I do not mean to be negative but when retailers barely provide enough training to their associates and usually do not staff stores with enough personnel, what’s the point of concerning themselves with the effectiveness of their AI tools? It seems that so many businesses are caught up with investing heavily in technology, which is fine, but they refuse to invest in the people who make the sales happen: the store associate. Today competition is the strongest it has ever been. Yet walk into any store and watch the sales lost as the customers who do go into the store can’t find an item or have a question but they can’t find a store associate to assist them and often when they do what do they hear, “You have to go online.” Technology is excellent, and we need to keep up with the times, but if you want to also keep up with your sales opportunities, you need to staff your stores with people and most importantly train them. Then we can concern ourselves about… Read more »

This is so cool! It is brilliant that Lowe’s and BBQ Guys have AI translators who focus on helping their internal teams understand what the AI data means to them and how it directly relates to their specific roles. The approach that they’re investing resources into translation and team communication is spot-on and it will give them a competitive edge, because their team will know what to do with the potentially extremely valuable information. Knowledge and information are power, but only if you know how to use it. Lowe’s and BBQ Guys are training their teams on how to use it.

Change management can be a huge issue for such initiatives — getting people to “believe” in the results is critical. Before this can happen you have to convince the individual that this can help them do their job and make their life easier. Do this and then show them the value they can add by providing their insights that improves the result and helps them.

I get the role of “translators” during the development cycle to ensure that the data scientists have the right context surrounding what they are looking at/building. But what happened to self explanatory analytics that are well-targeted/simple to understand and relevant to the person’s role?

This is all about communication. Anything that rocket/data scientists do must still be translated into concepts that are easier to understand. More importantly, these must be shown to positively impact the business and customer.

However, AI/ML is very much a black box scenario. Not everything is visible and in many cases, data scientists are testing out variations to their algorithms and training with different sets of data. It’s great to inform and outline the “Why are we doing AI?” question to departments, but giving them a say in what to test and improve is better still.

Lowe’s newsletter is a scalable idea that is a good starting point. But it can’t end there. When improving business ops, employees must be involved and understand what’s happening, which means “Data Science Training 101.”

I’ll echo Peter’s comments. Additionally, you can have the best AI insights ever, however, they need to be turned into very clear and specific action to be taken, sent to the right person at the right time to take the action, and then have the ability to confirm and measure the results to validate the value of that specific AI insight — did it result in a net increase to business value?

As with any new technology, training on the tool and change management are imperative for success. This is especially true for AI, which many feel is a magical “black box” that is difficult for people to understand and oftentimes hard to accept and trust. In some cases, the AI may be taking the place of work that some employees traditionally do and this could feel threatening to those employees. Training these employees on how AI will improve their processes and decisions is critical for success.