“Generative AI” has been a key phrase in PR and media for the past 18 months. However, a panel of venture capital (VC) and tech investors revealed during the Retail Innovation Conference & Expo this past June that they’re taking a more long-term view of AI. They’re focusing less on mind-bending consumer-facing experiences, like those found while using platforms like ChatGPT or DALL-E, and more on tangible business uses cases that drive concrete results.
“I’m not seeing many generative AI [solutions] really hitting consumers outside of like traditional ChatGPT or other things that people are using on large language models,” explained Brandon Yahn, Partner at Convivialité Ventures. “But I’m seeing much more of the effects of predictive AI that’s in a lot of consumer products, and while I think generative AI will eventually get there, I think the tools seem to be more productivity driven and enterprise driven, thus far.”
Panelists noted even companies that are already part of their portfolios are looking to bolster their solutions with AI. “We’re not actively looking for generative AI — it has just organically become a part of almost every single business that we’ve invested in,” explained Lindsay Lightman, Principal of RevTech Ventures, which invests in both retail tech companies as well as brands. “They’re leveraging it to make their algorithms stronger. They’re leveraging it to make their team stronger. We have a handful of different applications of AI in our portfolio, but they didn’t set out to be a generative AI company. They just leverage it to make them better.” One solution provider in the grocery space is using AI to support retailers’ inventory management, while another uses AI to scale the creation of different product designs.
Bob Ma, Head of Retail and Fintech Investing at WIND Ventures agreed that tools promising improved efficiency are top of mind; for example, automating content generation to drive scale and improving customer service through chat bots or call centers. One company, Yummy SuperApp, “has been able to integrate AI into their solution by first using AI to power conversational commerce so they can allow customers to order food delivery by chatting within a chat bot,” Ma explained. “They’ve also created a service that helps local restaurants that don’t have an online presence actually develop an ecommerce website using generative AI and within minutes.”
AI Efficiency Powers CX Pain Points
Panelists zeroed in on customer service as a clear area of opportunity—for investors, tech companies, brands and consumers alike. Service experiences are embedded into the entire customer journey — from social media to onsite and in-store — and AI capabilities can alleviate some of the most common pain points that both employees and shoppers face.
Lightman noted that one of RevTech Ventures’ “best portfolio companies” was Theatro. The core tech is extremely simple (an earpiece that sales associates wear while working on the store floor) but AI augments it by delivering associates relevant and timely information to help them do their jobs better. At the simplest level, the AI delivers reminders to smile; at the most complex level, it gives associates detailed information on product locations in stores as well as inventory levels.
“Historically, the tech was just human to human, but they’ve layered in AI to make that agent smart from day one instead of day 20,” she said. “It’s powered by a feedback loop, so when the AI spits out a response to you that’s not accurate or leading you in the wrong direction, you have to tell it and train it. “It’s all about that trust you build in the data and making sure that information coming through the AI is actually relevant.”
In this use case, associates act as an intermediary between consumers and the AI. But in instances when consumers need to use AI-powered technology on their own, the results can be mixed, especially if the solutions don’t always meet expectations, according to Ma.
“There has been tremendous growth in the number of Gen AI startups, and not all of them are actually delivering what they’re promising,” Ma said. “They’ll do a demo with their own data and their own systems, and they look really good, but once you get to the point where you’re actually integrating your data, your documentation and FAQs into the Gen AI system, it might not look as good. That’s the type of experience that we’ve had. It’s important to always test the Gen AI product with your own data.”
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