For artificial intelligence (AI) to live up to its promise, and for returns to spread beyond the semiconductor companies that have enjoyed most of the benefit so far, enterprises have to unlock greater value from the technology, according to Goldman Sachs Research. Ultimately, successful enterprise AI adoption will drive the economics for the entire supply chain.

“The general idea is that chip companies are supposed to thrive when their customers thrive,” writes James Covello, head of Global Equity Research. “They are not supposed to be thriving at the expense of the companies higher in the chain.”

Is AI living up to expectations?

Consumer adoption of AI chatbots “has been spectacular,” Covello writes. Generative AI reached approximately 53% adoption within three years (measured from the release of the technology’s first widely available product), according to the Stanford Institute of Human-Centered AI. That’s well above the initial trajectories of the personal computer and the internet over comparable time frames.

Most enterprises are yet to generate any returns from their AI spending, Covello notes, citing a study from the Massachusetts Institute of Technology and other reports. As a result, other parts of the AI supply chain are struggling as well. The companies making the models and the hyperscalers building the AI infrastructure are burning through cash and boosting their borrowing.

While semiconductor companies are seeing record revenue and profits, the overall dynamic is “unprecedented and unsustainable,” Covello writes.

Where are the investment opportunities in AI?

The team points out that about two years ago they recommended that investors take a “picks and shovels” approach to the AI boom, investing in semiconductor and semiconductor equipment companies (semi cap). Since then, these stocks have outperformed the market, while the hyperscalers mostly have not.

From here, Goldman Sachs Research expects hyperscalers to outperform semiconductor companies and those that make equipment for semiconductor companies, Covello writes.

For one thing, investors have become fairly skeptical of the returns the hyperscalers are likely to deliver. If enterprises begin to show returns from AI spending, investors may be willing to pay higher multiples on these stocks again. Even if returns on enterprise AI spending continue to be challenging, the hyperscalers may decide to trim their capital expenditures. This may be the best scenario for this trade, the team writes, causing a relief rally for the hyperscalers on better cash flow prospects and a selloff for semiconductors.

The worst scenario for this trade would be the status quo persisting and the hyperscalers continuing their huge capital outlays despite challenges for successful enterprise AI adoption. Investors would lose money on the trade in this scenario, as all the value of AI spending would continue to accrue to the semiconductor companies.

How can enterprises better use AI?

The big question according to Goldman Sachs Research is how companies can create economic value from their spending on AI. The researchers suggest they need to ensure that they are building their agentic AI on data that is structured properly, and they need to deploy, or orchestrate, AI models in a cost-effective manner. 

“Models will continue to improve at a fast pace, but, right now, model capability is not what’s holding back successful enterprise use cases,” Covello writes. “We believe data structure and orchestration are critical factors to unlock AI in the enterprise.” A new layer needs to be added between the enterprise and the model developers, he adds.

One goal for this “orchestration and deployment layer” would be to ensure that workflows are routed properly based on complexity and cost considerations. Low consequence, higher-volume workflows should be routed to simpler AI technology—open-source or lightweight models. Higher consequence tasks can go to the most advanced (and expensive) models, which will be reserved for environments where the cost of failure is high.

How should executives approach AI in their businesses?

The researchers provide an example of how this might play out at a hedge fund that wants to put AI tools in the hands of its employees. When someone queries the AI with what is essentially a glorified web search, that would be routed to a lower-end model. On the other hand, when building a new financial model or conducting complex valuation analysis across industries, the work might be routed to a more expensive, higher-powered model.

One approach that holds promise is to deploy small language models (SLMs), Covello writes. In contrast to the foundational large language models that attract attention, SLMs can be optimized for speed and lower cost, and they can be tailored to a specific workflow. Small models can be faster and increase accuracy even as they are much less expensive to train and use—showing the cost-effectiveness needed for successful enterprise AI deployment.

The new layer between the enterprise and the model providers would also address data issues. A basic example might be to ensure that a retailer’s data is aligned and accessible so that an AI tool can give helpful customer suggestions. That will only be possible if the databases for recommendation algorithms, customer behavior profiles, and current inventory are all properly organized and are not siloed.

“We believe organizations getting these building blocks in place will be key to unlocking AI economics in the enterprise,” Covello writes.

The researchers suggest that C-suite leaders may want to play the long game, given that some of what’s needed for enterprise AI success is not yet in place. “Slow down now so you can speed up later,” Covello writes. This won’t be easy, he admits, given the immense pressure executives face to show that they have a strong AI story—and the fear of missing out that seems to be driving shareholders and markets.

Which jobs and industries are likely to be disrupted by AI?

One place where markets have been watching for the impact of AI at the enterprise level is in large scale job replacement—and this has not yet occurred. Goldman Sachs Research has shown that, while AI is replacing some workers, these job losses are being partially offset by rising employment where AI augments human workers and boosts their productivity.

“We think the market needs to look beyond jobs to feel comfortable that there will eventually be return on investment,” Covello writes. That means exploring for profit pools that could be ripe for disruption by AI in the years ahead, he explains. Goldman Sachs Research’s equity analysts have identified a number of industries where large-scale profit disruption seems more likely, including advertising, software, cybersecurity, and transportation.

In the advertising business, for example, the creative processes could be disrupted and there’s potential for automated processes for ad generation. In transportation, as robotaxis and autonomous heavy duty trucks gain acceptance, the disruption to existing US markets could potentially be around $440 billion, the researchers find.

This article is being provided for educational purposes only. The information contained in this article does not constitute a recommendation from any Goldman Sachs entity to the recipient, and Goldman Sachs is not providing any financial, economic, legal, investment, accounting, or tax advice through this article or to its recipient. Neither Goldman Sachs nor any of its affiliates makes any representation or warranty, express or implied, as to the accuracy or completeness of the statements or any information contained in this article and any liability therefore (including in respect of direct, indirect, or consequential loss or damage) is expressly disclaimed.