Dynamic Finance: How the 12% Built a Capital Allocation Engine That Moves as Fast as AI

See also (wiki): ai-budget-cfo-decisions, ai-maturity-models

Executive Summary

• IBM IBV + Oracle surveyed 600 senior finance leaders (300 CFOs, 150 Controllers, 150 VPs FP&A) across 15 geographies and 18 industries in Q4 2025, in cooperation with Oxford Economics. Revenue band: $1B to $20B+. Only 12% of finance organizations combine strategic influence with digital agility — the empirically derived “advanced” cohort, validated via K-means clustering with MANOVA (Wilks’ λ = 0.113, p<.000).
• The 12% cohort makes innovation funding decisions 19% faster, executes strategy 37% more effectively (63% vs. 46%), and reports 21% higher ROI on ERP modernization and 10% higher ROI on EPM modernization than all other segments combined.
• The structural finding is not about AI spend. It is about decision infrastructure: the 12% have integrated ERP + EPM platforms, aligned planning cadences across strategy-finance-operations (94%), and have moved off the annual budget cycle toward continuous capital reallocation. Only 8% of all finance functions operate with fully dynamic planning — so even outside the advanced cohort, the fixed-budget world is still the default.
• AI is the amplifier, not the starter. 86% of advanced organizations have AI/ML fully integrated with planning systems; 76% have rolled out generative AI forecasting and scenario modeling; 90% operate with a fully implemented AI strategy guiding planning. Enterprise-wide, only 7% have operationalized AI-powered forecasting at scale — the gap is execution, not technology availability.
• The 12% treat AI as a workforce program, not a software program. 97% say reskilling for AI and strategic roles is an enterprise priority; 62% have already trained staff in data, analytics, and digital skills; they expect 65% of finance roles to be augmented by AI by 2030.
• Apply vendor caveat: IBM Consulting + Oracle co-publish. Both have direct commercial interest — IBM sells Oracle Cloud implementation services, Oracle sells the ERP/EPM platforms whose modernization ROI is being measured. Oxford Economics executed the survey; the statistical methodology is sound. Treat the outcome magnitudes as advertised maximums for the best cohort.

What “Dynamic Finance” Means — and Why It Is a Decision-Speed Problem

The study’s central argument is that finance is no longer defined by how well it tracks performance but by how decisively it shapes outcomes. The mechanism is speed of capital allocation. In AI-enabled markets, opportunities surface faster than annual budgets can react to them. Organizations that can reallocate capital continuously — toward AI initiatives that are working, away from ones that are not — capture value that the fixed-budget world leaves on the table.

Two dimensions define the advanced cohort:

• Strategic influence — finance’s contribution to enterprise value: innovation funding decisions, influence on strategy and investment, adoption of value-based KPIs beyond pure cost control.
• Digital agility — finance’s use of technology: scenario planning, integration of finance technology, AI maturity in forecasting and planning.

Only 12% score high on both. The remaining 88% split into three archetypes: strategically influential but operationally constrained (37%), digitally enabled but strategically peripheral (23%), and transactionally focused (28%). A CFO who has modernized the ERP but still does annual planning is in the 23% — capable but sidelined. A CFO who sits in every strategy meeting but still reconciles spreadsheets manually is in the 37% — influential but slow. Neither captures the full performance premium.

The Performance Gap — What the 12% Actually Get

Compared with all other segments combined, the advanced cohort reports:

The ERP and EPM ROI numbers are worth parsing carefully. The study does not claim that buying more Oracle produces 21% higher ROI. It claims that organizations that have both the strategic posture and the digital stack to use these platforms for continuous decision-making extract more value from their existing ERP/EPM investment than organizations that have installed the same software and use it for quarterly reporting. The software is necessary but not sufficient.

The Annual Budget Is Still the Norm — Even Though Almost Nobody Says It Works

The most uncomfortable finding sits in Figure 3: only 8% of all finance organizations operate with fully dynamic and responsive planning. The rest:

Forty-two percent operate with planning that is either mostly fixed or somewhat dynamic. For a CFO evaluating AI investments against an annual plan locked in November, this is the structural problem. An AI pilot that demonstrates traction in March cannot be scaled until the next budget cycle in November — by which time the market opportunity or the model capability has moved.

The 12% advanced cohort has solved this by moving to rolling forecasts and agile budgeting. 68% of advanced organizations use agile budgeting and flexible funding specifically for digital, AI, and innovation initiatives. 94% have aligned planning cadences across strategy, finance, and operations — meaning strategy does not set direction in January that finance then funds in November. They move together, continuously.

The Decision Infrastructure Stack

The 12% did not get here by buying more tools. They invested in what the report calls decision infrastructure: enterprise-wide financial and operational data platforms, scenario-based planning, and automated investment review. The advanced cohort’s stack looks like this:

• 80% operate a transformation office with dedicated finance leadership
• 66% participate directly in enterprise investment prioritization
• 92% report deep alignment between finance and technology leadership on transformation decisions
• 82% report decision-making timeliness improvements following EPM modernization
• ~70% report faster and more accurate forecasting post-EPM

The sequencing matters. ERP integration comes first — clean, unified transactional and planning data on a single platform. EPM modernization comes second — scenario modeling, rolling forecasts, automated variance analysis on top of that data. AI comes third — forecasting, scenario generation, next-best-investment recommendations embedded in workflows. Organizations that skip to AI without the platform foundation end up with AI outputs nobody trusts because the underlying data is not reconciled.

Where AI Actually Sits in Finance Today

Figure 5 maps the full AI adoption picture across all 600 respondents:

Ninety-three percent of finance functions have not operationalized AI-powered forecasting at enterprise scale. Forty-two percent are running AI alongside traditional models — a deliberate parallel-run stage that makes sense while trust is being built, but produces no efficiency gain until one is retired. The 7% that have fully scaled are almost entirely inside the 12% advanced cohort.

The advanced cohort’s AI posture is materially different:

• 90% operate with a fully implemented or enterprise-aligned AI strategy guiding forecasting and planning transformation
• 86% have AI/ML fully integrated with planning systems or embedded end-to-end
• 76% have rolled out generative AI-driven forecasting and scenario modeling
• ~9 out of 10 have robust AI governance frameworks and participate in internal innovation or transformation governance boards

Governance is not a brake on speed in this cohort. It is what enables speed, because the organization trusts the outputs.

The Workforce Program Behind the Numbers

The most actionable finding for a CFO or CHRO sits in the talent data. The advanced cohort treats AI as a workforce transformation:

• 97% say reskilling for AI and strategic roles is an enterprise priority
• 62% have already trained staff in data, analytics, and digital skills
• By 2030, they expect 65% of finance roles to be augmented by AI

This aligns with BCG’s 10-20-70 framework (technology is 10%, algorithms 20%, people and processes 70% of AI value) and McKinsey’s November 2025 finding that workflow redesign is the strongest predictor of EBIT impact from AI. The IBM/Oracle data extends the pattern specifically into finance: the functions that pair platform modernization with formal reskilling extract measurably more value than functions that buy the software alone.

How This Triangulates Against the Rest of the Corpus

The 12% advanced finance cohort echoes the repeated “small high-performer cohort” pattern in the corpus:

The 12% advanced finance figure sits higher than the 5-6% cross-functional high-performer bands — which makes sense, because finance organizations have a longer track record with ERP/EPM maturity than, say, HR or marketing. Finance is often further along the digital infrastructure curve, so the share that clears the “dynamic” bar is larger.

The 21% higher ERP ROI number pairs directly with the existing ibm-ibv-erp-meets-ai-2026.md finding that AI-bullish ERP adopters see 27% higher ROI. Different surveys, adjacent findings — both point to the same mechanism: ERP platforms become AI scaling vectors only when the organization is configured to use them for continuous decision-making rather than quarterly reporting.

The 7% enterprise-scale AI forecasting figure triangulates with BCG AI Radar 2026’s finding that only a fraction of AI investment produces scaled impact, and with Gartner’s April 2026 finding that only 28% of I&O AI use cases fully succeed. Scale is the rare outcome across every institutional dataset.

Credibility Rating

MEDIUM. The survey is methodologically serious: 600 senior finance leaders drawn in specific role proportions (300 CFOs + 150 Controllers + 150 VPs FP&A), 15 geographies proportional to market size, 18 industries, executed by Oxford Economics (credible independent research firm). The segmentation approach — eight maturity indicators standardized via z-scores, K-means clustering, MANOVA validation with Wilks’ λ = 0.113, p<.000 — is statistically legitimate and more rigorous than the typical consulting survey. The 12% advanced cohort is empirically derived, not a predefined marketing tier.

Apply vendor caveat: IBM Consulting and Oracle co-publish with direct commercial interest. IBM sells Oracle Cloud implementation services via its Garage approach. Oracle sells the ERP and EPM platforms whose modernization ROI is the report’s headline outcome metric. The “Oracle embedded finance” perspective box on page 10 is Oracle self-describing its own Oracle-powered finance function — a sales proof point framed as research.

The revenue band — $1B to $20B+ — means this is an enterprise and upper-mid-market dataset, not a true 200-2,000 person American mid-market dataset. A $150M professional services firm cannot clone the 12% playbook as-is. The directional findings (sequence ERP first, then EPM, then AI; pair platform with workforce reskilling; move to rolling forecasts) transfer. The specific ROI magnitudes do not.

Cross-reference the 21% ERP ROI / 10% EPM ROI figures against independent data before making capital allocation decisions. The directional finding — that the 12% with both strategic influence and digital agility outperform — is consistent with BCG, McKinsey, and Deloitte findings across their respective maturity cohorts. The specific magnitudes are IBM/Oracle’s and have no independent verification.

Key Data Points

What This Means for Your Organization

If you are a CFO at a $500M–$5B company, the question this report forces is not whether to invest in AI. It is whether your finance operating model can actually use AI. Three diagnostics are worth running in the next 30 days:

1. The planning cycle test. Can your organization reallocate capital mid-year without a full re-plan? If your AI pilots get funding only at the annual budget, your CFO function is in the 42% with fixed or somewhat dynamic planning — and every AI investment window is compressed to one decision per year. Move to rolling forecasts before buying more AI.

2. The integration test. Walk the data path from a transaction in your ERP to a forecast in your EPM to an executive dashboard. How many manual handoffs, reconciliations, and spreadsheets does that journey touch? The advanced 12% have eliminated those handoffs. If yours still exist, AI forecasting tools will layer on top of a broken data foundation and produce outputs nobody trusts.

3. The talent test. What percentage of your finance team has received formal training in data, analytics, and AI tools in the past 18 months? The advanced cohort sits at 62%. If yours is in single digits, your platform spend is outpacing your people’s ability to use it — and the platform will be blamed when the ROI does not materialize.

The sequencing is the insight. ERP integration first, EPM modernization second, AI forecasting third, workforce reskilling woven through all three. Organizations that buy AI tools before they have decision infrastructure end up paying for software that produces outputs their planning process cannot act on.

If this raised questions specific to your finance function’s readiness — particularly around how to phase platform modernization against AI investment without stalling the pilots you already have running — I’d welcome the conversation: brandon@brandonsneider.com

Sources

• IBM Institute for Business Value + Oracle, “Dynamic finance at work: Speeding decisions, scaling impact” (Research Brief, originally published 18 March 2026, survey fielded Q4 2025 by Oxford Economics, n=600 senior finance leaders across 15 geographies, 18 industries, revenue $1B–$20B+). Authors: Tina Mashiko (Oracle), Kevin Beyer (IBM Consulting), Arunava Saha (IBM Consulting), Spencer Lin (IBM IBV). Document ID: 15db1d60a641f6e4-USEN-01. Full PDF: https://www.ibm.com/downloads/documents/us-en/15db1d60a641f6e4 — Landing page: https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/strategic-agility-finance. MEDIUM credibility — statistically rigorous methodology (K-means clustering, MANOVA Wilks’ λ = 0.113, p<.000) executed by independent firm Oxford Economics; apply vendor caveat for IBM Consulting + Oracle co-authorship and commercial interest in ERP/EPM modernization services.

• IBM IBV, “ERP meets AI: Fortune favors the bold” (April 2026, n=1,500 executives). Related data point: AI-bullish ERP adopters report 27% higher ROI. Cross-corpus triangulation at research/04-consulting-firms/ibm-ibv-erp-meets-ai-2026.md.

• BCG AI Radar 2026 and BCG AI at Work 2025 (n=10,600) — 5% substantial-gains cohort pattern. research/01-ai-native-landscape/bcg-ai-radar-2026.md, research/01-ai-native-landscape/bcg-ai-at-work-2025.md.

• McKinsey State of AI, November 2025 (n=1,993) — 6% high-performer cohort with >5% EBIT impact. research/01-ai-native-landscape/mckinsey-state-of-ai-november-2025.md.

• Deloitte State of AI Enterprise 2026 (n=3,235) — 30% governance readiness, 20% talent readiness. research/07-adoption-challenges/deloitte-state-of-ai-enterprise-2026.md.

• Bain CFO Survey, April 2026 (n=102) — CFO-specific posture triangulation.

• Gartner, “AI Projects in I&O Stall Ahead of Meaningful ROI Returns” (April 7, 2026) — Only 28% of I&O AI use cases fully succeed. research/05-analyst-firms/gartner-ai-io-stalling-2026.md.

Brandon Sneider | brandon@brandonsneider.com April 2026