IT Operations Automation · Risk Management
Capita wanted to remove manual administrative burdens, accelerate decision making, and improve auditability and consistency in internal workflows such as training bookings and fire risk assessment review.
Reported impact
Capita reports workforce-wide time savings, lower cost-of-service, faster booking operations, better auditability, and improved assurance for safety-critical assessments.
Capita’s AI transformation here is an internally adopted operating model rather than a single packaged application. The company uses Multiverse as the external enablement platform to train employees at scale, then turns that training into applied AI projects inside day-to-day operations. Learners on the AI for Business Value, Level 4 programme identify manual bottlenecks, build targeted AI automations, and embed them in business workflows. In the example cited, Training Operations Manager Dave Collingwood created an AI-enabled booking workflow that takes inputs from the training team’s booking process and writes them into a more auditable, data-rich system, sharply reducing cycle time. Capita is extending the same pattern in Capita Fire and Rescue, where an internal AI agent reviews completed fire risk assessments to provide assurance at scale and reduce inconsistency in a safety-critical process.
Workflow data from teams such as training operations enters AI-enabled automations built by Capita employees after Multiverse-led training. The automation processes booking inputs and writes outputs into a more auditable, data-rich system used by the team. In Capita Fire and Rescue, completed fire risk assessments are fed to an internal AI agent, which returns assurance results for operational users reviewing the 6,000 assessments completed each year.
"Dave is now applying these skills to Capita Fire and Rescue, where he is developing an AI agent to provide 100% assurance on the 6,000 fire risk assessments completed annually, eliminating potential inconsistencies and enhancing public safety."
"He identified a major bottleneck in the training team’s booking process and built a solution that reduced the time required for a single booking from eight minutes to just 30 seconds."
"These efficiency gains represent a significant shift toward a data and AI-augmented workforce, where technical skills are used to remove manual administrative burdens and accelerate decision making."
"This single project saved 30 business days of manual work each year and created a more auditable, data-rich system."
"To date, Capita’s initial cohort has saved an average of 10 hours per week per learner, delivering over 400 applied AI projects and identifying 40 unique AI use cases."
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