AI is reshaping tax and finance talent strategy, says EY
Finance teams are being pushed to drive strategy while the ground keeps shifting. An EY survey of 1,600 tax and finance leaders shows 89% plan to upskill their workforce in the next two years. The goal: a tight mix of accounting depth, data literacy, and AI fluency.
As Stuart Lang of EY put it, the task isn't to replace people, but to build a training program that upgrades capability because "the skills of tomorrow are different from the skills of the past."
Why leaders are rethinking talent now
- More strategic demand: 79% rank providing insights for deals, supply chain shifts, and scenario planning as a top priority.
- Geopolitical pressure: 81% plan changes to supply chains, with direct tax and operating model impacts.
- Pipeline strain: A widening talent gap and retirements loom; 61% say senior retirements will significantly affect their function.
- AI urgency vs. data reality: 86% prioritize using gen AI and tech for innovation and insight, but only 16% feel very confident in their data strategy. Fewer than one in four report high data maturity in tax, and 64% lack a sustainable data-and-tech plan.
The new finance skill mix
Technical chops still matter. But leaders want a fusion of accounting, data, and product-thinking skills. David Helmer of EY sees teams hiring data scientists and AI specialists to sit side by side with tax and accounting pros-because the work now crosses disciplines.
- Core accounting and tax: Policy, controls, reporting, and judgment don't go away.
- Data and automation: Modeling, data quality, SQL/Python basics, workflow tooling.
- AI fluency: Use-case design, prompt skills, validation, and human-in-the-loop review.
- Intangibles: Problem-solving and critical thinking top the list-85% say they're essential.
Lang's take: future professionals must "translate what the data and AI are telling them, apply it to a business context, and make recommendations." That's the job now.
A 90-day plan for CFOs and Heads of Tax
- Rebalance the work: Map activities by value. Set a target to double time on strategic work and trim routine compliance via automation.
- Define role archetypes: Create a skills matrix (Accounting, Data, AI, Ops). Assess your people. Prioritize gap-closing.
- Launch focused upskilling: Short bootcamps on data fundamentals, gen AI use cases, controls, and prompt practices. Pair accountants with data partners.
- Fix the data basics: Establish a tax data model, owners, lineage, quality rules, and access policies. Write a 12-month roadmap that IT can execute.
- Pilot with guardrails: Start with 2-3 use cases (e.g., reconciliations, variance analysis, tax research summaries). Track cycle time, error rates, and reviewer hours.
- Strengthen governance: Model risk management, privacy, retention, prompt logging, and human review. Align with Internal Audit and Legal.
- Select platforms: Choose analytics, automation, and gen AI tools that integrate with your ERP and data lake. Avoid one-off point solutions.
- Redeploy and reward: Update job descriptions, KPIs, and incentives to reflect the shift to insight work.
What good looks like by 2026
- Time allocation: 40-50% of finance time on forward-looking analysis and scenario planning.
- Cycle time: Faster closes and compliance with fewer manual touchpoints.
- AI in the flow: AI-assisted tax provisioning, transfer pricing analysis, and document prep with controlled human review.
- Data maturity: A governed tax data set with a catalog, lineage, and quality SLAs.
- Team design: Cross-functional squads (Tax + FP&A + Data/AI) accountable for outcomes, not tasks.
- Learning engine: Ongoing microlearning, labs, and leaderboards that tie to promotions and pay.
How to upskill without slowing the business
- Microlearning > marathons: 60-90 minute sessions weekly beat two-day seminars that never stick.
- Practice on real work: Convert live tasks into training labs. Measure time saved and accuracy.
- Coach the coaches: Create "AI champions" inside each team. Office hours, code reviews, and playbooks.
- Hire for signals: Curiosity, systems thinking, and structured problem-solving over tool trivia.
- Show the path: Publish skill ladders and certification paths. Make progress visible.
Data and risk notes you shouldn't skip
- Document data sources, assumptions, and controls for every AI-assisted output.
- Keep humans in review for financial statements, tax positions, and filings.
- Stand up an AI policy that fits your risk profile. For a public framework, see the NIST AI RMF here.
Next step
If your team needs a structured way to build AI fluency by role, explore curated learning paths for finance and tax professionals here. Start small, measure hard, and ship wins weekly. That's how this sticks.
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