This is the third post in a mini-series on The Three E’s of AI Success. Catch up on the previous posts here.
As technology has evolved in the investment and wealth management landscape, designing for efficiency has always been a key objective. Typically, efficiency has been measured in quantitative terms—how many minutes or hours can be saved, how much throughput can increase, how much cost can be reduced. These hard metrics are essential for teams looking to optimize their business operations.
With the advent of AI, it’s time to expand our industry’s idea of efficiency, to assess impact in qualitative terms as well as quantitative. Operating at peak productivity should mean not just doing things faster, but also smarter and better—generating greater impact per unit of effort, more insight per interaction. Automating routine and manual tasks is just the beginning. AI unlocks incredible new opportunities for efficiency, in terms of detecting anomalies, surfacing hidden insights, and ultimately helping users make better decisions and take the best actions—faster, to boot.
At BetaNXT, AI accelerates and elevates the work we do for our clients and their clients. From our internal processes to our market-facing solutions, we’re infusing AI throughout our organization—and the resulting efficiency becomes a catalyst for broader transformation. It helps us automate the routine, untangle the complex and unlock new intelligence for our own teams as well as our clients. Below, we highlight a few key areas where AI is driving quantitative and qualitative impact at BetaNXT—and why they matter.
Automation at Scale
Automation is the most obvious application for AI, since it can do the work of many human employees in an instant. Across our organization, many historically labor-intensive tasks—such as document ingestion, data extraction and reporting—are now supported or fully automated with AI, freeing our teams to devote their time and thinking to higher-value activities.
For example, legacy workflows for corporate event setup once required extensive document sourcing, scrubbing and manual data entry. This slowed turnaround times and constrained our ability to scale. By implementing a production-grade AI platform, we have automated the majority of event capture and setup.
Regulatory filings processing is another area that has traditionally required hours of tedious, manual work sifting through dense documentation, extracting key information and reformatting data for reporting. Now, AI radically shortens the process, taking minutes instead of hours to move from ingestion to structuring to analysis.
The benefits of AI-enabled automation might be most appreciated in the areas of our business where volume experiences cyclical spikes. For instance, the short-but-intense proxy season each spring is demanding both for corporate issuers and for the operations teams who support them. Sourcing, validating and setting up proxy events is now fully automated at BetaNXT, giving our team members and our clients greater peace of mind.
In each of these cases, AI-powered automation doesn’t just deliver speed; it enhances service quality by freeing teams to focus on edge cases, exception management and deeper client engagement.
Augmented Intelligence
Beyond automation, we also like to think of artificial intelligence as augmented intelligence, technology that helps human users accomplish their goals not just faster, but better. With AI, our teams not only vastly expand and deepen their view of information—they also benefit from a sharp-eyed assistant that calls attention to where it is needed most. AI helps our teams identify anomalies, unlock hidden insights and recommend action items—processes that used to depend on deep manual inspection and cross-team coordination.
For example, we’re exploring an AI-powered call review tool that can instantly summarize calls—while keeping personal information protected—and spotlight the moments that really matter for our analysts. Instead of getting bogged down by hours of audio, this human-in-the-loop approach lets our team focus on what’s truly important: interpreting insights and making sense of trends. By reimagining the way we review calls, we hope to speed up analysis, make insights more consistent, spot patterns faster, and give our analysts more time for the work that makes the biggest impact.
Predictive analytics is another key area where AI is enhancing intelligence for our employees and our clients, through advanced models that identify key patterns and forecast future outcomes. One initial application coming soon will help firms anticipate and mitigate attrition risk among both investors and advisors, by observing activity patterns and surfacing early indicators of disengagement. Firms empowered with these insights on attrition signals can act quickly with proactive retention strategies—especially in the increasingly competitive quest for top advisor talent.
By delivering expanded visibility, earlier alerts and intelligent recommendations, AI-driven efficiency is enhancing the efficacy of our team’s work as well.
Engineering, Elevated
AI doesn’t just transform operations; it accelerates the pace and precision of product development itself. Our engineering teams use AI to elevate their productivity and enhance code quality as well.
Traditionally, code review and refactoring are labor-intensive and time-consuming—critical work, but difficult to scale. But now, by integrating AI, millions of lines of code are reviewed in minutes rather than days, enabling our teams to write and refactor code more consistently and quickly. Our human-in-the-loop approach pairs AI’s powerful capabilities with our engineering team’s institutional knowledge—a combination that leads to a higher-quality codebase, accelerated release cycles and products that respond more quickly to client needs.
When developers spend less time on repetition and more on innovation, that’s efficiency delivered through empowered engineering teams—not just time saved. Across our enterprise, AI-driven efficiency acts as a force multiplier and intelligence amplifier, too.
Looking Ahead
In our next and final installment of this series, we will wrap up with a look at our third E for AI success: Experience-First.