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4 minute read
Brendan McLoughlin, President of e123, recently participated in an executive education course at the Massachusetts Institute of Technology on Artificial Intelligence (AI) and its implications for business strategy. This is the thirteenth in a series of blog posts where he shares some of the insights he gained and how they apply to health insurance distribution.
In part one of this post, I introduced Harvard Business School professor John P. Kotter and his work Leading Change: Why Transformation Efforts Fail. In his research, Kotter identifies eight errors that companies commonly make when trying to implement change, and learning these lessons can be particularly useful for health insurance companies seeking to transform their business operations through the use of Artificial Intelligence (AI).
While AI represents a powerful toolkit for reimagining life and health insurance distribution, it is vital that business leaders treat AI implementations as the transformative events that they are. In part one, I reviewed the first four errors that Kotter identified: 1) failing to establish urgency, 2) not building a strong coalition, 3) lacking a clear vision, and 4) under-communicating that vision, and discussed how these mistakes in change management can doom AI implementations from the start. The last four errors focus more on project execution, and can be vital to prevent initiatives that could be transformational successes from fizzling into defeat.
While the first four errors are focused on vision, alignment and the overall strategy, the last four errors are more focused on executional issues that can derail change.
Error 5 - Not Removing Obstacles to the New Vision
Perhaps more than any organizational transformation, AI initiatives can elicit strong reactions from staff and executives alike. To some degree, having assembled a guiding coalition of leaders and communicating a clear vision to the organization will help create momentum for change, but these alone are not sufficient. Organizational obstacles that must be overcome will undoubtedly exist, and removing these obstacles requires conscious, concerted effort from leadership. Without this effort, change management will grind to a halt. Once again, identifying, tackling and removing obstacles can be challenging, but it is essential for leaders to take firm action, both to empower employees to drive change and to maintain the credibility of the change effort as a whole.
A great example of one of these obstacles is a “not invented here” mindset. A major health insurance marketer that I know has spent years and millions of dollars in a quest to build a massive, AI-driven data repository that will deliver strategic insights to their distribution system - an excellent use of what AI can offer. However, the project is constrained by an attitude of “not invented here”. The belief in the organization is that strategic advantage will be gained by them “owning” the systems and data collection, rather than what they “do” with the data. So despite the wide variety of tools available in the marketplace, the project has been bogged down trying to recreate wheels that already exist. Not only is this obstacle threatening the entire project, but it is also depriving the team of all important short-term wins.
Error 6 - Not Systematically Planning for, and Creating, Short-Term Wins
Major transformation takes time. Real impactful change can take years to deliver measurable results. But peoples’ enthusiasm for change has a much shorter lifespan. It is, therefore, advisable to consciously plan for and celebrate short-term, partial wins, which are a critical part of implementing long-term change.
This is an area where many insurance companies are getting AI wrong. Just because AI has the potential to completely revolutionize health insurance distribution, that doesn’t mean we as leaders should attempt to do it all. Rather than viewing AI as a massive, special project that needs a team of engineers and data scientists walled off from the rest of the organization, it might be more effective to instead view AI as a tool kit you can use to make quick, incremental improvements and start generating short-term wins.
Implementing an initiative with a focused scope can lead to realizing earlier success. For example, a machine learning (ML)-empowered assistant to spot potential commission errors or to flag agent bad behavior may not represent an earth shattering transformation, but could be a motivating first step to demonstrate to the organization the value of doing something new.
Error 7 - Declaring Victory Too Soon…
… or maybe ever.
One of the realities of continuous improvement is that it is continuous. There are always opportunities to improve, and developing a culture of transformation should be the ultimate goal of any change-minded executive. Too often, short-term wins are viewed as “good enough” by the very same forces that resist change in the first place. So while consciously planning for short-term wins is an important part of gaining long-term success, it is equally important to communicate milestones as steps along a path to a greater vision.
For example, initial implementations of AI to flag potential commission errors or bad acting agents should be celebrated in the context of a larger vision for the company. Insurance marketers can use AI to gain greater control over their distribution networks to attract, motivate and retain the best-producing agents, and create lasting, strategic advantage over their competition. In this context, the early wins are just the beginning of achieving the greater vision.
Error 8 - Not Anchoring Changes in the Company’s Culture
Kotter says “In the final analysis, change sticks when it becomes 'the way we do things around here’.” Driving change should be more than just implementing new ways of doing things. The ultimate goal should be to infuse the organization with the belief that constantly learning, growing and improving is a competitive advantage.
AI is certainly a tool that can improve business processes, cut costs and drive efficiencies. But more importantly, AI implementations can be a catalyst for creating a learning culture where improving and growing are encouraged, and where change is embraced as “the way we do things.” Cultivating a culture of being easy to do business with can be a powerful differentiating factor for health insurance carriers - motivating producers to focus on selling their products, driving profitable growth and creating sustainable advantage.
Conclusion
Successfully navigating AI transformation in health insurance requires more than just a focus on technology; it demands a comprehensive approach to change management. As Kotter's framework illustrates, avoiding common pitfalls such as failing to remove obstacles, neglecting to plan for short-term wins, declaring victory prematurely, and not embedding changes in the company's culture is crucial. By addressing these challenges head-on, health insurance companies can harness the full potential of AI, not just as a tool, but as a catalyst for continuous improvement and long-term competitive advantage. Ultimately, embracing a culture of change will position organizations to thrive in an increasingly complex and competitive market.
Want to learn more about e123 and the future of AI in insurance distribution? Get in touch here.
For prior posts in this series, click here or below:
