For the first time in business history, we are seeing something completely new: regular employees are using new AI technology faster than the companies they work for. This is not just another technology that needs a quick fix. Instead, it completely changes how companies adopt new technology. In the past, new technology moved from top leaders down to workers. Now, it moves from everyday workers up to leadership. The center of change has shifted from the boardroom to employee chat channels and personal accounts.
Only 9 percent of companies report being fully prepared culturally for AI integration—a figure that inspires approximately the same confidence as a paper umbrella in a hurricane.
“Individuals—human beings both in and outside of business—are adopting AI quicker than can be embraced at the enterprise level. As leaders, we’ve realized we’ve got a vulnerability here.”
—Toby Boudreaux, Global Vice President of Data Engineering at Publicis Sapient
So how does the C-suite lead change management when adoption speeds have already left organizational readiness in the dust?
As CFO, your relationship with AI differs fundamentally from your C-suite colleagues. While others rush to embrace the newest AI tools, you find yourself playing a more cautious role—not because you resist innovation, but because the financial data under your care demands thorough protection.
This tension—between embracing transformative technology and protecting sensitive financial information—creates a unique set of challenges that other executives simply don’t face.
“The sensitivity of the data we touch when we think about contracts or financial dashboards means that we cannot just use any AI because, by definition, AI stores what they are sent with a view to learn and evolve, and could use the data we share to answer a request from any other random user of the AI engine.”
—Eric Celerier, CFO Commercial Success at Publicis Sapient
The predictability of hourly billing—that fundamental economic equation where time equals money—is rapidly becoming as outdated as paper ledgers. As Celerier notes, “In the past, we were mostly selling to our clients time and materials... The arrival of the AI tools are changing that world. The usage of AI tools delivers a lot of added value for our clients. The classic financial models have to evolve accordingly.”
This shift demands creative financial thinking: outcome-based models. For example, clients might pay for results rather than hours, subscribe to AI-powered capabilities, or use hybrid approaches that combine traditional services with AI enhancements at varying price points based on customization levels and human involvement. The question isn’t whether to change your pricing models, but how quickly you can evolve them before market forces make the decision for you.
The finance professional who can’t explain how AI creates value is like an accountant who can’t explain a balance sheet—technically skilled but strategically limited.
This educational journey requires specialized AI literacy programs for finance teams focused not on technical implementation but on business implications—how these tools affect financial models, risk profiles, and regulatory requirements. Creating simulations that demonstrate how AI capabilities translate into customer value enables more accurate pricing and forecasting, turning finance from cost controllers into value interpreters.
“There’s a need to understand... what AI tools do we have? What do they do? How do they work? What is our competitive advantage in this field of expertise? To provide adequate support, we need to understand better what we are selling. If you don’t understand the nature and the value of the services your company delivers to its clients, you just cannot define a fair and accurate pricing model.”
—Eric Celerier, CFO Commercial Success at Publicis Sapient
The most delicate financial equation is the following: On the one hand, we are aggressively trying to reap the cost and time savings benefits from AI and transfer those to our clients. But at the same time, we need to fund AI development inside the organization.
Solving this equation requires sophisticated cost-accounting systems that separate AI development investments from client-billable work. Leverage financial models that measure both internal gains and client value creation, as well as reinvestment frameworks that direct some AI-driven profits back into capability development. This ultimately creates a virtuous cycle of continuous improvement rather than a one-and-done efficiency gain.
The shift from services to products demands entirely new commercial structures.
This product mindset requires different commercial models based on usage or capability access, metering systems that track AI utilization for consumption-based pricing, and contract templates that address previously unnecessary considerations like data rights, model improvements, and ongoing support arrangements. The finance team that can’t create these frameworks rapidly will find themselves unable to capitalize on their organization’s AI investments.
Bottom Line: The greatest financial challenge isn’t calculating AI’s return on investment but redesigning your entire financial governance system to protect sensitive data while simultaneously enabling the very AI experiments that could transform your business model.
"We are now starting to have cases when we are selling our AI tools and even sometimes embedding our AI tools into the clients' ecosystems, allowing the clients to use them. This requires a brand-new financial framework."
—Eric Celerier, CFO Commercial Success at Publicis Sapient
The executive suite now faces a profound choice: attempt to control a revolution already in progress or become its most thoughtful enablers, creating frameworks that channel its energy rather than contain it.
The C-suite’s value lies both in a decent understanding of AI capabilities (which will continuously evolve beyond any static comprehension) as well as in creating the organizational conditions where both humans and machines can continuously learn together.
What connects all successful AI transformations is humility—the recognition that no leader, regardless of title, fully comprehends the end state toward which we’re collectively evolving. The organizations that thrive won’t be those with the most advanced AI strategies on paper, but those that have reconstructed themselves, in difficult ways, to adapt continuously as AI capabilities expand in directions we cannot yet imagine.
The question isn’t whether your organization will transform—it’s whether that transformation will happen coherently, with intentional guidance from the C-suite, or haphazardly through a thousand unconnected adaptations.
The AI revolution won’t wait for your carefully orchestrated change management plan. It’s already happening, with or without your permission.