Gen AI growth for small and midsize retailers

Connect and conquer: Realize the power of generative AI in retail 

From ecommerce augmented reality features to smarter warehouse robots, generative AI (gen AI) advancements at Amazon and Walmart are making headlines. But where does the rest of the industry stand?

Unfortunately, Genpact research with HFS reveals that retail is one of the industries least likely to say gen AI is inspiring them to adopt new and disruptive ways of value creation.

To realize gen AI's full potential, small- to mid-sized retailers need to examine back and middle office functions to improve customer experiences on the front end – and there's no time like the present. In an industry with intense pressure on margins, getting gen AI right will become the difference between rapid growth and a slow decline.

Gen AI at work in the back and middle office

Retailers have traditionally invested significant resources in front-office functions such as sales, customer service, marketing, and ecommerce. But they've struggled to unlock the potential of middle- and back-office functions like inventory management, procurement, and finance, among others – this is also representative of the allocation of data and AI investments.

However, we know a successful gen AI implementation starts behind the scenes and is built on a well-architected data platform fueled by good quality data.

Here's how integrating the latest technologies with core operations gives retailers a competitive advantage:

Stronger supply chains

With many variables impacting supply chains, a lack of insight can make it difficult to optimize demand sensing and inventory management.

AI-driven forecasting can produce significant cost savings, reducing errors and product stockouts. This can reduce warehousing and administration costs, too. Gen AI can even generate alerts when product levels are low using inputs from warehouse cameras and live customer tracking. The technology takes the guesswork out of supply chain management so retailers can make the right decisions at the right time.

Data-driven pricing decisions

If a pricing strategy doesn't analyze customer information, market data, and seasonality, it can be an ongoing struggle to optimize working capital and avoid over- or understocking – or making decisions based solely on gut feeling.

Data and gen AI solve this problem by providing instant insight into real-time product demand. The technology evaluates basic purchasing trends as well as factors like which products get returned and where shoppers go next – while taking both in-store and online sales into account. With this accurate, data-driven perspective, managers can make more confident decisions on pricing and ordering.

Powerful procurement

Strong relationships with product vendors depend on consistent negotiation and communication. This isn't always easy as companies grow and supplier bases expand – and neither is balancing source cost and quality.

Gen AI's advanced analytics can help speed up closing deals and identify often-overlooked cost-saving opportunities.

Take Walmart – it has significantly reduced the time required for negotiations from weeks to days, enabling quicker decision-making. There's no reason this approach can't be adopted by small- to mid-sized retailers, too. Gen AI can also use information around delivery times and customer reviews to influence supplier scorecards, helping retailers decide which vendors to continue working with to support ambitious growth goals.

Exceeding customer expectations

Customers always want issues to be resolved promptly – and so do retailers. But the volume of inquiries many companies receive isn't easy to manage. Customer service chatbots enabled with gen AI can assist customers in finding answers faster. AI bots can also decide when and how to consult with employees – allowing staff to focus on the highest priority and most complex issues to streamline operations.

One example is how Amazon used Genpact's Cora ContactUs.ai customer experience management framework to help customers get devices repaired more easily. It manages routine inquiries, offers straightforward guidance, and simultaneously consolidates data from many sources to track repairs and uncover insights that improve the process. The existing back-office functions could play a significant role in helping retailers unlock the full potential of this capability.

Optimized digital storefront

In an increasingly competitive ecommerce landscape, retailers need a higher level of personalization to drive sales with the experiences consumers want and expect.

Gen AI can analyze customer intent insights to surface products that match shoppers' needs and interests. Then, it can support sales further by populating tailored product details that lead more customers to click or tap "add to cart." But retailers must remember that data makes all the difference – how well gen AI performs depends on the information gathered from the back end.

Enhanced in-store experiences

While online sales are on the rise, the in-store experience still needs innovation. Some retailers use an AI-powered scanning and checkout system to eliminate the need to wait in line. And when shoppers ask for items not found on shelves, AI tools within a connected data ecosystem should help in-store associates quickly locate inventory in storerooms.

Unfortunately, the HFS research shows that only 13% of retail execs believe that increased employee engagement is a top business benefit of AI, meaning most overlook the fact that improving employee experiences will also improve the customer experience.

Enterprise-level value is the future of gen AI in retail

The HFS research shows that 62% of retail executives believe gen AI adoption will increase individual productivity – yet 43% agree that a singular focus on productivity is misleading. Only a comprehensive gen AI strategy connecting back- and front-end use cases can break down silos to unlock value across entire retail organizations.

The bottom line? Small- to mid-sized retailers have the agility many larger competitors lack to successfully integrate data across every department – with the help of gen AI.

Of course, technological transformation doesn't happen overnight. More than half of retail executives say a lack of data quality or strategy is a top barrier to gen AI adoption. To overcome these obstacles, finding a partner with the right retail industry expertise is essential to deliver company-wide change. And it's the retailers that forge these partnerships to push the boundaries of gen AI that will thrive today and into the future.

This point of view was authored by Philip Ollapally, Data and AI in Retail Specialist, Genpact

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