The evolution of AI is having an unarguable impact across the business world as companies seek to implement new technology to increase productivity and efficiency. The increasing focus on the customer experience and data collection to facilitate better interactions across industries globally has driven changing customer expectations1. This, coupled with a shift in key demographics to tech-native Millennials and Gen Z, has meant that even traditionally slower-paced industries such as insurance or financial services are coming under pressure to provide near instantaneous decision-making, services, and support2. A recent study by IBM found that 84% of insurance organizations implementing or planning AI engagements cite improved customer satisfaction as a primary objective3. By making use of AI, businesses have been able to increase data collection and analysis, decision-making capabilities, and provide customers with 24/7 services at a rate that human employees could never replicate. As customer experience becomes a key differentiator, not only in attracting new customers but also ensuring retention - increasingly important during periods of economic uncertainty due to the expense involved in attracting new customers – it is likely that companies will have to turn to AI to keep pace with competition.4
However, the implementation of AI requires careful thought and human oversight as AI lacks the nuance in order to deal with complex situations or decision-making. Issues involving biases in data must be considered to prevent discrimination by AI systems. Companies also must ensure that they remain inclusive to the technologically illiterate and the vulnerable – who may not have access to tech or cannot use it effectively to fulfil their needs. Often, if a customer is having a problem, they want to know they can get in touch with a real person should they need to, and chatbots or automatic emails that do not provide the option to shift to a customer service agent can prove frustrating. Therefore, there is a delicate balance that must be struck between automation and knowing where a human touch is needed. This is something that is based on a variety of factors dependent on an individual business, including customer demographics, business needs, rate of growth, and employee numbers.
How can implementing AI change your business?
Data is key to personalisation and understanding your customers, and AI facilitates accessing and analysing more of it faster5. This enables a personalised customer experience throughout their journey, with increased customer interaction precipitating more data, a more accurate profile and, in turn, encouraging more interaction. The use of data to build detailed customer profiles, coupled with machine learning, provides greater opportunity for re-engaging and upselling or cross-selling to current customers in ways that are tailored to their needs and are far more likely to be successful6. By having detailed profiles, customers benefit from tailored products and services, such as insurance plans that properly fit their needs and ensure the best value7.
The new generation of AI-powered systems can also proactively request data from customers without human intervention, meaning more data can be collected without the need for further work by employees8. AI can easily assess behavioural patterns and instantly respond to the needs and sentiments of customers through machine learning recognition9. After gathering data, the AI systems can analyse it and take further actions to guide future customers through their purchase journey. Businesses that have integrated AI into their systems are now benefiting from mass data collection and analysis at speed, enabling informed decision-making and responsive flexible strategies.
Going a step further in personalising and improving the customer experience means predicting needs, and this area, in particular, benefits from the use of AI to do it well10. For example, a chatbot can proactively message customers who appear to be stuck on a page, learning from every interaction so that it can pinpoint customer pain points within the customer journey and ameliorate them, often before the customers themselves realise they need additional help.
Meaningful prediction utilising AI is also incredibly useful when it comes to business processes involving risk. By being able to quickly assess and analyse forms of structured and unstructured data to identify patterns, processes, and anomalies, AI can help risk managers and underwriters to improve decision-making11. Not only does this benefit insurance businesses by reducing the frequency and severity of allocated loss adjusting expenses (ALAE) in claims, but predictive AI can also help the customer by reducing instances of personal bias or human error12. Outcomes for customers as a result become more consistent, rather than identical cases having entirely different outcomes due to having different risk managers working on them. AI can also solve issues around customer dishonesty or error, which can make risk assessments inaccurate for insurance or financial services purposes13. Machine learning enables the use of more sources of data, for example, company reviews, social media, or press coverage, allowing for a more holistic view14.
However, while AI can eliminate bias, it can also introduce bias as a result of taking data at face value. This is a considerable issue for the insurance industry as cognitive analysis is key, when accessing unstructured data to reduce bias, something that AI struggles to do15. In many jurisdictions, regulators forbid the use of customer creditworthiness, gender, and race information in insurance pricing models and AI applications need to implement similar restrictions to avoid discrimination in decision-making16. Not only can failure to demonstrate compliance with privacy and data security laws and regulations within the context of an AI strategy result in irreparable damage to an insurance company’s brand and regulatory fines, but it can also have considerable ramifications for customers who end up feeling the brunt of unjust pricing or rejection17. The potential of AI to be biased, highlights the importance of human oversight when implementing software, ensuring more complex decisions that require nuance are addressed by a person, not a machine. AI decision-making should also be transparent so that any issues or faults can be easily identified and addressed through human oversight18.
By using a combined approach to decision-making across business functions, AI can take onboard a lot of the day-to-day legwork involved. This frees up employees to focus on complex cases that necessitate a more nuanced approach, maximising productivity and speeding up the customer journey19. As the customer demographic increasingly shifts to target Gen Z and Millennials, speed will become an essential factor in the customer experience, as generations that have grown up with technology are far more likely to expect immediate responses, quotes, and services, even from industries that historically have required longer or more complex onboarding, such as financial services or insurance20. For customer service, this means customer data and solutions to problems can be pulled up instantly, and chatbots can provide immediate responses to queries. For businesses, such as financial services, that require customer identification, virtual identification using AI means that customers can be verified through their phone camera immediately, rather than having to appear in person, speeding up loan approvals and transactions21. For insurance businesses or companies that have to assess risk, AI can sort through and capture vast amount of information from multiple sources, facilitating machine learnt decisions that speed up onboarding, approvals, pricing and more22. This not only takes pressure off of underwriters by automatically filtering out obvious cases but speeds up interaction lead times, improving customer satisfaction23.
Along with speeding up the customer journey and interaction times, AI also enables businesses to provide their customers with greater support. While AI cannot replace a good customer service team, it can fill in the gaps – AI doesn’t need to sleep and doesn’t have limited work hours, meaning that if your customer has a problem at 3 am, AI can offer support and, hopefully, a resolution24. Even if ultimately, the customer has to get in contact with customer service the following day due to a complex issue that your AI system can’t solve, your customers will still appreciate being offered some sort of initial support out of hours, as it indicates your prioritisation of their needs25.
As AI is increasingly implemented across industries, we should expect to see strides forward in terms of a streamlined and personalised customer experience. In areas like insurance, correct implementation could mean insurance becoming fairer, cheaper, and faster, with policies becoming more individualised26. These industries that require substantial risk analysis, including financial services, will see radical change as a result, as processes and approvals that would previously have taken days to accomplish will be reduced to a matter of seconds. To ensure businesses get the most benefit from AI systems, they need to think carefully how it can be implemented in alignment with their priorities and pinpoint which technologies will help them achieve their objectives27. Companies also need to acknowledge the certain areas that will never be able to be completely replaced with AI. To ensure inclusivity and avoid bias or discrimination the use of AI requires human oversight. Collaboration between AI and experienced employees who are able to evolve alongside and implement new technology to become more productive will be key going forward. While much of the administrative and repetitive work within industries can be taken on board by tech, the importance of nuanced thinking, creativity, and human experience cannot be overstated within business strategy. Ultimately industries sell to people, and as a result, that process, at least for now, requires a human touch.
11 Financier Worldwide; Business News Daily
15 Financier Worldwide; Dataversity; Accenture
19 Learn.g.2; Accenture; IBM
26 Forbes; Business News Daily