Recent research from EY highlights just how quickly AI adoption is happening for wealth and asset managers.
Their research finds that 95 percent of firms have already adopted AI across multiple use cases, and 78 percent are now exploring agentic AI to unlock deeper strategic advantages.
Yet despite this momentum, only slightly more than one-quarter of executives reported AI has made a substantial business impact.
That gap between adoption and actual value was front and center in a recent Tech It Up podcast episode featuring Alex Schlesinger, SS&C Advent’s Director of Product Management for AI.
In this post, we share highlights from that podcast, showing how investment managers can cut through the hype to deliver AI with lasting value.
Capitalizing on the AI Opportunity: 3 Key Factors Investment Managers Must Consider
AI can be a powerful resource for investment managers. But to harness that power, Alex recommends investment managers look beyond the hype and take a more strategic approach to their use of AI, one that takes into consideration the needs, resources, and human expertise of a firm's operation.
Here are the 3 factors Alex recommends firms consider when approaching AI projects:
Don’t Lead With AI: Lead with the Problem
All too often – in their eagerness to put AI to use – firms get caught up looking for a place to use it, even if the use case doesn’t make sense.
Instead, Alex suggests that firms define their business problems first and then determine whether AI will add real value. Research bears this out: McKinsey’s State of AI: Global Survey 2025 report surveyed companies across industries about their use of AI. Their findings indicate that when companies identified “high-value use cases" for AI they were better able to drive measurable outcomes compared with those that generally implement AI.
Clarity drives results. When organizations begin with a clear business challenge—operational efficiency, accelerated research, automated workflows, or improved client insights—AI shifts from a buzzword to a practical solution.
Prepare for Real Investment: AI Requires Money, Time, and Operational Resources
In the podcast episode, Alex notes that many firms still fall into the trap of believing AI will simply “do all the hard work for us.” This misconception downplays the cost, risk, data readiness, and operational effort required to deploy AI effectively.
Industry research reinforces this reality. The 2026 Supermodel II report from Carne Group found that 60 percent of asset managers admit to underinvesting in AI—even in critical areas such as fund launches.
As a result, Alex cautions firms to be wary of vendors promising out-of-the-box, plug-and-play solutions that deliver “instant results.”
Firms that commit to the full operational lift behind AI—people, processes, data, and governance—are best positioned to move beyond experimentation and achieve enterprise-level impact.
Iterate, Don’t Abandon: AI Learning as You Go
On the episode, Alex points to a striking statistic from an S&P Global survey: the percentage of companies abandoning the majority of their AI initiatives before they reach production has surged from 17 percent to 42 percent year over year. The report cites rising data costs, poor data quality, or security concerns as the primary reasons.
But Alex is clear: AI projects only truly fail when firms stop iterating. Early challenges aren’t a reason to quit — they’re part of the process.
Instead, he encourages investment managers to learn from each attempt and strive to understand what blocked progress and how they could use these lessons to build a better use case.
Firms that treat AI as an iterative capability — not a one‑and‑done project — are the ones that move past setbacks and ultimately unlock real business value.
Turning AI Momentum into Measurable Value
As the conversation on the Tech It Up Podcast makes clear, AI’s promise for investment managers is real. But realizing that promise requires firms to move beyond the hype—anchoring efforts in real business problems, committing the necessary operational resources, and treating AI as a capability that evolves over time.
When investment managers approach AI with clarity, patience, and a willingness to learn through iteration, hype gives way to measurable outcomes.
Learn more by listening to the “AI Everywhere: What's Real, What's Not, and What Works” podcast and contact us to continue the conversation.
