How AI helps asset managers provide more value to clients

How AI changes asset management distribution workflows and the outcomes leaders measure

Hayden posed a question to start: "[While] wholesalers with AI workflows are getting three, five hours back a day, what are they doing with that time? Are they playing golf? Or are they actually maximizing that added time to their day?"

His question highlighted the real opportunity for wholesalers to better nurture and grow relationships.

Vanessa shared two key themes for the Invesco team's operating model:

1. Enabling client-facing teams to deliver more informed, personalized engagement.
2. Evolving the go-to-market approach as clients increasingly interact with AI agents and new interfaces.

"With the advent of AI, you can really be a lot more intelligence-led," Vanessa said, "coupled with the ability to be a lot more personalized." Vanessa explained that by understanding the client better, her team is accelerating trust. She noted, "We're able to get to trust faster because we're more personalized."

Joe described the BNY Investments team's model for AI workflows, saying, "It's a combination of analysis and action." He added that this has dramatically shifted their approach to portfolio analytics.

"We now have a workflow that's streamlined that entire process and created an extreme focus on what matters most, which is that advisor's biases, their preferences, so it's the application to the client that creates a very different experience."

Voya's model is based around the guiding principle of "seek first to understand," according to Tiffani. She described using publicly available information to broaden the firm’s view of clients and learn how they prefer to consume information.

At the time of the panel, Voya was also beta testing analysis of meeting notes to better understand clients. These applications put client discovery ahead of deciding which product to discuss, and, per Tiffani, "help us be really differentiated in the way we approach those conversations."

Build AI workflows on trusted content and client data

Building a distribution operating model that scales requires a governed foundation of content and protected client data. As agents take on more work, the information and standards behind them become even more important.

"Centralizing the core of your business around the data, the content, and the governance standards is everything," Hayden said. Distribution leaders should build content governance into everyday workflows so client-facing teams can determine which source is current, whether its use is appropriate, and who needs to review the output.

Voya’s approach shows how employee experimentation can operate within those boundaries. Tiffani explained that employees develop ideas with technology colleagues and put them through a review process to assess their value to the business and clients. As she emphasized, "everything we do has to operate under a governance model, period."

"Bring technology, bring intelligence to them in their flow of work," Hayden said. Effective AI governance in financial services needs to support how teams prepare, share information, and follow through.

How shared priorities help teams turn revenue AI into results

Sharing client context across distribution teams helps everyone deliver more meaningful, relevant conversations.

Vanessa described how connecting AI point solutions supports that shared understanding.

"It brings these different point solutions together to drive that personalization, which accelerates getting to the needs of the client. It creates that shared consciousness to allow those engaging with the client to have more meaningful and relevant discussions," Vanessa explained.

Tiffani shared how Voya is aligning teams around common client priorities. "We brought together sales, marketing, and product under one house," said Tiffani. "We have to all function together as one unified organization that isn't operating in differentiated silos with different KPIs and different results that they seek to drive." Tiffani reported stronger partnership, communication, and information sharing following the move.

Voya is also building agents to share insights across those functions.

Joe emphasized distribution team alignment around a more complete client view and consistent standards. With this, he said, "you can have shared commercial priorities, a more complete client view, and a consistent set of standards." Connecting those priorities to the distribution operating model helps teams coordinate their expertise and contribute to financial services growth.

Hayden noted that when you have one KPI for the outcome you're trying to drive, everyone's measured on the same metric and working together. He emphasized that a win or loss tells a limited story, but that the life cycle of engagement that feeds all the way through the customer experience down to your customer support team is where the magic happens.

How asset managers can build skills and behaviors that drive AI adoption across teams

What’s the best way to think about AI-powered workflows? According to Hayden, it's about building systems around the behaviors that already produce strong performance, then reinforcing them with intelligence, governance, and trust.

Scaling those behaviors also requires the unified organization, as emphasized by Tiffani, with distribution teams working toward shared client outcomes across functions. Training, leadership participation, and shared practices help make that way of working part of everyday distribution.

Invesco introduced sales productivity consultants who work directly with frontline teams to capture and share effective behaviors.

Vanessa told the audience, "Make sure that across the entire organization, best practices are modeled." Sharing best practices gives other client-facing teams a practical starting point for scaling AI adoption.

Hayden described a similar starting point in his account of Royal London Asset Management. The firm built a foundation around strong existing behaviors across marketing and distribution before calling on AI to enhance them.

Voya paired required training across eight modules with an innovation challenge inviting employees to propose applications. But Tiffani noticed the ability to build agents was not translating into much activity. "So I made an agent. I made an agent, and I shared the agent." She described colleagues becoming more willing to contribute their own ideas after seeing her examples.

And for leaders building similar programs, practical support should include access to an approved content library, clear review routes, and opportunities to learn from what's being tested. You can build confidence internally when your client-facing teams can understand both what they can try and how to assess whether it helps.

In building familiarity with AI usage and "digital employees," Joe noted you have to "get people comfortable with a practical starting point." He used roadshow planning as an example of building confidence by eliminating hours or days teams spend planning.

By opening the space for teams to ask, "Should we have a roadshow?" or say, "Let's focus on these two cities," you drive confidence and winning behaviors. "Let's solve for the simple to create the comfort to then release the minds of everyone to really get into their creative."

Invesco's pilot versus passenger mindset similarly encourages employees to consider how AI can augment their contribution. This gives managers a clear coaching responsibility: help people use added capacity to strengthen trust in financial services through better client understanding.

Turn AI productivity into stronger client relationships

To get more value from AI, asset managers need to examine the workflows, processes, and responsibilities shaping distribution today. Tiffani urged firms to rethink those foundations as they introduce agents and models.

"You have an opportunity to revolutionize the way you do your work," Tiffani said. "Why waste the time trying to take an old model and just make it a little faster?"

The panel's experiences suggest five steps toward building a distribution operating model:

1. Choose a client outcome, such as more relevant guidance or stronger follow-through.
2. Map the workflow and identify the people responsible for each handoff.
3. Connect approved knowledge, appropriate access, and required review.
4. Train teams, model effective behaviors, and align incentives.
5. Measure relationship and business results alongside efficiency.

Keep the purpose close to what Vanessa described as "delivering more on behalf of the client." A proposed scorecard can help leaders assess whether workflow improvements support that aim:

Measurement level	What to measure
Workflow performance	Track turnaround time, rework, and adoption to understand whether AI helps teams prepare for meetings and follow up more efficiently while maintaining the quality of their work.
Relationship quality	Combine client feedback with evidence of relevant follow-through and deeper relationships to assess whether teams are using shared insights to better understand and address client needs.
Commercial results	Monitor opportunity progression, net flows, and share of wallet to evaluate whether improved workflows and more relevant engagement contribute to stronger business results over time.

Establish a baseline and review the measures together to understand where the workflow is improving and where further changes are needed.

"AI adoption takes off when people work better, not faster," per Hayden.

Not sure if your AI initiatives are actually driving outcomes? Join our upcoming webinar with Ignites to explore how to measure them. Register for the webinar.