Insurance in 2025 can be categorised as a period of uncertainty. Global economic conditions, highly competitive markets and evolving customer demands are all contributing to a new era for insurance firms.
These pressures are all contributing to the natural cycle of the insurance sector. The sector is currently pushing towards the end of a downcycle, meaning it is no longer about growth at any cost, but rather ensuring stability and profitability through efficiency. While firms might see reducing investment into innovation as an easy solution to their troubles, this short-term fix could be hazardous for their long-term prosperity.
esynergy, a technology consultant that builds scalable platforms, products and services, recently released a new whitepaper exploring how insurers can leverage technology to ensure long-term growth despite the current market pressures.
The whitepaper, ‘Unlocking tomorrow’s gains: Tech-driven strategies for Insurance leaders’, was created with support from in-depth interviews with senior industry leaders. It explores how insurers can preserve margins and build long-term resilience by strategically investing in data, governance, automation and a tech-driven culture.
In the current environment, firms are examining their balance sheets and searching for easy ways to boost their profitability, but they could risk making the wrong decision.
Graeme Howard, Group CIO at Benefact Group and Non-Executive Director at esynergy explained, “There is quite often a knee-jerk reaction to investment as margins start to fall, or if efficiency is not falling in line to where it needs to be. Companies look at their investment portfolio and quite often cut out the innovative or the newer ideas around technology and take a bit of a black and white view.”
While this can be great to get short-term margins under control, it can cause the firm to miss out on potential long-term growth. A firm might have two projects underway, Howard added, replatforming a policy admin system versus overhauling data into a better format to leverage an AI-powered agent. The firm might decide to cancel one of these, but the one they cancel could have been the one to provide both short and long-term growth. Similarly, putting a pause on innovation could impact the firm’s competitiveness and put them on the backfoot when the market takes a positive shift. Firms need to ensure they have a business-wide view when it comes to investments to ensure potential is not lost.
“It is a real area where you really need to collaborate. Not just technology, not just business leaders, not just operations and not just the leadership, everybody needs to come together and look at the holistic view of investment. That’s an easy mistake to make and one that many do.”
Another common problem that firms could face by reducing innovation, is continuing to have an overreliance on their legacy technology. While there has been a push for digital transformation, many firms are still plagued by outdated systems. It is also common for them to be a sort of patchwork solution that has other pieces of newer technology tacked on to it, Prasad Prabhakaran, Head of AI Practice at esynergy, explained. “This is bringing down innovation because you need to support and maintain these legacy estates.”
Balancing profit margins and innovation
Given the tight strings around the budget, firms are going to need to make tough decisions on how they can balance their profit margins with innovation.
For Howard, this all comes back to that sense of understanding the real driver of business value. “A lot of companies seem to invest in tech for tech’s sake, because it is interesting or exciting. But it doesn’t help profit margins and breeds negativity around the transformational goal.” It is essential firms have an aligned view of the cost versus benefit and balancing short-term pain for long-term gain, to ensure investments are going to impact profit margins over a longer period.
Prabhakaran also offered advice for insurance firms, encouraging them to take a test and learn approach. This is something that is more common within banking and asset management, where they have a sandbox style of system to experiment with new tools. Rather than waiting for a product to be built by someone else and buy it, firms should look to experiment in-house.
He said, “If you don’t experiment and if you don’t test and learn, you will be left out. The rate of change around AI and data is so fast, you cannot catch up by waiting to buy it when it is ready. That is not going to happen in the space for the next four or five years. So, it’s important to do this test and learn.
There is no need to transform the entire workflow and system at once, it is about identifying the easy wins for improvement and gradually changing things over time. “They have a better chance of success when they have something more steady in a sandbox,” Prabhakaran added.
Getting data sorted first
It is easy to be overwhelmed when it comes to technology. Whether it is an AI-powered tool that can automate countless mundane manual tasks, an improved data and analytics tool to transform underwriting and pricing capabilities, or a semi-autonomous agentic AI that can provide proactive support to an advisor. There are countless areas technology can be used within insurance. While a firm might be eager to explore some of these to bolster their efficiency and reduce costs, before getting started with that, Prabhakaran believes they need to fix their data.
“You cannot win with any of the new technology innovation without having your data sorted out, which is both your structure and unstructured data,” he said. It is estimated that as much as 80% of data used by insurers is unstructured. This could be PDF documents, images, text messages, voice calls and more.
Echoing a similar sentiment, Howard noted, “Without good quality data, you can’t do anything.” Insurance companies have large volumes of data, but often in poor quality, making it tough to use effectively. If the firm wants to leverage an AI model to automate underwriting tasks or provide an advisor with a tool aid with claims decisions, it needs access to quality data.
To overcome this, firms need to put data governance at the centre of everything, Howard added. Firms should establish a robust offensive and defensive data strategy that ensures data is handled the correct way and collated into a unified data lake. This creates a single source of truth that the entire business operates from and ensures technology solutions can get a greater context to generate improved insights.
Howard noted, “The insurance companies that win won’t be the ones with the best technology stack, they’ll be the ones that trust in their data to make decisions. That’s the crux of it for me.”
One area where this is paramount is with generative AI and LLM. These boast powerful use cases but are as only effective as the quality of data they have access to.
Prabhakaran explained that while AI models like Open AI and Gemini are extremely powerful, they are not designed for insurance. An insurance firm cannot simply start using the AI model and expect its output to be perfect for them, the models might understand basic insurance principles, but it would not understand an insurer’s specific pricing strategy or their underwriting principles. The insurer needs to plug their data into the AI model so it can tailor its output to be relevant to the insurer. “The organisations who can leverage these large AI models effectively are front runners.”
How esynergy can help
The insurance sector is one of the core verticals esynergy operates in. The firm and its team have gained a lot of experience working with companies of varying size and helping them grow. esynergy helps firms across three core areas: data, platforms and generative AI.
Focusing on data, esynergy empowers clients to take control of their data, helping to bridge the gap between raw data and actionable insights. It offers a custom approach for implementing a unified data experience so they can leverage innovative technology to streamline their operations. It guides clients so they can take advantage of generative AI, establish strong data governance frameworks, build the best data platforms and find the best data products.
As for platforms, esynergy is split between three phases. In the first phase, it helps with discovery, such as identifying inventory and capability, validating business cases and getting stakeholder alignment. Phase two is about mobilisation. This covers security and compliance, operating models and ensuring platform delivery. Finally, the last phase focuses on modernisation and migration to ensure the firm can use the new platform effectively.
Finally, esynergy supports firms with making use of generative AI. It guides insurers through understanding the potential business value of the technology, use cases, implementation, upskilling areas of the business, refining capabilities, creating enterprise AI framework and scaling the technology.
On a final note, Prabhakaran said, “We help them accelerate the rate of innovation and as well as try to reduce costs.”
For more insights into how to excel in the current market, download the whitepaper here.
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