ServiceNow implementations follow a predictable pattern. Launch brings excitement. Month three brings confusion. Month six brings quiet regret when promised ROI hasn’t materialized, reports contradict each other, and processes that worked in demos break in production. The failure isn’t technology or effort. It’s that organizations design workflows for today’s problems without considering tomorrow’s intelligence.
Richie Adetimehin, a ServiceNow strategic advisor and AI transformation leader, believes most organizations treat ServiceNow as a ticketing tool when it should function as a strategic engine.
The difference between systems that deliver and systems that disappoint comes down to workflow design. Leading with business outcomes instead of features, building governance that scales before introducing AI, and activating intelligence through data readiness rather than rushing automation.
Lead With Business Outcomes, Not Features
The foundation of AI-ready workflows starts with clarity of purpose. Before automating anything, define the why.
“Whether it’s reducing mean time to resolve by 20% or improving employee experience or cutting operational cost, AI thrives on clean data and intent,” Adetimehin explains. “Design your business processes that capture insights, not just tickets.”
Most implementations digitize existing processes without redesigning them around measurable outcomes. Teams automate ticket routing, for example, without defining success beyond faster assignment. They build dashboards without clarity on which metrics drive decisions. AI requires data that’s structured to reveal not just what happened but why it mattered.
When every workflow maps to measurable business outcomes, ServiceNow becomes a system of intelligence. Instead of tracking incidents, capture which incidents impact operations, which resolutions prevent recurrence, and which patterns predict future problems.
Build Governance That Scales Before You Need It
AI readiness isn’t technology. It’s trust and accountability around data, and that requires governance models that define ownership, compliance, and continuous improvement before automation begins.
Most organizations rush AI features before establishing foundations. Who owns data quality? Who approves automation that changes business processes? How are AI recommendations validated before they impact operations? Without answers, AI implementations create risk faster than they create value.
Working with one enterprise, Adetimehin established a governance framework that reduced downtime by 20% within six months and improved change success scores before any AI model was introduced. “That structure later became the backbone for safe, scalable AI adoption within the enterprise,” he notes.
The framework defined clear ownership for configuration management, established change approval workflows that prevented conflicts, and created continuous improvement processes that surfaced issues before they became incidents.
Activate Intelligence Through Data Readiness
Once business processes and governance are solid, AI can deliver impact. The key is data readiness. Clean, contextual data that supports smarter decisions.
“When automation and intelligence combine, you shift from reacting to incidents to preventing them,” Adetimehin explains.
AI readiness means data structured for machine learning. Incident records need categorization consistent enough for pattern recognition. Change records need outcome data that models can learn from. Service requests need context that virtual agents can understand and act on.
The application points become clear once the data is ready. Intelligent virtual agents resolve common requests without human intervention. Proactive incident prevention identifies problems before users report them. Automated change impact analysis predicts which changes carry risk before they’re deployed.
The impact over time is enormous. Virtual agents handle tier-one requests, freeing human agents for complex issues that require judgment. Predictive models surface patterns that prevent incidents before they occur. Automation reduces manual work while improving consistency.
From Ticketing Tool to Strategic Engine
After years of helping organizations bridge the gap between business intent and platform capabilities, Adetimehin believes that designing AI-ready service management workflows isn’t about chasing trends but about building for a future where intelligence is embedded in operations rather than bolted on afterward.
Lead with business outcomes so every workflow maps to measurable impact. Govern with clarity, so AI scales safely rather than creating new risks. Empower business processes with intelligence so the platform prevents problems instead of just tracking them.
When ServiceNow works smarter, your entire enterprise does too.
Connect with Richie Adetimehin on LinkedIn for insights on ServiceNow strategy and AI transformation.
