Limiting risk as retailers turn to AI-based solutions

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Retailers are adopting AI and mitigating risks.

Demand for AI-based solutions has grown significantly over the past several years, and it isn’t expected to slow down anytime soon.

In retail alone, the global AI market is already valued at $5.79 billion and is expected to grow to $40.74 billion by 2030. AI-based tools are being used to improve the customer experience, generate business, provide operational insights, and even help make improvements to the supply chain.

But as AI grows in popularity, it’s important for retailers to recognize the potential dangers that come alongside these advantages. AI will play a significant role in all industries moving forward, and understanding how to proceed with the appropriate level of caution will allow retailers to capitalize on AI’s capabilities without exposing themselves to unnecessary risk.

Why retailers Are flocking to AI

First, it’s important to acknowledge the many different ways AI is being leveraged within the retail industry. AI-based analytics are being used to help companies identify and predict things like high and low traffic times, which products visitors gravitate towards, future demand, and even when shelves are in need of a restock.

This data is incredibly valuable to help retailers more accurately anticipate their needs and plan their orders accordingly. Some retailers are even using AI in customer-facing ways, with AI-based solutions helping visitors by providing customized product recommendations and maps to the location in-store.

Many retailers are also turning to AI in an effort to alleviate ongoing supply chain challenges. AI’s skill when it comes to identifying patterns makes it ideal for seeking out redundancies, inefficiencies, and other potential supply chain issues. Just as AI can help a customer plot their ideal path through a store, it can also help logistics departments plot out shipping routes more effectively.

 Last, you can’t talk about AI in retail without also noting the critical role it plays in modern cybersecurity. A retailer today might have hundreds of thousands—even millions—of customer accounts and other identities to manage and protect.

Even just managing employee identities can be a challenge in retail, with seasonal, temporary, and young adult workers coming and going frequently. Manual management simply isn’t possible at today’s scale, and modern AI solutions are helping businesses ensure that they are keeping their systems as safe as possible against today’s increasingly persistent cyber threats.

Don’t let excitement outweigh caution

Those potential advantages are all very exciting, and it’s easy to see why retailers are so optimistic about the effect AI will have on the industry. But AI’s seemingly limitless potential has led to the widespread perception that AI is a sort of “magic bullet,” capable of solving every problem.

And while AI can certainly be leveraged to help solve a wide range of challenges, it is critical for businesses to deploy it carefully and intentionally and not as a one-size-fits-all solution.

AI solutions are great at helping organizations manage complexity, but they can be inscrutable—and if companies don’t understand how these solutions work, it can lead to significant problems down the line. When an AI solution makes a mistake or does something unexpected, it’s important to understand why. Suppose a California-based retailer implements an AI-based logistics tool, but it unexpectedly reroutes all of its shipments to Toledo.

That’s a big mistake—one with major financial implications—and if the retailer can’t pinpoint why it happened, they can’t guarantee it won’t happen again. An AI-based tool can probably make logistics more efficient, but don’t put blind faith in it. Know how it works.

Ensuring the integrity of these AI tools themselves is also a significant issue—and many organizations may not even realize it. It is critical to understand where AI-based solutions are drawing their information from, particularly if those solutions are being used to write software or generate code.

On top of that, it’s important to remember that AI doesn’t create things from scratch—it draws from existing information and iterates on it to create something new. That makes it a very powerful tool, but it also means that that quality of its input will significantly impact the quality of its output.

Transparency is critical for AI, and providers should be able to clearly explain why a code repository or library is considered safe. And of course, managing risk means having safeguards in place—even if that just means a human being looking at an AI-generated work schedule to check for obvious errors.

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