Turning ambitions into action: Delivering AI in the Transport sector

Turning ambitions into action Delivering AI in the Transport sector A practical guide to AI-powered transformation

Contents 4-7

The mobility AI landscape. What’s changed?

8-11

Key points from the AI guidance and plans

12-15

2026 and beyond: moving from experiment to reality

16-17

The delivery challenge

18-21

The path to production

22-23

What good looks like

24-25

Getting started: 5 practical steps

26-27

A partnership for delivery

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Why this guide?

And why now?

Safe and efficient movement of people and goods is at the heart of economic growth and sustainability. AI is massively accelerating digital transformation of mobility, enabled by new Government policy and investment. Yet here’s the reality: according to McKinsey, while almost all organisations are investing in AI, only 1% say their investments have reached maturity. So while AI is vital to deliver critical transport objectives, most AI projects have yet to reach full-scale implementation.

Who is this guide for?

This guide is aimed squarely at leaders, decision-makers and operational teams.

Whether you’re heading up digital transformation, involved in public service delivery, overseeing data governance, or working with transport providers and partners, this guide is for you. So you can bridge the gap from promising proofs of concept (PoC) to practical AI solutions that save time, reduce costs, increase performance, and improve citizen services.

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The Government’s Transport artificial intelligence plan sets out its agenda for the smart multi-modal transport ecosystem of the future.

Government agencies, local councils, transport operators and service providers are innovating to unlock the benefits of AI—from AI-powered workplace assistants, to predictive infrastructure maintenance, to smart traffic management.

In addition, the Department for Science, Innovation and Technology launched the Artificial Intelligence Playbook for the UK Government and the AI Opportunities Action Plan, both of which guide public sector teams toward successful and responsible AI deployment.

More recently, the Cabinet Office published The People Factor: A human-centred approach to scaling AI tools, building on current guidance to advocate an approach that prioritises organisational culture, skills, and behavioural change alongside technology.

Now is the time to take a joined-up approach, build momentum, and scale AI across the transport sector.

This guide bridges the gap between ambition and reality, helping you move swiftly from promising experiments to meaningful outcomes—securely, responsibly, and at pace.

Let’s get into it.

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The mobility AI landscape.

What’s changed?

A new intelligent mobility environment is evolving. AI is increasingly integrated into vehicles as well as physical infrastructure and assets, together with digital systems. Trusted data, shared between partners, is itself a critical asset. AI-powered transformation is urgent to maximise the efficiency, capacity, quality, resilience and sustainability of service delivery across the sector. Meanwhile, the Automated Vehicles Act heralds the arrival of driverless vehicles on UK roads in 2026, catalysing new business models and multisided markets.

As the Transport artificial intelligence plan makes clear, the UK is at a tipping point.

To harness the vast potential of AI, government agencies and their partners are being asked to turn policy into working solutions at speed. Some are still early in their AI journey—experimenting with pilots, wrestling with legacy infrastructure and siloed data, or trying to build capability on the fly.

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The shift from concept to reality isn’t just technical—as highlighted by the Cabinet Office’s People Factor paper and framework.

It’s cultural, operational, and deeply tied to service design. Becoming AI native—ensuring every process can benefit from intelligent automation, every person is equipped to work alongside AI, and every solution is designed with AI in mind—is critical to success.

Yet it’s more than that. In the transport sector in particular, innovation can be complex because it involves such a variety of partners, systems and objects (physical and digital; fixed and moving). AI-powered computer vision sensors monitor, analyse and predict behaviour on the roads to improve safety and reduce disruption. AI systems in control centres aid traffic management and law enforcement. Analysis of data from connected cars, buses, trucks and trains could provide vital insight into incidents for investigators.

Driving the advance of autonomous transport The driverless future is already here. The UK Government brought forward trials of selfdriving cars to 2026 with partners like Uber and Wayve. Multiple driverless bus trials are well underway, including services in Scotland and Cambridge. Teesside Airport has led the world with its trial of the first-ever integrated driverless passenger and baggage transport system. Additional commercial trials of autonomous taxi, bus and shuttle-like services will follow across the UK. Emerging technologies related to driverless mobility are predicted to create 38,000 jobs and unlock a £42 billion industry.1 In this context, the stakes are high and margin for error small. When done right, AI can transform outcomes, empower people, and optimise precious resources. Get it wrong, and you risk legal challenge, or unintended harm to the people who rely most on your service.

That’s why practical guidance and governance matters more than ever.

Without clear direction, stakeholder engagement and shared standards, AI rollouts have the potential to erode vital public trust, or simply fail to deliver.

What does it mean to be AI native?

Being AI native means embedding AI at the heart of how your department operates, serves users, and solves problems. It’s about empowering every team with AI capabilities, enhancing processes through automation and intelligence, and designing services and solutions with AI as a core element—not an afterthought. In practice, becoming AI native transforms the way teams work, helping you deliver more efficient, innovative, and responsive public services—all grounded in a responsible approach to adoption.

1 Next steps for self-driving vehicles as future

passengers help shape self-driving vehicles law, July 2025 7

Key points from the AI guidance and plans

The Artificial Intelligence Playbook for the UK Government sets out practical guardrails to help public sector teams use AI safely and effectively. It emphasises principles like meaningful human oversight, transparency, explainability, and the need to design for inclusion. Rather than treating AI as a black box, teams are urged to understand how systems make decisions and to be able to explain those decisions to the public. The playbook also stresses the importance of

robust data governance, bias mitigation, and clear accountability. This is especially important when AI is used in high-impact areas like transport.

The AI Opportunities Action Plan shifts the focus from ambition to implementation, highlighting practical steps for laying strong foundations and achieving citizen impact. It specifically calls out key steps including:

INVESTING IN DATA FOUNDATIONS

SCALING WHAT WORKS

BUILDING SKILLS AND ADOPTION

Quickly identifying and

Implementing a structured

Creating dedicated pathways

unlocking high-impact public

“Scan, Pilot, Scale” framework,

and programmes to rapidly

datasets, ensuring they’re

allowing departments to swiftly

develop the necessary AI skills

high-quality, accessible, and

identify viable AI opportunities,

across the public sector—

responsibly managed to

rapidly prototype them, and

ensuring AI literacy isn’t limited

accelerate innovation

then efficiently scale solutions

to technical roles but

that demonstrate clear value

integrated into operational teams

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Most importantly, the plan strongly advocates a citizen-first approach, prioritising AI use cases that directly improve public services and experiences both for citizens and employees. Real-world examples include: using AI assistants to do repetitive tasks better and faster, freeing up to 20% of an employee’s time; and drafting structured reports and forms with AI, cutting final document production times by 20-80%.

These practical, grounded applications not only streamline processes but free staff to focus more of their time on frontline, highvalue tasks, ultimately delivering faster, better, and more responsive services to citizens.

Complementing these practical steps, the Cabinet Office’s People Factor specifically addresses the human and organisational dimensions of AI adoption. It highlights the critical importance of user-focused strategies, structured into three key stages:

01. Adopt Encouraging initial uptake through targeted support, clear communication, and practical training.

02. Sustain Embedding AI into daily workflows by helping users develop consistent habitsand routines around the tools.

03. Optimise Continuously refining how AI tools are used in practice, proactively managing risks, and enhancing user skills and confidence.

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The Transport Artificial Intellige is a welcome framework for del greener, more integrated mobili works for everyone.

It emphasises the critical importance of Equitable and Responsible AI to serve and safeguard people and places. And it pinpoints key opportunities and challenges around: leadership (providing policy, guidance, governance, and oversight); skills and capabilities (ensuring all organisations are equipped to use, regulate, guide, and assure AI); infrastructure and data (enabling access and providing guidance and stewardship); applications (identifying and supporting to maximise public value); and engagement (sharing learning nationally and globally, collaboration, and open dialogue with the public).

All three of these documents clearly signal that the era of isolated pilots is ending. This represents a fundamental shift away from technology-driven experiments towards solutions deeply embedded in everyday service delivery.

At the same time, the available tools are evolving fast; if organisations don’t act, they risk falling behind, duplicating effort and wasting time and resources, or investing in solutions that don’t scale.

But it’s not just about seizing opportunity—it’s about managing risk.

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Making responsible progress together As the Department for Transport has outlined, trust and transparency are the foundations for the AI revolution. While autonomous vehicles are already in service in the US, China and parts of the Europe, the UK leads the way in shaping comprehensive regulation for their safe deployment.

What’s more, across the sector, volumes of data are exploding. Robust governance will remain critical to underpin the flow of data between moving assets, transport operators, control centres, local and central government, regulators, accident investigators, manufacturers and third-party providers.

ence Plan livering ity that An autonomous vehicle can generate over 1.2TB of data daily, equating to 500HD movies. 20 million journeys made by autonomous vehicles in 1 day would generate 24EB of data—comparable to streaming all movies and TV shows ever made over 300,000 times2

2 The new infrastructure of mobility, Version 1 and the Open Data Institute, 2025 11

2026 and beyond:

moving from experiment to reality Every team is at a different stage of delivering AI at scale, but most have moved beyond experimentation. Now the focus is on implementing value-driven solutions, managing the change, achieving operational excellence, and scaling what works. Here are four common scenarios we’re seeing across government—alongside critical considerations to keep front of mind.

01 –

02 –

“WE’VE EXPERIMENTED—NOW WE NEED TO SCALE” You’ve completed pilots and proven the tech

“WE’VE PROVEN VALUE, BUT WE’RE STUCK OPERATIONALISING IT”

works. Now it’s time to scale and

Your pilots showed clear promise, but

operationalise. At this stage, the focus shifts from proving concepts to robust, sustainable delivery. It’s about standardising infrastructure, embedding consistent data governance, and rolling out adoption plans that secure real buy-in from users.

Critical considerations. Clearly document outcomes, secure senior sponsorship, and build scalable infrastructure and processes.

integration into day-to-day operations is proving challenging. Here the blockers typically aren’t about the tech itself—they’re about cultural resistance, data integration hurdles, or gaps in skills and processes needed for sustainable operational delivery.

Critical considerations. Involve operational teams early to identify barriers and proactively plan for change management and skills development.

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If none of these sound like you, try mapping your work against the question: “What problem are we trying to solve…and why now?” The answer is often a better compass than any strategy document.

03 –

04 –

“WE HAVE MULTIPLE SOLUTIONS RUNNING, BUT THEY’RE FRAGMENTED”

“WE’RE DELIVERING, BUT NEED TO DEMONSTRATE IMPACT CLEARLY”

You have several AI solutions deployed, but

Your AI projects are operational, but now you

they aren’t yet joined up or delivering

face pressure to clearly demonstrate value to

coherent departmental impact. Fragmented

senior stakeholders, funders, and citizens. At

solutions often duplicate efforts, waste

this advanced stage, measurement,

resources, and create confusion. Now’s the

transparency, and ongoing refinement

time to step back, consolidate, standardise

become crucial. Effective monitoring and

your approach, and embed unified

evaluation are no longer optional—they’re

governance and oversight.

critical.

Critical considerations: Unify governance,

Critical considerations. Implement clear

adopt interoperable standards, and design

performance frameworks and consistently

modular infrastructure to support cohesive

measure, refine, and communicate outcomes

delivery.

linked to strategic priorities.

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When moving AI from experiment to reality makes sense (and when it doesn’t) A visual guide for transport leaders When it makes sense

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When it doe

Proven, measurable benefits: Clear outcomes from pilots showing tangible value

Unc cons

Repeatable, high-volume processes: Frequent, routine tasks that clearly benefit from automation

Pers cultu

Well-structured data environments: Accessible, clean, and consistent data available

Data or c

Clear citizen or staff impact: Directly improves user experience or reduces staff burden

Wea acco

Sustainable infrastructure: AI solutions built to scale and integrate seamlessly with existing platforms

Stra linke

esn’t

clear value or evidence: Benefits haven’t been sistently proven or documented yet

sistent operational barriers: Adoption, ural issues, or skill gaps remain unresolved

a readiness issues: Data quality, accessibility, onsistency hasn’t been sufficiently addressed

ak governance or oversight: Lacking clear ountability structures or ethical guardrails

ategically misaligned solutions: Not clearly ed to departmental priorities or citizen needs

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The delivery challenge As highlighted in the previous section, most teams now face the challenge of moving beyond successful pilots into scalable, operational solutions. But let’s be honest: getting AI into production is hard and even great ideas can stall. You can have a great use case, a capable team, and support from the top…and still get stuck.

The numbers tell the story: according to a recent study by McKinsey, only 6% of GenAI proofs of concept (PoCs) actually make it to production.

That’s a lot of effort with very little impact. So, what’s going wrong?

D u W e

Unclear busin hard to justif pilot phase. T measurable r

Lack of sponsorship: Pro who can unblock issues, support

User resistance: Even the best tools w If your frontline teams aren’t involved helps them, then adoption inevitably

?

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Ethics and compliance concerns: Public sector te (and rightly so). But concerns around explainabili can become dealbreakers if they’re not addresse

Data that’s not ready: Poor quality, patchy, or siloed data is undoubtedly the biggest barrier to getting out of PoC purgatory.

Without clean, consistent information to learn from and work with, even the smartest model will underdeliver

ness cases: If the value’s not obvious or the costs are y, it’s tough to get buy-in for your AI project beyond the That’s especially true when budget holders want fast, results

jects often stall without senior champions defend funding, and build cross-department

will fail if people don’t use them. d early or don’t see how the tool y suffers

eams tread carefully ity, oversight, or bias ed head-on

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The path to production

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BUILD THE CASE THAT MATTERS

GET THE RIGHT PEOPLE ROUND THE TABLE

Start with the citizen or end-user outcome you’re trying to achieve. Not “we want to use AI” but “we want to cut waiting times by 30%” or “we want to help service teams find relevant information in seconds, not hours.”

Frame your business case around time saved, experience improved, or quality increased. Make it tangible—hours per week, requests processed, or citizen satisfaction scores. Executive stakeholders need to see the “so what” clearly, and frontline staff need to understand what’s in it for them.

This isn’t just about having a project team. You need executive sponsorship from someone with real clout—ideally your Director General, Chief Digital Officer, or equivalent deputy director level who can actually unblock things when they go sideways. Your senior leadership team should include someone who genuinely understands digital delivery, not just someone who’s “good with computers.” Bring in your compliance, legal, and data protection teams from day one, not as an afterthought. Most importantly, involve people who will be using the solution.

Getting AI from pilot to everyday use isn’t about having the shiniest tech stack. It’s about solving real problems for real people and doing it in a way that sticks. The difference between projects that scale and those that gather dust often comes down to a few fundamentals that have nothing to do with algorithms.

DEVELOP YOUR AI DELIVERY APPROACH

PROCESS BEATS TECH EVERY TIME

Don’t just adapt your usual project methodology and hope for the best. AI projects need their own rhythm—shorter cycles, more testing, different risk patterns. Build an AI-specific development lifecycle that includes things like data quality checks, bias testing, and model performance monitoring from the start. Plan for regular “health checks” on model performance, not just system uptime.

Create checkpoints where you can pause, assess, and pivot if needed. Most traditional project frameworks weren’t designed for solutions that learn and evolve after deployment.

The specific AI tool you choose today might be outdated in eighteen months.

That’s fine – the technology landscape moves fast, and your approach needs to flex with it. What matters more is how you work: how you gather requirements, how you test, how you iterate, and how you learn. Build ways of working that can adapt to new tools rather than getting locked into today’s technology choices.

Think platforms and processes, not point solutions.

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The path to production

CREATE CLEAR GOVERNANCE THAT ENABLES, NOT BLOCKS Set up decision-making structures that can move at the speed AI projects need.

Establish an AI steering group with clear escalation paths and decision rights.

Define what constitutes an acceptable risk versus a show-stopper. Create lightweight approval processes for common changes such as model updates, feature additions, or performance tweaks. Good AI governance feels like guardrails, not roadblocks.

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COMMUNICATE LIKE YOU MEAN IT Don’t just announce when you’re going live. Talk about what you’re building, why you’re building it, and what it means for people’s daily work. Regular updates, honest about both progress and challenges, build trust and reduce resistance. Create space for questions, feedback and even pushback. If people understand the problem you’re solving and how it affects them, they’re more likely to engage constructively rather than resist change.

Remember that delivering your successful AI project isn’t a straight line. Things will go wrong. But recognising the traps early (and designing to avoid them) is what separates stalled pilots from sustainable success. That’s what delivery means and that’s what this next section is all about.

Get in touch

INVEST IN CAPABILITY, NOT JUST TECHNOLOGY:

DESIGN FOR TOMORROW’S CHANGES

Training isn’t a tick-box exercise at the end. It’s ongoing capability building that starts before you build anything. Help people understand not just how to use new tools, but why they work the way they do and how to spot when something’s going wrong. Build internal expertise so you’re not dependent on vendors for every adjustment. The goal isn’t just to use AI tools, it’s to become an organisation that can work intelligently with AI.

Assume your needs will evolve and your tools will change. Build on open standards where possible. Document your decisions and reasoning so future teams can understand why choices were made. Create feedback loops that help you spot when something’s not working or when needs have shifted.

Plan regular review cycles – not just “is it working?” but “is it still solving the right problem?” The most successful projects aren’t the ones that solve today’s problem perfectly—they’re the ones that create the foundation for solving tomorrow’s problems too.

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What good looks AI doesn’t have to change everything to make a difference.

In fact, many of the most effective and valuable projects are focused, low-risk, and quietly transformative—directly linked to improving how services are delivered. They automate routine tasks, help teams with complex decisions, or make public services easier to access.

Partnership is key. Across the transport sector, collaborations are happening between government, operators and partners to address efficiency, productivity, safety, capacity, quality and environmental challenges. The examples below show real impact, delivered at pace, without overengineering. Where AI is already making a difference on the ground.

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AI in action at National Highways Working with National Highways, Version 1 is applying AI to solve practical, everyday challenges, helping teams work smarter, make better decisions, and deliver more efficiently. These examples show what’s possible when AI is grounded in real operational needs.

Intelligent service navigation National Highways publishes extensive documentation for its services, including IoT Hub (connecting roadside devices), Transfer Hub (moving roadside data into the data warehouse), and provisioning environments for project teams. With so many capabilities available, it can be challenging for users and teams to understand what’s on offer, how to request it, and what to do next. Version 1 has introduced an AI-powered assistant that helps consumers identify their needs, find the exact information required, and navigate to self-serve—cutting the time from question to answer from hours to seconds.

s like AI-powered technical governance With a large number of delivery teams operating across the account, conducting in-depth Technical Assurance (TA) can be complex and time-consuming. AI now collects artefacts and transcripts from TA engagements and generates comprehensive reports. These reports demonstrate alignment with the Technical Assurance Framework, showing engagement health across eight domains including scope, security and compliance, operational readiness, and FinOps. Crucially, they compare with previous reports to highlight progress, flag concerns, and track changes over time. Delivery managers use these reports to govern their projects with greater clarity and confidence.

These aren’t isolated experiments. They’re practical applications embedded in dayto-day delivery, each one directly linked to the kind of outcomes this guide advocates: saving time, improving quality, enabling better decisions, and helping teams focus on what matters most.

Accelerating commercial readiness Version 1 produces and assesses a number of Rough Order of Magnitude (ROM) estimates for National Highways. AI is used to evaluate the readiness of each ROM before peer review, ensuring key areas are covered and producing a clear report on its readiness for client publishing. The result is faster, more consistent commercial outputs with fewer gaps reaching the review stage.

Data-driven cloud strategy When National Highways needed to assess potential cost savings and migration costs associated with moving to an alternative cloud provider, Version 1 used AI to analyse the collected data. This enabled the team to rapidly produce a detailed report that accurately identified key opportunities, provided t-shirt sizing of workloads, projected potential savings, and evaluated migration models creating a clear, evidence based foundation for strategic decision making.”

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Getting started: 5 practical steps You don’t need perfect conditions or a ten-year plan to start delivering with AI. But you do need a first beyond PoCs. Start by aiming to embed an AI native mindset—where people have the right skills, processes are intelligently automated, and AI is integral to delivering outcomes. Success depends on clear departmental alignment, robust leadership, and a structured approach to embedding AI across your teams.

Here are five clear steps to get you moving:

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01

02

SET A CLEAR VISION AND MISSION

SECURE VISIBLE SENIOR SPONSORSHIP

AI initiatives thrive with clarity. Start by defining precisely what AI success looks like for your department and ensure everyone understands how AI aligns to your strategic priorities. Clearly articulating your AI vision and mission—like Version 1’s ambition to become an exemplar AI native organisation—establishes a common direction, creates alignment, and accelerates practical delivery

Gaining executive-level support isn’t optional—it’s essential. Establish strong sponsorship from senior leaders who actively champion AI initiatives, clear barriers, secure resources, and visibly drive adoption across all departmental functions.

Effective senior sponsorship dramatically increases the likelihood of moving beyond experimentation to tangible, scalable success

03

04

05

ADOPT STRUCTURED CHANGE MANAGEMENT

ENGAGE, EDUCATE, EXPERIMENT

EMBRACE A HUMANIN-THE-LOOP APPROACH

Use structured frameworks—like the ADKAR model (Awareness, Desire, Knowledge, Ability, Reinforcement)—to systematically guide your teams through AI-driven change. Communicate clearly, explain benefits explicitly, and provide structured, continuous support to overcome resistance and build excitement

AI skills development is key.

Implement tailored, practical training programmes that encourage your people to engage, educate themselves through rolespecific learning, and immediately experiment to embed their new knowledge. This hands-on, practical approach builds lasting capability rather than superficial familiarity

People—not technology— remain central. Clearly define roles such as AI trainers, validators, orchestrators, collaborators, and decisionmakers to ensure human oversight and ethical AI use.

This “human-in-the-loop” model builds trust, reduces fear, and enhances productivity by creating clear boundaries and responsibilities

Following these steps won't just help you move from experimentation to execution—they'll enable you to scale your AI initiatives and deliver lasting impact for your teams, your citizens, and your department as a whole.

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A partnership for delivery Starting matters.

But delivery is what counts.

The UK’s drive to smart mobility is bold and exciting.

As outlined in the Transport artificial intelligence action plan, the private sector has an important role in unlocking AI’s benefits.

Creating a more intelligent and connected transport network needs shared vision, fresh thinking and close working between government, operators and partners. Many solutions just can’t be delivered by organisations operating in isolation.

Version 1 is a digital innovator and systems integrator, helping to accelerate the safe, efficient adoption of AI to deliver transport sector objectives. We have delivered as a trusted partner for some of the world’s leading transport organisations and their partners, with experience spanning road, railways and logistics operators and networks.

We help to shape and orchestrate digital transformation that delivers higher performance, capacity, efficiency and reliability, helping to create safer, greener, more resilient transport operations and experiences.

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We’re working as part of an evolving partner ecosystem to design and implement AI applications for smart mobility. From intelligent infrastructure management and technical governance, to cloud cost optimisation and AI-powered servicey delivery. Our work with National Highways demonstrates this in practice: embedding AI into everyday operations across service navigation, technical assurance, commercial readiness, and strategic cloud planning.

We’ve supported AI, automation and data transformation projects across government departments and agencies. From virtual assistants to risk analytics and intelligent triage, we’ve helped teams move from exploration to live services that save time, improve access, and support public value.

Where it makes sense, we bring accelerators and delivery frameworks like our AI Labs, automation tooling, or virtual assistant starter kits - all designed with ethical use and inclusion in mind. And through strategic partnerships with Microsoft and AWS, we help teams tap into secure infrastructure, innovation support, and trusted platforms.

But most of all, we work in partnership. That means listening closely, sharing what works, and helping deliver AI in a way that’s practical and real.

If you’re stuck, unsure where to begin, or ready to scale, we’re here to help.

Get in touch to see how we can support your next step - from sandbox to service delivery.

Contact: Guy Hodges, Head of Transport Guy.Hodges@version1.com

Version 1 is ranked as a leader by ISG in 2024 and 2025 in its global ProviderLensTM study on Generative AI Services, as Specialist in both Development & Deployment Services and Strategy & Consulting Services

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