Retooling the finance team’s skillset in the age of the agent

Just three years after the release of ChatGPT, the business environment we all work in has changed - dramatically, and for good.

Interacting with AI platforms represents a total paradigm shift for users - the skills we’ve all built up working in applications like Excel are not transferable. So what does this mean for the finance team? And how can leaders ensure their upskilling strategy is a success?

First, let's explore what skills the AI era demands.

What skills does a finance team need in the AI era?

Hard skills

• Data transformation, modeling, and visualization: Working with accurate data builds trust in every forecast.
• Predictive analytics and automation: Effective AI use improves forecasting and frees up time for strategic work.
• Prompt engineering and data fluency: Asking the right questions in the right way keeps AI outputs reliable.
• Systems integration and workflow design: Connected tools make planning seamless.

Soft skills

• Business partnering: Aligning financial insights with strategy enables better, faster decisions across the organization.
• Presentation and public speaking: Clear and compelling storytelling helps translate findings into action.
• Ethics and governance: AI practices that are transparent, compliant, and fair build internal trust in AI systems.
• Collaboration: Working with tech and other business teams improves the quality of insights.

With this in mind, let's now examine how to build an effecitve upskilling strategy.

1. Make it strategic

Upskilling succeeds when it is tied directly to business priorities. Finance leaders who embed skill building into strategic planning develop talent that is tailored to their organization’s goals and culture. In a market where technologies evolve quickly, the ability to learn faster than competitors becomes a key form of differentiation.

A strategic approach starts with identifying the capabilities that drive performance – such as predictive forecasting, automated reporting, and scenario modeling – and aligning training programs around them. The most advanced organizations treat upskilling as a measurable investment, tracking skill growth alongside key financial and operational metrics.

By positioning learning as a strategic pillar, finance leaders create a workforce that can adapt continuously to new tools, regulatory shifts, and market dynamics.

2. Make it everyone’s responsibility

Upskilling flourishes when it is championed across all levels of leadership. In leading organizations, senior executives, managers, and team leads share accountability for workforce development. This distributed model ensures that learning is connected to real work and that employees see how new skills contribute to shared goals.

Managers play a central role in guiding their teams’ progress. By integrating upskilling into performance conversations and project planning, they help transform learning from an occasional event into a continuous rhythm. Senior leaders reinforce this culture by modeling curiosity, celebrating development milestones, and ensuring that resources for training are visible and accessible.

When learning becomes part of leadership expectations, adoption accelerates and employees understand that growing their skill set is integral to achieving business outcomes.

3. Treat upskilling as change management

Upskilling is not only a learning exercise; it’s a transformation initiative that reshapes how teams work. Successful programs begin with clear communication about purpose and outcomes, giving employees a shared vision for how AI will enhance, not replace, their expertise.

Change management involves several deliberate steps:

• Defining which skills are critical to current and future success
• Creating learning pathways that connect to real roles and projects
• Providing time and flexibility for employees to learn within the flow of work
• Establishing feedback loops to refine programs based on results

Organizations that approach upskilling with the same rigor as major transformation projects see faster adoption and stronger alignment across teams. Finance leaders can reinforce this by assigning clear ownership, tracking progress, and integrating new skills into existing governance frameworks.

4. Design around motivation

Motivation grows when training feels relevant, attainable, and rewarding. Finance professionals are eager to learn when they can connect new AI skills to challenges they face every day, such as improving forecast accuracy or accelerating variance reporting.

The most effective programs are designed from the employee’s perspective. They minimize barriers, link skill development to meaningful career paths, and demonstrate how AI expertise leads to greater impact within the organization. Offering structured learning paths, recognition programs, and opportunities to apply new skills on live projects helps sustain engagement.

Upskilling thrives when employees see progress quickly. Short, outcome-driven modules and real-world applications turn learning into momentum that compounds over time.

5. Build your ecosystem

Reskilling and upskilling work best as part of an ecosystem that extends beyond the finance function. Partnerships with IT, HR, and data teams foster a shared understanding of technology and governance. Collaborations with external experts (from universities, training platforms, and industry groups) can also expand access to the latest tools and practices.

Ecosystem-driven programs also ensure that learning keeps pace with innovation. As new AI capabilities emerge, external partnerships help teams incorporate advances without waiting for internal systems to catch up.

By building this network of support, finance leaders create a self-sustaining environment for growth. Learning becomes a collaborative effort, supported by shared resources and reinforced by community insight.

Turning strategy into practice

Research from Harvard Business Review highlights that companies succeed when they approach reskilling as a strategic priority, make it the responsibility of every leader, manage it as transformation, design for employee motivation, and invest in cross-functional ecosystems.

Finance leaders who apply these principles can build teams that grow alongside technology. With clear strategy, distributed ownership, and structured learning opportunities, upskilling becomes an enduring source of agility and advantage – one that strengthens every decision, process, and plan in the era of agentic AI.