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

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

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

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

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

How can Gen4Geo help to optimize the geothermal energy process?

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

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

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

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

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

Key insights and learnings

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

Intelligent decision-making has never been so important, and agentic AI is a technology that can deliver the actionable insights the chief supply chain officer needs to build resilience and agility.

Intelligent decision-making has never been so important, and agentic AI is a technology that can deliver the actionable insights the chief supply chain officer needs to build resilience and agility.

To call the current business climate volatile is an understatement – and at enterprises across multiple industrial sectors, the people most keenly impacted by the resulting uncertainty are likely those responsible for managing their organization’s supply chains. These vital, logistical links are subject to powerful external forces – from economic and political factors to environmental impacts and changes in consumer behavior. It’s critical that the executives in charge of supply chains, and their teams, take advantage of every tool to make smarter decisions.

New, multi-AI agent systems can deliver the insights that not only make supply chains more resilient, but also help executives identify opportunities to reduce logistics costs. But organizations must be ready to take advantage of these powerful tools. Preparing for success includes creating the right roadmap and engaging the right strategic technology partner.

Common pain points in the chain

In my conversations with chief supply chain officers, I’ve identified several common pain points they’re keen to address. Most are being challenged to improve supply planning, reduce inventory cycle times and costs, better manage logistics investments, and do a better job of assessing risks associated with suppliers and other partners across their ecosystem.

A company’s own data is an important source of the information required to help CSCOs achieve these goals and to enable agentic AI. Unfortunately, legacy business intelligence systems are not up to the task. There are several ways in which they fail to deliver:

• Analytics systems rarely support strategic foresight and transformative innovation – instead providing business users with yet another dashboard.
• The results are often, at best, a topic for discussion at the next team meeting – not sufficient for a decision-maker to act upon immediately and with confidence.
• Systems typically fail to personalize their output to provide insights contextualized for the person viewing them – instead offering a generic result that satisfies nobody.
• Systems often aggregate data within silos, which means their output still requires additional interpretation to be valuable.

In short, many legacy systems miss the big picture, miss actionable meaning, miss the persona – and miss the point.

Based on my experience, I recommend an organization address this through multi-AI agent systems.

With the introduction of Gen AI Strategic Intelligence System by Capgemini, this could be the very system that bridges the gap between the old way, and a value-driven future. This system converts the vast amounts of data generated by each client, across their enterprise, into actionable insights. It is agentic: it operates continuously and is capable of independent decision-making, planning, and execution without human supervision. This agentic AI solution examines its own work to identify ways to improve it rather than simply responding to prompts. It’s also able to collaborate with multiple AI agents with specialized roles, to engage in more complex problem-solving and deliver better results.

How would organizations potentially go about doing this?

Establish an AI-driven KPI improvement strategy

First, organizations must establish a well-defined roadmap to take full advantage of AI-enabled decision-making – one that aligns technology with business objectives.

For CSCOs, this starts by identifying the end goals – the core business objectives and associated KPIs relevant to supply chain management. These are the basis upon which the supply chain contributes to the organization’s value, and strengthening them is always a smart exercise. The good news is that even small improvements to any of these KPIs can deliver enormous benefits.

The roadmap should take advantage of pre-existing AI models to generate predictive insights. It should also ensure scalability, reliability, and manageability of all AI agents – not just within the realm of supply chain management, but throughout the organization. That also means it should be designed to leverage domain-centric data products from disparate enterprise resource planning and IT systems without having to move them to one central location.

Finally, the roadmap must identify initiatives to ensure the quality and reliability of the organization’s data by pursuing best-in-class data strategies. These include:

• Deploying the right platform to build secure, reliable, and scalable solutions
• Implementing an enterprise-wide governance framework
• Establishing the guardrails that protect data privacy, define how generative AI can be used, and shield brand reputation.

An experienced technology partner

Second, the organization must engage the right strategic partner – one that can provide business transformation expertise, industry-specific knowledge, and innovative generative AI solutions.

Capgemini leverages its technology expertise, its partnerships with all major Gen AI platform providers, and its experience across multiple industrial sectors to design, deliver, and support generative AI strategies and solutions that are secure, reliable, and tailored to the unique needs of its clients.

Capgemini’s solution draws upon the client’s data ecosystem to perform root-cause analysis of KPI changes and then generates prescriptive recommendations and next-best actions – tailored to each persona within the supply chain team. The result is goal-oriented insights aligned with business objectives, ready to empower the organization through actionable roadmaps for sustainable growth and competitive advantage.

Applying agentic AI to the supply chain^*

Here’s a use case that demonstrates the potential of an agentic AI solution for supply chain management.

An executive responsible for supply chain management is looking for an executive-level summary and 360-degree visualization dashboard. They want automated insights and recommended next-best actions to identify savings opportunities.

An analytics solution powered by agentic AI can incorporate multiple KPIs into its analysis – including logistics spend, cost per mile, cycle time, on-time delivery rates, cargo damage, and claims. It can also track performance of third-party logistics service providers – including on-time performance, adherence to contractual volumes, freight rates, damages, and tender acceptance.

The solution can then apply AI and machine learning to optimize asset use through better design of loadings and routes. Partner performance can be analyzed – including insights into freight rates, delays, financial compliance, and lead times – and used to negotiate better rates.

The impact of this can include a reduction in logistics spend of approximately 10 percent, an opportunity to save approximately five percent through consolidation of routes and services, and a 15 percent improvement in transit lead time.

Capgemini enables this use case through an AI logistics insights 360 solution offered for the Gen AI Strategic Intelligence System by Capgemini. Just imagine this agent working 24/7 on your behalf; they don’t sleep, they don’t get tired, they don’t take vacation, and they’re completely autonomous.

Real results that relieve supply chain pressures

Capgemini’s modeling suggests that with the right implementation and support, the potential benefits include reducing overall supply chain spending by approximately five percent – including a 10-percent reduction in logistics spend. Other benefits include a three percent improvement in compliance, plus 360-degree order visibility and tracking.

Given that today’s supply chains are being subjected to so many pressures from so many sources, those are meaningful advantages that cannot be ignored.

^*Results based on industry benchmarks and observed outcomes from similar initiatives with clients. Individual results will vary.

The Gen AI Strategic Intelligence System by Capgemini works across all industrial sectors, and integrates seamlessly with various corporate domains. Download our PoV here to learn more or contact our below expert if you would like to discuss this further.

Telecom companies are shifting towards techco models. The right strategy, tech, and partnerships can accelerate this transformation and unlock new opportunities for growth.

The global telecommunications industry is grappling with significant challenges across both wireless and wireline service segments. The Canadian Telecommunications Association, for example, found that internet access prices fell by more than 15 percent from March 2023 to March 2024, while cellphone prices declined by approximately 26 percent.

The 2025 CRTC Canadian Telecommunication Market Report highlights a decrease in capital investments, falling from $10 billion in 2022 to $9.7 billion in 2023. This downward trend is largely attributed to sluggish GDP growth and persistent inflation, which have eroded consumer purchasing power and led to reduced spending across various market segments.

Compounding these issues, the entry of new competitors, along with slow-moving regulatory changes, creates additional headwinds for Tier 1 telecom providers. Traditional telecom businesses across voice and data services are facing mounting revenue and margin pressures.

New opportunities are emerging in B2B

However, there are bright spots within the enterprise sector. According to a report by GSMA Intelligence, the global enterprise market is a $400 billion opportunity, representing 35 percent of current mobile operator revenues.

In Canada, the enterprise ICT market is projected to grow at a compound annual growth rate (CAGR) of nine percent from 2023 to 2028, marking a significant upswing compared to traditional operator revenues, which are expected to exceed $188 billion over the next three years. This growth is explored in What’s next for telecoms, a 2025 Capgemini analysis from MWC which highlights how the sector is expanding beyond connectivity to seize new enterprise B2B service opportunities. As enterprise spending shifts towards more advanced technology services, telecoms are positioning themselves to deliver across the value chain, encompassing cloud computing, data centers, cybersecurity, the Internet of Things (IoT), analytics, artificial intelligence (AI), and blockchain.

With the rapid growth of the enterprise market, many operators are transitioning from traditional telecom models to “techco” models. This shift aims to create agile, technology-driven organizations capable of meeting the complex demands of mid-sized and large enterprise clients.

Telecom vs techco: What is the difference?

Telecoms have traditionally focused on building and managing communication networks that deliver mobile, internet, and voice connectivity. However, they are often constrained by complex processes, making agility and innovation difficult, especially when addressing sophisticated solution requirements.

Techcos are technology and customer-experience centric. They prioritize the development of innovative digital platforms, products, and services, and are designed to leverage emerging technologies to generate high-margin revenue streams and enhance customer experiences, with a strong focus on digital and AI-powered services.

Key characteristics of techcos:

• Techcos are digital-centric. Techcos deliver a new wave of digital and cloud-based services, including platforms, AI solutions, and applications.
• Techcos are agile. They emphasize faster time-to-market and customer satisfaction by fostering a culture of adaptability and continuous improvement.
• Techcos are driven by innovation and customers. Techcos prioritize innovation through the adoption of cutting-edge technologies such as cloud computing and AI. This enables a data-driven approach to service development, internal optimization, and operational efficiency.

How do telecoms make the transition and manage complexities?

With the scale of emerging revenue opportunities, more telecoms are embarking on the journey to become stronger techcos, seeking to build greater relevance and capture value in an increasingly competitive market dominated by digital-native players. The challenge is identifying the right opportunities and partners to deliver value and achieve transformation at scale.

Key considerations for enabling digital transformation

• Adopt a digital mindset. Embrace new ways of working, such as agile methodologies, rapid prototyping, and a fail-fast approach. These practices enable organizations to quickly adapt to evolving client needs and enhance responsiveness in dynamic markets.
• Invest in culture and talent. Reassess the critical skill sets required to drive innovation. This may involve conducting workforce audits to identify upskilling opportunities or rethinking recruitment strategies to attract creative, digitally fluent talent. The next generation of employees must be equipped to understand complex enterprise requirements and pursue high-value opportunities, moving beyond the traditional telecom focus on volume and velocity.
• Forge the right ecosystem partnerships. Collaborate with leading technology providers and innovation partners with the right skills, capabilities, and expertise to accelerate solution development and operational transformation. AI can drive operational efficiency, enable faster product releases, and support agile, data-driven decision-making.
• Transition from rigid, legacy systems to modern, flexible, and cost-effective architectures. This shift reduces operational complexity by automating routine tasks and integrating AI-driven processes, ultimately enhancing productivity and delivering stronger ROI.
• Take an automation-first approach. AI-powered automation is central to techco transformation. It enables the development of applications that improve service delivery, quality, and network availability, while also enhancing workforce effectiveness. Streamlined governance and development processes will accelerate solution deployment for enterprise clients and improve internal operational efficiency.

The agile telecom: Monetizing investments

As telecoms transition into techcos and transform their operations, one of the most pressing challenges remains monetizing their substantial network investments.

In this new techco model, telecoms will develop new assets such as open APIs to unlock the value of network capabilities, AI-powered application factories for network automation across public and private domains, and modern cloud offerings like sovereign AI to deliver AI-as-a-Service solutions. Development of these services pose new opportunities to expand these offerings to other enterprises, unlocking new sources of revenue growth.

However, the complexity of this transformation extends beyond technology. It requires a holistic shift across marketing, operations, and sales to fully realize the potential of these innovations. These new solutions will drive more enterprise-centric opportunities, demanding strategic partnerships across vendors, system integrators, advisory firms, and hyperscalers to accelerate monetization. These partnerships must evolve beyond opportunistic relationships into long-term, strategic alliances that are grounded in shared investment, risk governance, and mutual value creation.

Key enablers for monetization and transformation

The techco shift is starting and still has a long way to go

Telecoms are moving rapidly to enable this transformation. The past year has seen dramatic shifts in operating models, workforce strategies, and the delivery of new solutions and services to meet evolving market demands. In parallel, telecoms are increasingly demanding accountability and success-based models from their partners across marketing, operations, and delivery.

This evolution represents a fundamental shift in the DNA of traditional telecom organizations. It requires not only decisive, top-down leadership but also an internal mindset shift – recognizing that this transformation cannot be achieved in isolation. Ideally, this will lead to stronger, service-oriented partnerships that deliver better outcomes and experiences for customers.

With decades of industry experience, Capgemini has helped more than 600 telecom clients across 50 countries develop and deploy customized network transformation plans to monetize network services, deliver innovative, high-value solutions, and extend business reach across the digital ecosystem. Get in touch to learn more about how we use data, analytics, AI, and cloud to help telecom operators embrace a more agile way of working in a data-driven world.

The battery industry faces a huge challenge to scale up its production capacity.

The need for batteries continues to surge with unprecedented growth in the use of electric vehicles (EVs), the push for electrified public transportation, and increasing storage needs in the energy industry. The battery ecosystem is expected to receive an investment of more than $300⁽¹⁾ billion by 2030. To keep-up with the rapidly growing demand battery suppliers, EV and other manufacturers are looking for faster ways to build gigafactories and start industrialized operation. This means having battery manufacturing plants where GWh worth of battery capacity can be built and to rapidly increase production.

However, building and operating gigafactories at such a breakneck pace to meet these growing needs is not without challenges. Companies need to plan carefully to stay competitive and mitigate the risks associated with quickly expanding their high volume production battery plants. Speed is essential. Companies that can build batteries at scale and at cost first will take a large piece of the markets, while the later adopters might not survive.

Understanding the challenges

Producing batteries and their components is costly and highly complex. And as a multitude of new organizations race to market, even the incumbent players have acknowledged a need to modernize and transform their operations. The battery industry, collectively, are facing a multitude of challenges, but the main three are:

• Time-to-Market: It takes about 5 years from small-scale pilot factory to the completion of Gigafactory with stable production. Given the current demand for battery and battery components, this timeline is unacceptable. To remain competitive, manufacturing organizations need ways, to streamline their processes, to get gigafactories up and running faster at scale, and accelerate the replication to other locations.
• High scrap rate: It is not enough to start production in a gigafactory quickly. Today, far too many gigafactories suffer from a high scrap rate, above 30%. This waste and its associated costs are unsustainable from a business perspective, by 10% point scrap rate reduction save $200-$300⁽²⁾ million per annum for a 30GWh factory factory which largely pays off the initial investment. Beyond cost and delay, Gigafactories have a responsibility to become more sustainable, and reduce the environmental impact of these waste as scrap results in increased energy consumption and material losses.
• Traceability: The upcoming EU battery regulation is requiring traceability along the full battery value chain. Other parts of the world are sure to follow on similar lines. But besides this regulatory need for this complex process, understanding how to improve the battery product, manufacturing processes, and workflows requires the ability to look at every step of the production process from design to build. To do this, data needs to be collected, managed, and correlated. Associated AI algorithms need to be developed and trained. This is where traceability, together with data management, is key.

Leveraging a strong solution and implementation partnership

The powerful partnership between Siemens and Capgemini is boosting battery companies as they work to build gigafactories and ramp-up production. These two companies’ unique blend of technologies and professional services enables the battery industry to overcome the challenges fast and at scale, by for example:

• Taking a simulation-first approach to gigafactory development: By leveraging digital twins of the cell, pack and manufacturing processes as well as the gigafactory as a whole, organizations can design products virtually and commission optimal production lines, this minimizes the extensive prototyping process and avoids costly changes on the factory floor. With the digital twin primed, our collective solution saves twice the time during production ramp-up.
• Connecting the digital and physical manifestations of gigafactories: By integrating data from virtual and physical facilities, organizations can initiate end-to-end integration of the production process, accelerating physical commissioning. The integration of virtual and physical data identifies potential quality or production issues and helps organizations address these swiftly. Teams are supported physically on the shopfloor and digitally in the ramp-up with operations support for manufacturing at scale.
• Developing and deploying data-driven operations: Data and the use of AI are the backbone of productivity in gigafactory performance. The Siemens-Capgemini partnership starts from a data-centric architecture blueprint for the battery industry and associated ontologies, tailoring it to the clients best fit and requirements. Together, with end-to-end solutioning, deployment of hardware, software solutions, and services from enterprise level to shop floor are offered. This enables a fully data-driven operation with a closed loop, facilitates a highly scalable, flexible, and interoperable architecture. With this we can help companies achieve scrap rate reduction three times faster.
• Maintaining a secure platform: Cybersecurity is an ongoing concern. Organizations can ensure their gigafactories—and the vital data contained within—remain secure, by design and with associated operational cybersecurity services offered alongside.

But this isn’t all we offer. Siemens and Capgemini are providing even more to battery companies in terms of key capabilities. For example,  we are accelerating digital and procedural target blueprints, with standardized processes to support gigafactories seamlessly in the industrialization phase. This partnership enables organizations to efficiently implement end-to-end solutions that address the many challenges inherent to gigafactories — and help them remain competitive in a cutthroat marketplace.

Conclusion

To meet the growing demand for batteries, companies need to increase production quickly, cost-effectively, and on a large scale. Given the challenges include a need for speed, high scrap rate,  and quality increase, how can companies continue to grow? Traceability, sustainability, and a skilled workforce mean manufacturing organizations benefit. And when they leverage the expertise of partners who are well-versed in the risks and rewards involved with building, scaling, or transforming gigafactories, they can flourish. The partnership between Siemens and Capgemini offers companies a winning set of solutions, services, and industry know-how to help them reach their production goals and manufacturing targets.

To learn more about what Siemens and Capgemini’s revolutionary partnership can do for you, watch Puneet Sinha, Senior Director of Battery Industry at Siemens Digital Industries Software, and Pierre Bagnon, Vice President and Head of Intelligent Industry Accelerator at Capgemini, discuss the challenges and solutions involved with scaling gigafactory production to meet growing battery needs.

Sources :
(1)  IEA; Benchmarkminerals
(2) BatteryPower_Hitachihitech

Learn how automotive companies can use technology to build a resilient and sustainable EV battery supply chain through gigafactories.

The key to playing a decisive role in the growing electric vehicle market is producing enough batteries sustainably at a competitive cost, at scale, and at speed.

Industry analysts anticipate global demand for electric vehicles (EVs) will rise in the next few years, thanks in large part to trends in China. Despite signs of growth cooling a bit, particularly in the US, it’s still incredible when compared with other segments of the transportation industry. The long-term growth story is alive and well, and getting to market with a lead is as important as ever. A confluence of factors indicate that North America will take more of a role in producing the batteries needed for the worldwide transition.

The Capgemini Research Institute’s (CRI) recent report on reindustrialization strategies in North America and Europe found that 63 percent of organizations recognize the importance of establishing a domestic manufacturing infrastructure to ensure national security, and 62 percent acknowledge its significance for strengthening strategic sectors.

The research also revealed that the US stands out as a top location for gigafactories – large-scale manufacturing facilities for batteries and component parts. Fifty-four percent of executives surveyed from automotive, battery manufacturing, and energy companies said they are currently building or plan to build at least one gigafactory in the US. Meanwhile, 38 percent said this about continental Europe.

Automotive companies that understand how to unlock the potential of North American gigafactories stand to gain market share and position themselves as lynchpins in this emerging ecosystem.

But winning the gigafactory race will require a holistic enterprise architecture that enables data-driven business agility. Automotive companies can master this transition by accelerating speed to production, optimizing costs sustainably, digitizing end-to-end core business processes, and upskilling their workforce.

Increasing speed to market and reducing scrap rates

Battery production is still responsible for much of the EV’s price tag. As new competitors race to the market, even incumbent players understand the need to transform their operations to be competitive.

It typically takes about five years for an organization with a small-scale pilot factory to complete a gigafactory and stabilize production. To remain competitive and responsive to demand, companies need a streamlined process of getting gigafactories to world-class production.

An inefficient gigafactory launch could mean that up to 30 percent of early production ends up discarded. Reducing the scrap rate by just 10 percent can save up to $300 million annually for a 30 gigawatt-hour factory.

Unlocking solutions with digital twins and data

Organizations can use digital twins – virtual models of objects or systems – to recreate the cell, battery pack, manufacturing process, and factory. Digital twins enhance co-creation and simultaneous product and process engineering. By optimizing in a virtual environment, companies can design and commission production lines that minimize extensive prototyping and costly changes on the factory floor.

Building the factory virtually before physically can save months of work. Today, we estimate that digital twin leaders see 15 to 20 percent savings in operational efficiencies.

Companies can expedite commissioning real-world gigafactories and ramp up operations at scale, by integrating virtual and physical models to enable data-driven automation for proactive quality and production.

They should aim to establish a closed-loop operation based on a highly scalable and flexible architecture. A solid and standardized data platform will allow interoperability between different sources for a data-driven operations strategy, which enables analysis that could reduce a factory’s scrap rate.

Digital tools can also accelerate the path to recycling, making it safer, faster, cheaper, and easier. For instance, models can combine physical and chemical disassembly with data analytics and automation to enhance the precision of planning and executing recycling. In recycling and waste management, it’s not uncommon to disentangle complex materials into simpler substances for safer disposal.

Engineering resilient, sustainable supply chains

Gigafactories need a connected supply chain with visibility throughout transportation and material handling to operate effectively and produce enough batteries.

Manufacturing electric batteries often relies on procuring raw materials – lithium, nickel, graphite, manganese, etc. – from countries with geopolitical risk, which renders them vulnerable to sanctions and other political hurdles.

Meanwhile, the entire battery supply chain contributes to an EV’s lifetime emissions and could be subject to future climate-conscious legislation. While the battery supply chain is still developing, it’s important to build it right with sustainability and resiliency.

To build resilient supply chains for gigafactories, organizations will need a single thread to connect bills of materials, partner with reliable suppliers, and enable transportation networks for valuable cargo. This requires thorough analysis of potential partners across many countries, sourcing in the Americas when possible, signing long-term contracts (for ongoing delivery) if suppliers are in riskier geographies, and designing packaging to protect battery components during shipping.

Organizations should digitize the supply chain for a comprehensive view on sustainability – one that enables data-informed decisions and battery tracking for responsible end-of-life disposal that recycles materials, and aims toward circularity.

Empowering the workforce

Organizations can face challenges recruiting the highly skilled workforce needed for specialized gigafactory responsibilities, which diverge from traditional factories in many ways. For instance, employees may be expected to maintain complex robotic systems, utilize precision automation, interact with digital twins, or use data analytics for energy management in sustainable production. Few candidates in today’s job market have all the necessary skills that align with new gigafactory processes.

Gigafactories need thousands of employees ready for day one of production, meaning that hiring, training, and expert development must happen while the factory is still under construction.

A training program like the Capgemini Battery Academy can help organizations define skill requirements for potential employees and upskill these hires through virtual and augmented reality (VR and AR) training modules. The Capgemini Battery Academy develops and builds the necessary skills that transfer directly into the job on day one.

Capitalizing on growing interest in EVs

Annual global demand for passenger plug-in EVs is expected to grow 127 percent (to nearly 22 million vehicles) by 2026, compared to 9.7 million in 2022, according to S&P Global data.

Kelley Blue Book, a Cox Automotive company, estimates that US consumers bought a record-setting 1.2 million EVs in 2023, comprising 7.6 percent of all vehicles sold in the country – up from 5.9 percent the year before. That figure is expected to reach 10 percent by the end of 2024. EV sales are still rising, just not as quickly.

The slowdown in the US stems from the typical concerns when deciding between EVs and internal combustion engine (ICE) vehicles: range awareness, infrastructure reliability, maintenance costs, resale value, upfront costs, and so forth.

Despite this mild cooldown, automakers still see the long-term benefit of investing in EVs and batteries. In fact, my research indicates that federal support virtually negates near-term worries and incentivizes more aggressive investment in this sector.

The Biden administration’s Infrastructure Law and Inflation Reduction Act together mobilized more than $50 million toward climate resilience, which is encouraging domestic automakers to prioritize EV batteries and foreign manufacturers to open facilities stateside.

According to the Department of Energy, more than $120 billion of investments in the US battery manufacturing and supply chain have been announced so far – nearly $45 billion pre-IRA and around $85 billion post-IRA launch.

The CRI report found that nearly half (47 percent) of companies have already started investing in reshoring their manufacturing, which is expected to increase average onshore production capacity from 45 percent to 49 percent in just three years.

Now is the time to go full throttle.

What is your regulatory reporting telling you about your upstream processes?

As with anything in life, you get out what you put in. Higher quality inputs lead to better outputs. The same principle also applies to firms’ regulatory reporting, upstream processes, and risk management.

Where trouble begins

In our experience working with clients on regulatory reviews, many reporting issues originate in upstream processes and risk management. These problems often begin at trade booking and ripple all the way down to what is reported to regulators.

Many firms operate across multiple booking and risk systems, including those for collateral cash management and valuations. This complexity often comes from business growth, where new or “bolt-on” systems are added to existing architecture, avoiding the need for updates in the short term, but causing more trouble down the road. Information barrier controls – designed to separate private-side MNPI from public-side activity – further fragment systems and data flows.

Firms that handle this well usually have a consolidated data lake. However, not all firms have that capability, or they haven’t done it in a way that supports effective regulatory reporting. Furthermore, there are multiple end users of this collated data and the reporting data is a really good gauge of how data sits ‘upstream’ and will be used by different parts of the firm. If the quality is bad coming into the reporting layer, it will likely be bad upstream.

Inconsistencies pass through the system

Regulators now look for signs of weak internal controls. Investigations often reveal inconsistencies in data lineage – from upstream bookings and risk systems to the final reported figures. Tolerance for these gaps is decreasing, especially when they impact the accuracy of reports.

It is critical for firms to have a clear view of their risk and positions across all systems, and regulators expect this visibility to be reflected in regulatory reports.

Investing in a review

With the wave of regulatory rewrites slowing in 2025, now is a good time for firms to invest in a review of operational processes. Taking a closer look at trade processing flows can reveal underlying issues that may be compromising report accuracy.

An investment in understanding your data lineage and looking at the design of your internal data models has a track record of delivering operational alpha and ROI. The development of an industry standard data model, the ‘Common Domain Model’ (CDM) presents an opportunity to upgrade your internal data models in a way that will offer efficiency gains in data transparency and interoperability with other industry participants. In addition to regulatory reporting, there are other regulatory benefits to ensuring smooth data flows, such as supporting the upcoming requirements of the EU and UK T+1 accelerated settlements cycle. 

 A number of recent fines have mentioned that fined firms are bringing in a third party to review processes – an approach that is viewed favorably by regulators. What we typically find is that firms with an accurate and reconcilable reporting data set often have smoother upstream operations. To promote excellent regulatory compliance in the long term, upstream processes deserve a careful look.

Factories are transforming and becoming smarter through the introduction of powerful multi-agent AI systems.

In this blog, we’ll take a close look at how these revolutionary AI-powered systems can help drive the factories of tomorrow. 

A lesson from history 

The industrial revolutions of the past can be described in two ways: firstly, as the emergence of new types of power. The transition from using humans and animals to using steam power in the 18th century was a significant revolution that enabled huge productivity gains as well as transportation innovations and urbanization. Secondly, the industrial revolutions marked the emergence of specialization: splitting up work into smaller tasks, with dedicated humans or machines for each part of the process. This enabled standardization and mass production. 

Coinciding with this, education and knowledge became specialized as well – people were only trained on their individual part of a process. Eventually, the innovation of machinery introduced automated reactivity to factory processes. Machines could now use condition-based “if this, then that” actions to complete a task. 

In today’s factories, we are seeing the emergence of innovative multi-agent AI systems, which reflect the above themes in many ways, while also exhibiting some differences. In this blog, we’ll take a closer look at some of these new developments. 

What are multi-agent AI systems? 

Multi-agent AI systems consist of autonomous agents or bots equipped with AI capabilities that work together to achieve a desired outcome. An agent in this context can be defined as  “an entity which acts on another entity’s behalf.” In these multi-agent systems, AI agents cooperate to achieve the goals of people who own certain processes and tasks. 

Multi-agent systems can be thought of as having five dimensions in terms of complexity when compared to a single agent system: 

1. Single to multi – adding more agents. 

2. Homogenous to differentiated – having fundamentally different roles between agents. 

3. Centralized to decentralized – removing the need for a single/central point of orchestration. 

4. Generic to specialized – adding in different backgrounds and knowledge to create different expert agents. 

5. Reactive to proactive – agents that can act independently in response to changes in the environment, without needing to be prompted. 

Are there parallels with the previous industrial revolutions that suggest agents might accelerate the next one? 

Let’s take the principles of multi-agent AI systems and apply them to a smart factory.  

• Each machine can have its own AI agent, while multiple machines or types of work can be managed by supervisor agents.  

• Most industrial tasks require multiple machines to work together, either in a streamlined, one-piece flow or in batches. Even machines working in “islands” need to be coordinated for the work in progress to be controlled, with no idle time. This requires that many different roles need to be assigned to different agents.   

• Adding a decentralized AI management layer can be very beneficial for a factory. There are many advantages to having sub-teams of agents with the ability to act independently of each other and run different areas of a factory to meet objectives.  

• Each machine works in a different way, and each area of a factory requires specialized knowledge. Therefore, each agent needs its own pertinent information to be able to act effectively. Higher levels of agent specialization would be very valuable to a smart factory. 

• Agents would benefit from autonomously determining when and how they need to act, rather than waiting for permission or being told when to do so. If agents were connected to the market, they could independently decide what to do. For example, an agent might exhibit this reasoning: “although the plan says that we have to produce this mix, I will change it because I think that there will be an increase in that particular product due to X and Y.”  

Multi-agent AI systems deliver clear improvements to factory processes and outcomes, including reduced downtime and increased optimization and efficiency. We also have the ability to add AI agents to data processing tasks, such as image and video analysis. This unlocks the potential of understanding input data in ways that were not possible before.  

Unlocking new ways of understanding data in smart factories 

In-line process control (IPC) is an approach that provides immediate feedback and adjustments based on real-time monitoring to maintain desired performance, quality, or output. If this is done well, it improves efficiency and reduces waste. However, the approach is difficult to implement, especially in systems based around humans. There are many data sources that need to be reviewed and understood in real time, and very experienced individuals tend to be the ones relied upon for this task. This experience is hard to acquire, potentially expensive, and still may not be sufficient to get the best results. This is, therefore, a great area of opportunity for multi-agent AI systems, which are very good at taking in lots of information, understanding what it means, and making real-time adjustments.  

Let’s look at two examples of how this works. First, let’s say you are making potato crisps, and need to understand how the cooking time of the chrisps differs depending upon the size and growing conditions of the potatoes. This can be a complex problem involving lots of disparate data sources that a multi-agent AI system could cope with well. The system could also help to determine the root cause of any problems that arise. 

A second example: if you are processing rubber in an extrusion line, the composition of the raw materials, their current mechanical and thermal characteristics, and the line parameters all influence the quality and speed of extrusion. This is a very complex problem, and in-line process control performed by an AI multi-agent system could add a lot of value. 

Another advantage of this application is that it can be integrated into factories of varying levels of infrastructure quality. Sensors may not be perfect, and information from outside the factory may have data quality issues, but removing even some of the problems will give great productivity and quality benefits. This can be especially true if costly manual inspections could be streamlined, alongside the more obvious benefits of reduced waste.

Multi-agent AI systems are revolutionary for factories 

We see parallels between the industrial revolutions of the past and what we are seeing today in multi-agent AI systems being adopted into factories. The difference now is that we are not transitioning power sources from people or animals to steam, or substituting humans in physical parts of processes. Instead, we’re allowing AI to perform tasks where it is beneficial to do so, and where it can perform the task better than the human. It is also worth bearing in mind that the real world is messy, and multi-agent AI systems can help us have more resilience and be more flexible.  

New innovations like real-time AI processing on edge can accelerate the next AI-powered industrial revolution, and give similar productivity benefits as seen in the first one. The edge component is critical, as it is more responsive than cloud, permitting real-time control. It also offers higher levels of data security, enables off-line operations (which are critical to factories), and significantly reduces the cost of the operation. 

However, AI will likely not be operating alone. I believe we will have human-AI hybrid systems for quite some time, and this is in no way a bad thing. It will be essential that humans and AI work effectively together – because for AI systems to bring value, they need to empower people, rather than replace them.  

This blog article was written in collaboration with Ramon Antelo (Capgemini Engineering)

About the Generative AI Lab 

We are the Generative AI Lab, expert partners that help you confidently visualize and pursue a better, sustainable, and trusted AI-enabled future. We do this by understanding, pre-empting, and harnessing emerging trends and technologies. Ultimately, making possible trustworthy and reliable AI that triggers your imagination, enhances your productivity, and increases your efficiency. We will support you with the business challenges you know about and the emerging ones you will need to know to succeed in the future.  

We have three key focus areas: multi-agent systems, small language models (SLM) and hybrid AI. We create blogs, like this one, points of view (POVs) and demos around these focus areas to start a conversation about how AI will impact us in the future. For more information on the AI Lab and more of the work we have done, visit this page: AI Lab. 

As we approach 2025, organizations face a pivotal moment—navigating uncertainty while leveraging Intelligent Industry to turn volatility into opportunities for growth and innovation.

Success lies in resilience, sustainability, and technology-driven transformation, where the convergence of digital and physical systems enables businesses to thrive in an evolving global economy.

The key to long-term success lies in compound thinking—a strategic approach integrating digital transformation, physical engineering, and sustainability. By designing intelligent, efficient, and adaptive systems, businesses can unlock sustainable value and drive innovation. Transformation extends beyond the physical and digital; it requires an attitudinal shift—embracing software-centric models and force-multiplying solutions that create a lasting impact.

From Smart to Intelligent Products, Operations, and Services

Industrial companies are under mounting pressure to develop and deliver increasingly complex products at unprecedented speed and efficiency.  Manufacturing is shifting from traditional, linear processes—where humans direct machines—to dynamic, multi-directional models.   In this new paradigm, consumers demand directly influences production triggering automated manufacturing systems powered by Industrial IoT and AI-driven orchestration. This transformation extends beyond factory floors, shaping fully integrated supply chain ecosystems that optimize logistics, enhance responsiveness, and enable autonomous, robotic warehousing.

Imagine a factory that seamlessly adapts to market shifts in real time. The shop floor becomes a collaborative hub, where suppliers, engineers, and AI-powered systems work in sync to optimize efficiency. Technologies like Digital Twins, the Industrial Metaverse, and Software-Defined Factories bridge the digital and physical, fostering continuous innovation and agility across the entire manufacturing process.

To scale digital strategies effectively, companies must move beyond isolated pilots and siloed implementations. Seamless integration across product development, manufacturing, and operations is the key to greater flexibility, resilience, and sustainability. As industries accelerate toward software-defined products and services, operational agility will be the key to thriving in an era of increasing complexity, speed and sustainability demands.

Breaking Down Silos: The Convergence of Digital and Physical Realms

To drive cross-disciplinary collaboration and unlock new efficiencies, industrial leaders must break down traditional silos and merge the digital and physical worlds.  This convergence fosters creativity, sets new industry benchmarks, and accelerates innovation.  AI, edge computing, and software-defined architectures are rapidly redefining operational excellence. These technologies will power the next generation of Intelligent Industry, enabling smart factories that are more flexible, cost-effective, and sustainable.

Data First: The Foundation for Scalable Solutions

In today’s fast-moving digital economy, data is more than an asset—it is the foundation for intelligent, scalable solutions.  Organizations must adopt a data-first approach, leveraging virtual models of physical systems to enhance efficiency, drive innovation, and support long-term sustainability. Data-driven frameworks improve collaboration, agility, and real-time decision-making, enabling businesses to proactively shape the future rather than simply react to change.

The rise of Artificial Intelligence (AI) and agentic AI presents both challenges and opportunities. While these technologies streamline operations and optimize human-machine interactions, they require a fundamental shift in organizational processes and decision-making. Companies that integrate AI-powered analytics and automation effectively will gain a distinct competitive advantage, improving responsiveness, efficiency, and scalability.

To stay ahead in an increasingly complex landscape, enterprises must embrace agile, digital-first solutions that optimize workflows, accelerate time-to-market, and improve cross-functional collaboration. Model-driven methodologies, such as Digital Twins and Product Passports, enable organizations to simulate, test, and refine solutions before physical implementation—ensuring efficiency while aligning with ESG and sustainability goals. By harnessing these advanced technologies, businesses can build intelligent, connected ecosystems that foster innovation and long-term value.

Driving Efficiency Through Legacy Modernization

Digital transformation is the key to smarter, greener manufacturing. While investment levels vary, digital manufacturing initiatives typically account for 15-25% of total asset bases, with a significant portion focused on modernizing brownfield sites. Despite their challenges, these sites hold immense potential for transformation.

According to the Capgemini Research Institute’s report, The Resurgence of Manufacturing: Reindustrialization Strategies in Europe and the US, 60% of reindustrialization strategies in these regions focus on brownfield approaches. Although brownfield factories face unique hurdles, digital transformation is turning them into smarter, greener, and more resilient operations.

Cutting-edge technologies—AI, IoT, robotics, and digital twins—are revolutionizing legacy manufacturing by enhancing efficiency, reducing waste, and improving supply chain transparency. While new factories seamlessly integrate these innovations, brownfield sites must undergo legacy system modernization to remain competitive.

The impact of digital manufacturing is profound:

• AI-driven analytics optimize production processes.
• Predictive maintenance minimizes downtime.
• Automated energy management reduces emissions.
• AI-powered monitoring ensures consistency and quality.
• Robotics and AR solutions enhance productivity and safety.

A Roadmap for the Future

By adopting compound thinking and continuous innovation, organizations can transform today’s challenges into opportunities for growth. A commitment to sustainability, intelligent products, and adaptive operations lays the foundation for a resilient future—one where businesses drive progress and shape a thriving global economy.

Software-driven processes and products are key to increasing flexibility and efficiency while seamlessly integrating IT and OT. As industries continue their journey toward digital transformation, those who embrace agility, collaboration, and innovation will lead the way in defining the next industrial revolution.