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In my experience working with advisory firms on digital transformation, I’ve seen one challenge appear over and over again: Too much data and not enough connection between systems.
Advisors spend hours gathering information via different systems that should already be talking to each other. But by the time they make sense of it, the market has moved on. I’ve seen even well-established firms face this problem because their systems rarely communicate smoothly.
A few months ago, I spoke with a registered investment advisor who looked completely worn out. His team managed hundreds of accounts using Schwab, Tamarac, and Redtail. Every week, they spent hours reconciling spreadsheets that refused to align.
“We have all the data we need,” he told me, “but none of it talks to each other.”
That single line captures one of the biggest problems RIAs face today: Data fragmentation. Advisors sit on valuable information scattered across platforms.
According to a 2025 white paper by the World Economic Forum, only a fraction of firms have moved beyond pilot-phase AI deployments in financial services. The result: They spend more time managing numbers than managing relationships.
From Static Data to Living Intelligence
I later watched that same, aforementioned advisor’s story take a different turn, once he introduced AI analytics into his operations.
At first, he had doubts. He was concerned about the cost, the setup process, and training his team. But after getting tired of manual reports, he decided to try an AI analytics tool that connects all his systems.
The change was visible within weeks. His dashboards pulled data from Schwab, Orion, and Redtail and aggregated it all in one place. When a portfolio drifted off its target allocation, he received an instant alert. When a client’s engagement began dropping, he knew before the next quarterly review.
Industry research shows that financial firms embedding AI into workflows report meaningful benefits. A PwC analysis found that 79% of executives say their organizations are already adopting AI agents, with some reporting up to a 60% shift of team time toward insight-driven work.
AI didn’t replace his expertise; it gave him more space to use it. Instead of spending Mondays chasing missing data, his team spent that time planning outreach for clients who needed attention. They started identifying trends early, catching small shifts before they became major issues.
The Real Cost of Fragmentation
Before adopting AI, his firm would spend nearly ten hours a week on manual reconciliations. By the time reports were ready, the insights were already outdated. That lag wasn’t just frustrating; it was costly. Time spent verifying data meant less time spent with clients, creating blind spots that risked both performance and trust.
The Deloitte 2025 Investment Management Outlook shows that only 11% of firms describe their AI usage in distribution or client-facing operations as “heavy,” despite most firms recognizing the value of moving from concept to reality.
When automation handled the repetitive tasks, the firm saw clear improvements. Their work ran more smoothly, clients were more engaged, and the team had time and energy to focus on growth. The change improved not only productivity but also team morale.
How 1 Firm Took the First Step
When I asked him later what finally made him take the leap, his answer was simple: “We picked one pain point and fixed that first.”
Instead of trying to overhaul everything, they began by automating a single task: Performance drift alerts. Once the team saw how much time that saved, adoption spread naturally. They trained staff gradually, learned to read the data insights, and layered in more automation piece by piece.
That slow and steady approach worked. It turned skepticism into confidence and proved that AI wasn’t an overwhelming project. Rather, this slow adoption demonstrated over time that AI was a practical tool for better decision-making.
The Bigger Picture
What I’ve learned from working with advisors like him is that AI analytics isn’t about replacing human judgment. It’s about freeing up time for it. When systems are aligned, advisors can focus on what matters most and make faster, smarter decisions.
Those who learn to use AI not as a gadget, but as a partner in their process, are seeing more than just efficiency; they’re seeing growth, stronger client relationships, and renewed energy in their work. Firms that use real-time data will set a new standard for advisory success.
It all starts with one simple choice: Focus less on managing data and more on achieving results.
Naaz Scheik is the founder of SoftPak Financial Systems and keynote speaker at RIA Edge LA.
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Advisor Perspectives welcomes guest contributions. The views presented here do not necessarily represent those of Advisor Perspectives.
