The real question is: Why not leverage these tools to consistently capture alpha as the largest players are currently doing every day?

 Hortz: With such strong benefits, what are you experiencing as your greatest challenges? Are there misconceptions about AI that are preventing wider usage in professional money management?

Aferiat:  The biggest single misconception is that AI replaces the portfolio manager and trader, which cannot be further from the truth.  Rather, AI, as we just discussed, is the most advanced information tool that a manager can have. It is has a capability no team could ever execute, yet all the decisions remain with the manager. Think of it this way: why would a person choose to use a slide rule if a calculator is available?  We are strategically providing the benefit of information asymmetry for better decisions. So, paradoxically, AI does not replace the manager, but reallocates time and decisions to where he or she contributes the highest value.

Hortz: Where exactly are we in the development and adoption of AI for money management right now?

Aferiat: We are still in the early adoption stage, with some skepticism. Large firms have built these tools for in-house use every day, putting midsized and independent firms at a disadvantage. Firms that have embraced AI find the accessibility to better information to be immediate. Those who adopt AI as an input into their decision making process will benefit over time from first mover status, as those who delay will be in “catch up” mode. Also, I think there is some hesitancy around the term itself.  AI is in fact intuitive, with an almost zero learning curve once adopted. Next day use is the frequent use experience by those who are on-boarded.  That experience is backed by learning curriculums and client support we provide as users move to become “virtuosos.”

Hortz: Having growing adoption from professional traders, institutional managers and family offices, which have an affinity for advanced technology and alternative approaches to money management is one thing. What are the particular challenges in engaging RIA’s, asset managers, and wealth management offices with your AI machine learning services?

Aferiat: RIAs, FAs, Wealth Managers and Asset managers are faced with the same problem as any investor: does my strategy perform for my clients? So the real challenge is never around technology, but around performance.  Technology needs to be put in the service of that performance, and we can demonstrate that performance now over a 3 year period. So I think there is a communicative disconnect. People hear technology and they are already saturated with it. There’s a tech fatigue as in “I can’t take on any more.” Then there is fear around cost and fees.  We focus on their concerns, producing results and gaining AUM, with ease of use in respect to visualizations of the data or delivery of a raw feed if desired, at a more than manageable cost.

Hortz: From your practical experience in working with RIAs and asset managers, can you share a few use cases or case studies on how AI has been used most successfully in addressing their money manager’s challenges?

Aferiat: We have seen a variety of use cases leveraging Trade Ideas’ AI:

For individual RIAs, major impact is provided for those who directly invest on behalf of their clients. They use AI to develop ideas for defined segments of their client portfolios and as a research tool for other actions based on the algorithms the AI selects daily from the inventory of those it's developed, optimized, and monitored for consistent performance in capturing alpha. These RIAs are prepared with daily ideas, data, and direction that they can actively monitor and easily report vs. just quarterly fund performance.