As decision-makers in today’s rapidly evolving business landscape, you’re constantly seeking competitive advantages, ways to streamline operations, and robust security solutions. At Capgemini, we understand these imperatives, and that’s why we’re excited to share our insights on the IBM z17, a platform poised to redefine enterprise computing.

Over 70% of global transactions by value run on IBM Z. Every major bank, all three major global credit card companies, insurance companies, retailers and financial services clients use it, along with many other industries. In short it has long been the backbone for mission-critical workloads.

But our world is very different from early 2001 when IBM Z was first released; the IBM z17 brings the mainframe squarely into the AI age. It has a significant bump in raw processing power; but the key point is about intelligent integration, real-time insights, and unparalleled resilience – all delivered where your most valuable data resides.

The AI Advantage: Bringing Intelligence to Your Core Data

One of the most striking features of the IBM z17 is its deep integration of Artificial Intelligence. Powered by the new Telum II processor with its on-chip AI accelerator, the z17 enables:

• Real-time AI at Scale: Imagine scoring 100% of your transactions in real-time. The z17 can process hundreds of billions of AI inference operations per day with sub-millisecond response times. This translates to instant fraud detection, real-time customer interaction scoring, and immediate transactional insights without moving data off the platform.

• Generative AI Integration: Looking ahead, the z17 is designed to embrace generative AI and Large Language Models (LLMs) with the upcoming Spyre accelerator card. This will enable organizations to unlock new levels of productivity and innovation by leveraging generative AI directly on their most sensitive data, securely and efficiently.

Unwavering Security and Resilience in a Hybrid World

In an era of escalating cyber threats and stringent compliance requirements, security is paramount. The IBM z17 builds upon the mainframe’s legendary security features, further enhancing them:

• Quantum-Safe Cryptography: The z17 is designed with quantum-safe cryptography, future-proofing your data against emerging quantum threats. This proactive approach ensures your sensitive information remains secure for years to come.
• Pervasive encryption that protects data at rest, in motion, and in use.
• Hardware security modules and tamper-resistant components that enforce secure boot and runtime integrity.
• Continuous monitoring and automated threat detection embedded at every layer.

Driving Operational Efficiency and Resilience

Operational complexity can slow down innovation and inflate your costs. IBM addresses this with intelligent automation powered by AI—such as IBM watsonx™ Assistant for Z—which simplifies complex workflows using conversational AI and low-code tools.

The benefits include:

• Reduced manual errors and faster resolution times.
• Streamlined onboarding for new staff with easy-to-use AI assistants.
• Improved productivity that frees skilled personnel to focus on innovation.

Coupled with an 99.999999% uptime(that’s 6 nines after the dot), the z17 keeps your mission-critical systems running without interruption—minimizing costly downtime and ensuring business continuity under any circumstance.

Capgemini’s Perspective: Your Trusted Partner

At Capgemini, we believe the IBM z17 represents a significant leap forward in enterprise computing. Its focus on embedding AI directly into the core, coupled with enhanced security and seamless hybrid cloud integration, makes it a powerful platform for organizations looking to innovate, optimize, and secure their most critical operations.

With tried and trusted expertise in mainframe modernization, hybrid cloud, and AI, Capgemini helps unlock the full potential of your IBM z17 investments. By embedding AI into your core operations, you will enable smarter decision-making, enhanced productivity, and faster innovation—augmenting human capabilities to drive real business outcomes.

Ready to discuss how the IBM z17 can power your organization’s future? Contact your Capgemini client team today.

Microsoft has taken a bold step forward with the public preview of Microsoft Sentinel Data Lake, a new way to process and store SIEM data. This marks a significant milestone in the journey toward intelligent, scalable, and most importantly, cost-effective security operations with agentic AI.

What is Microsoft Sentinel Data Lake?

Built on Microsoft Fabric OneLake, Sentinel Data Lake offers a centralized repository for structured and unstructured security data. It’s designed to break down silos, enabling security teams to ingest, store, and analyze massive volumes of data from diverse sources – all in one place. This unified approach not only simplifies data management but also enhances the effectiveness of AI-driven threat detection and response.

The platform supports open data formats, making it easier to integrate with third-party tools and analytics engines. It also introduces tiered storage options, allowing organizations to optimize costs while maintaining access to critical data.

Why this matters for security teams?

Traditional SIEM solutions often struggle with scalability and cost-efficiency. Sentinel Data Lake addresses these challenges head-on by offering:

• Elastic scalability for growing data volumes
• Cost-effective storage tiers including hot, warm, and cold options
• AI-readiness for advanced analytics and automation.

This empowers security teams to move beyond reactive defense and embrace proactive, intelligence-driven operations.

Capgemini’s MXDR services: Amplifying the power of Sentinel and Unified SecOps

Capgemini has long been a strategic partner in Microsoft’s security ecosystem. As a member of the Microsoft Intelligent Security Association (MISA), Capgemini brings deep expertise in deploying and managing Microsoft Sentinel through its Cyber Defense Centers (CDCs).

Capgemini’s MXDR services are built on Microsoft Sentinel and the Unified SecOps Platform from Microsoft, and Capgemini and clients can take advantage of enhanced capabilities of the new Data Lake. These services include:

• Longer storage and retention choices
• Advanced threat hunting using machine learning and behavioral analysis on data beyond 90 days
• Integration with Microsoft Defender and other third-party security tools for a holistic defense strategy.

By leveraging Sentinel Data Lake, Capgemini’s MXDR services will be able to deliver even greater visibility, faster detection, and more efficient response – all while optimizing costs.

Looking ahead

The public preview of Microsoft Sentinel Data Lake is more than a product launch – it’s a paradigm shift. It sets the stage for a future where security operations are unified, intelligent, and scalable. With partners like Capgemini leading the charge, organizations can confidently navigate this transformation and build resilient, AI-powered defenses.

As manufacturers in the automotive industry ramp up their data management to prepare for the digital battery passport (DBP), they are laying the groundwork for future growth.  

The automotive industry is scrambling to solve a digital puzzle: How to cost-effectively track and compile data for every electric vehicle (EV) battery from raw materials to recycling, then share that data seamlessly across stakeholders. The EU’s DBP has made this challenge unavoidable. Starting as a regulatory requirement for EV batteries, the DBP is just the beginning, with full product passports for steel, aluminum, leather, and rubber already on the horizon. For automakers, the question isn’t whether to build comprehensive data tracking systems, but how quickly they can get ahead of the curve. 

The DBP: regulation for sustainability  

The Digital Battery Passport is not just a concept; it’s a legal requirement. The EU Battery Regulation mandates that from February 18, 2027, all EV batteries placed on the EU market must be accompanied by a digital passport. 

This regulation represents a landmark shift in how batteries are designed, produced, used, and recycled across the EU. It introduces a comprehensive framework to ensure that batteries placed on the EU market are sustainable, circular, and safe throughout their entire lifecycle. The DBP is an integral part of the EU’s broader push for sustainability, aligning with the European Green Deal and the Circular Economy Action Plan.  

The regulation mandates that each eligible battery must be accompanied by a digital record: the battery passport, accessible via a QR code. This passport must include data on the battery’s carbon footprint, material sourcing, performance, and end-of-life handling. The passport framework is expected to soon be extended to other vehicle components like steel and textiles. For manufacturers, this means DBP compliance is not optional. But those who act early can turn this obligation into a strategic advantage.  

The path to DBP compliance is lined with benefits 

The DBP will ensure transparency across the value chain and throughout a battery’s lifecycle. Simply by accessing the DBP, different parties can know the battery’s degradation and overall history, the source and recyclability of its raw materials, and more. Thus, the DBP will keep batteries in use longer, give their components a second life, and help the automotive industry achieve transparency and circularity. 

The foundation of compliance: an industry-wide challenge across 3 pillars 

Complying with the Digital Battery Passport (DBP) requires more than collecting data. It demands a robust, interoperable digital infrastructure capable of tracking, verifying, and sharing battery information across the entire value chain. At the core are three technical pillars. 

1. The first is data standardization and interoperability. To make DBP compliance operationally feasible, data formats must be harmonized to ensure compatibility across manufacturers, suppliers, and recyclers. Initiatives like the Battery Pass project (an industry guide to digital battery passports), Catena-X (a collaborative data ecosystem for the automotive industry), and the Global Battery Alliance (an industry partnership for ethical and sustainable battery production) are developing open standards and ontologies to support this. Use of digital data labels like GS1 identifiers, universally unique identifiers (UUIDs), and semantic data models will be essential for traceability. 
2. Secure and scalable data exchange is the second technical pillar of DBP compliance. Data must be accessible via a QR code on each battery, linked to a cloud-based or decentralized data repository. Technologies like blockchain or distributed ledgers are being explored to ensure data integrity, auditability, and trust across stakeholders. Application programming interfaces (APIs) and data-sharing protocols must be designed to comply with EU data privacy laws such as GDPR. 
3. The third technical pillar is lifecycle data capture and governance. Companies must implement systems to capture data at every stage: mining, refining, cell production, battery assembly, usage, and end-of-life. This requires integration with enterprise resource planning (ERP) systems, internet of things (IoT) sensors, and battery management systems. A strong data governance framework is needed to define ownership, access rights, and update mechanisms.

Many early adopters are supported by the end-to-end capabilities of Capgemini, and are already piloting digital twin architectures and traceability platforms to simulate and manage battery lifecycles in real time. These investments not only ensure compliance but also unlock new business models in reuse, resale, and recycling. 

For manufacturers, rising to the challenge brings a competitive advantage 

The automotive industry faces a particular challenge in preparing for—and eventually complying with—the DBP: its supply chain is extremely complex and includes many different actors around the world. This complicates Scope 3 emissions reporting, which includes the carbon footprint of every activity along the supply chain, both downstream and upstream. Collecting reliable, comprehensive data from every player is a tall task.   

Despite this challenge, forward-thinking automotive manufacturers have recognized the DBP’s long-term potential. They are seizing the strategic opportunities presented by more rigorous data collection, storage, and governance. Manufacturers that adopt new solutions and methods to stay ahead will gain a competitive edge by increasing and diversifying revenue streams. Plus, they are increasing circularity by sharing information on the age, origin, and usage of the battery’s components. With this data at hand, scarce raw materials like lithium and cobalt can be recycled or reused, cutting down on costs and reducing waste. 

The financial benefits are substantial: for example, optimized battery use and recycling pathways can improve profits by up to 58% for lithium iron phosphate (LFP) batteries and 19% for lithium nickel manganese cobalt oxide (NMC) batteries compared to traditional recycling methods.  

At the end of the day, the advantages and savings brought about by improving data management will far outweigh the costs of DBP compliance for proactive companies.  

Designing the ‘best case’ for your business 

Capgemini combines strategy and implementation to unlock opportunities. We bring our architecture blueprint, ready-made cloud and data governance solutions, and more to client conversations, so you do not need to start from a blank slate.  

We have deep expertise in designing the “best case,” i.e., the ideal way to adopt digital product passports such as the DBP. Our Product Traceability for Automotive solution is one tool that can help you reliably collect and share data with everyone in your ecosystem. 

To find out how you can unlock the advantages of the DBP, click here 

Mobility, meet action. 

You can also meet me at the upcoming IAA Mobility 2025 event to discuss about how we can together maximize opportunities, optimize data, create new models and drive efficient value from design to execution in the digital battery passport journey.

^{September 9-12, 2025 | Find us at Hall B1, Booth B22}

How robots that look like us are reshaping the workplace.

Once confined to science fiction, humanoid robots are stepping onto factory floors – not to replace workers, but to work alongside them. With the convergence of AI, robotics, spatial computing, and digital twins, enterprises now face a profound shift: automation with arms, legs, and reasoning skills. These human-shaped machines can adapt to existing environments, learn new tasks, and scale operations without disruption.

But the real breakthrough isn’t just technical, it’s collaborative. Humans, humanoids, and agentic AI systems are about to become one team. So how do we do it?

More flexibility, less disruption, better scaling.

Until recently, human-shaped robots – complete with a head, arms, and legs – were only speculative. But the first production models are now a reality from companies such as California-based Figure AI, which is building a factory expected to manufacture 12,000 humanoid robots per year. These will be game-changers for enterprises across multiple sectors – including manufacturing, life sciences, automotive, aerospace, defense, energy, utilities, and consumer products.

Robots and automation are nothing new on the factory floor, but legacy deployments involved purposebuilt machines and dedicated assembly lines. When manufacturing changes were required, a company would have to specify new robot designs and rebuild factories – which translated into significant financial investment and production disruptions.

Humanoid robots address these drawbacks by making the robot as adaptable as a human worker – capable of mimicking human gestures thanks to the rapid development of sensors and other hardware. What’s more, they are no longer just machines: humanoids are autonomous and adaptable physical reasoning agents equipped with cognitive capabilities. Artificial intelligence merges with robotics to take a physical form, making this the next big thing in AI and leading to the ultimate stage of automation on the shop floor.

Humanoids can be deployed to automate brown field operations without rebuilding, and humanoids can be easily integrated into existing industrial operations to perform undesirable, dangerous, labor-intensive, or repetitive tasks. Organizations can start small with a few humanoids working on focused activities, and then simply add more robots to scale up over time.

And when production needs change, companies will no longer have to retool. Instead, they can retrain both human employees and humanoid robots to undertake new operations in flexible workspaces.

Investigating the pathways to true convergence

That said, there’s work to be done to unlock the full potential of humanoids. These robots consist of an incredible package of sensors, controllers, motors, processors, and other hardware. At Capgemini’s dedicated AI Robotics and Experiences Lab, we’re exploring how to apply our expertise in agentic AI and LLM, computer vision, digital twins, data analytics, robotics, and sector-specific industrial processes to humanoid robots. Our goal is to help shape the technical convergence of human-centered, digital physical interactions between humans, systems of robots, AI-agents.

The hub of this convergence is a new virtual space in which most of the digital-human interactions will happen. In this space, unified and pre-trained data models, AI agents, edge AI, and digital twins merge with an interaction layer that leverages technologies such as real-time 3D and spatial computing. Data comes from an aggregation layer linking various data sources – including IS/IT/OT systems, sensors, and machines.

Agentic AI and physical AI then enable autonomous digital-physical interactions, with virtual AI-agents being able to act in the real world by interacting with AI agents housed in robots.

The challenge is how to best leverage technology enablers to unlock the productivity gains of humanoid robots with minimal operational disruption. To address this, Capgemini is adding agentic AI-enabled decision-making capabilities to a network of interconnected digital twins that replicate a physical industrial environment. These enhancements apply several key concepts in robotics and AI research:

· Vision language action models, which enable agents to understand and execute natural language instructions in a visual environment

· Reinforcement learning and simulation to real transfer learning, which enable the sharing of knowledge from various data sources, such as videos or motion capture, to train in virtual environments and then translate into real-world scenarios

· Teletraining, which enables a human to remotely control the agent to demonstrate desired actions

· System 1 and system 2 reasoning models, which are dual-process architectures that mimic a human brain’s ability to both generate fast, reactive responses and engage in slower, deliberate planning.

Collaboration in action

On the factory floor of the very near future, Capgemini envisions humans, AI-robots, and multi-agent systems working as a team to enhance operational efficiency, precision, and safety. Each participant will make important contributions to this, leveraging their unique abilities.

Human operators will bring their expertise and adaptability to bear upon the production process. Using spatial computing, they’ll supply guidance and oversight. They’ll also handle complex tasks that require fine motor skills and decision-making. And because humans can quickly identify and address unexpected issues, they’ll provide a level of problem solving expertise that enhances automated systems.

Humanoid robots will ensure precise handling and placement of parts, reducing the risk of damage and ensuring consistency. They can also perform repetitive tasks quickly and accurately, increasing throughput and reducing cycle times. And by taking over potentially hazardous tasks, robots will enhance workplace safety by reducing the risk of injury to human operators.

Agentic systems will continuously monitor the operational processes and environment to ensure each step is executed correctly. They will identify deviations, alert the team, and suggest corrective actions to help maintain workflow integrity and prevent errors. They can also make real-time decisions about whether operations require additional action or reinforced quality inspection process. Eventually, robots driven by virtual agents will be able to perform some of these actions.

Hybrid workforces will address pressing challenges

Industrial organizations today face pressing global challenges, including the need to contain costs, attract and retain talent through better employee experiences, and improve sustainability. Emerging solutions that build upon ongoing digital transformations – such as a properly designed and executed strategy to enhance workforces with humanoid robots – are essential to address these pressures. Companies that move first stand to gain the greatest rewards.

Start innovating now –

Establish your foundation. Leading companies are already undertaking complete digital transformations of their operations. This is essential for deploying humanoid robots – so make completing these transformations a priority.

Don’t delay. Humanoid robots are here today, and will soon be deployed in real-world environments – across multiple industrial sectors – to provide early adopters with significant competitive advantages. Enterprises can’t afford to wait.

Support, don’t replace, humans. The best outcomes will be achieved if humanoids are deployed as part of teams that also include human workers and multi-agent systems. Now is the time to determine how each team member’s strengths can best be leveraged.

Start innovating now

Establish your foundation
Leading companies are already undertaking complete digital transformations of their operations. This is essential for deploying humanoid robots – so make completing these transformations a priority.

Don’t delay
Humanoid robots are here today and will soon be deployed in real-world environments – across multiple industrial sectors – to provide early adopters with significant competitive advantages.

Support, don’t replace, humans
The best outcomes will be achieved if humanoids are deployed as part of teams that also include human workers and multi-agent systems. Now is the time to determine how each team member’s strengths can best be leveraged

Interesting read? Capgemini’s Innovation publication, Data-powered Innovation Review – Wave 10 features more such captivating innovation articles with contributions from leading experts from Capgemini. Explore the transformative potential of generative AI, data platforms, and sustainability-driven tech. Find all previous Waves here.  Find all previous Waves here.

CPR is a tangible world – but Kushal Dastenavar says that when customers want it all, businesses need to take advantage of the cost, time, quality, and value benefits that digital can bring to the physical

Life is straightforward in consumer products and retail. Said no one, ever.

Because that’s never been true, has it? And these days, it’s not just the nature of the challenges in CPR that is daunting – it’s their scale, too, and the pace at which they’re moving.

Now more than ever, markets are global and constantly morphing. Customers know what they want and when they want it, and so organizations need to find ways to streamline processes and increase efficiency. Technology can help them develop new business models, but it can be difficult to identify strategies that genuinely add value – especially when customers are pulling them in opposite directions.

How are shoppers pulling two ways? By simultaneously seeking eco-friendly options and low cost. As a result, CPR companies are obliged to achieve and maintain a three-way equilibrium between sustainability, affordability, and quality.

Let’s get digital – and physical

It’s a difficult balancing act – but a practical way forward is to address these challenges holistically. It’s not a case of simply throwing tech at it, any more than it’s only about radically overhauling physical operations.

Instead, CPR organizations need to find ways to bring together these two elements – traditional engineering and digital technology – so they can redraw departmental lines; align specialisms; make traditional processes more efficient, accurate, and scalable; improve sustainability without compromising on quality or margins; and offer new products and services that take advantage of strengths in both the physical and digital worlds.

Real-world examples: here’s one…

Let’s look at how these challenges are being tackled in the real world, and in particular how digital technology is being used to optimize physical performance.

A major multinational brewer has been looking to implement data and analytics transformation to achieve exactly this goal. In recent years it has introduced a number of different solutions across its infrastructure, all of which have been developed based on business needs and majority custom built. Some of them have grown in a non-homogeneous way, without a robust and standardized architecture, and with overlapping functionality. Furthermore, they have been costly and difficult to maintain, and present challenges to scale. What’s more, these disparate solutions provide information only, rather than a cohesive means of acting upon it.

All of this is why the company is now working with Capgemini on the implementation of a CAP-cloud platform (based on Azure) to bring contextualized process data from its breweries, filling, and packaging lines into a consolidated manufacturing data platform.

This approach will provide access to reliable real time data that serves as single source of truth for the supply chain. It will enable the company to set targets and monitor business performance through KPIs; to develop multiple use cases at scale, leveraging AI/ML to improve industrial performance and sustainability. It will accelerate solution deployment with minimized effort and investment across a global manufacturing footprint – and it will be easier and more cost-effective to maintain and to embed improvement evolution.

… and here’s another

A global CPR leader wants to optimize its manufacturing systems, reduce downtime, and increase platform efficiency. The challenges are considerable: there are more than 60 manufacturing applications supporting over 3000 manufacturing lines globally, providing a 24×7 support model with a product-centric approach.

The organization is currently working with Capgemini on a comprehensive new digital manufacturing operations solution, encompassing global operations and multiple international centers of excellence. A detailed transformation road map has been created, covering all aspects of operations, technical upgrades, and business outcomes. We’re collaborating with key product vendors on a cost-optimized delivery model that harnesses the best tools and GenAI to deliver benefits that are expected to include a reduction of downtime worth $6-8 million; site reliability engineering (SRE)-driven AI operations to reduce downtime by 34%; standardized service operations and monitoring; and a 25% reduction in (mean time to recovery (MTTR), once again via GenAI.

Two key takeaways

There are two factors implicit in the examples I’ve provided above.

The first of these is the usefulness of having an experienced partner. A knowledgeable solutions provider with a solid track record can provide valuable insights, best practices, and support during the transformation process.

The second key takeaway is the importance of a can-do attitude. CPR organizations working in collaboration with trusted partners can create a virtuous circle of energy and enthusiasm, maintaining and building momentum, and taking advantage of the power of digital to deliver lasting value in the physical world.

Major engineering and R&D-intensive (ER&D) businesses recognize the importance of combining digital and physical change. We recently published a report examining the views of ER&D leaders on the challenges they face and the solutions they propose. You’ll find the report here

Digital Twins & Smart Factories: The next evolution

At Siemens Realize LIVE 2025, we hosted a dynamic panel discussion with experts from AWS, NetApp, and Siemens to explore how cloud and AI technologies are reshaping automotive manufacturing. Led by Tarun Philar, VP of Digital Continuity at Capgemini, the session offered insights into emerging trends, real-world applications, and the transformative potential of AI.

Generative and Agentic AI: The next frontier

The panel explored the future of generative AI and agentic AI, technologies that will revolutionize product design and manufacturing operations. These tools enable faster iterations, smarter automation, and higher-quality outcomes, especially in the automotive sector.

As Rex Lam, Senior Solution Architect at AWS, explained, these tools are transforming rigid, manual processes into adaptive, autonomous systems. Today’s challenges—like coordinating changes across engineering, production, and supply chain systems often depend on sequential workflows and human oversight—leading to delays and errors.

Agentic AI changes this. It uses autonomous agents to manage cross-system processes in parallel, reducing bottlenecks and improving accuracy. Now more than ever, agentic AI is becoming integral to manufacturing—from data and insights to self-optimizing systems that adapt in real time.

“Generative AI and agentic AI are changing the way we engineer products. From optimizing manufacturing tasks to improving product design, these technologies are making a significant impact.”
— Rex Lam, AWS

Real-world impact: Success stories from the field

Rex Lam, shared how cloud and AI technologies are transforming every part of the automotive value chain – from design and development to manufacturing, sales, and customer service. Key trends driving this transformation include software-defined vehicles, smart manufacturing, and engineering Innovation.

Rex shared impactful success stories that support the tangible benefits of cloud adoption:

1. Smart Manufacturing: Cloud platforms unify factory data to optimize production. AWS-powered cloud platform connects 120+ plants, aiming to cut factory costs by 30% and reduce supply chain waste.
2. Engineering Innovation:  High-performance cloud computing accelerates design cycles. AWS helped an automotive customer moved its engineering workloads to the cloud and saw a 66% increase in software speed, improved availability of compute resources, and enhanced collaboration. As a result, this customer was able to test new concepts and bring new designs to market more quickly.

These real-world solutions prove how cloud platforms can deliver powerful ROI in a short time.

Accelerating digital transformation with AI

Dimitrios Dovas, Head of Cloud Product Management at Siemens, spoke about the rapid pace of AI-driven transformation in the automotive sector.

“The automotive space is transforming to digital very quickly, from design to production and service. AI plays a major role in cutting development cycles and delivering internal efficiencies.”
— Dimitrios Dovas, Siemens

This shift is part of a bigger comeback in manufacturing. A recent Capgemini Research Institute report shows that more companies plan to bring manufacturing closer to home – rising from 60% to 75% in the next three years. Global investments in modernizing factories are expected to grow from $3.4 trillion in 2024 to $4.7 trillion over the next three years. 

“With onshoring and nearshoring of manufacturing set to increase significantly over the next 3 years, it is driving investments in reindustrialization initiatives. We see companies increasingly make investments in their digitization of manufacturing, intelligent automation, predictive maintenance & energy management initiatives.”
— Tarun Philar, Vice President Digital Continuity & Convergence, Group Offer Leader, Capgemini

Cloud agility in action

Jesse Lafer, Solutions Architect for AI, HPC, & Data Lakes at NetApp, emphasized how cloud infrastructure enables agility and continuous innovation. Jesse explained that in manufacturing—where 24/7 uptime is critical—a secure, scalable hybrid data architecture is essential. This architecture ensures operational continuity while unlocking access to cloud-based tools that drive innovation.

Jesse Lafer shares how cloud brings agility, on-demand access to the latest and greatest technologies, and lessons learned from how other customers have solved similar business challenges using technology.

One of these examples includes collaboration for globally distributed Siemens Teamcenter end-users. This was accomplished through NetApp’s data caching capabilities between multiple on-premises locations and AWS regions. Data was shared securely and consistently across multiple locations to support both Windows and Linux end-users.

“In the cloud, you’re always going to have access to the latest and greatest technology. The cloud brings agility, allowing companies to move much faster.”
— Jesse Lafer, NetApp

AI as a copilot for change

Digital Twins are quickly becoming a must-have tool in modern manufacturing. A Capgemini study found that companies aim to boost system performance by 25%—either by designing more efficient systems or improving operations. Adoption of Digital Twins is expected to grow by 36% over the next five years, powering smarter factories and better decision-making.

The session wrapped with a forward-looking discussion on how generative AI will support change management:

“Generative AI will help accelerate change, reduce errors, and drive innovation. It will act as a copilot, assisting humans in managing change more efficiently.”

Partnering for digital continuity with Siemens

At Capgemini, we help businesses navigate their digital transformation journeys. Our deep expertise in cloud technology and AI enables us to deliver customized solutions that drive efficiency, innovation, and growth. Together with Siemens, we deliver end-to-end digital continuity through integrated business and IT/OT solutions. Our 20+ year partnership spans:
Software development

• Software development
• Requirements engineering
• Process control and instrumentation
• Advanced analytics and AI integration

Let’s drive the future together

It’s clear that cloud and AI technologies are unlocking new levels of efficiency and innovation in automotive manufacturing. Ready to accelerate your digital transformation journey? Connect with us to explore how we can help you lead the way.

With growing demand for more agile, sustainable, and localized production, Additive Manufacturing (AM) is shifting from prototyping to full industrial integration.

Additive Manufacturing, also known as 3D printing, is a process where a three-dimensional object is created by adding material layer by layer, based on a digital design. It contrasts with subtractive manufacturing, which involves removing material to form a shape. 

However, the scope of 3D printing today defines more than prototypes or design studies. Currently, Additive Manufacturing provides the reality of industrial series production of components in a tangible way. However, the path forward can be intricate, demanding meticulous planning, deep expertise in the AM domain, processes, and materials, with a focus on execution.

Capgemini joined the AM I Navigator initiative to be part of its holistic maturity model to shape the stages of industrialization in the AM industry, increasing interoperability in additive manufacturing.  By applying the AM I Navigator Maturity Model, we outline the essential stages for industrializing additive manufacturing, empowering companies to chart a strategic path toward integration.

The AM I Navigator emerged through collaboration with leading partners including Siemens, all working together to create a unified approach to 3D printing. This initiative is about embedding additive manufacturing seamlessly into traditional production systems. By leveraging methodology from industry-proven maturity models, we are helping organizations unlock the transformative potential of additive manufacturing at scale.

The AM I Navigator provides a structured, end-to-end methodology that empowers manufacturers to:

–               Assess their AM maturity

–               Accelerate adoption through data and insight

–               Integrate AM into existing digital production systems

Capgemini joined the AM I Navigator initiative to advance the industrialization of additive manufacturing (AM), together. Alongside industry leaders like Siemens, we will collaborate to help organizations unlock the full potential of industrial-scale 3D printing. This collaboration is a shared vision for scalable, interoperable, and automated additive manufacturing – rooted in a maturity model that guides smart manufacturers through every step of their AM transformation.

AM is a critical enabler of intelligent industry. By joining this initiative, we’re reinforcing our commitment to helping clients connect product design, digital thread, and smart factory strategies at scale. Together with our partners, we’re excited to shape the future of manufacturing, where additive is not an exception but a core capability.

Nicolas Rousseau, EVP, Chief Digital and Manufacturing Officer, Capgemini
Karsten Heuser, VP Additive Manufacturing, Siemens AG

Stefanie Schneider, Senior Alliance Manager, Siemens AG
Jens Huebner, Senior Manager and Lead AM Factory & AM I Navigator, Siemens AG
Ramon Antelo, CTO, Manufacturing and Industrial Operations, Engineering, Capgemini
Nicolas Rousseau, EVP, Chief Digital and Manufacturing Officer, Capgemini
Karsten Heuser, VP Additive Manufacturing, Siemens AG