9 Finance Automation Mistakes You Didn’t See Coming

I once watched a bot cost a mid-size firm $200,000. Overnight.

No, this isn’t one of those urban legends from a conference happy hour. It was real. Here’s what happened: the team had set up an automated payment bot to handle supplier invoices. Great idea, right? Except someone changed a supplier’s bank account details in the ERP… and nobody updated the automation. The bot cheerfully sent payment after payment straight into a fraudster’s account. By the time anyone noticed, two hundred grand was gone. Poof.

Now, don’t get me wrong, I’m all in on finance automation. I live and breathe it. Financial automation and automation solutions are transforming financial processes, but only if they are implemented thoughtfully. But I’ve also seen how easy it is to fall into traps that can turn your time-saving dreams into budget-burning nightmares.

In this guide, I’m pulling back the curtain on the most common (and costly) finance automation mistakes I see when finance teams jump into automation. When you implement finance automation, a strategic approach is crucial to avoid common pitfalls and ensure your automation solutions truly streamline your financial processes. I’ll break down:

• What goes wrong,
• Why it happens,
• And how you can dodge these landmines before they blow up your month-end close (or your career).

Automation is the shiny toy everyone’s chasing right now. You can’t scroll LinkedIn without seeing someone crowing about how they “eliminated 500 hours of manual work with a single bot.” But here’s the thing: automation isn’t magic. If you don’t build it on solid ground, it’ll crumble, and take your numbers, your team’s credibility, and maybe your bonus down with it.

Mistake No. 1: Skipping the Strategy

I get it. The idea of automating your finance processes is like dangling a slice of cake in front of someone who’s been on a diet for six months. It’s tempting to just grab the first tool you see and go. But here’s the brutal truth: diving into automation without a clear plan is how finance teams end up automating the wrong stuff—or worse, creating new headaches they didn’t have before.

What happens when you skip the strategy?

You end up:

• Automating low-value tasks while high-impact ones stay manual.
• Creating duplicate work because no one mapped the full process.
• Spending on tech that doesn’t actually solve your pain points—I’ve seen teams buy pricey RPA licenses and still manually copy-paste between systems!

Real-life case study: XYZ Corp’s vendor invoicing disaster

A mid-sized manufacturing company (let’s call them XYZ Corp) decided to automate their vendor invoice processing with robotic process automation. Sounds smart, right? The problem? They didn’t actually map out their process first.

The automation tool they chose worked great on paper. In reality, their upstream purchasing team was submitting inconsistent data, and their vendors used wildly different invoice formats. The bot had no chance.

Result? The automation amplified the mess. Error rates on invoices shot up 40%. Payments went to the wrong vendors, duplicates went out, and the AP team spent more time fixing bot mistakes than they ever spent processing invoices manually.

Here’s how to avoid this trap

Before you so much as open that automation tool:

• Map your current process. Document every step, decision, and handoff. Identify key processes that are candidates for automation, such as accounts payable, invoice processing, or other essential financial operations. This will help you see where automation can have the biggest impact.

1️⃣ Define your objectives

Ask yourself: What problem are we solving?Do you want:

• Faster month-end close?
• Fewer manual reconciliations?
• Better compliance?
• Support strategic initiatives? Write it down. Make it real. Automation without a goal is just expensive busywork.

2️⃣ Map your current process

I know, I know. Process mapping feels like homework. But trust me—if you don’t understand your current process, you can’t build a better one.

👉 Tip: Use sticky notes, whiteboards, Lucidchart, whatever works. Bring the team together and actually walk through each step. Identify any manual processes that could be improved through automation. Where’s the friction? What’s repeated? What breaks?

3️⃣ Design your future state

Once you know where you are, sketch where you want to go.
What’s staying manual? What’s ripe for automation? What systems need to talk to each other?

4️⃣ Prioritize

Not everything needs to be automated on Day 1. Pick the high-impact, low-risk wins first. Targeting tedious tasks for early automation—like automating a bank reconciliation before you tackle intercompany eliminations—can quickly boost efficiency and free up your team for more valuable work.

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⚡ Bottom line: If you don’t have a strategy, automation just makes your mess faster. Slow down, plan smart, and you’ll save yourself from becoming the cautionary tale at next year’s finance conference.

Mistake No. 2: Automating Broken Processes

Let me tell you a hard truth: automation doesn’t fix broken processes. It just speeds up the disaster. Think of it like this—if your car’s alignment is off and you put in a faster engine, you’re just going to crash sooner. Automation should be used to enhance operational efficiency, not just accelerate existing problems. Same thing happens in finance.

The classic blunder

I’ve seen finance teams eager to impress leadership (or desperate to claw back time) rush to automate clunky, outdated processes. The logic sounds innocent enough:

“Let’s just automate what we do today—then we’ll improve it later.”

It’s tempting to jump straight into automating repetitive tasks, but doing so without first optimizing the workflow can lock in inefficiencies.

Spoiler: later never comes. Instead, you end up with:

• Bots churning out errors at lightning speed.
• Automated reports no one trusts.
• More time spent fixing automation screw-ups than doing the work manually.

Real-life case study: The bank that almost automated itself into chaos

One client I worked with—a mid-cap regional bank—was ready to automate their payment approvals. On paper, it sounded like a win. But when we dug in, here’s what we found:

• The approval process was riddled with duplicate reviews, and the existing approval workflows were not clearly defined.
• Some approvers were rubber-stamping without looking at details, highlighting gaps in the approval workflows.
• There were no clear escalation paths when something flagged as suspicious, making the approval workflows ineffective for compliance.

If they’d automated those approval workflows as-is? They’d have scaled up the chaos and probably tripped a compliance landmine. Instead, we hit pause, redesigned the workflow, and then automated the cleaner, tighter process. Result? They cut approval times by 50% and reduced exceptions by a third.

How to avoid this mistake

Here’s the playbook I use before I touch a single automation tool:

First, map out your current process in detail. Don’t just look at the steps—dig into where things slow down, break, or get stuck. It’s crucial to identify bottlenecks during this assessment, as these pain points often reveal where inefficiencies and errors occur. Only after you’ve pinpointed these issues should you consider redesigning the process and then automating.

1️⃣ Redesign the process first

Sit down with your team and ruthlessly ask: Why do we do it this way? Eliminate steps that don’t add value. Combine approvals where it makes sense. Streamline handoffs. Consider using business process automation to help streamline workflows and optimize your process before moving to full automation. You want a process that’s lean and logical before you automate it.

👉 Example: Before automating journal entries, check if you can standardize templates, remove unneeded adjustments, or consolidate low-dollar items.

2️⃣ Pilot small and iterate

Don’t try to automate the entire process in one go. Pick a subset—say, payments under a certain threshold, or one business unit—and test your new streamlined workflow with automation. Be sure to include legacy systems in your pilot to uncover integration challenges early and address issues related to outdated platforms.

Adjust. Improve. Expand.

3️⃣ Document the redesigned process

This isn’t just busywork. Good documentation makes sure:

• Everyone’s on the same page.
• You can spot automation gaps.
• You’ve got a reference for audits or process reviews later.
• Accurate accounting records are maintained, supporting audit readiness and compliance.

Mistake No. 3: Over-Reliance & Automation Bias

Here’s a fun (read: terrifying) story. A client of mine proudly rolled out a shiny new AR automation. The bot was set to send out dunning notices to overdue customers. Cool, right? Except no one thought to double-check what it was sending. The bot spammed every customer—including those who had paid on time—with threatening reminders. This highlights why human intervention is crucial to catch errors that automation might miss. Imagine the joy of their sales team trying to smooth that over.

The trap of automation bias

When automation goes live, it’s easy to fall into what I call the set-and-forget syndrome. The mindset sounds like this:

“It’s automated. It’ll handle itself.”

Nope. Not even close.

What happens when you rely too much on automation:

• Bots amplify mistakes that humans would’ve caught (like dunning emails to good customers)—the human brain excels at catching context-specific errors that automation can miss.
• Exceptions pile up because no one’s monitoring the outputs.
• People stop thinking critically—because the machine “must be right,” overlooking the unique value of the human brain in complex decision-making.

It’s not just lazy—it’s dangerous. Because once trust in your automation erodes, guess who’s going to be manually double-checking everything at 11 p.m.? (Spoiler: you.)

How to build smart oversight

Here’s my battle-tested playbook to keep automation working with you, not against you:

Use dashboards to monitor key metrics and flag anomalies. Real-time visibility into automation performance through dashboards allows you to catch issues early, track spending, and ensure accurate, up-to-date financial data for better decision-making.

1️⃣ Set up exception alerts

Don’t just trust the bot. Build in exception reporting so you know:

• When something doesn’t match your business rules.
• When volumes spike unexpectedly (a classic sign of a runaway automation).
• When key steps fail or outputs don’t land where they should.
• When potential fraudulent transactions are flagged for review.

👉 Example: I always configure AR bots to flag when they’re about to send notices to top-tier customers. Human review required.

2️⃣ Schedule regular spot checks

Make it someone’s job—seriously—to review a random sample of automated outputs every week or month.

• Are dunning notices correct?
• Are journal entries posting to the right accounts?
• Is data flowing between systems as expected?

Regular spot checks contribute to improved accuracy in automated outputs by identifying and correcting errors early, ensuring more reliable financial data.

Spot checks catch issues before they snowball into real problems.

3️⃣ Track automation performance

Put KPIs on your automations just like you would a human employee.

• What’s their error rate?
• How many exceptions do they generate?
• How often do they require intervention?

👉 Bonus tip: Use dashboards (Power BI, Tableau, whatever floats your boat) to make it easy to monitor at a glance. Many automation tools offer built-in monitoring and reporting features, allowing you to track key performance indicators and quickly identify issues in your automated processes.

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⚡ Bottom line: Automation isn’t magic. It’s a tool—and like any tool, it needs oversight. Trust your automation, but verify. Your future self will thank you.

Mistake No. 4: Poor Data Quality & Manual Data Entry

Let me paint you a picture. You build what you think is a brilliant automation: sleek dashboards, auto-reconciliations, maybe even a bot pushing reports straight to your CFO’s inbox. Looks great—until someone notices the numbers don’t add up. Data accuracy is foundational to successful automation; without it, even the best systems will produce unreliable results. That’s when the fun begins: late-night fire drills, embarrassed explanations, and the soul-crushing realization that your automation was only as good as the garbage you fed it.

Why poor data kills automation

Automation is a multiplier. If your source data is clean, it’ll magnify your efficiency and accuracy. But if your data is a mess? Automation will happily spread those errors faster and farther than any human ever could.

• You’ll base decisions on bad numbers, including sensitive financial data, which can lead to compliance issues and undermine data integrity.
• You’ll waste hours chasing down root causes.
• And worst of all, you’ll lose trust in your automation—and so will your boss.

Real-life case study: The expense report mess

A large company I consulted for rolled out a slick expense report automation tool. The goal? Reduce manual review, minimize manual data entry and its associated errors, and speed up reimbursement. Except here’s what happened: their underlying expense category data was wildly inconsistent.

• Some folks tagged client dinners as “Meals.”
• Others used “Entertainment.”
• A few creative souls used “Miscellaneous.”

The automation had no chance. It flagged tons of legitimate expenses as outliers, missed actual policy violations, and turned what should’ve been a win into a headache. Finance spent more time fixing the automated output than they ever did reviewing reports manually.

How to dodge this disaster

If you want automation that actually works, here’s the no-BS checklist I follow:

• Audit your data sources for accuracy and completeness.
• Review historical data to identify patterns and recurring issues that could impact automation.
• Cleanse and standardize your data before feeding it into any system.
• Set up ongoing monitoring to catch new errors as they arise.

1️⃣ Audit your data before you automate

Look at the data your automation will rely on.

• Are key fields consistent?
• Are there obvious errors or duplicates?
• Do your categories, vendors, accounts, and cost centers line up across systems?
• Can you identify spending patterns that could inform your automation rules and improve expense management?

👉 Pro tip: Pull a sample data set and manually review it. If you’re seeing junk, fix it before automation touches it.

2️⃣ Set up data cleansing routines

Don’t just fix the data once and hope for the best. Build routines:

• Use Power Query, Alteryx, or whatever tool you like to standardize formats and categories. Leverage automation to automatically categorize transactions for consistency.
• Set validation rules so bad data gets flagged early.
• Automate the data cleaning before the main automation runs.

3️⃣ Monitor for data drift

Your data won’t stay clean forever. New vendors, accounts, products—all create opportunities for errors to creep in.

• Build reports to monitor key data health indicators (e.g., number of miscoded transactions, new categories popping up). Leverage real-time data to quickly detect anomalies and address issues as they arise.
• Make someone accountable for keeping data clean—don’t let it fall through the cracks.

Mistake No. 5: Ignoring IT, Governance & Regulatory Compliance

Picture this: a finance team gets all excited, buys an automation tool (or three), builds some slick bots… and then an ERP update hits. Suddenly, nothing works. Bots crash. Reports don’t run. And everyone’s scrambling to figure out what broke while IT stands on the sidelines saying, “We didn’t even know you were doing this!”

Sound familiar? I’ve seen this movie before—and trust me, it never ends well. Proper governance is essential not only for stability but also to ensure that all automation initiatives meet compliance requirements, reducing the risk of penalties and reputational damage.

Why ignoring IT is a fast track to failure

Here’s what happens when finance goes rogue:

• Automations break after system upgrades because no one coordinated with IT.
• Security holes open up—like bots with way too much system access, or sensitive data flowing through unapproved channels.
• Governance? What governance? There’s no central view of what’s automated, where it lives, or how it’s being monitored.
• Compliance issues arise—unsanctioned automation can lead to data inaccuracies and discrepancies, increasing the risk of regulatory or legal non-compliance.

It’s not about IT being control freaks—it’s about protecting your automation investment (and, let’s be real, your job).

Real-life case study: The ERP update from hell

A global firm I worked with had built a series of journal entry bots that interfaced directly with their ERP system, which served as the central platform for their automation strategy. Everything ran like a dream—until the ERP got a standard upgrade. No one had told IT about the bots, so the team didn’t factor automation testing into the upgrade plan.

Result? The bots failed silently for weeks. Journal entries didn’t post. Reconciliations were off. The team had to untangle the mess manually—and took a credibility hit in the process.

How to keep IT and governance on your side

1️⃣ Engage IT early and often

Don’t surprise them. Bring them in before you build. They’ll help:

• Validate that your automation plan plays nice with existing systems, including compatibility with current accounting systems for seamless integration.
• Set up secure connections and appropriate access controls.
• Ensure bots won’t break with routine system changes.

👉 Pro tip: Ask if your company has (or wants) a Center of Excellence (CoE) for automation. It’s a smart way to centralize knowledge, best practices, and oversight.

2️⃣ Formalize approvals and roles

You need clear governance:

• Who can build and deploy bots?
• Who reviews automation changes?
• Who monitors performance and compliance?

It is essential to define clear roles and responsibilities across all finance departments to ensure consistent governance and accountability.

Put it in writing. Share it. Stick to it.

3️⃣ Align on upgrade cycles

Work with IT to:

• Get visibility into planned upgrades/patches that might affect your automations.
• Schedule testing of your bots in staging environments before updates go live, making sure to include all automated systems such as those handling invoice data capture, scheduled payments, and approval routing.
• Build a rollback plan (just in case).

Mistake No. 6: Inadequate Testing

Let me tell you something that should be carved into the desk of anyone building finance automations: if you don’t test it, it will break.

I’ve seen too many teams build bots, dashboards, or reconciliation routines, or implement AI-powered automations, and rush them into production with nothing more than, “It worked once when I tried it.” That’s how finance automations go from hero to headline.

Why skipping testing is a recipe for disaster

Without proper testing:

• Bots fail in weird edge cases you didn’t think of (and finance is full of edge cases).
• Integrations break when something upstream changes.
• Errors don’t get caught until they’ve caused real damage.
• AI-powered tools have unique testing needs to ensure their advanced automation and data governance features work accurately and reliably.

Real-life cautionary tale: Knight Capital’s $440M oops

Knight Capital (a trading firm) pushed a software change live without proper testing. The system was responsible for handling a massive volume and speed of financial transactions, and when the error hit, it processed thousands of faulty trades in minutes. Within 45 minutes, their system racked up $440 million in losses. That wasn’t some evil hacker or freak market event—it was a test they should have run, but didn’t. Now, sure, that’s trading, but the lesson applies in finance: bad automation code can burn through money fast.

How to build testing into your automation game

Here’s how I approach it every single time:

Before deploying any solution, it’s crucial to test finance automation tools thoroughly to ensure they handle data governance, verification, and reconciliation processes accurately.

1️⃣ Run unit tests

Break your automation down into pieces. Test each one on its own:

• Does the bot log into the system as expected?
• Does your data import correctly?
• Does a calculation return the right result on a small test set?

2️⃣ Do full end-to-end scenario testing

Now test the whole process:

• Feed it realistic data—including outliers and edge cases.
• Run it across a full cycle (e.g., entire month’s worth of journal entries, not just one).
• Verify the accuracy of financial statements produced by the automation to ensure compliance and prevent errors.

👉 Pro tip: Include your downstream stakeholders in these tests. Let AP, AR, FP&A folks see the outputs before you go live.

3️⃣ Preview in production-like environments

Never go straight from development to live. Use staging environments that mirror production as closely as possible. Thorough testing in these environments helps prevent non-compliance with regulatory requirements by ensuring all standards are met before deployment.

And here’s the big one…

4️⃣ Always have a rollback plan

If something goes wrong (and someday, it will), you need a way to:

• Stop the automation cleanly.
• Revert any changes it made.

Mistake No. 7: No Post-Launch Monitoring

Here’s a dirty little secret I’ve seen too many finance teams learn the hard way: just because your automation worked at go-live doesn’t mean it’s working today.

I once worked with a financial services firm that launched an automated reconciliation between their ERP and bank feeds. Beautiful piece of work, accurate, fast, clean. And then… they forgot about it.

Six months later, someone noticed that a key mapping change in the ERP had thrown off the bot. For weeks, it had quietly misallocating cash receipts. The clean-up? Took three people two weeks, not to mention the awkward conversations with their auditors.

Why automations fail without monitoring

Here’s what happens when you hit “go” and walk away:

• Systems change. Bots don’t keep up.
• Data formats shift. Automation logic breaks.
• Exceptions pile up because no one’s watching.
• Neglecting monitoring disrupts financial operations, undermining internal controls and increasing the risk of errors or fraud.
• By the time someone notices? You’ve got a mess on your hands.

How to make sure your automations don’t die a slow, silent death

Here’s the post-launch playbook I swear by:

Monitoring is essential not only for catching errors early but also for ensuring reliable financial reporting, as it helps maintain accuracy and compliance in automated finance processes.

1️⃣ Define key metrics to track

Don’t just hope things are working. Measure:

• Failure rates (e.g., how often the bot errors out or skips steps)
• Exception volumes (are they creeping up over time?)
• Processing times (did that bot suddenly slow to a crawl?)

👉 Example: If you’re automating vendor payments, monitor for duplicate payment flags, rejected transactions, or unexpected volume spikes.

2️⃣ Set up real-time dashboards

Use whatever tools your company supports—Power BI, Tableau, Excel, Google Sheets with API pulls—just make sure you have a live view of automation health. Real-time dashboards not only help you monitor automation but also support proactive financial management by providing instant insights that streamline financial workflows and reduce manual errors.

I like dashboards that:

• Highlight failures immediately.
• Show trends over time (so you can spot when things start to drift).

3️⃣ Create an on-call or review rotation

Don’t let automations live in a black hole. Assign someone—weekly or monthly—to:

• Check logs.
• Review exceptions.
• Confirm outputs look right.

👉 Bonus tip: Rotate ownership so your whole team builds automation literacy. This approach also frees up time for the team to focus on strategic insights.

4️⃣ Document changes

Every time you tweak the automation, log it somewhere accessible. You’ll thank yourself during the next audit… or fire drill.

Real-life win: The institution that got it right

A large financial institution I worked with set up post-launch monitoring from Day 1 on their AR automation. Every exception triggered an alert. Monthly reports summarized bot health.

Within the first quarter, they’d identified and corrected small issues before they became big problems, and cut job failures by 60%. This case clearly demonstrates the benefits of automation, such as improved efficiency, enhanced data accuracy, and a stronger strategic focus enabled by effective monitoring.

Mistake No. 8: Overcomplicating Workflows

Ah yes, the automation Rube Goldberg machine. What starts as a simple automation idea somehow turns into a 47-step monstrosity involving five systems, two bots, a SharePoint folder, and someone’s cousin’s VBA script from 2013. Overcomplicating automations like this often means that critical manual tasks are left untouched, missing the opportunity to improve efficiency and reduce errors.

I’ve watched teams proudly show me these setups, until the first time something breaks. Then the pride turns to panic as they realize no one remembers how it actually works.

How complexity kills automation

Overcomplicating your automations means:

• They’re fragile. One minor change upstream—boom, the whole thing fails.
• They’re hard to maintain. People leave, documentation gets outdated, and suddenly no one knows how to fix it.
• They’re slow. Ironically, all that fancy automation ends up taking longer because it’s doing way too much.

Real-life facepalm: The budgeting workflow from hell

I consulted for a company that tried to automate their entire annual budgeting process, a critical component of financial planning that ensures better visibility into financial health and supports more strategic decision-making. What could go wrong? Well, here’s what they ended up with:

• A bot that pulled data from three different source systems.
• Another bot that transformed it and dumped it into a monster Excel model.
• A macro that spit out reports.
• A third bot that emailed those reports to stakeholders.

It worked… once. Then one of the source systems changed a field name. Everything broke. Took them two weeks and three consultants to untangle it.

How to keep it simple (and sane)

1️⃣ Start small

Pick one piece of the process to automate at a time. Example:

• Instead of automating the entire budgeting process, start with automating data extraction. This can improve visibility into cash flow by providing real-time insights from your financial data.
• Get it right. Then move to the next piece.

2️⃣ Design modular automations

Break your automation into clean, independent parts. That way:

• If one part fails, you can fix just that part without breaking everything else.
• You can reuse components in other automations (hello, efficiency!).

👉 Example: A data cleanup module can feed multiple bots—not just one.

3️⃣ Refactor regularly

Don’t let automations get bloated. Every few months:

• Review your workflows.
• See what can be simplified, combined, or eliminated.
• Update your documentation.

4️⃣ Document the flow

Yes, I know this feels tedious. But when something breaks at month-end close, future-you will want a clear map to follow. Clear documentation is especially critical when managing tens of thousands of transactions, as it ensures accuracy and efficiency at scale.

Mistake No. 9: Neglecting Change Management & Training

Let me be blunt: automation isn’t just a tech upgrade, it’s a people change. By automating routine processes, finance teams can focus more on strategic tasks like policy creation and resource allocation, rather than getting bogged down in repetitive work. And if you forget that, you’re setting yourself up for failure.

I’ve seen finance teams pour time and money into building rock-solid automations, only to watch them fall flat because… nobody actually used them. Or worse, people misused them because they didn’t understand how (or why) to change the way they worked.

What happens when you skip the people part?

Here’s what I’ve seen firsthand:

• Resistance to change — Your team clings to the old way because it’s familiar.
• Workarounds pop up — Folks bypass the automation because it’s “easier” (read: they don’t trust it).
• Errors increase — People misinterpret automated outputs or feed bad data into the system.
• Morale tanks — Instead of feeling empowered, your team feels blindsided and frustrated.

Real-life case study: The abandoned bot

I once helped a finance team automate journal entry creation for recurring entries, simple, solid automation. But here’s the kicker: they rolled it out without proper training or communication.

• Users didn’t trust that the bot was applying the right logic.
• Some folks kept doing it manually “just to be sure.”
• Others tried using the bot, hit a small error, and abandoned it altogether because they didn’t know how to get help.

In the end, the bot was gathering dust while the team was still swamped with manual work.

How to get your people on board (and keep them there)

1️⃣ Communicate the why

Don’t just say, “Here’s your new automation.” Tell the team:

• What it does.
• Why it exists (tie it to pain points they feel every day).
• How it will help them.

👉 Example: “This bot eliminates the need for those tedious manual journal entries, so you can focus on analysis instead of data entry.”

2️⃣ Train everyone who touches it

Not just the builders. Not just the team leads. Everyone who uses or relies on the automation needs to know:

• How it works (at a high level).
• What their role is in using it.
• What to do if something goes wrong.

Make training part of the rollout, not an afterthought.

3️⃣ Create feedback loops

Give users a way to:

• Report issues or ideas for improvement.
• Ask questions.
• Share wins (this helps drive adoption!).

👉 Bonus tip: A simple shared Teams channel, Slack group, or email alias can work wonders.

4️⃣ Celebrate the wins

When the automation delivers value, shout it out:

• “This bot saved us 10 hours last month!”
• “No more errors on recurring JEs!”