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Why your bank’s customer service needs to up the empathy – and AI may hold the key

P.V. Narayan
Jun 24, 2025

Marketing guru Shep Hyken once said “make every interaction count, even small ones.” This quote has always stuck with me because it’s so human, and because it explains why we feel a strong emotional connection to certain brands. We are more likely to become repeat customers if we experience good customer service, even in a small interaction.

It is well known that contact center agents are the face of any bank. They are on the front lines dealing with customer interactions and shaping your bank’s perception. Alas, the unfortunate reality is that today’s customer service isn’t standing up to customers’ needs. Consumers in 2025 expect more, and it’s on banks to step up.

Today’s consumer won’t stand for generic banking – they expect a personalized, seamless experience. More than that, they want it to feel human. Often, this demand lands with the staff at a contact center. But can we expect this staff to keep up with ever-growing customer expectations unaided? Or, even worse, can we expect the contact center to deliver a great experience when the perception is that banks are actively trying to automate away their jobs?

Capgemini’s World Retail Banking Report 2025 finds that only 16% of agents appear satisfied with their jobs. Attrition continues to rise, increasing the cost of recruitment and time spent training agents. In between, customers are looking for empathy in basic interactions – and instead find things impersonal and procedural.

I’m convinced we’ll do right by customers if we deploy technology to help overworked agents. Technology, after all, is a tool. The use of AI can help eliminate friction and let these agents deliver the kind of frictionless experiences that customers are hungry for. By implementing predictive AI capabilities, banks can prevent issues before they even occur based on historical patterns and trends, reducing the number of complaints and anomalies in real-time.

In the World Retail Banking Report, we sought to understand how 8,000 millennial and Gen Z customers view perhaps the single most important feature of their banking relationship: the card. The consensus was clear: there is room for improvement at every point of the customer journey. And there is a clear need for personal connection.

The worrying part of our research findings was the extent to which bank teams seemed aware of dissatisfaction among customers. Consider this: 68% of banking institutions acknowledged poor customer satisfaction as a major issue. What’s more concerning is that over 60% of bank marketing staff say they are overwhelmed by the number of applications they receive, and many banks acknowledge the KYC process can take days.

All of this is taking place against a backdrop of profound technological change. These changes have benefited nimble, digital-first players such as Monzo and Revolut. While they may seem small compared to the scale of US megabanks, they have succeeded in capturing valuable market niches. They did so by creating smooth digital experiences, broadening the aperture of services available and sidestepping much of the friction that can hinder established banks. They created real customer connection.

AI can let US banks build this connection too, removing bottlenecks in manual processes such as card applications. At a strategic level, it can inform banking strategy, create products with in-built personalization and close the customer service gap with the emerging neobank players.

By proactively predicting and addressing trends, the technology can assist banks in staying ahead of customer complaints and operational bottlenecks, making the process smoother for both agents and customers. 

However, AI can’t do it alone – many customers will still want the option to connect with a human being. After all, personal finance is personal, whether it’s a customer loan application or resolving a disputed charge. But AI can empower those humans, giving them a better insight into the customer’s situation and request.

For example, if a customer is angry about an unauthorized credit card transaction, a human agent augmented by AI can use sentiment analysis to detect the customer’s anger. The AI can then direct the query to an agent who has a high success rate in managing similar complaints and calming frustrated customers. AI can even proactively anticipate scenarios to help agents better serve customers.

Furthermore, by automating routine inquiries, AI allows agents to focus on complex, high-value tasks that require empathy, creativity, and judgment – attributes that customers are increasingly expecting. In this way, AI enables agents to provide more personalized service at scale, bridging the gap between human empathy and efficiency.

To put it simply, AI can make customer service agents much happier and more productive in their work. This takes more than a technology strategy: bank leaders will have to implement a thorough change management plan. That means educating employees about the potential of AI and their role in augmenting human capabilities, as well as clearly delineating what work will be done entirely by AI, and where AI will play a supporting role.

It’s also crucial that banks adopt a customer-centric AI strategy, focusing not only on operational efficiencies, but also how these technologies can directly enhance customer experience and employee experience. AI’s role is not just to solve problems faster, it’s to solve them better and with more empathy, while providing seamless self-service options and empowering agents to be more competent with contextual insights and continuous learning.

The bottom line: bank executives must push the boundaries of innovation to explore the potential of AI – in a safe and controlled fashion – that strives to deliver enhanced client engagement. It’s time to make every interaction count.

Author

P.V. Narayan

EVP and Head of US Banking and Capital Markets, Capgemini

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    Enhancing geothermal energy efficiency with Gen AI: Smarter energy solutions

    Bragadesh Damodaran & Amit Kumar
    18 Jun 2025

    Geothermal energy is a clean and reliable power source, but making it more efficient can be difficult. Systems like organic Rankine cycles (ORCs) are commonly used because they work well with moderate temperatures and are environmentally friendly.

    However, improving their performance requires careful control of factors like temperature, pressure, and flow.

    Traditional design and simulation tools can be slow and hard to use. That’s where Gen AI, Bayesian optimization, and large language models (LLMs) come in. These advanced technologies can make the process faster, smarter, and more user friendly.

    • Gen AI can create useful data, suggest design improvements, and support decision-making.
    • Bayesian optimization helps find the best settings to boost system efficiency.
    • LLMs can explain complex data and offer clear, actionable insights.

    By combining these tools with traditional engineering methods, we can build smarter, more efficient geothermal systems. This approach supports greener energy solutions that are easier to design, manage, and scale.

    How can Gen4Geo help to optimize the geothermal energy process?

    We partnered with one of India’s top institutes (IIT) to explore how geothermal power plants perform under different conditions. Our goal was to better understand and improve their efficiency.

    • Simulation and modeling
      We built detailed models of geothermal systems using Python and REFPROP to get accurate data. We focused on key parts of the organic Rankine cycle (ORC) and calculated important values like energy output and efficiency. To ensure accuracy, we also recreated the model in Aspen HYSYS, a trusted industry tool.
    • Smart predictions
      We used Gen AI to create a model that can predict how the system should operate to reach certain efficiency goals. This model was trained on real data and tested to make sure its predictions were reliable.
    • System optimization
      To find the best setup for the system, we used Bayesian optimization with a fast-learning model (XGBoost). This helped us quickly identify the most efficient configurations without heavy computing.
    • User friendly interface
      We developed a chatbot called Gen4Geo, powered by a large language model (LLM). It allows users – even those without technical backgrounds – to ask questions and get clear, helpful answers about the system.
    • A smarter, closed loop system
      By combining simulation, AI generated data, optimization, and a natural language interface, we created a smart, self-improving system. It helps design and manage geothermal plants more easily and efficiently.

    Bringing value to the geothermal extraction domain with AI and physical modeling

    Traditional methods for designing geothermal power plants can be slow, expensive, and hard to use without deep technical knowledge. Our new approach solves these problems by combining the power of artificial intelligence (AI) with proven physical models.

    • Faster, smarter design
      We use Gen AI to quickly create realistic data, which helps us test different design ideas much faster than before. This speeds up the entire process and leads to better, more efficient systems.
    • Cost effective optimization
      With Bayesian optimization, we can find the best system settings using fewer tests. This saves time and money while still delivering high performance.
    • Easy to use for everyone
      A breakthrough is our use of large language models (LLMs). These allow anyone from engineers to decision makers to ask questions and get clear, helpful answers. No need for deep technical skills.
    • Always improving
      Our system learns and adapts over time. As new data comes in, it gets smarter, helping us stay ahead in geothermal technology and improve performance under changing conditions.
    • A greener future
      By making plant design faster, cheaper, and more accurate, our method helps speed up the use of geothermal energy. It supports cleaner, more sustainable energy solutions that are also more profitable.

    Key insights and learnings

    We’re combining the power of thermodynamics and artificial intelligence (AI) to solve real world energy challenges. By using smart data models alongside traditional simulation and optimization tools, we can make geothermal power plants more efficient, faster to design, and more affordable. A key part of our approach is using Gen AI to create useful data for testing and improving system performance. Bayesian optimization helps us make smart choices quickly, saving time and money. We’ve also added a large language model (LLM) interface that lets users interact with the system using everyday language. This makes advanced tools easier to use, even for people without a technical background. This approach isn’t just for geothermal energy; it can also be used in other industries like oil and gas or hydrogen production. It opens the door to smarter, more sustainable, and more accessible energy solutions across the board.

    Author

    Bragadesh Damodaran

    Vice President| Energy Transition & Utilities Industry Platform Leader, Capgemini
    He is responsible for driving Clients CXO Proximity through Industry Infused Innovation and Partnerships, Thought leadership, building Industry-centric Assets and Solutions with Intelligent Industry focus aligning to Energy Transition, Smart Grid, New Energies, Water, Nuclear and Customer Transformations. Bragadesh is a seasoned ET&U Industry and Strategy Consultant in a career spanning over 24 years. Worked for major multinationals driving E&U Value chain strategies and CXO Advisory.

    Amit Kumar Gupta

    Program Manager, Energy Transition & Utilities- Gen AI for ET&U
    Amit brings over 18 years of expertise in the energy transition and utilities sector. As the Gen AI Lead in the ET&U industry platform, he specializes in asset development and industry intelligence, driving forward-thinking strategies and sustainable practices. He has spearheaded numerous innovative projects, developing industry-centric assets and solutions with a focus on intelligent industry practices. His extensive knowledge covers energy transition, smart grid, new energies, water, and oil & gas sectors while successfully collaborating with clients across various geographies, delivering impactful on-site solutions.

      Who leads in the Agentic Era: The Builders or the Adopters?

      Sunita Tiwary
      Jun 18, 2025

      We’ve entered a new phase of AI – one where systems no longer wait for instructions but actively reason, plan, and act. This shift from generative to agentic AI raises a defining question:

      Who will lead the next wave of transformation?

       Will it be the tech companies building the foundational models and platforms, or the industries embedding AI into real-world business workflows? The answer is clear: neither side can win alone. Agentic AI isn’t a plug-and-play solution—it’s a systemic leap that demands AI-native infrastructure, new talent roles, a culture of experimentation, and trust in autonomous systems. The future belongs to those who can bridge the gap between breakthrough technology and scalable, responsible value creation. In this article, we explore the evolving power dynamic between builders and adopters—and why service providers may be the unlikely accelerators of this new era.

      Agentic AI: Beyond Implementation to Transformation

      Unlike prior tech cycles, Agentic AI isn’t simply implementing a new tool or channel. It demands a complete rethink of how work is done, how decisions are made, and how value is created. To truly harness its power, industries need more than APIs and dashboards.

      They need:

      • Infrastructure readiness: scalable compute, data pipelines, and model orchestration.
      • Talent transformation: from prompt engineers to AI product managers, the skills needed are nascent and niche.
      • Mindset shift: a culture of experimentation, agility, and comfort with co-creating alongside AI.

      In this context, the true differentiator isn’t just having access to AgenticAI; it’s being prepared to reimagine how you operate with AI at the core.

      ROI, Talent, and the Black Box Problem

      While tech companies dazzle with breakthrough models and autonomous agents, industries face grounded realities:

      • ROI is uncertain unless use cases are tightly coupled with business outcomes.
      • Niche talent is hard to find, and even harder to retain.
      • The black-box nature of LLMs challenges observability, governance, and trust.
      • Security, privacy, and compliance must be rethought in the age of generative automation.

      This isn’t a plug-and-play revolution. It’s a systemic shift. Industries must invest not only in tools but also in readiness and resilience.

      The Evolving Power Dynamic

      Tech companies lead the way in building foundational models, toolchains, and agentic platforms. They control the tech stack, drive innovation velocity, and shape the ecosystem. Yet, they face challenges around monetization, trust, and the long tail of enterprise needs.

      On the other hand, industries hold the real-world context, proprietary data, and deep knowledge of customer behaviour. They define high-value use cases, drive adoption at scale, and ultimately determine where AI delivers impact. But they must also tackle integration complexity, change management, and readiness gaps.

      The new power players will be those who can navigate both worlds — translating the potential of Agentic AI into practical, governed, and scalable transformation across domains.

      Strategic Implications for Service Providers

      For service companies working with both tech builders and enterprise consumers, this creates a unique strategic opportunity:

      • Act as translation layers between Agentic AI innovation and industry needs.
      • Provide platformization strategies (moving from isolated tools and pilots to creating scalable, reusable AI foundations inside an enterprise) to help industries build internal capability, not just consume tech.
      • Build AI governance frameworks that bridge the black-box risks and enterprise trust requirements.
      • Offer talent incubation and skilling programs tailored to AI-first roles.

      Service companies must evolve from implementation partners to AI transformation enablers.

      The Real Winners: Co-Creators of Value

      Ultimately, the winners in the Agentic AI era will not be defined solely by who builds the most powerful models or the most dazzling demos. They will be the ones who can:

      • Align AI with business strategy.
      • Drive adoption with speed and responsibility.
      • Build ecosystems that are trustworthy, explainable, and human-centric.

      This is not just a race to innovate — it’s a race to transform. And those who can blend technology, context, and trust will define the next era of value creation.

      In this new landscape, co-creation is the new competitive advantage.

      Meet the Authors

      Sunita Tiwary

      Senior Director– Global Tech & Digital
      Sunita Tiwary is the GenAI Priority leader at Capgemini for Tech & Digital Industry. A thought leader who comes with a strategic perspective to Gen AI and Industry knowledge. She comes with close to 20 years of diverse experience across strategic partnership, business development, presales, and delivery. In her previous role in Microsoft, she was leading one of the strategic partnerships and co-creating solutions to accelerate market growth in the India SMB segment. She is an engineer with technical certifications across Data & AI, Cloud & CRM. In addition, she has a strong commitment to promoting Diversity and Inclusion and championed key initiatives during her tenure at Microsoft.

      Mark Oost

      AI, Analytics, Agents Global Leader
      Prior to joining Capgemini, Mark was the CTO of AI and Analytics at Sogeti Global, where he developed the AI portfolio and strategy. Before that, he worked as a Practice Lead for Data Science and AI at Sogeti Netherlands, where he started the Data Science team, and as a Lead Data Scientist at Teradata and Experian. Throughout his career, Mark has worked with clients from various markets around the world and has used AI, deep learning, and machine learning technologies to solve complex problems.

        Decarbonizing transport by 2050: which alternative fuels will lead the way?

        Capgemini
        Graham Upton and Sushant Rastogi
        Jun 13, 2025

        Transport accounts for over one-third of CO₂ emissions from end-use sectors globally, and emissions have grown by 1.7% annually between 1990 and 2022—faster than any other sector.

        To align with net-zero goals, emissions from transport must fall by more than 3% per year through 2030 and continue to decline steeply beyond that, despite rising demand and increasing complexity across the sector. (Source: IEA – Transport Sector)

        On this urgent but complex journey to decarbonize, the transport sector, especially aerospace and automotive, faces the dual challenge of growing demand while meeting increasingly strict environmental targets. Additionally, rising government regulation and public pressure are pushing airlines, automakers, and other transport operators toward cleaner fuels and energy sources.

        The production of biofuels, a critical alternative to fossil fuels, faces several technical challenges. For example, used cooking oil requires significant pretreatment, agricultural waste is difficult to process, and algae-based fuels remain costly and unscalable. These challenges stem from both the type of feedstocks used and the conversion processes required to make them usable across aviation, automotive, and other mobility applications.

        There is an expanding range of biofuels in development such as biodiesel, bioethanol, biogas, and others but each presents unique hurdles depending on the raw materials and technologies involved.

        Here, Graham Upton (Chief Architect, Intelligent Industry) and Sushant Rastogi (New Energies SME, Energy Transition & Utilities) explore how alternative fuels are evolving and how aerospace, automotive, and infrastructure players can use them to offset carbon emissions while enabling mass sustainable mobility.

        Biofuel feedstocks: diverse sources, diverse challenges

        Biofuels can be derived from various feedstocks, but each presents distinct technical, environmental, and economic challenges:

        • First-generation feedstocks (food crops):
          Derived from crops like corn, sugarcane, and soybean, these are well-studied and widely used. However, they raise “food versus fuel” concerns, consume large land and water resources, and contribute to environmental degradation such as deforestation and nutrient runoff.
        • Second-generation feedstocks (non-food boimass):
          Include agricultural residues, forestry waste, and energy crops. While they don’t compete with food supply, they are harder to collect, transport, and process due to their structural complexity and geographic dispersion.
        • Third-generation feedstocks (algae and microorganisms):
          Can be cultivated on non-arable land and produce high yields of biodiesel, but the current technology is energy-intensive, water-demanding, and not economically scalable. (Reference: IEA Bioenergy Task 39, “Algal Biofuels: Landscape and Future Prospects,” 2022.)
        • Waste oils and fats:
          Sourced from used cooking oils and animal fats, these feedstocks avoid land-use conflict but are limited in global supply and require extensive pretreatment due to high impurity levels.
        • Fourth-generation biofuels:
          Produced using genetically engineered microorganisms to enhance yield and efficiency. While promising, they face high R&D costs, regulatory barriers, and significant scalability hurdles. (Reference: IRENA, “Advanced Biofuels – Technology Brief,” 2021.)

        Processing costs for many of these advanced biofuels remain 2–3 times higher than conventional fuels, limiting their commercial competitiveness. (Source: World Bank, “Biofuels for Transport: Global Potential,” 2020.)

        Achieving net-zero emissions in transport—particularly in hard-to-abate sectors like aviation—requires a multi-pronged approach:

        • Optimize biofuel feedstocks and processing technologies
        • Scale up production economically
        • Align infrastructure development and supportive policy frameworks

        A diversified and innovative strategy is critical to reduce costs, increase resource efficiency, and ensure sustainable, scalable biofuel adoption across sectors such as automotive and aerospace.

        Biofuel production: a comparative view of process challenges

        Producing biofuels is technically demanding. Each type—bioethanol, biodiesel, and biogas—faces unique process-related challenges in terms of efficiency, cost, environmental impact, and scalability. Here’s a side-by-side comparison:

        Biofuel typeKey feedstockCore process challengeEfficiency barrierEnvironmental impact
        BioethanolLignocellulosic biomass, sugar cropsComplex pretreatment to break down plant fibresTraditional yeast inefficient at fermenting all sugar typesHigh energy input in pretreatment and fermentation
        BiodieselWaste oils, vegetable oilsImpurities reduce process efficiencyHigh-quality feedstock required; catalyst separation is complexExcess glycerol by-product requires responsible disposal
        BiogasOrganic waste, manure, food wasteFeedstock inconsistency affects gas yieldAnaerobic digestion requires precise conditionsRequires gas purification to meet fuel quality standards

        Each of these fuels needs process optimisation to reduce cost and improve performance—such as advanced enzymes, improved catalysts, or integrated upgrading technologies.

        Summary insight:

        To unlock biofuels at scale in high-emission sectors like aviation and automotive, industry must address core production hurdles by:

        • Innovating cost-effective conversion technologies
        • Enhancing feedstock flexibility
        • Minimising waste and emissions

        Can these challenges be solved through material and process optimization?

        Producing biofuels efficiently and with minimal environmental impact requires significant technical optimization across the value chain:

        • Enzyme and catalyst development enhances performance in bioethanol and biodiesel production.
        • Process integration and energy efficiency, particularly in energy-intensive stages like distillation and gasification, are crucial.
        • Upgrading technologies for biogas and bio-oil must meet high fuel standards, often requiring expensive, multi-stage purification.

        While these innovations support net-zero targets in aviation and transport, most remain expensive and limited in scale without broader industrial and policy support.

        Where the focus needs to be: scalability and economic viability

        Even with technical solutions in place, scaling biofuel production to meet global transport demand is challenging:

        • Higher production costs vs fossil fuels
        • Fragmented, globalized supply chains
        • Need for new or upgraded processing and distribution infrastructure

        Current infrastructure is largely fossil-based. Biofuel integration in sectors like aerospace and heavy mobility requires system-wide investments across storage, pipelines, airport fuelling systems, and more.

        To succeed, biofuels must be backed by strong market mechanisms: subsidies, tax credits, blending mandates, and long-term regulation to encourage adoption across carbon-intensive industries.

        Conclusion

        Decarbonizing the transport sector by 2050 is a critical challenge and to meet net-zero targets, emissions must decline by over 3% annually through 2030 and continue to decline steeply beyond that – despite rising demand. This transition is particularly complex for high-emission sectors like aviation and automotive, which face mounting regulatory and societal pressure to adopt cleaner energy sources. Biofuels, ranging from first-generation food crops to advanced fourth – generation engineered organisms, offer a promising alternative but each type presents unique technical, environmental, and economic hurdles. These include high production costs, limited scalability, and complex processing requirements. Feedstocks such as waste oils, algae, and agricultural residues require significant pretreatment and infrastructure adaptation, while innovations in enzymes, catalysts, and purification technologies are essential to improve efficiency and reduce emissions. However, without strong policy support market incentives, and investment in infrastructure, biofuels remain commercially uncompetitive.

        Achieving scalable, sustainable biofuel adoption will require a coordinated strategy that enhances feedstock flexibility, optimizes production processes which aligns with broader energy and transport systems.

        How Capgemini can help you decarbonize

        Capgemini brings deep expertise in decarbonizing transport and industrial energy systems. We partner with global clients to define, develop, and deliver innovative fuel and infrastructure strategies.

        In aerospace, we assessed market demand for medium-range planes by 2030 and evaluated the feasibility of hydrogen-powered aircraft—helping clients plan for the next generation of zero-emission aviation.

        In maritime, we partnered with Newcastle Marine Services, the University of Strathclyde, O.S. Energy, and MarRI-UK to retrofit diesel vessels with hydrogen propulsion using Liquid Organic Hydrogen Carriers (LOHCs).

        Impact metrics:

        • Emissions reduced by >90% per vessel during trials
        • GPS and energy data collected over 48-hour missions
        • Demonstrated LOHC integration without redesigning onboard systems

        Capgemini enables transport clients to make informed decarbonization choices—from strategy to implementation. Our approach includes:

        • Strategic fuel and tech assessments
        • Infrastructure and policy alignment
        • Business case development
        • Digital prototyping and scaled deployment

        We also leverage Internet of Things (IoT) and Artificial Intelligence (AI) to optimize biofuel supply chains, enhance efficiency, and reduce carbon footprints across the value chain.

        👉 Learn more about our experience in energy transition and mobility innovation

        Authors

        Sushant Rastogi

        Oil & Gas SME, Energy Transition and Utilities Industry Platform, Capgemini
        Entrusted to drive Oil & Gas Digital Strategy & Consulting at Capgemini, leading business development, decarbonization, and digital transformation initiatives. With deep expertise across Upstream, Midstream, and Downstream including Petrochemical sectors, he crafts tailored solutions, fosters partnerships, and promotes AI/ML adoption, contributing to sustainable energy transitions.
        Graham Upton

        Graham Upton

        Head of Technology & Innovation, Capgemini Engineering UK
        Capgemini can help clients seize opportunities in transport decarbonisation by leveraging its expertise in digital transformation, engineering, and sustainability. We can support innovation in biofuel technologies, optimise supply chains, and navigate regulatory landscapes. By enabling scalable, cost-effective solutions and infrastructure adaptation, Capgemini empowers clients to lead in sustainable mobility and meet net-zero targets amid rising demand and complex challenges.

          Capgemini named a leader and customer favorite in The Forrester Wave™: SAP Services In Europe, Q2 2025

          Elisabetta Spontoni
          13 Jun 2025

          I am delighted and proud to share that the prominent industry analyst firm Forrester has named Capgemini a leader and customer favorite in The Forrester Wave™: SAP Services In Europe, Q2 2025.

          As I read the Forrester report, I was reminded of Wordsworth’s reflection: “Life is divided into three terms – that which was, which is, and which will be. Let us learn from the past to profit by the present, and from the present, to live better in the future.” William Wordsworth.

          Our journey in SAP services mirrors this timeless insight. Our past has laid the foundation. Our present is where we deliver value. And our future is where we shape what’s next.

          It’s all about time — and how we use it to lead.

          The past

          Becoming a leader in any field doesn’t just happen. It has taken many years to build the team we have today at Capgemini. Our experience and expertise stem from a decades-long mission to create a solid foundation of skills, ranging from visionary senior leaders to thousands of dedicated, capable, and qualified technical experts worldwide. But it is the positive outcome of this commitment that counts. It is the satisfied customers who return to us again and again, and who are willing to recommend us to other clients and analysts, such as Forrester.

          In some ways, delivering the SAP project is the easy part; building a culture of collaboration, trust, and determination demands hard work. It was gratifying to see exactly this recognized by Forrester when they noted, “Customers particularly appreciate that Capgemini’s teams can break down delivery silos; they report that they couldn’t ask for more from the service provider.” 

          The present

          I can reflect on the past and build a strategy for the future, but I also have a day job. Each day, I embrace the challenges and opportunities that come my way while ensuring smooth operations and competent service delivery (not to mention the endless conference calls).

          Unsurprisingly, we spend a lot of time focusing on technologies that will simplify delivery, ease migrations, or prevent problems. As highlighted in the Forrester Wave report, we invest both time and money in creating world-class intellectual property that “covers all aspects of SAP project delivery, from strategic ambitions to testing, incident management, and continuous improvement.” We have also deployed artificial intelligence – in its many forms – across all aspects of our services. As I noted in a LinkedIn post from this year’s Sapphire event in Orlando, the shift to AI is happening now. It is no longer about pilots and isolated use cases; we are already in the realm of end-to-end intelligent processes, and we are assisting clients such as CONA Services with our portfolio of AI-led SAP offerings designed to augment business processes, accelerate time to market, and upskill the workforce.

          The future

          I hope it goes without saying that I am determined for Capgemini to maintain its leadership status with analysts – and, more importantly, with our clients, both existing and new.

          We will continue to be at the forefront of SAP service delivery, and we will do this by:

          • Investing in the creation of superior services built on new technologies such as artificial intelligence, edge computing, IoT, and more
          • Creating award-winning methodologies and IP
          • Placing research and innovation at the center of our strategic development
          • Building on our already strong relationship with SAP
          • Seeking to solidify our SAP offerings through partnerships and acquisitions.

          Thank you, once again.

          Earlier this year, when Forrester named us a leader in their Forrester Wave™: SAP Services, Q1 2025, I stated in a blog post that “We set out to be a world-leading provider of SAP services with a complete vision and an unchallenged ability to execute. Analyst recognition is a welcome and happy result of our hard work and strategy. Together with an excellent team of leaders around the world, we have built – and continue to refine – a determined strategy to achieve success for our clients.”

          This remains my heartfelt commitment.

          So, once again, I am honored to say such a remarkable achievement doesn’t simply happen. It requires time and is preceded by immense hard work, solid strategies, and the skills of a great team of over 30,000 SAP consultants, engineers, and leaders. And more importantly, it happens thanks to clients who are eager for innovation and open to change. To all of you, once again, I say, “thank you.”

          Find out more

          Discover the full report on Europe and Global leadership ranking and learn more about our SAP services leadership.

          Author

          Elisabetta Spontoni

          Group Offer Leader for Digital Core
          Elisabetta is responsible for driving the Digital Core offer lifecycle end-to-end. This entails building value add service offers around enterprise core processes and applications transformation, building innovative assets and solutions that position Capgemini leadership in the field, and promoting the offers in the market. She is also leading the packaged solutions portfolio in the ERP and supply chain space and strategic learning and certifications for the global packaged solution business line.

            Explore our SAP partnership

            Scaling Up: A Strategic Imperative for the Defense Industry

            Andreas Conradi, Matthieu Ritter, Elodie Régis and Frédéric Grousson
            Jun 13, 2025

            Beyond the Buzzword: The Real Stakes of the “Production Ramp-Up”

            Current armed conflicts serve as a stark reminder of the critical importance of maintaining substantial stockpiles of weapons, personnel, and ammunition. This presents a major challenge for European defense manufacturers, who have traditionally focused on producing complex, high-tech weapon systems in small quantities.

            How can the industry make the leap to mass industrialization?
            What short-term solutions can be implemented to scale up production of existing equipment?
            And how can the product lifecycle be reimagined to better integrate manufacturing and ramp-up considerations?

            Accelerating Production: A Long-Term Endeavor

            Industrial ramp-up is not a new challenge for defense stakeholders, particularly in the aerospace and space sectors. For years, production management has been a central concern. However, recent conflicts—most notably the war in Ukraine—have reignited the urgency, highlighting the reality of high-intensity warfare and the critical need for mass production.

            This demand now confronts European manufacturers historically focused on high-end, small-series technological equipment, primarily for export. The production apparatus must now adapt to a new strategic landscape, while contending with significant constraints: production lines designed for precision rather than volume, and legacy designs from the 1980s and 1990s that are often incompatible with modern digital tools and manufacturing methods.

            Meeting this imperative requires a profound transformation of industrial models—from design processes to manufacturing capabilities.

            “The defense sector must transition from a kind of small-batch high-tech craftsmanship to full-scale industrialization. If I were to use an analogy, I would say it’s like moving from luxury watchmaking to premium mass market”, Andreas Conradi, Head of Defense Europe.

            Many manufacturers operating in both civilian and military markets have historically concentrated their efforts on the civilian segments, driven by strong growth dynamics in sectors such as naval, aerospace, and space. This focus has led to a pronounced separation between civilian and military activities—reinforced by defense secrecy requirements and cultural factors—limiting the transfer of experience and industrial synergies between the two domains.

            In this context, meeting the current surge in demand requires reactivating production lines and increasing throughput—a lengthy and difficult process that cannot easily be accelerated. Timelines are further strained by the loss of critical skills (due to retirements, outsourcing, and post-COVID effects) in a sector with high technical demands, where the time required to build expertise is significant. Recruitment challenges are also exacerbated by mandatory security clearance procedures—which can take up to a year—and by the sector’s limited appeal to certain talent profiles.

            Finally, the ecosystem remains highly fragmented, with a dense network of SMEs with limited investment capacity. This hampers the ramp-up of the supply chain, especially since digital continuity between stakeholders remains weak, making it difficult for major contractors to monitor progress effectively.
            Finally, the ecosystem remains highly fragmented, with a dense network of SMEs According to Matthieu Ritter, Head of Aerospace & Defense France, “We are seeing a consolidation movement in the sector, which should accelerate around major manufacturers and the arrival of dedicated investment funds. But all of this takes time.”

            Between Lean and Digital Pragmatism

            According to Andreas Conradi, “Production ramp-up is probably the most complex issue for the defense sector, because you have to change everything: how you define needs, manage spare parts, design systems, produce them, organize the supply chain, and so on.”

            To tackle this major challenge, three levers can be activated in the short and medium term:

            1. Capacity Increase and Productivity Gains
              This involves boosting capacity and improving productivity per assembly line through the reintroduction of lean practices. Many ramp-up projects have already been launched, such as adding extra teams to enable 24/7 operations. However, Elodie Régis notes: “This lever has already been activated in most organizations, with limited results due to recruitment difficulties and because the entire production ecosystem must be mobilized—logistics, quality assurance, maintenance, methods teams, etc.”
            2. Expanding Existing Lines
              This consists in duplicating certain stations identified as bottlenecks. “However, this already involves higher level of work on buildings and infrastructure, and presents complexity in execution while maintaining ongoing production”, adds Elodie Régis.
            3. Optimizing Overall Production Organization
              Complementary to the first two levers, this includes shortening the critical path with suppliers, consolidating the supply chain, and integrating elements of digital transformation when they can quickly deliver productivity gains without compromising capacity. For example, we are seeing the implementation of “single source of truth” architectures, consolidating all ramp-up stakeholders into a single, secure, and shared data lake. This approach optimizes the use of available data, facilitates planning and tracking of parts, tools, skills, and operations, identifies breakpoints and risk areas in the supply chain, enables “supplier recovery” initiatives, and secures valuable productivity gains.

            “Ultimately, building new factories or production lines is such a long-term endeavor that it cannot be the sole answer to the defense sector’s immediate ramp-up needs”, concludes Elodie Régis.

            Learning from Today to Better Prepare for Tomorrow

            Defense industrial programs must meet exceptionally high technological, technical, and security requirements, which have not always accounted for industrial constraints. One of the key challenges for future programs will be to reconcile and more closely align the worlds of engineering and manufacturing in order to simplify and standardize designs. This includes, for example, integrating best practices from the civilian sector, using model-based systems engineering (MBSE), leveraging simulation and collaborative tools, and harnessing recent digital innovations—such as generative artificial intelligence and cloud computing—with digital continuity at the core of the process.

            The defense sector must also anticipate and incorporate new constraints into its roadmap to successfully scale up production, including:

            • The growing role of low-cost or “disposable” systems (e.g., drones), which challenge traditional mindsets,
            • Circular economy principles, to address future tensions over strategic resources (steel, titanium, aluminum, etc.) between civilian and military sectors,
            • The rationalization of long and vulnerable supply chains, with significant sovereignty implications.

            This transformation requires a fundamental shift in collaboration methods, particularly among industrial players, as well as a renewed focus on the human dimension: reinforcing the sense of purpose in missions, evolving mindsets in a world of highly specialized engineers, and developing employee skills to enhance agility. This evolution is essential to more effectively respond to military needs and to adapt to a constantly evolving geopolitical context.

            Authors

            Andreas Conradi

            Executive Vice President | Head of Defense Europe
            Since March 2023 Andreas has been Executive Vice President and Head of Defense Europe at Capgemini. As such, he is responsible for Capgemini´s business with the Defense Industry as well as Defense Ministries and Armed forces in Europe and NATO. Andreas is a proven defense sector expert with sustained successful track record as top official at the helm of the German Ministry of Defense including as Chief of Staff to Defense Minister Ursula von der Leyen. Based on more than two decades of experience, he has a deep understanding of the structure and function of the public and private defense sector in Europe including the set-up and management of national and international armament programs.

            Matthieu Ritter

            Head of Lifecycle Optimization for Aerospace
            Matthieu has a Master’s Degree in Aeronautical engineering from ENSPIMA, Bordeaux Institute of Technology (INP) and more than 15 years of experience in the A&D industry where he works with clients on integrated solutions from engineering to aircraft maintenance, modification, and end of life management. Matthieu joined Capgemini in 2018 and has since been supporting A&D clients in the convergence of the physical, digital, and human worlds to accelerate the transformation of products, services, systems, and operations with the ultimate goal of creating more value for customers.
            Elodie Regis

            Elodie Regis

            VP, Aerospace & Defense, Capgemini Invent
            Elodie is Vice President at Capgemini Invent, leading 2 main topics : industrial ramp-up in the Aerospace & Defense and Skywise. She has a diverse background including Quality Director in a factory for the Automotive Industry and work as a Consultant for 18 years. She has developed her expertise on A&D Manufacturing, Quality and Supply Chain while designing and building new factories, supporting shopfloor workforce transformation, and operations excellence.

            Frédéric Grousson

            VP, Head of Aerospace & Defense, Capgemini Engineering
            Frederic is Dr.-Eng in control system and has joined the group in 2000, and has worked since then in the Aeronautic sector for many customers with a huge experience at Airbus account in the industry sales team since 2015, he now leads the Aerospace and Defense sector globally for Capgemini Engineering.

              Zero trust and users: Cutting through the noise

              Lee Newcombe
              Jun 12, 2025

              I’ll admit – trying to explain zero trust without relying on the usual jargon and buzzwords is no small feat. But here goes.

              At its core, cybersecurity aims to ensure the right people have the right access to the right systems and data at the right time. Breaches tend to occur when any one of these elements goes wrong. Over the years, we’ve leaned heavily on user identity and associated access controls – think usernames and passwords – but that approach has its flaws, both in effectiveness and in user experience. So, what have we learned over the years?

              • Organizations work in collaborative ecosystems – building metaphorical walls is ineffective due to the sheer number of holes you have to punch through them.
              • Users don’t want to be confronted by security. They will work around your controls if they are too onerous. Transparent security is more effective.
              • Segmentation is important. Many ransomware attacks succeed because attackers, once inside, face minimal resistance in moving laterally across systems. Today’s focus on operational resilience puts a spotlight on the need to reduce that blast radius.

              And this is where “zero trust” comes in.

              You’ll often hear “never trust, always verify” when it comes to zero trust. It’s not necessarily wrong, it’s just not particularly helpful. The underlying philosophy is really “assume compromise”: start from the assumption that anything and everything in your IT ecosystem may be compromised; that could be the user themselves, their credentials, their laptop, the network they are using, or any combination of the above. How do you secure your systems and data if anything or everything is broken? Well, you start by building up trust, from that position of no trust whatsoever.

              How can I build up trust in the user’s laptop? Is it one of ours? If so, can we give it a certificate it can use to identify itself? Is it configured in-line with our policy? Perhaps we can run a policy check. Has it been compromised? What does the endpoint security agent we have installed on the device say? From that position of untrusted, we’ve now built up a degree of trust – assuming that the security tooling providing those checks is effective! (That trust thing again, eh?). What about the user? Well… have they presented the right credentials? Are they accessing from their usual location? At the usual times? In this case, we’re now making decisions based on previous patterns of behavior, and this is where AI can help, particularly machine learning which can raise an alert and/or deny access should behavior be seen as outside of normal baselines. We can score the trustworthiness of each and every access request, and grant access if a request is deemed sufficiently “normal.” What about the network? Frankly, we don’t really care, we’re going to encrypt all the traffic and so the network is just a way of transporting data backwards and forwards. Just pick quantum-safe algorithms if your threat model demands it.

              One of the nice things about modern approaches to zero trust is that it connects the user to their applications rather than to the underlying network on which those applications are hosted. If you group the applications appropriately then you get that benefit of segmentation. An attacker may be able to compromise the application to which they have been given access, but they will then only be able to traverse to whatever other systems that application can see rather than having access to the full underlying (often flat) network. You can reduce the blast radius of a compromise.

              There are also some tangential, not security-specific benefits available. When you think about traditional ways of doing security, you’ll likely find a fairly complex stack of security technologies in place within the data center. Wouldn’t it be nice to be able to simplify that stack? Perhaps reduce the total cost of ownership in terms of overall license and support costs? All while still delivering the same security capabilities? That’s where technologies like Zscaler can help. Centralize those security capabilities and deliver them from the cloud. This does, however, mean you are placing a LOT of trust in your “zero trust” security services provider. An irony that is not lost on most security professionals, but another reason why I do grumble somewhat about the term.

              In summary, “zero trust” is really just a way of delivering the dynamic, context-based security controls that modern business demands. You can choose the authentication credentials you want to use to provide the user experience you desire. Every access request is checked, such that if something goes wrong in the period between a user accessing Application A and asking to access Application B then you can deny the second request. You are only providing access to applications and not networks and so you reduce the risk of full network compromise. You can simplify your legacy security tooling and deliver much of this from the cloud, supplemented by other technologies (e.g. endpoint security) where it makes sense to do so.

              Businesses may not want “zero trust,” but they probably will want the outcomes described above – improved user experience, reduced total cost of ownership, and improved operational resilience. Sometimes it’s helpful to forget the buzzwords and focus on the outcomes. The final post in this series will talk about how we help our clients to do just this.

              In the next post, we will explore zero trust and devices – because yes, machines have identities too.

              Know about the author

              Lee Newcombe

              Expert in Cloud security, Security Architecture, Zero Trust and Secure by Design
              Dr. Lee Newcombe has over 25 years of experience in the security industry, spanning roles from penetration testing to security architecture, often leading security transformation across both public and private sector organizations. As the global service owner for Zero Trust at Capgemini, and a member of the Certified Chief Architects community, he leads major transformation programs. Lee is an active member of the security community, a former Chair of the UK Chapter of the Cloud Security Alliance, and a published author. He advises clients on achieving their desired outcomes whilst managing their cyber risk, from project initiation to service retirement.

                Future of Work: Transforming workplaces with human-machine collaboration

                Muhammed Ahmed
                May 27, 2025

                “The workplace of the future is redefining the way humans and technology coexist. AI is no longer just a tool for productivity or efficiency – it’s become an integral part of the modern workforce. A new operating model is emerging, where humans and intelligent AI agents collaborate to unlock unprecedented possibilities.” – Muhammed Ahmed 

                Humans ignite a spark. Technology amplifies the flame. Together, they are unlocking new levels of creativity, accelerating innovation, and empowering us to solve challenges once thought impossible.  

                This dynamic partnership between humans and technology is reshaping how we collaborate and achieve our goals. At the forefront of this transformation are AI and advanced collaboration tools, enabling humans and their digital colleagues to work together seamlessly as though they’re physically side by side. These next-generation technologies are becoming critical differentiators. Organizations that adapt and embrace them will be better positioned to lead with innovation, while others risk falling behind.  

                A new way of working together 

                Today’s workplaces are saturated with digital tools. Each day, employees toggle between an array of digital tools that enable them to effectively carry out their day-to-day tasks and communicate in real-time with team members from across the globe. Of these tools, collaboration technologies are the ones currently in the spotlight. A recent study from Microsoft found that 85% of workers feel these technologies are a “critical area of focus,” underscoring their essential role in the modern workplace.  

                Coupled with the importance of collaboration tools is generative AI. Recent research from the Capgemini Research Institute (CRI) found that 80% of organizations have increased their investment in Gen AI since 2023, underlining its immense potential to enhance productivity and creativity across industries.  

                How technology is leaving its mark 

                Organizations are already exploring how they can integrate collaborative technologies and Gen AI into their businesses. A leading financial services firm recently launched LLM Suite, an AI assistant that enables the firm’s personnel to leverage Gen AI across many tasks, including drafting emails and writing reports. Boosting productivity across the business, this tool is a promising development that is slated to drastically enhance the firm’s value chain over the coming years.  

                The benefits of Gen AI aren’t only being felt within the financial services sector. The technology is also leaving its mark on the media and entertainment industry. A German media organization recently developed a solution that leverages LLMs to streamline its editorial process. It does so by reducing the time editors spend searching for topics and suggesting text elements that reduce the time spent per article. Set to completely revolutionize digital journalism, this solution is yet another example of how Gen AI will transform workplaces across industries.  

                Need for checks and balances 

                Despite their growing importance, these technologies come with their own set of challenges. While these intelligent agents and digital tools can autonomously handle mundane tasks and assist human co-workers across a wide range of functions, a standardized operating model to effectively manage and govern this hybrid workforce is currently lacking. Organizations are grappling with how to best integrate these two distinct, yet complementary, types of team members for optimal performance and seamless human-machine collaboration. 

                Furthermore, while Gen AI uplifts creativity and productivity, enterprise applications often require careful review and robust guardrails to ensure accuracy and reliability. Similarly, while real-time communication and a suite of digital tools can enhance performance, they also increase the risk of distraction and digital fatigue. 

                These complexities highlight the need for continued research, refinement, and responsible investment in these technologies. It’s a priority that remains top of mind for business leaders as they navigate the evolving workplace landscape. 

                A glimpse of the future 

                Workplace collaboration tools and Gen AI are set to deliver unseen levels of innovation and efficiency for businesses, positioning these technologies as key enablers for success.  

                Organizations that act now – by embracing intelligent technologies, investing in talent, and equipping their people with powerful digital tools – will lead and stay ahead of the curve in this new era of work. 

                Learn more 

                • TechnoVision 2025 – your guide to emerging technology trends 
                • Synergy2 – a new trend in We Collaborate 
                • Voices of TechnoVision – a blog series inspired by Capgemini’s TechnoVision 2025 that highlights the latest technology trends, industry use cases, and their business impact. This series further guides today’s decision makers on their journey to access the potential of technology.

                Meet the author

                Muhammed Ahmed

                Portfolio Manager, Financial Services
                Ahmed leads strategic initiatives around emerging technologies for the global financial services business at Capgemini. As a strategy consultant, he has rich and diverse experience working with global clients driving complex technology transformation programs, delivering tangible business outcomes.

                  Capgemini and NVIDIA: Pioneering the future of AI factories with Capgemini RAISE and Agentic Gallery

                  Mark Oost
                  June 11, 2025

                  Capgemini and NVIDIA’s strategic collaboration provides an innovative AI solution designed to transform the way enterprises build and scale AI factories.

                  This work is aimed to assist organizations, particularly those in regulated industries or with substantial on-premises infrastructure investments, deploy agentic AI into their operations. By leveraging NVIDIA AI Enterprise software, accelerated infrastructure, and the Capgemini RAISE platform, companies can expect a seamless, high-performance AI solution ready for the future.

                  Managing AI at scale

                  Capgemini RAISE is our AI resource management platform, able to manage AI applications and AI agents across multiple environments within a single managed solution. This enables organizations to separate their solution from systemic risk and, leveraging NVIDIA NIM microservices, can centralize AI evaluation, AI FinOps, and model management. The business can then focus on delivering AI-augmented work, while the AI Risk Management team focuses on managing risk, costs, and technical challenges. 

                  This is a paradigm shift, placing the AI Factory at the center – and not only for private implementation, but as the global point for AI management.

                  “This new collaboration with NVIDIA marks a pivotal step forward in our commitment to bringing cutting-edge AI-powered technology solutions to our clients for accelerated value creation. By leveraging the power of the NVIDIA AI Stack, Capgemini will help clients expedite their agentic AI journey from strategy to full deployment, enabling them to solve complex business challenges and innovate at scale.” – Anne-Laure Thibaud, EVP, Head of AI & Analytics Global Practice, Capgemini

                  Benefits for modern enterprises

                  Imagine the ability to deploy agentic AI capabilities with a single click. Our partnership extends the reach of the Capgemini RAISE platform, bringing these capabilities to NVIDIA’s high-performance infrastructure. This enables companies to realize value more swiftly, and reduce total cost of ownership and deployment risk. Additionally, with the NVIDIA Enterprise AI Factory validated design, we guide organizations in building on-premises AI factories leveraging NVIDIA Blackwell and a broad ecosystem of AI partners.

                  Some of the other key features to support the navigation of complex, agentic AI solutions include:

                  • Rapid prototyping and deployment: Speeding up the deployment of AI agents through ready-to-use workflows and streamlined infrastructure, minimizing time-to-market.
                  • Seamless integration: Embedding AI agent functionalities into current business systems to enhance automation, operational efficiency, and data-informed decision-making.
                  • Scalability and governance: Deploying AI agents within strong governance models to ensure regulatory compliance, scalability, and consistent performance. Capgemini RAISE provides specialized agentic features – such as governance, live monitoring, and orchestration – to provide centralized management and measurable outcomes.

                  Scaling AI in private, on-premises environments

                  Our solution is designed to help organizations rapidly scale AI in private, on-premises environments. It supports key requirements such as data sovereignty and compliance to meet regulatory and data residency mandates. It also ensures resiliency and high availability for business continuity, security, and privacy controls for air-gapped environments. This solution delivers ultra-low latency for a diverse set of real-time use cases like manufacturing or healthcare imaging, and edge or offline use cases for remote, disconnected environments.

                  Alongside NVIDIA, we are bringing the power of Capgemini RAISE to on-premises infrastructure. This open, interoperable, scalable, and secure solution paves the way for widespread AI adoption. To illustrate our capabilities, we are launching the Agentic Gallery, a showcase of innovative AI agents designed to address diverse business needs and drive digital transformation.

                  Capgemini and NVIDIA have collaborated on over 200 agents, leveraging the NVIDIA AI Factory to create a robust ecosystem of AI solutions. This collaboration has led to the development of the Agentic Gallery, which is set to revolutionize the way businesses approach AI.

                  Is your organization ready to place the power of an AI Factory at the center of its business? Get in touch with our experts below.

                  Meet the authors

                  Mark Oost

                  AI, Analytics, Agents Global Leader
                  Prior to joining Capgemini, Mark was the CTO of AI and Analytics at Sogeti Global, where he developed the AI portfolio and strategy. Before that, he worked as a Practice Lead for Data Science and AI at Sogeti Netherlands, where he started the Data Science team, and as a Lead Data Scientist at Teradata and Experian. Throughout his career, Mark has worked with clients from various markets around the world and has used AI, deep learning, and machine learning technologies to solve complex problems.

                  Itziar Goicoechea

                  Agentic AI for Enterprise Offer Leader
                  Itziar has more than 15 years of international experience as a tech and data leader, specializing in data science and machine learning within the e-commerce, technology, and pharmaceutical sectors. Before joining Capgemini, she was Director of Data Science and Machine Learning at Adidas in Amsterdam, leading a global team focused on AI solutions for personalization, demand forecasting, and price optimization. Itziar holds a PhD in Computational Physics.

                  Steve Jones

                  Expert in Big Data and Analytics
                  Steve is the founder of Capgemini’s businesses in Cloud, SaaS, and Big Data, a published author in journals such as the Financial Times and IEEE Software. He is also the original creator of the first unified architecture for Big Fast Managed data, the Business Data Lake. He works with clients on delivering large-scale data solutions and the secure adoption of AI, he is the Capgemini lead for Collaborate Data Ecosystems and Trusted AI.

                    The generative AI evolution in the Brose supply chain

                    Maid Jakubović
                    9 May 2025

                    Brose has more than 14,000 suppliers worldwide – and that means communication can be a challenge. Brose had already transformed its supply chain by creating a single sign-on portal that allowed suppliers to access back-end applications. Now, by adding generative AI, it is delivering even more innovation to make life easier for suppliers.

                    Brose is a global automotive supplier that builds mechatronic components and systems for doors, seats, electric devices, and electronics in 69 locations in 25 countries. One out of every two cars built in the world contains at least one Brose product.

                    Streamlining supplier communication

                    In 2023, the company worked with Capgemini and SAP to co-innovate a supplier integration app built on SAP’s Business Technology Platform (BTP). This proof of concept became the Capgemini Supplier Integration for Automotive (CSI4Auto) tool, and delivered a single digital gateway and central collaboration platform for the company’s 14,000 suppliers. The solution eliminated time-consuming, complicated, and resource-intensive daily processes.

                    CSI4Auto at Brose provides suppliers with a single sign-on to access back-end applications, with central access to any cloud or on-premises application out of the box. And supplier administrators can easily manage new user onboarding, while self-registration allows supplier employees to sign on for different legal entities. The content available to a supplier or legal entity was controlled based on what was relevant. The streamlined process enhances user autonomy and ensures a more efficient and transparent collaboration.

                    The optimized workflow paid big dividends. The new supplier integration application delivered an 80% reduction in manual effort, 50% faster supplier user onboarding, and a 20% decrease in support volume.

                    Solving the next challenge

                    While CSI4Auto solved an immediate business challenge, onboarding new employees on the supplier side still had some lingering hurdles. Suppliers usually receive specifications and quality standards in extensive documents. New employees would spend a lot of time manually reviewing the documents to find the right information for their role.

                    Language was another obstacle. Working in 25 countries means documents need to be maintained in multiple languages, requires a significant effort. And it was more material that employees needed to wade through before they could find the right information.

                    Introducing AI-supported innovation

                    Brose needed to provide relevant information easily, while reducing the administrative burden. The answer: the Supplier Chatbot.

                    Working with Capgemini, Brose harnessed the power of generative AI to create a chatbot specifically to serve its supplier community. The chatbot is trained on the supplier documents and is ready to answer questions. The advantages include the following:

                    • Quick answers: Employees can ask specific questions and receive precise information immediately, skipping the tedious document searches.
                    • Always available in any language: The AI enables continuous support for suppliers worldwide in any language, without concern for time zones – even without previously translated documents.
                    • Role-based answers: The chatbot provides tailored information based on the role of the person making the inquiry.

                    Added to CSI4Auto, the chatbot is an intelligent, user-friendly solution for supplier portals, and it increases the efficiency of collaboration across the supply chain.

                    Capgemini and Brose brought the Supplier Chatbot from idea to reality within a few weeks, because:

                    • The modular CSI4Auto architecture enables the seamless integration of new innovations
                    • AI services in SAP BTP support rapid market introduction
                    • The co-innovation model combines the expertise of Capgemini, Brose, and SAP to allow joint pilots to be designed, implemented, and tested quickly.

                    Enhancing the supply chain

                    Supply chain transformation is challenging. Streamlining supplier communications adds efficiency and great collaboration. Using CSI4Auto and the Supplier GPT, companies can optimize processes and future-proof the organization to ensure the supply chain continues to operate smoothly. Improved workflows help everyone.

                    AI technologies can solve some of the most complex problems facing supply chains. By embracing innovation, companies can reshape workflow operations for the better.

                    Capgemini champions co-innovation to foster sustainable and shared solutions that lead to a competitive advantage. Digital platforms are indispensable, and processes must constantly adapt. We want to elevate digital collaboration between companies and suppliers to achieve better business outcomes.

                    To find out more about how we made this solution possible, reach out to me on LinkedIn.

                    Author

                    Maid Jakubović

                    Supply chain expert, Automotive cloud initiative with SAP
                    Maid is a managing Business Analyst with more than 15 years’ experience as an automotive industry specialist. He spends most of his time working directly with clients and has a thorough understanding of the automotive business. He believes that the automotive industry is a leader in innovating to address highly competitive and challenging markets and he is a vanguard of creative innovation. He is renowned for his pragmatic, results-focussed style of leadership.