AI laggards can still come out ahead

AI is virtually everywhere now. We’re seeing it in every commercial, after every Google search and even possibly in Will Smith’s music videos. It doesn’t need any free press. But it keeps getting more.

A detailed McKinsey industry study on AI usage demonstrated that companies in the AI Leader category are seeing six times revenue growth when compared to their laggard counterparts. That’s an incendiary statistic and it blew the doors off the AI use-case discussion, turning it from an abstract issue to an all-out arms race.

From possible to profitable

The AI wave of possibility has begun to fade and, as we move into 2026, the issue has now turned to applicability. It’s changed from what’s possible to what’s real and what’s bunk. This is a much more difficult question.

Not only that, but the revenue being gained from AI is continuously growing. In other words, the stakes are increasing dramatically each day. One global survey demonstrated that the top-line gainers — that is, those who are increasing their revenue from AI by 6% to 10% or higher — are skyrocketing. In certain industries like service operations, supply chain or corporate finance, these rates are up by 300% or more.

In other words, the AI cream of the crop are getting massively efficient and profitable off their newfound success — and laughing all the way to the bank at everyone else. All these factors together make for an extreme (and largely understandable) level of FOMO for those outside the cool kids’ club.

Without a sense of direction, they’re spending wildly and blindly, trying to do whatever they can to catch up. Of course, this is a recipe for going nowhere fast and now AI laggards are wondering what went wrong, what they can do and if they missed the boat for good.

You’re not as late as you think you are

The answer to this is a resounding no. Despite a whopping 41% of businesses reporting that they’re worried about being behind in AI, they should take comfort in the corollary. A mere 8% of companies are actually AI leaders rather than laggards, meaning they are maximizing the full AI toolkit to its foremost potential. That means most people claiming to be front-runners are likely fibbing.

While the stakes are indeed rising higher and it’s imperative to utilize AI in your workflows, the odds are high that most aren’t too far gone. If you’re already AI-ready, you’re barely behind at all. Ultimately, it comes down to possessing a proper understanding of one’s needs and the organization. With that, you can catch up with anyone. Do not give up hope.

Few cases shatter the laggard illusion more than JP Morgan’s AI rollout. In late 2023, the AI frenzy was becoming increasingly ubiquitous among the Fortune 500. Big companies were all assembling their own tools, apps and chatbots, while JPM’s strategy was seemingly stuck back in the pre-AI world, which may as well have been the 1800s.

But JPM wasn’t without a plan, nor were they even really behind. They were on time for when AI became truly viable and valuable for their clients. This was a genuine strategic deployment: calculated, focused and unmoved by irrelevant outside competition.

While JPM was cautiously working on their comprehensive plan, Fintech startups like Betterment and Wealthfront rolled out their own versions of AI-driven financial assistants. At first glance, these seemed cutting edge. But in 2024, a lifetime later in AI-rollout years, JPM finally released its IndexGPT and other suite of AI tools. It became evident that rather than rushing for a release date, they were merely leveraging massive internal datasets and compliance frameworks that the smaller players lacked — providing immediate credibility as well as real customer traction that was expected for a financial behemoth of its caliber.

Speed without fit is theater

The lesson here isn’t necessarily about waiting for the dust to settle. It’s a story about proper execution. At the time all the smaller competitors were rolling out their piecemeal solutions, JPM wasn’t waiting. The risk of a rushed product was not worth it for them, laggard or not.

The startups and fast movers have become sideshows — their credibility called into question. This slow and steady approach showed that getting AI fast bears no value when you’re not getting it right.

Integration is not seamless, nor should it be. It means understanding every nook and cranny of one’s business — which is often a fluid and laborious endeavor.

Leaders need to know where the value is for AI with their organization and where it can be scaled up. The best way to do this is by starting small. Begin by hitting singles and doubles, racking up a few easy and scalable wins. This is essential before trying to ramp up to a full-scale transformation effort. The quicker you can get to value, the faster you can get ahead and maximize it on a broader scale.

The difference between front-runners and fast-followers is their relative aptitude at deploying and scaling.

This addresses the two biggest AI hurdles. The two foremost fears surrounding AI are the workforce challenges, particularly managing the change, cultural shifts and training requirements. Starting with small, repeatable wins helps streamline this transition process.

Similarly, 60% of business leaders say that integrating AI tools with legacy systems is the most daunting challenge. A slow and steady approach means knocking out two birds with one stone.

In my experience, success stems from conducting one-to-one workshops that identify, pinpoint and prioritize each client’s pain points and potential use cases. This helps us professionals figure out which problems to attack head-on and which to leave for a later date. We then build a prioritized list of methods and solutions to address these specific problems in a way that best suits each client.

Most self-identified laggards simply don’t know where to start. They feel the FOMO and think they have to do everything and more. The best answer is also the easiest: Start small. If companies can do that, they’re instantly at the front of the pack.

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