Artificial intelligence is rapidly reshaping the insurance industry, not by replacing insurance professionals, but by helping them work smarter. As insurers, brokers, underwriters, and UMAs face growing customer expectations, increasing compliance demands, and mounting administrative workloads, AI is enabling faster learning, better decision-making, and more efficient ways of working.
For many insurance businesses, AI can feel like a significant leap from traditional ways of working. In reality, successful AI adoption is often the result of a broader digital transformation journey. Before businesses can benefit from AI-driven learning and decision support, they need digital processes, connected data, and modern systems that can support it.
✅ Step 1: Digitise core processes
Digitise products, policies, claims, and customer journeys to create accessible, connected data.
✅ Step 2: Automate routine work
Use automation to reduce manual administration and free employees to focus on higher-value activities.
✅ Step 3: Use AI internally
Implement AI to support employee learning, knowledge access, decision-making, and everyday productivity. Starting with internal AI allows organisations to build trust, governance, and confidence before extending AI into customer-facing experiences.
✅ Step 4: Introduce Customer-Facing AI
Extend AI to customer service, self-service, personalised experiences, and faster communication.
The insurance industry is facing a growing capability gap. A shortage of experienced talent, legacy systems, increasing risk complexity, and limited specialist advisory capacity are placing greater pressure on brokers than ever before. Compounding this, insurance professionals can spend up to half their working hours on administrative tasks, reducing time available for client engagement, risk analysis, and strategic advice.
As insurance becomes more complex, the challenge is no longer simply acquiring knowledge; it is applying that knowledge effectively and at scale. The table below highlights key industry challenges and the digital and AI-driven solutions emerging to address them.
| Industry challenge | Digital and AI response |
|---|---|
| Legacy systems: Manual quoting, policy administration, and claims processes slow broker responsiveness and increase operational friction. | Digital workflows: Automation, APIs, and self-service portals streamline processes and enable faster client service. |
| Administrative burden: Time spent on repetitive tasks limits client-facing and advisory work. | Intelligent automation: AI automates routine activities, freeing brokers to focus on higher-value interactions. |
| Knowledge overload: Brokers must keep up with evolving products, regulations, and market conditions. | AI-assisted learning: AI delivers relevant knowledge and guidance in real time, reducing reliance on traditional training. |
| Rising client expectations: Customers expect fast, personalised, digital-first experiences. | Enhanced client service: AI helps brokers deliver quicker responses, tailored recommendations, and proactive support. |
| Skills gaps: Rapid industry change makes it difficult for brokers to continuously upskill. | Embedded upskilling: AI supports on-the-job learning, helping brokers turn knowledge into practical expertise. |
The insurance industry is evolving faster than traditional training can keep up. One-off workshops, annual certifications, and classroom-style learning were designed for a world where knowledge changed slowly. Today, brokers, insurers, underwriters, and UMAs are navigating shifting regulations, emerging risks, new technologies, and rising customer expectations in real time.
As a result, modern upskilling needs to move beyond teaching people what to know and focus on helping them apply that knowledge as part of their everyday workflow.
Traditional learning follows a “train, then work” model; knowledge is acquired separately from daily tasks, and much of it is forgotten before it can be applied. AI changes this by embedding learning directly into the workflow.
AI enables:
✅ Real-time answers and guidance
✅ Context-aware recommendations
✅ Continuous, on-the-job learning
✅ Faster access to relevant knowledge
Starting with internal AI also allows organisations to build trust, governance, and confidence before extending AI into customer-facing experiences.
The result is a shift from information to action, helping employees make faster, more confident decisions while continuously building practical expertise.
The future of insurance belongs to organisations that can learn, adapt, and evolve faster than the market around them. AI is helping make this possible by supporting better decision-making, automating routine tasks, and enabling employees to access the information they need when they need it.
However, AI is only as effective as the systems and data that support it.
Businesses still reliant on legacy technology, disconnected data, and manual workflows will struggle to realise the full benefits of AI. Before organisations can scale AI successfully, they need a strong digital foundation built on connected processes, accessible data, and streamlined customer journeys.
This is why many insurers, brokers, underwriters, and UMAs begin their AI journey with digital transformation. By digitising core insurance processes and automating repetitive work, organisations create an environment where AI can be adopted more effectively, governed more confidently, and integrated more seamlessly into day-to-day operations.
The organisations seeing the greatest success with AI are not necessarily those adopting the latest tools first. They are the ones investing in the digital infrastructure, processes, and culture needed to support long-term innovation.
The journey to AI adoption starts with digitisation, grows through automation, and evolves through the intelligent use of AI.
If your organisation is looking to build the digital foundation required for AI adoption, Briisk’s insurance digitisation experts can help you modernise products, processes, and customer journeys, creating the connected ecosystem needed to support AI at scale.
To learn how a connected digital insurance ecosystem can prepare your business for what’s next, click the button to book a demo.
What are the benefits of AI in insurance?
Some of the key benefits of AI in insurance include:
→ Improved customer experience
→ Reduced administrative workloads
→ More efficient claims and policy processes
→ Continuous employee learning and development
What’s the difference between automation and AI in insurance?
Automation follows predefined rules to complete repetitive tasks, while AI can analyse information, identify patterns, generate insights, and support decision-making. Many insurance businesses begin with automation before introducing AI capabilities.
How can InsurTech platforms help insurance businesses prepare for AI?
InsurTech platforms help insurance businesses modernise legacy systems, digitise workflows, connect data, and automate routine processes. By creating a connected digital ecosystem, organisations are better positioned to implement AI successfully and scale innovation over time.
How insurance businesses can prepare for AI adoption: A practical roadmap for digitisation, automation & upskilling: A practical roadmap for brokers, insurers, underwriters and UMAs looking to digitise operations, automate workflows and use AI effectively.
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