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:

Neither of us woke up one-day thinking, ‘I want to work in aerospace and defense.’ Yet here we are, passionate advocates for an industry we stumbled into almost by accident. 

One of us started in law, consulting, and even oil and gas. The other moved from a background in telecommunications to A&D. Our journeys, though, share a common thread: the desire to be part of something bigger, something that matters. 

We often forget that idea when crafting the job descriptions, we hope will attract the best and the brightest. We expect candidates to already be passionate about our industry without telling them why they should be.

The problem isn’t the work – it’s how we talk about it. We need to show that A&D isn’t just about hardware; it’s about solving humanity’s biggest challenges.  

An industry at a crossroads 

While so many of us have discovered the hidden gem that is A&D, the industry itself is grappling with a less glittering reality.  

The A&D industry isn’t exactly known for its agility. Don’t get us wrong: aerospace is cutting-edge stuff, but many companies are held back by long development cycles, heavy regulation, and a focus on traditional hardware production. Unlike rapidly evolving sectors like software and services, A&D companies often struggle to integrate the latest innovations quickly. 

This creates a widening chasm between the industry’s talent needs and its ability to attract top candidates. Today’s emerging professionals seek dynamic environments where they can engage with cutting-edge technologies and make a tangible impact. A&D offers these opportunities in abundance yet struggles to communicate this reality to potential recruits effectively. 

The result is a talent pool that often overlooks A&D in favor of industries perceived as more innovative, despite our sector’s critical role in shaping the future. 

Global conflicts and shifting budgets only compound these challenges, highlighting an urgent need for skilled professionals in a rapidly changing landscape. 

But how do we bridge this gap? The answer may lie in one of our most basic, yet overlooked tools: our job postings. 

Our job postings don’t tell the whole story 

Imagine you’re a young professional browsing job posting, seeking a role where you can make a meaningful impact. You come across an A&D listing. Instead of being inspired, you see a dense block of text filled with KPIs, technical jargon, and rigid requirements. It’s hardly the motivational call to action you were hoping for.

The way jobs are advertised doesn’t promote the characteristics of the roles that would attract diverse candidates. Job ads need to evolve to reflect the broader impact and purpose of the roles. Rather than listing qualifications and responsibilities in dry, technical terms, job ads should tell a compelling story about the difference one can make in the industry.  

For example, instead of saying, “Requires proficiency in XYZ software and 5+ years of experience,” a job ad could say, “Join our team and use cutting-edge technology to solve complex problems that safeguard our nation and improve lives. Your expertise in XYZ software will directly contribute to innovations in defense systems that protect millions.” This approach doesn’t discard the important technical requirements but places them within an inspiring narrative. 

By showcasing how roles in A&D contribute to national security, technological advancement, and societal well-being, we can attract individuals motivated by purpose and impact while still ensuring we recruit candidates with the necessary technical skills and experience. 

Crafting new narratives  

Addressing the PR challenge 

Our industry faces a significant PR challenge amidst ongoing geopolitical conflicts and debates around defense spending. It’s no surprise some of the younger generation is hesitant to make the jump. The ethical considerations are complex, and the public perception of our work can be polarizing.  

Yet, beyond these contentious issues, our industry is at the forefront of technological advancement, from developing sustainable aviation solutions to pioneering space exploration.  

The issue isn’t that we’re behind the times – it’s that we’re not telling our story well enough. We need to shine a spotlight on the diverse, impactful work happening across A&D. This includes: 

• Highlighting roles beyond traditional engineering, such as those in environmental sustainability, data science, and digital transformation. 

• Showcasing our commitment to solving complex global challenges, from climate change to national security. 

• Emphasizing the opportunity for individuals to work on projects with far-reaching societal impact. 

• Illustrating the collaborative nature of our work, where diverse teams come together to tackle multifaceted problems. This collaboration extends to both private and public sectors. We also engage in international cooperation, working with allies and partners around the world to drive innovation and address global challenges. 

By refocusing our PR efforts, we can attract individuals who are passionate about making a difference.  

Practical steps for effective storytelling 

Effective storytelling can transform how potential recruits perceive the A&D industry. Here are some practical steps for achieving this: 

• Showcase real-world impact: Move beyond listing technical requirements. Instead, vividly illustrate how roles contribute to addressing critical global challenges. Highlight projects that have tangible effects on national security, space exploration, or environmental sustainability. 

• Appeal to purpose-driven candidates: The next generation of professionals seeks meaningful work. Demonstrate how A&D roles offer opportunities to tackle significant issues, from advancing clean energy technologies to pioneering breakthroughs in artificial intelligence for humanitarian applications. 

• Emphasize innovation and cutting-edge technology: Showcase the industry’s role at the forefront of technological advancement. Describe how A&D professionals work with state-of-the-art tools and technologies, often years ahead of commercial applications. 

• Highlight career growth and diverse opportunities: Illustrate the vast array of career paths within A&D. From engineering to cybersecurity, from project management to research and development, emphasizing the potential for diverse and evolving career trajectories. 

• Share collaborative achievements: Share stories of diverse teams solving complex problems. For instance, describe how interdisciplinary groups develop new sustainable aviation technologies or digital defense systems, showcasing both innovation and collaboration. 

• Connect to global impact: Frame roles within the context of global challenges and opportunities. Whether it’s contributing to climate change mitigation through more efficient aircraft design or enhancing global connectivity through satellite technology, emphasize the far-reaching implications of A&D work. 

Purpose-driven talent: The lifeblood of A&D’s future 

The aerospace and defense industry isn’t just competing for contracts; we’re in a global race for talent. Our future hinges on our ability to attract, retain, and nurture the brightest minds from diverse backgrounds. 

Without effectively communicating our story, we risk losing the next generation of innovators to industries that have mastered the art of self-promotion. Software companies, tech giants, and even gaming industries have captured the imagination of young talent with narratives of innovation, impact, and exciting work environments. 

Yet, the work we do in A&D is critical to global security, technological advancement, and societal progress. If we don’t get that point across, we risk having impressive technology but no one to implement the critical tasks of defending, building, and connecting the nations.  

The question then becomes: How do we craft a narrative that makes the next generation of talent say, “That’s where I want to be”?  

Learn more about Capgemini Aerospace and Defense: 

Digital Continuity in Aerospace 

Digital Twins in Aerospace and Defense 

Intelligent Supply Chain for the Aerospace and Defense Industry  

Lifecycle Optimization for Aerospace and Defense 

The Rise of Total User Experience

In the rapidly evolving world of enterprise service management, the expectations of enterprise users have undergone a significant transformation. The stark reality is that 94% of a website’s first impressions are tied directly to its design, with a preference for simplicity expressed by 41% of customers. The concept of Total User Experience (TUX) has emerged as a pivotal focus area, especially for tools like ServiceNow that are at the forefront of this change. Investing in user experience design is crucial, with studies showing that every dollar invested in UX results in a $100 return, a staggering 9,900% ROI. The modern user demands not just a functionality but an integrated, intuitive, and seamless experience that caters to their every need within a single platform. This shift represents both a challenge and an opportunity for enterprise platforms to redefine service management. 

Leaping from isolated tools to seamless integration 

Gone are the days when employees were content to navigate through multiple portals to accomplish their tasks. Today, digital transformation has set the stage for a new era where simplicity, efficiency, and convenience are not just preferred but expected. Speaking of convenience, the digital age demands mobile optimization, with 57% of customers avoiding businesses that lack a well-designed mobile site. For ServiceNow and similar platforms, this statistic highlights the urgency of ensuring mobile-friendly interfaces, as 50% of users will abandon a mobile-unfriendly site, even if they are fond of the business. 

One-stop shop for enterprise needs 

Today’s enterprise users are looking for more than just a collection of unrelated tools and platforms; they seek a cohesive, integrated experience that caters to a wide array of needs within a single, accessible interface. This underscores the necessity for platforms like ServiceNow to prioritize design that is not only appealing but straightforward, as 94% of negative feedback is design-related, highlighting the critical role of aesthetics and functionality in user satisfaction.

Raising the bar for seamless enterprise experiences 

Modern enterprise users, accustomed to the seamless experiences offered by consumer applications and platforms, now demand similar standards in their professional tools. They anticipate an ecosystem where diverse functionalities are not only available but are interconnected in an intuitive manner. They seek comprehensive IT service management, not limited to incident reporting but encompassing proactive solutions, predictive maintenance, and tailored IT advice, all within an accessible dashboard. This expectation extends to integrated human resources services, simplifying a range of processes like leave management, performance reviews, and training program participation, without the hassle of juggling multiple systems or paperwork. Additionally, the platform is expected to offer seamless facility and asset management, enabling easy reservations, issue reporting, and real-time tracking of company cars, meeting rooms, and other assets. Users also desire streamlined financial and administrative tasks, including expense reporting, reimbursements, and budget management, all achievable through this singular, integrated portal. This holistic approach aims to eliminate the need for multiple logins and interfaces, offering a comprehensive, frictionless experience across various organizational needs. 

Going a few steps beyond traditional needs 

The one-stop shop model’s ambition goes beyond just streamlining traditional enterprise operations; it envisions a comprehensive platform that encompasses wellness and work-life balance through features supporting mental health resources, wellness programs, and community engagement, all without navigating away from the platform. It aims to promote professional growth with customized learning paths, offering access to a wide array of online courses and opportunities for further education aligned with individual career objectives. Moreover, it seeks to foster a collaborative environment by integrating social tools that facilitate knowledge sharing, project management, and enhanced team interaction. 

Building platforms users love 

Integrating diverse functionalities into a single, user-friendly platform presents a complex challenge for providers like ServiceNow. However, this also opens up unparalleled opportunities to innovate and deliver a superior user experience that sets them apart from the competition. The key lies in developing a transparent yet multifunctional layout that caters to the varied needs of users.

ServiceNow as a pioneer in total user experience 

ServiceNow has been at the forefront of this transformation, reimagining how services are delivered within enterprises. By offering a cohesive platform that addresses the full spectrum of user needs, ServiceNow exemplifies how innovative solutions can meet the high expectations of today’s users, showcasing success stories that highlight the platform’s impact on enhancing enterprise service management. 

Innovations that are changing the game 

Looking ahead, the landscape of user experience is poised for transformation through several key trends. ServiceNow is expected to embrace anticipatory design, utilizing data analytics to predict and meet user needs proactively, thereby boosting efficiency and user satisfaction. A growing emphasis on inclusivity will see the integration of accessibility features, such as screen readers and high-contrast modes, ensuring compliance with standards like the European Accessibility Act and broadening user accessibility. Ethical design principles will play a crucial role in safeguarding privacy and data security, building user trust. Enhancements in user interaction will come through engaging micro-interactions and the adoption of dark mode as a default setting to enhance user comfort. The introduction of 3D designs and immersive AR/VR technologies promises more dynamic and engaging experiences, merging digital with physical realities. The role of UX designers is set to evolve, focusing on creating comprehensive experiences that go beyond the screen. Lastly, a continued shift towards simplified and minimalist interfaces will aim to improve usability and reduce cognitive load, prioritizing essential functionalities. 

The unstoppable march toward a unified user experience 

The drive toward total user experience in enterprise service management is more than a trend; it’s a fundamental shift in how users interact with digital platforms. ServiceNow’s commitment to evolving alongside the trends mentioned above not only showcases its role as a pioneer but also sets a benchmark for what’s possible in creating integrated, efficient, and enjoyable user experiences. As we move forward, the fusion of innovative design principles with technological advancements will continue to redefine the boundaries of enterprise service management, making it an exciting journey for users and providers alike. 

Quantum Computing has the potential to revolutionize many industries by solving complex challenges that classical computers cannot yet solve or that they struggle to solve (*), given the enormous computational requirements and the long time to get the needed results.  

It is important to note that not all challenges will require quantum technology. Quantum computing excels at specific types of calculations, such as optimization, cryptography, and simulating quantum systems. 

Quantum however requires revisiting current approaches to overcome limitations of current models and costly build-and-test cycles. This is the case for example in aerospace (aerodynamics), in energy transition (modelling and simulating energy storage materials, such as Li-ion batteries and fuel cells) or financial services (portfolio optimization). 

With quantum technology rapidly maturing and business benefits becoming clearer, industries now realize that quantum will play an undeniable role in solving todays and tomorrow’s specific challenges in the future.  

This quantum advantage will translate into a competitive advantage, which is why organizations are eager to embrace the quantum adoption paradox: for transformative projects that require quantum capabilities, organizations must now take explicit action towards becoming ‘quantum-ready’ in due time. This includes assessing their organization’s challenges that can be effectively addressed with quantum algorithms and identifying tasks where quantum can offer significant advantage over classical computing methods.  

As we embark organizations on this transformative journey, our preferred partnership with Pasqal, a quantum computing pioneer, is a key asset. Pasqal’s unique approach to constructing quantum processors using ordered neutral atoms in 2D and 3D arrays, a method that leverages Nobel Prize-winning technology, sets them apart in the quantum landscape. These neutral atom quantum processors offer a realistic pathway to near-term quantum advantages and provide exciting opportunity for enterprises to begin developing applications today, making them a game-changer in this technology. 

In 2023, we became a strategic investor in Pasqal through Capgemini Ventures’ CVC fund. Our investment and preferred partnership are built upon a solid collaboration since 2019 which crystallized in the EQUALITY consortium, as part of the EU’s Horizon Europe initiative. 

Capgemini quantum endeavor is fronted by our Quantum Lab focusing on applied quantum research and builds on our internal capabilities ranging from consulting to engineering as well as on interaction with our external partners such as Quantum. All this enables us to integrate world-class quantum hardware and software solutions into quantum-centric supercomputing stacks, giving enterprises a competitive advantage through an unparalleled quantum ecosystem. 

Together with Pasqal, we help organizations realize the full potential of quantum by taking quantum from R&D to production, focusing primarily on augmenting existing high-performance computing workflows. Capgemini’s role in this partnership is to provide strategic guidance and practical implementation support, accelerating the transformation and operationalizing our clients’ adoption of quantum technology.  

Our joint involvement with industry leaders like Airbus and the German Aerospace Center is a good illustration of the multiple exploratory use cases: they include battery and fuel cell design, airfoil aerodynamics, fluid dynamics, space mission optimization, materials design, multidisciplinary optimization as well as space data analysis. 

Our commitment to supporting organizations in adopting the quantum paradox towards achieving quantum value is unwavering. We truly believe that partnerships are the driving force behind innovation quantum adoption. The recently announced partnership between IBM and Pasqal is a significant step forward, and will no doubt contribute to the accelerated adoption of quantum technology. 

_{(*) The promise of unprecedented computing power to solve current intractable problems is a very attractive proposition for quantum computers. But these quantum computers also have the potential to represent a significant threat to the security of many cryptographic systems that we currently use. This issue, referred to as “Y2Q,” is anticipated to require one of the largest global migration programs affecting most of the information and communication systems since Y2K. Read more about this in our report Y2Q: A journey to quantum safe cryptography and in this article from the World Economic Forum.} 

Discover More about Pasqal and our partnership.

All debates around Gen AI made me realized that the fact that it is creativity that debate circles around (picture generation, texts, sounds, music), i.e. all that we regarded as “human” traits, and that would be the last ones to be automated.

Before that we would replace all “boring jobs”, all routine, etc. Now those jobs seem, in many cases, to be quite difficult actually.

Because they require context, common sense reasoning and often planning. Like cleaning your room. Like driving a car in Lima, Peru or Oslo. Like performing bureaucratic tasks which involves or have to adhere to laws, regulations, etc.

All this made AI to go from “technical” (give me your data and we build a model and run it, update it. full control in all processes) to something that involves language, human communication, creativity and thus sociology started to play a role. And psychology. And even geo-politics become part of the discussion.

Today I had the chance to discuss these topics at the core of Dutch politics, with Barbara Kathmann and Ufuk Esmer in the building of the “Tweede Kamer” (House of Representatives) in Den Haag. One thing we definitely agree on is that it is long overdue that there is a public debate on Artificial Intelligence, benefits, but also its place, its needs and its risks in society.

We will have a more in-depth debate tomorrow at our Capgemini Leidsche Rijn offices on this. Looking forward to it!

(And the state of the public digital services, ref picture from an information stand, really shows the need for a good vision and strategy on digital services).