Gen AI in retail: Finding opportunity amid disruption

Here are five key areas where gen AI is changing the retail and consumer goods worlds, from product development through customer service.

Even when business leaders aren’t directly discussing generative AI, they’re grappling with its influence. This is especially true with gen AI in retail and consumer goods, where the technology is rapidly becoming integral to every role and function, from sales and service right on up to the C-suite.

In fact, research published by Cognizant predicts that within the next decade, generative AI could disrupt up to 90% of jobs, with knowledge workers experiencing the most significant change.

But there is an important distinction to be made: Disruption does not mean displacement. In fact, one of the most common misperceptions about gen AI is that this technology will replace people. In fact, it will replace tasks.

Retail leaders and workers alike need to reframe their thinking of this technology, viewing gen AI in retail not as a replacement for humans but as a tool that can enhance, augment and automate work, making people more efficient and effective.

In many ways, this transition has already begun. Gen AI is being deployed across the retail value chain to drive improvements in both employee and customer experiences, as well as business performance. Here are five areas where gen AI is impacting the retail and consumer products world and examples of companies that are embracing this technology to strengthen agility, resilience and customer satisfaction.

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Five ways gen AI is changing retail and consumer goods

1.    Speeding product development

With the help of generative AI, product development has entered a new era of efficiency and creativity. Whether it’s launching a new product or reimagining an existing one, key tasks—including identifying new ingredients, developing product variations, creating labels or designing packaging—can now happen at an unprecedented pace, enabling faster innovation and reducing time-to-market.

For example, PepsiCo uses generative AI, in conjunction with other advanced technologies, to develop new flavors and shapes of Cheetos in six weeks’ time compared with the typical six- to nine-month product development lifecycle. Using this approach, product engineers can experiment with various combinations of product characteristics to precisely control for product flavor and appearance.

The company also uses generative AI to reformulate its recipes to meet changing consumer preferences. For instance, the AI can analyze consumer feedback and ingredient interactions to produce healthier snacks without compromising taste or quality.

2.    Creating high-value experiences

Generative AI-enabled tools can help bridge the gap between data-driven insights and how retailers use that information to power great customer experiences.

For example, Bath & Body Works is creating a generative AI-driven tool that enables customers to describe a scent using a conversational interface and receive personalized recommendations from a catalog of 200 options. The “fragrance finder” results will also be customized based on variables such as season, location, lifestyle and preferences.

3.    Optimizing supply chain operations

Gen AI is redefining how retailers manage many supply chain functions, with the goal of improving speed, agility and resiliency.

An example is demand forecasting. Traditional methods look backwards, relying on historical data and statistical models that do not account for emerging trends, real-time market shifts or external disruptions.

With the help of gen AI-enabled tools, planning teams can better predict demand by analyzing a wider array of first-party and third-party data sources, including real-time market data, social media, news articles, industry reports and weather forecasts. Equipped with natural language models, planners can also ask the AI-enabled planning system questions about demand predictions or the impact of certain factors, such as inflation rates or tariffs, and receive answers in plain language.

For example, HanesBrands launched a gen AI assistant that seamlessly integrates with a variety of messaging platforms such as Microsoft Teams, Slack and WhatsApp to support natural language queries. The tool helps teams more quickly evaluate changes, model scenarios and make better decisions.

4.    Automating procurement

Another critical, but sometimes overlooked, application of gen AI in retail relates to procurement. For example, retailers are turning to generative AI to streamline document creation within core procurement processes. AI-powered tools can produce draft contracts, agreements and other documentation, using templates, vendor profile details and other existing assets. Integrated tools, such as gen AI-enabled search features, can also help procurement teams more quickly validate details within those documents, such as supplier contacts, contract terms and payment timelines.

These tasks, which typically take hours of manual support, can now be done in minutes by AI tools, the output of which can then be reviewed by humans. This represents substantial time savings, especially for large retail organizations managing hundreds of vendors, all of which require customized documentation.

5.    Redefining customer service

One of the most prominent use cases for generative AI in retail is customer service. By automating responses to routine questions and common complaints, gen AI-enabled tools can let support reps take a more supervisory role, enabling them to oversee multiple accounts and prompting them to step in only when their expertise is required. Service reps can also use gen AI-powered tools themselves to quickly review similar past cases and craft personalized responses, improving speed, accuracy and productivity.

Our latest research with Oxford Economics reveals that generative AI could assist with or fully automate the vast majority of customer service tasks. But rather than replacing people or entire teams, it will reshape the work they do.

For example, a gen AI-powered agent could be integrated with inventory and logistics systems, enabling it to provide specific and timely answers to customer questions about the status of their order. The AI agent could also recognize patterns based on system alerts, enabling it to proactively prepare communications to send to affected customers. It could also create a script that service reps could follow for callers that initiate contact.

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Source: Cognizant Research

Achieving excellence with gen AI in retail

Most retailers accept that generative AI is a critical part of their future. But as with all advanced technologies, the value won’t necessarily come from the tool itself but how the organization uses it.

Here are four best practices identified by our research that will help retailers position gen AI as an economic catalyst and draw the maximum return from their investment.

• Engage and inform employees. Organizations must upskill their workforce and equip them with the gen AI-enabled tools and solutions that will help them perform tasks more efficiently. As part of this transition, it’s essential to openly address the impact of this technology on people, including concerns about potential job replacement. Acknowledging and addressing these fears is the key to building employee trust and ensuring successful gen AI adoption.
  
  
• Innovate don’t stagnate. Over the past two years, we’ve advised clients to get started with gen AI. Now, we’re focused on keeping the momentum going. The technology is advancing rapidly, which means that organizations need to keep evolving as their gen AI strategy matures over time.
  
  
• Build confidence with transparency. Instilling trust in this technology is the key to business adoption and achieving AI-driven productivity gains. To foster this engagement, organizations must be transparent about how the technology is being leveraged and foster a culture of knowledge-sharing. Companies must also adopt regulatory and ethical standards to ensure they are using the technology in a safe, secure and responsible way.
  
  
• Meet the needs of AI-enabled consumers. Our research reveals that by 2030, AI-friendly consumers will be responsible for up to 55% of consumer purchasing activity. To remain relevant in this future landscape, retailers must understand what consumers are and are not comfortable with when it comes to using AI to discover, buy and use products and services.

For more information, download our latest research “New Minds, New Markets."