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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 in helping enterprises become future-ready leveraging the power of disruptive technologies such as blockchain, quantum computing, 5G, IoT and metaverse.

    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.

        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

          Global Product Owner
          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.

            Legacy applications, revived by agentic AI

            Capgemini
            Stefan Zosel and Sebastian Baumbach
            Jun 9, 2025

            Capgemini’s innovative AI agent tool is helping organizations in the public sector and beyond to reduce the cost and time of modernizing their legacy applications

            For years, and across industries, rapid developments in digitalization have been creating challenges for companies around adapting to technological change. Particularly challenging are “legacy issues” such as outdated applications or obsolete software that need transferring to current technologies to stay maintainable.

            This has unfortunately resulted in us talking about legacy modernization for so long that the first modernizations are already due to be modernized again.

            A legacy modernization often involves rewriting the existing application code almost completely, as the original solution was likely based on a different technology or programming language. A software development team could still do this work manually, but it would involve considerable effort.

            This is where Gen AI-augmented software engineering comes into play. It allows the development team to automate repetitive tasks by outsourcing them to generative AI. But while providing developers with simple, recurring code fragments is an exciting way to increase productivity and reduce costs, it only marginally reduces the effort involved in a legacy modernization. As a result, these projects remain manual, time-consuming and costly.

            Figure 1: Capgemini research: Turbocharging software with AI
            https://www.capgemini.com/au-en/insights/research-library/gen-ai-in-software/

            Bar chart showing maximum and average time savings from generative AI across four software engineering tasks: documentation, coding, debugging, and project management.

            How Capgemini’s AI agents are transforming legacy modernization

            At Capgemini, we have developed an innovative approach that takes advantage of agentic AI coding agents to significantly reduce the time needed to modernize legacy applications.

            Our AI agent tool – a sophisticated multi-agent system – is purpose-built to make legacy systems future-safe. We have designed it to support software teams in migrating custom-built applications from outdated technology stacks to modern platforms.

            At the heart of the solution is the orchestration of a collaborative team of AI agents. This allows development teams to automate a large portion of the modernization process (see figure 2), resulting in a far more efficient, scalable approach to modernizing and migrating software.

            Figure 2: Development focuses on defining what needs to be done and leaves much of the processing to the AI agents

            Let’s call an AI agent to do the job

            Unlike traditional chatbots that simply return responses, AI agents take ownership of tasks and actively drive them forward. They operate autonomously, optimizing based on new information or past mistakes. But they can also interact with large language models, other agents, or non-AI tools such as compilers.

            In Capgemini’s AI agent tool, multiple agents collaborate to modernize a legacy application and transition it to a new technology stack. A human orchestrator defines the overall migration process, providing a structured set of instructions to guide the agents.

            The instructions transfer Capgemini’s deep expertise to the agent, both in understanding the legacy system and in designing the target software architecture. They also determine the specific role each AI agent is assigned in the migration.

            So that the transition runs smoothly, the roles of these AI agents mirror those in a human development team migrating a legacy application (see figure 3). A software developer agent analyzes the existing source code and rewrites it using the target technology. A testing or quality assurance (QA) agent then validates the code against predefined test cases. If any tests fail, the QA agent provides detailed error messages and returns the code to the developer for revision.

            Once the code has passed all the tests, a DevOps agent takes over to build the complete application and checks it for runtime issues. In this way, every function of the original application is faithfully reimplemented in the new technology stack.

            Figure 3: Get the job done – the power of agentic AI agents

            An applicable approach across sectors

            At Capgemini, we are already using this approach with many clients in the global public sector and beyond.

            A German organization, for example, was looking for a solution to modernize its approximately 40 outdated applications. The client could not develop those applications any further but also recognized the need to integrate new features and switch to a modern technology platform.

            Migrating all those legacy applications manually would have been very time-consuming and costly. Thanks to our AI agent tool, though, a large part of this previously manual migration could be automated. The amount of development effort needed dropped correspondingly, and the project costs fell significantly – freeing up the client to concentrate on developing innovative features.

            Would you like to try Capgemini’s AI agent tool for yourself?

            By automating the process, our tool makes it faster and more cost-effective to switch legacy applications over to new and future-safe technologies.

            What’s more, as every migration path is different, we customize our tool to the modernization context each time.  The enablement team will support you with analyzing your specific migration paths and conducting pilots.

            Finally, in case you are wondering, the question of sovereignty does not play a role here. That is because the AI agents run both in your public cloud environment and “air-gapped” on-premise.

            Authors

            Stefan Zosel

            Capgemini Government Cloud Transformation Leader
            “Sovereign cloud is a key driver for digitization in the public sector and unlocks new possibilities in data-driven government. It offers a way to combine European values and laws with cloud innovation, enabling governments to provide modern and digital services to citizens. As public agencies gather more and more data, the sovereign cloud is the place to build services on top of that data and integrate with Gaia-X services.”

            Sebastian Baumbach

            Capgemini Global Product Owner
            “Generative AI and intelligent agents are transforming the way governments modernize applications and deliver digital services. These technologies are no longer emerging – they’re already reshaping public sector innovation. Instead of long development cycles, these technologies enable faster, more adaptive solutions that better respond to the needs of citizens. The shift toward AI-powered architectures is not just a technological upgrade but a strategic imperative for the future of public sector IT.”

              Agentification of AI : Embracing Platformization for Scale

              Sunita Tiwary
              Jun 4, 2025

              Agentic AI marks a paradigm shift from reactive AI systems to autonomous, goal-driven digital entities capable of cognitive reasoning, strategic planning, dynamic execution, learning, and continuous adaptation with a complex real-world environment. This article presents a technical exploration of Agentic AI, clarifying definitions, dissecting its layered architecture, analyzing emerging design patterns, and outlining security risks and governance challenges. The objective is strategically equipping the enterprise leaders to adopt and scale agent-based systems in production environments.

              1. Disambiguating Terminology: AI, GenAI, AI Agents, and Agentic AI

              Capgemini’s and Gartner’s top technology trends for 2025 highlight Agentic AI as a leading trend. So, let’s explore and understand various terms clearly.

              1.1 Artificial Intelligence (AI)

              AI encompasses computational techniques like symbolic logic, supervised and unsupervised learning, and reinforcement learning. These methods excel in defined domains with fixed inputs and goals. While powerful for pattern recognition and decision-making, traditional AI lacks autonomy, memory, and reasoning, limiting its ability to operate adaptively or drive independent action.

              1.2 Generative AI (GenAI)

              Generative AI refers to deep learning models—primarily large language and diffusion models—trained to model input data’s statistical distribution, such as text, images, or code, and generate coherent, human-like outputs. These foundation models (e.g., GPT-4, Claude, Gemini) are pretrained on vast datasets using self-supervised learning and excel at producing syntactically and semantically rich content across domains.

              However, they remain fundamentally reactive—responding only to user prompts without sustained intent—and stateless, with no memory of prior interactions. Crucially, they are goal-agnostic, lacking intrinsic objectives or long-term planning capability. As such, while generative, they are not autonomous and require orchestration to participate in complex workflows or agentic systems.

              1.3 AI Agents

              An agent is an intelligent software system designed to perceive its environment, reason about it, make decisions, and take actions to achieve specific objectives autonomously.

              AI agents combine decision-making logic with the ability to act within an environment. Importantly, AI agents may or may not use LLMs. Many traditional agents operate with symbolic reasoning, optimization logic, or reinforcement learning strategies without natural language understanding. Their intelligence is task-specific and logic-driven, rather than language-native.

              Additionally, LLM-powered assistants (e.g., ChatGPT, Claude, Gemini) fall under the broader category of AI agents when they are deployed in interactive contexts, such as customer support, helpdesk automation, or productivity augmentation, where they receive inputs, reason, and respond. However, in their base form, these systems are reactive, mostly stateless, and lack planning or memory, which makes them AI agents, but not agentic. They become Agentic AI only when orchestrated with memory, tool use, goal decomposition, and autonomy mechanisms.

              1.4 Agentic AI

              Agentic AI is a distinct class where LLMs serve as cognitive engines within multi-modal agents that possess:

              • Autonomy: Operate with minimal human guidance
              • Tool-use: Call APIs, search engines, databases, and run scripts
              • Persistent memory: Learn and refine across interactions
              • Planning and self-reflection: Decompose goals, revise strategies
              • Role fluidity: Operate solo or collaborate in multi-agent systems

              Agentic AI always involves LLMs at its core, because:

              • The agent needs to understand goals expressed in natural language.
              • It must reason across ambiguous, unstructured contexts.
              • Planning, decomposing, and reflecting on tasks requires language-native cognition.

              Let’s understand with a few examples: In customer support, an AI agent routes tickets by intent, while Agentic AI autonomously resolves issues using knowledge, memory, and confidence thresholds. In DevOps, agents raise alerts; agentic AI investigates, remediates, tests, and deploys fixes with minimal human input.

              Agentic AI = AI-First Platform Layer where language models, memory systems, tool integration, and orchestration converge to form the runtime foundation of intelligent, autonomous system behavior.

              AI agents are NOT Agentic AI. An AI agent is task-specific, while Agentic AI is goal-oriented. Think of an AI agent as a fresher—talented and energetic, but waiting for instructions. You give them a ticket or task, and they’ll work within defined parameters. Agentic AI, by contrast, is your top-tier consultant or leader. You describe the business objective, and they’ll map the territory, delegate, iterate, execute, and keep you updated as they navigate toward the goal.

              2. Reference Architecture: Agentic AI Stack

              2.1 Cognitive Layer (Planning  and Reasoning)
              • Foundation Models (LLMs): Core reasoning engine (OpenAI GPT-4, Anthropic Claude 3, Meta Llama 3).
              • Augmented Planning Modules: Chain-of-Thought (CoT), Tree of Thought (ToT), ReAct, Graph-of-Thought (GoT).
              • Meta-cognition: Self-critique, reflection loops (Reflexion, AutoGPT Self-eval).
              2.2 Memory Layer (Statefulness)

              To retain and recall information. This is either information from previous runs or the previous steps it took in the current run (i.e., the reasoning behind their actions, tools they called, the information they retrieved, etc.). Memory can either be either session-based short-term or persistent long-term memory.

              • Episodic Memory: Conversation/thread-local memory for context continuation.
              • Semantic Memory: Long-term storage of facts, embeddings, and vector search
              • Procedural Memory: Task-level state transitions, agent logs, failure/success traces.
              2.3 Tool Invocation Layer

               Agents can take action to accomplish tasks and invoke tools as part of the actions. These can be built-in tools and functions such as browsing the web, conducting complex mathematical calculations, and generating or running executable code responding to a user’s query. Agents can access more advanced tools via external API calls and a dedicated Tools interface. These are complemented by augmented LLMs, which offer the tool invocation from code generated by the model via function calling, a specialized form of tool use.

              2.4 Orchestration Layer
              • Agent Frameworks: LangGraph (DAG-based orchestration), Microsoft AutoGen (multi-agent interaction), CrewAI (role-based delegation).
              • Planner/Executor Architecture: Isolates planning logic (goal decomposition) from executor agents (tool binding + result validation).
              • Multi-agent Collaboration: Messaging protocols, turn-taking, role negotiation (based on BDI model variants).
              2.5 Control, Policy & Governance
              • Guardrails: Prompt validators (Guardrails AI), semantic filters, intent firewalls.
              • Human-in-the-Loop (HITL): Review checkpoints, escalation triggers.
              • Observability: Telemetry for prompt drift, tool call frequency, memory divergence.
              • ABOM (Agentic Bill of Materials): Registry of agent goals, dependencies, memory sources, tool access scopes.

              3. Agentic Patterns in Practice

              (Source-OWASP)

              As Agentic AI matures, a set of modular, reusable patterns is emerging—serving as architectural primitives that shape scalable system design, foster consistent engineering practices, and provide a shared vocabulary for governance and threat modeling. These patterns embody distinct roles, coordination models, and cognitive strategies within agent-based ecosystems.

              • Reflective Agent : Agents that iteratively evaluate and critique their own outputs to enhance performance. Example: AI code generators that review and debug their own outputs, like Codex with self evaluation.
              • Task-Oriented Agent :Agents designed to handle specific tasks with clear objectives. Example: Automated customer service agents for appointment scheduling or returns processing.
              • Self-Learning and Adaptive Agents: Agents adapt through continuous learning from interactions and feedback. Example: Copilots, which adapt to user interactions over time, learning from feedback and adjusting responses to better align with user preferences and evolving needs.
              • RAG-Based Agent: This pattern involves the use of Retrieval Augmented Generation (RAG), where AI agents utilize external knowledge sources dynamically to enhance their decision-making and responses. Example: Agents performing real-time web browsing for research assistance.
              • Planning Agent: Agents autonomously devise and execute multi-step plans to achieve complex objectives. Example: Task management systems organizing and prioritizing tasks based on user goals.
              • Context- Aware  Agent:  Agents dynamically adjust their behavior and decision-making based on the context in which they operate. Example: Smart home systems adjusting settings based on user preferences and environmental conditions. 
              • Coordinating Agent :Agents facilitate collaboration and coordination and tracking, ensuring efficient execution. Example: a coordinating agent assigns subtasks to specialized agents, such as in AI powered DevOps workflows where one agent plans deployments, another monitors performance, and a third handles rollbacks based on system feedback.
              • Hierarchical Agents :Agents are organized in a hierarchy, managing multi-step workflows or distributed control systems. Example: AI systems for project management where higher-level agents oversee task delegation.
              • Distributed Agent Ecosystem: Agents interact within a decentralized ecosystem, often in applications like IoT or marketplaces. Example: Autonomous IoT agents managing smart home devices or a marketplace with buyer and seller agents.
              • Human-in-the-Loop Collaboration: Agents operate semi-autonomously with human oversight. Example: AI-assisted medical diagnosis tools that provide recommendations but allow doctors to make final decisions.

              4. Security and Risk Framework

              Agentic AI introduces new and very real attack vectors like (non-exhaustive):

              • Memory poisoning – Agents can be tricked into storing false information that later influences decision
              • Tool misuse – Agents with tool or API access can be manipulated into causing harm
              •  Privilege confusion – Known as the “Confused Deputy,” agents with broader privileges can be exploited to perform unauthorized actions
              • Cascading hallucinations – One incorrect AI output triggers a chain of poor decisions, especially in multi-agent systems
              • Over-trusting agents – Particularly in co-pilot setups, users may blindly follow AI suggestions

               5. Strategic Considerations for the enterprise leaders

              5.1 Platformization
              • Treat Agentic AI as a platform capability, not an app feature.
              • Abstract orchestration, memory, and tool interfaces for reusability.

              5.2 Trust Engineering

              • Invest in AI observability pipelines.
              • Maintain lineage of agent decisions, tool calls, and memory changes

              5.3 Capability Scoping

              • Clearly delineate which business functions are:
              • LLM-augmented (copilot)
              • Agent-driven (semi-autonomous)
              • Fully autonomous (hands-off)

              5.4 Pre-empting and managing threat

              • Embed threat modelling into your software development lifecycle—from the start, not after deployment
              • Move beyond traditional frameworks—explore AI-specific models like the MAESTRO framework designed for Agentic AI
              • Apply Zero Trust principles to AI agents—never assume safety by default
              • Implement Human-in-the-Loop (HITL) controls—critical decisions should require human validation
              • Restrict and monitor agent access—limit what AI agents can see and do, and audit everything

              5.5 Governance

              • Collaborate with Risk, Legal, and Compliance to define acceptable autonomy boundaries.
              • Track each agent’s capabilities, dependencies, and failure modes like software components.
              • Identify business processes that may benefit from “agentification” and identify the digital personas associated with the business processes.
              • Identify the risks associated with each persona and develop policies to mitigate those. 

              6. Conclusion: Building the Autonomous Enterprise

              Agentic AI is not just another layer of intelligence—it is a new class of digital actor that challenges the very foundations of how software participates in enterprise ecosystems. It redefines software from passive responder to active orchestrator. From copilots to co-creators, from assistants to autonomous strategists, Agentic AI marks the shift from execution to cognition, and from automation to orchestration.

              For enterprise leaders, the takeaway is clear: Agentification is not a feature—it’s a redefinition of enterprise intelligence. Just as cloud-native transformed infrastructure and DevOps reshaped software delivery, Agentic AI will reshape enterprise architecture itself.

              And here’s the architectural truth: Agentic AI cannot scale without platformization.

              To operationalize Agentic AI across business domains, enterprises must build AI-native platforms—modular, composable, and designed for autonomous execution.

              The future won’t be led by those who merely implement AI. It will be defined by those who platformize it—secure it—scale it.

              Author

              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.

                Data centers to cloud: A strategic shift with FinOps

                Deepak Shirdhonkar
                Deepak Shirdhonkar
                May 30, 2025

                Harnessing Financial Operations for Smarter Cloud Transitions

                Technology is transforming every organization and business, driving them to surpass economic development. Many enterprises are continuously adopting and shifting workloads to the public cloud, expecting numerous benefits such as flexibility, scalability, agility, and cost savings. However, with the myriad of options available for cloud adoption, there is also a risk of uncontrolled expenditure. It is quite common for enterprises to express that they are not receiving the benefits they anticipated from the shift from data centers to the cloud. The following section of this article delves into those key challenges in detail.

                When an organization decides to move to the cloud, it starts with migration planning. Often, gaps in migration planning, inadequate assessments, lack of cloud-ready staff, complex designs, failed migrations, rework, and app or tool dependencies extend migration timelines beyond expectations. Businesses pay for their existing on-premises infrastructure while incurring new expenses for cloud migration, leading to a migration bubble. Additionally, migrating only a portion of the infrastructure while leaving other components on-premises prevents businesses from enjoying the full benefits.

                Our practical experience shows that merely migrating workloads from on-premises or co-located data centers to the cloud is not enough. Regardless of the chosen hyperscaler, issues arise when clients overlook cloud best practices, leading to challenges in cloud governance and cost management. It is evident that many enterprises are still approaching cloud adoption with a data center mentality and are hesitant to embrace essential cloud features like autoscaling, on-demand provisioning, and self-service, which have the potential to drive significant innovation.

                The shift from data centers to the cloud has also disrupted traditional procurement processes by empowering developers with greater purchasing authority. It enables engineers to spend company funds with just a click of a button or a line of code, bypassing the lengthy conventional procurement procedures including purchase requisitions, calling tenders, vendor scouting, and purchase orders.

                Due to these challenges, monthly bills from hyperscalers can spiral out of control, extending the payback period for investments and negating the benefits of cloud transition. Therefore, it is crucial to develop a comprehensive migration strategy with operational governance controls to avoid potential pitfalls and adhere to cost optimization goals, commonly referred to as FinOps. This approach helps free up budgetary funds and accelerates the shift to the cloud. Enterprises must ensure their personnel are cloud-ready and have strong procedures to analyze expenditures and identify key cost drivers. Assessing available cloud resources is also advisable for optimization.

                The primary goal of every organization is to lower technological costs, and the cloud is no exception. As companies continue to invest more in the public cloud, recurring cloud run costs will increase. This trend underscores the growing importance of FinOps as a recognized financial management discipline.

                Author

                Deepak Shirdhonkar

                Deepak Shirdhonkar

                Senior Hyperscaler Architect, FinOps Lead & Full Stack Distinguished Engineer
                Deepak is a seasoned professional with 18 years of rich experience in architecture, transformation projects, and developing and planning solutions for both public and private cloud environments. Deepak has extensive technical acumen in AWS, Google, FinOps, and Network. Academically, Deepak holds a Master of Technology in Thermal Engineering from Maulana Azad National Institute of Technology. Deepak serves as the Lead Architect for Cloud Delivery in CIS India at Capgemini. Throughout Deepak’s career, Deepak has taken on various roles, including Technical Lead, Infra Architect, and Cloud Architect.

                  From Design to Delivery
                  Why aerospace and defense should expand MBSE into manufacturing

                  Capgemini
                  Capgemini
                  May 30, 2025
                  capgemini-engineering

                  The history of systems engineering is rooted in the need to manage and integrate complex projects with significant components or ‘systems’, especially during times of rapid technological advancement. So, it should come as no surprise that the concept grew from the large-scale military endeavors required during the Second World War. The need to ensure everything worked together efficiently gave birth to the systematic planning and coordination methods that remain at the heart of modern systems engineering concepts.

                  Whilst the underpinning principles of systems engineering have remained unchanged, how they are applied in practice is constantly evolving, as large-scale industrial projects push the boundaries of complexity and scale. This remains especially relevant in aerospace and defence (A&D). Today’s A&D systems are more intricate and connected than ever before. Consider autonomous robotics surveillance systems; electronic warfare operations; or the current generation of long-haul passenger aircraft — all of which involve layers of complexity beyond the conventional industry programs for which systems engineering was originally designed.

                  Smart systems engineering shows promise in production

                  The rise of intelligent digital technologies has also dictated how systems engineering is practiced.

                  It has driven the evolution of common methodologies into Model Based Systems Engineering (MBSE) – in which, advanced tools allow engineers to create virtual twins of complex systems. These improve design and testing whilst smoothing the integration of new technologies such as artificial intelligence (AI) and autonomy, ensuring their introduction is safe and efficient, and predicting their effects on the overall system. The emergence of MBSE now offers companies a way to design smarter, collaborate better, and innovate faster, creating virtual twins and limiting the need to build a physical prototype until everything has been simulated and tested digitally first.

                  Why MBSE for A&D manufacturing?

                  MBSE has already been transformational for the design and development of novel A&D systems, but Capgemini and Dassault Systèmes believe that it has the potential to achieve much more. This is why we are working together to explore the application of MBSE further along the A&D product lifecycle, into manufacturing and production.

                  MBSE is highly relevant here because it is very effective at streamlining processes, improving quality, and managing complexity – some of the biggest challenges for large scale manufacturing teams in the A&D industry. By applying the same digital tools used in the development of a new system to its manufacture, A&D companies can simulate the required production process, including assembly lines, resource allocation, and workflow. This gives them the visibility to optimize the production schedule, minimize bottlenecks, and improve efficiency all before physical production begins.

                  MBSE’s ability to foster more effective collaboration between the many moving parts of a large production operation means it is an effective way to remove internal silos that can slow down and complicate large projects. MBSE makes this possible by bridging the gap that exists between design and production and the various teams within. It offers a single source of truth, using digital tools that integrate both processes. This has become essential because in this environment, engineers and production teams are often separated, and when they do work together, they rarely speak the same language. Both create dangerous gaps in the system lifecycle that can result in delays, waste, and cost. MBSE gives both groups a way to connect through a common view of real time data about both of their worlds, and helps them avoid issues such as mismatched specifications or unclear instructions. This is particularly important in the delivery of large A&D projects such as 6th generation fighters or high earth orbit satellites, which often involve intricate assemblies of hundreds of thousands of components, all reliant on each other and all feeding into a considerable overall system. Here MBSE can help production teams ensure that every part fits together correctly by defining precise relationships between components and systems at the earliest opportunity, reducing human errors during assembly.

                  Capgemini and Dassault Systèmes join forces

                  At Capgemini and Dassault Systèmes, our teams have combined their respective experience of MBSE to offer a disruptive capability specifically designed for A&D production. Our collective experience spans every aspect of systems engineering, digital transformation, and production processes throughout the lifecycle of aerospace and defence systems, giving us a unique perspective on how the theory of MBSE can be applied in practice for tangible benefits.

                  We also recognise that MBSE is not a magical solution to address every manufacturing challenge. But we can see it is already proving powerful for supporting the identification of high-level solutions and the subsequent articulation of detailed designs for A&D systems. We believe that MBSE has the power to enhance A&D manufacturing by improving efficiency, quality, and agility, ensuring that the complex systems we are designing today for the future of aerospace and defence can be built accurately and delivered on time. 

                  Accelerating Aerospace & Defense System Production

                  Introducing Model-based Systems Engineering

                  Capgemini Engineering
                  Read more

                  9 production challenges MBSE can help the Aerospace and Defense industry meet

                  Capgemini
                  Capgemini
                  May 30, 2025
                  capgemini-engineering

                  In our first blog on the topic of Model Based Systems Engineering (MBSE) we looked at the bigger picture – where systems engineering came from, how its evolution into MBSE has become an important opportunity for Aerospace and Defense (A&D) innovators, and why it should also be integrated into their production environments.

                  In this blog we are going to delve deeper into how MBSE can help A&D companies solve some of their most pressing production challenges – outlining the nine that our customers tell us they experience most often.   

                  1. Bridging the Gap Between Design and Manufacturing

                  MBSE provides engineers a way to create a single digital repository of all the information related to a project. This acts as the single source of truth and is used to integrate design and manufacturing teams – giving everyone visibility and access to data from every system and process involved. This ensures that manufacturing teams have access to accurate, up-to-date information about the product. It helps avoid issues like mismatched specifications or unclear instructions, which can lead to production delays or errors. And it provides a common language for both engineers and production teams to use, bringing together two very different worlds that have traditionally struggled to understand each other.

                  MBSE also enables manufacturing and production teams to approach challenges with a System of Systems (SoS) perspective. This gives them a view of the wider environment in which individual production systems take place that recognizes how they all connect to create complex integrations, working together to achieve a higher-level capability of that no single system could achieve alone. As A&D programs become larger, more complex and more intricate, this is a way to make sure teams are aware of global production challenges that could be missed if individual products or processes are simply viewed in isolation.

                  2. Enhancing Production Planning

                  MBSE allows A&D manufacturers to simulate the production process in a virtual environment before physical manufacturing begins. By creating a comprehensive 2D digital simulation of assembly lines, resource allocation, and workflow, manufacturers can identify potential inefficiencies, bottlenecks, or conflicts in the production process early on. By leveraging MBSE’s predictive capabilities, production teams can test different scenarios, adjusting schedules, workforce distribution, and equipment usage to optimize efficiency. This means that manufacturers can make data-driven decisions about how to best allocate resources, whether it’s ensuring that critical components arrive just in time or that personnel with the right expertise are positioned where they are most needed.

                  3. Supporting Complex Assembly

                  The scope of modern A&D systems is becoming vast. They often involve intricate assemblies with thousands of components. each with precise tolerances, dependencies, and functional relationships. They also require a blend of multiple types of technologies including software, advanced materials, electronics, and sensors. Small mistakes can result in much bigger problems downstream. A single misalignment, incorrect specification, or missing part can cause costly delays, rework, or even mission-critical failures. MBSE provides a structured, model-first approach to managing this complexity by defining precise relationships between components, systems, and subsystems – integrating all subsystems from the outset. This ensures that every part is correctly positioned, oriented, and integrated within the larger system or SoS. Engineers and production teams can use these digital models to validate component interactions, identify potential fit or alignment issues before production begins, and simulate the step-by-step assembly process.

                  Furthermore, MBSE enables seamless communication across teams involved in different stages of the assembly process. This includes creating a a single source of data that connects design intent to the physical assembly process. By providing a single source of “truth” in this way, all stakeholders—designers, engineers, technicians, and suppliers—are always aligned with the latest specifications and assembly instructions. This is particularly valuable in large-scale A&D programs, where different teams may be working on different sections of an aircraft, spacecraft, or Defense system, often across multiple facilities or even countries.

                  4. Quality Assurance and Testing

                  MBSE integrates quality assurance and testing into the digital engineering process to help teams prepare for manufacturing, ensuring defects are identified before production begins. By simulating and validating processes within a virtual environment, manufacturers can detect potential weaknesses, optimize performance, and reduce costly rework.

                  MBSE also standardizes testing protocols, providing a unified reference for evaluating compliance and streamlining quality control across production sites. This is particularly important in A&D where the scale and complexity of systems means teams are often spread across multiple sites and countries – all with different infrastructure. And it simplifies the regulatory compliance process by maintaining a comprehensive digital record of all testing and validation, ensuring adherence to industry standards while expediting certification.

                  5. Facilitating change management

                  In A&D production, changes to requirements or designs are inevitable due to evolving customer needs, regulatory updates, supply chain constraints, or technological advancements. Managing these changes efficiently is crucial to maintaining production schedules, ensuring quality, and minimizing cost overruns. MBSE provides a structured, digital approach to change management by integrating real-time updates into a unified digital simulation that is already used as the single source of truth  by the production team.

                  Rather than relying on fragmented documentation and manual updates, MBSE ensures that any design or process modification is instantly reflected across all related components, systems, and workflows. This automatic propagation of changes reduces the risk of inconsistencies, miscommunication, and outdated information reaching the factory floor. And because engineers, production teams, and suppliers all work from the same updated model, maintaining alignment and avoiding costly errors caused by working with obsolete specifications.

                  MBSE also improves impact analysis by enabling manufacturers to simulate and assess the consequences of proposed changes before implementation. By analyzing how modifications affect system performance, assembly sequences, or supply chain logistics, manufacturers can make data-driven decisions that balance efficiency, cost, and feasibility. This predictive capability helps prevent disruptions and ensures that changes enhance rather than hinder production.

                  6. Supply Chain Integration

                  Large-scale industrial manufacturing in the A&D sector relies on intricate, multi-tiered supply chains, with components sourced from numerous suppliers across different regions. Ensuring that each supplier delivers parts on time, to the correct specifications, and in sync with production schedules is critical for maintaining efficiency and avoiding costly delays. MBSE enhances supply chain integration by providing a standard system modelling approach and creating a common communication framework. This not only aligns suppliers with manufacturing requirements but provides them an easy way to engage up and down the supply chain to ensure seamless collaboration and coordination across all stakeholders.

                  This is partly down to MBSE’s ability to integrate supplier data directly into the design and production workflow. By linking supplier-provided digital models with the overall system architecture, manufacturers can conduct virtual fit and performance tests before parts arrive at the assembly line so integration issues become less likely and all components work together as intended.

                  MBSE also supports supply chain resilience by enabling real-time monitoring and predictive analytics. Manufacturers can track the impact of supply chain disruptions—such as material shortages, shipping delays, or regulatory changes—on production schedules and system performance. By simulating different sourcing scenarios within a virtual twin of all manufacturing operations, companies can identify alternative suppliers or adjust production timelines in advance, mitigating risks before they escalate.

                  7. Faster production ramp up and scalability

                  Adhering to delivery schedules for mission-critical capabilities is paramount for A&D programs. Manufacturers are increasingly turning to MBSE to significantly reduce the timeline from initial concept to delivering a functional product to customers. MBSE facilitates more efficient and accurate design iterations, enabling earlier entry into production. This approach allows companies to scale up production rates more rapidly and with greater assurance. ​

                  8. Compliance and Traceability

                  In most large A&D projects, every part of the manufacturing process must meet strict regulations and standards. MBSE provides a detailed, traceable digital record of how designs and processes comply with these requirements, making audits and certifications easier. This is invaluable for regulatory compliance, certification, and quality assurance, particularly in highly regulated industries such as A&D. It also improves collaboration between teams by providing clear visibility into the evolution of product designs and manufacturing processes.

                  9. Cost Control and Risk Reduction

                  MBSE contributes to significant cost savings throughout the product lifecycle. By catching design flaws early and reducing rework, companies can avoid expensive changes later in development. This methodology also streamlines compliance with industry regulations, helping manufacturers avoid costly penalties and production halts.

                  And by identifying these potential production challenges early in the planning phase, MBSE helps manufacturers anticipate and address material constraints, process inefficiencies, and integration issues before they become costly roadblocks. Effectively, by simulating different scenarios and evaluating the impact of various constraints, MBSE allows teams to make informed decisions that optimize efficiency and resource allocation. This proactive approach ensures that production processes remain on schedule, reducing the likelihood of unexpected delays or last-minute redesigns that themselves can lead to short- and long-term financial consequences.

                  Many of these challenges are already material for aerospace and defense companies. They will only become more onerous as products and systems continue to increase in complexity. Act now and start to develop your MBSE capability for production teams before they do.

                  Accelerating Aerospace & Defense System Production

                  Introducing Model-based Systems Engineering

                  Capgemini Engineering
                  Read more