The robots are here.

Artificial intelligence (AI) continues to build its presence across global industries, so its progression into the asset management sector is only natural. But AI can be understood differently by different people, so definitions are important.

One should think of AI as a field in computer science that builds "intelligence" into electronic systems. These AI systems are able to perceive data environments, learn, and take actions to achieve objectives. This definition presents a compelling value proposition for AI versus traditional portfolio management systems with respect to today's growing data environment. The key technological advantage of AI investment platforms is their overall flexibility to appropriately process massive amounts of dynamic market data and identify investing opportunities.

AI can help us better understand what to trade, and how to trade it. Important AI capabilities include: consuming, learning, and aggregating growing volumes of data across mediums, continually adjusting portfolio risk based on observed market signals (and the concurrent removal of rigid factor-based criteria), and the ability to connect new market signals and derive an optimized portfolio free of human bias.

High-performing systems should be able to assimilate and manage both structured and unstructured data in a timely manner, and appropriately process erroneous or blatantly fake financial news. Many early stories of AI investment systems described the brute force design of jamming massive data sets into the conceptual "black boxes" and allowing the machine to produce a series of recommendations. Better solutions exist and are more suitable for the explosion of available investment data and the demand for system operational observability.

These more transparent and higher-performing AI data processing systems are likely to drive increased asset flows into the space. As this evolves, investors should recognize that not all AI is created equal.

In reviewing the emerging class of AI-powered funds, an effort should be made to understand how they process data -- the same kind of due diligence that would be done on a human portfolio manager. The truth is that AI investment platform design will vary significantly, and in turn influence performance. But it is also true that the strongest AI-powered approaches coming to market do not operate as "black boxes" but instead are overseen by teams that should be able to clearly articulate their respective approaches. They should also explain just what drives their algorithms and the types of opportunities that have been engineered to uncover.

Even with the recent AI technological developments and improved commercial accessibility, many investors will struggle to understand the potential value in well-designed AI investment solutions (no long term AI track records are yet available). Early adopters of this emerging technology stand to be the largest beneficiaries. Platform users will benefit from growth in their investment knowledge base facilitated by machine learning algorithms.

Investors will likely experience tailwinds from incremental investments as AI success stories continue to emerge. About 90% of electronic data was created in the past two years, and in two years from now we will likely be saying the same thing; AI will become a necessary tool for global investors.

AI is already disrupting the ETF industry and it is having spillover effects into other investment vehicles. Millions of market signals, news articles, and social media posts are processed by currently operating funds to produce thousands of hypothetical test portfolios which are further distilled down into daily trade recommendations. There has been an increase in the number of usually tight-lipped hedge fund managers admitting to using AI after these AI ETFs launched.

Others will eventually catch on that more cost-efficient asset management structures exist through the use of AI.

For all its advances, it's unlikely that AI will immediately replace human managers and analysts. But what it will do is to allow them to more efficiently manage the overwhelming amounts of market data.

As AI investment products produce out-performance numbers, we anticipate the same type of shift we saw from taxis to Ubers. As you consider the future shape of the investing landscape, ask yourself this question: are there taxis in your portfolio?

By: Chida Khatua

Khatua is co-founder and CEO of EquBot, the firm behind the first AI-powered ETF and the first international equity-focused AI powered ETF.

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