Across bus maintenance depots from Glasgow to Plymouth, something seismic is happening. Tools that keep Britain’s buses on the road are going digital, and artificial intelligence (AI) is moving from pilot projects into everyday workshop practice. What may have seemed to be decades away, such as vehicles warning engineers before parts fail, and automated fault triage, for example, are now an operational reality for many UK operators.
The transformation within bus engineering has accelerated this year as more operators go digital, with big advances in the interconnectivity of systems. Managers now have a level of visibility they simply didn’t have before, giving them real-time insights into every facet of the engineering operation, increasingly aided by AI. Large operators and bus manufacturers are partnering with AI firms and software developers, in areas from timetable optimisation to predictive maintenance platforms.
Go-Ahead Group, for example, has accelerated electronic vehicle inspection and digital workflows across its regional bus operations, replacing paper checks with mobile apps, that feed data into maintenance systems, giving the operator predictive workflows and digital record-keeping.
In London, First Bus has equipped 150 engineers with tablets to provide real-time data from ten garages. Daily updated information is displayed through dashboard graphs and charts showing the performance of every aspect of fleet maintenance at each location. These dashboards are continually updated and show the status of everything from inspections to defects, MOTs to vehicles off-road, stock issued and mechanical lost miles.
“Digital dashboards are simply brilliant as they provide a complete management oversight of the performance of each garage,” says Chris Tong, Engineering Director at First Bus. “We can simply click on a graph and drill down into more detail to see what’s behind the figures and immediately see what’s going on.”
“We now have a proper insight into our maintenance costs, which is something we didn’t have before. We can undertake cost analytics to minimise wastage, streamline stocks, and with maintenance history, we can identify recurring defects, manage warranties, and use parts more efficiently.”
So why is this happening now? We consider three converging trends:
Electrification and new fleet complexity
The shift to hybrid and electric buses increases the need for sophisticated diagnostics and energy-management strategies. Pilot programmes show predictive models are especially valuable for electric fleets, where battery availability and charging schedules are mission critical.
Abundance of data
Buses generate huge volumes of CAN-bus, telematics and sensor data (e.g. temperature, vibration, battery metrics, fault codes). Cloud connectivity and better IoT hardware make that data transferable and usable. Operators that collect and utilise this data can move from time-based servicing to condition-based and predictive maintenance, giving them a cost and competitive advantage.
Policy and strategy support
National and local transport strategies in the UK actively encourage the use of AI and digital tools in transport, creating incentives and offering frameworks for safe, accountable deployment. That regulatory momentum makes investment less risky and more aligned with wider decarbonisation and reliability goals.
Whilst there are clear tangible benefits for operators, such as fewer roadside breakdowns, reduced unplanned downtime, lower spare-parts inventory costs, and improved fleet availability, all of which translates directly to better reliability for passengers and lower operating cost per mile, there are obstacles still to overcome and this will take time.
Challenges for the industry around the integration of AI within maintenance include:
Data quality and internal silos
Many operators struggle with fragmented systems and inconsistent data formats. The answer is not just better models but disciplined data engineering and cloud-based repositories. There are a number of specialist software providers working in the PSV sector that offer integration services to bridge legacy systems.
Skills and cultural change
Workshops are staffed by experienced engineers who rightly prioritise safety with years of hands-on know-how. Successful AI adoption needs to align engineers with data scientists, treating AI as an assistive tool, not a threat to jobs. This requires investment in upskilling and apprenticeships, so staff can trust and understand the role of AI.
Governance, safety and regulation
AI in transport must be explainable, auditable and safe. The government’s transport AI action plan and local authority pilots emphasise accountability and workforce impact assessments to ensure deployments are responsible.
Cybersecurity
Connected fleets must protect their operational data and passenger privacy. This means robust security by design and supplier risk management. Operators are increasingly including cyber clauses and security certifications in the procurement process for new technology and systems.
Imperial Engineering viewpoint
Commenting on this digital transformation and the future of bus depots, John Dwight, Imperial Engineering’s Sales Director, said:
“With the rise in the adoption of AI, we can expect maintenance to become ever more predictive and integrated with operations. As EV fleets grow, AI-driven energy management and predictive battery health will be as important as traditional engine diagnostics. There is already major demand for multi-skilled engineers, so this demand will only intensify as manufacturers and then operators roll-out more advanced technology. Ultimately, we don’t believe that AI will simply replace a skilled mechanic, but it will make them better. Faster to diagnose, equipped with the right part, and freed from routine paperwork to focus on complex repairs. For operators, the smart deployment of AI is not a tech fad but a practical lever to improve reliability, support electrification, and deliver better service to passengers.”
