Execs may note the importance of AI but not understand how best to use it. And yet it’s true that leveraging AI should be a mandate from the C-level down. Without a culture of experimentation at the core of a brand’s strategy, it’ll be more difficult to reap the benefits of AI. In order for this to stick (and work), retailers need to be data-driven, flexible, and IT-first – often a hard sell in an industry dominated by legacy issues and interdepartmental silos.

They are probably aware and probably unsure as to how it is best used. They need to differentiate it from automation, which is where productivity gains might be found. Hard to believe there’s trillions in value being left on the table – sales are a relatively zero-sum game so if somebody is picking up value with AI, somebody is losing. And that is the biggest obstacle – unrealistic expectations about what AI can do for you short term and long term.

Often, when there is a new technology solution in the market, there’s a lot of media attention centered around the theory of technology instead of its practical use. AI can do many things, but it’s only helpful to retailers when used to solve an existing priority business problem faster, better, and cheaper. For example, AI is great for personalized product recommendations. Amazon generates 35 percent of its sales through its recommendations engine. Similarly, Halla’s AI engine is trained using recipe data and natural languages to easily plug into a retailer’s e-commerce and/or scan and go platforms.

Retailers are not aware of the potential impact because few use cases exist that show that AI adds tremendous value. It’s challenging to measure the impact and costly to implement solutions, especially data-focused solutions because the systems are not in place to enable it. Outside the standard e-commerce recommendations, demand forecasting, intelligent routing scenarios, and some robotics, there haven’t been major strides in applying AI to retail. The big drawbacks are tied to lack of data infrastructure, real-time data access and AI/data talent in the retail space. Regardless of retail exec awareness, retailers are unable to move towards major AI powered results without a data science foundation. For retailers it’s a matter of practicality.

AI deserves the attention it is getting but, for the algorithms to work, they need clean data – and too few organizations are dedicating the effort to clean up their data and generating single customer profiles. Without those foundations in place, a great deal of money will be wasted on AI as the ROI will be depressed by poor data management practices.

In many businesses, AI is still a solution looking for a problem, so its potential to drive improved business outcomes is not well understood. Analytics teams need a C-suite sponsor behind a strategic initiative that puts AI’s potential to work in a visible way. They will also need to take the black box off of AI to help explain the “why” behind the outcomes it drives in a way that can be broadly understood and ultimately increase its leverage. Only then will AI be recognized for the impact it is having and be fully embraced as a meaningful business tool.