SAP can transform data into actionable insights for your business.

Many organisations are becoming familiar with SAP Business Data Cloud (BDC) and its role in unifying SAP’s data and analytics portfolio. However, there’s often less clarity around how SAP Analytics Cloud (SAC) fits into that picture and how it’s being extended through a growing set of Intelligent Applications.  

These pre-built, embedded applications are one of the most significant developments within BDC but are frequently overlooked in early-stage discussions. They provide a new way to deliver rapid, tangible insight across business domains. As embedded components, they reduce time to value and allow organisations to benefit from BDC’s unified data infrastructure.

Understanding BDC Architecture

BDC brings SAP’s data landscape into a single, streamlined environment. At its core are Data Products: predefined, governed data building blocks for areas such as finance or HR. These can be used directly in analytics or planning without duplication, ensuring consistency and reducing manual effort.

SAP Datasphere underpins BDC as the semantic layer, providing governance and business definitions across all assets, while SAP’s integration with Databricks enables advanced analytics and machine learning without replicating data. 

On top of this clean, modern core, SAC provides the visualisation and planning layer. It enables browser-based interaction with governed data in Datasphere and BW, removing the need for fragmented tools or complex data movements.

What role does SAC play?

Within BDC, SAC is the main entry point for business users. It brings together dashboards, reporting and planning in a single environment powered by live data. Because SAC integrates actuals with plans, it supports Extended Planning and Analysis (xP&A) across finance, operations and other domains. This helps teams respond more quickly to changing conditions, using real-time inputs without manually reconciling data across systems. 

BDC enhances this by incorporating real-time operational data into planning scenarios. SAC’s AI tools, including the Joule co-pilot, provide contextual insights directly within the interface. The integration with Databricks extends access to advanced models, making data science more accessible to non-technical users.

Intelligent Applications = instant value 

Intelligent Applications (previously known as Insight Apps) are domain-specific analytical solutions that run directly within SAC. Designed for functions such as HR, finance, supply chain and procurement, they come pre-loaded with KPIs, logic and planning scenarios.

Examples include Finance Intelligence, which quick starts general operational finance reporting, and People Intelligence, where workforce management is provided in a few clicks.

These apps are activated from within the BDC workspace and appear in SAC as ready-to-use dashboards or planning environments, complete with their own data models. They consume curated Data Products from Datasphere, avoiding the need for custom data pipelines or duplication. They also inherit definitions, hierarchies and access permissions from the semantic layer.

This significantly reduces deployment friction and allows faster delivery of value. For instance, People Intelligence, due in late 2025, will support workforce planning by offering insights into skills distribution and compensation. Other upcoming apps will focus on ERP, customer intelligence and financial forecasting.

A strategic future for SAC

SAC is central to SAP’s long-term analytics vision. As BDC becomes the standard platform, SAC will be the primary interface for analytics, planning and decision support.

It does more than display data. SAC connects directly to governed sources via Datasphere, maintaining consistency and reducing duplication. It allows users to model scenarios and align plans with actuals, all within the same environment.

SAP is investing in making SAC more intelligent and more embedded. Intelligent Applications will continue to expand, offering business users structured, guided analytics aligned with best practices. With tools like Joule and a growing application layer, SAC is becoming a platform for embedded intelligence that helps automate parts of the decision-making process.

Migration and adoption: what are the challenges and opportunities?

Transitioning to BDC varies by organisation. Some are migrating from traditional BW, others already use SAC and Datasphere, and some are adopting SAP analytics for the first time. All face the same challenge: achieving a smooth transition that preserves value and sets the stage for modern analytics. 

As your business and technology transformation partner, Capgemini can help you assess your current architecture, determine BDC readiness, and design tailored migration plans. For those on BW, a common approach involves lifting content into BW PCE first, then reusing logic in Datasphere over time. Others focus on simplifying pipelines and adopting Data Products to reduce modelling overhead. 

Intelligent Applications often play a key role in these journeys. Capgemini supports clients in preparing compatible data models, identifying valuable use cases, and customising apps where required. This ensures a faster route to benefit and a solid foundation for scaling.

Strategic considerations for leadership

BDC shifts analytics from an infrastructure focus to an outcome-driven model. By embedding SAC and Intelligent Applications into daily operations, businesses reduce custom development effort, accelerate delivery, and improve agility.

BDC’s semantic layer, supported by a knowledge graph, provides trusted, explainable insights. This is essential for regulatory compliance and responsible AI. Its multi-cloud support allows organisations to adopt flexible deployment models and prepare for future innovation, including generative AI.

Licensing for Intelligent Applications is included with SAC only when licensed as part of BDC. Organisations using SAC standalone will not have access to these apps by default. Pricing is based on named users, with additional charges for planning users or advanced functionality. Understanding this model early is important for budgeting and deployment planning. Capgemini helps clients map licensing to roles and use cases, ensuring maximum return on investment.

Moving forward with confidence – how Capgemini can help

SAP BDC represents more than just a platform shift. With SAC and Intelligent Applications at its core, it changes how insight is delivered: faster, embedded, and governed from the outset.

For technical teams, it provides a reason to rationalise data models and focus on reusability. For business users, it provides consistent and actionable insight without waiting on bespoke development.

Realising this value requires early planning. As a trusted SAP Business Partner, Capgemini can help you shape your BDC roadmap in a way that aligns technical strategy with business outcomes. Whether you are modernising BW, scaling SAC, or implementing Intelligent Applications, we provide the guidance and delivery capability to make the transition successful and future-proof.

Get in touch to start the journey today. 

In our latest retail-focused point of view, ‘Five tried and tested operational principles for retail resilience’ , we explored the key challenges facing today’s UK retailers and the mindset shift needed to maximise profit, reduce costs, enable all-channel growth, and connect with customers.

Though these operational principles are relevant and actionable across the industry, grocery stands apart in the world of retail, with its own set of challenges. It’s fast-moving, margin-tight, operationally complex, and considered one of the most competitive markets in the world. While other sectors can afford to hold stock, test pricing strategies, or absorb inefficiencies, grocery retailers operate in an environment where every decision – from supplier negotiations to shelf placement – can make or break profitability.

At a time when retail price inflation is at its highest in over a year and consumers are tightening their belts on discretionary spending, the pressure on grocery retailers has never been greater. Navigating this landscape requires more than just experience – it demands bespoke, data-driven solutions tailored to the sector’s unique challenges.

Grappling with a grocery price war

Consumers are demanding lower prices but delivering them sustainably is a high-wire act. A third of all grocery spending now comes via promotions, with tactics like discounter price matching becoming the norm. But while promotions drive volume, they often erode profitability. The challenge? Planning pricing strategies that are both effective and sustainable.

Meanwhile, shrinkage – from theft, spoilage, or errors – continues to eat into margins. Retailers are investing heavily to get it under control, but without the right data and tools, it’s difficult to pinpoint where losses are occurring and how to prevent them.

Asda’s recent, aggressive pricing strategy sent shockwaves through the market in its aim to make Asda 5-10% cheaper than competitors. Their statement of intent impacted the share prices of Tesco, Sainsbury’s, and M&S – illustrating how a bold pricing move by one player in a highly competitive market can trigger a chain reaction, affecting not just pricing strategies but also investor confidence and company valuations.

Retailers must now walk a tightrope: invest in price to win market share, but without sacrificing profitability. This is where strategic pricing, granular cost modelling, and end-to-end visibility become critical. Can grocers identify and invest in “hero products” that shape price perception, while balancing margins across the rest of the range?

Channel mix: growth vs. Margin

Tesco’s latest figures show online grocery sales have surpassed their pandemic peak, now accounting for 13.5% of UK revenue. While this channel is a growth engine, it comes with a cost: online fulfilment is significantly more expensive, eroding margins. Retailers must align on a margin measure that surfaces the true cost of omnichannel operations. This will provide the business case required to invest in technologies that optimise fulfilment efficiency and help inform ranging decisions in the online assortment.

The double-edged sword of QuickCommerce

A survey by Retail Gazette found that over 65% of major retailers have partnered with at least one quick commerce delivery service provider to meet the growing demand for quick deliveries. This trend allows customers to receive their groceries within minutes, enhancing the overall shopping experience and catering to the modern consumer’s preference for speed and convenience.

But with this boom comes a dilemma. Retailers know they must partner with these quick commerce platforms to be seen as a convenience brand, but not only does it disrupt traditional operations – pulling staff away from tills, demanding rapid fulfilment, and driving higher item pricing to cover platform margins – it’s also through these channels that they lose key customer information. As soon as a customer opts for an Uber Eats or Deliveroo delivery option, they are no longer a customer of the grocer. Which results in a fragmented customer journey and patchy data – making it harder to drive personalised, cohesive experiences and, ultimately, revenue.

The key is implementing the right tools and using that data effectively, to:

• Personalise promotions
• Inform assortment decisions
• Drive long-term customer value

Discover how to master data & AI to power the next generation of retail here.

The most forward-thinking retailers are already years ahead with this – leveraging data (and AI) to drive efficiency, profitability, and customer satisfaction. But how can those whose data & AI strategies aren’t quite as mature catch up? Read all about mastering your data & AI strategy in this detailed blog from our colleague Conor McGovern, Lead Enterprise Data & Analytics in the UK and globally, and global lead for Generative AI Strategy.

Is the EU trade deal a supply chain silver lining?

Amid the challenges, there’s a glimmer of good news. The recent UK/EU trade deal promises to reduce the bureaucratic burden of importing goods, lowering costs and friction in the supply chain. Retailers with deep supply chain visibility and agile pricing models will be best positioned to capitalise – seizing first-mover advantages and capturing market share before competitors can react.

Navigating the profitability puzzle

To thrive in this environment, grocery retailers must master the art of profitability across multiple dimensions:

• Gross Margin Management: Lowering COGS through supplier partnerships and dynamic pricing tools like electronic shelf labels.
• Inventory Turnover: Reducing waste and optimising stock levels, especially for perishables.
• Product Mix: Balancing loss leaders with high-margin private label and premium products.
• Shrinkage Control: Minimising theft and errors through better systems and training.
• Operational Efficiency: Leveraging automation and just-in-time logistics to reduce overheads.
• Data-Driven Decisions: Applying AI and analytics to optimise assortment, pricing, and promotions.

Introducing bespoke tools to a bespoke challenge

Whether it’s optimising assortment, adjusting pricing, or forecasting demand, data-driven decision making is the key to unlocking profitability in grocery. With the right tools, you can move from reactive firefighting to proactive strategy.

In our point of view, we highlighted the product profitability tool we built for a leading grocer to bring to life key operational cost data in a tailored, interactive dashboard.

This BI platform:

• Calculates net margin – including operational, overhead, shrinkage, and pricing costs – at the product and category level
• Visualises margin variation across categories and suppliers
• Supports better-informed assortment planning decisions
• Models the impact of changes to pack size, supply routes, shelf space, and ranging decisions – i.e. removing product X and replacing with product Y
• Supports AI-driven forecasting and planogram optimisation

And most importantly, it’s tailored to your operation. We can modify the tool to reflect your unique business model & supply chain and tackle your challenges & goals.

Let’s talk

The grocery sector in 2025 is a battlefield of price, perception, and performance. Retailers who succeed will be those who can balance short-term competitive tactics with long-term strategic resilience—investing in digital infrastructure, mastering cost control, and using data to drive smarter decisions.

The winners won’t just be the cheapest. They’ll be the most agile, the most insightful, and the most resilient.

If you’re interested in learning more about our product profitability tool – or if you’re facing any of the challenges mentioned above – get in touch. We’d love to explore how we can help you build a smarter, more resilient business.

The secret to general merchandise profitability

In our latest POV, ‘Five tried and tested operational principles for retail resilience’, we examine the key challenges UK retailers face and the mindset shift required to maximise profit, reduce costs, enable all-channel growth, and connect with customers.

General merchandise categories like clothing, home goods, electronics, and entertainment, are particularly sensitive to inflation, shifting consumer behaviour, and global supply chain volatility. These categories often experience frequent price fluctuations due to their high elasticity, making profitability a moving target.

In the years following the pandemic, inflation has significantly increased operating costs – further compounded by the effects of geopolitical tensions and, more recently, tariffs. These mounting pressures are prompting retailers to reassess their pricing strategies and supplier partnerships to sustain profitability.

So, what actions are key players in the general merchandise Retail industry taking to ensure they’re ahead of the game in protecting their product profitability? And what can the rest of the sector do to follow suit?

Why is cost control key for Retail in 2025?

According to Lloyds’ Business Barometer, the UK retail landscape in 2025 was forecasted with cautious optimism as inflation has started to ease, interest rates are stabilising, and real household disposable income has grown by 3.3% over the past year. Despite this optimism, British retail sales for non-food stores, such as clothing and department stores, fell by 1.4% between April and May. This is telling us consumer sentiment remains fragile, shaped by lingering economic shocks, geopolitical tensions, and the impact of new tariffs and regulatory costs.

Retailers are preparing for a possible £5bn surge in operating expenses this year, driven by wage increases, National Insurance hikes, and the full implementation of packaging Extended Producer Responsibility (EPR) rules. Retailers are right to remain cautious, focus on resilience, margin protection and operational resilience.

What profit protecting strategies are general merchandise retailers taking?

Understanding end-to-end costs

To safeguard profitability, general merchandise retailers should take a comprehensive view of their cost base, factoring in packaging, transport, storage, and replenishment, while also navigating global tariffs and geopolitical uncertainty.

Despite a slight drop in sales, the Very Group returned to profitability due to continued and diligent cost control and a focus on higher-margin products. Their disciplined approach to cost management and product mix optimisation is a blueprint for the general merchandise market. The retailer focused on pushing sales of high-margin product, increasing operational efficiencies at their automated fulfilment centre to process and dispatch orders quickly, and technology investments to a cloud-based platform to improve customer experience and reduce friction in the purchase journey.

Exploring global opportunities

In response to newly imposed tariffs GM companies may be exploring strategic sourcing, such as reshoring or exploring new trade routes, but setting up new sourcing and manufacturing can cause cost-inflation so pricing strategy will remain a key consideration. They will need to be mindful of how price-sensitive their customer is to understand how much additional cost the company is prepared to absorb, or to pass along to their customer. They will need to leverage price elasticity and strategic promotions to determine customer sentiment.

Many UK retailers are reliant on global supply chains, and they will feel direct impact from rising tariffs on imports from the US, China, and Europe. To combat the tariff challenges, businesses may need to reconsider their suppliers, reshoring operations or seeking alternative trade routes. Positive action is being taken across the UK to alleviate margin pressure, with a UK-India trade deal that will reduce tariffs on India’s clothing and footwear exports. This will improve supply chain diversification, offer more attractive sourcing destinations and improved cost competitiveness without compromising on quality.

Designing strategies to remain competitive

Retailers are navigating a rapidly evolving landscape shaped by economic pressures and shifting consumer expectations. In response to the cost-of-living crisis, shoppers are becoming more value-conscious and we see shoppers trading down to own-label products, reducing discretionary spending, and limiting basket sizes. To remain competitive, retailers must adopt strategies that go beyond pricing, focusing on disciplined cost control, accurate demand forecasting, and strategic expansion to meet changing customer needs and protect margins.

As the retail environment becomes increasingly digital, a new layer of complexity is emerging: the rise of agentic consumers, where AI-powered shopping agents make decisions on behalf of human shoppers. These agents filter choices based on price, availability, ethical sourcing, and personal preferences – reshaping how products are discovered and purchased. For retailers, this means product data accuracy, localisation, and algorithmic visibility are becoming as critical as price and promotion. Adapting to this shift will be essential for capturing local demand and safeguarding profitability in an AI-mediated marketplace.

Sustainability and conscious consumerism: a strategic lever for profitability

As general merchandise retailers strive to protect margins and future-proof operations, sustainability and conscious consumerism are no longer peripheral concerns – they’re becoming central to commercial strategy. Today’s consumers are increasingly making purchasing decisions based on ethical sourcing, environmental impact, and brand transparency. This shift is not just a reputational imperative; it’s a profitability opportunity.

Marks & Spencer is embedding sustainability into its core business model to drive profitable growth. Initiatives include carbon footprint reduction across its supply chain through cutting plastic packaging and hangers at different stages of the customer journey, sustainable sourcing of raw materials, and customer engagement using transparency tools.

Retailers that embed sustainability into their supply chain through circular product design, low-impact packaging, and carbon-efficient logistics can unlock cost savings, reduce regulatory risk, and build stronger customer loyalty. For example, the implementation of Extended Producer Responsibility (EPR) rules is driving retailers to rethink packaging and waste, turning compliance into a catalyst for innovation.

By aligning operational efficiency with environmental responsibility, general merchandise retailers can meet the demands of both regulators and customers – while building more resilient, future-ready businesses.

How can digital transformations fuel profitability?

Intelligent forecasting is paramount

The macro-economic and geopolitical climate has been challenging to demand forecasting and promotion planning. Retailers need to be savvy about how changing customer behaviour will affect purchasing patterns. We recognise opportunity for AI-driven integrated business planning – for example implementing AI-driven demand forecasting to optimise planning through predictive analysis from AI analysis of historic data, sales trends, store behaviour, and customer traffic. This both frees up planners’ time, enabling them to be more strategic, and supports reducing overstock and stockouts while minimising waste.

As eCommerce continues to expand, retailers are under pressure to balance growth with profitability. Rising fulfilment costs and high return volumes are eroding margins, prompting a shift toward integrated, omnichannel strategies. By blending physical and digital operations, retailers are not only improving customer engagement but also driving operational efficiency and long-term value.

For example, Holland & Barrett is celebrating its second consecutive year of double-digit growth, attributed to its significant investment in its digital transformation strategy. This digital transformation has been focused on improving stores, technology, and new product development.

Capital investment to fuel transformation in value fashion

As the value fashion sector faces mounting pressure from shifting consumer expectations and rising operational costs, strategic investment is becoming a key lever for profitability. New Look’s recent multi-million-pound funding round of £30m signals a renewed focus on transformation, with capital earmarked for modernising its store estate, enhancing digital capabilities, and optimising supply chain operations. This investment reflects a broader trend among high-street retailers to future-proof their business models through targeted reinvestment, ensuring they remain agile, relevant, and profitable in a rapidly evolving retail landscape.

Unlocking operational excellence with AI

AI is rapidly becoming a cornerstone of operational excellence in general merchandise retail. From automating demand sensing to simulating supply chain scenarios, agentic AI empowers supply chain and operations teams to make faster, more informed decisions that directly impact profitability.

According to Salesforce, 73% of UK retail decision-makers are increasing investment in agentic AI, with the most significant gains seen in unifying commerce platforms – connecting inventory, logistics, and customer data to streamline fulfilment and reduce operational overheads.

In our POV ‘Agentic AI in Supply Chain: Transforming Operations & Decisions’, we explore how agentic AI complements traditional automation by introducing autonomous decision-making capabilities. This evolution enables dynamic inventory allocation, real-time exception handling, and predictive scenario planning – transforming how retail supply chains respond to volatility and complexity.

The most resilient retailers embrace strategic agility

In 2025, general merchandise retailers are operating in a landscape defined by complexity but also opportunity. Inflationary pressures may be easing, but rising operational costs, shifting consumer behaviours, and global trade disruptions continue to challenge profitability. The most resilient retailers are those embracing strategic agility: rethinking cost structures, diversifying supply chains, and investing in digital transformation.

Success will hinge on the ability to blend operational discipline with innovation. This means considering cost containment, omnichannel models to meet customer demand, and using data-driven insights to fine-tune pricing and promotions. There will be opportunities for retailers to begin leveraging AI, for purposes such as enhancing forecasting and planning. Retailers that take a proactive, end-to-end approach through balancing cost control with customer-centricity, will be best positioned to protect and grow their margins in a volatile market.

Ultimately, profitability in general merchandise retail will not be won by caution alone, but by those bold enough to adapt, invest, and lead with insight.

Get in touch if you’re looking to take control of operational costs and turn insight into action. We’d love to help.

The European B2B passenger car market is undergoing a seismic shift. Here we delve into the transformative trends shaping this market – from the allure of flexible leasing to the surge in electrification – and how OEMs can successfully adapt to this dynamic landscape.

As the European automotive landscape undergoes a seismic shift, Business to Business (B2B) passenger car sales are emerging as a critical growth engine for Original Equipment Manufacturers (OEMs). With true fleet registrations (all registrations by legal entities/companies) now accounting for over 30% of new car sales in most EU markets¹, the B2B segment is not just important – it is the next battleground for market dominance.

The Complexity Behind B2B Sales

Unlike Business to Customer (B2C), B2B sales are a multi-layered ecosystem involving Original Equipment Manufacturers (OEMs), leasing companies, dealers, and a diverse range of customers – from small and medium sized enterprises (SMEs) to large corporate and public institutions. This complexity demands tailored strategies, robust digital infrastructure, and a deep understanding of customer needs.

Market momentum: growth and electrification

The European B2B market is on a steady upward trajectory. In the EU5 (UK, France, Germany, Spain, and Italy) true fleet registrations are projected to grow from three million in 2024 to 3.2 million by 2029.

Electrification is another defining trend. More than 40% of the company car market is projected to be electric by 2029, driven by regulatory pressures to reduce emissions, and supported by Battery Electric Vehicle (BEV) tax incentives. These factors are particularly influential in segments where there is price parity between Internal Combustion Engine (ICE) and BEV alternatives.

Electrification is another defining trend. More than 40% of the company car market is projected to be electric by 2029, driven by regulatory pressures to reduce emissions, and supported by Battery Electric Vehicle (BEV) tax incentives. These factors are particularly influential in segments where there is price parity between Internal Combustion Engine (ICE) and BEV alternatives.

However, there are challenges as well. Unpredictable residual values in the used electric vehicle market are threatening leasing companies’ profits, leading them to seek deeper discounts from OEMs. Competition from Chinese OEMs, tariffs, and trade instability are further squeezing OEMs’ profit margins. Despite these challenges, the B2B fleet market remains a key growth area for OEMs and should be prioritised as the industry evolves.

What’s driving the shift?

Several forces are converging to reshape the B2B landscape:

1. Cost efficiency: Fleet deals offer better Total Cost of Ownership (TCO) through bulk discounts, tax benefits, and predictable maintenance costs.
2. Flexibility: Leasing and subscription models provide adaptable contracts and access to a wider range of vehicles.
3. Sustainability: Regulatory pressure and corporate ESG goals are accelerating EV adoption.
4. Digitalisation: Fleet managers demand seamless digital experiences -from vehicle configuration to telematics and aftersales.

On top of this is the major force of shifting customer expectations. According to research by Capgemini’s automotive team in Germany:

• Flexibility is king: 78% of leasing companies cite flexibility in vehicle choice and 76% in contract duration as top customer needs.
• Customisation is rising: 71% of managers expect growing demand for individual service modules – like cafeteria-style leasing packages or menu of options.
• Alternative mobility is mainstream: 78% of companies now offer public transport allowances, bike leases, or cash-for-car schemes.
• Subscriptions are gaining ground: 54% of fleet managers already use or plan to use subscriptions, with 40% viewing them as a viable alternative to long-term leasing.

Major perceived differences in customer needs between the EU markets, based on a survey of LeaseCo managers

These trends reflect a broader shift in consumer behaviour – away from ownership and toward access, convenience, and personalisation. These factors are also driving a change in preferred financing products. Operational leasing and subscriptions are on the rise.

Operational leasing is the fastest-growing financing method, expected to grow by 17%, reaching over 1.1 million by 2028¹. This growth is fuelled by:

• EV Affordability: Leasing helps align monthly EV costs with ICE vehicles.
• Convenience: All-inclusive packages simplify fleet management.
• Predictability: Fixed costs appeal to CFOs and procurement teams.

As for subscriptions, a 2023 survey by Dataforce shows that 40% of fleet managers view subscription models as alternatives to long-term leasing, while 33% see them as complementary.

Meanwhile, LeaseCos are consolidating and evolving. They are exploring ultra-short-term rentals to compete with subscription providers and meet rising expectations for flexibility. AutoFintechs are also entering the fray, with a projected 9.2% compound annual growth rate through 2031¹.

How can OEMs benefit from this opportunity?

To thrive in this evolving market, OEMs must activate four key profitability levers:

• Deploy low-depreciation models to reduce residual value risk.
• Offer modular service packages (menu systems) to capture flexibility premiums.
• Manage aftersales and uptime guarantees to generate recurring revenue and monetise connected services for fleet management.
• Recoup value through second-life leasing and resale optimisation.

Additionally, OEMs must adapt their B2B sales models to offer flexible contracts and customisable service modules. Partnerships with finance and leasing companies should be made more agile to provide innovative financial solutions.

Moreover, enhancing customer experience with digital tools like fleet configurators, fleet management systems and data-driven personalisation is crucial. This approach builds direct customer relationships, boosts loyalty, and unlocks strategic opportunities.

How Capgemini can help

Capgemini is well-placed to assist OEMs in managing this complex landscape. Our end-to-end fleet-specific transformation framework includes:

• Vision and strategy: We have helped German and Japanese OEMs set goals and target customer segments for fleet management, focusing on flexibility and efficiency.
• Digital infrastructure: We assisted Swedish, Japanese, Italian, and German OEMs in implementing CRM systems, pricing engines, and telematics to support low-depreciation models for leasing and resale.
• Sales enablement: We have supported Japanese OEMs in developing scalable B2B sales models, offering modular service packages to maximise flexibility premiums.
• Customer experience: We are experienced in helping global OEMs create seamless customer journeys across awareness, purchase, and aftersales, managing uptime guarantees for recurring revenue.

Whether you aim to expand your leasing operations, digitise your B2B sales, or accelerate the adoption of electric vehicles, Capgemini is ready as your business and technology transformation partner to help you navigate the next era of mobility.

Get in touch with our experts to find out how we can tackle your specific business challenge.

The third instalment in a cyber security series from Capgemini and RenewableUK explores how human behaviour remains the most exploited vulnerability in modern cyberattacks, what practical steps can be taken to mitigate this, and what we can all learn from Brad Pitt.

Greeks bearing gifts

Over 3,000 years ago, in the now infamous former city of Troy, defenders rejoiced as the Greek army was vanquished after a decade-long siege. The surrounding bay was clear of warships and the beaches empty of military tents. A huge wooden horse was the only indication they were ever there.

Had they not been so exhausted from battle and jubilant with victory, more sober Trojan minds might have questioned this conspicuous Greek gift. On this day, however, scepticism did not prevail. And so, it was not the mighty walls of Troy that were breached, with the perimeter still holding fast, nor was it the imposing gate that had shattered. It was not iron or wood, nor cement or stone, that ultimately laid Troy low. Rather it was trust, and manipulation of that all too human emotion.

Our technology has come a long way in the subsequent three millennia. Today we invest in firewalls, endpoint detection, and sophisticated scanning to protect our assets. But our amygdala – the part of the brain that processes emotions like fear – is much the same as our Trojan forebears. When we’re in a rush, links can seem convincing. After a long day, when we’re prompted to update our password, surely “BradPitt2004Troy” would do? It’s rarely modern cyber security defences that fail, but instead misplaced human trust often proves to be the weakest link.

People are the most exploited attack surface

Technology evolves and threat actors certainly innovate. But, year after year, the majority of security breaches still arise from human behaviour. Whether through deception, mistakes, or deliberate misuse, attackers increasingly target the people within organisations when seeking to open the proverbial gates. There are four primary ways in which human vulnerabilities typically manifest in cyber security breaches, though it is worth noting that these methods are rarely used in isolation, and Capgemini has tracked how frequently these occur:

1. Phishing and social engineering (68% frequency)
   A message appears benign or routine, but hides a threat, such as a file, link, or seemingly urgent request. Such threats often play on emotion through authority, urgency, or reward.
2. Credential theft and misuse (30% frequency)
   Usernames and passwords are the gate keys. Reusing passwords or choosing weak ones makes them easy to steal, guess, or phish.
3. Human error (28% frequency)
   Mistakenly CCing the wrong person, uploading the wrong file, or exposing data in shared documents. All are small mistakes with potentially big consequences.
4. Malicious insider threats (6% frequency)
   A trusted user goes rogue. Whether motivated by revenge, coercion, or negligence, they knowingly violate policies to harm their organisation.

When human risk becomes real

To illustrate how human-centric risks manifest in real world scenarios, we can look at four notable incidents which embody these primary categories:

1. NHS ransomware attack, 2022 (phishing and social engineering)
   In early 2022, the National Health Service (NHS) experienced a significant cyber incident involving a phishing campaign that targeted official email accounts, offering a stark illustration of how such an attack can compromise entire swathes of critical national infrastructure. 139 NHS email accounts were compromised and used to distribute over 1,157 phishing emails over a period of several weeks. The compromised accounts were used to send emails, often impersonating NHS.net – the email, diary and directory system for health service employees in England and Scotland – to trick individuals into providing personal or financial information, leading to the exposure of sensitive data for around 80,000 individuals. The Information Commissioner’s Office (ICO) later fined the responsible IT department £3 million for failing to implement adequate security measures, including the absence of multi-factor authentication (MFA). In this case, the attackers exploited human trust and the lack of basic security protocols, leading to widespread service disruption and data compromise, and underscoring how social engineering tactics, combined with insufficient security practices, can have far-reaching consequences.
2. The Colonial Pipeline attack, 2021 (credential theft and misuse)
   In May 2021, Colonial Pipeline from Texas to New York fell victim to a ransomware attack after hackers accessed it via a compromised password. The password had been used for several accounts on the network, meaning the hackers gained extensive access through it. They were, in effect, able to open multiple doors using a single key. The breach led to fuel shortages across the Eastern United States and a ransom payment of $4.4 million, resulting in widespread societal disruption from a seemingly minor oversight.
3. Facebook’s cloud misconfiguration, 2019 (human error)
   In 2019, security researchers from the software company UpGuard discovered that over 540 million Facebook user records were publicly accessible through misconfigured Amazon Web Services (AWS) cloud servers. The exposed data encompassed user IDs, comments, reactions and, in some cases, passwords. This incident was not the result of a sophisticated cyberattack but stemmed from human error, specifically the failure to properly configure the system. The developers neglected to implement basic security measures, such as password protection or encryption, leaving vast amounts of personal data vulnerable to unauthorised access. This breach underscored the potential impact of human errors, with even well-intentioned developers capable of inadvertently exposing sensitive information through simple missteps.
4. ‘The Tesla Files’, 2023 (malicious insider threat)
   In May 2023, Tesla disclosed a significant data breach affecting over 75,000 current and former employees. The breach was traced back to two former employees who, in violation of Tesla’s IT security and data protection policies, misappropriated confidential information and shared it with a German media outlet. The leaked data included names, contact information, social security numbers, and employment details. Investigation revealed that the insiders had accessed and exfiltrated over 100gb of sensitive data, which was subsequently named ‘The Tesla Files’. Investigations revealed that these former employees had grievances with Tesla’s management, underscoring how internal dissatisfaction can become a catalyst for malicious actions.

Empowering individuals to mitigate human-centric cyber risks

The good news is that organisations and their employees are equally able to solve the challenges of human cyber risk, and below are practical steps that RenewableUK members can implement to protect themselves and their organisations:

1. Phishing
   Organisations should participate in regular phishing simulations, which can significantly reduce the likelihood of falling for real attacks. Individuals should be sceptical of unsolicited communications, always verifying the authenticity of unexpected emails or messages, especially those requesting sensitive information or urgent actions. If in doubt, they should adopt a ‘better safe than sorry’ approach and flag the email as suspicious.
2. Credentials
   Organisations should enable multi-factor Authentication (MFA), which is able to block over 99.9% of account compromise attacks. Individuals should use strong, unique passwords, whilst avoiding reusing passwords across different accounts, and utilising password managers to generate and store complex passwords securely.
3. Human error
   Organisations should communicate best practices to their workforce, ensuring all cyber security practices are clearly understood and regularly updating their team’s knowledge to minimise the risk of inadvertent errors. Individuals should double-check before sending emails, especially those containing sensitive information, as well as verifying the recipients and attachments to prevent accidental data leaks.
4. Malicious insiders
   Organisations should foster a culture of integrity by encouraging open communication and ethical behaviour to deter potential insider threats. Individuals should report suspicious behaviour through the appropriate channels if they notice unusual activities or policy violations.

Whether it concerns ancient Troy or tomorrow’s Tesla, human vulnerabilities remain consistent targets for attackers. Addressing these human-centric risks demands ongoing vigilance, regular training, and a proactive security culture.

What happens when the Trojan Horse learns to knock? Stay tuned for upcoming articles that discuss the impact of AI and what this means for the future of digital security.

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. 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. 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 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 today’s evolving public sector landscape, the demand for efficient, transparent, and citizen-centric services has never been greater. The partnership between Capgemini and ServiceNow offers a powerful response to this challenge—combining cutting-edge technology with deep transformation expertise to reimagine how public services are delivered and experienced

This blog explores how agentic artificial intelligence (AI) is reshaping both employee and citizen experiences, and why Capgemini and ServiceNow are uniquely positioned to lead this transformation.

Introducing Agentic AI: From Reactive to Proactive

Unlike traditional AI, which responds to prompts, agentic AI takes initiative. It can plan, decide, and act autonomously to achieve goals—without needing step-by-step instructions. In a public sector context, this means:

• Proactive Service Delivery: Identifying issues before they escalate and resolving them autonomously.
• Autonomous Task Management: Coordinating multi-step workflows across departments.
• Enhanced Decision-Making: Synthesising data to support faster, more informed actions.
• Human-Centric Design: Freeing employees from repetitive tasks so they can focus on meaningful work.

Empowering Public Sector Employees

Capgemini and ServiceNow are helping public sector organisations create intelligent, efficient, and engaging employee experiences:

• Automating Routine Tasks: ServiceNow’s AI automates repetitive processes; Capgemini tailors these to maximise impact.
• Predictive Analytics: ServiceNow predicts incidents; Capgemini helps interpret and act on the data.
• Personalised Support: ServiceNow delivers tailored experiences; Capgemini ensures they are intuitive and inclusive.
• Continuous Improvement: ServiceNow gathers feedback; Capgemini drives adoption and iteration.

Together, they enable public sector teams to work smarter, not harder—boosting engagement, reducing attrition, and improving service delivery.

Elevating the Citizen Experience

Agentic AI isn’t just transforming internal operations—it’s revolutionising how citizens interact with public services. Here are some of the most impactful use cases:

• Healthcare Access: AI agents can schedule GP appointments, send reminders, and triage symptoms—reducing wait times and improving outcomes.
• Tax and Benefits Support: AI can resolve tax queries, track welfare applications, and proactively update citizens—cutting through bureaucracy.
• Local Services: From reporting potholes to graffiti removal, agentic AI can log, route, and track issues across departments—ensuring faster resolution.
• Permit and Licence Management: AI can guide citizens through applications for parking permits, business licences, or event permissions—ensuring compliance and ease.
• Education and School Services: Parents can apply for school places, track transport eligibility, or manage special education needs—all through AI-guided workflows.
• Public Safety and Crisis Response: AI can coordinate emergency logistics, notify citizens, and track resource deployment in real time.

These capabilities not only improve satisfaction but also build trust in public institutions.

Real-World Impact

• Healthcare: A major hospital system used ServiceNow and Capgemini to automate HR tasks, improving staff engagement and enabling more time for patient care.
• Local Government: A council automated IT service management with ServiceNow and Capgemini, reducing incident resolution times by 40% and achieving significant cost savings.

Looking Ahead: The Future of Public Sector AI

As AI continues to evolve, agentic AI will play a pivotal role in shaping adaptive, intelligent, and human-centric public services. Capgemini and ServiceNow are at the forefront of this shift—empowering organisations to deliver experiences that are not only efficient but also empathetic and inclusive.

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

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.

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.