The Hidden Cost of Errors in Your Accounts Payables Process

Organizations want faster cycle times, lower costs, and better accuracy. AI-Powered Intelligent Automation can deliver those outcomes—when it is deployed with clear controls, good data, and human oversight. This guide highlights the pitfalls we see most often and how to avoid them.

What Are the Top Pitfalls Teams Face with AI Automation?

The most common pitfalls are:

Trying to replace people instead of redesigning workflows
Weak data and access controls
Picking the wrong use cases for automation with unclear KPIs
Insufficient governance and risk management
Underinvesting in training and change management

Will Your Employees Resist Automation or Fear Job Loss?

Many will—at least at first. Recent surveys show the concern is real, not theoretical:

52% of U.S. workers say they’re worried about the future impact of AI at work.
75% of Americans believe AI will lead to fewer job opportunities over the next decade—that belief alone can dampen adoption and morale if leaders do not address it.
55% of U.S. workers say they rarely or never use AI chatbots, such as ChatGPT or Gemini, at work.

The takeaway: fear and resistance is normal. But it’s also manageable if you show your employees how automation can still keep them in control and improve their workday instead of outright replacing them.

Quick Comparison: Pitfalls At a Glance (And How to Fix Them)

Pitfall	Do this instead	How you’ll know it’s working
Replacing people outright	Let a Digital Assistant, powered by Intelligent Automation, gather information and draft routine work. Keep a human reviewer for important approvals and unusual situations.	More work is done right the first time. Fewer corrections. Customers get faster, clearer answers.
Messy data and wide permissions	Clean up your data first. Build and try the automation in a safe test environment. When the results hold, open permissions gradually—and keep human checks on the important steps.	Fewer mistakes. Issues are caught early instead of after the fact. You can see who changed what and when.
Vague use cases and goals	Start with one repetitive, rules-based task (e.g., invoice intake or order-status replies). Agree up front on the time and cost you want to save.	The team spends less time on busywork. The job moves faster week over week. You can point to dollars and hours saved.
Little governance or testing	Set simple rules: which tasks the software can finish, and which always need a human reviewer. Test changes before go-live and keep a quick rollback plan.	Fewer surprises after launch. If something goes wrong, you can undo it quickly and get back to normal.
Minimal training or role redesign	Explain why you’re automating. Train on real examples. Update roles so people focus on approvals, service, and improvements—not copying and pasting.	Staff are less stressed and more engaged. Adoption goes up, complaints go down, and your best people stay.

Pitfall #1: Are You Trying to Replace People With AI?

Fully replacing teams with AI and Intelligent Automation removes human judgment where it’s needed the most, making processes fragile and error-prone.

According to a video report by Economy Media, 55% of businesses that replaced employees with AI regret it and are shifting toward training and supervision. In the video, several examples of this pitfall are showcased, such as:

- Duolingo: Their “AI-first” shift reduced contractor work but was followed by noticeable lesson-quality issues, with some courses seeing up to 42% errors and a reported 18% drop in user retention in the first quarter after changes.

Klarna: Replaced a large portion of its support staff with chatbots and within three months, resolution time increased by 27% and unsatisfactory interactions rose by 35%. This resulted in a prompt, public pivot to “quality human support.”

Telstra: Cut 2,800 roles while adopting AI, then saw customer response times increase by up to 25%.
Taco Bell: Trialed automated voice ordering across hundreds of locations, but then quickly limited the rollout due to order and billing errors that hurt customer experience.

In each case, rapid automation paired with workforce cuts completely backfired.

What You Can Do Instead:

Rather than fully replacing employees with AI, a better approach is simple: the automation prepares the work and fulfills routine, repetitive tasks, and then a human reviewer verifies and makes decisions where judgment matters. This approach keeps people in control, improves resilience, reduces risks, and raises quality.

Implementing a tool, such as a Digital Assistant powered by Intelligent Automation, means that your team can spend less time on routine, mundane tasks. Instead, they are utilizing their talents where it matters the most: resolving unusual scenarios, making critical approvals, and making strategic improvements to the work at hand.

Pitfall #2: Are Your Data Foundations and Access Controls Strong Enough?

If your organization is starting with reference data that is stale, full of duplications, or riddled with errors, then AI and Automation just moves that bad information faster. Additionally, if your software and programs have broad permissions and access control, this creates the risk of producing errors at scale.

While it can be enticing to dive into automation, it’s critical to start with a solid foundation.

This means mapping out workflows and establishing a single source of truth before automating processes, adding in human validation layers, and only opening up access and permissions where necessary. You’ll also want full auditability, with the ability to track back log inputs, prompts, outputs, and changes.

What You Can Do Instead:

More often than not, setting up a strong foundation is cumbersome for organizations.

Instead of adding an additional task to your already-busy team, you can hand-off the entire automation implementation to experts. At Valenta, we take all of this off your plate to ensure there are layers of human validation, audit trails for every automation, and that your data stays secure.

Pitfall #3: Did You Pick the Right First Use Cases and Define Clear KPIs?

Over the past several years, many organizations have gotten caught up in the idea of “Digital Transformation.” While the concept is exciting, it is also vague.

Because of this, many companies struggle with where to begin, which leads to:

Analysis Paralysis: You have lots of ideas but the fear of choosing the best first step prevents you from taking any action at all.
Flashy Pilots Over Clear KPIs. Instead of mapping out clear goals and KPIs, and trying to find a best fit for them, you seek to inspire and end up choosing use cases that don’t produce the meaningful results you’re actually after.

What You Can Do Instead:

Meaningful automation doesn’t need to be flashy. Start with structured, repetitive processes and quantified outcomes, and avoid vague goals.

Anchor Your Decisions in Business Math. Try thinking in terms of cost savings, time savings, and error rate. These are tangible metrics that can prove ROI.
Select Processes That Fit AI Well. Some good examples are invoice intake, reconciliations, vendor data checks, claims intake, and order-status responses. The common theme shared between these processes? They are repetitive, routine, and rules-based.

Pitfall #4: Do You Have Governance and Risk Management in Place?

Without documented rules, teams improvise. That is how risky shortcuts happen, and why errors occur. You don’t need an extensive 50-page policy for every process you automate—just a clear framework that everyone follows.

Establishing governance starts with documentation of your existing process and risk assessment. As you develop and implement process automation, you will need to test and monitor, and update your governance framework based on these findings.

What You Can Do Instead:

This is a critical phase before any process automation goes fully-live. By leaving automation implementation up to Valenta, you can trust our team of experts to thoroughly test, monitor, and validate before any automation goes live. Everything about the workflow is tracked, and your team will have a clear framework to move forward with.

Pitfall #5: Are You Investing Enough In Training, Roles, and Change Management?

Tools do not change outcomes on their own. People need clarity on why automation is here, what will change in their role, and how success will be measured.

An MIT-linked analysis found that only 5–7% of AI initiatives showed clear, near-term revenue impact. Most companies missed the mark because of thin planning, weak data, and limited training.

AI and Automation can improve processes tenfold, give time back to your team, save money, and make your life easier—but, when you’re implementing it on your own, it’s critical to invest in change management.

What You Can Do Instead:

Developing and implementing AI Automation for your organization can be a huge undertaking. That’s why Valenta offers AI-Powered Intelligent Automation delivered as a service.

With deep expertise in automation, we can custom-build Digital Assistants that can take over your existing workflows. You won’t have to change the software, programs, or systems you currently use. Your employees won’t have any disruption to their day-to-day or how they work.

How Can You Implement AI Automation Responsibly?

It’s not glamorous, but slow and steady truly wins the race.

Checklist

Pick one measurable bottleneck. Example: reduce reconciliation time from 8 hours to 2 hours at steady state.
Harden data and access. Ensure a single source of truth, grant permissions only where necessary, and include layers of human validation/verification.
Include risk management.
Enable people. Clear “why,” hands-on training, updated KPIs.
Monitor and review. Keep audit logs and track metrics like time savings, error rate, and more.
Iterate and expand. Scale only after metrics are stable and the process has proven a ROI.

Conclusion: Pair AI With Oversight to Elevate People and Outcomes

The fastest path to value is not to remove people from the loop. It is to design the loop so that AI-Powered Intelligent Automation removes repetitive steps, enforces controls, and surfaces decisions—while humans provide judgment, handle exceptions, and drive improvement.

With the right guidance, teams can scale automation that is fast, safe, and measurably effective.

How Valenta Designs AI-Powered Intelligent Automation With People In Control

Discovery & Value Model: We begin by mapping your end-to-end workflow to see where time and money are being lost—things like rework, handoffs, and delays. From there, we set specific, measurable targets for time saved, quality improved, and overall return on investment.
Human-In-The-Loop By Design: For actions that carry real risk—such as payments, access changes, or policy exceptions—the software prepares the work and a person approves it.
Data & Access Hardening: Before anything goes live, we connect to the right system of record and add checks that stop bad data at the door. The software is given only the access it needs, every change is recorded, and you can always see who did what and when.
Enablement & Change: We update your standard operating procedures, create short playbooks, and coach the team on real examples. We also align goals and measurements so people are recognized for approvals, service, and improvements—not manual copy-and-paste work.
Continuous Improvement: After launch, we watch the results closely: speed, quality, customer impact, and where items still need a human. We tune the workflow regularly and roll out updates to the AI’s settings and instructions in a controlled, reversible way.

FAQs

What is the #1 mistake companies make when implementing AI for process automation?
Trying to replace people instead of redesigning workflows with a human-in-the-loop for validation.

How do we choose the first AI use case for our organization?
Pick one repetitive, rules-based task with volume—like invoice intake, reconciliations, or order-status replies. Set a baseline for time and cost today, then set a target for hours and dollars saved before you build.

What does “human-in-the-loop” mean in AI automation, and when do we need it?
It means a person reviews or approves specific steps. You should use it for actions that could impact money, customers, compliance, or data quality.

How do we introduce a Digital Assistant without disrupting our team?
Explain the goal (remove busywork, keep complex judgment with people), provide real examples, and update roles so employees spend more time on strategic work, approvals, service, and improvements—not mundane, manual tasks.

How do we address employee fears about AI replacing jobs?
Be direct. Show which tasks the software or automation will take over and which decisions stay with people. Celebrate early wins where time saved was reinvested in customer care, analysis, or fixing chronic issues.

How fast should we scale AI automation across departments?
Expand only after the first workflow runs smoothly for multiple cycles and the numbers hold. Add one new process at a time, reusing the same guardrails and playbooks so quality stays high.