A new IBM Institute for Business Value research report argues that many enterprise operating models have become obstacles to AI value. Based on a survey of 2,000 senior technology and business leaders across 16 countries and 17 industries, the report finds that 82% of C-suite executives say functional silos block value, while 55% of organizations are already developing or deploying an agentic AI operating model.
IBM describes the emerging alternative as the “interconnected enterprise”, an AI-first operating model organized around agentic workflows instead of departments.
In this model, AI agents handle routine execution and operational decisions, while human experts focus on judgment, oversight, ethics, exceptions, and trade-offs.
Why It Matters: The report suggests that agentic AI will not deliver its full value through isolated automation projects. To scale autonomous operations, enterprises may need to redesign how work flows across business functions backed by interoperable data and real-time orchestration.
- Functional Silos Are Becoming a Structural Barrier to AI Value: IBM’s research places fragmentation as the primary issue holding back autonomous execution. While traditional operating models were designed around specialized departments, AI agents increasingly need to operate across functions to complete end-to-end workflows. That shift is already underway as 75% of executives say they believe AI will significantly redefine global service delivery models by the end of 2026, while 61% are actively dismantling boundaries to build a more unified digital culture.
- The Workflow Is Becoming the New Unit of Enterprise Performance: The research suggests that agentic AI could change how enterprise work is measured. 56% of executives expect next-generation delivery models to be priced around measurable outcomes, such as customer impact and risk exposure, instead of transactions or headcount. That reframes workflows as business-value engines where AI agents execute across systems and partners, while human experts focus on oversight and trade-offs that directly affect enterprise outcomes.
- Data Interoperability Is the Foundation for Autonomous AI Decision-Making: The report argues that autonomous AI depends on data that is accessible but also consistent and connected across the enterprise. IBM says 77% of leaders are investing in data quality and orchestration, while 59% now see interoperable AI capabilities across business functions as a priority. These investments are becoming necessary safeguards to ensure agentic systems make decisions based on complete and reliable information, not fragmented data.
- Digital Twins as the Control Layer for Agentic Operations: Enterprise digital twins are described as real-time models that mirror operations and help govern autonomous workflows. According to the report, 82% of executives say a unified digital twin control plane is essential for autonomous operations, and 72% expect real-time simulation and scenario analysis to become core features of next-generation service delivery.
- Governance May Determine Whether Agentic AI Scales: IBM finds that autonomous workflow adoption depends on six capabilities, including change management readiness, AI governance, data governance, real-time shared data integration, system interoperability, and financial integration. When all six mature together, organizations are 5.4 times more likely to adopt autonomous workflows. The report identifies change management and AI governance as especially important, stating that the shift is as much organizational as technological.
Go Deeper -> The blueprint for agentic operations – IBM
Trusted insights for technology leaders
Our readers are CIOs, CTOs, and senior IT executives who rely on The National CIO Review for smart, curated takes on the trends shaping the enterprise, from GenAI to cybersecurity and beyond.
Subscribe to our 4x a week newsletter to keep up with the insights that matter.
