In recent years, the finance function has shifted from incremental efficiency gains to strategic investments in artificial intelligence (AI). As organisations face tightening margins, evolving regulatory burdens, and demand for faster decision-making, finance leaders are reallocating budgets—moving beyond legacy systems into AI-enabled workflows. This article explores this transformation in depth, describes real human experiences, and outlines how finance teams can prioritise, invest, and execute AI initiatives effectively.
1. Why the Shift Is Happening
1.1 Cost & Efficiency Pressures
Finance organisations are under pressure to do more with less. According to a survey, 32% of finance leaders cite cost-efficiency as their top transformation priority. The CFO+1 AI is viewed as a lever to reduce manual work, accelerate close processes, and optimise cash flow.
1.2 Budget Re-prioritisation
Even while many organisations are pausing general capital expenditures, AI remains a protected investment area. For instance, a survey by Gartner found that 37% of finance leaders have already paused some capital spending for H2 2025, yet AI investments remained a top priority. Gartner This reflects a belief that AI isn’t an optional luxury but a business-critical capability.
1.3 Strategic Shift Beyond Automation
Where earlier technology spend might focus on ERP upgrades or point-automation (RPA), today’s leaders view AI—especially generative AI and advanced analytics—as transformational. A report by KPMG found 71% of organisations use AI in finance operations, and 57% say the return-on-investment (ROI) is exceeding expectations. KPMG
1.4 Human Experience: Finance Teams Feel the Change
“We automated the monthly close for our international business unit using AI-driven matching and variance-analysis. Instead of 10 days of manual work, the team now takes just 3 days—allowing us to shift focus to strategy.”
This quote from a senior finance manager reflects a growing sense that AI is unlocking time for value-added work, not just replacing it.
2. How Finance Functions Are Investing in AI
2.1 Targeted Use-Cases for Efficiency
Finance teams are prioritising AI in functional areas with high volume and predictable interactions:
- Order-to-cash (O2C) / Cash flow optimisation: Over 80% of companies report investing in AI to improve cash-flow despite budget cuts. GlobeNewswire
- Risk and compliance: Financial institutions have ramped AI investments in fraud detection, AML (anti-money laundering), and operational risk management. tabinsights.com+1
- Financial reporting & forecasting: The repeatable nature of these tasks lends itself to AI-driven acceleration. KPMG+1
2.2 Budget Allocations Are Changing
High-performing finance functions report spending nearly twice as much on enterprise-wide AI activities as their peers (13% vs 7% of IT/finance budget). KPMG
Furthermore, long-term commitment is signalled: 78% of CFOs aim to increase AI investment in the next 12-18 months. The CFO+1
2.3 Human Experience: Leadership Mindset
A CFO of a European manufacturing firm commented:
“We reduced our headcount in transactional finance, and re-invested into an AI investment pool. Our team now focuses on insights and value-creation.”
This illustrates a shift: instead of purely cost cutting, the savings are being channelled into AI and human redeployment.
3. Key Enablers for Success
3.1 Clear Value & Focus
In the survey by BCG (“How Finance Leaders Can Get ROI from AI”), median ROI reported was around 10%—below targets. bcg.com The differentiator: high-ROI teams focus relentlessly on value, not experimentation, and treat AI as part of broader finance transformation.
3.2 Change Management & Skills
Investment alone isn’t enough. Many report lack of technical capability or unclear strategy as barriers. Gartner+1 Human experience reinforces it: finance teams must be reskilled from transactional tasks to oversight, decision-making, and analytics.
3.3 Governance and Controls
As AI moves from pilot to production, governance becomes critical. KPMG found that leaders are more likely to employ controls assurance, central AI teams, and audit frameworks. KPMG
3.4 Data Infrastructure
AI relies on high-quality, integrated data—something many finance functions still struggle with. Human commentary in forums highlights that while AI initiatives are promising, data siloes and legacy systems remain a bottleneck. Reddit
4. Roadmap for Finance Leaders
- Identify the “why”: Define what efficiency, speed, or cost-structure outcomes you aim to achieve.
- Pick high-impact use-cases: Prioritise workflows with high volume, manual effort, or complexity (e.g., invoice processing, cash application, variance analysis).
- Build a pilot, measure fast: Produce early wins to gain momentum and budget.
- Allocate budget explicitly: Create an AI pool within the finance/IT budget, rather than tagging it as part of generic transformation.
- Reskill teams: Shift roles from data entry to analysis and oversight.
- Establish governance: Ensure transparency, auditability, and compliance for AI-driven workflows.
- Scale with rigour: Once pilot is successful, roll out more broadly with clear metrics (cost savings, cycle time reduction, decision-use improvement).
- Communicate results: Use ROI metrics and human stories to build stakeholder support.
5. Challenges and Mitigations
- ROI Expectations Too High: Many target 20%+ ROI yet report median around 10%. Manage expectations. bcg.com
- Reliance on Data Quality: Without clean, integrated data, AI failures increase.
- Talent Gaps: Upskilling and hiring are critical.
- Regulatory/Control Risk: Especially in finance, AI must respect audit trails and controls.
- Budget Misalignment: If AI is seen as a cost rather than productivity lever, it may be cut. Note: Larger organisations are protecting AI spend even when other CAPEX is paused. Gartner
6. Final Thoughts
Finance leaders are now shifting from optional AI experiments to mission-critical investments. The budget moves being made reflect that AI is seen not just as an efficiency tool but as a strategic enabler. With the right focus, governance, and change management, AI in finance can deliver accelerated decision-making, cost savings, and resilience.
The organisations that treat AI as a key line item—rather than a side project—will lead the next wave of finance transformation. For CFOs and finance teams, the message is clear: invest wisely, measure quickly, scale deliberately.
