ServiceNow says AI is now central to how work gets done

ServiceNow's chief customer officer Chris Bedi says AI, agentic agents, and streamlined workflows are being scaled to speed operations, improve productivity, and enhance employee and customer experience; internal training and an AI platform reached more than 95% employee use and saved an estimated 340,000 hours last year.

Chris Bedi, ServiceNow's chief customer officer, describes his role as helping customers succeed with the platform while scaling AI, automation, and simpler operations. He told Beena Ammanath of the Deloitte AI Institute that AI is now central to how work gets done and that leaders should align on outcomes such as speed, productivity, and experience. ServiceNow has introduced AI learning days and a gamified training platform to help employees use and build with AI. The company reports more than 95% employee use of its internal AI tools and an estimated 340,000 hours saved last year. Key points: - Leaders should align C-suite and operational teams on measurable outcomes such as speed, productivity, and experience. - ServiceNow rolled out AI learning days and a gamified platform that more than 95% of employees used, which the company estimates saved about 340,000 hours last year. - Agentic AI is already handling routine work at scale at ServiceNow, including about two-thirds of accounts receivable cases and a reported 20% improvement in nondisclosure agreement processes. - The "2026 Workflow Automation Outlook" from ServiceNow and Deloitte describes a connected ecosystem where human and machine collaboration can drive end-to-end outcomes. - Organizations are planning for an agentic workforce, roles for AI enablement and people management, and tools to monitor agent performance as deployments scale. Summary: Bedi frames the shift as practical: combining AI, data, and workflows to save time and simplify work so employees can focus on higher-value tasks. Organizations are planning for wider deployment of agentic AI, setting targets for operational groups, and building capability to monitor and retrain agents as performance changes. Reported next steps include reviewing operations and establishing targets for AI adoption.