At Zuora’s latest Women Leading the Next Era of Finance event, I joined Lisa To of Anthropic, Priyanka Mohan of Amazon Web Services, and Debra Skorupka of PwC for a candid conversation about redesigning finance work around AI. While our earlier conversation explored how curiosity shapes leadership, this time, the questions became more operational: Which workstreams can AI lead? How do we make time for change management? How can finance help business partners act on analysis sooner? My takeaway was that leaders are focused on turning AI adoption into better ways of working.
Decide where people need to lead
Starting with the outcome helps teams decide where AI can take on repetitive work and where people need to lead.
In finance, it’s easy to focus on where AI can save time. But efficiency alone shouldn’t be the goal. Before redesigning a workflow, we should ask what outcome we’re trying to improve. Is it faster decision-making? Better insights? More time for strategic partnership? The outcome should guide how we design the workflow: what AI can lead independently, where humans remain in the loop, and where human judgment needs to lead. It’s not about removing people from the process, but being intentional about where and how they contribute.
In forecasting, AI can help consolidate data, identify variances, and model scenarios, while finance interprets the drivers, challenges assumptions, and recommends actions. In a more transactional process, such as travel expenses, AI might take on much of the process end-to-end, with people stepping in for exceptions and approvals. Customer-facing interactions require a more human-led approach, particularly where judgment and relationships matter.
That distinction gives workflow design a purpose. The question becomes not just what AI can do, but what experience and business outcome we want to create.
Make room for people to learn
One tension in our conversation was how to make time for thoughtful change management when the timelines for change keep getting shorter. As AI takes on parts of a workflow, people still need to understand how their responsibilities and relationships with other teams will change.
For finance leaders, that means creating space for this transition. AI adoption isn’t just about teaching people new tools. It’s about helping the team redefine where they add value. For example, if AI automates the first draft of variance analysis, the team’s responsibility may shift from preparing the analysis to challenging assumptions, interpreting drivers, and influencing decisions. Leaders need to make time for people to learn, experiment, and adapt as their roles evolve, rather than expecting them to take on new responsibilities while continuing to work the same way.
Give finance the context to help
Finance shouldn’t be a gatekeeper that enters the conversation only when a decision needs approval. We should be strategic partners from the beginning. This starts with understanding what our business partners are trying to achieve. Without that context, we risk becoming a roadblock by rejecting an idea before understanding its purpose. Joining a business leader’s weekly staff meeting, for example, helps us understand their priorities and find a workable path forward together.
Reliable data and trusted context are essential to that partnership. As AI enables teams to move faster and operate more independently, strong governance becomes even more important. Frameworks such as RACI (a responsible, accountable, consulted, and informed matrix) and COSO (the Committee of Sponsoring Organizations of the Treadway Commission) can help clarify accountability, establish appropriate controls, and decide where human oversight is needed.
The goal isn’t to slow the business down, but to give teams the confidence to act within clear guardrails.
Build skills for changing work
Agility, communication, and curiosity help teams adapt as responsibilities shift. That means getting comfortable with unfamiliar technology, letting go where appropriate, and continuing to test and learn as capabilities evolve.
It was energizing to reconnect with this community and hear how others are navigating these changes. Seeing analysis influence decisions sooner can make the work more rewarding. That’s a useful test for AI adoption: whether it helps people contribute more meaningfully to the business or find greater satisfaction in their work.
Ultimately, the measure of successful AI adoption isn’t how many workflows we’ve automated, but rather how effectively we’ve elevated finance from number crunchers to strategic architects of the business.