Implementing a next-generation contact center solution can help insurers drive meaningful outcomes from client interaction data to deliver more meaningful, emotive, and connected relationships with their policyholders.

39% of insurance executives said they face hurdles in technology readiness on the journey to life insurance modernization, according to Capgemini’s Insurance Top Trends 2024. At the same time, policyholders face barriers related to product adoption, such as product complexity (39%), limited awareness (39%), and lack of trust (28%).

According to Capgemini’s World Life Insurance Report 2023, 33% of the world’s population will be aged over 50 by 2050. To overcome these barriers and retain relevancy and trust among older policyholders, insurers can leverage data from multiple sources to generate single views of their customers. They can also utilize advanced technologies such as cloud computing, advanced analytics, and AI to digitally empower their agents and interact with customers.

Speech analytics is one such technology for unraveling the potential of data to make informed decisions. But how can the analysis of speech drive business outcomes? And why does it hold such huge potential for the insurance industry?

Driving business outcomes from interactions data

Deriving business outcomes from sound is as much an art as it is a science. While harnessing cutting-edge speech analytics technology to unlock data is difficult enough, turning this data into meaningful business process outcomes represents a huge challenge for insurers.

Indeed, organizations often struggle to apply data purposefully. But when it’s built around analyzing recorded calls to gather information on the persona, behavior, and needs of the individual customer, extracting meaningful insights from data buried in client interactions can enrich customer experience and drive operational efficiency.

Leveraging AI-enabled speech analytics

Leveraging Generative AI in contact center interactions can help insurers enhance operational efficiency and nurture strong customer relationships. Speech analytics leverages conversational intelligence that uses natural language processing and machine learning to convert spoken words into text for gaining insights into customer sentiments and needs.

GenAI offers highly personalized interactions, superior customer experience, and heightened policyholder engagement and satisfaction – all while reducing operational costs. However, insurers must also grapple with the dual challenge of having to explain decisions based on GenAI, while also adhering to regulatory compliance in handling customer data and data privacy due to the sensitive nature of information within the insurance sector.

In short, implementing a next-generation contact center solution that leverages a persona-influenced service design, AI, and advanced analytics can help you drive more meaningful, emotive, and connected relationships with your policyholders.

To learn more about how Capgemini can help your insurance company unlock the true potential of data to drive a seamless, intelligent, and connected customer experience with your policyholders, contact: bhaskar.amit@capgemini.com or aneta.szporak@capgemini.com.

Generative AI can make recommendations that will transform decision-making for organizations – but how can people trust the answers Gen AI provides? Knowledge Graphs can play a vital role in ensuring the accuracy of Gen AI’s output, bolstering its reliability and effectiveness.

In Douglas Adams’ The Hitchhiker’s Guide to the Galaxy, a supercomputer called Deep Thought is asked for the answer to “Life, the universe, and everything.” After 7.5 million years, Deep Thought responds “42.” Representatives from the civilization that built Deep Thought immediately ask how it arrived at the answer, but the computer cannot tell them. When Adams wrote this scene in the 1970s, he was (arguably) making a joke – but today, many people find themselves in this situation when interacting with generative AI (Gen AI).

Gen AI works by drawing upon millions of pieces of data – a volume that’s impossible for humans to effectively analyze. Businesses are excited by its potential to deliver valuable insights and make well-informed predictions – but if different Gen AI tools are asked the same question and give different answers, how could an organization decide which result is more correct? How would a person fact-check the responses?

Addressing the shortcomings of unstructured, implicit data

The challenge relates to the Large Language Models Gen AI relies upon. An LLM can contain massive amounts of data, but it’s commonly stored in an unstructured, implicit manner. This makes it difficult to investigate how a Gen AI tool arrived at its answer.

Since the release of ChatGPT in late 2022, Neo4j and Capgemini have been working independently / collaborating to overcome this challenge by using Knowledge Graphs. These store complex, structured data and the relationships between them. Instead of relying solely on LLMs to directly generate database queries, our solution incorporates a high-level interface that allows the LLM to interact seamlessly with a Knowledge Graph via database query templates. These templates serve as structured frameworks, guiding the LLM to fill in specific parameters based on the user’s request. This simplifies the task for the LLM by abstracting away complex logic. (See Figure 1.) This separation of concerns ensures the LLM focuses on natural language understanding and generation, while the query templates handle the technical aspects of database interaction – improving the overall accuracy and efficiency of retrieval.

In this example, the query template uses a vector search to locate relevant nodes within the Knowledge Graph that correspond to the entities present in the user’s question. This identifies the nodes relevant to the query, which are then used to retrieve neighborhoods or shortest paths around the nodes within the graph. This helps contextualize the retrieved information and provides a more comprehensive answer to the user’s query. More information about this specific query template is available in this blog post.

Tailored templates

Query templates can be tailored to discrete domains such as finding dependencies within supply chains or executing aggregation operations for business intelligence purposes, enabling organizations to address specific challenges. This more targeted approach best leverages the LLM’s capabilities to generate insights by ensuring they are not only relevant but deeply informed by the underlying data structures, helping enterprises to efficiently transform their raw data into actionable intelligence.

That said, the complexity of business requirements often exceeds what a single query template can accommodate when an LLM interfaces with a Knowledge Graph. Therefore, it’s essential to embrace an adaptive approach, providing a rich assortment of query templates that can be selectively deployed to match specific business scenarios. Leveraging the LLM’s capability to invoke functions, Gen AI can dynamically select and employ multiple query templates based on the context of the user’s request or the specific task at hand. This results in a more nuanced and flexible interaction with the database, and significantly amplifies the LLM’s ability to solve intricate business intelligence and analytics problems. (See Figure 2.)

This LLM-powered movie agent uses several tools, orchestrated through carefully designed query templates, to interact with the Knowledge Graph.

• The information tool retrieves data about movies or individuals, ensuring the agent has access to the latest and most relevant information.
• The recommendation tool provides movie recommendations based on user preferences and input.
• The memory tool stores information about user preferences in the Knowledge Graph, allowing for a personalized experience over multiple interactions.

More information on this movie agent project can be found on GitHub.

“We expect Knowledge Graphs to help Large Language Models embrace iterative processes to improve their output.”

Democratizing data and empowering business users

The Knowledge Graph acts as a bridge, translating user intent into specific, actionable queries the LLM can execute with increased accuracy and reliability. By allowing any user – regardless of technical knowledge – to inspect how the LLM arrived at its answers, people can validate the information sources themselves. Benefits include:

• Results that are explainable, repeatable, and transparent. This can enhance trust in Gen AI in everything from research and discovery in life sciences to digital twins in sectors such as manufacturing, aerospace, and telecommunications.
• Better-informed and better trusted business decisions
• Freed up time for experts such as prompt engineers to concentrate on tasks that require their specialized skills.

As we look ahead, we expect Knowledge Graphs to help Large Language Models embrace iterative processes to improve their output. Our enthusiasm is shared by other experts in the field including Andrew Ng at DeepLearningAI, underscoring the widespread recognition of their transformative capabilities. As we help create the future, it’s clear the journey with these intelligent systems is only just beginning – and is moving much faster than Deep Thought ever did – so it’s critical that people are given the means to fact-check generative AI as it evolves.

INNOVATION TAKEAWAYS:

TRUST IS IMPORTANT – Knowledge Graphs can boost confidence in the output from Gen AI systems – making it easier for people and organizations to embrace them.

TOOLS FOR THE TOOL – With Knowledge Graphs, Large Language Models can dynamically employ multiple query templates to match specific business scenarios, making interactions with Gen AI more nuanced.

DEMOCRATIZING DATA – By making it easier for everyone in an organization to interact with generative AI, Knowledge Graphs can free up experts to focus on tasks that require their specific skills.

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.

New research from Capgemini’s Quantum Lab focuses on both industry challenges and quantum computing theories to define practical, scalable and cost-efficient applications for neutral atom quantum computers.

Neutral atom quantum computers are pushing the boundaries of quantum computing, allowing for more complex and higher-quality calculations. Exploring their potential and the most impactful use of this emerging tech is multifaceted and requires an understanding of its technological capabilities, industry-specific expertise, and a mastery of the quantum computing theory. Capgemini’s recent investment in Pasqal, a pioneer in quantum computing, known for creating innovative quantum processors using neutral atoms arranged in 2D and 3D structures, demonstrates the growing interest in the application of quantum computing to deliver clear business value. How do we move from the abstract and theoretical, and how exactly do we evaluate what may or may not be a good application?    

A good methodology to evaluate what good applications look like 

To evaluate whether an application is suitable for neutral atom quantum computers, one must consider the end-to-end algorithm, considering both classical and quantum calculations. For example, neutral atom quantum computers, when running in analog mode, solve a specific mathematical problem. That is to say that it finds a maximum independent set (MIS) [see definitions] in a unit disk graph (UDG) [see definitions]. Finding an MIS in a UDG means identifying the largest possible group of nodes within the graph that are not directly connected to each other by an edge, see Figure 1. The class of combinatorial optimization problems, however, is a lot bigger than just the class of MIS problems on a UDG. This means that not all combinatorial optimization problems can be solved by the neutral atom quantum computer natively. The problems must be mapped to a form that can be solved by the neutral atom quantum computer. This can add significant computational costs, which cannot be neglected but sometimes are. The result of cutting these costs can drastically favor the quantum computer, which is unfair. That is why end-to-end considerations must be made.  

Let’s look at an example as applied to banking. The mathematical problem of optimizing a portfolio of financial assets to minimize risk, called portfolio optimization, can be translated into a maximum clique problem [see definitions], which is equivalent to the MIS of the complement graph [see definitions], i.e., the graph where the nodes are connected if, and only if, they are not connected in the original graph. This complement graph is going to be very dense when starting from a sparse graph. In general, that means the complement graph is difficult to encode on a neutral atom quantum computer due to the physical constraints of a neutral atom quantum computer. The cost of mapping and scaling in comparison to classical systems, in addition to the overall integration of classical and quantum processes, are critical factors to assess. 

Within Capgemini’s Quantum Lab, we have begun two research projects to assess the suitability of neutral atom quantum computers for different applications. The first looks at how to map a problem so it can be run on a neutral atom quantum computer. As these computers can only solve a very specific class of problems – an MIS on a UDG – the challenge is to translate a business problem into an MIS on UDG in order to understand how commercial value can be derived. Several methods exist for this. One of which is a method published by QuEra that translates any MIS problem on a graph to an MIS problem that can be run on a neutral atom quantum computer.¹ The challenge with this approach is that it can lead to high overhead and only works if the quantum computer is able to find exact solutions. 

But do users always need exact solutions? Most of the time, the answer is no. A parcel delivery service does not need to know the exact best route. They just need a good route before the day starts. Even if there exists a route that improves the solution by 0.001%, that might not be worth spending hours or even days of compute power. 

That is why, in the second research project, we are considering the scaling of the quantum solutions compared to classical approximate optimization solvers. By using QUARK, an open-source benchmarking framework created by software engineers at BMW and an ever-growing community,² we will create random (unit-disk) graphs and let both the approximate classical algorithm and the (simulated) neutral atom quantum computer attempt to find large independent sets. Then, we measure the (estimated) runtime and quality of the solutions and extrapolate. The goal is to find structures of graphs where the classical methods suffer, but the quantum computer is still able to provide large independent sets.  

Finally, we will combine the two research projects and define a methodology to assess the potential of neutral atom quantum computers. We will focus on specific business problems, and we will consider the whole (end-to-end) algorithm. This includes translating it into a suitable format for the quantum computer, running the quantum algorithm, and interpreting and validating the results. We will compare the performance of a hybrid (quantum-classical) pipeline with a purely classical one. Factors such as accuracy, speed, scalability, and computational (classical) cost will all be considered. This will help us identify the domains and scenarios where neutral atom quantum computers can offer a significant advantage over classical methods. It will also reveal the challenges and limitations that need to be overcome to realize their full potential. 

Where to look for good applications 

So, where do we start to find these applications to assess? Assessment generally begins with either a technology or industry approach. Typically, quantum computing companies start with the technology because they focus first on what they know about quantum computers. Let’s take a deeper dive into this and look at three problem formulations: maximum independent set, graph coloring [see definitions], and minimum vertex cover. 

Using a technology approach to find applications 

Maximum independent set 

It is natural to start with the maximum independent set problem on the unit disk graph that’s been mentioned previously. The goal of the MIS problem is to find the largest set of nodes in a graph that have no connection to each other. An application of this mathematical formulation, for example, is finding the best placement for antennae so that their signals do not interfere with each other. The challenge, though, is to reframe other problems in a similar way. For example, one could try to formulate a traffic optimization issue as an MIS by creating an abstract graph where traffic routes are nodes that share an edge where the routes share a road. The challenge is that this very specific traffic problem is mapped to MIS-UDG, potentially creating high overhead results in the need for more and better qubits to find a solution. These extra qubits don’t contribute to solving a ‘larger’ problem; they are only necessary to map the problem. Furthermore, solutions to the mapped problem might not be actual solutions to the actual problem. This is especially true for the approximate solutions to the mapped problem.  So our challenge is to find formulations of business problems that fit MIS as close as possible. 

Sometimes, the problem does not require finding one set of nodes but many sets of nodes that are all independent of each other. An example of this would be planning in a production plant, where different tasks can happen at the same time, but not two tasks that require the same resource (e.g., a specialized machine). In this case, the solution to the planning problem is a collection of sets of nodes that don’t share edges. This is an example of graph coloring, wherein a specific color is assigned to all nodes, such that no two connected nodes have the same color as depicted in the figure above. 

To find a coloring with a neutral atom quantum computer, the problem once again has to be translated into an MIS problem. There are multiple ways to then solve the problem, but they mostly rely on the fact one color is an independent set and that the optimal coloring contains large independent sets. With a classical optimizer and the results from the quantum computer, a classical computer can find a “good” coloring with high probability.³ To reinforce this approach, Capgemini’s Quantum Lab built a demonstration that solves a planning problem using neutral atom computers: Link to Demo

Minimum vertex cover 

In some cases, you do not want all solutions to be independent from each other; rather, you want every node to be connected to at least one other node in a set. A good use case would be immunization, where not everyone needs to be inoculated to stop transmission. Instead, those who are not inoculated need to be surrounded by those who are immune, as seen in the figure from Wurtz et al.⁴. This is called a minimum vertex cover problem. Luckily, the problem is equivalent to the MIS-problem. The opposite of a maximum independent set is a minimum vertex cover. Thus, a neutral atom quantum computer can assist in finding a solution. A challenge now is that usually, the minimum vertex cover only becomes interesting in large graphs. For example, a healthcare organization developing an immunization strategy for a country would require a minimum vertex cover on a graph of millions of nodes. That is much larger than can be embedded on the neutral atom computers.  

The risk in taking a technology-approach, is that the focus in on the specific tool (quantum computing) may lead to proposing cumbersome algorithms, like in the traffic optimization example above. A mathematician might view ‘a constant overhead’ as a small problem, but in practice, this might make the quantum approach unfeasible. Furthermore, focusing on the technology may push to ignore other possible solutions through classical computing. 

Using an industry approach to find applications 

Good applications of neutral atom quantum computers can also be discovered by leveraging industry expertise, which is the most common approach for industry leaders. However, focusing solely on bespoke challenges within specific industries and then trying to fit in quantum technology as an afterthought could risk not taking ample account of the technological constraints. This is why ‘quantum Gen AI’, ‘massive parallel computing’, and ‘quantum big data’, make no sense from a technology perspective. 

A more refined industry approach in the context of neutral atom quantum computing is to look for optimization problems in an industry (e.g., known NP-hard problems) and see if there exists a way to transform them to MIS on UDG. However, this method also has a flaw, albeit more subtle and often overlooked, which is that there could be a structure in the problem that could be exploited by classical computers. This exploitation can result in a good (sometimes approximate) solution in sub-exponential time with a classical computer, which means that the problem is solved. In these cases, trying to tackle a problem with a quantum computer will almost certainly not result in the favorable performance for the quantum computer. While this might result in solving their problem, there might not be a quantum advantage.  

Capgemini’s Quantum Lab approach 

Given that the technology approach often overlooks the industry’s needs and that the industry approach often overlooks the technological constraints (of classical and quantum computers), there is a chasm between the two worlds. Capgemini’s Quantum Lab tries to bridge that gap by explicitly looking for classically hard problems where a solution will deliver value. Capgemini’s strong industry expertise and close relationship with industry leaders allows us to test our applications continuously.  

Closing 

In conclusion, the journey to finding good applications for neutral atom quantum computers is iterative and collaborative. It requires a deep dive into both industry challenges and quantum computing theories, with a focus on practicality, scalability, and cost-effectiveness. While the use cases are not fully known, ongoing research and collaboration between industry and quantum computing experts are vital for uncovering them. Establishing a robust ecosystem is crucial for the development and identification of practical quantum applications. The partnership between Capgemini and Pasqal exemplifies the commitment to this exploration and the promise it holds for the future of computing. 

Definitions

• Maximum independent set problem: The maximum independent set (MIS) problem is a combinatorial optimization problem that asks for the largest subset of nodes in a graph such that no two nodes in the subset are adjacent. This problem has applications in various domains, such as wireless network design, social network analysis, and bioinformatics. However, finding the maximum independent set of a large graph is computationally hard, and classical algorithms may take exponential time to solve it. Neutral atom quantum computers offer a potential speedup for this problem by exploiting the dynamics of the Rydberg blockade.
• Rydberg blockade: The Rydberg blockade is a quantum phenomenon that arises when atoms are excited to a highly excited state called a Rydberg state. In this state, the atoms have a very large electric dipole moment and experience a strong dipole-dipole interaction. This interaction creates an energy shift that depends on the interatomic distance and the angular momentum of the Rydberg state. As a result, two atoms cannot be excited to the same Rydberg state within a certain distance, which is called the blockade radius. Thus, the excitation of one atom to a Rydberg state blocks the excitation of nearby atoms. This effect can be exploited to create entanglement and implement quantum gates between atoms. 
• Unit disk graph: A unit disk graph (UDG) is a type of geometric graph where the nodes represent points in the plane, and two nodes are connected by an edge if and only if the distance between them is at most one. UDGs can be used to model wireless networks, where each node has a fixed transmission range of one unit. Neutral atom quantum computers can find MISs on a UDG by using the Rydberg blockade, because two atoms cannot be in the Rydberg state at the same time if they are close to each other. 
• Maximum clique problem: The problem of finding the largest subset of nodes in a graph that are all connected to each other. 
• Complement graph: A complement graph of a given graph is a graph that has the same set of nodes but the opposite set of edges. That is, two nodes are connected by an edge in the complement graph if and only if they are not connected by an edge in the original graph. 
• Graph coloring problem: A graph coloring problem is a combinatorial optimization problem that asks for the minimum number of colors needed to assign a color to each node of a graph such that no two adjacent nodes have the same color. Graph coloring problems have many applications in scheduling, map coloring, register allocation, and Sudoku puzzles. Finding the optimal coloring of a graph is NP-hard, which means that there is no efficient algorithm that can solve it in polynomial time for any graph. 

Nguyen, M.-T. et al. Quantum Optimization with Arbitrary Connectivity Using Rydberg Atom Arrays. PRX Quantum 4, 010316 (2023).

Finžgar, J. R., Ross, P., Hölscher, L., Klepsch, J. & Luckow, A. QUARK: A Framework for Quantum Computing Application Benchmarking. 2022 {IEEE} International Conference on Quantum Computing and Engineering ({QCE}) (2022) doi:10.1109/qce53715.2022.00042.

da Silva Coelho, W., Henriet, L. & Henry, L.-P. Quantum pricing-based column-generation framework for hard combinatorial problems. Phys. Rev. A 107, 032426 (2023).

Wurtz, J., Lopes, P. L. S., Gemelke, N., Keesling, A. & Wang, S. Industry applications of neutral-atom quantum computing solving independent set problems. Preprint at http://arxiv.org/abs/2205.08500 (2022).

Building strong relationships with key partners to create a robust retail media network ensures you continue to deliver enhanced value to your customers and brand partners.

According to McKinsey, retail media networks are currently worth $45 billion and are projected to reach $100 billion in three years – making them the next big thing in advertising.

This rapid shift is transforming how brands interact with consumers and how retailers generate new revenue streams by monetizing their shopper data. But what are these networks and what’s fueling their growth?

What is a retail media network?

A retail media network is an advertising platform managed by a retailer to manage its ad inventory and control the placement of ads from various brands across its owned channels (digital, in-store, etc.) and other paid media channels. This includes ads and videos on its website, app, and in-store digital displays and banners.

These networks are made up of three main components: the advertiser (the brand or media buyer), the publisher (the retailer owning the digital and physical points of purchase), and a technology platform that connects them.

Fueling retail media networks

There are several factors currently driving the growth of retail media networks. These are:

• The rise of ecommerce – the shift to online shopping has given retailers access to a wealth of first-party data, including consumer purchase behaviors, preferences, and demographics. This enables them to target consumers and create new revenue streams by monetizing their advertising – all while reducing spend
• Cookie deprecation and the need for first-party data – with Google’s deprecation of third-party cookies, brands are seeking data collection and targeting alternatives. Retailers, armed with extensive first-party customer data, are well-positioned to fill this gap, which encourages them to collaborate closely with retail media networks to create more personalized advertising
• The disruption of traditional channels – traditional advertising channels, such as TV, have been shrinking for several years, and recent declines in ad revenues for established platforms like Meta and Google signal significant disruption in the rapidly changing advertising ecosystem. However, retail media networks provide brands with the ability to display ads to high-intent consumers closer to the point of purchase, which helps increase conversion rates
• The presence of lucrative margins – the prospect of setting up a retail media business is highly lucrative for retailers as it can significantly impact their profitability. For example, Amazon’s advertising revenues grew to $46.9 billion in 2023, an increase of over 24% compared to the previous year
• The rise of non-endemic advertisers – beyond traditional retail brands like CPGs, non-endemic brands are also benefiting from retail media networks. Walmart Connect, Walmart’s ad platform, has started offering offsite media to brands that do not sell at Walmart but offer complementary products and services. This approach positively impacts businesses outside of the retail sector such as automotive, entertainment, financial services, fast-food, and travel providers.

Collaboration builds successful retail media networks

In conclusion, while the potential of retail media networks is immense, it’s still very early days. Retailers need to build an entire ecosystem of capabilities from media operating models and streamlined workflows to the right partnerships with agencies, all while providing a personalized experience for shoppers and enhanced campaign performance for advertisers’ retail media spend.

Even with a robust process and advanced technology, scaling media planning, activating many channels simultaneously, and measuring across multiple advertisers seamlessly remains challenging for many retailers. The increasing deployment of Gen AI and intelligent automation solutions can help accelerate speed to market for campaign activations and provide tangible results for brands and retailers.

Building a robust and successful retail media network is a journey. Nurturing strong relationships with key partners is an effective way of ensuring you continue to deliver enhanced value to your customers and brand partners.

To learn how Capgemini’s Connected Marketing Operations is helping Albertson’s Media Collective leverage intelligent process automation and Gen AI to create ad formats, bolster media planning capabilities, and build precise audience segmentation, contact: abha.singh@capgemini.com or isha.b.gupta@capgemini.com

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.