AI Summit: Succeeding in retail with AI

Rashed Haq from Publicis.Sapient talks about data, skills and outsourcing

Retailers might find it challenging to start with AI and machine learning – but the job gets simpler as their efforts progress, according to Rashed Haq, global head of AI, Robotics and Data at Publicis Sapient, a digital transformation consultancy owned by French advertising giant Publicis.

At the AI Summit in San Francisco, Haq presented a session on applications of AI technologies in retail. He noted that machine learning projects are much easier to scale if the same models and data are constantly repurposed across the organization.

“I was giving examples of how businesses are using it across the board,” he told AI Business.

“And the advantage of using [AI] more broadly across the business, rather than individual departments, because if you look at what’s going on in marketing, and what happens in the e-commerce channel, some of the data and many of the algorithms can be reused. So, integrating those on a single platform makes sense.

“if you look at the demand forecasting models within the supply chain, they’re built on historical sales data. But if you add the clickstream data and the search data from the e-commerce site, and what the customers are doing, the accuracy improves significantly. So being able to use AI across all of those [operations] improves the quality of the results, the performance of the business, and the advantages – you have a shared data platform and a shared AI platform, which reduces the cost.

“I would say probably about 10 to 15 percent of retail businesses are doing this in some form or other.”

Another point Haq made was that that the choice between developing AI skills in-house or outsourcing this work to third parties – frequently discussed in the industry – was actually a false choice.

“Bill Joy famously said that no matter how many smart people you have inside the company, there are more smart people outside of your company. And if you try to choose one or the other, there are big downsides to both.

“You do have to build your team internally, particularly for the decision-making components you want to keep in-house. You also want to bring partners from outside, whether those are consulting companies like ourselves, or startups, or other companies that have some of the products that you can use and integrate into your APIs. You can also build your models on your own, but there’s no reason to build something that somebody else has already built.”