The Bespoke Operating Model.

What if We Rethink This Entirely?

AI capabilities are changing constantly. The economics of work are changing. Processes that made sense only a few years ago can suddenly be reconsidered from the ground up. Trying to follow every development is, in Jen’s words, “an impossible task.”

So she advises leaders to resist chasing every new tool or capability and return to the business itself.

“I’m not trying to chase all of these shiny balls that are dangling in front of my team,” she says. “I need to be able to figure out strategically what is going to make my company be a market leader, operationally efficient, run lean, and still continue to be innovative.”

The answer, she believes, will look different for every company.

“The work is to understand your own strategy and become comfortable building an enterprise model that is genuinely unique to you,” adds Jen.

AI Is Moving Deeper Into the Organization

AI is showing up in FP&A, operational efficiency, close processes and order-to-cash, as well as in broader conversations about how companies prioritize AI investments. “It’s really everywhere,” she says. “It’s permeating throughout the entire enterprise.”

Jen says discussions that previously tended to happen with CFOs or CAOs are increasingly happening within GBS and other operating teams. They are asking how AI can handle more routine work, identify risk, monitor events and create greater straight-through processing—allowing people to concentrate on the situations where judgment and intervention actually matter.

That pushes AI into the mechanics of how the enterprise runs. And as that happens, Jen believes the cultural challenge becomes inseparable from the technical one.

Organizations cannot simply give employees a three-hour AI class and declare the job finished. Leaders need to encourage experimentation in the work itself. Jen doesn’t see experimentation as the opposite of discipline. She sees it as increasingly necessary to discover what is possible. “Every single day you should be using it for five things that you do,” she says.

That repeated exposure is what begins to produce new ideas about how work could be redesigned. And there is something Jen finds energizing about the uncertainty.

“No one has the answer,” she says. “You can experiment and even if you’re wrong, so what? Most people will be wrong.”

The fundamental value proposition for finance, in her view, hasn’t changed: reduce the amount of time people spend on manual work so they can focus more on insight, risk and proactive management of the business.

“The goal is still the same,” she says. “The way in which it takes shape is the how, and that to me is what will continue to evolve.”

Finance Leadership Now Requires a Different Kind of Curiosity

What differentiates finance leadership now is the ability to work across the organization: to connect finance with IT, HR, the front office and customer-facing teams and create what Jen describes as an integrated ecosystem of continuous improvement. That requires vision. It also requires someone willing to champion a different mindset.

“If all you care about is results or accuracy, you’re going to miss a lot of the journey that comes with finding out new ways of doing things.”

The journey asks leaders to question processes they have spent years optimizing, and sometimes systems and organizational structures they helped build.

“It’s not looking at the same process and saying, ‘Here’s where we apply automation. Apply it in step three, apply it in step six, apply it in step 13.’” Instead, she says, the thinking needs to be closer to: “Know point A, know point Z, and figure out whether everything in between still needs to exist.”

This work changes the modernization question from “Where can AI help?” to “If we were designing this work today, would we design it this way at all?”

AI Is Also Changing Who Can Create Value

Natural-language coding and other AI tools mean people no longer necessarily need traditional technical backgrounds to build applications or solve certain kinds of technology problems. Someone who can frame a problem clearly, learn quickly and work intelligently with AI may be able to develop capabilities that once required a much narrower educational path.

“The availability and the diversity of the types of AI tools that exist now enable a much broader use,” she says. “It levels the playing field.”

For early-career professionals, the implications are particularly striking. Someone a year out of school can use AI to learn at a speed that would have been difficult to imagine only a few years ago.

Jen’s advice is unequivocal: “Don’t use it at your peril.”

That has consequences for hiring, too. Jen believes finance and HR need to work much more closely to identify the leaders the organization will need next. AI acumen, knowledge and proficiency are rapidly becoming part of the baseline.

When AI Becomes Embedded

Jen has a simple way of thinking about where organizations are headed. Most are still somewhere between exploring and piloting. Then comes scaling. Ultimately, AI becomes embedded.

“You should strive to have AI embedded within your entire organization,” she says. At that point, it stops being a series of isolated initiatives. Work begins to flow differently. More routine processes happen automatically. Information reaches people with greater context.

Decisions and risks are surfaced continuously rather than discovered after the fact. That is the intelligent operating model Jen sees beginning to take shape.

But there is no standard blueprint for getting there—and perhaps that is the most important point.

The companies that make the most of this moment may not be the ones that identify the “right” operating model first. They may be the ones that get much better at designing, testing and continuously reshaping their own.

As Jen puts it, we are still in a period where “nobody knows.” For finance leaders accustomed to having the answer, that can feel uncomfortable. It may also be where the opportunity begins.