As Kesh Thukaram, Co-Founder of Best Insurance and excitare.ai, sees it, the risk, protection and advisory landscape is undergoing one of the most profound transformations in its history. Artificial intelligence is no longer a distant concept or a back-office experiment confined to large technology firms; it is rapidly becoming the infrastructure that underpins how modern advisory businesses operate, compete and grow.
In Kesh’s view, the firms that will lead the next decade are those that move beyond surface-level adoption and embed AI at the heart of their operating model.
The capabilities of AI have advanced significantly over the last few decades, evolving from traditional rules-based systems and machine learning models to something far more sophisticated.
Today’s AI systems sit in the everyday tools and platforms used by modern-day businesses. They generate content, analyse complex information, write code and interact in everyday language.
The way in which people talk about AI has also changed, particularly over the last few years.
AI is no longer just about innovation or a ‘nice to have’, it’s a survival mechanism for gaining a competitive edge, and those that fail to adapt are at risk of becoming redundant.
Thinking about ‘whether’ to do something with AI is no longer good enough. Instead, advisory firms should be thinking about how to embed AI into their business in a way that genuinely transforms how it operates.
Understanding the three distinct categories of AI and how they can work in practice is crucial to achieving success, especially for those firms looking to put together a clear and effective business strategy.
Predictive AI and smarter decision-making
Predictive AI is a technology that uses historical and current data to make predictions on future trends and events. In the world of insurance, this can be used to make underwriting decisions, predict claims and improve risk assessment processes by replacing outdated manual underwriting procedures.
Despite rapid advancements in technology, manual underwriting processes are still surprisingly common throughout the insurance industry. Predictive AI models can help improve these archaic practices by identifying patterns in vast amounts of data within seconds.
The advantages for advisers are faster and more accurate risk assessments, improved insurer selection and more rapid turnaround times for clients. With predictive AI systems continuing to become more reliable, those advisers who understand how these tools shape pricing and acceptance criteria will be better positioned to help customers.
Generative AI and hyper-personalisation
The next stage of the technology is called generative AI, which goes one step further by learning patterns and generating new outcomes rather than replicating them. For insurers and reinsurers, this can be used to predict loss ratios and solvency requirements, enabling them to bring in a concept known as hyper-personalisation.
Hyper-personalisation means that insurance pricing will no longer sit at a scheme level. Instead, it can move to a one-to-one level where each individual risk is assessed on its own merit, therefore removing any generalised bias within the risk pool.
As underwriting practices continue to become deeper and more behaviour-driven, customers are also more likely to change the way in which they interact with insurers and advisers.
Many will begin to be more forthcoming with personal data as they start to understand that superficial answers are less effective when it comes to pricing risk. Instead, divulging more information means they are more likely to benefit from personalised and tailored cover.
Agentic AI: transforming client interaction
One of the most significant changes to the way in which advisory firms operate is likely to come from Agentic AI, which looks set to revolutionise the entire customer interaction.
Agentic AI systems can already autonomously handle tasks and customer interactions through automated responses, voice bots and meeting note-taker tools, and all advisers, regardless of size, should be taking advantage of this.
Employing a full-time member of staff to handle routine inbound phone enquiries for example, can cost tens of thousands of pounds a year. In contrast, an AI-powered voice bot can be trained to manage straightforward requests relating to claims for a fraction of the cost and will be available 24/7.
It’s important to note however, that Agentic AI isn’t about replacing humans, it’s about escalation, learning and business efficiency. When Agentic AI cannot solve a query, it passes it on to a human agent who can help. The system then learns from that response, helping to reduce the likelihood and need for escalation next time.
Over time, this will then help to standardise service levels and improve customer experience and satisfaction by providing a first contact resolution. It also helps businesses save money on routine tasks such as answering the phone, helping to improve profitability.
Workplace transformation
Where Agentic AI has the ability to truly transform businesses however, is through the handling of multiple leads via different chat interfaces such as phone, video, and messaging in one integrated CRM system.
While the usual procedure would be to acknowledge an enquiry and call the customer back at a more convenient time, Agentic AI systems can immediately engage prospective clients by offering a selection of communication choices, leading to faster response times, consistent service standards and improved conversion figures.
Forward-thinking firms that embrace these opportunities and utilise AI to redesign business models, reduce costs, accelerate training and deliver simpler and more personalised services and products stand themselves in good stead for future success.
