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Amid all the excitement over the business transformation that will be driven by the appetite for artificial intelligence, the reality of what companies will do about implementing AI over the next 12 months might be a little different.
Will AI truly deliver in-year benefits that revolutionise the way the business works or could it prove to be another expensive multi-year project?
Money to spend
With some insurers posting great results from 2023, we are seeing more companies starting to spend money on transformations. But with money to invest in big multi-year programmes, the temptation is to buy something big and shiny without establishing whether it will deliver the required benefits for your business requirements..
Many of the conversations we have with clients around digital enablement focus on data quality and business processes. The user experience is central to changing business processes and there’s been plentiful discussion in recent years about user-led design, prompting many companies to build their workflows.
Typically, this is a slick front end with a lot of clever stuff behind it. But it's the clever stuff behind it that's the interesting part, especially when talking about AI transformation.
AI versus IA versus data
In the underwriting world, the current debate is whether their strategic focus should be on AI or intelligent automation (IA); technology that can mimic human behaviour versus technology that automates tasks previously performed manually.
If properly mapped out to change certain business processes and automatically update internal systems, IA can remove the need for human intervention in repetitive tasks and deliver cost and speed benefits.
Arguably, every transformation programme that is currently going ahead will have an element of IA and in the insurance sector for example, businesses are looking closely at applying IA to automated underwriting, policy renewals, compliance and claims.
It’s apparent that certain lines of business can be relatively easily automated, assuming underwriters have the necessary data and can apply a few simple rules. It should also be possible to add some AI on top of that automation, such as chatbots for customer services and claims support.
However, whether an insurance business intends to use IA to expedite claims and automate underwriting, or use AI tools to access new markets and deliver new digital products, it all comes back to getting the data right. The reality is that most clients we speak to say their priority is to improve their data integrity.
Investing in innovation
Despite clear advantages to deploying AI-assisted tech, there is a reluctance to commit to significant spending. Those projects currently going ahead in the underwriting world are likely to be pilots, and there’s a lingering concern about putting AI at the core of the business because of the perception it could end up making unwanted decisions given a carrier's portfolio and risk appetite.
But with everyone developing an AI strategy, there’s a need for businesses to acknowledge that their strategy requires investment in a proper innovation hub, with the right people to deliver it and the right culture in place to enable delivery.
That investment should also involve change communications to get everyone in the company on board, appointing change champions across the business to drive engagement and, crucially compensating those champions properly.
Another common failing in the delivery of change programmes is under-investment in education and training. As well as driving engagement, investing in training employees to use new systems and technologies has an optimisation component. If staff are not fully conversant with a new technology there is a risk that businesses will not end up utilising the full range of capabilities the tech offers, and therefore the benefits realised are underwhelming.
Getting to grips with AI
While some believe the AI boom is overhyped and large parts of the insurance sector will shy away from getting to grips with the technology, it’s our belief there will be exponential adoption of AI over the next 12-18 months, across multiple industry sectors and business processes.
In the years ahead, we can expect to see a growing divide between those organisations who either lack the capital to invest in AI and are unsure where to implement it, versus those who grapple with AI, see their business accelerate as their data improves, and bring new products to market more quickly.
