Why 80% of Enterprise AI Pilots Fail: It's the Data

Gartner has released its 2026 Magic Quadrant for Process Intelligence, naming Celonis the top-ranked vendor for the fourth consecutive year. The report marks the first time Gartner has used the "Process Intelligence" label, replacing the old "Process Mining" category.

The name change reflects a shift in what enterprise software buyers need. Process mining analyzed historical records to show how work moved through company systems. Process intelligence adds real-time monitoring and predictive analysis. It is now positioned as live operating infrastructure for AI, not just a reporting layer.

Enterprise AI budgets are growing fast. The problem is that most of the money is going to waste.

Companies spent US$37 billion on generative AI in 2025, up from US$11.5 billion the year before. Yet 80% of enterprise AI pilots never make it to full deployment. The gap between AI ambition and AI results keeps widening, and the culprit is not the AI itself.

The real bottleneck is context. AI systems are only as useful as the business data they can actually understand. Without a clear picture of how an organization's operations work, AI agents make bad decisions or no decisions at all.

Enterprise AI's Context Problem

A Celonis survey from early 2026 found that 85% of enterprises want to become an "agentic enterprise" within three years. But 45% say their biggest barrier is getting AI to understand how their business actually operates.

Gartner projects that AI agents will drive half of enterprise decisions by 2027. Organizations with successful AI initiatives invest four times more in their data and analytics foundations than those that struggle. "There is no AI without process intelligence," said Alexander Rinke, co-CEO of Celonis, in a CNBC interview last year.

What Companies Are Doing With It

Florida Crystals, one of the largest cane sugar producers in the US, expanded its Celonis contract in late 2025. The company uses process intelligence across finance, procurement, and supply chain to give AI agents the operational context needed to process invoices automatically and manage orders without human intervention.

Renault Group uses Celonis to convert raw data from its internal systems into structured models that AI can actually read. "Using object-centric process mining, we can go from having the data as it is in the original system to a well-structured model that makes sense to the AI," said Julien Nauroy, who leads process intelligence at Renault.

APAC Process Intelligence Adoption

85% of APAC organizations now see broad AI adoption as a strategic priority, and 78% are already creating new leadership roles to oversee AI deployment. The intelligent process automation market is projected to nearly double from US$13.6 billion in 2022 to US$25.9 billion by 2027.

For marketing leaders in the region, the Gartner result signals where enterprise technology priorities are heading. Competitors including SAP Signavio, IBM Process Mining, and ARIS from Software AG are all in the same space. ServiceNow also entered this year with a real-time data foundation product.

The companies getting the most from AI right now are not those with the biggest models. They are the ones that built the plumbing first.