Why is operational efficiency important?
Enhanced operational efficiency isn’t just great for business; it’s the difference between sinking and swimming in today’s insurance business ecosystem. Balancing your organisation’s input and output by reducing costs, streamlining resources, and increasing revenue with better service and repeat custom, is something that should be hard-wired into every company’s culture. If operational efficiency is hard-wired into an organisation, it’s more likely to be successful in the long-term.
So, how do we make our operations more efficient?
There are multiple strategies that can be employed to improve operational efficiency, from capacity planning to enhanced information sharing and collaboration. But today, one of the core areas to understand and develop is the use of AI and Robotic Process Automation (RPA) – it’s probably the most effective way to streamline your operations and take your business to the next level.
RPA versus AI
If your employees are getting dragged down by repetitive, dissatisfying and time-consuming tasks, then consider applying RPA (Robotic Process Automation) to your business processes.
Think of RPA like an automated workflow, ticking along, doing the same repetitive process in a predictable way. To distinguish, pure AI deals with uncertainty and idiosyncrasy, often completing tasks differently over time. At the moment, AI doesn’t deal with predictability all that well, but can be useful in coming up with different pathways to solve complex problems.
Integrating RPA with existing systems and rules engines can automate entire workflows. This can create better cohesion across different parts of the business, from data silos to email and spreadsheets, enhancing company-wide processes and freeing up employees to carry out more complex, service-enhancing tasks and product innovation.
Embedding AI capabilities will enhance decision making powers, giving you and your team insights and solutions that inform company strategy and outputs.
Optimising Legacy Systems
There’s a misconception that companies have to strip out legacy systems to integrate AI technology. Joost de Jonge from our AI partner, ExoBrain, argues that to capture the value of AI in a timely way, companies must be comfortable with experimentation and agility:
“AI is changing so rapidly that if you spend an age replacing systems, you’ll miss the opportunities that AI can offer your business right now. Low-cost experimentation with company datasets, through the creation of a centre of excellence and the necessary guardrails in place, accompanied by an agile mindset, will separate the winners from the losers in the race for AI capitalisation.”
This goes some way towards overcoming the road block of “Frankenstein” systems, built incrementally over time. These types of systems create operational drag but can be optimised, and their lifespan can be increased, allowing firms to develop the company’s future technological capabilities.
Integrating Machine Learning and AI
How is AI best embedded into your business? Joost recommends the phased approach of: “Augmentation”, “Automation” and “Autonomy”.
Augmentation is all about enhancing decision-making and operational efficiency through the use of AI tools. For example, you could use AI-powered analytics tools to process large volumes of data for underwriting and risk assessment, whilst also using machine learning algorithms to identify trends in claims and customer behaviour which would, in turn, provide marketing and product insights. Deploying chatbots to handle basic customer inquiries and helping customers choose the right policy based on their needs through an AI-driven recommendation system is also something that many leading insurers are already doing. It’s important, at this stage, to invest in training staff and creating a centralised knowledge sharing base for your internal teams. Encouraging collaboration creates cohesive business practices and workflows, improving overall operational efficiency.
Automation streamlines processes to improve efficiency and reduce costs. Applied to claims processing, you could create AI systems that can evaluate claims, detect fraud and facilitate quicker approvals or use image recognition technology to assess damage from customer accidents. For example, Fukoku Mutual Life uses AI-powered systems to analyse medical documents and calculate payouts. This has led to a 30% increase in productivity and significant cost savings.
With underwriting, you can further speed up the process by developing automated workflows and algorithms that determine policy pricing faster and more accurately. By automating policy management workflows, AI can update policy changes and notify customers of changes or renewals or even identify when customers may need policy adjustments.
After the first two phases, your company should be on the path to creating fully autonomous parts of the business which drive innovation. In this phase, your company could embark upon predictive modelling projects and risk assessment with systems that learn from new data and make suggestions for insurance products. Whilst at the moment AI doesn’t have end-to-end process capability, there may be a future where the customer journey process is fully automated, from onboarding and quote generation, to policy issuance and personalised follow-ups and renewals.
When integrating AI into the business, here are some watchouts and considerations:
- Data Privacy and Ethics: ensure that you comply with GDPR, the FCA and the PRA and implement ethical guidelines for the use of AI. Make sure systems are updated as and when AI regulations come into force.
- Change Management: foster a culture of innovation within the organisation to encourage the acceptance of AI technologies across the board. Employ change management champions to facilitate your AI transformation
- Partnerships: Collaborate with AI Specialists and vendors to access cutting-edge solutions and expertise
- Performance Metrics: your change managers should define KPIs to measure the success of AI initiatives, including customer satisfaction and operational efficiency.
To address these issues, ExoBrain recommend creating a centre of excellence for AI governance and strategy, portfolio management, best practice cultivation and AI trend monitoring.
Balancing your workforce with AI
It’s important to note that the most successful companies today and in the future will have to retain a human touch. Joost suggests that we must try not to think of AI in a linear fashion and as a replacement of whole end-to-end processes. AI works best by taking away drudgery and freeing workers up for expanded value creation and innovation.
However, in reality, there are a lot of inefficiencies in workflows that need to be identified, eliminated and augmented, or automated through the use of AI. When considering your resource model, the company needs to understand when and how to outsource certain functions while integrating AI and RPA to create a flexible, cost-effective workforce. A hybrid model can combine human expertise with AI-driven processes to optimise outcomes.
Simply put, implementing AI should help the business focus on empowering employees to enhance their productivity and morphing human job roles into more interesting and satisfying vocations.
At The Siena Partnership, we work with our clients to recruit insurance specialists and help their organisations innovate and deliver business success.
There’s so much more we can talk about on this issue. If you’d like to continue the conversation with our insurance expert, Tom Goodyear, contact him here: tom@thesienapartnership.com