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This newsletter helps CFOs think better about AI decisions. Each edition tests frameworks from the CFO AI Playbook against real evidence and delivers one concrete action.

This week: Research shows a stark reality. 97% of executives see the CFO as "the department of No". Finance remains trapped in transactions. Use AI to bridge the gap.

The Playbook publishes Q1 2026. Your feedback shapes the final work.

AI Turns CFO from No to ROI: Upskill, Don't Downsize

Datarails research reveals a brutal perception gap: 97% of finance teams are still seen as the "Department of No" by their business partners. A separate Datarails survey of 270 US CFOs shows 57% expect AI to reduce finance headcount by 2026.

Escaping the Department of No requires more than new tools. Finance remains trapped in transactions: FP&A Trends research shows 45% of analyst time still goes to data cleaning and reconciliation. That leaves little room for the business partnering that changes how operations see us. As Saurabh Jain, CFO at Siemens Healthineers, warns: "By 2030, 38.6 million potential FTEs will be displaced by automation. Storytelling with data will be a skill differentiator for the FP&A professional of the future."

The Wrong Response

Headcount reduction sounds like the obvious play. AI automates transactions, finance shrinks, costs drop. But McKinsey's Finance 2030 research calls this a false choice. Their data shows finance leaders achieved similar cost improvements to average performers while spending 19% more time on value-added activities. The difference: they shifted work from transactions to analysis, not from people to unemployment.

Where Datarails reports 57% of CFOs expect finance teams to shrink with AI, Gartner finds CFOs struggle to source the talent needed to realise their AI ambitions, a challenge that will intensify.

SAP's CFO Insight Report counters the headcount reduction narrative: 81% of CFOs see themselves as primary growth drivers, with optimizing costs (69%) and automation/AI (58%) as their top priorities. They're not choosing cost-cutting or growth. They're using automation to fund growth. That's the strategic play the 57% expecting headcount cuts are missing.

How AI Bridges the Gap

The time savings are real. Maria Azatyan, a CFO who has guided AI and robotics startups through Series B for over 20 years, puts it concretely: "My team members needed one month to work with data. Today with AI, we do it in two minutes." But that efficiency doesn't mean fewer people. It means her team can service more customers, take on more projects, and shift from data collection to strategic analysis.

For example, McKinsey reports at a global consumer goods company, a gen AI assistant helps finance professionals deliver insights on budget variances to business leaders in different divisions and markets. The tool replaces manual number crunching, saving an estimated 30 percent of finance professionals’ time.

Across multiple industries, companies are developing and deploying decision support agents, enabled by gen AI and agentic AI, to substantially reduce the time the finance team needs to make resource allocation decisions. Instead of manually pulling reports and stitching together insights across functions, these teams now generate complex scenarios using natural language during planning sessions.

Specific AI implementation varies by organization, of course. Across a handful of finance functions where it has been adopted robustly, McKinsey observed that finance professionals spend 20 to 30 percent less time crunching data. They devote the saved time to their role as business partners who support strategy execution.

SAP's CFO Insights found manual processes jumped from 1% to 38% as a top internal challenge in just two years. Finance leaders aren't discovering new problems, they're recognising automation opportunities that were invisible before AI made them visible. The 43% who predict AI will improve forecasting accuracy and 38% expecting mundane task automation aren't planning headcount cuts. They're redirecting capacity.

The Skills That Matter

FP&A Trends surveyed 500 finance professionals across 68 countries on which skills matter most for the future. The results challenge assumptions: Influencer ranked first at 37%, followed by Storyteller at 21% and Data Scientist at 20%. Analyst came last at 5%. The skills shift for FP&A includes data analytics (FP&A and data science will converge), architectural thinking (how do systems connect), data lineage awareness (where do numbers come from), data storytelling and influencing skills (translating AI insights into business action). Less time crunching, more time advising and co-designing systems.

Paul Barnhurst, founder of The FP&A Guy, frames the evolution: "Technical skills become table stakes. If we truly want to drive value we're going to have to differentiate with soft skills." The trajectory moves from yesterday's accounting and reporting, through today's business partnering and data science, toward tomorrow where soft skills become the differentiator.

This doesn't mean junior roles disappear. They transform. Entry-level finance will focus more on human oversight: validating AI outputs, investigating exceptions, verifying audit trails. That's not checkbox work; it develops real financial judgment.

But here's the paradox: the better the automation, the more critical the fundamentals become. Azatyan requires every team member to manually create a balance sheet from P&L and cash flow data, no ERP, no AI, just understanding. "If a person is sure that an ERP system does everything correctly and they don't need to know this information, it's not true. Now it's a very interesting time when you understand how finance works, how every report works, and you can check everything that AI does." You can't validate what you don't understand.

The Investment That Matters

BCG research on organizations achieving breakthrough AI value reveals a counterintuitive resource allocation: 10% to algorithms, 20% to data and technology infrastructure, and 70% to people, processes, and cultural transformation. This 70/20/10 pattern explains why technology-first approaches fail. AI implementation is primarily a human capital challenge.

The principle that emerges is progression, not flux: employees should move permanently toward strategic work rather than oscillating between AI-assisted and manual tasks. The time efficiency AI creates opens new opportunities for team development. Azatyan now gives team members chances to grow with AI, taking on work that previously required senior-level expertise. But the foundation remains non-negotiable.

Who Got It Right

Companies that approach AI strategically are using it to elevate finance teams rather than replace them.

Siemens, for example, implemented AI‑powered reconciliation and financial close automation across its global operations, reducing monthly close time by over 40% and cutting manual reconciliations by 70%. This freed finance professionals to focus on strategic analysis and decision‑support, demonstrating that investing in skills and capabilities, not downsizing, is the path to real ROI from AI.

JPMorgan Chase has deployed its COIN (Contract Intelligence) AI platform to automate the review of complex legal and financial documents, enabling the system to analyze approximately 12,000 agreements in seconds, a task that previously consumed around 360,000 labor hours per year. This shift didn’t just cut manual effort; it allowed legal and finance professionals to redirect their time toward higher‑value work. At the same time, the firm has publicly emphasised the importance of upskilling its workforce for the AI era, launching initiatives focused on preparing employees and broader communities for AI‑related skills needs. By investing in training and talent development alongside AI deployment, JPMorgan demonstrates how large organizations can combine technology adoption with workforce transformation to capture value and evolve roles for a future‑ready finance function.

The Shift Required

Vena's 2025 survey exposes a troubling gap: while 57% of finance teams now use AI, only 4.6% prioritize AI skills in hiring decisions. We're adopting the technology without building the capabilities to use it effectively.

McKinsey's 2024 update is explicit: the arrival of generative AI highlights "a need for reskilling and upskilling finance professionals" and calls for "reinvestments in finance staff capability building."

The path from Department of No to strategic partner doesn't run through headcount reduction. It runs through capability building that matches the BCG 70/20/10 pattern, role design that enables progression not flux, and skills development that prioritizes influence and storytelling alongside deep financial fundamentals.

What's working in your organisation? Are you seeing upskilling programs that actually shift finance toward strategic partnership? Or is the headcount reduction playbook still dominating? Share your experience.

3 Levels of Finance Relevance

Where does your team sit, and what’s at risk?

Two recent frameworks help answer this question. Liping Qi, CFO at MicroSurgical Technology, maps FP&A maturity across three levels based on the questions you answer. The Corporate Finance Institute identifies the skills each level now demands. Together, they create a roadmap for staying relevant.

Level 1: Explain (What)

Qi’s entry point: tell stakeholders what happened. “Output dropped 10%, so sales dropped 10%.” This is obvious math that doesn’t provide much insight. Everyone can look at the numbers and figure it out.

CFI confirms this work is already disappearing. AI automates data collection and consolidation, variance analysis calculations, and standardized report generation. Two-thirds of finance professionals say AI will save up to 200 hours of FP&A work annually. Most of those hours come from Level 1 tasks.

If your team lives here, they’re competing with software that doesn’t need sleep.

Level 2: Predict (Why)

Qi’s middle tier requires business acumen. Why did output drop despite adding headcount? Because new hires needed training from experienced workers, which hurt productivity. The ability to dig out the “why” and predict outcomes if root causes aren’t addressed is immensely critical, he writes. Without understanding root causes, we cannot fundamentally cure the problem.

CFI frames the skills this demands: AI literacy to validate outputs and spot bias. Programming capability with Python and SQL to pull data directly rather than waiting for IT. Data visualization with Power BI and Tableau, now essential rather than optional.

But here’s the threat. FP&A Trends research shows AI users already rate 73% of their forecasts as good or great, compared to 42% for non-users. AI is learning to predict. The 2025 survey found AI/ML users spend 39% of their time on high-value activities, nine percentage points more than non-users. The algorithm is climbing toward Level 2.

Level 3: Prescribe (How)

Qi’s highest tier: recommend solutions. After finding the root cause, challenge yourself to address how we fix this. Like a doctor prescribing a cure. His example: phase future hiring so new workers can learn while experienced workers still meet production needs. Or create training videos so onboarding doesn’t pull people off the line.

CFI calls this strategic advisory and business partnership. The shift from number-crunchers to strategic business advisors who guide decision making. The specific skills: narrative creation that transforms complex outputs into compelling stories that drive action. Business interpretation that understands what data means for strategy. Trust building that makes you the go-to advisor for financial decisions.

These tasks resist automation entirely, CFI argues, because they rely on distinctly human capabilities. Strategic planning requires pulling together different viewpoints, predicting market shifts, and adjusting assumptions based on business context that numbers alone can’t capture. Stakeholder communication means building relationships, adapting to diverse audiences, tailoring messages for maximum impact. Cross-functional collaboration demands empathy and adaptability that AI systems cannot generate independently.

Qi agrees humans still have the edge at Level 3. But he asks the uncomfortable question: how long can I be convinced?

The diagnostic

Think about your team’s last five contributions to business decisions.

How many explained what happened? How many predicted why? How many prescribed what to do next?

If most answers cluster in Levels 1 and 2, you’ve identified your upskilling priority. The organisations getting this right aren’t asking whether AI will replace finance. They’re asking how fast they can move their people to Level 3 before the algorithm catches up.

This week's articles

This Week's Action

1. Test the Fundamentals: Have one team member build a balance sheet from P&L and cash flow. No ERP, no AI. 90 minutes reveals who understands the architecture versus who navigates systems. You can’t validate what you don’t understand.
2. Map Your Skills Reality: List your team’s last ten contributions to business decisions. Categorize: data delivery, analysis, or influence. Fewer than three in influence? That’s your upskilling priority.
3. Redesign One Role: Pick your most transaction-heavy role. For its top three tasks: Could AI handle this with human oversight? What judgment would oversight require? If answers point to validation and exception investigation, that role is ready to move permanently toward judgment work.

What surprised you? Reply and tell me. I read every response.

Go Deeper: The CFO AI Playbook

The frameworks in this edition: Budgeting for AI investments with BCG's 70/20/10 framework, role design and building AI ready finance teams.

The Playbook covers the full decision journey: from ROI measurement and budgeting through governance, internal controls, and data quality. XAI and architecture

I'm finalising chapters 11-12 now. Updates at www.quipucfo.com.