As AI Adoption Accelerates, Brands Search for Competitive Edge

Not a single day passes without a headline touting the latest and greatest uses for artificial intelligence. Brand executives are taking notice, pouring millions into the adoption and implementation of AI. In Gartner’s 2019 CIO Survey, which included more than 3,000 CIOs from 89 countries, AI technology was ranked as the technology most likely to be disruptive. Despite their enthusiasm for AI, however, CIOs still showed a lack of certainty over the best way to implement the technology and get their newest marketing strategies off the ground.

That uncertainty has the potential to negatively impact brands’ bottom lines, and it’s an issue that industry insiders like Cerebri AI co-founder Jean Belanger are working to combat.

At Cerebri, Belanger uses AI to help enterprises understand their customers better. Cerebri’s technology automates certain actions and measures the value of every customer interaction. Some of the tasks now handled by AI are things that human employees previously managed, and Belanger says brands, and businesses in general right now, are grappling with the idea that some occupations will decline, and others will grow, as the use of AI accelerates.

“Some may choose to see AI as a threat — whether a robot [can] do my job better, faster, and cheaper; but the business approach is to figure out how artificial intelligence can give your business or brand an edge and help you to serve your customers better,” Belanger says.

AI is not about reducing head counts or substituting capital for labor, Belanger says. Although the technology can help business executives understand their customers better, and automate certain practices, Belanger advises that brands should be careful about replacing anyone who really understands their customers.

With so much uncertainty over how best to implement AI technology, Belanger believes brands need to start looking for thought leadership and subject matter expertise to help them see through the fog. A basic idea is all that’s really necessary for brands to get started with AI. In fact, Belanger says it’s actually better for brands to avoid trying to create global data lakes with data they don’t already have. All a brand needs to do is have legacy systems, then send the AI system that data, which shouldn’t be very difficult to do.

“My view is that Nike got it right — ‘Just Do It,’” he says.

Belanger advises brands to start with a smaller project, one product in one country, usually with 100,000 or fewer customers. Then, pick a target—either customer engagement or tactical financial results. The bigger the enterprise is, the more it should be spending on building internal data science teams. This is especially true for businesses in the financial services, wireless, and automotive industries, where there is a lot of digitally recorded data.

If attracting enough talent to build out an internal data science team is the issue, then Belanger recommends aligning with a vendor that can offer support. AI vendors have an easier time attracting and retaining talent because many vendors are pure-play AI and that’s where most data scientists want to work.

“The thing to remember is you are not buying software features per se in all of this. You are buying insights, best actions to take when,” Belanger says.

What brands cannot afford to do is sit back on the sidelines while their competitors test the waters with AI.

“Can a brand or public organization in today’s mobile age afford to wait for customers to come to them? The answer is emphatically no,” he says. “AI provides us with an opportunity to gain an edge here. Look at all the companies that wish they had mastered digital media earlier in the cycle. E-commerce? You can do a lot with a few hundred thousand dollars—all in, your internal costs and vendor costs.”

Stephanie Miles is a senior editor at Street Fight.Rainbow over Montclair

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