AI is resetting finance. 2026 is the scale year.
AI isn't a buzzword anymore. It's moving from talk to balance-sheet impact across forecasting, planning, and strategic decision-making. As data flows faster, the right question isn't "What can AI do?" It's "How will AI transform the enterprise-and how do we build for it?"
What CFOs are saying
Across industries, finance chiefs are shifting from pilots to enterprise playbooks. The focus is clear: measurable value-faster decisions, leaner operations, and predictive insights that sharpen competitive advantage.
Zane Rowe, CFO at Workday, put it this way: "There has never been a more exciting time to be a CFO with AI unlocking new opportunities for value creation through unprecedented data and insights. Most of the focus has been on experimentation and discovering the art of the possible, but this year, leaders will shift from 'What can AI do?' to 'How do we build the foundation for scale?' They will manage a more nuanced AI portfolio that balances launching pilots with rolling out proven solutions, and they will prioritize the unglamorous but critical work of data governance, process redesign, and maintenance of new technologies. Success in 2026 will be defined by how we mature our AI strategy to ensure it is both agile, durable, and enterprise-grade."
Mandy Fields, CFO at e.l.f. Beauty, added: "From where a CFO sits, AI simultaneously helps broaden our view to get a better macro picture and can help put a sharper focus on very specific points of interest. e.l.f. Beauty is growing globally, and AI has visibility across it all. Going into next year, we'll continue to explore how we best leverage AI in finance to lean into its strengths. It's a pretty similar approach to our high-performance teamwork culture in which we encourage the team to pursue and thrive in the areas where they have expertise, learn continuously and move at e.l.f. speed."
From pilot projects to enterprise-scale AI
Finance leaders are signaling a clear next step: 2026 will be the year AI scales across the enterprise. Pilots and proofs of concept are giving way to production rollouts tied to clear metrics-cycle-time reduction, working-capital gains, forecast accuracy, cost-to-serve efficiency, and margin lift.
This shift comes with new demands. Governance, data integrity, process redesign, model lifecycle management, and human oversight are now core finance capabilities, not side projects.
Governance and data: the non-negotiables
Without clean data and firm controls, AI becomes noise. CFOs are co-owning standards for data lineage, model risk, and auditability with CIOs and compliance leaders. Frameworks like the NIST AI Risk Management Framework can help align policy with execution.
Expect tighter integration with internal audit, clearer thresholds for human review, and documentation that stands up to scrutiny-from regulators, boards, and customers.
Hot topics on every finance agenda
- ROI of AI: moving from anecdotes to unit economics, cash impact, and payback windows.
- "Prompt-a-thons": structured sprints to translate domain knowledge into reusable prompts and workflows.
- The CFO as "chief capital officer": rethinking funding models for data platforms, copilots, and shared AI services.
- FP&A acceleration: scenario planning, driver-based models, and continuous forecasting grounded in live data.
Practical moves for 2025-2026
- Build an AI portfolio: split bets across quick wins (AP/AR, close, reconciliations) and scalable platforms (data foundations, copilots, decision engines).
- Lock in data quality: define golden sources, ownership, and SLAs; eliminate shadow datasets that break models.
- Stand up model governance: versioning, monitoring, drift alerts, and human-in-the-loop checkpoints.
- Tie to hard metrics: set targets for forecast error, days to close, DSO/DPO, and unit-cost reductions.
- Redesign processes, not just tasks: remove handoffs, standardize inputs, and automate end-to-end flows.
- Upskill the team: prompt craft, query skills, and basic model literacy for FP&A, controllership, and treasury.
- Update controls and audit trails: capture who-approved-what, with explainability for model-assisted decisions.
- Plan change management: communicate roles, set usage guidelines, and reward adoption with measurable outcomes.
Where this is heading
AI is expanding the CFO's field of view while sharpening focus on what moves cash, margin, and risk. The winners will pair ambition with discipline-treating AI as a portfolio, not a one-off tool, and building the groundwork that makes scale possible.
If you're mapping tools and training for your finance team, explore curated options here: AI tools for finance.
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