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ARTIFICIAL INTELLIGENCE

The Golden Age of AI for Retail

When Amazon acquired Whole Foods, stock prices of other retailers dropped drastically. Why was this the case?

Is it only because of Amazon or is there more to this? We know historically that Amazon excels in Artificial Intelligence and already does a lot to incorporate it into their business, for example autonomous drones, autonomous robots for collecting goods, and of course all the intelligence that goes into the Amazon website. This gives them a huge boost in both cost savings and margin but what if Amazon applied the same technique to Whole Foods and made them completely intelligent? Is this nonsense or could it become reality? Everybody would love a store where you could just walk in, grab everything you need and walk out, letting the AI in the store assess what you have bought and automatically pay for you. No hassles with lines, packing goods and paying with cards. Nonsense? No, Amazon Go stores do exactly this and we see that shopping with the help of AI is becoming a reality.

So why is Amazon focusing on the retail market? If we look at the recent case study carried out by Capgemini Research Institute, we see that AI has a $300 billion+ opportunity for those retail companies that are able to scale and expand the scope of their existing deployments. But the report also found that just 1% of use cases by retailers have achieved this level of deployment today.

So what is the issue here? Why are retailers not achieving this level of maturity? First and foremost there is a lack in AI infused strategies. Not a strategy on how to use AI but how AI is infused into all parts of our business (in other words the whole value chain). A Leadership commitment is key in enabling this.


If we go back to Amazon, Jeff Bezos already pointed out that AI is in its golden age and that it will improve every part of Amazon’s business. This doesn’t mean we should all build advanced autonomous drones, and this is exactly the second issue that a lot of retailers are facing. When they commit to start implementing AI into their business they are aiming for difficult use cases that have in general no impact on their business. When drawing up the strategy, it is wise to include projects that are 'low-hanging fruits' at the beginning of the process in order to achieve small wins and to help build and drive the momentum but also making it clear that AI will not solve all your problems, yet. Defining a process of selecting the right use cases while keeping in mind the ease of development and also value from a consumer perspective is key. For example developing an object detection that detects dogs is relatively simple however it doesn’t necessarily have a use case with a lot of value and developing an object detection tool that can recognize all products in your store can become quite intensive and maybe it does not fit the capabilities within your team.

Lastly I want to address data strategy, a lot of retailers come to me with the question ‘If we hire an AI expert can we create AI?’ No. AI requires data and without a good data practice its difficult to move directly into AI. Assessing your data practice and building a data-strategy are needed to begin your journey. Building an AI strategy can be difficult but with a successful scale up and execution there is a 300 billion+ market out there waiting for you and Amazon is leading the way.

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