AInxiety in retail: why retailers must deploy Artificial Intelligence

Across every industry, generative artificial intelligence (GenAI) has burst into the spotlight, catapulting the potential for AI solutions to new heights.

According to recent market reports, 92% of Fortune 100 companies are actively using OpenAI’s generative AI technology and 84% believe AI will have a significant impact on their operations in the short term – writes Kearney.

AI’s impact is certainly being felt in retail, where the projected market value of artificial intelligence is expected to reach $55.5 billion by 2030.

Generative AI, supported by increasing applications of traditional AI and machine learning (ML), is driving unprecedented change in key areas of the retail business—particularly to improve the customer experience, boost productivity, and become more efficient across the end-to-end supply chain.

AI’s promise to transform the retail industry is enormous.

And that promise is increasingly becoming a reality for generative artificial intelligence as computing cost falls and model accuracy improves.

For instance, the cost of training deep learning models is decreasing 50 times faster than Moore’s Law, and Kearney analysis projects hardware and software costs will drop by approximately 40 to 60 percent annually.

At the same time, generative AI model performance is getting around 2 to 4 percent better than human performance year over year.

The “why” of AI in retail is pretty clear: while boosting automation and collaboration used to take several years to accomplish, generative AI and ML can help retailers supercharge their supply chain capabilities in mere months.

With these technologies, retailers can more quickly move from descriptive to predictive to prescriptive and, ultimately, to autonomous supply chains as their digital maturity grows.

This is extremely relevant for all players in the retail ecosystem, including leaders and new entrants, as competitive advantage could quickly disappear unless companies define clear action paths to capitalize on what these technologies offer.

The big question now is, where and how should retailers focus their efforts to begin reaping the rewards from AI? Leading retailers offer some clues.

AI’s impact: supply chain operations and customer engagement

Two major areas of artificial intelligence use cases in retail are end-to-end supply chain operations and consumer-focused applications.

In supply chain operations, AI can help retailers optimize their footprint by enabling better store or retail location decisions informed by market forecasts and competitive intelligence; creating localized and targeted assortments based on AI-driven insights; and developing highly efficient supply chain operations leveraging predictive and prescriptive analytics (to boost forecasting accuracy and optimize sales and operations planning).

Walmart, for example, has implemented artificial intelligence technology across a set of stores that scans the shelves, alerts employees when to bring out products, and automatically adjusts inventory levels and demand forecasts.

The company also is testing an AI-powered inventory management system that constantly monitors and redistributes stock across store locations to get products where they’re in high demand before customers even request them.

Similarly, MercadoLibre, one of the largest e-commerce retailers in Latin America, is using AI to help analyse data on product demand, sales history, and market trends to ensure efficient inventory levels and minimize product losses and shortages.

On the consumer side, GenAI is a natural for personalization of marketing (for example, developing ads and content recommendations based on customer purchasing history to create unique experiences) and product descriptions (for instance, tailoring descriptions based on user preferences and behaviour). It also excels in providing efficient and customized customer support and query resolution.

At Walmart, GenAI is playing a growing role in search optimization and shopping recommendations. The company is currently deploying a GenAI-enhanced search experience so it better understands context and allows customers to search by specific use cases (such as a birthday party with a specific theme), which generates more relevant results and saves consumers time.

MercadoLibre is taking aim at detecting fake ads. Its set of AI and ML technologies can analyse more than 5,000 variables in less than a second to identify, pause, or eliminate—in real time—ads that don’t comply with e-commerce standards.

How other retailers can capitalize on AI’s potential

Retailers such as Walmart and MercadoLibre are at the forefront of using AI to improve their business. Other retailers that have been hesitant to embrace AI need to do so to avoid falling behind—because AI is here to stay and will drive new levels of performance and innovation, and likely entirely new business models, for those that successfully deploy it.

Nevertheless, many retailers continue to struggle to get started and identify which use cases they should prioritize and to determine how to drive collaboration and adoption throughout the organization.

This “AInxiety” can lead to paralysis and a tendency toward either too timid of an approach that delivers little to no value or to avoiding deciding altogether.

To be sure, AI certainly brings significant changes to those that embrace it, and it’s filled with complexity and a good degree of risk.

The key to eliminating AInxiety is to first think of AI as a journey, with specific incremental steps and experiments that lay the groundwork for wider adoption in the enterprise—and eventually add up to a big impact. No retailer can expect to achieve what Walmart has right out of the gate.

In parallel, retailers can move into execution mode, developing the foundational elements required to fully leverage AI.

These include specialized data governance for both traditional and generative AI (they’re similar, but not the same) and the right talent and skills to build and run AI models.

It also includes building a robust data infrastructure, implementing advanced analytics tools, and fostering an environment that promotes experimentation (and quick failure to ensure constant improvement).

Retailers should encourage a culture of innovation by not just improving AI literacy but urging employees to explore new ways to use AI to stay ahead of the curve and unlock AI’s full potential.

Finally, strong change management must be in place to help the company’s people understand how AI can and will change the ways they work, train them in the use of AI tools and solutions, and foster widespread adoption.

Next, it’s time to define where to start. Here, retailers should evaluate business processes to identify areas to add value, and then deeply evaluate relevant high-potential use cases and pressure-test business cases to select a short list for development.

Collaboration across business units and stakeholder alignment are crucial in this step. Proofs of concept for each use case on the short list follow (on average, a six- to 10-week exercise), with iterations based on learnings and impact that lead to eventually building and scaling the use case companywide and handing over the AI solutions created to operational teams.

In most cases, working with the right partners can significantly accelerate capability development and execution.

Thus, it’s important for retailers to get to know the ecosystem, as new AI applications, solutions, and partners emerge daily. Partnerships play a vital role in driving open innovation to remain at the cutting edge of AI. Open innovation helps optimize the ideation process, improve productivity, reduce development and deployment time, and boost bottom-line benefits.

As part of evaluating the ecosystem, retailers also should assess their own internal AI capabilities, resources, and skill sets to see where they have important gaps partners could fill—while considering factors such as cost, time-to-market, and potential for long-term competitive advantage.

A transformative impact for retailers

With generative AI leading the charge, artificial intelligence is poised to transform retail as we know it. Indeed, the potential transformative use and value of GenAI in retail exceeds that of many other industries due to retail’s unique characteristics.

For example, a business-to-business (B2B) company that sells a predictable number of widgets every month could certainly use the technology to improve planning and streamline its operations, but its impact wouldn’t be as great as it would be for retailers, which have a much larger and more diverse customer base and far greater variability of demand, supply chain complexity, and competition.

These are perfectly suited to generative AI’s power to wade through enormous amounts of structured and unstructured data from virtually limitless sources and not only develop insights on how retailers should respond, but in a growing number of cases, actually do the responding on its own.

Imagine having a GenAI tool that is perfectly tuned into consumers’ wants and needs and serves up a steady stream of highly tailored offers.

Or one that can deeply understand the demand at each store, down to the individual product level, and orchestrates the upstream supply chain to ensure every store has exactly what it needs to meet that demand in real time—no more or no less—and at the optimal cost to the business.

That’s the beauty—and power—of generative AI, and it’s why every retailer should be working toward embedding the technology in the heart of its business.


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