A gen-AI-powered assistant transformed how a leading roadside assistance provider diagnosed roadside breakdowns, reducing unnecessary callouts, improving fleet efficiency, and enhancing service precision.
Context:
As vehicle technology evolved, manual triage processes came under pressure. Sub-optimal call centre diagnostic data capture often led to misdiagnosed faults, unnecessary callouts, and excessive costs. A more intelligent and scalable approach was needed to improve diagnostic accuracy at the first point of contact.
What we delivered:
Strategic assessment of diagnostic data quality and architecture readiness
Design and testing of an AI-powered Next Best Question (NBQ) diagnostic assistant (agentic bot for call handlers)
Proof of value established through interface testing and dataset performance
Identified operational improvements up to £30m in annual savings by reducing additional callouts and improving resource deployment
Outcome:
The initiative evidenced:
Improved triage accuracy and first-time fault identification
Reduction in unnecessary roadside callouts and tows
Optimised fleet utilisation and resource planning
Strengthened customer experience through faster, more targeted support
Testimonial:
“We focused on creating something that didn’t just demonstrate technical promise but delivered measurable value early. By combining cutting-edge gen-AI capability with deep operational knowledge, we built a solution grounded in the real world and scalable across the business.”
– Dan Blagojevic PhD, Chief Data Scientist, Optima Partners
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