Earlier this year, we highlighted how AI-driven robotics is reshaping the boundaries between humans and machines—ushering into a new era of intelligent, adaptive systems. This evolution is now taking shape through the rise of physical AI. In this blog, we’ll showcase the core components of physical AI and explore how business leaders can take advantage of this transformative shift. 

The last few years have been a boon for AI. We now have foundational models for generating high-quality and reliable text, images, and video. We are also starting to benefit from agentic AI systems that can operate with autonomy, undertake goal-directed behavior, and adapt their behavior based on changing circumstances.  

But what about Robotics? How have we helped robots act more like humans? The answer is that we have made tentative progress. We have systems that enable robots to handle specific tasks within constrained environments.  

However, asking robots to go beyond these confines and act more like us is problematic. The world is built for humans, and changing the environment to suit a new age of robotics would be too expensive. Enabling robots to become more like us requires a nuanced approach – physical AI, where AI is integrated into physical systems for environmental interaction. 

Creating a foundational model for robotics means enabling machines to adapt to our world. We must visualize and codify the millions of tasks we perform daily and use this data to help robots perform tasks collaboratively.  

This is no straightforward task. The journey to a foundational model for robotics will involve many hurdles. Progress will rely on contributions from multiple parties. Let’s consider how your business can get involved and how advances in AI and edge computing can help us build foundational models that support the long-term emergence of physical AI.  

A new state of urgency around robotics 

Robotics is on the verge of a major leap—much like the recent revolution in generative AI. The hardware components, batteries, and actuators, which cost up to 50% of the actual bill of materials, have fallen by double-digit numbers over the past few years.  

This cost reduction means innovations that might have taken up the better part of a century will be completed in a decade or less. We are moving to a future where robots can work alongside humans, bridging the digital and physical worlds. With advanced sensory integration, deep reasoning, and dexterous manipulation, these robots will adapt to their environment, moving quickly and effectively.  

Agile companies in certain regions, particularly China, are already optimizing their supply chains and iterating on robotic innovations. The companies that create value from physical AI will win the race. So, how can your business stay competitive and thrive in this new era? 

Towards general-purpose robotics 

Business leaders must recognize that AI and humanoids are coming together to move robotics beyond structured manipulation and autonomous navigation to general-purpose robotics, where navigation and manipulation seamlessly integrate. 

This co-evolution of AI and humanoids should be your guide to effective competition in the race towards physical AI. AI, rather than hardware, will be the key differentiator that powers the move to general-purpose robotics, and its foundational model will draw on three main engines: 

• Reinforcement Learning – Rewarding robots for achieving a set business goal and not just completing a discrete task. 

• Agents – Using pioneering work on the industrial metaverse and digital twins as the source data to create the simulations that build the foundational model for robotics. 

• Vision Language Action – Combining vision models that detect an object, scene, or visual input with neural networks trained on thousands of actions. 

The huge amount of training in Vision-Language-Action (VLAs) enables robots to perform actions without programming. They can work on the fly to complete actions they might not have previously encountered. These pre-trained robots can collaborate, with their foundational models interacting to complete tasks effectively. 

Asking the right questions about Physical AI 

As we stated at the outset, the journey towards humanoids is challenging. General-purpose robotics helps us to visualize how a foundation model for robotics is a realistic end objective, but we can’t be complacent. 

Let’s take a step back and think about how humans operate: some things we do intuitively, like recognizing a friend’s face in a crowd, and some things require a more deliberate approach, such as solving a math problem. The same process is true with robotics in an age of physical AI: some systems will need fast, reactive responses and can be handled by transformer models; other systems are more analytical and will require huge computer power and pre-trained VLAs to interpret complex scenarios and plan accordingly. 

Cost will be a crucial concern. The foundational models that support generative and agentic AI often rely on the cloud. Pushing processing requirements to the cloud creates huge costs. These costs will be prohibitive in an age of Physical AI, where robots will need to connect to supporting computer systems rapidly and repeatedly. The key to handling these ever-growing computational requirements is edge computing. The Edge will help your business manage data demands cost-effectively by moving compute to where the data resides, reducing the requirement for expensive cloud services, and minimizing data transmission costs.  

At Capgemini, we’re not here to give you all the answers. We’re here to help you ask the right questions, because that’s when you get creative, unlock inspirational ideas, and generate new value.  

As we enter the era of physical AI, we can help you explore the possibilities, challenge assumptions, and shape the future of robotics—together. 

“We must start thinking about how we’ll make the foundational model for robotics. Nobody knows exactly when it will happen, but if we think about AI and the progress during the past three years, I think the call for action starts becoming more and more important.”  – Kary Bheemaiah, CTIO Capgemini Invent 

“VLA is the core of the foundational models powering these humanoids. The world is full of actions, and VLA is just a coming together of all this data. It’s an ingenious idea where now you can look at an object and drive an action without programming.”  – Dheeren Velu, Head of Innovation & AI, AUNZ 

“At the intersection of AI, robotics, and computer vision, advanced training methods like reinforcement learning and VLA are driving a profound transformation: automation endowed with arms, legs – and reasoning. No programming is required.  But the true breakthrough isn’t just technological – it’s collaborative. Humans, humanoid robots, and virtual AI agents are about to operate as cohesive teams, elevating efficiency, precision, and safety to unprecedented levels.” – Alexandre Embry, CTIO, Head of the Capgemini AI Robotics and Experiences Lab  

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Navigating the new industrial landscape – Capgemini  

Robotics and computer vision are two complex fields that have existed for decades. Yet in the past ten years, things have shifted – and continue to evolve rapidly.

Robotics, once limited to basic automation and repeatable motions in isolated environments, is now expanding to address broader challenges. Traditional industrial robots operated at a safe distance, executing predefined tasks in static environments. 

Meanwhile, computer vision, once fragmented into subdomains like image processing, geometry, and optics, has undergone a transformation. The rise of artificial intelligence has unified these domains and propelled computer vision to the forefront of innovation. 

Today, a new convergence is taking shape — one that merges perception, reasoning, and physical action into integrated systems. This is the promise of Physical AI: the ability for machines not only to process information intelligently, but to act upon it in the real world. And at the heart of this evolution lies the rise of Vision-Language-Action (VLA) models — architectures that combine what a robot sees, what it understands through language, and how it decides to move or manipulate its environment accordingly. 

We’re already seeing early signs of this shift. For example, new-generation robots can now interpret a voice command like “pick up the red cable next to the panel,” visually locate the object in context, and perform the action — all thanks to VLA architectures that connect perception to natural language and motor execution. 

In industrial settings, robots once confined to repetitive welding behind safety cages are now operating side by side with humans — navigating busy factory floors, identifying parts, adapting to shifting workflows, and contributing dynamically to production without the need for constant reprogramming. 

Though often treated as separate disciplines, robotics and vision are deeply intertwined. Today’s robotics is no longer just about repetition — it’s about adaptability in dynamic, unpredictable environments and what better way to enable intelligent action than through perception? After all, around 80% of the information processed by the human brain comes from visual cognition. It’s only logical to equip robots with powerful vision systems if we want them to act meaningfully in the world. 

When vision meets movement 

The fusion of sight and motion is redefining how robots interact with the world around them. 

A robot that interacts intelligently and adapts to its environment relies primarily on its ability to perceive, interpret, and understand the world around it. Much like humans reconstruct their environment from limited focal information, vision systems must extract meaning from incomplete, noisy, and ambiguous data. 

In both humans and machines, vision is not passive — it’s an active process of interpretation, selection, and decision-making. And this principle applies directly to robotics. An efficient humanoid robot must incorporate biomimetic principles, enabling it to understand and act upon its surroundings as humans do. 

That’s why giving robots the ability to “see” is not just an enhancement — it’s a requirement for safe navigation, interaction, and decision-making. In collaborative environments, such as modern industrial settings where humans and robots coexist, real-time perception is essential to avoid collisions and adapt to changing conditions. 

We are moving from conventional robotics and siloed vision systems to intelligent robotics powered by integrated perception. Where traditional robots acted blindly within controlled environments, AI-driven robotics must now interpret complex scenes and operate in the real world — fluid, noisy, and often unpredictable. 

Applications across industries 

From factories to farms, vision-powered robots are reshaping work across every sector. 

Thanks to breakthroughs in both robotics and computer vision, it’s increasingly plausible to anticipate radical changes in how we design, manufacture, and operate across countless industries. 

Many tasks that are still carried out manually — repetitive, sometimes non-standard, and often labor-intensive — could be augmented or replaced by intelligent robots. For instance, repetitive part handling is physically demanding and costly. Delegating such tasks to machines allows humans to focus on less exhausting, more meaningful work. 

A more complex case is visual inspection. Today, for each inspection station, there’s a dedicated process — sometimes manual, sometimes automated, often a mix of both. But with computer vision and robotics, we can envision versatile, autonomous visual inspection systems capable of adapting across product types and conditions. 

And these examples extend well beyond quality control in operations: think of hazardous operations, where robotic systems can prevent human exposure to danger, or required round-the-clock tasks, where robots can operate continuously without fatigue avoiding dangerous error. 

From perception to autonomy 

Seeing is just the beginning – true autonomy emerges when machines understand what they see. 

Attaching cameras to a robot and detecting a few objects doesn’t make it autonomous. While the progress in computer vision is undeniable, real autonomy lies in the transition from raw detection to contextual scene understanding. 

Detection allows a system to identify known elements — objects, markers, obstacles — typically in controlled environments. But the real world is rarely so clean. In industrial settings, in cities, or in natural environments, robots face variability, ambiguity, and noise. That’s where true autonomy begins: not just recognizing what’s in front of them, but understanding what it means, how it changes, and what to do about it. 

This shift requires a deeper integration of perception, cognition, and action. For example, in a fulfillment center scenario, a robot must move from: 

• Identifying a box to understanding that it’s fragile and just fell off a conveyor belt 
• Seeing a person to predicting their trajectory and adjusting behavior safely 
• Detecting a machine to interpreting that it’s idle and requires assistance 

It’s about reasoning, prioritizing, and reacting in real time, based on complex visual input. And this isn’t just a matter of better algorithms — it requires: 

• Multi-modal fusion (combining vision with sound, touch, or contextual data) 
• Learning on the edge (to adapt quickly to new situations without retraining centrally) 
• Generalization (being able to apply learned behaviors to unseen environments) 

In other words, we move from reactive systems to proactive agents capable of operating in the unknown. This is especially vital in dynamic or high-stakes environments — from co-working with humans on factory floors to exploring disaster zones or navigating crowded streets. 

Autonomy is not binary — it’s a spectrum. And the closer we get to human-like understanding of space, intent, and consequence, the more fluid, intelligent, and reliable robotic behavior becomes. 

Ultimately, perception is the lens but autonomy is the leap. 

From seeing to thinking and doing: The rise of physical AI 

Perception alone is not enough — intelligent robots must connect vision, language, and action into one seamless cognitive loop. 

A new wave of intelligent robotics is taking shape — one where vision alone isn’t enough. The frontier is now Physical AI: systems that combine what a robot sees, what it understands, and what it does. At the heart of this evolution are Vision-Language-Action (VLA) models, which merge visual perception, natural language understanding, and physical execution into one unified architecture. This enables robots to go beyond detecting objects — they can now follow instructions, understand goals, and adapt their actions accordingly. 

These models open the door to more intuitive, adaptive robotics in factories, hospitals, and homes — creating machines that collaborate, learn, and act in complex environments. While still an emerging field, Physical AI is rapidly becoming the foundation of truly intelligent autonomy. 

Challenges in the loop 

More intelligence means more complexity – and a greater need for safety, ethics, and control. 

With increasing perceptual capabilities come significant challenges. One key issue is robustness: computer vision systems can be vulnerable to variations in lighting, background, and unexpected events. 

There’s also the challenge of trust and explainability. When robots make decisions based on complex visual input, humans must understand why and how those decisions are made — especially in safety-critical environments. 

Additionally, there’s a computational burden: processing high-resolution video streams in real time, running deep models at the edge, and doing so efficiently and sustainably is still an ongoing technical frontier. 

Moreover, and perhaps most importantly from an ethical perspective, we must ask: What tasks should we delegate to machines? How do we ensure that intelligent robots augment human work in responsible ways? 

Shaping the future together 

Empowering the next generation of robots starts with the choices we make today. 

The fusion of computer vision and robotics is one of the most promising frontiers in technological innovation. It offers a glimpse into a future where machines are not just tools but perceptive collaborators. 

To realize this future, organizations must invest not only in algorithms and hardware, but in talent, infrastructure, and governance. It requires cross-disciplinary collaboration — between engineers, ethicists, designers, and decision-makers. 

Those who act now — by embracing intelligent technologies, fostering experimentation, and building trust — will shape the future of robotics not as a distant vision, but as a practical, human-centered reality. 

As warehouse operations evolve, smart warehouses and new technologies are helping organizations balance growing operational challenges and deliver goods efficiently

Running a warehouse today involves overcoming a changing set of obstacles. Alongside the rapid growth of e-commerce, warehouse managers must navigate an ever-changing landscape of customer demands, sustainability challenges, and resource shortages. Additionally, they must consider regulatory requirements and skills management. In this environment, cost optimization becomes an increasingly difficult task. To improve their chances of success and stay competitive, warehouse managers need to adopt the latest technologies such as a smart warehouse management system. The concept of the “smart warehouse” represents an evolution, understanding the maturity of your warehouse operations today is crucial to seizing future opportunities.

Warehousing operations are faced with multiple challenges

Navigating the complexities of today’s warehouse operations involves addressing a variety of challenges, including:

• Customer needs: Managing continually changing customer needs requires the agility to quickly adjust to fluctuating volumes and meet increasingly high service level expectations.
• Climate challenges: Environmental responsibility is a significant and pressing issue, compelling warehouses to embrace their role in ensuring adherence to sustainability targets.
• Resource scarcity: Labor shortages and inventory shocks create a scarcity that clashes with the growing demands of consumers. Efficient operations and focusing labor on complex client value driven tasks is becoming paramount in finding innovative ways to navigate these constraints and deliver results.
• Strict regulatory frameworks: Regulations, particularly concerning traceability, add layers of complexity to supply chains. Warehouses must be compliant with these regulations while effectively managing logistics across geographical borders.
• Competency management: The constant turnover of employees and subsequent loss of knowledge become significant hurdles to long-term productivity, demanding constant effort to maintain a skilled and knowledgeable workforce.

The first step in warehouse optimization and transformation 

To meet customer needs, warehouse managers must optimize warehouse space and identify areas for improvement. For that to happen, warehouse managers need to be able to assess and visualize capacity and demand at the facility level and below. As new companies enter the market, the need for warehouses to be more competitive – and provide complex service offers – has increased. 

The evolving role of warehouse management systems

Warehouse management systems (WMS) fulfill a critical role in warehouse transformation. They provide a centralized software interface for processing, managing and monitoring operational processes. WMS have significantly evolved over the last few years. And this evolution includes cloud warehouse management systems which are currently in high demand. The popularity of the move to cloud is due, in no small part, to the fact that WMS that use cloud computing offer unparalleled flexibility, scalability, and cost efficiency. 

Applying a core model to your deployment approach shapes your WMS in a way that addresses your needs across all geographies to enable faster transformation projects at scale. 

But, nowadays, modern WMS are moving beyond streamlining core processes. They are increasingly incorporating new use cases like workforce management, enabling dynamic task allocation and improved labor efficiency. This is ultimately driving operational excellence in warehousing. 

As well as preparing a foundation for warehouse operations, WMS also set the stage for further optimization efforts, such as integrating with automation technologies to provide established business rules, communicate orders and enhancing the data visibility needed to unlock data-driven improvement use cases. WMS systems act as a pivotal enabler for the transformation of traditional warehousing into smart, connected operations.  

Warehouse automation and robotics

Warehouse automation that integrates into WMS has been accelerated by multiple technological leaps and fueled by massive investments from marketplace players. The need to address both traditional objectives (cost efficiency, service and quality), as well as new challenges (labor scarcity, assortment complexity) has made the decision to invest in automation easier to justify. 

While automated guided vehicles (AGV) and autonomous mobile robots (AMR) are the most visible tip of the iceberg within the solutions landscape, there is no “one-size-fits-all” approach making it important to consider the full scope of solutions within the warehouse and all the variables at hand. 

Warehouse automation and robotics solutions can boost labor productivity by 85%¹ allowing businesses to not only reduce labor costs but also face head on the shortages some experience. For instance, Exotec’s Skypod system with its put-to-light cells and orchestration engine enables end-to-end automated order fulfillment and achieved a throughput and efficiency increase of more than 600% for one of its customers. 

Data-driven optimization and IT/OT integration

A proper IT/OT strategy, including operational tools and IT integration, and a data factory approach, is just as important as the technology itself. It is key to empowering the supply chain as a strategic asset and to continuously identifying new business cases. Data analytics enables warehouses to harness vast amounts of data, created by their systems, to drive efficiencies, costs and CO2 footprint reduction through real-time monitoring and data-driven decision making. This data can be harnessed to drive extended visibility solutions such as digital twin, transportation control towers and process mining solutions to take operational issue diagnosis and resolution to the next level.   

Towards smarter and more sustainable warehouses 

Generative and agentic AI tools unlock a new enhanced layer of data intelligence. Revealing a realm of unprecedented adaptability and resilience will certainly revolutionize supply chains. Warehouse space optimization is a great example of how Gen AI allows you to go one step further than with traditional AI.  Discriminative AI can be used for warehouse layout constrained optimization, considering factors like distance travelled and tasks duration minimization, reducing congestion, and products category. Generative AI then enables layout generation for visualization, simulation of a wide range of scenarios (e.g. seasonal peaks) and suggestion of optimal layout. 

Finally, it is equally as important for industry stakeholders to prioritize sustainability as an integral part of warehouse optimization. By leveraging the power of technology and adopting sustainable practices, warehouses can not only enhance operational efficiency and customer satisfaction but also contribute significantly to mitigating the environmental impact associated with their operations. We believe this can be achieved through a combination of sustainable execution and sustainable assets. 

Sustainable execution involves implementing initiatives that promote the more sustainable use of existing assets and resources within warehouses, while “sustainable assets” focus on designing and utilizing assets that are inherently sustainable, such as eco-friendly warehousing facilities and sustainable primary materials. From reducing energy consumption and optimizing packaging to implementing effective waste management practices, new technologies also offer the potential to revolutionize the way warehouses operate in an environmentally conscious manner.  

Partnering for end-to-end warehouse transformation

As warehousing solutions continue to advance, it is crucial for industry stakeholders to collaborate with the right partners in their warehouse transformation journey. At Capgemini we provide end-to-end support for organizations, from the analysis of your current situation and exact context, the selection of the most suitable technologies that align with your goals and needs, the definition of the right implementation strategy and delivery execution, to the set-up of a continuous improvement approach to ensure your warehouse remains at the forefront of innovation. 

“As manufacturers face increasing pressure to deliver faster, smarter, and more sustainable operations, the way we design and build factories is undergoing a radical transformation. At Capgemini, we’ve been working with global leaders to rethink traditional approaches – leveraging digital twin technology to bring agility and intelligence to the factory floor.” 

–  Alexandre Embry  

A global consumer products company wanted to make building new factories simpler, smarter, and more efficient. Instead of starting from scratch each time, we helped them create a digital tool that lets teams design and compare factory setups virtually, choosing everything from product types to packaging lines. With built-in visuals, data dashboards, and AI-powered insights, the tool is now helping them plan better, move faster, and make more informed decisions. 

Reimagining factory design for the digital era

Designing a new factory is a complex, capital-intensive endeavor. Our client wanted to eliminate disruption points and boost both capital efficiency (CapEx) and operational efficiency (OpEx). The question: how could they standardize factory design globally while tailoring it to specific consumer goods?  

So, we innovated the process from the ground up. Instead of treating each new factory as a bespoke project, we built a plant configurator that lets engineers design production lines using a modular and digital-first approach. From selecting product types and packaging sizes to choosing suppliers and automation levels, users can now configure entire factories digitally, complete with 3D models, scanned documents, and real-time KPI dashboards. 

Building the Digital Twin: How we made it real 

We assembled an innovation team of business experts, data modelers, business analysts, 3D and digital twin specialists, and programmers, to develop the Digital Twin Configurator. Our solution helps create new digital twin content dynamically, on demand. To achieve this, we leveraged our Digital Twin Cockpit solution based on Microsoft assets and developed as part of Capgemini’s AI Robotics and Experiences Lab. It merges the assets built in our Lab with Microsoft data, AI and cloud standards, such as Copilot, Power BI, and several Azure components, enabling faster and consistent review of source standards and produced plant models. 

The tool guides users through each step of setting up a new production line—letting them choose product types, factory layouts, and equipment options, much like customizing a kitchen. Teams can compare different designs based on cost, energy use, and water consumption. The AI speeds up data entry, and built-in dashboards help track key metrics like emissions and operating costs. 

One of the biggest challenges was making sure the tool could handle many different factory types and still keep everything connected from the first design to final construction. 

Results delivered and the road ahead 

Our client now has a centralized, standardized, and replicable architecture for factory design. The digital twin configurator enables: 

• Setting up factories faster and more efficiently 

• Making smarter decisions about where to invest and how to maintain equipment 

• Comparing different factory setups using key data like energy use, water consumption, and operating costs 

The system is already helping top management make data-driven decisions. As the configurator evolves, it’s poised to become a blueprint for global factory design—scalable, smart, and sustainable. 

Learn more about our AI Robotics & Experiences Lab

The convergence of scientific computing and engineering is accelerating innovation in unprecedented ways.

As different sectors seek to tackle complex physical systems, optimize design and simulation, and unlock the next wave of scientific breakthroughs, Capgemini’s association with Wolfram stands as a powerful milestone. Together, we’re combining decades of expertise in symbolic computation, generative AI, and systems engineering to create what we call the Capgemini co-scientist framework—an intelligent assistant built for engineering rigor.

At the heart of this collaboration lies a shared belief: generative AI is transformative, but it must be grounded in scientific accuracy, auditability, and domain reasoning to truly serve the engineering and scientific community. To enable the robustness that scientists expect from their tools, Wolfram Language brings all the infrastructure needed for a scientific reasoning engine: unmatched breadth of symbolic computation, algorithmic modelling, and knowledge representation, resulting in a co-scientist that doesn’t just generate answers in a typical LLM way—it actually understands the problem and queries verifiable facts to produce trustworthy results.

From Natural Language to Verified Computation

What makes the co-scientist novel is its ability to go beyond text-based generation. By combining large language models with Wolfram’s symbolic reasoning capabilities and curated computational knowledgebase, users can input a natural language query and receive responses that are not only contextual but computationally synthesized.

Imagine asking co-scientist to simulate the thermal behaviour of a new material under varying conditions—or to optimize the control logic of a complex mechatronic system. Rather than simply returning a list of suggestions, the co-scientist can use real Wolfram Language code to compute precise equations, model dynamic systems, and integrate directly with engineering workflows in different environments.

Hybrid AI in Action

This collaboration brings the vision of Hybrid AI to life—an approach that blends language model fluency with symbolic reasoning, scientific simulation, and rigorous rules. It’s this hybridization that unlocks reliability and traceability in safety-critical domains such as aerospace, automotive, and industrial automation.

Hybrid AI enables iterative co-design, traceable decision-making, and seamless collaboration between AI systems and human experts. Our joint solution with Wolfram represents a concrete step toward AI systems that are not only assistive but trustworthy.

Engineering a Better Future, Together

Capgemini’s Augmented Engineering strategy is about more than just productivity—it’s about elevating human expertise through AI, enabling organizations to solve harder problems faster. Our work with Wolfram builds a bridge between natural language interfaces and the rigorous world of scientific computing, ultimately empowering engineers, researchers, and product teams to think more freely, design more confidently, and innovate more responsibly.

As this collaboration evolves, we are excited to bring the power of the co-scientist to real-world use cases—from sustainability analytics and advanced manufacturing to systems engineering and intelligent product development. This is the future of engineering: collaborative, explainable, and scientifically grounded.

Read more about the collaboration with Wolfram here

The United Kingdom (UK) is striving to bring about an electric vehicle (EV) revolution. EV adoption and rollout for UK citizens is an important part of the UK achieving long term sustainability goals.

However, the UK is currently falling behind the required charging infrastructure to support this revolution. According to the UK Infrastructure Bank, in 2023 an average of 1,600 charge points were installed per month – under half the required 3,250 to meet forecasted demand. The UK government also has an estimated shortfall of funding of £1.5 billion to build the necessary charging infrastructure to meet its goal of having 100% of all new vehicles being zero emissions by 2035.

Our geospatial analytics community at Capgemini Invent believe that geospatial data should be an important component in supporting the EV revolution, and policymakers should be fully utilising its potential.

In this article, they will review how the UK’s 2030 Geospatial Strategy supports the EV revolution, the key challenges the EV revolution faces in the UK, and finally how geospatial techniques offer solutions to support the transition to EVs.

Can the 2030 Geospatial strategy support the electric vehicle revolution in the UK?

The UK’s 2030 Geospatial Strategy, released by the UK Geospatial Commission, outlines a strategic framework for the utilisation of geospatial analytics and techniques by the public sector to support economic growth, while also fostering environmental stewardship and enhancing social well-being.

The strategy offers missions and opportunity areas for increasing the use of geospatial techniques to support a wide array of sectors. In relation to the electric vehicle revolution, it outlines a comprehensive framework and offers insights on how the public sector can further support electric vehicle adoption and infrastructure by utilising geospatial data and analytics.

The Challenges

Infrastructure building

The UK government estimates that 300,000 charge points will be needed to support a full EV rollout by 2030. The announcement by the UK government of a delay for this target gives more time, but UK is still short of EV charge points. As of March 2024, there are 59,590 EV charging points located across 32,322 charging locations. Building this infrastructure and the selected locations of charge points present a significant challenge – so what is preventing expansion of the network?

One of the main blockers is a lack of funding and financial incentives for the expansion. Although the UK government has committed £1.6 billion towards the UK’s charging infrastructure such as the Local EV Infrastructure (LEVI) fund and the Rapid Charging Fund (RCF), additional funding is still required as stated previously. Without sufficient investment and incentives for charge point infrastructure, EV rollout may be delayed and pushed further back.

Charge anxiety

Charge anxiety is a growing phenomenon challenging the adoption and rollout of electric vehicles. This can be defined as the fear that charge points are unreliable, too costly, and too sporadic to use effectively. This anxiety is interconnected with the need to expand the charge point network in the UK. Public confidence in electric vehicles is increasing, with over half of motorists between 16-49 stating they would switch to an electric vehicle within the next ten years. However, a general anxiety exists that the charge infrastructure is insufficient, and charge points themselves fail to meet consumer needs.

Equity and access:

Ensuring equitable access to the charge point system remains a critical challenge for UK citizens. Various segments of society encounter difficulties when trying to utilise the charge point network. One significant factor is the urban-rural divide. A County Councils Network report revealed that rural drivers have access to only one charge point per every 16 kilometres whereas London drivers enjoy a more favourable ratio of one charge point for every 1.2km.

Figure 1 below shows the rural/urban classification of local authority districts, and Figure 2 shows the number of EV chargers per km² for the same boundary areas. Comparing these maps visually shows that local authorities with a higher proportion of urban areas tend to have a higher density of EV chargers compared to those that are more rural. The boxplot in Figure 3 shows this to be true. The average density of EV charge points increases in local authorities categorised as more urban.

The second major factor is those living with no at-home access to charge points. This can be for varying reasons including living in a flat or not having access to off-street parking. The challenge remains that if citizens cannot access charge points at home, they will be forced to use public charge points as alternatives. This can result in higher costs relative to at-home chargers and poses a challenge in providing access to charging points for economically-disadvantaged individuals within society.

Solutions

Demand prediction and population movement

Geospatial analytics can provide detailed insights on origin-destination (O-D) movements, how travel patterns are changing, and where they differ from region to region. Traditionally this O-D flow data has been captured through census data. This has limitations based on sample sizes, frequency of updates and type of journey. Anonymised mobile phone data is rapidly becoming a more promising source. It has the advantage of modelling estimates for smaller areas and can generate more frequent and timely outputs, meaning it can respond to changing travel trends faster.

Geospatial analytics has a critical role to play in unlocking the travel flow insights. Utilising this data correctly can inform the prioritisation of where EV charge points are located and how future funding could be allocated. This aligns with the aims of the 2030 Geospatial Strategy which outlines the power of population movement data and how aggregated and anonymised data from mobile phone and apps can be utilised by the public sector.

Business fleets

Business fleets will play a critical part of the EV transition, however there are significant operational, financial, and logistical challenges that need to be considered to ensure feasibility. Combining advanced routing algorithms with geospatial analytics can help businesses scenario model fleet performance under a range of different conditions. This can help answer key strategic questions through data-driven analysis: What fleet mix will best suit business needs? Will depots have sufficient capacity to suit charging requirements? How adaptable is the fleet to meet future demands?

From an operational standpoint, geospatial analytics can be used to combine live data feeds from charge points, with vehicle routes, range, capacity, delivery requirements and driver schedules, to optimise routes – by minimising driver downtime and maximising efficiency.

Data and pricing

To combat the problems of equity and access, the effective use of data and standardised pricing are important to ensure all can benefit from both geospatial data and electric vehicles. Demographic, property, street, and traffic data can ensure all areas are adequately supplied with charging infrastructure.

Pricing is also an important aspect of ensuring equity between regions.  The UK government has tried to reduce charge anxiety through implementing regulations such as the Public Charge Point Regulations 2023. The regulations implement key changes including standardised pricing metrics to help improve consumers’ experience with charge points.

The future is geospatial and electric

Many challenges still exist for the transition to electric vehicles to succeed. The need to build the required infrastructure for electric vehicles and the charge anxiety that still exists amongst the British public will hinder any EV revolution. However, the 2030 Geospatial strategy seeks to alleviate some of these concerns through offering a strategic framework to enable the use of geospatial data and techniques by the public sector. Geospatial analytics is key in supporting the EV rollout and to enable consumers to make informed travel decisions in the future.