Defining AI-First: Navigating the New Vocabulary of Artificial Intelligence

Defining AI-First: Navigating the New Vocabulary of Artificial Intelligence

When any substantial change captures our attention—whether it’s a trend in fashion, a new business model, or a cultural movement—a new language often emerges.

From that language arises a new vocabulary, and that vocabulary eventually proliferates to the point that many of us adopt it.

But sometimes this vocabulary proliferates so quickly that we find ourselves using it without really knowing what it means.

And perhaps nowhere is this more apparent right now than within the vocabulary of AI.

If you’re anything like me, you’ve read and heard the phrase “AI-first” (used as both a noun and an adjective) thrown around quite a bit in the last year.

And, if you’re anything like me, your understanding of what it means is nebulous at best.

But this lack of understanding says less about you and me and more about this moment that we’re in, which, by any form of measurement, is still incredibly new and moving incredibly fast.

The reason you feel a lack of clarity around the phrase AI-first is because there’s no clear definition of what it means. Or maybe a better way to say it is this: it’s up to you to define what AI-first means to you and your organization.

Back in 2021, prior to the launch of the first commercially available Large Language Model, internationally-renowned startup investor Ash Fontana published a book called The AI-First Company.

He wrote, “AI-First companies put AI to work, prioritizing it within real budgets and time constraints. AI-First companies make short-term trade-offs to build intelligence in order to gain a long-term advantage over their competitors.”

Of course, Fontana wrote these words before the launch of ChatGPT, which means his understanding of what AI-first meant, and might ultimately mean, suffers under the burden of not having all the information we have now.

I’m not sure it’s still true that companies have to make short-term tradeoffs to build intelligence, because the cost of building meaningful intelligence from the data you already own is falling at a rapid pace, and the tradeoffs, if any remain, are diminishing.

Despite the book’s title, Fontana doesn’t really get us any closer to an acceptable, agreed-upon definition of AI-first, which means that it’s ultimately up to us to determine whether AI-first, regardless of how we define it, is something to which we should aspire.

So, in an effort to move this conversation forward, I’ll try and unpack my own definition of AI-first.


AI-first or strategy-first?

In the brand and marketing work I do for clients, I’ve long believed in strategy-first, that is, that strategy must always come before execution.

This frustrates our clients at times, which I understand to some degree, because they feel that speed sometimes trumps precision, but it’s not a point I’m willing to concede for this simple fact: if the strategy isn’t right, the execution won’t matter.

Regardless of the type of strategy we’re discussing—positioning, brand, customer-acquisition, GTM, marketing, you name it—if the strategy is wrong, then the rest of what you do will either be ineffective based on assumptions and erroneous conclusions, or effective based on nothing more than chance.

My preference, even now, in the AI Age, is to have strategy lead the way.

This gets a bit meta when AI itself becomes the strategy or when AI is used to determine the strategy.

Nevertheless, what I’m getting at here is that both strategy-first and AI-first are mindsets.

If you declare that you are an AI-first company, you’re taking a position that indicates that technology is important and that you’re investing in it as an organization.

You’re letting the market know that you’re aware of the sea change happening right now, and not only are you observing it, you’re also wading deep into the waters to see what’s swimming around.

What you’re not saying is that you’re going to use AI for everything.

In that sense, AI-first is more of a direction than a destination.

In his new book, Co-Intelligence: Living and Working with AI, Wharton professor Ethan Mollick posits four principals for working with AI, which he calls “co-intelligence.”

Principle one states, “Always invite AI to the table.”

He writes, “You should try inviting AI to help you in everything you do, barring legal or ethical barriers. As you experiment, you may find that AI help can be satisfying, or frustrating, or useless, or unnerving.”

He goes on to say that familiarizing yourself with AI “allows you to better understand how it can assist you—or threaten you and your job.”

Mollick’s point dovetails nicely with world-renowned AI expert Lance B. Eliot’s position, which I agree with: “The more that you can think like the machine, the greater the chances you have of successfully contending with the machine.”

At this point my own AI journey, I would say that both my business and my approach as a knowledge worker are AI-first.

I’ve used AI to assist with strategy, to proofread my work, to brainstorm, to suss out keywords, to analyze list data, to perform market research, to create ICPs (ideal customer profiles), to role play business scenarios, to create recipes, to come up with new workout routines, and to help me with techniques for crate training a dog I’m currently fostering.

In my view then, to be AI-first is exactly that—to look to AI for a solution before you look to anything else.

You may not like what it comes up with, and you may not ultimately use it, but that’s perfectly okay.

AI-first is a choice, not an obligation.

It’s also still, after about a thousand words of trying to define it, open to interpretation.

But that’s the thing about learning a new vocabulary, isn’t it? That it’s at least partly open to interpretation?

Sometimes it’s easier to know what something means, to understand the intent behind it, than to accurately define it.

Perhaps, then, it doesn’t actually matter that we come up with a succinct definition of what AI-first means.

In the end, maybe all that matters is we grapple with the words and wrestle with the definitions, adding our own particular nuance and understanding to the conversations happening around us.

Maybe all that really matters is that each of us engage with the language and interrogate the vocabulary, defining AI-first for ourselves, keeping in mind that AI is a powerfully versatile tool that’s shaping and molding our future.

So regardless of where you land, and regardless of how you define it, remember that AI-first is a direction you can travel, even if you’re not quite sure where that direction will ultimately lead.

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