In today’s fast-evolving retail landscape, data and AI are no longer optional – they’re existential. In our recent POV, ‘Five tried and tested operational principles for retail resilience’ we briefly highlighted how AI can enable dynamic decision-making at scale and act as a co-pilot to commercial, supply chain, and store operations teams. AI is not the silver bullet, but it goes beyond traditional analytics by explaining, recommending, and acting in a way that maximises product profitability.

The most forward-thinking retailers are already years ahead, leveraging these technologies to drive efficiency, profitability, and customer satisfaction. They’ve made conscious decisions to master all things data & AI to remain competitive.

But for those whose data & AI strategies aren’t quite as mature, how can they catch up?

Let’s unpack the top use cases for data & AI in retail and learn how to put in place the critical foundations to get there.

Top use cases for data & AI in retail

• AI-powered recommendation engines – Generative AI (Gen AI) can power commercial recommendation engines that automate millions of micro-decisions. These systems optimise product suggestions, pricing, and promotions in real time – boosting conversion rates and customer satisfaction.
• Demand forecasting & inventory optimisation – AI models can predict demand with high accuracy, helping retailers reduce overstock and stockouts. This leads to better inventory turnover, lower costs, and improved customer experience.
• Dynamic pricing – AI can be used to adjust prices dynamically based on demand, competition, and customer behaviour[CM1] . This ensures competitiveness while protecting margins.
• Customer segmentation & personalisation – Advanced analytics enable hyper-personalized marketing by segmenting customers based on behaviour, preferences, and lifetime value.
• Supply chain optimisation – AI helps streamline logistics, predict disruptions, and optimise delivery routes – making supply chains more resilient and cost-effective.

To get to these levels of sophisticated granularity and speed of response, you need highly automated data, analytics, and execution capabilities.

This means:

• Solid, near real-time data foundations
• As automated as possible data & AI sitting on top of those foundations
• Considerations for where you might use agentic AI for decision-making and execution – for example, solving complex sales and customer service queries or deciding real-time pricing decisions in an online environment – with the right guardrails and human oversight

That’s the end goal. So, how do we get there?

• Improving data literacy and culture across the organisation

First, success in data and AI starts with a top-down belief in its existential importance. Data transformation isn’t just technical – it’s cultural.

Many organisations choose to move in the data foundations direction first before trying to advance their culture and literacy. But one of the characteristics right alongside data foundations is concerted efforts to raise the level of data literacy and confidence and belief in the data.

In our ‘Data-powered enterprises’ research, we found 80% of surveyed data executives in our ‘data masters’ category had defined a strategy to become a data-powered organisation, compared with 61% of others. Also, 81% of data executives in the ‘data masters’ category stated that all business areas in their organisation had a defined data/analytics strategy and roadmap, compared with 64% of others. For example, for a large grocery retailer currently undergoing a data transformation programme, a significant pillar is a dedicated central organisation with 100+ people dedicated to driving data literacy and culture, and data & AI enablement.

Of course, getting to this point requires building trust in data – closing the gulf that often exists between IT and business execs with a combination of data foundations and data literacy and culture. There’s no point spending time and money cleaning your data, building models and tools, then putting them into the hands of end-users and decision-makers who choose not to use them.

Overall, organisations that focus more on behaviours (culture, change management, leadership) and foundations together reap the most benefits.

• Strong end-to-end data & AI strategy framework

Without strong data foundations, AI cannot deliver value at scale, reliably or repeatedly. This strategy framework should include:

• Clean, governed, and secure data
• Fit-for-purpose data architecture
• Scalable data platforms
• Real-time data pipelines

Protecting your data as an asset is crucial. This covers all elements of your data foundations; protecting and securing data with your standards and governance, sufficiently good data quality and master data management processes. It’s implementing the right operating model, establishing structured approaches to data management and making sure that you’ve got modern fit for purpose, data architecture, and technology.

This is a key pillar in any data transformation. We see many of our clients getting stuck in the proof-of-concept (PoC) trap – spending vast amounts of money on ideas that they simply can’t scale. In fact, 75% of data executives surveyed by the Capgemini Research Institute cited large-scale deployment of generative AI PoCs as a major challenge.

Instead, we always recommend a proof-of-value (PoV) to demonstrate  that a solution not only works but also delivers measurable business benefits, such as increased efficiency, cost savings, or improved customer outcomes. Broader than a PoC, this will often include real data, user testing, and performance metrics.

Having good, consistent standard quality data foundations means you’re much more likely to be able to repeatedly and reliably scale more advanced analytics and AI applications that you build on top.

• Building AI models that operate with independence

As customer expectations shift toward seamless, real-time digital experiences, retailers are under increasing pressure to deliver instant, personalised interactions – especially in areas like pricing and promotions. This evolution demands not only robust real-time data infrastructure but also advanced analytics and AI systems capable of making autonomous decisions at scale. After all, when rolling out dynamic pricing for millions of customers, keeping a human in the loop becomes impractical. Impossible, even.

Therefore, you need to design AI models that can operate with a high degree of independence. This requires a carefully architected operating model where automation is governed by clearly defined rules, ethical boundaries, and oversight mechanisms. Strong governance frameworks are essential to ensure that these autonomous systems make decisions that are not only fast and scalable but also aligned with business values and regulatory standards.

Partnering for the AI-powered future of retail

Mastering data and AI is no longer a competitive advantage, it’s a necessity[CM4] . But getting there requires more than just technology. It demands a cultural shift towards data-centricity, robust data foundations, and the ability to scale transformation across the enterprise. It pays to bring partners [CM5] on board to help you navigate these complexities.

As your business and technology transformation partner, Capgemini’s team of data & AI and retail experts can bring:

• Deep retail industry expertise – With decades of experience working with global retailers, we understand the nuances of your business and the evolving expectations of your customers.
• Leading data & AI capabilities – From real-time data platforms to advanced AI models and Gen AI applications, we help you unlock value at every stage of your data journey.
• Scaled transformation delivery – We don’t just design strategies – we implement them at scale, embedding change across people, processes, and platforms.

Whether you’re just starting your data journey or looking to scale AI across your enterprise, Capgemini can help you build the foundations, culture, and capabilities to thrive in the AI-powered future of retail.

Get in touch to start that transformation today.

In the evolving landscape of public sector organisations, the need for efficient, transparent, and citizen-centric services is paramount. The partnership between Capgemini and ServiceNow offers a robust solution to these challenges, combining advanced technology with deep industry expertise to enhance public sector operations. This blog explores the benefits of leveraging Capgemini and ServiceNow together, with a focus on the transformative power of agentic artificial intelligence (AI) and its impact on employee experience.

Introducing Agentic AI: The Next Frontier

While traditional AI responds to commands, agentic AI takes initiative. It can plan, decide, and act autonomously to achieve a goal—without needing step-by-step instructions. For example, instead of simply responding to a service request, agentic AI could identify a recurring issue, initiate a resolution workflow, notify stakeholders, and follow up with a summary—all without human prompting.

This shift is especially powerful in the public sector, where complexity, compliance, and scale often slow down service delivery. Agentic AI enables:

• Proactive Service Delivery: Anticipating needs and resolving issues before they escalate.
• Autonomous Task Management: Managing multi-step workflows across departments.
• Enhanced Decision-Making: Synthesising data to support faster, more informed decisions.
• Human-Centric Design: Freeing employees from administrative burden so they can focus on meaningful work.

Synergy in the Public Sector

The collaboration between ServiceNow and Capgemini brings together the best of both worlds: ServiceNow’s powerful digital workflow platform and Capgemini’s deep expertise in digital transformation. Together, they are empowering public sector organisations to create intelligent, efficient, and engaging employee experiences.

• Automating Routine Tasks: ServiceNow’s AI capabilities automate repetitive processes. Capgemini tailors these automations to maximise impact.
• Predictive Analytics: ServiceNow predicts incidents and disruptions. Capgemini’s data analytics expertise helps make informed decisions.
• Personalised Experiences: ServiceNow analyses employee behaviour to deliver tailored support. Capgemini ensures these experiences are engaging and intuitive.
• Enhanced Collaboration: ServiceNow’s AI-powered tools facilitate seamless communication and teamwork. Capgemini drives adoption and fosters a culture of collaboration.
• Continuous Improvement: ServiceNow monitors performance and gathers feedback. Capgemini ensures organisations stay ahead of the curve.

Enhanced Employee Engagement and Productivity

Employee engagement is crucial for the success of public sector organisations. Studies have shown that engaged employees are more productive and provide better service. ServiceNow’s employee centre improves engagement by providing a unified experience for employees, managers, and support departments. This leads to increased employee satisfaction and a reduction in service desk calls related to internal issues.

Capgemini’s people experience transformation approach further enhances this by addressing broader aspects of employee engagement, such as inclusivity, well-being, and work-life balance. By focusing on these areas, Capgemini helps create a positive and supportive work environment.

Proactive Problem Management and Automation

ServiceNow’s proactive problem management capabilities identify recurring issues, reducing the number of support requests and improving overall efficiency. The platform’s automation features streamline repetitive tasks, allowing public sector employees to focus on more strategic activities. This not only improves productivity but also enhances the overall employee and citizen experience.

Capgemini’s expertise in operational change management, adoption strategies, and UX design ensures that these capabilities are effectively implemented and adopted. By providing tailored solutions that meet the unique needs of public sector organisations, Capgemini helps maximise the impact of ServiceNow’s automation and problem management features.

AI-Driven Insights and Continuous Improvement

ServiceNow’s robust analytics and reporting tools provide valuable insights into public sector processes, enabling organisations to make data-driven decisions. Capgemini enhances this by incorporating AI capabilities, which continuously analyse interactions and feedback to suggest enhancements and optimisations. AI-driven insights can identify patterns and trends that might not be immediately apparent, allowing for proactive adjustments and improvements.

For example, AI can analyse citizen feedback to identify common issues and suggest solutions, improving service delivery and satisfaction. Additionally, AI can predict future service demands based on historical data, enabling public sector organisations to allocate resources more effectively.

Real-World Impact: Case Studies

• Healthcare: A major hospital system partnered with ServiceNow and Capgemini to enhance HR service delivery. By automating routine HR tasks and providing personalised support, the hospital improved engagement, reduced turnover, and enabled staff to focus on patient care.
• Local Government: A council used ServiceNow’s AI to automate IT service management. With Capgemini’s support, the council reduced incident resolution times by 40%, improved employee satisfaction, and achieved significant cost savings.

The Future of Employee Experiences in the Public Sector

As AI evolves, agentic AI will play a pivotal role in transforming employee experiences. The collaboration between ServiceNow and Capgemini is at the forefront of this shift—empowering organisations to create workplaces that are not only efficient but also adaptive, intelligent, and human-centric.

By embracing agentic AI, public sector organisations can unlock new levels of autonomy, innovation, and impact—ensuring their people are equipped to thrive in the digital age.

Conclusion

The partnership between Capgemini and ServiceNow offers a comprehensive solution for transforming public sector operations. By combining ServiceNow’s powerful technology with Capgemini’s deep industry expertise, public sector organisations can create a unified, efficient, and engaging work environment that drives productivity and satisfaction. Whether it’s through improved operational efficiency, enhanced employee engagement, proactive problem management, AI-driven insights, agentic AI for decision-making, seamless integration, scalability, enhanced security, cost efficiency, or improved citizen satisfaction, the synergy of Capgemini and ServiceNow delivers significant benefits for public sector organisations looking to thrive in today’s dynamic landscape.

Transform your analytics with compelling data storytelling, where the magic lies not just in the numbers, but in how you present them to drive impactful decisions.

Have you ever sat through a meeting where someone is explaining a report that is so busy and packed with numbers, but you leave without learning a thing?

Maybe the content was solid, but the delivery just didn’t land. You’ve probably experienced it – monotone voice, no structure, no spark. And let’s be honest, in analytics and tech, that happens more than we’d like to admit. We’re often so focused on the accuracy of what we’re saying that we forget to think about how we’re saying it.

In my own career, I’ve learned that it’s not just about technical ability. Storytelling, communication and confidence when speaking can completely change how your message is received. That’s why I believe soft skills are just as important as technical skills in AI and analytics.

Why Storytelling Matters in Analytics

Analytics is about uncovering insight. But if you can’t explain your findings in a way that people connect with and understand, then it doesn’t matter how complex your model is or how clean your data is. It just won’t stick.

When we present dashboards, reports or analysis, we’re often hoping to drive change or make decisions. Data doesn’t speak for itself, you do. And the way you present that data is the bridge between insight and action.

I do a lot of personal development around building communication skills, especially public speaking and presenting. It’s something I genuinely enjoy and try to pass on to others in my team and community. But it’s not about becoming a TED speaker overnight. It’s about understanding that presenting is a skill you can build over time, and that small changes can make a big difference.

The Science of How People Take In Information

Here’s something that always stuck with me: when people take in information, only 7 percent comes from the actual words we say. The rest? Around 23 percent is from how we say it – our tone, pitch and pace. And 70 percent is body language.

So, when we present analysis and insights, it’s not just about being factually correct, it’s about being engaging, confident and human. Are you making eye contact? Do you sound like you care about what you’re saying? Are you giving your audience a reason to care?

The most effective analysts I’ve worked with aren’t just great with Python or Power BI. They know how to read the room, connect with people, and tell the story behind the numbers.

Making Your Presentations More Engaging

There are so many practical things we can do to improve how we present data. A few that have worked for me:

• Use analogies and metaphors – Help people relate data to something they already understand. It makes the complex feel simple.
• Give structure – Start with the ‘so what’, then walk through the supporting points. Avoid drowning people in detail too early.
• Visual storytelling – Don’t just show a graph. Explain what it means, why it matters, and what you recommend.
• Vary your tone and pace – If you sound bored, your audience will be too. Pause to let key points land. Emphasise what matters.
• Practice out loud – It sounds simple, but reading a presentation in your head isn’t the same as saying it aloud. You’ll quickly spot what’s clunky or unclear.

Sharing What You Know

What’s been most rewarding for me is not just developing these skills myself, but helping others grow in this space too. Whether that’s through speaking clubs, mentoring or informal coaching, I’ve found that everyone benefits from learning how to tell a better story with their work.

Soft skills often get labelled as “nice to have” in technical roles, but I would argue they are essential. A technically brilliant piece of work that no one understands or acts on is a missed opportunity.

If we want to influence, lead and make an impact with our data, we need to bring it to life.

So next time you are building a presentation or walking someone through a dashboard, ask yourself – what story am I telling? And how can I make it land?

Bringing It All Together: The Capgemini Approach

Here at Capgemini, we recognise that being a great consultant goes far beyond technical expertise. That’s why we invest time and energy into helping our people become well-rounded consultants – those who cannot only solve the complex data problems but communicate the insight in a way that lands with impact.

Because at the end of the day, we know it’s not just about delivering dashboards or building models, it’s about adding value to our clients. And that happens when we combine the power of data with the power of communication.

We’re hiring for a number of roles across SAP, Data and Analytics. Rewrite your future with Capgemini.

The way in which we construct must undergo a fundamental transformation. Traditional methods won’t solve today’s challenges; what we need is a radical reset. Rethinking outdated models is essential to deliver faster and more cost-effectively, with greater resilience, lower carbon emissions, and infrastructure built for the future. But how?

Part of Capgemini’s ‘Future of Energy Transition and Utilities’ series

Capgemini’s What’s NOW | Constructing Tomorrow event – hosted by our Applied Innovation Exchange – united leaders across energy, utilities, transport, mobility, manufacturing, and engineering to discuss just that. Together, we explored how to reimagine infrastructure delivery, embrace digital innovation, and create a resilient, future-proof foundation.

Read on for our key takeaways from the event.

The old ways are obsolete

The UK’s infrastructure sector is at a tipping point. Ageing assets, a growing population, and mounting sustainability demands persist despite record investments. Projects face an average 20-month delay and cost overruns of up to 80%. With major investments like BlackRock’s acquisition of Global Infrastructure Partners and Ofwat’s £88 billion water initiative, innovation is no longer optional – it’s critical.

However, according to Capgemini partner Autodesk’s Construction Disconnected report, we’re starting from a place of fragmentation:

• Teams – 35% of time is wasted on non-optimal activity. E.g. waiting for info on a construction site
• Projects – £280 billion is spent on annual re-work costs created by poor project data and communication
• Business – 95.5% of all data is wasted. The average AEC firm uses 26 TB of storage space on data but almost all of it goes unused as it passes down the chain. O&M teams reproduce all the same data again and again
• Legacy collaboration – manual workflows lead to ineffectiveness and time wasting

We’re also up against massive challenges:

• More expenditure – an increase of £ 1.2 trillion from 2021 to 2030, with most sectors experiencing 1.7x increase from previous cycles.
• Limited resources – workforces are moving to other growing industries/geographies and supply chains are stretched thin.
• Shifting targets – an increased focus on the natural environment and building resilience.
• Regulatory requirements – the complexity and frequency of regulatory changes necessitate robust systems for monitoring and implementing compliance measures

Without adopting a digital-first mindset, progress is impossible. It’s critical to rethink how we build, design, and deliver in the face of these challenges.

Digital must be part of the construction process

Construction is trailing behind other industries in realising the benefit of digital advancements. Integrating digital delivery with physical engineering can significantly boost capital delivery productivity, enhancing project outcomes and performance across all lifecycle stages by:

• Ensuring your workforce can adapt to this changing landscape
• Aligning unconnected processes through a complex supply chain

Our perspective is that we need a step change in strategies to drive a fundamental shift in construction performance:

• Digital engineering and AI maturity. The growth and capability in Digital Engineering platforms & AI solutions today vs. three years ago is game changing. The opportunity is here and now, not three years away – digital business strategies should be planned across multiple horizons.
• The business case is compelling. Speed, productivity, lower overheads, reduction in overruns, and reduced variances all positively impact the bottom line, as well as investment and delivery plans.
• Status quo, incremental change will fail. We need disruption and bold ambition to drive change. History suggests the same outcome five years from now, where incremental adjustments are the norm, planning horizons are too long, and too “tomorrow” biased.
• Delivery models must evolve. There needs to be a core shift in intelligent owner/operator models, more deeply integrated with delivery vehicles and supply chains. We also see the requirement for a Digital Delivery Partner model as well as new internal digital engineering skills. Read more about our thoughts on this shift here.

Exploiting digital isn’t just limited to building – there are scores of benefits to drive from introducing digital into your operations. Gen AI can enhance your material specifications, design documentation, quality checking, business cases, and much more. It’s a win/win for CapEx, OpEx, and fundamental business performance.

AI can transform your construction and operations

At What’s NOW | Constructing Tomorrow, some of Capgemini’s most innovative AI partners showcased their digital construction solutions. Including:

• Autodesk’s Autodesk Docs – a Common Data Environment that enables everyone to access the same repository of information. Supporting all types of file formats, including PDFs, data, drawings, and info models, it provides fundamental collaboration capabilities, connecting people, processes, and data to improve performance.
  
• BlackShark.ai’s AI-driven geospatial platform that converts sensor and pixel inputs into scalable, intelligence-ready data for GIS and 3D environments. Within construction, this software can rapidly create large scale 3D terrains to be used for:
  • Logistics planning and simulation
  • Presentations
  • Proposal submission
  • Virtual design
• Siemens’ Xcelerator platform that connects multiple buildings of any size, and of any connectivity under one unified platform approach. Giving the building owner full transparency of the construction progress; the engineering consultant management of design reviews with multiple disciplines/stakeholders; the builder and contractor management of information during production with a frictionless handover; and the facilities manager a complete handover that meets operational requirements.

Change is a journey; change is a mindset

In a wrap-up panel discussion, led by Paul Haggerty, Vice President, UK Market Head for Energy Transition and Utilities at Capgemini, we explored the cultural shift needed to drive construction forward in the digital age.

Key insights included:

• Legacy mentality vs. digital mindset –Addressing the key challenge of moving from an analogue to a digital approach requires a fundamental shift in thinking, led by leadership. For example, starting afresh by digitising only the essentials rather than absolutely everything.
• Balancing digital and analogue –Integrating digital advancements with existing analogue systems is challenging, particularly in large utilities. A harmonious blend is needed to ensure efficiency and effectiveness.
• Driving change through people –Bringing people along on the digital journey is crucial. Blockchain can play a role in ensuring payment confidence within the supply chain, but collective buy-in is essential for successful change.
• Addressing AI/human workforce concerns – AI can significantly enhance job performance when properly integrated. Leaders need to guide their teams through the transformation and show them their place in the digital workforce to alleviate anxiety about AI replacing human jobs.
• Involving procurement teams – Including procurement teams in the digital transformation – not just focusing on those connected to digital or the asset – is vital to avoid safety and risk issues. Resistance to change due to safety concerns with new processes can create significant obstacles.

Get in touch

Across industries, construction stands at a pivotal moment. The challenges of outdated methods, fragmented data, and increasing demands necessitate a radical shift towards operational change and digital integration. The journey towards a digitally driven sector is not without its hurdles, but the opportunity is immense. By leveraging advanced digital tools and fostering a culture of operational excellence in delivery methods coupled with disruptive innovation, we can transform the way we build and operate. It’s time to move beyond incremental changes and adopt bold, disruptive strategies that will drive significant improvements in performance and sustainability.

The insights shared at What’s NOW | Constructing Tomorrow event underscore the urgency of embracing digital innovation to enhance productivity, reduce costs, and build resilient infrastructure for the future. We must act now.

If you’re ready to explore how digital solutions can revolutionise your construction projects, our team of digital experts is here to help. Get in touch with us today to learn more about how we can support your journey towards a smarter, more efficient future.

The Future of Energy Transition & Utilities.

Capgemini’s ‘Future of’ series explores the challenges facing global energy and utilities businesses today and the opportunities these challenges create. Discover how, with vision and ingenuity, you can accelerate the pace of digital adoption across the value chain, delivering both quick wins and long-term dividends in the future. For your business, your consumers, and the environment.

Explore the rest of our ‘Future of’ series here.

In the recent ‘The State of the Border’ report, 1 in 8 businesses indicated that they have struggled to organise a state-authorised ‘Official Veterinarian’ (OV) to sign UK export health certificates (EHCs) in 2024. EHCs are required for moving food, plant and animal products internationally, and as a result such issues cause a direct impact on trade across the border.

Current Challenges:

The UK faces an estimated 11% shortage in the veterinary sector workforce. Meanwhile, over the past six years demand for vets has increased due to:

• Post-Brexit certification regime
• Rise in pet ownership post-Covid
• Managing increasingly frequent disease outbreaks 

The wider spread of disease means more restrictions on animal keepers to move and export their livestock, stifling economic activity and growth. Total losses from endemic livestock diseases are estimated to cost the UK livestock sector between £290m and £710m per year (BBSRC). Historically, the cost of each exotic disease outbreak has ranged between £2m and £3bn (Defra, as cited by the Royal College of Veterinary Surgeons). 

The UK APHA (Animal and Plant Health Agency), an Arm’s Length Body (ALB) of Defra relies on veterinarians to help monitor animal health and report outbreaks of infectious ‘notifiable’ diseases in farm animals. In the event of a disease outbreak, vets work directly with APHA and animal keepers to implement control measures such as culling, movement restrictions and disinfection protocols as well as to disseminate information about disease prevention, biosecurity measures and veterinary medicines.  

The shortage of veterinary staff increases the risk to effectively responding to animal disease outbreaks: a significant consideration given the increasing frequency and severity of animal disease outbreaks.  Climate change is a key factor contributing to this increase. For example, a warming climate in the UK has extended the midge activity period by 40 days since the 1980s with potentially severe consequences for animals susceptible to the bluetongue virus. (The Pirbright Institute). As such, the continued shortage of veterinary capacity in the UK risks endangering animals, public health, food security, and economic stability. 

How Technology Enabled Field Services Can Boost Veterinary Efficiency

Technology-enabled field services transformation can make more effective use of limited veterinary resources in the UK, whether during a disease outbreak or as a matter of course.  Lessons from other industries like gas, utilities and rail and others that also rely heavily on efficient field services, provide a blueprint for advancements in veterinary field operations.

Modern field services tools have been shown to help reduce downtime, improve efficiency, boost staff satisfaction, and combat carbon emissions – all of which are areas of strategic importance for the veterinary sector and to Defra.  To maximise impact and drive the adoption of best practices, field services transformation must follow a carefully considered pathway.  Organisations must focus on three key personas pivotal for field services to work effectively:

• The dispatcher
• The field operative  
• The customer

Generative AI is paving the way for more efficient and effective field services and is a key enabler of this transformation.

The Dispatcher is responsible for scheduling the field operatives. Their challenge is one of optimisation and rapid response: getting the most out of the field workforce while being able to mobilise rapid response to emergencies. 

Modern AI-equipped field services tools focus heavily on empowering the dispatcher. They make effective use of data and targeted automation to minimise task repetition and improve decision-making. For example, generative AI can significantly improve work order creation and scheduling. AI can help create work orders from unstructured information like emails or voice calls, reducing double entry and errors. This streamlines the process and ensures accuracy. 

In ‘business-as-usual’ scenarios, such tooling helps plan optimum routes from job to job and minimise vehicle journey times. AI can help match people with the right skills and certifications to jobs, forming optimal crews, and minimising travel.  It is helpful in making optimisations based on live adjustments for traffic or roadworks and responding dynamically to cancellations or rescheduling requests. AI can also handle rescheduling when unexpected events occur, such as an engineer calling in sick. This dynamic rescheduling can be done autonomously or semi-autonomously, ensuring that operations run smoothly even in the face of disruptions.  

With these tools and AI-based recommender systems, we’ve witnessed a fundamental rethink of the traditional job scheduling approach. Some organisations are moving away from a set schedule communicated to the field operatives in advance and subsequently adjusted when disruptions and delays occur. Instead, they are embracing a fully dynamic schedule like those used in gig economy scenarios. In these cases, field operatives and crews ‘subscribe’ to a ‘queue’ of jobs and are only presented with the details of their next job when their current job is completed. 

Organisations that have used these tools have seen as much as 13% improvement in asset maintenance efficiency creating significant cost savings. We’ve also witnessed route optimisation creating an important secondary benefit of 10-15% reduction in CO2 emissions (Capgemini, Scottish Water) 

Additionally, IoT integration is also crucial for predictive maintenance. For example, water level sensors can be used for flood management, and smart seals in vehicles carrying perishable goods can ensure quality and compliance. AI can analyse data from these sensors to predict potential issues and proactively address them. Smart seals in vehicles carrying perishable goods can monitor temperature and quality, automatically triggering inspections if safety thresholds are breached. Crucially, these tools also shine in emergency situations. Inbuilt visualisation tools, automation and AI maximise the speed and effectiveness of any emergency response that a dispatcher needs to mobilise. The dispatcher can quickly and easily reprioritise the schedule and re-direct critical resources to where they are needed the most. This can be done by manually leveraging powerful visualisation tools or with help from a bespoke optimisation model tuned against critical business KPIs to aid the dispatcher in maximising impact. Autonomous agents can either automatically or semi-automatically adjust schedules, proposing new plans that a human can validate.

The Field Operative is tasked with getting the job done, be it routine maintenance, addressing an animal’s health needs or responding to an emergency. Their challenge is one of information – knowing where to go, what needs doing and what to bring.  

We’ve seen as much as a 19% capacity creation when enabling field operatives with AI-based in-field tools. Such tooling, usually packaged in a dedicated app on their work phone or tablet, is designed to be available ‘on the go’ and work in areas of limited connectivity. 

Having access to AI benefits field workers in several important ways. Voice-to-text annotations allow field workers to describe observations as they see them, eliminating the need for after-job report creation. This is particularly useful in environments where typing is challenging due to safety gear or other constraints. 

Natural language queries enable field workers to ask questions and get answers from corporate knowledge and job-specific instruction manuals without needing to search through large documents. This improves efficiency and ensures that workers have the information they need at their fingertips. Some tools also allow field staff to remotely contact an expert via a video link and collaborate on the issue. 

In its most advanced application, AI can also help in analysing images taken by field workers and informing a potential diagnosis, such as assessing the severity of a disease in an animal. This can be further enabled with mixed reality headsets which can overlay information directly onto what the operative is seeing.  Benefits of in-field tooling extend beyond AI use-cases. Operatives also greatly benefit from having a complete history of the customer information, service history and the specific job. Timely prompts and job-specific checklists ensure crews bring the correct equipment, tools, and supplies to complete the job. We’ve witnessed a 25% improvement in first time fix rates because of these improvements (Capgemini, Scottish Water).

The Customer: No transformation would be complete without careful consideration of the end-recipient of the services being provided.

To meet the ever-increasing customer expectations for seamless service, organisations have integrated their in-field workflows with other customer touchpoints to create a coherent cross-channel experience. In this mode, field staff are increasingly seen as the literal ‘face’ of the organisation they represent. For example, customers expect field staff to have the relevant details of any prior interactions they may have had with the organisation providing the service, whether that’s via phone, webchat, email or during earlier visits.  

Customers also expect to interact with the field services in new and different ways. For example, some organisations provide interactive maps so the customer can check when the crew is expected to arrive or provide real-time email or text updates on the progress of the job, or any disruptions they may face and any delays in completing it.  

To further empower the customers and reduce the demand on often stretched field services, organisations are also focused on leveraging the latest technology and AI to enhance self-service. Generative AI can make self-service multimodal, allowing users to upload images or speak to a voice bot. For instance, customers can take a photo of a charging point and benefit from AI automatically analysing the image to diagnose issues and provide guidance. This approach can be applied to various scenarios, such as self-diagnosing health issues with plants or animals.  Additionally, AI can enable self-service booking for appointments, such as scheduling a vet visit, which can be done 24/7 without needing to call during office hours. This not only improves convenience but also ensures that services are always accessible.

 Conclusion

The need for a standard risk-based approach, powered by modern digital field services technology, AI and automation is key to managing field services and animal disease outbreaks.

Disease outbreaks can be all-encompassing events that require a rapid, co-ordinated and decisive response. This is difficult to truly master with a shortage of skilled veterinary resources who are already stressed and overworked and is further impacted by limited data sharing between private veterinary practices and parties involved in response to any emergencies.  

Lessons learned and best practices across the industry can serve as a valuable benchmark of what the art-of-the-possible could look like to inform the next generation of veterinary field services in the UK that are fit for the needs of the animal keepers, veterinarians, the economy and the wider community.   In today’s fast-paced world, GenAI is revolutionising how we approach various tasks and processes and brings game-changing efficiency gains. By leveraging AI, we can indeed enhance efficiency, create more robust services, and provide better customer and employee experiences.