As UK councils face mounting road maintenance costs and shrinking budgets, many are asking: is AI-powered road inspection worth the investment?
Decision makers need to understand, quite rightly, the costs versus the benefits of this technology whose use is becoming increasingly widespread. Here we’ll look at a data-driven comparison of AI vs traditional road inspection methods, including costs per kilometre, ROI, and real-world savings seen by councils who are early adopters of these solutions.
The Landscape
Councils today face enormous road maintenance backlogs and tight budgets. In 2023, Parliament warned that without better upkeep and monitoring strategies the local road repair backlog (already over £15 billion) will worsen. Historically, councils have relied on manual surveys and inspectors on foot or in vehicles – methods that can be slow, labour intensive and sometimes unnecessarily costly.
For perspective, a US analysis (cited by Carahsoft) found that manual pavement inspections can cost $100–200 per mile (roughly £80 - 160 per km) when accounting for crews and vehicles. That’s an astonishing outlay. These traditional methods are not only costly and time-consuming but they also often limited in scope.
With road maintenance costs rising and budgets under pressure, UK councils are increasingly turning to AI-powered road inspections to cut costs and improve results.
Traditional Inspections vs AI-Powered Monitoring
Traditional inspections involve scheduled surveys (often by specialist vehicles or foot patrols) that record defects by eye. These methods are time-consuming and typically cover limited mileage per day. They are also reactive: crews only repair potholes after they appear.
In contrast, AI-driven monitoring uses sensors and machine learning to proactively scan for faults. Dashcams on ordinary highway vans or even bin lorries continuously collect images, and AI software automatically flags potholes, cracks, faded markings or missing signs. The result is far faster data collection. Recent reports illustrate that AI can inspect roads roughly 4× faster than manual checks and provide a live, up-to-the-minute digital inventory of defects.
One crucial factor that’s often raised in discussions?
“Does this technology replace inspectors?”
This is a critical consideration for all stakeholders in highway maintenance, including decision-makers, budget holders, and operational leads, who must balance safety, efficiency, and cost-effectiveness.
The answer? AI systems are designed to augment, not replace, human inspectors. As Route Reports explained in a recent article, AI “is not intended to entirely automate the inspection process” but to “streamline and augment” it. Automating repetitive tasks frees inspectors to focus on judgment calls and complex repairs. In practice, councils using AI can let machines do the “heavy lifting” of data gathering while engineers verify and act on the findings.
Key benefits of AI-based inspections include:
- Consistent, objective defect detection
- Continuous coverage (day or night)
- Integration with asset-management systems to trigger repairs automatically
By eliminating redundant manual patrols, councils can cut field costs dramatically. For example, one study found that AI can eliminate up to 90% of traditional inspection routes, slashing time and labor needs. Reduced fieldwork also means less vehicle fuel, fewer overtime hours, and a safer process (inspectors no longer step into live traffic to survey roads).
“As a result of the AI, highways staff will no longer need to step onto the road to manually carry out inspections, enabling them to track more defects across what is one of the country’s busiest highway networks. Potholes that don’t need immediate attention will also be regularly tracked to ensure they are dealt with when needed.” (County Councils Network, County Spotlight)
Economic Case: Efficiency Gains and ROI
In financial terms, AI inspections have shown strong returns on investment. Consider some data:
Labour savings:
- Essex Highways found that after deploying an AI video-inspection platform, their teams saved about 1,800 staff-hours per year, and eliminated a backlog of ~10,000 citizen enquiries, by automating data entry and defect logging. That alone translated into roughly £1 million in annual savings for Essex.
Reduced crew costs:
- An Essex pilot also reported that using AI on driven safety inspections meant a two-person crew became one, “removing the need for one of the operatives”. Even a single operative’s time is expensive, so cutting crews yields direct savings.
Coverage and productivity:
- Carahsoft’s analysis notes that manual inspections (at $100–200 per mile) become prohibitively costly on large networks. By slashing 90% of those routes, AI gives much greater coverage for minimal extra cost. In one US case, automating debris and guardrail scans alone saved a state DOT $900,000 by avoiding unnecessary field visits
Data-driven decisions:
- Beyond immediate savings, AI yields better budgeting and preventive maintenance. The UK’s new PAS 2161 standard encourages data-based road management and government analysis indicates this shift could unlock £300 million a year in efficiencies for councils. AI tools feed rich condition data into asset management systems, so councils can target treatments where they’ll extend lifespan most effectively – importantly avoiding expensive emergency fixes later.
In short, while there is an upfront cost for AI hardware and software, the ongoing savings dwarf these investments. As one user put it: “with AI data, road inspection doesn’t have to drain the budget - it actually expands visibility while reducing costs”
AI Augments Inspectors, It Doesn’t Replace Them
A common concern is that AI might cut jobs. In reality, councils stress that human expertise remains central. At Route Reports we firmly believe that AI is a tool to enhance inspectors’ work, not replace them. The human expertise is central to excellent delivery, and will remain so. However, by handling monotonous data collection, AI frees engineers to focus on high-value tasks (complex repairs, community engagement, etc.)
Every AI-generated defect is still reviewed and prioritised by an engineer. Whilst using this technology inspectors can now spend more of their day planning repairs based on AI data and less time driving repeated routes or entering data by hand. Councils also report faster resident response: with images and maps at their fingertips, engineers can quickly answer citizens’ queries without needing to send someone back on foot. In practice, AI creates a collaboration: technology spots the pothole, people decide how and when to fix it.
Case Study: Essex Highways
Essex Highways’ experience offers concrete numbers. In 2024 they integrated Route Reports’ AI platform for their routine safety inspections (Section 58 surveys). Dedicated camera devices captured the road and AI automatically detected defects, feeding them into Essex’s Confirm management system. The impact was immediate.
Essex reports that 100% of its inspections are now on schedule, and it has saved around 1,800 labour hours per year through the automation. Crucially, the new system generated roughly £1 million of annual savings by avoiding duplicate data entry and focusing resources. (An independent award nomination even notes Essex’s benefit-cost ratio has improved dramatically.) In practice, the AI lets Essex engineers find and fix minor defects earlier; for example by flagging faded markings or surface cracks – so that fewer large potholes form in the first place.
Moreover, Essex’s shift to AI paid for itself in other ways. The council’s internal report noted that what began as a Covid-era innovation (needing only one driver in the car) ended up cutting costs:
“AI was implemented for driven inspections… The use of AI for this service also created financial savings by removing the need for one of the operatives for driven inspections.” Essex Highways
That single change reduced crew expenses permanently. In short, Essex is spending less time and money per km of road inspected, while also catching more issues earlier.
Case Study: Surrey County Council
Beginning in early 2025, Surrey has been equipping its highways vehicles with AI-enabled cameras to log defects in real time. Surrey officials note that inspectors no longer need to step onto the carriageway to report potholes – instead the system automatically records and geolocates them. This means survey work is faster and safer, and crews can inspect more of Surrey’s busy road network each week.
Surrey expects major efficiency gains: their cabinet member explains that being able to “proactively log and fix potholes” will help keep roads in better condition for longer. While Surrey is still in rollout, it already anticipates that the faster detection and reduced fieldwork will allow its existing crews to cover far greater mileage at little extra cost. In other words, the council can do more with the same budget.
Overall, Surrey’s move is expected to pay for itself. The council is spending part of its existing road maintenance budget (approximately £300 million over 5 years) on resurfacing and inspections. By using AI to catch minor defects early and prioritise repairs, Surrey aims to reduce repeat pothole formation and lower future repair costs. In short, staff time and money are being refocused on fixing roads, not simply chasing symptoms. The AI system also creates a permanent video record (“Route View”) so engineers can re-inspect virtually, which further cuts out extra site visits and public inquiries. All of these factors point to positive ROI: cheaper routine surveys, fewer emergency jobs, and better-targeted resurfacing down the line.
Read more on Surrey’s successful adoption of AI at Highways Magazine
Conclusion: A Worthwhile Investment
The data and case studies show that AI-driven roads inspection pays for themselves in UK councils. Councils that have adopted AI report substantial resource savings (time, labor hours and money) while actually improving coverage and defect detection. Even conservative estimates (like $100 per mile manual costs and 90% fewer field routes) imply that surveying hundreds of miles can be done at a fraction of the previous cost. Meanwhile, smarter data supports better long-term planning. With new DfT standards (PAS 2161) coming in, technology neutral options like AI are explicitly encouraged; smarter maintenance strategies and tools set to dramatically improve efficiencies for councils.
Ultimately, the question “is this worth it?” seems to be answered by yes, especially at scale. Councils are discovering that a modest investment in AI inspection hardware and software can deliver big dividends: less wasted patrol time, quicker fixes, and more of the maintenance budget devoted to actual repairs rather than overhead. Critically, these systems are being used to supplement, not supersede and replace, skilled highway teams. When inspectors and AI work together, roads stay safer and maintenance budgets stretch further.
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Further reading:
- Overcoming Initial Scepticism of AI Driven Inspections (Article)
- County Spotlight
- Further information on Essex Highways AI Adoption
- DFT’s PAS 2161 Standard launch
Further information on Surrey County Council’s AI Adoption
