When I speak with heads of sales and distribution across financial services companies, one topic is consistently on their radar: using technology—artificial intelligence and machine learning, in particular—to enable their organizations. For some of these leaders, this topic is core to their vision of the future: They are already investing in new selling and marketing approaches and the technology to enable them. For others, it’s reluctantly on their agenda—the brainchild of some other leader who is promising transformational change—if only salespeople would follow the next best action.


Distribution leaders are right to be thinking about this topic, regardless of their perspective. That’s because AI-augmented distribution isn’t just an idea, it’s a reality—and it’s already making an impact for many companies. Distribution leaders should be thinking about the case for change in their organization, as well as the potential barriers.

When contemplating the impact of AI-augmented distribution, some will say to focus on the customer experience—that advisor or broker or investor journeys will be improved, and value will be created as a result. While I believe all of that to be true, my advice is to first focus on the cold, hard math instead. And for that, I go back to the foundational distribution equation:

  • Quantity of customer engagements
  • Quality of those engagements
  • Context of those engagements
  • Cost of each engagement

When combined, those four factors—quantity, quality, context and cost—describe the productivity of any distribution system. Increasing quality of engagements, for example—by presenting the right content, or improving the effectiveness with which you do so—will increase distribution productivity, assuming the other factors remain constant. And improving multiple factors simultaneously can have multiplicative effects on production.

To my mind, that is the aim of AI-augmented distribution: to employ AI or machine learning to improve the quantity, quality and context of customer engagements at an appropriate cost, thereby increasing distribution productivity. And my view of distribution is comprehensive, including all touch points: external sales, internal sales, marketing, product specialists and key accounts.

We’ve worked with companies to deploy AI-augmented distribution, and I can say definitively that the factors described above can be impacted. Here are a few examples:

  • By sending marketing campaigns to individual customers at the right time in the selling process, we’ve seen open and click rates increase by 15 to 20%.
  • In our initial deployments, about 65% of AI-augmented suggestions (or plays) are accepted by field-based salespeople—a sign that the context resonates for them.
  • By our estimates, top-line sales can be increased by 5 to 7% through combined improvements in the quality and context of distribution activities. For asset management, AI provides the unique ability to impact not only gross flows but also redemptions. The joint impact could mean anywhere from a 10 to 15% increase in revenues.

I believe that distribution leaders—be they optimistic or skeptical—shouldn’t spend much time thinking about if AI-augmented distribution will work. Instead, they should think about how it will work for them because the biggest barriers to success won’t be the machines, it will be the people. In particular, I see five areas where distribution organizations will need to adapt in order to take full advantage of the productivity gains promised by AI-augmented distribution:

  1. Sales needs to change. Salespeople are independent by nature, so figure out how to enable them to take advantage of AI-augmented recommendations. What will be your stance on adoption? Is it a requirement or a suggestion? Think about how your compensation and measurement will change.
  2. Marketing needs to change. Marketing campaigns will be automated and dynamic, so there will be no more need for list pulling. Instead, the demand for content will rise, as will the need to catalog that content. And the marketing department will increasingly play a role in shaping the sales department’s actions. Is your organization ready for that change?
  3. Mindsets, especially at the most senior levels, need to change. Since the sales and marketing boundaries must change, senior leaders must optimize interactions for advisor value and asset manager value, and promote these efforts to home office personnel. AI augments (or complements) human capabilities; it’s not a competition.
  4. The planning process needs to change. AI-augmented distribution requires planning across the totality of the customer promotional journey. Firms aren’t going to ask machines to invent strategy or content on the fly. Instead, AI and machine learning will direct and optimize the deployment of the strategy and tactics, which means that firms will need to establish regular, cross-functional planning processes as an input for distribution. Do those processes exist today?
  5. Technology and data management needs to change. Many firms have been working for years to assemble the data and technology toolkit needed to enable AI-augmented distribution, but there will also need to be tools to connect that ecosystem and make the whole system work. The ecosystem should be built with these orchestration engines in mind.

I’ll leave the above four topics for another series of blogs, but since the promise of AI-augmented distribution has become a reality, it’s certainly time to shift our thinking from the “if” to the “how” and “when.”