• Start thinking about AI monetization opportunities down the value chain, says Ayako Yoshioka.
  • AI can boost productivity and enhance margins for non-tech companies, she said.
  • Yoshioka is a senior portfolio manager at Wealth Enhancement Group.

As the AI hype rages on and Big Tech companies pour billions into capital expenditures on the technology, investors are increasingly asking the trillion-dollar question: what are the applicable use cases of AI?

So far, most of the AI winners have been confined to players further up the value chain, such as data center providers and semiconductor chip producers at the backbone of AI technology. As the buildout phase of AI matures, investors are looking further down the value chain for monetization opportunities, whether it be for consumers or businesses.

Ayako Yoshioka, senior portfolio manager at Wealth Enhancement Group, sees three parts of the market that could see profit enhancements from the adoption of AI. She shares the following recommendations for investors who are looking for AI trades other than data centers and HVAC systems.

3 non-tech AI opportunities

There's significant potential for the healthcare sector to reap the benefits of AI. One of AI's key strengths is natural language processing, or the ability to understand and process large volumes of human language.

And there's no shortage of that to be processed in healthcare.

"There's so much data within healthcare that can be combed through and used as the source material for all the machine learning that needs to happen in order for AI to get developed," Yoshioka said.

AI can help process and summarize medical data, making medical records and bills more accessible.

Yoshioka also sees potential in areas of drug discovery and diagnostics. Companies like Google are developing AI-enabled tools to detect patterns from data or images. These developments could help clinicians diagnose diseases with increased accuracy. Other applications include personalizing medicine and treatments, accurately calculating medication dosages, and aiding in drug discovery and design.

Security, both physical and cyber, is another area that could be revolutionized by AI. Regarding physical security, companies can use AI to analyze their surveillance data and predict future crime by identifying distinct objects and people, Yoshioka said.

AI can create more holistic data insights and integrate incident reports, maintenance logs, alarm systems, and other disparate data sets. Similarly, AI can detect and flag cybersecurity anomalies, accidental data leakage, and potential threats.

Lastly, there are various AI use cases within the financial sector.

"Credit card companies are pretty savvy," Yoshioka said. "Visa and Mastercard have talked about AI and its ability to detect fraud."

Earlier this year, Visa announced a generative AI-powered solution for detecting cases of credit card fraud by assigning risk scores to transactions. Similarly, Mastercard developed a similar technology to detect potentially compromised cards.

Large banks like JP Morgan and Goldman Sachs also use AI to comb through and sort large quantities of historical financial data, facilitate financial research, and provide investment advice.

Investors interested in gaining exposure to these sectors can do so through funds such as iShares US Healthcare ETF (IYH), First Trust Nasdaq Cybersecurity ETF (CIBR), and Financial Select Sector SPDR Fund (XLF).

The future of AI applications

For now, Yoshioka sees these use cases as productivity enhancements. "They're not going to necessarily be a revenue generator, at least not yet," she said.

Even so, downstream applications of AI should benefit many companies. Yoshioka points out that non-tech sectors are benefiting from the mega-caps that spent billions to develop these technologies. This makes it easier for the companies applying the technology to enhance their margins, since they didn't have to make the initial investment during the buildout phase.

"That's probably the benefit for a lot of these companies who are going to be utilizing AI versus those who are going to be building out the capabilities of AI," Yoshioka said.

Yoshioka provides a cautiously optimistic outlook on the future of AI. As AI development continues, she recommends that investors keep their eyes out for downstream applications of the highly expensive technology, which could take some time. Yoshioka points to technologies like AWS and Azure, which grew quietly for much of their early life.

"From a stock perspective and an investment perspective, you want to weigh the whole sentiment and exuberance around the potential of AI and balance that out with the practicality of what the reality of AI is going to be from a monetization standpoint," Yoshioka said.