A CFO playbook for 2027 workforce planning

As CFOs enter budgeting and workforce-planning season, many of the most important questions about AI are no longer theoretical. Many organizations have been discussing the future of work, human-AI collaboration, and workforce readiness for months. Most of those with fiscal years starting in January are already having to decide where AI-related budgets are more needed in investment, operating, and talent decisions, or if they remain in pilot phase.

For CFOs, the stakes extend beyond finance transformation. As both stewards of financial data integrity and key decision makers regarding enterprise AI investment, CFOs’ experience with controls, evidence, and decision rights can help shape how the broader organization adopts AI.

The choices made in this planning cycle will likely help determine whether AI becomes a force multiplier or an expensive missed opportunity. To enable AI to become a force multiplier, CFOs may need to intensify their focus on AI workforce planning, identifying which AI investments will create meaningful business value, how AI will reshape finance work, and whether their workforce has the skills to adapt.

Deloitte’s 2026 Global Human Capital Trends research found that organizations focused solely on technology are 1.6 times more likely to fall short of expected returns on AI investments than those taking a human-centric approach. Organizations leading in intentional human-AI work design are nearly 2.5 times more likely to report better financial results.

These findings reinforce that technology is an enabler; people create differentiation by applying human judgment, redesigning work, and turning AI into measurable business value. Yet many organizations continue to layer AI onto legacy systems and processes rather than reimagining how humans and AI can interact, collaborate, and make decisions.

When staffing for AI, a central question is whether they have intentionally designed the work, workforce, and ways of working—and put the leadership and change enablement in place—important to capturing AI’s value.

Work aspects of transformation in finance

Three priorities for CFOs

1. Move from AI experimentation to intentional work design. Organizations should identify where humans and AI are explicitly designed to work together across finance processes and design how people and technology work together, including which activities are best suited to each and why.
   
   AI will likely assist or augment much of the finance work, enabling both people and the nature of work to move up the value chain, while AI-powered work may replace some activities and deliver new or more efficient outcomes.
   
   In some areas, headcounts may increase as finance talent and technology increasingly work together. CFOs should therefore assess whether AI-generated capacity is being intentionally converted into redesigned work and higher-value outcomes or captured through labor-cost and workforce-structure optimization rather than simply used to perform existing tasks faster.
   
   In addition to cost savings, work design discussions should also focus on the quality and speed of decisions people can make.
   
   
2. Fund workforce readiness beyond technology adoption. Finance leaders should determine what level of AI fluency, data literacy, business-partnering skills, and broader talent strategy is required for the next 18 months and whether reskilling plans need to be reflected in the 2027 workforce budget.
   
   Beyond reskilling and upskilling, this may include decisions about workforce mix, role design, and hiring at the entry- and early-career levels. For example, AI-fluent talent can help accelerate workforce readiness and embed new ways of working, while experienced professionals can provide the business judgment and context needed to apply AI effectively.
   
   This approach calls for a talent strategy aligned with both business and AI strategies, one that addresses the workforce dimension of transformation, including trust, culture, leadership, and change enablement.
   
   
3. Srengthen accountability and orchestrate capabilities across the enterprise. AI-generated outputs require human oversight, nuance, and context. Finance leaders should prioritize protecting financial data and intellectual property, clarifying who is accountable for material decisions, and evolving controls as AI becomes more embedded in reporting and planning. At the same time, working closel with human resources, legal, operations, and technology can help finance leaders better orchestrate people, skills, data, and systems around business-critical outcomes.
   
   Many challenges and opportunities related to AI in finance now warrant greater consideration in annual planning. The annual budgeting and workforce-planning cycle is typically the most natural time for leaders to align investment with the intentional redesign of finance work, workforce, and organization, to turn AI from an experiment into measurable business performance.