Why common benchmarks for AI readiness in the CPG industry are off the mark, and what you can do about it.

There is a prevailing attitude about AI in the consumer packaged goods (CPG) industry, and it revolves around how ready an organization needs to be in order to begin fully leveraging AI and Machine Learning in their business. That view is that AI is costly, labor intensive, takes a long time to see positive ROI, and, in general, is something that only organizations with dedicated, highly technical staff can spearhead. This can be an antiquated way to look at AI, but one that stems from a time not so long ago when it was partly true. Technology has changed rapidly in the last year. Now, these changes have gone overlooked by most, but they have broken down the barriers for all tiers of CPG organizations to leverage the power of AI across their entire business–from the highly technical data scientist, to the business user who knows they have significant data to work with internally or via a 3rd party. In this article we’ll look at why the old way of thinking about AI has been turned all on its head.

Shifts are widespread and run deep

There are shifts in every industry, no doubt. But one would be hard pressed to find an industry whose fluctuations can be felt as integrally as those of CPG. 

Members of the industry feel it in their roles as they work to meet the ever-intensifying demands of customers along with shifting economic conditions. And they, like everyone, also feel it when they clock out from their professional lives and go home—as they, too, are consumers of these goods. What’s more, they’re looking for the same seamless and personalized experiences that they seek to give to their own customers.

Reflecting on their professional lives, they’d be able to tell you about some of the shifts happening. They’d tell you about turmoil in the industry, as newcomers—often Tier 2 brands who have been around a while but have yet to become true mainstays—compete to make a name for themselves. These professionals see the supply chain becoming ever more complex. They see brands and retailers becoming competitors rather than allies, as private label goods become more common. Combine all of that with a (at times) hesitancy to give the green light to investments in technologies that capitalize on the rich streams of the data CPG companies have access to right under their own roofs, and there is an interesting situation.

Throughout all this, the legitimate benefits of artificial intelligence (AI) loom large over the future. And it’s no mystery, as the trope of AI as a savior for industry is as common in popular culture as it is in board meetings. So, naturally, CPG organizations have been looking for ways to put its potential to work in intensified ways for more data-driven decisions.

However, there have been varying degrees of success achieving this.

The Archetype Dilemma

In 2019, an enlightening study from Bain & Company sought to bring clarity to this very thing and established what they deemed the “5 archetypes that distinguish leading companies as AI frontrunners.” 

The useful, eye-opening study cast the industry vis-à-vis AI as one that has its believers, its laggards, and those that run down the middle. That type of distribution in attitudes doesn’t sound that unique at all. But what is a bit hard to fathom is how CPG, an industry that has data pouring in from every step on the value chain, can find itself skewing so indecisive. It is now non-news that CPG companies scaling their use of advanced analytics and AI can expect revenue gains around 10%. So, why were there still so few instances of successful AI deployment in the industry as compared to others?

Times have changed… and so have the benchmarks

One of the kickers of that study is that for the majority of those questioned, their own personal assessment of AI readiness was way off the mark when it came to “actual readiness” as measured by the research team.

A lot has changed since that study—in business, globally, as well as in the CPG industry itself. Challenges that were present in 2019 are still here and have, in fact, only intensified. But despite the dramatic differences that exist merely one year after that study was published, advancements in AI delivery assure that those same respondents questioned today would be “ready for AI.”

Attitudes and stigmas can stick around a long time. What’s more, because of feedback loops that happen thanks to how search engine algorithms work, internet traffic and search engine results pages both mirror and amplify those attitudes. A quick check on Google for the simple, common question “is my business ready for AI” (give it a try) generally brings up a first page that fully reinforces the sense that AI is lengthy, arduous, and costly. Numerous articles talk about what a business “needs to do before even approaching AI.” Most of the traffic never even makes it to the second page of results.

A compound problem

There are profound shifts happening in CPG, that is clear. But a lot of these changes fall into categories most able to be addressed by AI. There are many factors in the value chain that are intensive for business analysts and where AI can augment decision making. Predictions and forecasting, trend analysis, etc. are all areas that are essential enough to have a bulk of knowledge resources dedicated to them. In the new operating rhythms, constant uncertainty has made every resource count that much more. Where does that leave you when you need to allocate those resources elsewhere to take care of other projects? It’s hard to make the choice when so much has become business critical in its own way. 

A new benchmark for AI readiness in CPG

What’s not clear for a lot of companies is that it’s easy to get going at any stage of readiness, provided you know where and how to start. And for most CPG businesses who want to get quick revenue-driving wins, that place is with a self-serve, SaaS AI business platform like Kepler.

The very fact that a platform like Kepler exists renders moot many notions that, merely a year ago, fed into an evaluation of such a high barrier of entry for AI. Running on a cloud-based infrastructure, there are none of the costly hardware investments that are so common with deep-dive in-house solutions. Its self-serve nature is specifically tailored for the non-technical user, and this equates to a sort of “plug-and-play” level of ease when compared to other AI solutions almost to the point it could be considered an everyday tool. And with that level of usability comes a more democratized access to AI. This means that you can put AI in the hands of any business user as a way of doing business every day.

There’s a shift… and there’s a solution

The CPG industry is changing–fast. Keeping up with customer demands, forecasting in an ever more complex supply chain, monitoring and acting through omnichannel touchpoints, etc. all requires a lot of mindshare. These are also some of the prime candidates for AI’s assistance, provided the data is there. And in most CPG organizations, it is. So, what’s stopping the players at all levels from moving forward with AI? Chances are they’re merely unaware that the benchmarks to readiness they judged themselves by a year ago have changed. The era of the self-serve SaaS AI platform seems to have come at just the right time. When human decision making and knowledge resources have never been more important, the idea of enabling more users with the power of an AI business platform like Kepler is one that future leaders will look back on fondly.

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