You walk into a large home improvement store looking for a washer assembly to fix your kitchen faucet (and, let’s be honest, a few tips from the staff on how to not mess up the installation). But when you get there, it takes 10 minutes to track down an employee. And the one you find only knows about gardening supplies.
Frustrating!
Now picture that associate equipped with a Gen AI-powered copilot device that could quickly call up the correct part, locate it in the store, and offer a step-by-step guide on how to install it. Not only have you gotten exactly what you needed, but the whole transaction unfolded with a level of speed and efficiency that you probably weren’t expecting.
This is just one example of how Gen AI can transform the in-store retail experience. Scale it across millions of customers, and suddenly you start to see an impact on margins. In fact, Generative AI adoption is estimated to deliver between $240 billion and $390 billion in economic value for retailers in the coming years. Further, 71% of retail executives expect consumers to increase their use of Gen AI for shopping this year.
Advertisement
But are retailers prepared to capitalize on this potential?
From Theft Prevention to Staff Enhancement: Retail Gen AI Use Cases
A copilot tool like the one described above is just one of many possibilities for making the in-store experience smarter and faster with Gen AI. Other potential use cases include:
- Streamlining inventory management and the supply chain by combining computer vision and Gen AI to enable more accurate demand forecasting.
- Implementing more effective loss prevention – a persistent and expensive problem for retailers – by using Gen AI-powered tools to analyze customer behaviors, transaction patterns and anomalies in store operations that can identify theft or fraud.
- Enhancing product information by using Gen AI to bring catalog, web and mobile descriptions and details together for in-store use.
- Recommending in-store offers by drawing on customer data to provide a more personalized shopping experience.
- Addressing the retail labor shortage by optimizing staffing levels and employing dynamic real-time scheduling.
- Upgrading in-store digital signage to optimize retail media networks and brand promotions by changing content displays based on current in-store conditions, such as where customer traffic is moving at a given time.
- Generating synthetic data to mimic actual customer data – while avoiding any privacy and security concerns – when running a variety of in-store promotional scenarios.
- Improving on-shelf availability and avoiding stockouts by using computer vision in-store to provide real-time shelf updates and replenishment requests.
The goal in all of these Gen AI applications is to increase efficiency and relevance, which will lead to higher margins. But despite the potential, not every retailer is ready to jump.
Obstacles Ahead: The Challenges to Gen AI Adoption in Retail
While retail businesses would normally clamor for any tool or tactic that might win them a fraction of a percentage point of margin, many leaders still have reservations about whether Gen AI is the answer. And in many cases those reservations are well founded, especially as they relate to things like…
Legacy technology: A shining new Gen AI experience will still need to integrate with the retailers’ underlying platforms and data ecosystems. Typically, many retailers’ legacy infrastructure and systems aren’t ready for a seamless integration, creating a gap in terms of possibilities, speed of innovation and actual value to their customers. That’s why getting an accurate perspective on those infrastructure and platforms in need of modernization is a very important step in any attempt to integrate and roll out innovative Gen AI cases.
Cost effectiveness: While Gen AI offers the potential for cost savings through maximized efficiencies, concerns about its ROI are especially pronounced in retail. After all, tight margins in the industry mean every investment needs to pay off.
Use case uncertainty: Proof-of-concept experiments are showing retailers the possibilities of Gen AI, but they’re concerned about whether these use cases are scalable.
Diving In: How Retailers can Get Started with Gen AI
Those obstacles can be tough to overcome. But as more competitors begin to adopt Gen AI solutions, retailers that hesitate are likely to get left behind.
That’s why getting started (even at a basic level, such as creating product descriptions in several languages or developing targeted promotions based on past customer behavior) is so important – as is taking the right approach. Here’s what we recommend:
- Identify a problem: Too many AI implementations go wrong because the tail is wagging the dog. That is, a company will start with the solution – a particular AI tool – and try to find a way to fit it into what they’re doing. A better approach is to start with an existing problem that you clearly understand and determine how AI might be used to solve it.
- Start small: Not every tool is going to be a fit for every company. Gen AI demands a willingness to try and fail, which is why starting small makes so much sense – failed experiments are easier to take when they’re isolated and not so expensive.
Find areas where Gen AI can make an impact without necessarily upending your entire operation. If it works, those immediate incremental wins will serve as good examples of what the tech might achieve on a larger scale. The goal is to move toward a flywheel of continuous improvement. How can those little victories lead you to organizational transformation? - Focus on adoption: Everyone these days wants to be seen as “AI-first.” But completely upending your operations or disillusioning your employees is not a road to success. Can everyone in the organization see the vision? Are they excited about it? If AI is creating more friction than it relieves, you’ll be running in place.
For Gen AI Success, Start Small and Scale Fast
Plugging in a Gen AI tool to solve a specific problem isn’t terribly complicated, but retailers envisioning a more transformative journey have a challenging road to navigate. From the number of solutions available to the speed at which the technology is evolving, there are plenty of variables to consider and plenty of pitfalls to avoid.
The winners in this race are going to be those retailers that take a measured and decisive approach to Gen AI experimentation and implementation, starting with an assessment of their current maturity level and an AI adoption roadmap with clear ROI for each milestone. These will be the companies that have their operations end up moving faster and smarter – and making more money in the process.
Marcelo Vessoni is SVP, Digital and Head of Retail at CI&T, a global technology transformation specialist for large enterprises and fast growth clients that helps retailers engage customers, increase sales and drive greater operational efficiencies.
