Complexity has become a bit of a buzzword. It seems wherever you look, every business is promising to simplify complexity, without really specifying which bit. After all, what in life and business today isn’t complex? However, complexity is the genuine state of play for the aerospace & defense industry. In battling supply chain issues, cost constraints, rapidly evolving technology, lack of cross-industry and inter-departmental cohesion, and constant changes, ‘complex’ may feel like the only way to describe the many challenges your business is facing.

As a Chartered Engineer with a background in defense, it’s these challenges my clients enlist my help with every day. And thankfully, the solutions aren’t too complex. If you’re heading to Farnborough International Airshow (FIA) this year, you can experience live demonstrations of them at Capgemini’s booth or chalet. Plus, my esteemed colleagues and I will be on-hand to show you how bespoke versions of them can be applied to your business.

You’ll find more information on our presence at FIA at the bottom of this blog. But before then, let’s break down the answers to the industry’s biggest questions.

How can technology help my business balance cost constraints, innovation, and quality, with increasing product complexity?

If this challenge resonates, it’s because much of the industry is facing the same thing. Balancing cost constraints with the need for innovation and quality is a perpetual challenge, especially in a highly competitive market. Simultaneously, the products we develop are increasingly complex and we must quickly adapt to them to enable effective and efficient development and management across the full product development lifecycle.

The key to solving this? Firstly, Systems Thinking. A way of working that requires probing challenges, asking questions, and interrogating what systems need to achieve before jumping straight to implementation. Taking this “big picture” view of connected wholes rather than isolated parts enables you to break down traditional siloes and is core to efficient product development.

Moving forward, by combining Systems Thinking with advancements in digital technologies (both tools and techniques), we can start to address our high-level problem, drive-up efficiency improvements, and manage our products as a holistic system. There are two relevant approaches here, which you can see in action at our FIA booth or chalet.

1. Model-Based Approaches. This is essential for advancing traditionally document-centric approaches – which are entrenched with inefficiencies and inaccuracies – and opens the door to more advanced modelling and simulation capabilities. It enables the linkage of models to create a system that understands its boundaries, its contradictions, and can communicate the reasons for its design changes. It is the bridge between requirements and low-level high-fidelity technical models created by domain specialists. Visit us at FIA to: Discover how this approach can help you manage complexity, reduce risk, and increase productivity with a demonstration of C-Pulse – a Medical Transportation Drone, developed from scratch by Capgemini from a Model-Based Systems Engineering (MBSE) model.
2. Digital Twins. This term can mean different things, including a digital twin’s simple iteration: attaching several sensors across a system to get a snapshot. However, at Capgemini we always push to utilize MBSE at the core, then expand on it to link up different technologies within the system. Ultimately, it allows us to use the digital twin across a product’s lifecycle and gain a continuous, overarching view, rather than one moment in time. Visit us at FIA to: Understand how digital twins can help you can make data-driven decisions, enhance customer experiences, drive critical efficiency, reduce carbon footprint and uncover new revenue opportunities, with a demonstration of our RT3D airport operations platform – a full digital twin of a North American airport. Learn more about MBSE and Digital Twins here.

How do I break down siloes in my organization to improve collaboration?

Lack of collaboration is a prevalent issue across the A&D industry; not only between organizations but between in-house engineering domains. Though it’s common to organize teams into capabilities, this approach creates siloes between them – separate data, separate tools, separate simulations and testing – which only drives inherent problems and inefficiencies across their work. Engineering is multi-disciplinary and no one team is more important than the need to produce a seamlessly integrated product to meet our customer needs. 

So how do you go about bringing them together? Digital technologies play a big part in enabling and enhancing collaboration; digital continuity breaks down silos by bringing people together not just in ways of working, but also in the tools and data.

What can often happen in siloed teams is that each works on their own element of product development with success, but when those elements come together in the physical testing stage, they fail. Our goal, conversely, is to ‘fail fast’ – increasing early simulation and verification activities so we can quickly identify and fix the problems, allowing us to focus spend on the development and management of our products rather than fixing problems later in the lifecycle.

Visit us at FIA to: Explore the possibilities of reducing component production time by bringing together specialists from across business functions, through our component design demonstration.

What part does agility play in all of this?

The thread that runs through these challenges is change. There will always be change – whether that’s in technology, capability, user expectations, customer requirements, or anything else on the extensive list of variables. As an industry, we must enable our teams to adopt to and respond to change, instead of spending all their time trying to manage it. Faced with a change in design requirements? Ask yourself how you can accept it, and feed it into the solution, instead of pushing back on it and sticking with what’s always been done.

Our industry requires an approach to engineering that welcomes and embraces change, and agility is crucial to adopting that. Combining agile philosophies, a Systems Thinking approach, and collaborative engineering, backed up with Digital Continuity across the lifecycle, we accelerate our engineering capabilities and drive the resilience needed to adapt to change.

Capgemini’s vision for FIA, collaboration, meet acceleration, wasn’t devised simply because it rolls off the tongue. We truly believe if you can collaborate, you’ll combat your, and the industry’s, toughest challenges, and together we can accelerate towards an intelligent future that is connected and sustainable.

Learn more:

Digital Continuity for the Aerospace Industry

Digital Twins in Aerospace and Defence

Intelligent Supply Chain for the Aerospace and Defence Industry

Lifecycle OptimiZation for Aerospace and Defense

Capgemini’s event gathered eco-conscious leaders to explore how companies can drive positive outcomes for people and the planet

Capgemini brought together sustainability experts and environmentally conscious professionals recently for its New York Sustainability Connect event at its office in the bustling Union Square neighborhood of Manhattan.

The event on June 18, 2024 assembled experts from various industries for an evening of networking and lively discussion on different aspects of our collective transition toward a cleaner future. The topics included the current state of play for sustainability in the Americas, climate risk in financial services portfolios, and job creation in the growing green economy.

Current state of play for sustainability in the Americas

Our presenters:

• Vincent Charpiot, EVP and Head of Group Sustainability Accelerator at Capgemini (moderator)
• Shobha Meera​, Chief Corporate Responsibility Officer at Capgemini
• Sol Salinas, Global Executive Vice President & North America Sustainability Lead​ at Capgemini
• Satish Weber, Head Executive VP of the Sustainability Financial Services Strategic Business Unit at Capgemini.

Discussions around environmental, social, and governance (ESG) factors too often amount to little more than “motherhood and apple pie” – feel-good platitudes that do little to advance the conversation.

The panelists from Capgemini wanted the conversations to be meaningful by addressing the potential benefits and obstacles, from strategy to execution, and avoiding cheerful reassurances. The upshot of the first panel was that embracing sustainability is no longer an option: it’s a business necessity.

Internationally, business value is a leading – if not the leading – driver of sustainability initiatives. In the US, adhering to regulations tends to top the list of incentives.

Of course, sustainable projects that don’t deliver quantifiable outcomes will ironically not be sustainable for that organization. But delay won’t help either, because the competition will be adopting new, clean technologies that will accelerate business objectives while earning trust from the public and investors.

In recent years, sustainability has become a hyper-driver of innovation in product, business, and service models.

Opportunities to secure funding for sustainable projects have never been as plentiful. If there was ever a time for successful business cases around adopting sustainable practices or sourcing renewable energy – whether wind, solar, geothermal, hydropower, ocean energy, etc. – that time is now.

The Biden administration is investing around $2 trillion in sustainability, through the CHIPS and Science Act, the Inflation Reduction Act (IRA), and Infrastructure Investment and Jobs Act (IIJA).

Nevertheless, the biggest challenge is the high investment costs, which means access to capital is a key enabler and financial services have a crucial role to play.

Climate risk in financial portfolios

Our presenters:

• Alex Tepper, Global Head of Ventures & Leader in Sustainable Futures at frog, Capgemini Invent (moderator)
• Sandro Chen​, Banking Engagement Lead at Climate X
• Maritzabel Mayoral, ESG Coverage Vice President at MSCI
• Ashley Cooke​, Institutional Client Coverage, Alternatives/Renewables at HSBC.

Both private and public funds are crucial to developing new technologies for ESG projects. Private companies and venture capitalists take significant risks to fund promising new technologies. Then the public sector enables large-scale implementation and adoption.

Investors have the unenviable task of not only determining which startups and established businesses herald the greatest returns, but also deducing which sustainability efforts are in good faith.

The truth eventually comes out, so any financial backing built on shaky sustainable practices could dissipate. It’s important for investors to have an accurate picture of a company’s sustainability claims early on.

Greenwashing, the act of promoting vague or misleading commitments to sustainable practices with minimal or zero legitimate effort to reduce environmental impact, is sadly all too common.

Providers of investment decision support tools and services can help people sift through all the relevant information to determine which companies live up to their eco-friendly messaging.

It’s still difficult to evaluate ESG investment opportunities without a standardized, recognized data source. At the moment, hundreds of different companies gather and organize similar datasets, with sometimes diverging or even contradictory information. The sooner the industry establishes and embraces a single, open source of ESG data, the better for all involved.

In the past, anyone investing in ESG was liable to hear someone suggest he or she was “giving up their returns.” But this misunderstanding has slowly changed, as more companies find success in the space and sustainable business practices are better understood as a profit center rather than a cost center.

It’s also worth noting that many promising investment opportunities exist in emerging markets, which contribute less than 14 percent of global greenhouse gas emissions but are more vulnerable to the effects of climate change.

Green jobs

Our presenters:

• Alex Hammer Ducas, Senior Strategy Director and Private Sector Lead at Purpose (moderator)
• Kevin Eckerle, Director of ESG Performance, Operations, and Consumer Health at Bayer
• Caitlyn Brazill​, Chief Revenue Officer​ at Per Scholas
• Matthew Beller​, Senior Advisor at the NYC Mayor’s Office of Talent and Workforce Development.

Many positions we think of as green jobs weren’t available just a few years ago. And we don’t yet know all the new green positions that will open up in the near future.

This applies just as much to emerging, explicitly green jobs and traditional roles – such as controller or accountant – that will increasingly focus on sustainability. 

A major company in the past may have had a single job for a sustainability expert, but now it likely has many experts on various aspects of sustainability and roles that incorporate the movement’s concerns.

Unfortunately, a skills gap separates many jobseekers from openings in sustainability. Given the fluid nature of the green job market, one of the best ways to prepare young adults for a career in sustainability is through technological training.

Organizations like Per Scholas, a nonprofit committed to equitable education access based in the Bronx, NY, provides tuition-free skills training to people typically excluded from tech careers.

Preparing people for tech careers that support the sustainability transition helps address social and environmental challenges simultaneously.

Embracing the apprenticeship model of job training, which is more common in Europe than the US, could also help connect young people land new jobs in the burgeoning green economy.

Developing a greener tomorrow

Despite some headwinds, today’s market is promising in terms of accelerating a more sustainable future. Throughout the Americas, the transition toward sustainability is characterized by meaningful progress and persistent challenges.

Whether looking to build a career or invest, considering ESG factors early and often can help people stay on the cutting edge of technology and business trends.

New York Sustainability Connect concluded with networking opportunities, so the attendees could discuss what they had heard and develop relationships, perhaps even partnerships.

There are ongoing challenges to accelerating sustainability, but green initiatives incentivize cooperation and goodwill, whereas business as usual can incentivize avarice and suspicion. After all, the effects of pollution and anthropogenic global warming affect everyone and it’s going to take collective (in addition to individual) action to mitigate their most harmful consequences.

But with events like New York Sustainability Connect, we can start to make meaningful changes, together.

The current spike of Gen AI is prompting more and more people to wonder when AI will surpass human intelligence (and in many disciplines, the bar is already passed).

Interestingly, this is a debate which is far from new. Actually, as early as 1950, one of history’s most brilliant minds published a scientific paper in which he posed the question ‘’Can machines think?’’. This was Alan Turing, who would later become famous for the ‘’Turing Test’’ to determine a machine’s ability to exhibit intelligence. Turing also invented one of the first computing devices in history (the Turing machine) by the age of 24.

But it was during World War II that Turing made some of his greatest contributions to humanity. Working at the Allied codebreaking center of Bletchley Park, Turing and his team managed to crack the Nazi Enigma code, which significantly contributed to the Allied victory.

Even after the war, he made lasting contributions to the fields of mathematics, biology and what would eventually be called Artificial Intelligence.

But despite all of this, Turing was persecuted and even put on trial for homosexuality. He ended up committing suicide in June 1954 (although there is still some debate whether his death might be an accident).

Alan Turing’s legacy is vast and multifaceted. His life story is also a powerful reminder of the consequences of prejudice. As Pride Month comes to a close, it reminds all of us of our responsibility to build, protect and defend an inclusive work culture in our organizations, empowering talented minds like Turing to thrive regardless of our respective gender identities and sexual orientations.

The process of screening natural persons, legal entities, and transactions applies in a variety of AML contexts—including adhering to sanctions and identifying adverse media and politically exposed persons (PEPs). It’s integral to a satisfactory AML and sanctions program, but rife with errors, backlogs, improper decisioning, and outsized costs, and increasingly difficult for financial institutions to manage properly. These institutions are struggling to bring more efficiencies and better risk management to their screening systems, understanding that throwing human resources at the problem is not the answer.

The benefits of employing modern artificial intelligence (AI)-based technology to enhance screening processes are compelling, including retrieving relevant information, executing researches, analyzing data, making initial decisions on alerts, and generating and publishing detailed reports and an audit trail. Of note is that AI can be used to derive a very curated matching logic, one that’s far more advanced than the fuzzy logic method that’s been used for many years and allows for a better ranking of the probability that a match is a real one.

But how do institutions identify the modern, innovative tools that are best to enhance their screening systems, and then acquire and implement that technology in a seamless manner that doesn’t create additional risk? To help understand and address the challenges and opportunities in the screening arena, Capgemini, together with its partners Hummingbird and WorkFusion, hosted at the Harvard Club in New York City on June 20 an industry roundtable that included senior representatives from a range of financial institutions.

“It was a frank, valuable exchange of views. I was so impressed by the thoughtfulness and candor of these industry leaders in sharing information about the challenges they’re facing regarding their screening processes.”

Supriyo Guha, Senior Director & FCC Practice Lead, Capgemini

In the initial portion of the roundtable, the key issues related to existing screening processes were reviewed. They include:

• The frequency of updates to the OFAC and other sanctions lists;
• The strict liability associated with OFAC violations;
• The complexity of recent activity-based Russia sanctions;
• Dealing with various regulatory obligations and supplementary considerations across geographies;
• Not having sufficient information on counterparties;
• Budgeting and staffing constraints;
• The extremely high level of false hits;
• The huge amount of “noise” (i.e., insufficient or immaterial data – a particular problem with adverse media monitoring and dual use goods) that surrounds alerts.

Several participants mentioned the significant operational challenges in the trade finance space alone, including manual inputs from letters of credit and other documents resulting in numerous errors, and the lack of ability to identify and analyze key informational items in an efficient fashion. The attendees also noted that, in addition to sanctions, the handling of export controls poses an increasingly larger burden.

The discussion then moved to how financial institutions use, or plan to use, AI-based tools. It was pointed out that currently, AI intervention is applied primarily on the alert adjudication side and not to up-front screening, where it’s also needed. AI also is being looked to address the problem of duplication of due diligence reviews and to promote better sharing of information among teams and throughout the organization.

The group discussed how human productivity can be enhanced by AI, given that the traditional performance of screening reviews and alerts is an arduous process that, over time, can wear down and demoralize human analysts.

“Enabling individuals to collaborate with an AI-based system frees them from performing menial tasks such as copying, pasting, and data gathering and review, allowing them to work on higher-value investigations and, thus, be used in a more productive, strategic way.”

Art Mueller, Vice President—Financial Crime, Banking, and Financial Services, WorkFusion

Indeed, several organizations have replaced first level human review of screening alert hits with purpose-built algorithms for screening names and payments and analyzing them in real time. In complex situations, there’s a hand off to a human analyst, with alert review and transaction history provided in one place for efficient and easy review.

Finally, the conversation turned to lessons learned as to how best to introduce and implement modern AI-based tools. The process of incorporating AI into a screening system includes a number of steps, such as model selection, training, testing, and validation; integration of models with existing systems; user training and adoption; and ensuring continued compliance with all applicable laws and regulations. As with any AI system, a huge consideration is ensuring comprehensive, quality data. Data accessibility, sourcing, consistency, privacy, and security all are critical, along with integrating end-to-end workflows to allow for a seamless stream of information.

Implementation challenges identified by the roundtable participants included model governance, ensuring the ability to trace where the large language model is receiving its information from, resistance to change, skills gaps, legacy system compatibility, data security concerns, and user training and adoption.

Given the highly regulated nature of the financial industry, another key consideration is ensuring regulatory acceptance.

“The good news is that financial regulators globally have, in recent years, embraced AI-driven innovation as an appropriate if not necessary development in addressing financial crime.”

Joe Robinson, Co-founder & CEO, Hummingbird

In this regard, important aspects to regulatory acceptance were identified, including ensuring explainability, transparency, accountability, and proper model risk and data management.

In sum, the benefits of employing modern AI-based tools to enhance screening processes are compelling, and have been embraced by financial industry regulators. However, implementing these tools presents challenges that require careful planning, internal support, collaboration between IT and business units, attention to regulatory imperatives, and a strategic approach to ensure a smooth integration.

The continued advancement of digital assets, including cryptocurrencies, security tokens, and other blockchain derivatives, is building a new era in the capital markets. This shift extends beyond trading, by revolutionizing the logging, clearing, and settling of transactions within the post-trade sphere. As digital assets establish their presence, they reveal significant challenges and unique opportunities. These developments have the potential to reshape the financial services industry.

Streamlining operations and mitigating risks

Digital assets expedite and streamline transaction processes far beyond the capabilities of traditional financial tools. Underpinned by blockchain technology, they facilitate transactions that are not only faster but also settle in real time, paving the way for atomic settlement. This eliminates the need for the protracted settlement periods typical of legacy systems, thereby reducing counterparty risks and boosting market liquidity.

The revolutionary role of smart contracts

Smart contracts are a pivotal innovation in utilizing digital assets post-trade. Embedded directly into blockchain code, these contracts execute automatically, upheld by a decentralized network of computers via network-wide consensus. For example, in a bilateral trade, both parties must agree on the trade economics before the contract is considered upheld and the trade is written to the ledger. Smart contracts can automate the complex and labor-intensive tasks of post-trade operations, from compliance verification to dividend issuance and managing corporate actions. This automation potential may significantly reduce operational costs and curtail human error, streamlining the entire post-trade process.

Navigating the integration minefield

Yet, for all their advantages, digital assets present formidable integration challenges within the traditional capital markets framework. Regulatory clarity is still, at best, a work in progress globally, as authorities grapple with appropriate frameworks to govern these digital assets. Moreover, the existing technological infrastructures of conventional financial institutions often require extensive overhauls to accommodate blockchain transactions, necessitating significant investments in new technology and workforce retraining.

Evolving regulations

As the impact of digital assets becomes more apparent within financial markets, regulators are under pressure to evolve existing legislation to include these innovations. The trajectory of these evolving regulations will critically shape the digital asset landscape within capital markets. Clear, consistent regulatory directives are vital to balancing fostering innovation, ensuring market stability, and safeguarding investor interests.

The path ahead

The impact of digital assets on the post-trade sector signals a pivotal transformation in capital market operations. Though the journey ahead is fraught with regulatory, technological, and operational complexities, the promise of enhanced efficiency, reduced costs, and bolstered security presents a compelling case for broader adoption of digital assets. As the market landscape adapts, stakeholders must remain flexible, leveraging new technologies and adapting to emerging paradigms to stay competitive in this evolving arena.

2024 is the year for scaling AI

The Capgemini RAISE™ framework signifies a new era of advancement and evolution for generative AI. It embraces the fast-evolving and highly experimental development of the technology and adapts to the rapid pace of innovation. RAISE delivers accelerators and learnings to harness the power of AI and generative AI while focusing on sustainability, scalability, and trustworthiness.

Generative AI has emerged as a groundbreaking force, democratizing innovation across industries. Open source and commercial models alike have become widely available, leveling the playing field for those eager to harness their potential.

However, as approachable as generative AI may seem, navigating its complexities is no small feat. Within just a year, the field has seen seismic shifts in paradigms and underlying technologies. Organizations are exploring generative AI, recognizing its value as a catalyst for innovation and revenue growth. The Capgemini Research Institute underscores this trend, revealing that nearly 90% of organizations plan to prioritize AI, including generative AI, in the next 12 to 18 months. The question is: are organizations ready to transition from mere exploration to achieving tangible results?

Generative AI generates new content, ideas, or solutions by learning from vast datasets. This extends from creating realistic images and text to generating code and innovative solutions across various fields.

As generative AI solutions are being constructed, decoupled development has led to redundancy and inefficiency. This disconnected approach gives rise to multiple issues: identical open-source models running on separate GPUs, increasing costs and complexity; commercial APIs used in disparate applications, preventing better vendor deals due to split volumes; and the repeated development of similar applications without performance comparison or monitoring.

The year 2023 was a time for experimentation; 2024 is the year for scaling. To address these challenges and herald a new era of development, we crafted the Capgemini RAISE™ framework to enforce sustainability, reusability, and trustworthiness throughout the code, establishing a robust AI partnership with our clients. The Capgemini RAISE™ framework streamlines the development process, ensuring that generative AI solutions are built on a solid, efficient, and cohesive infrastructure.

Exploring the Capgemini RAISE™ framework

Capgemini RAISE™ is a gateway to a future of trustworthy, scalable, and sustainable AI. While many companies concentrate on solutions, Capgemini RAISE™ shifts the focus to infrastructure – the foundation of both the service and solution layers.

Capgemini RAISE™ is built on modularization. It promotes the development and deployment of reusable components as independent services, covering an array of AI and generative AI models, tools, and data services. This includes both commercial APIs and open-source models.

At the core of the Capgemini RAISE™ framework is a uniform pipeline structure, ensuring cohesion and efficiency throughout development and deployment. It emphasizes efficient deployment of open-source models, leveraging shared GPUs for optimal resource utilization, minimizing environmental impact, and maximizing performance. Its unified management system facilitates easy comparison, efficient deployment, and thorough monitoring of both open-source models and commercial APIs, improving scalability and enabling cost savings as new models emerge.

Adaptability and experimentation are critical, as is embracing a diverse mix of technologies and staying open to future changes. As H. James Harrington noted, “Measurement is the first step that leads to control and eventually to improvement,” highlighting the importance of enforced evaluation. Such processes not only enable rapid comparison of changes in the experiment stage but also ensure smooth transitions to new ideas and models.

Capgemini RAISE™ continually identifies and builds reusable components to enhance trust, efficiency, and performance in AI and generative AI applications. Its current offerings include model cascading for cost savings, prompt optimization for performance improvement, and RAG services for scaling enterprise text retrieving service. The framework is committed to evolving and refining its services, empowering its users to embrace ultramodern technology.

Capgemini RAISE™ provides a path towards:

• Trustworthy AI, through rigorous evaluation, testing, and monitoring
• Scalable AI, by embracing modularization, DataOps, MLOps, DevOps, and governance
• Sustainable AI, focusing on cost optimization, reusability, and efficiency.

Pioneering the future

The Capgemini RAISE™ framework is setting the pace towards a future where AI’s potential is fully unleashed. This next phase in Capgemini RAISE™’s evolution is pioneering tomorrow’s innovations.

Innovative deployment and customization. The future of Capgemini RAISE™ is marked by an even greater emphasis on customization and efficiency. Leveraging the latest advancements in large language models (LLMs), Capgemini RAISE™ is poised to offer an even more refined infrastructure setup. This includes specialized pipelines for deployment, fine-tuning, and data management, designed to streamline the AI development lifecycle from conception to deployment.

Tailored data and model training. A standout feature for Capgemini RAISE™ is its enhanced capability for organizations to craft their own high-quality datasets for training or evaluation purposes. This ensures the data meets the specific needs of each project and also elevates the quality of model training. Coupled with the Capgemini RAISE™ training framework, organizations will have the flexibility to develop custom models, pushing the boundaries of what’s possible with generative AI.

Cost-effective model selection. A novel aspect of the RAISE framework is its intelligent model selection service, designed to optimize resource allocation by matching the most suitable model to each task. This reduces costs and amplifies the effectiveness of AI initiatives.

Leading the way into tomorrow

Capgemini RAISE™ is pioneering innovative solutions that address today’s challenges and anticipate tomorrow’s opportunities. As large models advance in capability, emerging agents face the challenge of dynamically decomposing tasks and choosing the right tools for completion. Capgemini RAISE™ provides these agents with an ever-growing toolbox, incorporating a comprehensive catalog of information and standardized endpoints.

We invite you to reach out. Together, let’s shape the future of AI, leading the charge into uncharted territories of possibility and success.

INNOVATION TAKEAWAYS

MODULARIZATION FOR EFFICIENCY: Break down AI development into reusable components with RAISE, optimizing resources and streamlining processes for enhanced efficiency.

TRUST AND SCALABILITY: Ensure trustworthiness and scalability in AI solutions with RAISE’s evaluation, testing, and monitoring mechanisms.

FUTURE-PROOF INNOVATION: Stay ahead of the curve with RAISE’s commitment to adaptability and customization, empowering organizations to pioneer tomorrow’s AI solutions.

Interesting read?

Capgemini’s Innovation publication, Data-powered Innovation Review | Wave 8 features contribution from leading experts from Capgemini and esteemed partners like Dassault Systèmes, Neo4j, and The Open Group. Delve into a myriad of topics on the concept of virtual twins, climate tech, and a compelling update from our ‘Gen Garage’ Labs, highlighting how data fosters sustainability, diversity, and inclusivity. Embark on a voyage of innovation today. Find all previous Waves here.

Today’s businesses are experiencing a dynamic shift as they eagerly strive to embrace generative AI to stay competitive in an ever-evolving market

Generative AI (Gen AI) is the subset of AI that focuses on the creation of new content, such as text, images, video, audio, and software code autonomously using sophisticated machine learning models called deep learning models. Though traditional AI works through machine learning to complete tasks and learn or make decisions independently, it cannot create new information.

The future of supply chains, fueled by generative AI, will transcend traditional optimization and enter a realm of unprecedented adaptability and resilience. Gen AI’s unique ability to create, simulate, and predict will revolutionize areas like demand forecasting, where it can generate nuanced scenarios based on vast datasets and real-time signals.

Gen AI will be instrumental in achieving self-healing supply chains. While traditional AI can analyze data and identify patterns to predict potential disruptions, Gen AI takes it a step further. It can create and simulate a wide range of scenarios, enabling the supply chain to proactively identify vulnerabilities and generate innovative solutions to mitigate risks before they materialize. Gen AI’s ability to create novel solutions and adapt to changing circumstances is crucial for achieving true self-healing capabilities in supply chains.

Where Gen AI departs from traditional AI

Imagine a supply chain where traditional AI acts like a vigilant guard, constantly scanning for potential threats. It might identify a potential delay in a shipment due to weather conditions, raising an alarm. Gen AI, on the other hand, is a resourceful problem solver. It takes that alarm and springs into action, not just identifying the problem but also brainstorming and generating a multitude of potential solutions. It might propose rerouting the shipment, reallocating resources to expedite other deliveries, or even proactively contacting alternative suppliers to ensure seamless fulfillment. In essence, Gen AI transforms the supply chain from a reactive system to a proactive one, where problems are not just identified but also actively and creatively solved before they disrupt operations.

Currently, 30% of supply chain leaders actively plan to deploy generative AI for supply chain in the next six months.^[i] The projection for increased adoption is driven by Gen AI’s ability to significantly reduce costs, improve efficiency, harness complex data, and increase revenue within business units that deploy the technology. Moreover, a recent Gartner survey of 127 supply chain leaders found that ‘Chief Supply Chain Officers (CSCOs) are dedicating 5.8% of their budget to Gen AI in 2024.’ The mass adoption of this emerging technology is further validated in the same report, with the finding that only ‘2% of respondents say they have no plans to leverage Gen AI.’^[ii]

Capgemini Invent recognizes the vast potential of Gen AI to revolutionize supply chains. While numerous opportunities exist, this perspective focuses on two key areas: scenario modeling and demand planning. In future communications, we will explore additional applications of this transformative technology. Additionally, we will highlight how with the right approach, businesses can unlock and scale the benefits of Gen AI.

“In the world of generative AI, supply chains will evolve from reactive systems to proactive networks, anticipating needs, optimizing resources, and seamlessly adapting to changes in real time.”

Phil Davies – Global Supply Chain leader, Capgemini Invent

Supply chain scenario modeling: a strategic perspective

In an era of unprecedented disruptions, supply chains are under immense pressure. Natural disasters, geopolitical events, pandemics, and even minor internal operational hiccups can trigger cascading effects that lead to production delays, shortages, and financial losses. In the urgent search for solutions, many business leaders are beginning to explore Gen AI’s capabilities as a means to tackle this issue.

The cost of not managing such disruptive supply chain risks effectively is immense, as evidenced by the billions of dollars lost in recent years. A recent Gartner study revealed that ‘75% of supply chain leaders expect an increase in high-impact disruptions compared to the rate of disruptions over the past 5 years.’^[iii] This highlights the urgent need for innovative solutions. For many organizations, Gen AI has proven to be invaluable in this regard.

Gen AI doesn’t just predict the future; it creates it!

Gen AI: the game-changer in scenario modeling

Scenario modeling is a crucial capability of the supply chain, enabling companies to simulate various risk scenarios and prepare appropriate responses. There is a potential for Gen AI to start leveraging models to simulate potential disruptions, companies can gain valuable insights and develop effective contingency plans.

Generative AI in supply chains can revolutionize scenario modeling by creating diverse, novel scenarios beyond historical data, analyzing unstructured sources, such as news and social media. By synergistically combining Gen AI’s generative capabilities with existing AI/ML techniques, supply chain scenario modeling can achieve a new level of sophistication. This powerful combination enables organizations to anticipate disruptions, explore innovative strategies, and make more informed, data-driven decisions, ultimately leading to improved efficiency, resilience, and adaptability in the face of ever-changing market conditions.

Gen AI will exclusively build upon the existing AI and machine learning (ML) capabilities in supply chain scenario modeling to create a more powerful and comprehensive approach. While reinforcement learning (RL), simulation modeling, and agent-based learning provide the foundation for optimizing decision-making, Gen AI is capable of so much more. Unlike traditional AI, Gen AI’s ability to process vast complex data and generate insights facilitates more comprehensive and proactive risk management in today’s supply chains. By modeling the impact of everything from equipment failures and labor shortages to geopolitical events, natural disasters, and cyberattacks, businesses can gain a deeper understanding of their vulnerabilities and develop proactive mitigation strategies.

Gen AI will democratize scenario modeling, enabling leadership to run simulations directly through large language models (LLMs), reducing the need for specialized modelers and tech developers.

The future is what we make it

Despite being its most prominent capability, this transformative technology can unlock benefits far beyond scenario modeling. In fact, Gen AI does not just predict the future; it creates it. Gen AI enables companies to stress-test their supply chains in a virtual environment, experimenting with different scenarios and responses to find the most effective solutions. This ability to “play out” potential disruptions before they occur provides a level of preparedness and resilience that was previously unattainable.

Companies that embrace Gen AI-powered scenario modeling can gain a significant competitive edge. They can achieve the following outcomes:

• Anticipate disruptions: By simulating potential risks, companies can identify weaknesses in their supply chains and take preemptive action to mitigate them.
• Optimize operations: Scenario modeling can help companies optimize inventory levels, allocate resources more effectively, and design more resilient transportation networks.
• Respond faster to crises: When disruptions do occur, companies with Gen AI models can quickly assess the situation, generate alternative scenarios, and choose the best course of action.
• Innovate and adapt: By continuously learning and adapting to new data, Gen AI models can help companies stay ahead of the curve and respond to changing market conditions.

Clearly, there are great potential benefits to integrating Gen AI for scenario and risk modelling!

Demand planning: Bridging the Gaps

Demand planning is a critical component of supply chain management that involves forecasting to ensure a company can meet future customer demand. The future of demand planning is not just about better predictions – it’s about unlocking a new level of understanding. Gen AI is not replacing current AI and ML models; it’s supercharging them. Imagine a demand planning system that does not just crunch numbers, but grasps the nuances of market shifts, consumer behavior, and global events. This is the promise of Gen AI. By synthesizing vast amounts of structured and unstructured data, it creates a dynamic, real-time picture of demand. This is not just about accuracy; it is about adaptability. Supply chains become agile, responding to disruptions with foresight, not hindsight. It is about empowering planners with insights that go beyond numbers, enabling them to make strategic decisions that drive growth and resilience.

These qualities set Gen AI apart from traditional AI capabilities that cannot generate new data or content and are restricted to analyzing existing datasets only. This enables planners to experiment with different scenarios to stress-test their supply chains and formulate optimal strategies for a variety of market conditions.

In today’s market, we are seeing Gen AI-powered demand forecasting systems improve forecast accuracy by up to 10% already by using Gen AI to predict and optimize the forecast. It incorporates market data and signals in addition to historical order and shipment information, employing a library of probabilistic and deep learning models to identify accuracy and reduce bias across product, geography, and time hierarchies.^[iv]

Gen AI presents a powerful opportunity for supply chains leaders to empower their demand planners, who can in turn guard against uncertainty and transform their supply chains in ways that will ensure they thrive despite uncertainty.

Scaling and readiness for Gen AI

As part of the Gen AI revolution, a multitude of use cases have been identified where the technology can potentially drive or unlock significant business value. However, recent research highlights that more than 85% of Proof-of-Concepts (PoCs) for Gen AI have failed to move to production. Therefore, it is crucial to thoroughly assess their feasibility, ascertain their true value, and evaluate their scalability within an organization to fully realize the associated benefits.^[v]

To support this, businesses should look to establish processes and create a shift in mindsets whereby use cases can move beyond ideation to be fully assessed, tested, deployed, and adopted to deliver value. Leaders must move away from traditional, reactive risk management approaches and embrace a proactive, data-driven mindset. Based on our experience, the following guidelines are provided as the core pillars to enable successful Gen AI in supply chain deployment.

GenAI business readiness

• Gen AI Strategizing: organizing dedicated workshops to clearly define your Gen AI strategy and identify use cases that will provide the most benefits.
• Build the right toolkit: Leverage frameworks and toolkits to develop Gen AI solutions from proof of concept to enterprise ready.
• Benefits at scale: Implement the right operating model, people, processes, technology, risk management, and controls to safely scale across the organization.

Finally, leaders in organizations will play a pivotal role in driving the adoption and implementation of Gen AI in supply chain scenario modelling. To succeed, leaders must prioritize the following actions: Championing change, defining a clear vision, and facilitating continuous improvement.

Final thoughts: The future of supply chain management

Whilst addressing two of the many supply chain opportunities in this report, it is clear that Gen AI will make an impact across the End-to End (E2E) chain. With that in mind, here are some parting considerations:

• The future of supply chain management belongs to those who are willing to embrace change and harness the power of AI and Gen AI.
• This is not just a technological challenge; it’s a leadership challenge. The companies that succeed will be those that have the vision, courage, and agility to transform their supply chains for the emerging age of AI.
• This is not just another tech trend; it’s a paradigm shift. Generative AI is democratizing access to sophisticated risk modeling capabilities, enabling even smaller companies to compete on a global scale with unprecedented resilience.

The future of supply chain management is here, and it is powered by AI and Gen AI. Leaders must seize this opportunity to transform their supply chains, ensuring they are not only resilient but also capable of thriving in an increasingly unpredictable world.

Capgemini has multiple frameworks and project blueprints to support and accelerate the development, deployment, and operation of Gen AI within supply chain. Contact our experts to learn more: