Finance chiefs are under pressure to "do something with AI." Here's how to deliver results fast
Boards are getting louder. Nearly half of CFOs (44%) say leadership is pressing them to implement AI across finance, yet most teams are still testing and guessing.
For practical frameworks and case studies designed for finance leaders, see AI for Finance.
According to Basware's new report, six in ten finance leaders (61%) say their organization rolled out custom AI agents largely as experiments. One in four still don't fully know what an AI agent looks like in practice. Pilots are fine-until they stall. It's time to turn experiments into outcomes.
Agentic AI is where ROI is showing up
Two-thirds (66%) of finance leaders say the hype around agentic AI tops any recent tech shift, and three-quarters are still figuring out how to use it. But results are breaking through the noise.
Overall AI ROI jumped from 35% to 67% year over year in the survey. Companies using third-party platforms already embedded with AI agents outperformed all others, averaging roughly 80% ROI. As Basware's CEO, Jason Kurtz, put it: "We've reached a tipping point where boards and CEOs are done with AI experiments and expecting real results."
For automation and agent-focused use cases and guidance, see AI Agents & Automation.
Start where the money moves: Accounts Payable
Finance leaders are clear on the first target. 72% say Accounts Payable (AP) is the most obvious starting point for agentic AI because it's manual, data-heavy, and tied directly to working capital, compliance, and risk.
Basware has spent 40 years building one of the largest, high-quality AP data sets and has processed more than two billion invoices. Their agents apply context-aware predictions so teams spend less time digging through invoices and more time deciding and acting.
Practical use cases finance teams are prioritizing now
- Automating invoice capture and data entry (30%)
- Cash flow management (24%)
- Scenario modeling and forecasting (23%)
- Lower operating costs (21%)
- Running real-time risk and market analysis (20%)
- Automating financial reporting and reconciliations (20%)
- Streamlining compliance checks and regulatory filings (19%)
- Detecting duplicate invoices or potential fraud (19%)
- Reducing overpayments or duplicate payments (18%)
Build vs. buy: speed, control, and total cost
You can stitch together point solutions, build from scratch, or deploy a platform with embedded agents. The Basware data shows the last option is delivering the fastest path to ROI for many finance teams.
Basware's Invoice Lifecycle Management platform, embedded with agentic AI, is built to run end-to-end processes. Their InvoiceAI applies generative and agentic AI, natural language processing, and deep learning across the invoice lifecycle to autonomously process invoices with higher speed, accuracy, and compliance. If you need results this quarter-not next year-platform-first is hard to ignore.
Why some teams win-and others don't
The survey shows the gap clearly. 71% of teams with weak AI returns acted under pressure and without direction. Only 13% of top performers fell into that trap.
As Jason Kurtz said: "AI for AI's sake is a waste. Agentic AI can deliver transformational results, but only when it is deployed with purpose and discipline. That means embedding AI into finance workflows, grounding agents in trusted data, and governing them like digital employees."
Your 90-day playbook to move from pilots to performance
- Define outcomes and guardrails: Pick 2-3 business metrics (e.g., AP cycle time, on-time payment rate, DPO, first-pass accuracy). Set budget, data access rules, and approval thresholds.
- Choose one "cash-now" use case: Start with AP automation or invoice matching and exceptions. Keep scope tight and measurable.
- Pick the platform: Favor embedded agentic AI with proven finance workflows, strong controls, and audit trails. Validate SOC 2, role-based access, and PII handling.
- Get the data ready: Map sources, fix field-level issues, and define golden sources for vendors, GL codes, and tax. Your agents are only as good as the data they see.
- Pilot with clear SLAs: Run a 4-6 week pilot on a defined invoice cohort. Track latency, accuracy, exception rates, and rework hours saved.
- Govern like "digital employees": Assign owners, escalation paths, and approval limits. Log decisions. Review agent performance weekly, then monthly.
- Scale and compound: Expand to cash forecasting, fraud detection, and reporting automation once AP stabilizes. Reinvest time saved into working capital and scenario planning.
What this means for finance leaders
The board's ask is simple: results, not pilots. The path is, too: start with AP, use embedded agentic AI, set hard metrics, and govern with the same discipline you expect from your team.
Do that, and the ROI numbers reported in the study are within reach. Skip it, and you'll keep burning cycles on experiments that don't move the P&L.
For governance best practices, see the NIST AI Risk Management Framework here. For a curated view of practical tools in finance, explore AI tools for finance.
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