By James Aspinwall — February 21, 2026
Celonis dropped their 2026 Optimisation Report earlier this month, and the headline number should worry every CTO who just signed off on an AI budget: 76% of enterprises admit their current processes are holding back their AI ambitions. This from a survey of 1,649 business leaders across companies pulling in $500 million or more in revenue.
The ambition is there. The readiness is not.
Everyone Wants to Be an Agentic Enterprise
The survey paints a picture of near-universal enthusiasm. 90% of organizations are already using or exploring multi-agent systems to automate complex decision-making. 85% want to become a fully “agentic enterprise” within three years. And 89% of leaders view AI as their single biggest competitive opportunity.
These are not cautious numbers. This is an industry sprinting toward a finish line it hasn’t built the track for.
The Two Barriers Nobody Wants to Talk About
The top obstacles aren’t budget or technology. They’re human and organizational:
- Internal expertise gaps (47%) — Nearly half of organizations don’t have the people who understand both AI and their business well enough to bridge the gap.
- AI can’t understand business context (45%) — The models are powerful, but they’re flying blind. They don’t know how your procurement workflow actually runs, why your finance team approves invoices the way they do, or where the bottlenecks hide in your supply chain.
And here’s the deeper problem: 58% of process and operations leaders say their departments still don’t operate seamlessly together. You can’t build an AI agent that orchestrates across departments when those departments can’t orchestrate with each other.
The ROI Trap
This is the number that should keep executives up at night: 82% of decision-makers believe AI will fail to deliver ROI if it doesn’t understand how the business actually runs.
Think about what that means. Four out of five leaders surveyed are essentially saying: we know this won’t work the way we’re doing it. They’re investing anyway, hoping the operational readiness catches up to the spending.
It won’t. Not automatically.
Process Intelligence: The Missing Layer
Celonis — being Celonis — naturally argues that Process Intelligence is the answer. Their President Carsten Thoma puts it plainly: “For AI to truly work for the enterprise, it needs more than just data. It needs operational context.”
Strip away the vendor pitch and the core insight holds up. AI agents that execute isolated tasks are table stakes. The hard part is building agents that understand end-to-end workflows: how a customer order flows through sales, inventory, fulfillment, and finance. How an exception in one step cascades through the rest. How to optimize across departments rather than within silos.
This is the gap between a chatbot that answers questions and an agent that actually runs part of your business.
What This Means for Mid-Market Companies
The survey focused on enterprises with $500M+ revenue — 80% of respondents sat in the $2-10 billion range — but the implications hit harder for mid-market companies.
If massive enterprises with dedicated AI teams and eight-figure budgets are struggling with operational readiness, smaller organizations face the same barriers with fewer resources. But they also have an advantage: less legacy process debt, fewer departmental silos, and the ability to move faster when they get the approach right.
The playbook for mid-market AI adoption isn’t “buy what the Fortune 500 buys, but smaller.” It’s:
- Map your processes before you automate them. You can’t give an AI agent context you don’t have yourself.
- Start with end-to-end visibility in one value chain. Pick your most critical workflow — order-to-cash, procure-to-pay — and understand it completely before adding AI.
- Hire for the intersection. The 47% expertise gap isn’t about finding AI engineers. It’s about finding people who understand both the technology and how your business operates.
- Build context layers, not just model integrations. The AI needs to know what “normal” looks like in your business before it can improve anything.
The Bottom Line
The Celonis report confirms what practitioners have been sensing for a year: enterprise AI has an execution problem, not an ambition problem. The technology is ready. The organizations are not.
The companies that win won’t be the ones that deploy the most agents. They’ll be the ones whose agents actually understand what they’re doing — because someone took the time to make the business legible to the machines.
Source: Celonis 2026 Optimisation Report — Survey of 1,649 business leaders across APAC, DACH, Europe, India, and the US. Industries: Manufacturing (18%), Banking (15%), Automotive (13%), Tech/Software (11%). Conducted June-July 2025.