In our recent webinar on the human aspects of AI-augmented distribution, our panel of ZS experts discussed why implementing AI requires change management. To influence behavior, we need to shape experiences (what we sense and process), mindset (what we feel and the decisions we make) and behavior (what we do or don’t do).

As the use of AI intensifies around us, asset managers and insurers will start to incorporate the capability within their distribution models. Over the next few years, they’ll use AI to determine the right touchpoint (whether through marketing or through sales reps), the right advisor to call, the right time for it and the right message. However, many may fail or fall short of their maximum impact because of their inability to change behaviors to fit the new operating models. These poorly implemented “ivory tower” solutions often fall short in these areas:

• Vision and sponsorship: Overcoming barriers to AI implementation means establishing a common set of aspirations addressing what will be delivered, how the customer experience will change and the mutual benefits created. Naturally, the vision must incorporate the voices of the C-suite, sales, marketing, analytics, IT and the like. Out of this conversation a champion(s) who will sell the idea and drive the implementation will arise.
• Feedback loop with frontline sales and marketers: A crucial aspect of helping frontline employees in sales and marketing process the changes related to AI is involving them in creating the solution. As day-to-day users of the AI insights, salespeople and marketers will create a steady pulse on how well customers are responding to the touchpoint experience, how well it worked for sales and marketing and what could be done to further improve it. Creating this low-friction feedback loop (both through technology and conversations) helps the humans and the AI solution adapt and evolve.
• Cross-functional team effectiveness: AI pushes teams across the organization to work at an uncomfortably faster pace, with short turnaround times and rapid response windows, so the increase in agile pods makes sense. But it often takes these teams, most of whom have never worked together before, months to gel. Providing the engagement framework, guidelines and processes for the pods to function is a precursor to success.
• Data savvy: Even if the insights generated by the AI solution are not as good as the best insight created by a human, they must be accurate. That calls for deep and clear understanding of the metadata (sources, transformations and update frequencies) as well as the limitations of the data. Salespeople and marketers must feel conviction when following AI guidance. Good data is the bedrock of that conviction.

In the end, AI solutions will help augment human activities so that sales reps can be more efficient, better equipped and more targeted in their interactions with advisors. These benefits will ultimately lead to lower costs and higher revenues, as long as organizations correctly design, implement and drive adoption to the field.