As CFOs step into 2026, they face two key challenges: driving growth while managing financial risk and embracing AI for cash flow optimization. But how do CFOs pursue aggressive growth while championing innovation?
In a recent CFO Dive webinar, How Top CFOs Will Win 2026, three finance veterans mapped out exactly how to do this. Jacob Brunton, Finance Transformation Lead at Ahead; Nick Levine, Accounting & Fintech Consultant, and John Vaillancourt, Strategic CFO at SeatonHill Partners shared practical tips for CFOs who need to move fast without losing control.
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What Moves the Needle Most for CFOs? Cash Flow Optimization
When asked what part of B2B payments automation moves the needle most for CFOs and accounts receivable (AR) organizations, the consensus centered on cash flow efficiency and clarity.
Brunton framed it directly: operational efficiency is what makes cash flow more manageable and predictable. “By automating various aspects of the order-to-cash cycle, especially with leveraging AI, you can realize some significant wins for both operating efficiency and cash flow. Automation can really strengthen your cash forecasting by giving you a cleaner timeline on when you can expect that cash.” Get the AI guide for cash flow optimization.
Levine approached it from a talent perspective. Finance professionals didn’t choose their careers to process data. Automation frees teams from administrative work that drains their potential. When you automate the grunt work, your team elevates into the strategic analysis they actually want to do.
The takeaway: Speed matters, but accuracy matters more. Automated invoicing doesn’t just get bills distributed faster; it reduces the errors that lead to financial disputes and delays that slow the flow of cash. Clean invoicing on the front end means smoother cash flow on the back end.
Where Should CFOs Start with Cash Flow Optimization? With Quick Wins
If you can only automate one thing to drive cash flow optimization, where do you start?
Cash application and payment matching are great starting points for faster cash flow. Vaillancourt and Levine both agreed. This is where most teams struggle with manual work, and it’s where results are immediately measurable. High invoice-to-remittance match rates (in the high 90s) signal a strong automation process.
Electronic invoicing with delivery and payment confirmation. Brunton emphasized the front end of the AR process. Between customer portals, outdated email addresses, and organizational changes, invoices get lost constantly. Automating delivery, confirmation, and payment status monitoring prevents downstream collection problems before they start.
Expand payment methods with multiple options. Levine focused on the quality of the buying experience. Give customers their preferred payment method — wire transfers, cards, flexible financing — and make it accessible through an online portal on any device. The easier you make it to pay, the faster cash comes in. Learn how to expand card payment methods while lowering costs.
There’s no right or wrong place to start but consider where manual work is highest and results will be most visible. Build momentum and confidence with quick wins before tackling more complex areas like collections analytics.
How Should CFOs Build a 90-Day Plan for Cash Flow Optimization?
For CFOs with efficient processes but no clear AI strategy for optimizing working capital, the prospect of transformation can feel paralyzing. Here’s a sensible plan that doesn’t require ripping and replacing your current tech stack.
Days 1-30: Organize & Prepare
The panelists offered these key tips. Use the first 30 days to structure data and identify weaknesses.
- Clean your master data. Customer records, payment terms, contact information, portal access — if it’s messy now, automation will just move “garbage” faster through your system. Data hygiene should come first.
- Benchmark current performance metrics. DSO (Days Sales Outstanding), match rate, bad debt ratio, cost per invoice. These become your “before AI” baseline. Here’s a great list of KPIs to benchmark each AR function.
- Map your AR workflow in detail. Document the full order-to-cash process, following the life of an invoice. How does data flow from Sales to your ERP and to the AR team? What are the invoicing rules for different products and customers? Where are processes automated and where are the manual handoffs? Where do exceptions occur?
- Ask: Where is the connectivity broken? Which functions require employees to work across multiple systems – how many? If there’s a gap between systems, is there an ERP-agnostic solution that can plug it easily?
AI can be a big help at this stage. It can identify where data is out of sync between systems and organize unstructured data, making reconciliation processes and end-to-end visibility easier.
Days 30-60: Pilot & Test
Streamline processes around your newly structured data, then identify one high-impact and one low-risk use case to pilot.
- Keep the pilot contained. Limit it to one business unit, a small subsidiary, or a specific integration. Levine emphasized this minimizes risk and separates the experiment from the rest of the organization.
- Typical starting points: Cash application and payment matching, or electronic invoicing and delivery.
- Make it measurable. Define success metrics upfront. Compare pilot performance against your baseline performance. Are DSO, match rates, and accuracy improving?
- Assess training needs. For any new integrations, does the wider team need training before you scale?
Days 60-90: Review & Scale
Use the final 30 days to build the business case for wider implementation.
- Compare pilot KPIs against the wider organization. If the results are strong, you have the data to justify expansion. If not, pause, and adjust before scaling.
- Expand gradually. Establish governance, data quality controls, and change management to encourage adoption.
“Ideally, the goal is using AI to aim for end-to-end automation,” said Levine. “That should be the long-term finish line.”
How Do You Know When Your Investment is Really Paying Off?
The panelists shared their experience to help CFOs target the best two or three performance metrics used to judge whether AR and B2B payments automation are really paying off.
DSO remains the gold standard. All three panelists agreed on this. If you’re sending invoices with 30-day terms and your DSO is anywhere near 30, you’re doing well. But DSO alone doesn’t tell the full story. Here’s what else to track according to the panel:
- Bad Debt / Write-Offs: This signals a deteriorating relationship and has implications. It also requires intervention which is a drain on human resources.
- Payment Delay Reasons: Track not just payments that are late, but why. Are disputes coming from purchase orders or invoice discrepancies? Explore customer non-response and satisfaction issues. Volume and trends over time reveal systemic problems.
- Cash Forecast Accuracy: Compare your 13-week rolling forecast against actuals, especially from a receivables standpoint. This identifies where assumptions are off and what other metrics you should be tracking. Get the guide to cash flow accuracy.
- Operational Cost per Invoice: AI should reduce labor costs and free the staff’s time. This is the efficiency metric executives should care about.
How Can CFOs Help AR Teams Overcome a Resistance to AI Technologies?
Many finance teams view AI as a threat to their jobs. Success depends on how leadership frames the technology.
Levine offered a powerful mantra: “AI won’t replace you, but you might be replaced by someone who knows how to use AI better than you.” The reframing works because it’s true. AI allows finance professionals to move away from routine admin tasks and focus on relationship-building and commercial strategy — work that actually adds value and can’t be automated. Don’t miss this eBook, Redefining AR: A Playbook for Modernizing Processes and Uplifting Teams.
Brunton said this: “AI can help to free them from what might seem like a prison of monotony in their day-to-day repetitive grind. You can paint a picture of their future role in an elevated capacity where they are freed up by AI to do things that are much more strategic.”
The key to adoption: transparency. Vaillancourt advised involving teams early in defining automation rules. When staff see AI as a tool they control, rather than automation that controls them, adoption rates increase.
Sometimes CFOs and other financial leaders themselves also need to embrace AI, ensuring responsible AI practices and ethical data practices.
Trust in AI: What Finance Leaders Need to Embrace Artificial Intelligence
With AI Automation, what Governance Practices Should CFOs Prioritize?
The risk of fraud and compliance will always remain, but there are best practices to reduce it. The panel stressed that automation does not mean “set it and forget it.” In high-stakes decisions, human judgment should never be replaced by machines.
Vaillancourt outlined the core principles that must remain, regardless of the AI tech stack:
- Audit Trails: Logging every action under a specific user or automation identity.
- Exception Management: Ensuring high-risk transactions are flagged for manual review.
- Segregation of Duties: Maintaining checks and balances even within digital workflows.
Here’s how Billtrust ensures a framework of AI governance.
Customer Onboarding as a Critical Control Point
Brunton highlighted onboarding as a major friction point where silos between Sales, Legal, and Finance introduce risk. Without clear handoffs and responsibilities between teams, you can leave yourself exposed to fraud while simultaneously slowing down your sales engine and damaging customer satisfaction.
The complexity multiplies when you have vastly different product lines. Do you review a customer for all potential offerings upfront, or track which products they’re approved for and require additional review before cross-selling?
These decisions need to be made cross-functionally, documented clearly, and built into your workflows. The goal is robust checks and balances that don’t slow down the sales cycle.
Levine added that customer risk profiles change over time. Rather than treating onboarding as a one-time exercise or annual review, use automation for monitoring. Learn how agentic AI can be used to continuously monitor and lower credit risk.
How Can CFOs Make the Case to the Board?
How do you communicate AR automation initiatives so they’re seen as a cash flow innovator?
Frame it as strategic growth, responded Levine, who emphasized that AR process automation generates cash faster and reduces bad debt. More cash in the bank creates opportunities: pay bills faster to save money, hire new employees, enter new markets, and develop new products. Effective cash flow optimization increases long-term financial resiliency.
Boards care about ROI, said Vaillancourt. If you can demonstrate that cutting DSO by 3 days frees up $5 million in working capital, that’s money investors don’t have to put into the business. Emphasize risk mitigation and compliance safeguards. Boards don’t want lawsuits or personal liability.
Build credibility slowly by demonstrating early wins before asking for larger investments.
Final Thought: The Strategic Controller
The CFOs who win in 2026 will be the ones who use AR automation as a working capital solution — not just to freeing up time but freeing up cash availability for strategic investment. They position themselves as innovators who drive value for growth-oriented enterprises.
Get in touch to explore strategies for improving AR performance and cash flow optimization in 2026.
