whole for data collection.