Companies that teach AI how work actually gets done are five times more likely to report very successful results, according to a new study of Global 2000 leaders.
Everyone wants AI agents. Almost no one is ready for them.
That’s the central finding of the Process Context Study 2026, from The Hackett Group in partnership with process intelligence company ARIS. The survey of more than 200 senior business leaders at Global 2000 companies points to a widening gap between enterprise AI ambition and operational reality.
The numbers behind the readiness gap
86% of respondents say agents cannot be deployed reliably without process context.
76% expect process context to be very important or critical within three years.
22% say their organization has comprehensive, real-time visibility into end-to-end processes and workflows.
Organisations with greater experience of process context are five times more likely to report very successful AI outcomes than those with limited experience.
What is “process context”?
Process context is the business understanding that tells AI how work really happens: how workflows, rules, controls and system interactions connect across the enterprise.
“Data tells AI things about your business. Process context tells AI how your business works,” said ARIS CEO Guillaume Bacuvier.
Rick Gardner of The Hackett Group says the issue grows more urgent as AI shifts from assisting people to executing work. Agents, he argues, need to know not just what they can do, but how work should get done.
Where companies are seeing the payoff
Rather than attempting one massive transformation, the research points to targeting high-value workflows first:
Financial planning, analysis, and accounts payable/receivable
Spend analysis, contract management, and procurement
IT service desks and automated incident management
Recruitment, employee onboarding, and customer operations
Among organisations using process context, the top benefits reported were:
78%: faster execution and shorter cycle times
75%: fewer manual, repetitive tasks
71%: better process quality and consistency
69%: higher-quality decision-making
67%: stronger governance, risk management and compliance
The roadblocks: cost, data, and silos
Cost, data quality, and organisational silos account for more than 70% of the primary obstacles to establishing process context.
Governance is the widest gap of all:
Only 34% are confident they can govern AI-driven decisions effectively.
Just 18% have mature, enterprise-wide AI governance frameworks.
46% place AI accountability solely with IT, leaving business ownership unclear.
The takeaway
Bacuvier says the next phase of enterprise AI is about deploying the right agents into the right processes. “The Agentic Enterprise won’t arrive through one giant transformation,” he said. “It will emerge process by process.”
For leaders racing to deploy agents, the message is clear: the smartest model in the world still needs to understand your business before it can run it.