How AI modernizes asset management distribution workflows

What today's asset management firms need to scale AI responsibly: Trust, sound data, and capable teams

As it relates to leveraging artificial intelligence in day-to-day business operations, Ekta spoke to her team's patience and not getting wrapped up in "chasing the next tool or the next model."

Instead, Ekta emphasized a more intentional approach that's all about "making Capital better, and that also helps our associates, but more importantly, our investors and our partners."

Accomplishing this means thinking about it across the entire ecosystem, according to Ekta. In asset management, executing with speed means doing so in control and with the proper governance in place.

"We can only move as fast as the speed of trust," said Ekta. "And so for us, being really clear about these frameworks and what we're trying to accomplish helps us really feel good about what we're doing and helps us set the intentional pace of doing this."

Ekta's overarching point: You can't build client trust with unreliable data.

In the poll we ran during the webinar, about a third of attendees ranked data quality and system alignment as where the AI execution gap hurts their companies the most.

Grant Thornton's 2026 Asset Management AI Impact Survey offers a similar benchmark: Around 31% of surveyed asset managers cited data quality and integration as a top scaling barrier, while 53% pointed to governance and compliance barriers as contributors to AI underperformance.

For Ekta's team, data remains a front-and-center aspect of their strategy.

"Data has always been a consistent theme for us," and our leadership fundamentally understands it," said Ekta. "We have such a robust program around managing our data, owning our data, governing it, organizing it."

Ekta added that the investment predates the current AI wave and is now amplifying new capabilities. She also explained how governance helps her firm move faster.

"At Capital, we see governance more as an enabler, not the brake," Ekta continued. "And the analogy we love to use here is that you can drive faster on a paved road than an unpaved one."

With clear ownership, safe testing environments, and guidance from legal and compliance, firms can move AI initiatives forward with greater confidence. By tailoring controls to each use case's risk, teams can move faster where less oversight is needed and apply closer review where more is at stake.

How a layered AI strategy connects distribution efficiency with differentiated growth for asset managers

Peter noted that a layered strategy turns AI from scattered activity into distribution results. He started with the cost of having no plan.

"It's important to have a strategy," said Peter. "Without one, it can be, just many random experiments happening across the firm."

PGIM organizes its strategy into three layers:

• Base layer—firm-wide tools: This layer gives everyone at the firm a baseline of AI capability. PGIM provides Microsoft Copilot to its distribution teams, investment teams, and other functional areas, who use it to research, draft emails, and draft notes.
• Middle layer—function-specific tools: This layer applies AI to the specific jobs of a function. For distribution, that means content generation across web, email, and campaign channels, content operations that make publishing across those channels more efficient, AI quality check, and meeting summarization and note-taking. Together, the two lower layers are asset management content automation built for returning time to distribution teams.
• Top layer—differentiated solutions: This layer builds AI around PGIM's own data to generate insights, enhance processes, and expand what the firm can do. Peter described it as AI for opportunity. "It's mainly proprietary solutions that we're building," he noted. "It's using our own data. It's to give insights to enhance processes to do things that we just couldn't do previously."

Peter's team also set the controls that hold the layers together. He drew a firm line on human review.

"We don't generate anything that isn't reviewed by a person, and I think that's even maybe a little bit too passive of a statement," said Peter. "We're using AI to generate drafts, to generate ideas," but that the follow-up and actual creation are human.

"It's about engaging our control partners," Peter added. "So that's compliance, that's legal, that's risk. "It's about engaging them early, having them a part of the process, and really being partners."

With investment content compliance built in from the start, asset management teams can use AI quality checks to support human review and approval of client and investor-facing material.

As AI takes on routine tasks, employees need training to review its outputs, apply its insights, and use the time they save to deliver more value to potential and existing clients.

Why connecting data, systems, and human accountability closes the AI execution gap

Ekta and Peter made it clear: Getting value from AI starts with connecting data, tools, and teams.

Capital Group's approach establishes trust through clear ownership and governance. PGIM's strategy puts those foundations to work through tools that support everyday tasks and capabilities that set the firm apart.

For distribution teams, AI is only useful if it can draw on complete client records and accessible, approved investment content. On the webcast, I encouraged firms to examine the basics:

• Is content tagged correctly in their digital asset management system?
• Are both sellers and marketers using the systems available to them?
• Can those AI-powered systems share the information teams need to act?

Consider a wholesaler's workflow after an advisor meeting.

By accessing an AI-generated summary alongside earlier conversations in the CRM, the wholesaler can quickly identify next steps. With approved collateral available in the same workflow, they can select relevant resources and draft a follow-up email for review.

Wholesalers can then use the advisor's engagement with that content to prepare for the next conversation, spending less time piecing together information and more time building the relationship.

By building human review into the workflow, teams can catch errors in investor-facing communications before they undermine client confidence. Apply that same discipline to each stage of implementation to turn AI investment into measurable improvements:

1. Map the work. Choose one distribution workflow and identify the data, content, systems, and people needed to complete it.
2. Set review points. Define which particular decisions and materials need approval and who provides it to distribution teams.
3. Connect the necessary tools. Bring together the systems that support the workflow, such as CRM, enablement, and meeting capture.
4. Measure the results. Track time saved, accuracy of AI output, adoption of AI tools, and overall client engagement activity.
5. Improve before expanding. Use the findings from your AI audit to correct problems and decide where to apply the technology next.

By reducing manual preparation and follow-up, asset management firms can give distribution teams more time to strengthen client relationships and build trust. Tracking how those improvements affect engagement and AUM growth helps firms identify where AI delivers value and expand with confidence.