AI in Accounting: Why a Unified Order-to-Cash Foundation Must Come First
Key takeaways
- AI in accounting only drives results when built on a unified, automated order-to-cash system.
- Without clean, connected data, AI simply magnifies inefficiencies instead of solving them.
- Modernize O2C first to unlock meaningful AI use cases, from contract review to forecasting, without overwhelming your team.
Many organizations invest in AI in accounting to handle everything from contract reviews to forecasting and improving collections. This technology saves time and improves accuracy—but here’s the catch—AI in revenue accounting is only effective if a company’s underlying processes are already optimized.
Here’s what the research is revealing: without full integration and automation across the order-to-cash (O2C) process, most AI tools don’t help at all. In fact, they might actually overwhelm resource-strapped finance and accounting teams.
In this article, you’ll learn why so much of AI in accounting is still just hype, which barriers get in the way of adding value, and practical ways accounting leaders are building the right foundation for future AI tooling success.
Frankly, if we had implemented the AI tool before we modernized our order-to-cash process, I think the AI tool would have flagged everything as an anomaly. Being on our new system helped enable us to get the most benefit out of the AI tool, so that it really was able to learn and pick out what was truly nonstandard and really be able to help the team.
— Rachel Noel
Sr. Director of Quote-to-Cash and Revenue Accounting at Zuora
The AI Hype vs. Accounting Reality
Vendors make big promises when it comes to AI in revenue accounting. Automated reconciliations, smart assistants, and predictive analytics sound tempting because, with the right foundation, they truly can fuel greater efficiency and help make space for more strategic work. But despite these big promises, many accounting leaders and their teams are still burdened by a massive amount of manual work.
So, what’s really happening here?
As more accounting teams adopt AI tools, they’re noticing a common trend: AI tools lighten the load for revenue accountants and often expose or even exacerbate problems instead of fixing them.
We call this the AI paradox. According to The Modern Finance Leader report, 93% of finance and accounting leaders now prioritize AI when evaluating new financial technology, and nearly 9 in 10 already use AI tools within their stack. However, 79% still report being bogged down by manual work, and 50% cite manual errors as their biggest operational challenge.
The SaaS sector shows an even sharper disconnect:
- 85% have AI integrated into their tech stack
- 97% say manual order-to-cash tasks still slow their teams down
- Nearly six in 10 name manual errors as a top issue
While AI in accounting is now nearly ubiquitous, it clearly isn’t a standalone productivity solution for revenue accounting teams. If your underlying data, processes, and system architecture are fragmented, automation can’t fix what’s fundamentally broken.
Order-to-Cash Technology Gaps Limit AI Effectiveness
Without a streamlined order-to-cash process, AI is less of a solution and more of a mirror that reflects the problems in accounting operations that technology alone can’t fix. AI in accounting can be a tremendous solution, but to make it effective, finance teams need the proper foundation.
Understanding the gaps in your current setup is essential for building the proper infrastructure for AI in accounting. Here are the leading factors that may prevent AI tool effectiveness:
Fragmented Data
AI thrives on connected, high-quality data. However, most accounting teams are still reconciling information from multiple systems, and billing, CRM, ERP, and revenue tools often fail to communicate well. In fact, 95% of SaaS finance and accounting leaders say technology gaps are hindering their O2C process, and more than half (54%) describe those gaps as severe.
Warning Signs to Watch For:
- You spend more time reconciling than reviewing — pulling data from CRM, billing, and ERP just to tie out one revenue schedule.
- The same contract amendment looks different across systems, causing timing mismatches and unexplained differences during close.
- Usage or consumption data lives outside the billing system, and you’re still uploading CSVs every month to recognize revenue.
- Deferred revenue balances don’t match across billing and ERP because updates in one system don’t flow automatically to the other.
- Finance leadership asks for a consolidated revenue forecast, but your team needs multiple exports and pivot tables to produce it.
- Auditors keep flagging “data integrity” as a risk area, even when you haven’t changed your controls.
If your team acts as the “data bridge” between systems, your automation foundation might not be ready for AI.
Brittle Processes
If you layer AI accounting automation tools on top of broken processes, it magnifies these inefficiencies instead of fixing them. This means that even if you invest in automation, most teams will still need to use spreadsheets and manual reconciliation. As a result, 79% of finance and accounting leaders say manual O2C tasks still overwhelm their teams after implementing AI solutions. In SaaS, the issue is nearly universal: 97% of leaders report that manual work continues to slow operations, with 53% experiencing these disruptions frequently.
Warning Signs to Watch For:
- Every contract amendment requires manual intervention: new billing terms, new SSPs, or a fresh set of spreadsheet adjustments.
- Your “automated” revenue rules break whenever a deal includes usage, a ramp schedule, or a custom bundle.
- You need manual journal entries to correct timing differences between billing and revenue every close.
- An AI or automation tool produces more exceptions than resolutions — because your underlying data logic isn’t clean.
- Retroactive deal changes in CRM don’t cascade to billing or revenue, forcing you to unwind prior-period recognition.
- Team members hesitate to trust system outputs and double-check everything manually “just in case.”
If new AI or revenue automation tools aren’t reducing manual work, your end-to-end O2C process may need an overhaul.
Limited Team Capacity
Without structural automation, accountants spend their time firefighting transactional issues instead of focusing on analysis or strategy. More than half (56%) of finance leaders say their teams are overworked due to the manual effort required for complex allocations and adjustments. In SaaS businesses, that number jumps to 82%, which shows how deal complexity and patchwork systems are pushing accounting teams beyond their capacity.
Warning Signs to Watch For:
- Your month-end or quarter-end close always feels like an all-nighter sprint, even after implementing automation tools.
- Senior accountants spend more time fixing allocations and revenue schedules than analyzing results or forecasting.
- Auditors ask for standard data pulls, but it takes your team days to locate, reconcile, and validate the supporting files.
- New team members can’t learn the process without shadowing a veteran because it’s undocumented or dependent on tribal knowledge.
- Team members are burned out and taking longer to complete close tasks quarter over quarter.
- You have no capacity to take on new initiatives like predictive analytics or AI-assisted forecasting, the manual work never stops.
If your team has no time to focus on analysis or strategic projects, your automation isn’t actually scaling capacity.
Without unified automation across order-to-cash, AI tools can actually increase operational noise. They generate more exceptions and require more manual interventions, creating more problems than they solve. Instead of embracing AI as a solution to these inefficiencies, companies need to address these structural problems first.
Why Unified Order-to-Cash Automation Comes First
AI only delivers value when it runs on clean, connected data. Before finance teams can rely on predictive analytics or smart assistants, they need a foundation that eliminates friction and connects every step of the revenue process.
A unified O2C foundation helps accounting leaders:
- Create capacity. Automation removes repetitive tasks and frees up accountants to focus on higher-value work. Instead of chasing exceptions, your team can focus on higher-value tasks like analysis and forecasting.
- Connect workflows. Unified O2C streamlines data flow between quoting, billing, payments, and revenue recognition. This setup eliminates gaps in reconciliation that can lead to false positives with the AI.
- Build trust. Automating O2C first gives you a single source of truth. A silo-free setup provides the team with real-time transparency into contract changes, billing events, and revenue schedules. Over time, this setup builds confidence in both the data and the decisions that stem from it.
Accounting process automation lays the foundation for meaningful AI adoption. Once you have a stable foundation, you can thoughtfully layer in AI tools that amplify your team’s efforts without overwhelming them.
Before Zuora, our finance teams were constantly buried in manual tasks: managing complex contracts, reconciling disparate data, correcting billing errors. The automation Zuora brought has liberated them to focus on strategic analysis and optimization. We’ve seen our automated workflow tasks double.
— Sid Sanghvi
Head of Finance Business Applications, Asana
Practical AI Applications Once the Foundation is Set
After laying the foundation through process optimization and automation, there are so many ways to implement AI in accounting. With unified automation in place, AI can be applied to use cases that truly reduce work, such as:
- Contract review. AI automatically scans complex contracts and flags any anomalies or inconsistencies. This approach prevents downstream errors and saves hours of manual review.
- Collections management. Overcome the challenges of collections with AI models that can predict which customers are most likely to delay payment.
- Forecasting. Analyze real-time data on revenue, churn, and payment trends in less time with AI in accounting. Using this tool, your team can model multiple scenarios, forecasting changes from pricing strategies or changes in customer behavior.
- Exception handling. Over time, AI gathers enough data to accurately pinpoint anomalies that require your team’s attention. It reduces the noise and overwhelm, highlighting only the exceptions that need human expertise.
Making the Most of AI Applications in Finance
From forecasting and anomaly detection to pricing optimization, AI empowers finance teams to move from manual processing to strategic decision-making. Learn how finance leaders are making the most of the latest AI tools.
The Path Forward for Accounting Leaders
Implementing AI in accounting requires a smart strategy and proper resource allocation. A successful transformation effort hinges on the order in which you implement new changes. Follow this sequence to lay a solid foundation for AI:
- Automate end-to-end O2C. Eliminate manual reconciliations, disjointed workflows, and redundant systems that require reactive approaches that overwhelm your team.
- Unify the platform. Ensure that billing, collections, and revenue processes share a common language so data can flow freely across the revenue lifecycle.
- Layer in AI. Don’t implement AI across all of your processes at once. Start with high-impact but low-risk areas at first, such as contract review. This approach amplifies team capacity without complicating their workflows. Over time, you can layer in additional AI use cases after this initial proof of concept.
AI in accounting comes with a lot of hype. And while the excitement around AI is understandable, many organizations embrace this technology without doing essential groundwork first.
AI has a place in your workflows, but as an accounting leader, you need to look beyond the hype and ensure your organization’s data and processes are ready for AI.
Instead of adding new tools for the sake of innovation, this process will make AI a true accelerant for your organization.
See how Zuora streamlines the order-to-cash process and makes organizations AI-ready: Book a Zuora product demo now.
Frequently Asked Questions
1. Why is a unified order-to-cash system essential before adopting AI in accounting?
A unified order-to-cash (O2C) system ensures that all financial data—across billing, revenue, collections, and forecasting—flows through a single, connected source of truth. Without clean, automated O2C processes, AI tools simply magnify inefficiencies instead of solving them. Modernizing O2C first allows AI to deliver accurate insights, reduce manual work, and improve forecasting.
2. What happens if you implement AI in accounting without automating order-to-cash?
AI built on fragmented systems can’t differentiate real anomalies from data inconsistencies, often creating false exceptions and extra work for accounting teams. Instead of driving efficiency, it increases operational noise and manual reconciliation. That’s why automation and data unification must come before AI implementation.
3. How does AI improve accounting once order-to-cash is automated?
Once order-to-cash automation is in place, AI can streamline processes like contract review, collections management, forecasting, and exception handling. AI models can detect revenue anomalies, predict late payments, and automate reconciliations—all powered by accurate, connected data from a unified system.
4. What are the most common barriers to effective AI in accounting?
The main barriers include fragmented financial data, brittle manual processes, and limited team capacity. Research shows 97% of SaaS accounting leaders still struggle with manual order-to-cash tasks even after adopting AI. Without addressing these foundational gaps, AI tools can’t function effectively or scale with business growth.
5. How can finance leaders prepare their teams for AI-driven accounting?
Finance leaders should first focus on automating and unifying their order-to-cash process. This means connecting billing, revenue recognition, and collections into one platform to eliminate manual reconciliations and ensure data integrity. Once that foundation is in place, they can safely layer in AI for predictive analytics, forecasting, and anomaly detection.