5 tips for choosing the right AI model for your business

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The growth of generative AI (gen AI) has been driven by high-profile large language models (LLMs), such as Open AI's GPT-4o, Google's Gemini, and Anthropic's Claude. 

However, while these larger models hog the headlines, another set of models has been gaining traction. Some experts believe small language models (SLMs) could be the future of gen AI.

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According to research firm Gartner, while LLMs have traditionally dominated the development of language models, SLMs offer potential solutions to key challenges identified by functional leaders, including budget constraints, data protection, privacy concerns, and risk mitigation associated with AI. Business leaders might have to choose between larger and smaller models as they explore gen AI. 

So which will win the battle? Five business leaders give us their opinions.

1. Consider domain-specific opportunities

Claire Thompson, group chief data and analytics officer at financial services giant L&G, said she expects small and large models to have a place in business activities. However, she also thinks today's high-profile models could be tweaked for new use cases.

"I can see a situation where some of the LLMs could start to be trained on specific topics to get more detail out of them, and I can see that beginning to happen more and more," she said.

While there is a gap for domain-specific models, Thompson told ZDNET she's unsure if many companies would dedicate human and financial resources to in-house development.  

"I don't know whether you'd build your own," she said. "When I talk about building models, it's more about leveraging existing models internally and using your data in a secure environment to achieve results."

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However, whether large or small, Thompson said the future is about domain-specific models.

"I think we will start to get more tailored models," she said. "You could see, for example, how you might tailor a model around medical information, climate topics and ESG, and asset markets. It's those specific use cases where you could get more bespoke models coming out."

2. Pick the right horse for the course

Nick Woods, CIO at MAG Airports Group, is another digital leader who said the future of gen AI is probably a blend of large and small models.

"I don't think it's one size fits all," he said. "And I think the model you select depends on the use case in your business."

Woods told ZDNET it's not unusual to hear professionals say the organization should spin up an AI program. His response? "No, it's the last thing we should do."

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Woods said executives should focus on the business transformation agenda and decide which tools, including gen AI, can help deliver the right outcomes. "So, for example, we may want to run a small, specific model on the edge to go and solve a particular use case around something like spotting when an air bridge has docked," he said.

"I might run something different when attempting to create a model for a question like, 'What does global air traffic look like, and how will it react to weather changes?'"

In short, said Woods, choosing a model is about picking the right horse for the course.

"I think you will see many small models deployed at the edge at scale for particular use cases," he said. "That's almost inevitable. However, I still think you'll see some big models prevailing."

3. Consider the context

Gabriela Vogel, senior director analyst in the Executive Leadership of Digital Business practice at Gartner, said her conversations with CIOs suggest small, domain-specific models have an important role to play -- at least in the shorter term.

"The clients I speak with are trying to find and create models applied to a specific context," she said. "They're not necessarily big, general models, but ones specifically tied to small databases for a particular application."

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Vogel told ZDNET that more and more companies are moving from exploration to production gen AI services using SLMs.

"They're making this shift because they've tested a lot," she said. "They've seen what works and doesn't with bigger models, and then they're trying to go more specific and apply that approach. That's what I've personally seen with my clients."

4. Go small to reduce hallucinations

Ollie Wildeman, who leads customer satisfaction at Big Bus Tours, said the choice of SLM or LLM depends on the use case -- and for many companies, the selection is likely to be smaller rather than bigger.

He told ZDNET how Big Bus Tours uses Freshworks Customer Service Suite, an omnichannel support software that includes AI-powered chatbots and ticketing. The company also uses an AI-enabled virtual assistant from Satisfi Labs that connects to its website and deals with basic customer queries.

"Satisfi's AI technology only takes data from the specific companies they work with," he said. "The company's technology is not connected to large-scale AIs, like ChatGPT or other tools -- they're doing it themselves."

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Wildeman said this contained approach creates business benefits -- executives can be sure how their data is used carefully to produce outputs.

"In that way, your data is safer because you know where it's coming from and what processes they're using," he said. "Also, you get fewer hallucinations because you know the model you're using is designed for the type of business you're in."

These results lead Wildeman to conclude that smaller, domain-specific models will be important for enterprises.

"I think for businesses, the choice of model is going to be more specific, whereas probably for the general user, these massive free models that you see everywhere will be popular."

5. Focus on your first-party data

Rahul Todkar, head of data and AI at Tripadvisor, said the right model for a company might not just be a question of big or small.

Professionals may try both models. However, Todkar told ZDNET that purpose-built and customized models are the future of AI, whether they're defined as big or small.

"Take the example of Mistral 7B, which is a relatively small model in the context of other LLMs, but it does fantastically well when you look at specific tasks," he said. "So, to me, the future is about customizable models."

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Todkar suggests the key to AI success is ensuring the model uses your data securely and effectively.

"It's not the training size or the features in the model that matter, but rather it's about taking that model and applying it in your context with your first-party data," he said. "That's when you move beyond off-the-shelf models and can use the insights from your data. So, the answer is going to be somewhere in the middle."