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Gen AI: When It Will Come Down to Cost

Generative AI promises transformation for enterprise CX, but that doesn’t mean it’s necessarily the right technology solution for everything the contact center wants to accomplish. The devil’s in the details, and you can get an excellent summary of many of those details on No Jitter, where Senior Editor Matt Vartabedian has a two-part interview (Part 1Part 2) with Christina McAllister, Senior Analyst at Forrester.

In Part 1 of the interview, McAllister notes that, “We’re still really early in the adoption phase,” adding, “I have not seen many big companies necessarily getting attributable value from their deployments – they’re mostly still piloting these solutions.” She confirms the widely-held belief that gen AI-driven call summarization appears to be one of the early quick wins that can save agents time at comparatively low risk.

One of the most valuable through-lines in the interview is that, in discussing different scenarios, McAllister frequently returns to the issue of cost, and how enterprises ensure that gen AI delivers ROI. She focuses on the pricing model of large language model (LLM) use, emphasizing that if customers are charged for each time they query the LLM, their costs will grow as the solution scales within the enterprise. So enterprises need to understand how they’re being charged for use of gen AI technology, and understand the technology well enough to ascertain whether they’re using the most cost-effective implementation.

The good news is that McAllister told Vartabedian that she’s seeing enterprises pilot gen AI applications such as summarization with cost analysis as a key element of those pilots. However, she adds that she’s uncertain about whether the vendors have really developed long-term pricing strategies that work for them as well as the customer. She concludes, “If the cost of ownership of a solution that uses generative AI stays constant, there's a level of diminishing returns where you're not going to be able to crunch an agent's efficiency any lower than a certain point. But, you'll still be paying that fee to the generative model.” At that point, she suggests, enterprises will have to question their gen AI spend, and vendors will have to revisit their pricing models.

The whole interview is a really sharp analysis of where gen AI is today in CX, and where it could be going, based on the kinds of real-world concerns—i.e., cost—that enterprises grapple with every day.

And for some more real-world takes on AI, Enterprise Connect 2024 is the place to be the week of March 25. Two of our sessions, Reality Check: How Not to Fall Behind in AI and Conversational Intelligence 2024: The Rise of Domain-Specific Language Models, offer an unbeatable combination: the AI experts from Opus Research, together with a panel of enterprise users who will talk about their real-world experiences. We’ve also got sessions centered on AI research from McAllister’s Forrester colleague Max Ball, and Robin Gareiss of Metrigy. And we’ve got a case study session presented by Michael Altieri of Medtronic, who will discuss conversational and generative AI in enterprise omni-channel services. Plus, attendees to Altieri’s session will have the opportunity to stay another 45 minutes after the session to network and discuss the topic further.

So Enterprise Connect 2024 is the place for insights, information, and conversation on gen AI in enterprise communications/CX tech. I hope to see you in Orlando!