Harnessing Financial Operations for Smarter Cloud Transitions

Technology is transforming every organization and business, driving them to surpass economic development. Many enterprises are continuously adopting and shifting workloads to the public cloud, expecting numerous benefits such as flexibility, scalability, agility, and cost savings. However, with the myriad of options available for cloud adoption, there is also a risk of uncontrolled expenditure. It is quite common for enterprises to express that they are not receiving the benefits they anticipated from the shift from data centers to the cloud. The following section of this article delves into those key challenges in detail.

When an organization decides to move to the cloud, it starts with migration planning. Often, gaps in migration planning, inadequate assessments, lack of cloud-ready staff, complex designs, failed migrations, rework, and app or tool dependencies extend migration timelines beyond expectations. Businesses pay for their existing on-premises infrastructure while incurring new expenses for cloud migration, leading to a migration bubble. Additionally, migrating only a portion of the infrastructure while leaving other components on-premises prevents businesses from enjoying the full benefits.

Our practical experience shows that merely migrating workloads from on-premises or co-located data centers to the cloud is not enough. Regardless of the chosen hyperscaler, issues arise when clients overlook cloud best practices, leading to challenges in cloud governance and cost management. It is evident that many enterprises are still approaching cloud adoption with a data center mentality and are hesitant to embrace essential cloud features like autoscaling, on-demand provisioning, and self-service, which have the potential to drive significant innovation.

The shift from data centers to the cloud has also disrupted traditional procurement processes by empowering developers with greater purchasing authority. It enables engineers to spend company funds with just a click of a button or a line of code, bypassing the lengthy conventional procurement procedures including purchase requisitions, calling tenders, vendor scouting, and purchase orders.

Due to these challenges, monthly bills from hyperscalers can spiral out of control, extending the payback period for investments and negating the benefits of cloud transition. Therefore, it is crucial to develop a comprehensive migration strategy with operational governance controls to avoid potential pitfalls and adhere to cost optimization goals, commonly referred to as FinOps. This approach helps free up budgetary funds and accelerates the shift to the cloud. Enterprises must ensure their personnel are cloud-ready and have strong procedures to analyze expenditures and identify key cost drivers. Assessing available cloud resources is also advisable for optimization.

The primary goal of every organization is to lower technological costs, and the cloud is no exception. As companies continue to invest more in the public cloud, recurring cloud run costs will increase. This trend underscores the growing importance of FinOps as a recognized financial management discipline.

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

Hey there! We’re excited to share that Capgemini will be at the Pharma MES conference in Berlin. We’ve got a lot to say about Manufacturing Execution Systems (MES) in the pharmaceutical industry, and we’re breaking it down into three blogs. In this first one, we’ll give you a snapshot of what’s happening today, including the main challenges. Keep in mind, this is our view on the current state of MES—there’s no single truth, and perspectives can vary.

In our previous blog, we discussed the current state of Manufacturing Execution Systems (MES) in the pharmaceutical industry, highlighting the challenges and limitations faced today. As a natural sequel, this blog explores the future advancements in MES, promising greater integration, flexibility, and efficiency, and their potential impact on pharma manufacturing.

Seamless Integration
In the future, we think that MES will be seamlessly integrated with other systems, creating a unified digital ecosystem. Users will be able to interact with a single, cohesive system without realizing the underlying complexities. This seamless integration will eliminate the need for custom-built interfaces and will ensure a smooth data flow across all systems.

Enhanced User Experience
The future MES will also offer an intuitive user experience. Users will be able to interact with the system effortlessly, akin to driving a car without thinking about its mechanics. This user-friendly interface will make it easier for personnel to access and utilize data, thereby improving overall efficiency.

Support for Different Pharma Manufacturing Types
Future MES systems will have the capability to support continuous manufacturing, personalized medicine manufacturing, and existing batch manufacturing.

Shift in Licensing Models
Many pharma manufacturing companies will experiment with and adopt a SaaS licensing model. This shift will also motivate medium and small-sized companies to implement MES solutions.

Advanced Technologies

1. AI and Machine Learning: AI and ML will play a significant role in the future of MES. These technologies will enable predictive analytics and anomaly detection, enhancing decision-making processes. For instance, AI can help identify variability in product output and suggest potential root causes, making it easier to address issues proactively.
2. Generative AI: The integration of generative AI will allow users to interact with MES through natural language, making data retrieval and analysis more accessible. This will enable operators to ask questions and receive insights in a conversational manner, further simplifying the use of MES.
3. Cloud-Based Solutions: The shift towards cloud-based MES may offer greater flexibility and scalability. Cloud solutions can make it easier to deploy and manage MES across multiple sites, providing a unified platform for global operations. As these systems mature, clients will evaluate on-premises vs. cloud risks and consider changes to their strategies to take advantage of benefits outlined above. However, addressing technical constraints like latency and ensuring robust security measures will be crucial for widespread adoption.

Sustainability and Efficiency
Last but not least, Future MES will contribute to sustainability goals by optimizing resource usage and reducing waste. Integrating sustainability metrics into MES will drive greener manufacturing practices. For example, MES can help monitor and reduce energy consumption by optimizing equipment usage and minimizing waste.

Conclusion
The future of MES in the pharmaceutical industry is bright, with advancements in integration, user experience, and technology set to transform manufacturing processes. By embracing these innovations, pharma companies can achieve greater efficiency, flexibility, and sustainability in their operations.

1. Balancing Capacity Growth and Standardization 

One of the core themes of the discussion was the delicate balance between growing manufacturing capacity for some Manufacturers and the need for standardization across multi-site MES deployments. As industries expand, especially in global manufacturing environments, the integration of contract manufacturers has become essential. However, scaling up comes with significant investments, and aligning organizational expectations across different sites remains a challenge. 

Key Insight: Balancing capacity growth with standardization is critical for successful MES programs, especially when integrating with contract manufacturers and managing large-scale, Greenfield deployments. 

2. AI/GenAI and Data Challenges 

The integration of AI/GenAI with MES was another hot topic. Although enthusiasm is high, and proofs of concept exist, there are significant hurdles to overcome. For instance, the proprietary nature of manufacturing data may not always align well with large language models, which limits their utility in some cases. Additionally, skill levels among colleagues, especially in certain languages, can pose challenges for widespread GenAI adoption. GxP validation is another significant concern. 

Key Insight: While AI and GenAI are promising, there’s a significant need for foundational work and data strategy alignment before these technologies can be fully realized within MES environments. 

3. Pathways for AI/GenAI Integration with MES

Following on the current and future states of AI/GenAI, the session also distinguished between different areas of focus within MES deployments. As one example, AI/GenAI can be used to develop and translate manufacturing recipes; In another example, it can be used to optimize manufacturing goals, such as enhancing flow through constraint points. The former is an example of enabling program design and rollout, while the latter is an example of value delivery from the MES investment. The consensus was that while program implementation efficiency/accelerating using advanced technologies is beginning, these techniques are not yet being fully leveraged to optimize broader manufacturing processes. 

Key Insight: AI/GenAI potential in MES is far-reaching, but its full use in optimizing manufacturing goals is still in its infancy. At present, most of the AI/GenAI efforts in MES have been in system design and rollout vs. in use of MES data to drive Manufacturing goals.

4. Risk Assessment and GxP Validation

As industries adopt more advanced technology applications and SaaS models within MES, the importance of leveraging risk assessments, particularly in GxP-validated environments, was emphasized. Discussions also highlighted the benefits of learning from out-of-industry benchmarks, particularly the automotive sector, which has managed to accelerate technology deployments with a high degree of standardization and efficiency. 

Key Insight: Risk assessment and validation processes must be tailored for advanced technologies and SaaS models, leveraging best practices from industries like automotive to drive efficiencies.

 5. Unlocking Value from Data

The session also considered the challenge of turning vast amounts of data into actionable insights. While MES deployments generate large volumes of information, the consensus was that most organizations are still at the stage where more data is being gathered and stored vs. serving value extraction. The true promise of MES will be realized when organizations can effectively use this data to optimize manufacturing processes and achieve greater efficiencies. 

Key Insight: The true value of MES comes from transforming data into actionable insights that optimize operations and unlock efficiencies in real-time. 

6. Organizational Alignment and Change Management

The session also touched on the importance of organizational alignment when deploying MES and that driving change requires an understanding of the operational realities on the factory floor. Engaging operators where they are, while maintaining strong sponsorship from top-level management, is essential to ensure that MES initiatives do not lose momentum. Organizational ownership of MES itself is also variable (IT, OT, or hybrid) and in some cases has shifted in ownership, with further exacerbates challenges in driving change.

Key Insight: Effective change management for MES deployments involves bottom-up engagement with operators and top-down support from leadership, ensuring alignment across organizational layers. 

7. Leadership and Operator Engagement

Another key change management takeaway was the role of leadership in fostering an MES-friendly operating environment. Engaging operators from the outset and considering their perspectives throughout the MES deployment process is crucial. Leadership must drive the conversation, ensuring that technology implementation aligns with operational goals. 

Key Insight: Leadership must play a proactive role in integrating operator perspectives and driving the MES deployment forward. 

Closing Thoughts and Conclusion

The session concluded with a sense of achievement and an acknowledgment of the challenges ahead. While considerable progress has been made in MES development, the journey toward the MES of the future is far from complete. That said, the group left the session energized, with a shared commitment to pushing the boundaries of what MES can achieve. 

The “MES of the Future” session at MES Berlin highlighted both the progress and the challenges on the road to advanced MES deployments. From balancing capacity growth with standardization to leveraging AI/GenAI, the discussion covered a broad spectrum of critical topics shaping the future of manufacturing. As industries continue to adopt innovative technologies, there’s a shared understanding that while we’ve made great strides, there is still much work to be done. We’re excited to be on this journey and would love to connect with others who are passionate about MES and the future of manufacturing. Feel free to reach out to discuss further or share your own experiences. 

The challenge of text summarization in financial services

The financial services industry generates an immense volume of documentation daily. From customer interactions and regulatory filings to legal proceedings and risk assessments, organizations must process, interpret, and act upon large amounts of unstructured data. Traditionally, this has been a time-consuming and labor-intensive process, often susceptible to human error and inconsistencies. As regulatory frameworks evolve and customer expectations rise, the demand for accurate, efficient, and standardized document summarization has never been more critical.

In banking, institutions must navigate a constantly shifting regulatory landscape. Compliance teams are responsible for reviewing extensive regulatory filings, risk reports, and audit documents—any misinterpretation can result in significant financial and legal consequences. Beyond compliance, customer service operations require rapid access to key insights from call center interactions to enhance service efficiency. Additionally, loan and credit risk assessment teams manually analyze financial statements, credit histories, and other documents to determine creditworthiness, a process that is both time-intensive and costly.

The insurance sector faces similar challenges, particularly in underwriting, policy management, and claims processing. Insurance providers must constantly interpret complex regulatory changes while ensuring accurate policy underwriting and risk assessment. Claims processing teams review medical reports, legal documents, and third-party assessments to determine coverage and fraud risk. Manual document reviews in these areas not only slow down operations but also introduce inconsistencies that can impact decision-making.

The increasing complexity of financial services documentation makes manual summarization an unsustainable approach. Generative AI (GenAI) offers a powerful solution by enabling automated summarization of key insights from various documents. However, assessing the quality of AI-generated summaries remains a challenge. Traditional evaluation methods, such as ROUGE and BERTScore, rely on human-generated references, which are not always available or practical for large-scale financial services applications.

Introducing QAG-based automated summarization evaluation

Question-Answer Generation (QAG) for automated summarization evaluation provides a breakthrough, offering a reference-free approach to ensuring both completeness and accuracy in AI-generated summaries. Instead of comparing summaries to predefined references, QAG-based evaluation gauges summarization quality by generating factual questions from the original document and checking whether the AI-generated summary provides correct answers.

Experimental results

Optimization techniques for QAG were implemented that included limiting truth extraction and using custom question templates to improve evaluation performance.

This enhanced QAG-based evaluation approach was then tested on four real-world transcripts. In each test, both the default QAG model and our optimized approach were implemented. The following table summarizes the results:

Overall, the experimental results reveal a significant leap in alignment scores, rising from a baseline of 56% to over 70%, while coverage scores experienced an even greater boost, increasing from 70% to 90%. These enhancements demonstrate the effectiveness of the refined approach in producing more accurate and comprehensive AI-generated summaries.

Wide-ranging use cases in banking and insurance

By implementing QAG-based evaluation, financial institutions can improve the reliability and accuracy of GenAI-powered summarization across multiple business functions. In banking, it ensures that compliance reports, customer interactions, and financial risk assessments maintain factual integrity. In insurance, it enhances underwriting decisions, policy management, and claim evaluations. The following is a sample of several key use cases in financial services.

Banking use cases

• Call center interaction summarization: Customer service teams manage a high volume of customer interactions, often recorded in call center transcripts, chat logs, and emails. GenAI can summarize these conversations, extracting key themes, customer concerns, and sentiment trends, enabling more efficient issue resolution. With QAG-based evaluation, AI-generated summaries ensure that no critical customer concerns are overlooked, allowing for more personalized and proactive customer support.

• Audit report summarization: Internal audits are a critical part of risk management in banking, yet the process is often time-consuming and labor-intensive. AI-powered summarization helps highlight key discrepancies, compliance violations, and recommended actions from audit reports, improving the efficiency of risk and compliance teams. With QAG-based evaluation, banks can ensure that summarized audit findings remain aligned with the original reports, reducing the chances of oversight in risk assessments.

• Credit risk assessment: Evaluating a borrower’s financial health requires the review of credit reports, financial statements, and loan histories, often spread across multiple documents. GenAI can consolidate key financial indicators into a structured summary, allowing risk analysts to make faster and more informed lending decisions. By applying QAG-based evaluation, banks can verify that these summaries accurately reflect the borrower’s financial status, reducing errors in credit risk assessments.

Insurance use cases

• Underwriting and risk assessment: Insurance underwriting requires the evaluation of extensive data, including health records, financial documents, and previous policy claims. GenAI-generated summaries allow underwriters to quickly assess risk factors, policy eligibility, and pricing considerations. With QAG-based evaluation, insurers can confirm that these summaries capture the full scope of risk assessment criteria, reducing underwriting errors and improving decision-making efficiency.

• Policy management: Managing policies involves handling a large amount of unstructured documentation throughout the policy lifecycle. Any modifications initiated by insurers or customers require careful reassessment. GenAI streamlines this process by efficiently condensing information from various sources. By applying QAG-based evaluation, insurers can confirm that AI-generated summaries align with policy terms and regulatory requirements, enabling them to allocate more time to strategic tasks such as customer service and relationship management.

• Claims processing: Whether for auto, healthcare, or commercial policies, claims processing is a complex, documentation-heavy task that demands significant time and effort when done manually. GenAI automates the extraction of critical details from diverse records. QAG-based evaluation ensures that all necessary claim details are preserved, reducing operational costs, expediting claim settlements, and improving overall customer satisfaction.

These use cases highlight just a few of the many ways QAG-based evaluation can be applied in financial services. Potential applications extend far beyond these examples. Depending on an organization’s specific needs, QAG-based evaluation can be adapted to review AI-generated summaries across a wide range of business functions, including regulatory reporting, contract analysis, investment research, internal policy compliance, and more.

Driving accuracy, efficiency, and trust in AI-generated summarization

As financial institutions increasingly rely on GenAI to streamline document processing, ensuring the accuracy and reliability of AI-generated summaries is paramount. QAG-based automated summarization evaluation provides a reference-free, scalable, and precise method to assess summarization quality, addressing one of the key challenges in AI adoption. By evaluating summaries based on factual correctness and content coverage, QAG-based evaluation offers a structured approach to verifying AI outputs without the need for human-generated reference summaries.

The benefits of integrating this approach in banking and insurance are far-reaching. Banks can enhance decision-making by quickly extracting key insights from financial reports, compliance documents, and customer interactions. This leads to faster responses to regulatory changes, improved operational efficiency, and a more seamless customer experience. In the insurance sector, QAG-based evaluation improves underwriting accuracy and claims processing efficiency, ensuring that AI-generated summaries are both comprehensive and aligned with business objectives.  

Now is the time for financial institutions to embrace AI-powered summarization with QAG-based evaluation. To explore how this approach can elevate your organization’s AI-driven summarization efforts, contact Capgemini’s Financial Services Insights & Data team today.  

Generic proposals are losing deals

Insurance RFP responses are starting to feel like they’ve been photocopied over and over. Brokers and clients today are no longer just flipping through proposals hoping to find a winner—they’re expecting them to speak directly to their unique needs. The days when you could get away with templated, one-size-fits-all responses are behind us. In insurance, trust is built on understanding, and understanding is signaled through specificity.

In fact, many proposals don’t even get past the first skim because they sound like they were written for any client, not this client. The root issue is that generic responses signal a lack of investment in the relationship. Insurers risk losing out on high-value deals, wasting time and resources crafting responses that don’t convert. As our work with numerous global insurers has revealed, many of these generic documents—especially cover letters and executive summaries—were not even being read by brokers due to their lack of relevance.

Generative AI for hyper-personalization in insurance

Now, let’s imagine a private, enterprise-trained generative AI assistant that doesn’t just regurgitate past language, but crafts messages so tailored they make your clients feel like VIPs. That’s the magic of a custom, private GenAI assistant.

This assistant is no off-the-shelf chatbot. It’s trained on your historical RFP data, your previous client interactions, your industry nuances, and even your internal product literature. It understands how you communicate and what your clients care about. More importantly, it learns and evolves. With the help of Agentic AI, a modular framework powered by specialized AI agents, this assistant goes far past simple auto-fill. It reads the RFP, summarizes the client ask, constructs the top winning themes, and proactively drafts personalized responses, summaries, and even intelligent suggestions for improvement.

This is where hyper-personalization becomes real. By utilizing structured and unstructured data alike, the Gen AI assistant pulls out the most relevant insights and shapes them into messaging that resonates. It compiles data from its entire knowledgebase to craft a tailored solution to the client’s problem. It’s not guessing, it’s contextualizing. That means proposals land stronger, faster, and with far better chances of hitting the mark.

MongoDB: The motor powering AI-driven personalization

Behind the scenes, MongoDB plays a crucial role in making all this magic possible.

Their flexible document model allows for rapid ingestion of diverse data types including past RFPs, client correspondence, marketing decks, and everything else imaginable. This structure is perfect for insurers juggling massive volumes of semi-structured and unstructured data.

MongoDB Atlas Vector Search is particularly crucial here.  It enables the Gen AI assistant to rapidly identify, rank, and re-rank the most relevant information based on contextual relevance, delivering responses that are both timely and precise.

Its globally distributed architecture—available across AWS, Azure, and GCP in over 115+ regions—makes it an ideal foundation for building large-scale, enterprise-grade Gen AI applications. By embedding Vector Search directly into the core database, MongoDB eliminates the need to sync data between separate operational and vector databases. This simplification reduces complexity, minimizes the risk of errors, and significantly shortens response times.

Keeping both operational and vector data in a single system also improves performance through reduced latency and advanced indexing capabilities. For organizations building out agentic Gen AI capabilities, MongoDB further supports Graph RAG (Retrieval Augmented Generation) architectures, enhancing contextual accuracy and scalability across use cases.

However, insurance is a heavily regulated industry and data security is critical. MongoDB also offers enterprise-grade encryption, access controls, and supports compliance with key data privacy regulations.

Case study: Less robotic, more calibrated and compelling RFPs at a global insurer

A recent standout example of our custom, private GenAI assistant in action comes from a global insurer who started with a modest request: Can we hyper-personalize our RFP cover letters better? The ask was simple and they were merely looking for a few bullet points to make things feel less robotic.

What we were able to create for them was a revolution in how they respond to RFPs. In just five weeks, our team implemented our custom, private GenAI assistant that not only delivered personalized bullet points but also crafted full executive summaries and tailored cover letters. These were not piecemeal templates—they were coherent, compelling, and calibrated to the specific opportunity at hand.

The feedback we received was immediate and enthusiastic. The Chief Innovation Officer and the Sales leadership team pushed for scaling the solution to other areas. It wasn’t just a productivity gain, it was a reputation builder. Brokers began to take notice. The insurer wasn’t just responding faster; they were responding smarter.

Business impact, check! Strategic outcomes, check!

By implementing a custom, private GenAI assistant, insurers gain access to a scalable, cloud-native platform that integrates easily with existing systems—whether it’s a CRM, document management platform, or internal knowledge base. Beyond the technical flexibility, the real impact lies in how this approach transforms stagnant, siloed data into living insights that power tailored client engagement.

The platform supports more consistent and efficient proposal development by reducing manual effort, accelerating turnaround times, and improving the quality and relevance of responses. Teams can focus less on reformatting and more on building client relationships. Meanwhile, the built-in security and governance measures ensure that every interaction meets enterprise compliance standards, protecting both client data and institutional knowledge.

Insurers using this model report stronger broker engagement, better win rates, and faster RFP response times. Operational costs drop due to reduced manual formatting and response drafting. From a technical perspective, RAG-enhanced GenAI can offload up to 35% of compute cost compared to full LLM inference on raw content, thanks to targeted document retrieval and short-form reasoning tasks.

As organizations use this solution over time, feedback loops from won/lost deals can be fed back into the model for retraining, improving response quality and alignment. As the assistant matures, it can serve as a strategic enabler across adjacent workflows—claims review, renewal briefs, or even sales coaching.

The future of insurance RFPs

Custom private GenAI assistants represent a rare intersection of technical maturity and business impact. When combined with MongoDB’s robust data orchestration capabilities and Capgemini’s proven technology blueprint, this solution becomes more than a digital enhancement—it becomes a strategic advantage.

Organizations that embrace this model transition from reactive, templated proposal development to proactive, context-rich client engagement. With the ability to generate intelligent, personalized content at scale, they not only improve operational efficiency but also strengthen their competitive position in a high-stakes market.

This isn’t just about responding faster—it’s about responding better. As expectations around relevance, precision, and value continue to rise, the future of insurance RFPs will belong to those who invest in intelligent automation and meaningful personalization.

The path forward isn’t generic. It’s personal, scalable, and ready to deliver lasting impact.

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