You Can't Skip the Offseason and Expect to Win on Saturday

College football is finally here. For me, that means Clemson football is back, and there aren't many Saturdays this fall when I won't arrange at least part of my day around watching the Tigers.

As fans, we spend a lot of time thinking about what happens on Saturday: the big plays, the coaching decisions, the turnovers, and ultimately the final score. But we also know that a lot of those games were won or lost months earlier: in winter workouts when nobody was watching, in summer conditioning, in the film room, in recruiting and player development, and across thousands of practice reps that never make SportsCenter. By the time the team runs down the hill and the scoreboard comes on, most of the work that determines what we're about to see has already happened.

I've been thinking about that a lot as I watch companies approach AI.

Everyone wants Saturday. They want the productivity gain, the AI agent, the automated workflow, the faster decision. They want the use case they can point to and say, "Look what AI did for us." There's nothing wrong with wanting those things. The problem is expecting Saturday results without putting in the offseason work. And right now, that's where a lot of organizations are struggling.

Deploying AI and being ready for AI are not the same thing

The technology is moving fast. What seemed difficult or expensive a year ago can often be built or bought quickly today. That speed makes it tempting to believe that becoming an AI-enabled organization should happen just as fast. It shouldn't, and it can't.

The less visible work is usually what determines whether an AI initiative creates lasting value:

• Is the data good enough to support the decisions you're asking AI to influence?

• Are leaders aligned on where AI matters most, or is every function chasing its own priorities?

• Do you know where AI is already being used across the organization, including the tools employees adopted without asking anyone?

• Have business processes been redesigned around what AI makes possible, or has AI just been layered on top of how work has always been done?

• Do your controls still work when some of the judgment they were built to oversee is now being performed by AI?

• Do people understand where they're still accountable for decisions, even when AI did much of the work?

None of that makes for a particularly exciting demo. But neither does a winter workout.

That's the shift I keep seeing: the technology is becoming more accessible, while the organizational questions are becoming harder. AI touches data, governance, business processes, and controls. It changes jobs and responsibilities. It affects how decisions get made. It creates new risks. And it forces leadership teams to decide where they want to move quickly and where they need to be more deliberate.

You can launch a new AI tool without addressing any of this. You just can't build a sustainable AI capability that way.

That's why I think some companies are in for a surprise over the next few years. They'll have plenty of AI: licenses, agents, pilots, automations, maybe hundreds of use cases. But having a lot of AI isn't the same as being good at AI.

What does your offseason look like?

A football team doesn't spend January trying to predict every play it will run in October. It builds capability: getting stronger, developing players, installing systems, building chemistry, evaluating talent, fixing weaknesses, drilling fundamentals.

Companies need to think about AI the same way. The goal isn't to anticipate every use case your organization will ever pursue. That's impossible given how fast the technology is changing. The goal is to build an organization capable of recognizing the right opportunities, making good choices, executing quickly, managing the risks, and learning from what happens.

In practice, that means:

• Getting leadership aligned on what you're trying to accomplish with AI, and where it can meaningfully change the business rather than just speed up existing tasks

• Building the data foundation those opportunities depend on

• Establishing governance that lets the organization move with confidence

• Rethinking processes and controls for work now being done differently

• Preparing people not just to use AI tools, but to exercise judgment over the work those tools produce

Most importantly, it means building the ability to do all of this repeatedly, as AI keeps evolving. That's the offseason. It's not as exciting as the touchdown, but it's what makes the touchdown possible.

Saturday is coming

One of the things I love about college football is that eventually you have to play the game. Recruiting rankings don't matter anymore. Preseason predictions don't matter. The hype doesn't matter. The ball gets kicked off, and you find out how prepared you really are.

We're entering that stage with AI. The experimentation period isn't ending, but the expectations are changing. Boards and executive teams want to know where the value is.

Finance leaders want to understand the return. Risk and compliance teams want to know how AI is being governed. Employees want to know what it means for their work. The scoreboard is starting to matter.

The organizations that win won't necessarily be the ones with the most AI tools, the biggest budgets, or even the most impressive playbooks. They'll be the ones that built a team capable of executing when Saturday arrived.

You can install a few new plays during game week. You can't build the team on Friday night.

About Root Idea

Root Idea helps CFOs protect the business from AI decision risk. Root Idea works directly alongside finance teams to map AI influence, establishes decision governance controls that hold up to board scrutiny, and delivers training and change management to make governance stick.

If your organization is scaling AI and governance hasn't kept pace, that's exactly the conversation we're built for. Learn more at rootidea.ai.