Artificial intelligence (AI) is hot. Funding for AI startups has steadily increased over the past decade as a host of innovative tech entrepreneurs have stepped forward to solve a broad range of business problems. In many respects, AI is a natural fit for the insurance industry. This business is a numbers game; it’s about understanding correlation, predicting risk and identifying trends and anomalies, so the current rush to adopt AI doesn’t come as a surprise.
AI is indeed powerful — but it’s not magic. For years, tech visionaries and Hollywood screenwriters have offered us visions of a future in which machines exhibit human-like thought patterns combined with a virtually unlimited capacity to consume and digest new information. Digital assistants like Siri and Alexa have furthered those impressions, using natural language processing to understand human speech and respond to simple requests. They have reinforced the notion that artificial general intelligence (AGI) is already upon us.
Back in 2005, Gartner published and branded its now-famous “Hype Cycle.” A new category of technology emerges, a flood of VC money follows and, eventually, most early-stage companies fail or are acquired by bigger players. Ultimately, the majority of hyped technologies succeed in providing net positive value but not before they’ve been through the stage that Gartner calls “the trough of disillusionment.” That stage can be painful; it’s where a lot of startups fail. It’s also where a lot of once-promising internal projects are relegated to the trash heap.
There are, however, some extraordinarily good reasons for the current hype around AI. Insurers are achieving remarkable efficiencies and using it to transform claims management, improve risk assessment and detect fraud. The key, generally speaking, is to use AI to augment and enrich existing business processes rather than replacing them wholesale. The human touch still matters, but it can be rendered far more effective with the assistance of natural language processing, machine learning and predictive analytics.
To be successful with AI, executives should adopt the view that this technology serves as a key component within their overarching business strategy. As such, it requires careful analysis and forethought. With the right approach, insurers can achieve impressive returns on AI investments. That’s not a future promise; it’s already happening today.
Successful deployment of AI requires a careful balance between visionary optimism and cautious pragmatism. As insurance executives plan their AI investments, here are some best practices that will help to ensure successful business outcomes:
- Start with a list of your biggest challenges, then identify a path to solving them, one step at a time. Think beyond innovation and experimentation. Engage with stakeholders throughout your organization with the aim of identifying specific problems and operationalizing AI to achieve tangible benefits in the near term.
- Assess the opportunity for solving those challenges by reengineering (not necessarily replacing) existing business processes to incorporate AI. Map various AI technologies against those challenges. Aim at augmenting human intelligence — not replacing it.
- Assess your organization’s capacity to adopt and integrate AI, including fundamental competencies in AI technology, data access and data quality, change management and willingness among the target users to adopt proposed solutions.
- Get senior management involved … but at the right time. AI is strategic, and the C-suite needs to be informed and involved. They can bring vision, energy and support to your AI initiatives, provided that you engage them at the appropriate stage in the process. If senior management is involved too early, they could specify new initiatives that are ill-suited to AI. Early C-suite involvement can also frequently lead to inflated expectations. A better approach is to bring well-considered proposals that multidisciplinary teams at ground level have fleshed out.
- Make this a cross-functional exercise. Develop AI project teams that incorporate a range of perspectives and skill sets and don’t limit them to a single team. By creating multiple groups, each with its own dynamic and operational focus, your organization will benefit from a greater diversity of ideas.
- Start an AI-related education and skills program now. Even though you may not be sure yet of your specific needs for retraining and reskilling, begin to make education offerings available now that will help your workers adapt to future changes. Such programs will pay dividends down the road, giving your organization a head start in the change management process.
See also: 3 Steps to Demystify Artificial Intelligence
As you evaluate technology, plan your pilot rollout and eventually operationalize AI within your company, here are some additional factors that will contribute to your success:
- Before the pilot starts, set a timetable and criteria for deciding whether to go into production. This timetable will add rigor to the decision-making process and put pilot project advocates on notice that implementation is an important consideration from the very beginning.
- Adopt technologies that can scale and that can be used by your intended audience. If, for example, a chatbot is ill-suited to serve your customers as a primary channel, don’t adopt it with the vague hope that it will improve substantially soon.
- Get your data in order. AI relies on high-quality data, and it benefits from a holistic view of information enabled by integration. Assess your organization’s ability to unify and harmonize your data and to ensure its accuracy, consistency and completeness.
- Make sure AI can interface well with your existing systems. Select initiatives to prioritize needs to consider “the last mile” of implementation. Get your IT teams involved early so they have a hand in creating a feasible solution.
Finally, it’s important to be flexible and transparent and to manage expectations. Some pilots will prove to be impracticable fairly early. That’s to be expected, but stakeholders should understand that AI pilot projects are like a portfolio of investments; some will succeed while others will not. AI isn’t the answer to every problem, but insurers that neglect to get on board will be eclipsed by those that do. Be willing to learn from successes and failures and apply that knowledge to your future endeavors.
As first published in PropertyCasualty360.
