Artificial intelligence has become one of the defining investment themes of this cycle. Yes, we may be hearing about the AI pullback as a valuation reset. However, that doesn’t change the underlying scale of adoption for this theme. And that’s evident even “under the hood” of ETFs. There’s a growing number of ETFs not investing in AI but becoming dependent structurally on AI. In other words, AI is increasingly being used to construct and manage ETFs. I highlight a few in this note.
AI-Enhanced ETFs Distinct From Thematic AI ETFs
AI-enhanced ETFs differ from traditional AI thematic ETFs. On one hand, AI ETFs like the ROBO Global Artificial Intelligence ETF (THNQ) invest in companies that build and lead AI technology and infrastructure. AI-enhanced (or AI-powered ETFs), on the other hand, have varying investment strategies (but typically focus on broad US or international equities). These strategies generally use machine learning models to assist with security selection, portfolio weighting, or risk management.
Unlike traditional passive ETFs, these strategies tend to be fully active (in certain cases, the AI is applied to the index). Unlike classic quant funds, they emphasize adaptive learning systems rather than static factor formulas. The common thread is adaptability. These models typically undergo regular retraining, allowing exposures to evolve as new data emerges. These strategies also often still use skilled human managers.
AI-Enhanced ETFs: The Next Evolution of Quant Investing
These are some of the AI-enhanced ETFs you may have heard of:
The Amplify AI Powered Equity ETF (AIEQ) tracks EquBot’s AI Powered Equity Index, which is built with IBM Watson technology to select securities based on machine learning, sentiment analysis, and natural language processing.
The EquBot model uses AI to analyze up to 10 years of historical data across news, social media, earnings reports, and other financial statements. The system ranks U.S. equities daily based on their probability of outperforming under current conditions (using AI forecasted returns), while maintaining volatility characteristics similar to the broad U.S. equity market. The starting universe are the constituents of the iShares Core S&P Total US Stock Market ETF (ITOT) — a broad-based U.S. equity ETF that is composed of small to large U.S. companies. AIEQ’s top 10 holdings include familiar Magnificent Seven names with some lesser-known stocks like TE Connectivity (TEL) and IQVIA Holdings (IQV).
The Qraft AI-Enhanced U.S. Large Cap ETF (QRFT), the Qraft AI-Enhanced U.S. Large Cap Momentum ETF (AMOM), and the LG Qraft AI-Powered U.S. Large-Cap Core ETF (LQAI) make up Qraft’s suite of AI-enhanced ETFs. AI/financial industry experts established Qraft, pioneering the use of AI in the investment process, in 2016. These strategies blend traditional factor investing with machine-learning; however, the product design is still led by humans.
QRFT is an AI-enabled large-cap strategy that dynamically shifts among the five traditional factors (quality, size, value, momentum, and low volatility). It pairs AI-discovered opportunities with familiar factor building blocks under human oversight. AMOM applies a similar concept. However, it centers the portfolio on momentum exposure. It uses AI to complement momentum factor investing in security selection and portfolio construction. LQAI seeks to hold 100 U.S. large-cap stocks every four weeks based on highest conviction AI signals generated by a proprietary engine developed with LG AI Research. AMOM is the largest ETF of the three and is heavily tech-weighted (~50% of the portfolio) with a few outliers like Eli Lilly (LLY) and Goldman Sachs (GS) in its top 10 holdings.
The WisdomTree U.S. AI Enhanced Value Fund (AIVL) and the WisdomTree International AI Enhanced Value Fund (AIVI) invest in equities that exhibit value characteristics based on the selection results of a proprietary, quantitative AI model developed by Voya Investment Management. The strategy evaluates a broad universe of U.S. or international companies. It uses AI-driven analysis of valuation metrics, fundamentals, financial quality, and other quantitative characteristics. It has the goal the goal of identifying undervalued stocks that traditional screens may miss.
The model is designed to enhance (not replace) conventional value investing with idiosyncratic alpha potential. Historically, these portfolio objectives and strategies changed in January 2022. Prior to January 2022, the funds were called the WisdomTree U.S. Dividend ex-Financials Fund (DTN) and the WisdomTree International Dividend ex-Financials Fund (DOO).
The Pictet AI Enhanced International Equity ETF (PQNT) and the Pictet AI Enhanced US Equity ETF (PQUS) are Pictet’s AI-enhanced ETFs, with PQUS newly launched in February 2026. They are positioned as low-cost core equity sleeves driven by AI with an investment model designed to be transparent and factor-neutral versus the market. Active returns are intended to be pure alpha and are delivered by a team with 25 years’ experience in quant investing.
Under the hood, Pictet frames this as “Quant 2.0” proprietary machine-learning models. These models are trained on a large feature set (Pictet cites roughly 400 characteristics) across long histories (around 15 years) and repeatedly tested across different economic backdrops. Pictet notes it retrains and updates the model quarterly. Portfolio constraints (including factor/sector/geography neutrality) are still specified and monitored by experienced human managers. Notably, Pictet’s expense ratios are the lowest of the group.
Bottom Line
AI-enhanced ETFs aren’t entirely new. Systematic and quantitative investing have been around for decades. However, the AI technology powering these strategies continues to evolve under the leadership and monitoring of human experts. The scale and speed at which models can process data, adapt to new information, and refine signals has increased. It has the potential to add value for advisors.
Originally published on ETF Trends
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