Will the Corporate Investment in AI Pay Off?

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.