Skills
About
I help leaders translate ServiceNow AI (Now Assist and agentic workflows in ITSM) and broader GenAI investments into measurable business outcomes without losing control, security, or compliance. My focus is simple: prioritize the right use cases, define the outcomes that matter, assign clear ownership and decision rights, and deliver quick wins that scale. I lead AI-enabled service transformation across complex enterprises, aligning executive priorities (MTTR reduction, cost-to-serve, productivity, risk reduction, employee experience) to a practical portfolio of AI use cases and KPIs. The work goes beyond “go-live”: I help build the operating model—intake and prioritization, value tracking, governance, and continuous optimization so AI adoption is sustainable and trusted. In ServiceNow, I drive end-to-end modernization across ITSM, Employee Center, CMDB/CSDM, EA (formerly APM), CSM, and HRSD connecting platform strategy to real operational outcomes. For ITSM specifically, I help leaders deploy Now Assist and agentic workflows with speed and discipline: clear guardrails, human-in-the-loop controls where needed, auditable workflows, and a security-first approach that still enables rapid iteration. A consistent thread in my work is strengthening the AI foundation: improving data quality, standardizing key service and CI data, reducing noise and bias, and ensuring the right telemetry exists to measure performance and risk. The goal is reliable automation and decision support grounded in trusted data, transparent logic, and measurable impact. In parallel, I serve as Service Design Lead for Saama’s Clinical Analytics regulatory products (PAa, QOSa, CSRa), designing AI-enabled services and governance that meet regulatory expectations while accelerating time-to-insight and decision-making for clinical and safety teams. I also invest in capability-building, mentoring ServiceNow professionals into high-impact roles so organizations develop lasting transformation strength, not dependency. If you’re looking for a partner to de-risk AI, accelerate value realization in ServiceNow AI, and build a governed path from use case to outcome at enterprise scale, I’m open to connecting.
Richie Adetimehin
Published content
expert panel
No longer confined to analytics dashboards and recommendation engines, AI systems are now initiating transactions, approving workflows, flagging anomalies and even orchestrating other software agents. With this sudden increase in autonomy, business leaders are left asking: Where should humans step back—and where must they stay firmly in control? According to a 2025 McKinsey survey on the state of AI, nearly nine out of 10 organizations now report using AI in at least one business function, yet most are still early in scaling these technologies and many lack robust governance and risk controls. As artificial intelligence advances from advisory tools to agentic systems capable of multi-step planning and execution, the leadership challenge shifts: defining not just what AI can do, but what it should do. Members of the Senior Executive AI Think Tank—a curated group of experts in machine learning, generative AI and enterprise-scale transformation—argue that the real issue isn’t capability but accountability. Across their industry expertise, they all converge on one theme: The boundary between human judgment and machine decision-making must be dynamic, evidence-based and anchored in responsibility. Here is how they recommend drawing—and redrawing—that line.