Enterprise AI delivers its greatest value when organizations break down barriers between teams, systems, and data. This National CIO Review article explores why collaboration is becoming a critical factor in scaling AI initiatives and achieving stronger business outcomes. Connect with Capgemini to discuss how these trends may influence your organization's technology strategy.
Why are traditional functional silos limiting enterprise AI value?
Traditional operating models were built around specialized departments, but agentic AI needs to work across functions to complete end-to-end workflows. According to IBM Institute for Business Value research, 82% of C-suite executives say functional silos block AI value.
These silos create fragmentation in processes, data, and decision-making. As AI agents start to handle more routine execution and operational decisions, they need access to interoperable data and cross-functional workflows, not isolated systems.
The shift is already underway: 61% of organizations are actively dismantling boundaries to build a more unified digital culture, and 75% of executives believe AI will significantly redefine global service delivery models by the end of 2026. For CIOs and CTOs, this means AI strategy can’t be limited to isolated automation projects; it requires rethinking how work flows across the enterprise.
What is an interconnected, AI-first operating model?
IBM describes the emerging alternative to siloed organizations 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 many operational decisions across systems and functions.
- Human experts focus on judgment, oversight, ethics, exceptions, and trade-offs.
This approach reframes the workflow as the new unit of enterprise performance. In fact, 56% of executives expect next-generation delivery models to be priced around measurable outcomes—such as customer impact and risk exposure—rather than transactions or headcount.
For technology leaders, this means reimagining operating models so that AI agents can execute across business functions, while people concentrate on higher-value decisions that directly influence business outcomes.
What foundations are needed to scale agentic AI and autonomous workflows?
The research highlights several foundations that determine whether agentic AI can scale beyond pilots:
- Data interoperability and quality: Autonomous AI depends on data that is accessible, consistent, and connected across the enterprise. 77% of leaders are investing in data quality and orchestration, and 59% now see interoperable AI capabilities across business functions as a priority.
- Digital twins as a control layer: Enterprise digital twins act as real-time models that mirror operations and help govern autonomous workflows. 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 and change management: IBM identifies six capabilities as critical—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.
For CIOs and senior IT leaders, this points to an agenda that goes beyond technology selection. It requires coordinated investment in data, architecture, governance, and organizational readiness to truly reshape how work gets done with AI.
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