AI: All-Inputs

In recent years, cooking shows have risen in popularity. Watching pros and amateurs alike apply fire, ice, pressure, time and know-how to transform raw ingredients into inspired culinary experiences is a universal human pleasure. 

And the first step of every cooking show is the same: the careful, loving search for ingredients. They use words like “beautiful” in describing produce – there’s real passion in what these raw, unwashed inputs represent in their craft.

Even with the advent of innovative technologies and techniques (sous vide, molecular gastronomy, compression cooking, 3D food printing, air frying, and so on), the crucial importance of ingredients – the inputs – endures. Even the most skilled artisan, technology, and gastronomic vision are only as good as the basic ingredients.

I’m betting you follow the metaphor, but just in case… We are the chefs. Data are our ingredients.

However, unlike the cooking competitors splashed across screens everywhere, within the AI hysteria, we’re giving very little attention to the most important component in this bright new future – quality ingredients. Obviously, when it comes to AI, our industry is talking about little else. And yet I hear very little consideration for this pivotal question of “What inputs, ingredients, and data are we applying AI to…?”

It’s akin to a top chef competitor announcing, “I will use sous vide!” Then, when asked what would be sous vide, answering, “Oh, whatever’s back in the pantry, I guess.” Mmmm.

Don’t you see the frying pan parallels?

Consider the abridged history of MRX.

The year is 1953. The focus group facility is born and promises to dramatically reshape the consumer marketing landscape. As early insights pioneers prepare to interview willing participants and observe them behind the sanctity of mirrored glass, the team encounters the inevitable question: “What should we ask them?” 

The year is 1973. We can now dramatically expand national reach and respondent access via telephone research. As those first interviewers begin to dial, they ponder, “Wait, what should we ask them?” 

The year is 1993. Digital! We can reach them on the WWW! Wait, what should we ask them?

2003. Mobile is the answer! We can reach them anywhere and everywhere. But… what are we looking for, and how will we know when we’ve found it? 

2013. Big data. Now, we don’t have to ask them anything – the answers will present themselves by synergising massive data sets! Fizzle. Because behind all the hype lay more flawed, mediocre inputs. More data ≠ better data.

2023. Okay, this time, it’s for real. AI everything. The AI’s will show us the way.

Just as in the London Tube, one must “mind the gap,” here, at the dawn of AI, we must also mind the hysteria. The history of our industry provides cautionary tales to expand thinking and relevance beyond the mere hype of the AI revolution.

I remember my first study on mobile during that massively hyped revolution. At its core, It was just a basic, crummy survey – but on the phone!

Likewise, the victors in this new space will be those who focus first, not on the technology but on the inputs. Unique, insightful inputs ­– not the same vanilla data repackaged. AI is a tool to enhance our work, not replace it. Like digital in the 2000s, AI can help us work smarter ­– if we use it right.

However, at the moment, the words, promise, ideas, and hysteria feel reminiscent of those previous – dare I say – bubbles.

To be clear, AI is not a bubble. The technology clearly promises truly revolutionary capabilities and ways of thinking and working. But, just as with digital and all the rest, AI starts with the same old challenge: clearly defining the problem. And aligning stakeholders. And communicating simply. AI augments human intelligence by tackling tedious tasks, not by solving problems for us. 

So before launching new AI experiments, scrutinise your inputs by asking 3 questions in the spirit of an avant-garde chef:

What dish are we creating?

I know – obvious. But just as a chef assesses the task at hand based on the context – from killer ballpark food to white glove fine dining, this still remains scarce in many insights and strategy efforts. 

Are we just trying to “segment the market” (invariably leading to the proverbial non-actionable segmentation deck)? Or are we trying to crack the code on how to attract incremental households via promotional shopper activation?

What are our ingredients?

Are our ingredients capturing true human behaviours and motivations? Or just weak proxies and assumptions? The quality of outputs depends on it.

AI's full potential emerges when combined with richer inputs from new data sources and advanced methodologies. Don't just dust off and word-smith a bad survey with AI; use AI to mine the gems hidden in both structured and unstructured data.

Who’s our diner?

Just as top chefs think of their dishes as stories to be told to their audience of diners, so remains the remit of shopper and consumer insights. Telling the organisation: “We used AI” will get people in the room. Delivering a dish they can relate to, from fresh, unique ingredients, is what will earn the rave reviews.

The future belongs to those who feed AI something new. The same inputs will get the same outputs, no matter how slick the tech is.

Smarter AI starts with braver inputs.