Are you wondering whether you should be investing in quantum? For leading tech executives, this is a no-brainer.

While quantum technologies have been around for a few decades, the latest developments – which actively use quantum properties of subatomic particles – are nothing short of spectacular. Much like, say, the internet in the late 1980s, commercial quantum applications are still in their infancy; however, mass adoption could be closer than you imagine.

The latest advances promise an exponential speed-up over the best available supercomputers (via quantum computing), tap-proof communications (via quantum-secure communications), and ultra-precise measurements (via quantum sensing). These recent breakthroughs mean that commercial adoption may occur before the end of the 2020s. Our recent research into quantum technologies[1], Quantum Technologies; How to prepare your organization for a quantum advantage nowconfirms this. Learnings from this research uncover four broad imperatives for organizations to invest in quantum technologies as soon as possible:

1. Quantum technologies promise significant performance gain for specific applications. Quantum advantage – the ability to drive significantly improved performance vis-à-vis classical systems – is closer than ever before to becoming a reality. In 2021, Goldman Sachs predicted that quantum computing could begin to yield quantum advantage in practical financial applications within the next five years.[2] In recent years, a number of organizations have moved quickly to take advantage of emerging possibilities:
   • Airbus set up a quantum technology applications center in 2018 to work on a set of problems in the field.[3] In 2019, it collaborated with experts around the world to solve a five-problem quantum challenge around flight physics.[4]
   • JP Morgan used a hybrid classical-quantum approach to determine the optimum portfolio balance of financial assets, which it claims can be scaled to work with portfolios of any size.[5]
   • Pharma major GSK is exploring how quantum optimization approaches could scale in the future in the area of drug development.[6]

It is worth noting that quantum technologies will yield an advantage in very specific application areas and will require extensive experimentation and trials. Finding these applications will take time and availability of skills, which are in short supply as of now.[7]

2. Organizations have begun experimenting with quantum – including your competitors. Our research on quantum technologies found that about one in four organizations (23%) is either working or planning to work on quantum technologies in the near future. Most of these organizations come from China (43%) and the Netherlands (42%); however, early adopters exist in all geographies we surveyed. André König, Managing Partner at Entanglement Capital, a venture capital firm focused on quantum technologies, told us, “Any company that does not start this journey today is a company that is at severe risk of losing any sort of meaningful position within its industry in the next 5–10 years.” 

3. Rapidly expanding investment in quantum tech. Our research also found that organizations are committing greater funds to quantum tech: 20% of all organizations and 85% of those working or planning to work with quantum expect to increase investments in the next year. In 2021, quantum startups raised over $800 million in venture funding – an increase of more than 70% since 2020.[8] Governments aren’t lagging behind, either – in August 2020, the US government announced an investment of over $600 million in quantum tech.[9] France has pledged €1.8 billion ($1.83 billion) for quantum technologies, whereas the UK has announced plans to invest £1 billion ($1.2 billion) in becoming a frontrunner in quantum technologies.[10]

4. Breakthrough advances in quantum technologies are accelerating. In the first half of 2022, several major breakthroughs have significantly accelerated the development of quantum tech. IBM, one of the leading providers of access to quantum hardware, is reportedly less than a year away from introducing the world’s first universal quantum computer with a capacity of over 1,000 qubits – a significant increase over its current hardware with 127 qubits.[11] Researchers from Austria and Germany have, for the first time, demonstrated a set of computational operations on two logical quantum bits in such a way that errors caused by underlying physical operations can be detected and corrected – making error-free quantum computation possible for all applications and removing a key barrier to the adoption of quantum algorithms.[12] Nadia Haider, Lead Applied Electromagnetic Scientist at QuTech, says, “Our ability to really work with this technology has exponentially increased. There is more trust that this is not only useful for fundamental research, but we can bring it to real applications.”

In the quantum communications space, researchers at TU Delft recently successfully demonstrated a technique called quantum teleportation to send data across three physical locations (an operation previously only possible with two locations).[13] These linkages can enable data transfer without the data being lost or tapped into. The idea is to scale these connections to create a network linking an increasingly large number of sites – effectively creating a quantum internet. Even compared with the current state-of-the-art quantum key distribution (QKD) technology, this would advance quantum communication to a whole new dimension.

The case to invest in quantum technologies has never been stronger. This could be the time to harness quantum advantage and gain a competitive edge.

[1] Capgemini Research Institute, Quantum Technologies: How to prepare your organization for a quantum advantage now, March 2022. For this research, we surveyed over 200 R&D and innovation executives from global organizations and conducted in-depth interviews with over 30 world-renowned experts on quantum tech.

[2] Financial Times, “Goldman Sachs predicts quantum computing 5 years away from use in markets,” April 2021.

[3] Aviation Today, “OpenQKD fuels European quantum computing research potential in Aerospace,” December 2, 2019.

[4] WSJ, “Airbus CTO sees quantum computing taking off in aerospace industry,” January 23, 2019.

[5] The Quantum Insider, “JP Morgan Chase Bank research team brings portfolio optimization a step closer in NISQ era,” November 1, 2021.

[6] The Next Platform, “GlaxoSmithKline marks quantum progress with D-Wave,” February 24, 2021.

[7] ZDNet, “Quantum computing skills are hard to find. Here’s how companies are tackling the shortage,” November 2021.

[8] Crunchbase, “Quantum technology gains momentum as computing gets closer to reality,” May 2022.

[9] The Verge, “US announces $1 billion research push for AI and quantum computing,” August 2020.

[10] BusinessFrance.fr, “€1.8 billion in funding for quantum technologies,” January 2021; Gov.UK, “£1 billion investment makes UK a frontrunner in quantum technologies,” June 2019.

[11] IBM, “IBM unveils new roadmap to practical quantum computing era; plans to deliver 4,000+ qubit system,” May 2022.

[12] Phys.org, “Error-free quantum computing gets real,” May 2022.

[13] The New York Times, “‘Quantum Internet’ inches closer with advance in data teleportation,” May 2022.

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.

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.

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

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

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

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

The past

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

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

The present

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

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

The future

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

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

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

Thank you, once again.

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

This remains my heartfelt commitment.

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

Find out more

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

Both artificial intelligence (AI) and generative AI (Gen AI) play a critical role in RISE with SAP transformations. The latter though, Gen AI, offers specific capabilities for both enriching the deployment of RISE with SAP, and maximizing its potential.

Let’s take a brief look at what these capabilities are.

From a big-picture perspective, Gen AI and RISE with SAP share many common goals. Both, after all, are dedicated to driving business transformation. They also increasingly share the same “space” with Gen AI assistants such as Joule, which is now integrated into SAP’s RISE bundling.

But as with any technology that for many sits somewhere between potential and practical value, Gen AI – particularly in the context of RISE with SAP – still raises many “how” questions:

• How can it reduce the risk and help optimize the actual deployment of RISE with SAP?
• How can it enable clear business value, in areas such as procurement and accounts payable?

The first step to answering these questions is understanding what’s realistically possible. Yet it’s also important to understand what’s suitable, given the fact that a mix of standard AI and automation will most likely be the answer for 80 percent of current Gen AI use cases.

Gen AI for delivering RISE with SAP transformations

The true value of Gen AI, in the context of a large, multi-year change program, is its ability to accelerate delivery, reduce the risk profile, and improve efficiency. For example:

• It can be used as an assistant during implementation workshops, helping answer questions, and creating in-depth reports.
• Based on these discussions, Gen AI can then create functional specifications, technical designs of custom objects, and configuration documents to significantly ease the manual burden.
• There’s also an important data migration benefit with Gen AI automatically mapping and transforming data from legacy systems to SAP S/4HANA.
• Last but not least, Gen AI can be used to generate code, thereby accelerating the software development lifecycle and ensuring quality assurance.

According to the latest Capgemini Research Institute report, Generative AI in organizations 2024, organizations have seen a fourfold increase in the deployment of generative AI, with 20 percent boosting investments and realizing tangible benefits like enhanced customer engagement and operational efficiency. These benefits are also key drivers behind using Gen AI for RISE with SAP implementations.

Overall, the correct application of Gen AI can have a huge impact and cut the costs of a RISE with SAP implementation by up to 15–20 percent.

Gen AI for delivering business value as part of the implementation

Outside of the delivery conversation, which certainly helps accelerate the benefits of the transformation, what really makes Gen AI a long-term enabler of the value of a transformation based on RISE with SAP are the business use cases. These create the transformational outcomes that provide the all-important business case justification.

That is why the activation of embedded AI/Gen AI use cases, as well as the identification of additional ones, should always be part of the solution design.

SAP is heavily investing in integrating Gen AI features into core business processes to automate, optimize, and bring contextual navigation to any task. These capabilities sit at the heart of the Joule offering, which is available via the RISE and GROW with SAP offerings.  These are usually industry-specific and cover a wide range of areas such as:

• Supply chain resilience
• Recruitment matching
• Predictive analytics.

The value underpinning these activities will manifest in the form of more automated and independent processes, enhanced productivity, and more informed decision-making. Yet equally, it’s about streamlining the way users interact with systems – and making the process easier and more intuitive for creating highly specific outcomes.

Obstacles to change

Gen AI may represent a major change in enterprise technology, but its introduction alone does not guarantee success. This is where change management enters the picture, because in reality Gen AI demands changes to standard operating procedures:

• For people, that means overcoming long-established habits (“I’ve always done it this way…”) and skills resistance (“I’m an expert developer, and don’t need Gen AI…”).
• For processes, Gen AI inevitably requires a degree of fine-tuning to maximize the outcomes it delivers.

There can also be an understandable wariness of the technology itself, with concerns extending from ethical considerations to practical day-to-day issues relating to data security and the introduction of bias into any system.

Embracing what’s possible

Despite these potential obstacles, it’s an undisputed fact that Gen AI drives better business outcomes. Depending upon the use case, there is also significant value in using the technology now, as an ever-growing number of organizations can confirm. The difference made by RISE with SAP is that it makes adoption far easier and more compelling.

RISE with SAP might not represent the totality of an organization’s AI strategy, but it does enable the key capabilities such as SAP Business Technology Platform (SAP BTP) that are critical to ongoing innovation.  Embedding Gen AI into RISE with SAP is also central to SAP’s long-term AI roadmap, making it important for customers to embrace the opportunity. This is where Capgemini can help, working with our SAP clients to help them:

• Identify the right use cases for Gen AI, while also deploying standard AI, automation, and hyper-automation
• Access the tools and accelerators needed to speed up the delivery of projects
• Utilize our deep relationship with SAP to surround their Gen AI journey with added reassurance
• Identify new use cases that enrich the ones out of the box from SAP
• Provide tools and frameworks for a safe, trusted, and cost-effective use of Gen AI.

Final thoughts

Improving forecast accuracy, lowering inventory costs, and detecting fraud – these and more use cases represent the ultimate goal of Gen AI projects. With Gen AI, a user can chat with a system, ask it to create a report, a purchase order, or a line of code, and receive increasingly personalized responses. This is the new reality as enabled by RISE with SAP and supported by all the experience and insight available from Capgemini.

It all points to an exciting future.

Read our next blog part of the series.

Gen AI is certainly one of today’s hottest boardroom topics. Organizations of every shape and size are looking at the technology and asking questions of how it can help optimize the core tasks, processes, and workflows that underpin everything they do.

Equally, and as covered in previous blogs, the application of Gen AI is almost boundless – from inspiring large RISE with SAP transformations to reimagining the software development life cycle.

Yet while big-picture thinking on the full potential of Gen AI is a critical, ongoing endeavor, it’s also vital to follow up such analysis of what’s possible with practical use cases.

Knowing what Gen AI can do in creating new content, simplifying the analysis of complex data streams, and streamlining information access is important – but so is understanding how this capability can be applied to day-to-day realities. So, with that in mind, let’s look at opportunities for applying Gen AI within a specific industry sector – in this instance manufacturing.

Getting productive

Speak to any manufacturer about their operational challenges, and common issues soon emerge relating to the seemingly endless task of improving productivity. A task made excessively complex by the myriad variables involved, ranging from material availability to unexpected machine breakdowns.

What’s more, surrounding any manufacturing process is a wealth of structured and unstructured data – typically residing in SAP and non-SAP systems – that if available in a timely manner can provide both advance warning of upcoming problems and options for immediate resolution.

In effect, efforts to overcome the “productivity barrier” are ultimately focused on turning this raw data into actionable insight. To this end, many technologies, from manufacturing execution systems (MES) to advanced analytics, are already employed. Indeed, much of the required insight can be made available and embedded into established workflows.

But with its ability to be trained to follow precise rules, analyze vast data sets, detect discrepancies, and provide tailored responses, Gen AI can truly “democratize” the flow of insight across manufacturing operations. This is a capability that in turn lowers the barrier for people to discover (or receive) timely intelligence on which to base their decisions.

Advancing the journey to Industry 4.0 (and beyond)

The Capgemini Research Institute’s report, Harnessing the value of generative AI: 2nd edition – Top use cases across sectors, highlights how organizations are leveraging generative AI to enhance operational efficiency, foster innovation, and unlock new revenue streams across industries, including manufacturing.

Here are a few examples of manufacturing capabilities currently being developed by Capgemini:

Asset availability – with Gen AI models able to create real-time forecasts of capacity, predict potential machine stoppages, and maintain a more dynamic form of production scheduling that can react instantly to any stoppages or lack of available resources/labor.

Product quality – where quality checklists incorporate voice controls and real-time updates based on product-specific quality intelligence, to fast-track the process both at the assembly line and within the warehouse operation.

Workforce self-service – with Gen AI used to diagnose the root cause of high priority issues, suggest possible resolutions to users, and propose long-term solutions to prevent recurrence – as well as the process and time needed to implement them.

Supply chain optimization – where Gen AI combines historical data with real-time forecasts to create highly detailed bills of materials (BOMs) and matching this to existing stock levels and known supplier availability to cover predicted shortfalls.

Time to insight

With all these use cases, the value of Gen AI comes in its ability to provide a simplified interface between users and a bewildering array of complex data. Answers can be found without using the technology, but often in a way that requires too much time and effort to make the process viable – as well as a basic skill level for performing such analysis. Which is why, in the past, key insights that could transform both user productivity and customer relationships were often left hidden in the detail.

An example of Gen AI turning vast data sources into meaningful insight can be found in a current project with a semiconductor manufacturer using SAP solutions. As with any Gen AI initiative, the work has originated from a clear operational problem:

• The client has a portfolio spanning thousands of products and components – each accompanied by a mass of documentation.
• Responding to customer queries means people having to navigate through these design and specification documents to find answers – an exhaustively inefficient process.
• As a result, customers may not always be presented with the ideal product recommendations, and receive a slow response to any urgent sales inquiry.

Where Gen AI can help is in bringing together the structured and unstructured documentation surrounding each product. This data can then be queried via a conversational chat window, with responses increasingly tailored to the expectations and personalities of individual users. As a result, sales and technical employees can now ask questions – such as “what’s the best components mix to meet a customer’s precise specification?” and “what’s the fastest, most economical and sustainable way to supply these products from our global operation?” – and receive answers in seconds.

Final thoughts

Gen AI opens a window into an organization’s collective knowledge and intellectual property. It is a trained intelligence able to interpret a user’s intent and produce the most relevant data possible. It’s about radically shortening time to query and providing a layer of contextual understanding that helps advance the collective ambition for Industry 4.0 and beyond.

Challenges exist in introducing an industrialized Gen AI solution that can be trusted to consistently deliver authentic answers, from architecting the right solution to implementing the correct policies and controls. But as Capgemini is routinely demonstrating, these issues can be quickly solved with a robust implementation process and in-depth industry expertise. The hardest part remains the conceptualization of different use cases and imagining how and where Gen AI can complement existing processes – while inspiring new ways of tackling old problems.

For CFOs, and the finance and procurement functions in general, Gen AI continues to offer significant potential.

Indeed, only two years ago a Gartner survey suggested that 80 percent of CFOs expected to increase spend on the technology by the end of 2024.

This is unsurprising of course given the CFO’s ongoing preoccupation with cost and risk, and the many repetitive, time-consuming, manual tasks that routinely need to be undertaken to manage these metrics. Hence the attraction of Gen AI as a tool able to generate insights at speed to transform the way organizations answer questions such as:

• What suppliers should we avoid doing business with?
• How can we make the entire bank reconciliation process touchless?
• How can we maintain oversight on the key trends relating to contracts?

With its ability to process vast amounts of data quickly and accurately, alongside the use of natural language processing to generate human-like text, the opportunities presented by Gen AI across both finance and procurement are extensive. AI agents can further enhance these processes by acting as intelligent assistants that can interact with various systems and users. They can automate routine tasks, provide real-time insights, and facilitate decision-making by integrating data from multiple sources. All of which makes the first challenge that of identifying specific use cases for delivering more dynamic operations. So, let’s take a brief look at the main candidates to help shape expectations.

Enabling a dynamic transformation of P2P

The delivery of Gen AI often involves the building of a bot to generate content for end users. This makes perfect sense for CFOs when the content supports their wider efforts to streamline processes, mitigate risk, and reduce levels of manual intervention. Hence a common starting point being the procure-to-pay (P2P) process to enable outcomes such as:

• Sourcing and procurement: analyzing supplier data (delivery times, quality standards, etc.) to identify the most reliable and cost-effective suppliers.
• Vendor invoice matching: complementing the invoice reconciliation process and augmenting vendor invoice matching to replace Optical character recognition (OCR)technologies.
• Payment anomaly detection: enabling the early identification of accounting irregularities or deviations from established policies, procedures, or thresholds.
• Financial insights: generating recurring financial reports, automating the import of data into templates, and delivering board-level insight into performance.
• Planning and analysis: reducing forecasting cycles down from weeks to minutes and conducting more sophisticated data querying from different sources.

Getting more intelligent about suppliers

Another area ripe for Gen AI utilization is vendor management. Again, the objective being to help simplify and streamline standard processes, from onboarding a new supplier to validating invoices. These processes involve vast third-party data sets, ranging from audit information, sustainability data, quotations, and even social media reviews. The result of all this data, analyzed and turned into actionable insight by Gen AI, is an elevated capability for assessing and quantifying suppliers based on the key metrics of cost and risk – alongside more time efficient processes. For CFOs, this means having a virtual assistant that can handle everything from generating financial reports to analyzing market trends, thereby freeing up time for more strategic activities.

For example, Gen AI can help select a supplier, based on detailed cost comparisons (including all related costs), then convert a request for quotation into a purchase order. It can also help detail potential supply gaps and recommend alternative suppliers – both existing and new – based on a stated risk profile. These capabilities and more are introducing new cost efficiencies to both finance and procurement teams, while easing the move toward more dynamic sourcing.

Streamlining the AP process

Alongside benefits for the P2P process comes Gen AI’s involvement in a touchless accounts payable (AP) process. The value here is easy to imagine, given the huge amount of data involved in picking the correct invoices and translating them to the required format – before the manual scrutiny even begins!

Where Gen AI offers huge potential is its ability to automatically scan all these paper invoices and extract the correct data and processing invoices – while also providing recommendations on the fields being captured. In doing so, the Gen AI bot is bringing a greater “touchless” element to AP, as it can:

• Be trained on historic data – to understand key fields and what marks an invoice as “correct” versus “problematic”
• Match invoices to purchase orders and receipts – while handling complex scenarios like partial deliveries and multiple invoices for a single order
• Clarify whether invoices are PO or non-PO based
• Route invoices for approval based on predefined rules and exceptions
• Analyze patterns and anomalies to detect potential fraud or duplicate invoices.

AI agents can also ensure compliance by continuously monitoring transactions and flagging any anomalies, thus reducing the risk of fraud and errors. By leveraging AI agents, CFOs can achieve greater efficiency, accuracy, and agility in their operations, ultimately driving better business outcomes.

The Intelligent procurement study 2024 report by Capgemini Research Institute highlights how generative AI is reshaping procurement by driving efficiency, mitigating risks, and fostering innovation in a rapidly evolving landscape. Ultimately, these capabilities are helping redefine AP workflows and are significantly cutting back on what tasks need to be completed by humans – and the time required to do them. This is a development that quickly leads to a sizeable cut in invoicing processing costs, while also helping inspire greater velocity of cash flow.

Final thoughts

It’s important to note that these Gen AI capabilities and more are already in the here and now. Capgemini has clear offerings in each area, built within the RISE with SAP framework, delivered in the SAP BTP layer, or through hyperscaler tools like Microsoft Power Platform, and available as plug and play solutions. With them, CFOs and procurement leaders can finally remove the burden associated with traditional tasks. From contract reviews and validations to fraud detection and financial forecasting, Gen AI can be called upon to do much of the heavy lifting – or at least the more time-consuming, repetitive tasks.

Not that it finishes there. Gen AI is also now able to assess the strategic insights needed by the board, reviewing key dashboards, and providing personalized summaries for those involved. This “narrative design” further reduces the time needed to pull such insights together, while automating the alignment of summary insight with key performance indicators. It’s just another example of how Gen AI is helping fast-track journeys toward the intelligent business.

“Everyone is obsessing over agentic AI, and rightfully so. When it comes to operational agility, autonomous agentic systems are set to deliver game-changing benefits to enterprises. In the coming years, the successful integration of these systems won’t just be a good idea, it’ll be the defining factor that separates industry leaders from the rest of the competition.” – Rajesh S. Iyer 

In our world, there are many kinds of agents. Travel agents help us book travel plans, with everything from flight bookings to hotel reservations falling under their jurisdiction. Sports agents help professional athletes navigate the legal and business side of sports, enabling clients to maximize their career and financial opportunities. Secret agents typically deal with top-secret matters.  

What about AI agents? Regarded for their intelligence and ability to tackle business challenges with flexibility and precision, AI agents have quickly become a hot topic for business leaders. The same goes for autonomous AI systems, which are growing increasingly more prominent within organizations.  

While the terms agentic AI and autonomous AI are often used interchangeably, these systems have distinctive qualities that set them apart. Autonomous AI refers to systems that can operate independently within predefined parameters, like self-driving cars or factory robotics. On the other hand, agentic systems are equipped with a deeper sense of agency. These systems are designed to actively pursue goals, dynamically adapt strategies, and make context-dependent decisions. In short, all agentic AI is autonomous, however not all autonomous AI is agentic.  

As more organizations look to integrate AI agents and autonomous AI systems into their operations, a new kind of partnership between people and technology is emerging – one that’s pushing businesses to learn and evolve. 

Making a real-world impact: from education to finance 

The benefits of AI agents and autonomous AI systems are already materializing across industries. In an effort to enhance its learning experiences, a US-based non-profit education company recently started leveraging an artificial intelligent platform that autonomously supports educators and students. Providing teachers with an online teaching assistant and students with an online learning coach, this system helps break complex educational goals into actionable tasks – completely revolutionizing the classroom experience.  

The financial sector is also reeling in the benefits of autonomous agentic systems. In the US, a major bank is using Edge AI to autonomously handle tasks like interest rate queries, account openings, and fund transfers, drastically improving operational efficiency. Across the globe in India, a leading digital lending and savings platform is leveraging an Ema’s AI Employee to automate its customer support services. Since integrating the agent into their operations, the platform has managed to automate 70% of its support tickets in multiple languages, delivering a vast reduction in costs and faster ticket resolution times. 

As organizations continue to leverage these systems and the technology itself continues to develop, benefits such as those mentioned here are just the beginning of a much broader transformation.  

Looking forward: a bright future ahead  

Agentic systems are at the forefront of the next wave of automation and AI. Representing a powerful shift for enterprises, these systems are positioned to improve operational efficiency, workplace collaboration, and customer satisfaction – transforming how organizations across industries pursue their strategic objectives.  

Though the benefits of agentic systems are certainly apparent, human oversight and the continuous adaptation of these systems are paramount for their success. Collaboration between humans and technology must remain at the core of any agentic system to build trust, safeguard privacy, and ensure resilience. As challenges like missing data, system outages, or other unexpected conditions arise, businesses must be able to adjust their systems at speed. Addressing this confluence of factors will dictate whether organizations successfully integrate autonomous agentic systems into their value chains. 

These agents aren’t just tools, but rather catalysts for change capable of unlocking new levels of productivity, personalization, and innovation. The path forward is full of promise for those who are ready to embrace the next chapter of AI-powered business operations. As humans and machines continue to collaborate, the possibilities are only beginning to unfold. 

Important Definitions 

Agentic AI  

Agentic AI refers to AI systems that can act and reason autonomously, collaborate with humans, adapt to changing environments, and use enterprise tools. These systems are designed to act with goals in mind, and are capable of making decisions, taking initiative, and carrying out complex tasks to achieve specific outcomes. 

Autonomous AI  

Autonomous AI refers to AI systems that can operate and process data without human interaction or oversight. These systems perform tasks independently and continuously learn from input data to become more efficient over time. 

Learn more 

• TechnoVision 2025 – your guide to emerging technology trends 
• Autonomous Agent Alliance – 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.