How Vertical-Specific Models Will Unlock The Value of GenAI
Those who overlook vertical-specific models when integrating GenAI into their customer experience will see poorer results and increased costs, writes Avaya’s Tvrtko Stosic.
A recent CMP report revealed that, despite wide interest in voice and chat bots within the customer service industry, the majority of self-service capabilities are actually based on traditional IVRs. That same research identifies end user adoption as the main inhibitor of voice and chat bot expansion. This is hardly surprising, and here’s why.
The majority of bots today are based on rigid scripting and pre-configured scenarios. Interactions with them usually require high effort and frequently cause frustration on the customer’s side. That’s why a lot of hope for increasing customers adoption has been pinned on generative AI and its ability to humanise voice and chat bot conversations. Indeed, with its human-like conversational capabilities, ability to show empathy, understand complex inputs and solve complex issues, generative AI is bringing a real revolution to self-services applications.
On the other hand, the majority of large language models (LLMs) today, like Chat GPT-4, are horizontal or general. Those LLMs are versatile and widely applicable across various industries. Their generalised capabilities makes them readily available for companies from different sectors who want to automate their customer service operations via voice and chat bots. But this versatility is not without some drawbacks.
Due their general nature, horizontal generative AI models need to consider trillions of parameters to produce good results. This increases the required consumption of processing resources (and their associated prices). Plus, horizontal AI requires high effort and skilled resources so that it can be customised to a specific company’s needs. You’ll need data injections, fine tuning and prompt engineering to enable company-specific support. We know from experience, having built out Avaya Ada.
Finally, the dangers of bots providing bogus answers or even hallucinating is always present when it comes to general LLMs.
Despite these challenges, horizontal models have proven to be useful in customer service and, if managed correctly, can bring significant value. That said, great improvement is already on the horizon with the evolution of generative AI towards vertical models. Vertical models will be tailored for specific industries or domains and will be based on domain-specific knowledge and expertise. Consequently, they will be much better at addressing specific vertical needs and challenges – and they’ll come with more stringent privacy and security features.
LLMs based on vertical models will require far less customised training, data injections and fine-tuning. And being smaller, industry-specific models will further decrease the price of automated interaction handling – because they will consume far less processing power compared to general LLMs. Therefore, it goes without saying that vertical LLM-based bots will deliver even a faster and more compelling return on investment compared to horizontal models. Plus, accuracy will be significantly improved, and the risk of hallucinations will be significantly reduced.
Early examples of vertical LLMs are Google’s Med-PaLM 2, optimized for healthcare, and Microsoft’s BioGPT, specialized for scientists in biomedical research.
Considering the advantages of vertical generative AI models, it’s hardly surprising that Gartner named vertical GenAI models as one of its top trends for 2024, predicting that, by 2027, over half of GenAI models used by enterprises will be domain-specific, up from 1% today. Those who overlook vertical-specific models when integrating GenAI into their customer experience will see poorer results and increased costs.
Tvrtko Stosic is Customer Experience Consultant at AVAYA
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