Unlocking AI & Data in Insurance: Key Challenges and FAQs

Published 27/02/25 under:

The insurance industry is evolving rapidly, and businesses that fail to modernise risk falling behind. From fragmented customer data to missed cross-selling opportunities, insurers, brokers, MGA’s, & Digital Eco-System providers face significant challenges that impact efficiency, customer experience, and profitability.

A recent Earnix Trends Report surveyed 400 insurance executives and highlighted key concerns:

• Modernising legacy systems
• Integrating AI into operations
• Improving efficiency
• Managing and integrating external data
• Enhancing fraud detection and prevention
• Keeping pace with competitors
• Delivering superior customer experiences

With customer expectations rising and competition increasing, digital transformation is no longer optional—it’s essential. But where should you start? Here’s how AI and data can unlock new opportunities and where Kerv Digital can help.

The Power of a Single Customer View

A fragmented view of customer data creates inefficiencies, missed sales opportunities, and disjointed customer experiences. The key to overcoming this is a Single Customer View (SCV)—a unified, real-time snapshot of each customer, integrating data from various sources.

With a SCV, businesses can:

• Provide seamless, personalised customer experiences
• Align marketing and sales communications
• Improve cross-selling and upselling effectiveness
• Make data-driven decisions that enhance profitability

Mapping the customer journey is also crucial. Whatever your role in the Insurance end to end value chain you must ask: What are customers thinking at each stage? What questions do they have? By aligning marketing content with the buyer journey, insurers can nurture leads more effectively and drive higher engagement.

Cross-Selling & Upselling: A Missed Opportunity

A recent Kerv Digital poll found that a high proportion of  businesses in the sector have a low product-to-customer ratio, typically in the 1.1–1.4 range. However, world-class businesses consistently achieve a ratio of 2 or higher—meaning they successfully cross-sell multiple products to each customer.

The key to improving this? Blended Digitisation. By leveraging AI and automation, businesses can elevate & personalise their approach to identifying customer needs, recommending relevant products, and personalising their offerings in real time.

AI & Data: The Game Changers

Many businesses in the sector are either experimenting with AI or feel stuck in a state of paralysis. But AI is only part of the equation—true transformation comes from combining AI with automation, data consolidation, and a robust digital ecosystem.

The Data That’s Being Left on the Table

Currently, businesses across the Insurance market only leverage 20% of their available data—the structured, easy-to-process information. The remaining 80% consists of unstructured data (videos, images, emails, social media posts, chat logs, and research documents), which often goes untapped.

Businesses that unlock & harness unstructured data gain a competitive edge by:

• Hyper Personalising their customer experience – taking personalisation & customer relevance to another level
• Extracting insights from large documents – Automating key information retrieval
• Improving claims processes – AI can detect fraud and anomalies
• Enhancing internal efficiency – Using AI-powered assistants (e.g., Microsoft Copilot) to support employees
• Automating financial operations – Matching invoices and resolving accounts

The Earnix Trends Report highlights AI’s increasing impact on business processes. While only 6-10% of executives recognised AI’s significance on their businesses in 2024, the proportion of businesses that feel that AI will have a significant impact on their business is projected to reach 25-28% in 2025. The most common AI applications in insurance include risk assessment, pricing, customer experience, claims management, & increasingly digital marketing.

Common AI & Data FAQs in the Insurance Sector

Q1: We have customer data in multiple systems—how can we use AI effectively?

We recognise the important fact that you can’t get very far with AI without first sorting out your data. Having multiple, disparate and disconnected data sources is a common challenge, especially with financial services and insurance sector businesses, where it is not unusual to have numerous systems, legacy tech, and ‘data silos’. This issue can be compounded and made even more complex through mergers and acquisitions, potentially bringing even more systems and data into play. 

However, it is also where there is potentially tremendous value, as combining and analysing multiple sources of data may help to reveal multiple dimensions to the needs, behaviours, trends and opportunities within your customers. It can also open up potential to automate and streamline processes that otherwise require manual intervention across systems. 

Generally, our approach is to implement a cloud-based Data Platform (we typically use Microsoft Fabric and Azure Synapse) to pull key data from the various sources, creating a consolidated single customer view and using that as the basis for employing AI, plus other capabilities such as automation, marketing and even sales and customer services. 

We like to focus on the outcomes our customers are trying to achieve. So rather than “how do we use AI?” the question is “what are the priorities for the business?” AI (and the data powering it) might be the answer to some of them. 

AI comes in many forms, providing capabilities ranging from advanced analytics and modelling through the generative AI and assistance for staff (such as Microsoft Copilot) through to automation and ‘agentive’ AI which can act autonomously.  

Q2: Do we need to replace our entire tech stack to implement AI?

This is a concern we come across a lot and is closely related to Question 1. 

Remembering that data is powered by AI, the pragmatic approach is to start with data consolidation using an intelligent Data Platform.  

The Data Platform extracts relevant data from existing systems (and potentially provides data back to them) without the need to replace everything – which can be hugely expensive, risky, time-consuming and disruptive to the business. 

You then have the consolidated, single customer view from which to start leveraging AI and other modern technologies to extract value from your data. 

When it comes to updating and replacing legacy systems, having a properly architected and implemented Data Platform can be a significant advantage. Although not quite as simple as ‘plug and play’, transitioning from one data source (e.g., legacy underwriting system) to another (e.g. new cloud-based solution) is straightforward with a Data Platform. This helps to maintain consistency with analytics, automation, etc. while you replace legacy systems at a pace that suits your business. 

Q3: Where do we start?

Knowing where to begin is a common hurdle for insurance firms embarking on digital transformation. To keep this brief, we have two high-level suggestions:

1. Consultancy – At Kerv Digital, we use a highly focused consulting approach to prioritise needs, understand dependencies, ‘envision’ solutions, and produce a roadmap. This usually takes only a handful of weeks and gets you to the start line of your programme and/or helps form a business case for the next stage.
2. Proof of Concept (PoC) & Pilots – Start small. Rather than investing in a large-scale programme, a better approach can be to invest in one or two ‘Proof of Concept’ (POC) or pilot projects. This could involve building a POC around a particular AI business case or conducting a small-scale pilot within a team (or subset of a team). Not only can this approach help to prove (or disprove) the usefulness of the tech, it is typically a huge learning experience and can also form an important part of your change management and user adoption strategy.

How to Build a Data-Driven Future

So to conclude, in order to succeed in the digital age, insurers must:

✔ Consolidate data for better decision-making

✔ Test AI with low-risk applications (e.g., internal use, automation pilots)

✔ Leverage advanced analytics for deeper insights

✔ Integrate AI into the customer journey to enhance engagement

✔ Prioritise data and technology in business strategy

✔ Adopt a people-first approach by training and supporting staff in new technologies

Balancing Innovation & Cost

Digital transformation requires investment, but it must be strategic. By building strong business cases and demonstrating ROI through early wins (e.g., AI-powered automation reducing manual workload), businesses can secure buy-in and funding for future innovations.

Kerv Digital: Your Partner in Insurance Sector Innovation

At Kerv Digital, we help businesses unlock the full potential of AI, automation, and data-driven decision-making. Whether you’re modernising legacy systems, integrating AI, or improving customer experiences, we’re here to guide you every step of the way.