RWRetailWire
Back to Discussions
DISCUSSION

Are retailers set up to scale the value of AI investments?

Written by Gib Bassett

Photo: Getty Images/PeopleImages

According to McKinsey, the retail industry has the potential to create $1.7 trillion of value, or 12.39 percent of total sales, from artificial intelligence. How much value do you think AI creates for a retail business? Before answering such a question, you must first have established goals and the capacity to track use case performance, plus a way to translate it into metrics that matter: sales, margin, customer value, retention, productivity or efficiency. This is important because companies that achieve outsized benefits from AI fund their use case roadmaps on the backs of successful projects. You simply cannot do this if you approach AI within silos and buried within business processes. AI is most often employed to automate tasks within processes. This means you have to isolate the impact of AI on the process in order to measure effectiveness. Fortunately, for those early in their AI journeys, most initial use cases that pass muster during prioritization are based on lots of historical data just ripe for machine learning. So, the baselines for use cases targeting improved personalization, recommendation or recognition should be readily available. Retailers already proficient with AI have built up the technical, organizational and governance foundations necessary for significant value scale. Your goal should be the same, but you cannot get there overnight. That is why before you head down any AI use case path you should:
  1. Ensure you have the means to establish a baseline from which you expect to improve.
  2. Have the data to support the baseline analysis — data that is updated at the right frequency and is available in the right format; data that connects actions performed by AI to business metrics.
  3. Be capable of conveying use case results in business metric terms to the executives who fund and depend on the AI.
  4. Plan for next steps: If results fail to meet expectations, make changes or retire the use case and leverage the learnings into the next one. If results are positive and your executives seek to improve further, build upon what you have.

Discussion Thread0