Retail and Gen AI: Now Scale Those Terrific Early Returns

Retailers have had access to generative AI tools for more than a year now—time enough for almost all to see the new technology’s undeniable power. While the speed and scope of experimentation have varied across the industry, most retailers are now convinced that generative AI will rapidly improve productivity, easing the industry-wide pressure on margins through an array of cost savings.

Some retailers have gone further. Recognizing generative AI’s potential to redefine how people shop, they’ve experimented in areas such as conversational search and personalized apps. The early success of these experiments has bred confidence that generative AI will significantly boost revenues. And that boost won’t just be in the medium term. Enhancements such as SEO-friendly automation of product pages and AI-written summaries of customer reviews are likely to lift sales in the short term.

The first year of the generative AI era has also caused retailers to think hard about its long-term impact. Some see a reckoning on the horizon, fearing that if they don’t exploit the technology fully, they will be disintermediated. One worry is that big tech companies will muscle in on the early stages of the shopping journey, such as inspiration and curation. Another fear is of being shouldered aside by digital insurgents that are simply faster at implementing generative AI in a compelling way.

With so much in flux, it’s hard to quantify the longer-term risk—existential or otherwise—posed by these scenarios. However, executive teams don’t need a crystal ball to know that moving from experimentation to scaling should be their strategic priority for generative AI today. That goes for all retailers, no matter how far they got with pilots in 2023 and early 2024.

Accelerated scaling is vital because shoppers are starting to adopt AI tools at scale in their daily lives, thanks to products such as ChatGPT and Microsoft’s Copilot. Behind-the-scenes AI improvements at early-adopting retailers, such as natural language search and customer review summaries, are also beginning to reshape the act of shopping itself. If retailers don’t soon expand generative AI beyond a few test-and-learn corners of their website or a scattering of internal use cases, they won’t keep up with the transformational benefits the technology will bring to the customer experience as it goes mainstream. Shopper expectations are already rising.

Retail’s thin margins are another reason scaling is so important. Generative AI–powered productivity enhancements and cost savings could be a game changer for retailers in the vanguard of adoption, boosting their margins and providing fuel to grab share from slower rivals.

Scaling is never easy, of course. Although the rollout of generative AI will be a lighter lift than many big tech investments, with progress counted in months rather than years, the new technology will profoundly alter the nature of jobs across a wide range of retail functions, which creates a particular scaling challenge. But retailers should be energized by the task. The return on investment for generative AI initiatives is shaping up to be extraordinarily promising. Indeed, if retailers were to rank every investment opportunity across their business in terms of the multiples likely to be returned, scaled generative AI projects would probably monopolize the top 10.

Getting family-sized benefits from generative AI

So, how can retailers move from standalone pilots to a more scaled approach to generative AI that will give them an edge in key strategic areas? One powerful approach that’s particularly well suited to retail involves prioritizing families of use cases that share similar objectives and technical foundations. By grouping initiatives like this, retail executive teams can chart a path to productivity and revenue gains that are meaningful at the company level, not just for an individual function.

Such an approach can accelerate and lower the cost of development (through a greater reliance on reusable modules and methodology) while streamlining maintenance (through updates that cascade down from the master use case). It can also demystify the generative AI journey for the broader organization, give greater visibility to the adoption effort, and make it easier to set up teams to seize both short-term and longer-term opportunities.

One highly promising family of use cases centers on personalizing the customer experience, including the way a retailer communicates with shoppers. That promise is already starting to be realized by one retailer at the forefront of strategic generative AI adoption. Its early personalization efforts have included an AI-powered conversational shopping assistant that is on track to boost its conversion rate by 7 percentage points or more and is likely to generate additional revenue amounting to 24 times the investment required.

Our experience advising on generative AI globally suggests that many retailers should be able to increase their revenue by 5% to 10% overall through a suite of personalization initiatives, including shopping assistants like the one mentioned above, enhanced search, and localized shopper recommendations (see Figure 1).