From identifying critical issues to recommending next steps, AI has become invaluable for delivering answers fast. But turning those answers into action is much harder. AI agents need access to enterprise data through a shared foundation that supports AI across the enterprise.
Many CIOs are discovering that traditional approaches to data readiness aren’t enough. Gartner predicts organizations will abandon 60% of AI projects unsupported by AI-ready data through 2026.
Getting your data ready for AI requires an understanding of how the business works and the ability to act within enterprise workflows. Specifically, CIOs should focus on what’s known as the four C’s: connect, control, contextualize, and converge.
- Connect: Bring data together for the full picture
In many enterprises, the data AI needs is scattered across business applications, operational systems, and cloud platforms, each with its own access model and ownership. Without the full picture, decisions made by AI are unreliable. In addition, legacy database systems can’t keep up with the speed of AI agents, delaying reports and transactions. A unified data foundation provides AI with real-time access to any system in minutes, without complex integrations or data duplication.
- Control: Give AI trusted guardrails
Only 53% of organizations can trace AI decisions back to the agent or model that made them, according to DigiCert. Many enterprises have dozens of AI projects running, and leaders can’t tell which data feeds into which model, who approved it, or whether any of it is compliant. Instead of tracking AI through committees and spreadsheets, automated governance ensures every AI instance is secure, compliant, and accountable. These guardrails ground decisions in trusted data, making each new AI initiative faster and safer than the last.
- Contextualize: Ground AI in your business
Context connects information to business logic, workflows, organizational relationships, and priorities so AI understands what the data means. For example, a service disruption affecting a customer-facing application should be handled differently than the same issue in a non-critical internal system. Business context helps AI agents take immediate action that reflects business priorities rather than generic rules.
- Converge: Turn insights into action
The final component of data-ready AI is convergence — bringing AI agents, data, and workflows together where work happens. Valuable insights are typically trapped in reports instead of driving action. Convergence embeds intelligence directly into business processes so enterprises can scale AI confidently and efficiently.
EXL faced this challenge when staffing client engagements. Information about employee skills, availability, and project demand was spread across systems, making resource decisions slow and inconsistent. By creating a real-time operational view of its workforce, EXL reduced resource allocation cycle times by 50% and prevented $1 million in revenue loss in a single year.
Lay the foundation for autonomous work
The future of enterprise AI depends on the foundation beneath it. When AI can understand the business and operate within the flow of work, it becomes part of how the organization runs rather than another tool employees simply turn to for answers.
Learn how ServiceNow’s Workflow Data Fabric can connect enterprise data, add business context, and help AI move from generating answers to taking action.