AI adoption case study: Transforming job allocation and logistics at Thames Laboratories
techUK’s AI adoption collection of case studies showcases examples of how organisations are Seizing the AI Opportunity, either through the adoption of AI models within their organisations, or by developing AI tools that can be leveraged by others.
By shining a light on these use cases, techUK hopes to demonstrate examples of best practice from across sectors and from organisations of all sizes.
1. Challenge:
The company’s legacy job scheduling system was mostly manual, a resource-consuming operation that routinely resulted in sub-optimal customer service, inefficiently utilised, dissatisfied workforce, and decision making that was slow to adapt to real-time developments; all at a high environmental cost. The cumbersome logistics of dispatching engineers to attend calls at client locations resulted in significant operational losses.
The core challenges associated to improving job scheduling in Thames Labs:
- Meeting conflicting objectives, such as increasing productivity whilst keeping staff workloads and carbon footprints within manageable limits.
- Managing subtle, dynamic inter-dependencies between job priorities, preconditions, and engineering staff skillset.
- Adapting to unforeseen changes in staff availability (engineers stuck in traffic) and to last-minute job booking modifications (postponements, new requirements added to the initial ask) with cascading implications on other scheduled jobs.
2. Solution:
This would be the AI application itself. Add some text around the model itself and any other details about how it was produced. Avoid highly technical language.
The team of experts from Aston University and Thames Labs developed an innovative, explainable AI solution that automated the company’s onsite engineering service provision. This resulted in more evenly distributed workloads, better customer care and a lower carbon footprint. The AI tool leverages the strengths of evolutionary intelligence to analyse the company’s historical job allocation records and efficiently detect patterns, both value-adding and detrimental to the business. This enables scheduling incoming jobs in a way that perpetuates the former whilst avoiding the latter, whilst dynamically adjusting to realtime data (last-minute job alterations, unforeseen changes in engineers’ circumstances, traffic developments).
The intelligent job scheduling algorithm was successful at:
- Assigning jobs to (teams of) engineers appropriately skilled to complete them, such that the itinerary of an engineer on a given day is as short as possible (i.e., the jobs assigned to the engineer are in a tight geographical cluster and close to the engineer’s base location) and the time spent by an engineer attending jobs is evenly distributed across days (avoiding allocations where jobs are amassed towards the end of the week).
- Creating job schedules that met operational constraints: even workload distribution across all engineers on the payroll, meeting income targets, attending jobs in logical order (avoiding cases where the kitchen cabinet fitting team arrives on-site before the plumbing and electrical wiring have been completed), etc.
- Reducing time spent on the road by company vehicles, thus decreasing the organisation’s carbon footprint and supporting the net zero agenda.
3. Barriers:
- Conflicting, ambiguous feedback from company engineering and admin staff trialling the algorithm whilst performing their daily tasks.
- Incongruous legacy database entries: job and engineer metadata, such as geospatial location, priority and completion status, were often incomplete or misaligned.
- Incompatibilities with frontend requirements: the AI algorithm outputs needed careful calibration to match the way job information was presented to company staff in the user interface.
- Schedules with limited explainability: non-technical stakeholders found it difficult to grasp the rationale behind the AI-generated job allocations, causing increased resistance to adopting the automated system within their daily work routines.
4. Impact:
Thames Labs has successfully adopted the innovative AI-powered job scheduling system to replace their legacy logistics but not their staff whose bandwidth is now freed of mechanical tasks and available for creative, fulfilling work leading to a sense of accomplishment and professional satisfaction as well as superior service for clients. The company is also making the intelligent job allocation tool available to the community in the form of SaaS.
The AI solution was benchmarked against the company’s legacy manual scheduling system in three practical test scenarios, each entailing intricate job-to-engineer-skills matching combined with travel and service time optimisation, all while reducing the company’s carbon footprint and increasing the quality of its services (and, implicitly, its profits). The results show that the performance of the AI algorithm is on par with that of the manual variant on one dataset and significantly surpasses it on the other two. Across the first and third datasets, an engineer’s daily tour in the AI-generated schedule is less than half as long as in the manual variant, daily CO2 emissions are cut by almost 40%, the drop in fuel consumption exceeds 35% and operation costs are reduced by 37%.
An admin team leader within Thames Labs has said:
It has been good to consider ways to maximise the use of surveyors’ time. The new system has made us more aware of how we are booking the appointments, allocating them to a specific surveyor, and deciding how long they should take.
Charlotte Burton, Thames Labs Development Manager has said:
Working on the project has really challenged our thought processes and our approach to scheduling work. It has prompted us to question why things have been done a certain way—whether it’s truly the best approach and if it is, how the information in everyone's mind is translated to our systems. The development has enabled us to optimise schedules much faster than we could manually. Planning large programmes of work around complex skill sets and KPI parameters used to take days but can now be completed in minutes. This allows the team to review the output, make any necessary adjustments to the inputs, and re-run the schedule far more efficiently.
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Kir Nuthi
Kir Nuthi
Kir Nuthi is the Head of AI and Data at techUK.
She holds over seven years of Government Affairs and Tech Policy experience in the US and UK. Kir previously headed up the regulatory portfolio at a UK advocacy group for tech startups and held various public affairs in US tech policy. All involved policy research and campaigns on competition, artificial intelligence, access to data, and pro-innovation regulation.
Kir has an MSc in International Public Policy from University College London and a BA in both Political Science (International Relations) and Economics from the University of California San Diego.
Outside of techUK, you are likely to find her attempting studies at art galleries, attempting an elusive headstand at yoga, mending and binding books, or chasing her dog Maya around South London's many parks.
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Usman Ikhlaq
Usman Ikhlaq
Usman joined techUK in January 2024 as Programme Manager for Artificial Intelligence.
He leads techUK’s AI Adoption programme, supporting members of all sizes and sectors in adopting AI at scale. His work involves identifying barriers to adoption, exploring solutions, and helping to unlock AI’s transformative potential, particularly its benefits for people, the economy, society, and the planet. He is also committed to advancing the UK’s AI sector and ensuring the UK remains a global leader in AI by working closely with techUK members, the UK Government, regulators, and devolved and local authorities.
Since joining techUK, Usman has delivered a regular drumbeat of activity to engage members and advance techUK's AI programme. This has included two campaign weeks, the creation of the AI Adoption Hub (now the AI Hub), the AI Leader's Event Series, the Putting AI into Action webinar series and the Industrial AI sprint campaign.
Before joining techUK, Usman worked as a policy, regulatory and government/public affairs professional in the advertising sector. He has also worked in sales, marketing, and FinTech.
Usman holds an MSc from the London School of Economics and Political Science (LSE), a GDL and LLB from BPP Law School, and a BA from Queen Mary University of London.
When he isn’t working, Usman enjoys spending time with his family and friends. He also has a keen interest in running, reading and travelling.
Sue Daley OBE
Sue Daley OBE
Sue leads techUK's Technology and Innovation work.
This includes work programmes on cloud, data protection, data analytics, AI, digital ethics, Digital Identity and Internet of Things as well as emerging and transformative technologies and innovation policy.
In 2025, Sue was honoured with an Order of the British Empire (OBE) for services to the Technology Industry in the New Year Honours List.
She has been recognised as one of the most influential people in UK tech by Computer Weekly's UKtech50 Longlist and in 2021 was inducted into the Computer Weekly Most Influential Women in UK Tech Hall of Fame.
A key influencer in driving forward the data agenda in the UK, Sue was co-chair of the UK government's National Data Strategy Forum until July 2024. As well as being recognised in the UK's Big Data 100 and the Global Top 100 Data Visionaries for 2020 Sue has also been shortlisted for the Milton Keynes Women Leaders Awards and was a judge for the Loebner Prize in AI. In addition to being a regular industry speaker on issues including AI ethics, data protection and cyber security, Sue was recently a judge for the UK Tech 50 and is a regular judge of the annual UK Cloud Awards.
Prior to joining techUK in January 2015 Sue was responsible for Symantec's Government Relations in the UK and Ireland. She has spoken at events including the UK-China Internet Forum in Beijing, UN IGF and European RSA on issues ranging from data usage and privacy, cloud computing and online child safety. Before joining Symantec, Sue was senior policy advisor at the Confederation of British Industry (CBI). Sue has an BA degree on History and American Studies from Leeds University and a Masters Degree on International Relations and Diplomacy from the University of Birmingham. Sue is a keen sportswoman and in 2016 achieved a lifelong ambition to swim the English Channel.
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