Automation Won’t Save You From Bad Deduction Data
- The HRG Team
- 50 minutes ago
- 6 min read
If you work in finance, accounts receivable, or accounts payable, you’ve probably seen the promises in your inbox:
“Let artificial intelligence handle your deductions.” “Automate disputes end-to-end.” “Close more claims with fewer people.”
Some of that is real. Automation and artificial intelligence (AI) can significantly accelerate processes and reduce manual work.
But there’s a significant limitation almost nobody talks about:
AI only knows what has already happened. Retailers are changing policies in real time.
When a major retailer updates its routing guide, adds a new chargeback type, or quietly tightens its defectives rules, your AI engine has no lived experience to draw on. It is still predicting the future based on the past.
If your deduction data is messy and your interpretation of retailer policy isn’t up to date, automation doesn’t fix the problem. It just helps you get the wrong answer faster.
That’s where human experts plus technology become a genuine competitive advantage—especially when those experts live and breathe deduction policy every day.
At Woodridge Retail Group, our Deduction Recovery solutions are powered by HRG, giving suppliers that exact blend: deep human expertise, supported by smart tools.
Let’s unpack what that actually means in practice.
What automation and AI are genuinely good at
Before we poke holes in the “robots will save us” story, it is worth being fair.
Modern deduction tools are very good at:
Pulling claim data from retailer portals and emails
Matching chargebacks to invoices and shipments in your enterprise resource planning (ERP) system
Auto-attaching documents like bills of lading, proofs of delivery, and packing lists
Routing disputes to the right person or team based on rules
Highlighting patterns that look similar to past invalid claims
In other words, AI and workflow tools are fantastic at:
Doing repetitive work consistently
Moving information between systems
Reducing basic manual errors
That is all extremely valuable. No one wants a human keying the same reference number 500 times if a machine can do it.
The trouble starts when we ask the technology to do something it is not built for: understanding context that has changed.
The uncomfortable truth: AI is a rearview mirror
Artificial intelligence is trained on historical data.
It does a great job of answering questions like:
“What kind of shortage claim used to be invalid?”
“Which codes used to indicate a likely system error?”
“Which claim patterns used to correlate with successful disputes?”
But retailers are not static.
They:
Update routing guides
Change shortage tolerance thresholds
Launch new audit programs
Adjust calendars, event windows, and compliance fine structures
Those changes don’t show up in your historical data right away. They show up in:
New policy PDFs
Updated portal rules
New line items on remittance advice
Emails from buyers and compliance teams
A human expert reads that new policy and says, “Okay, this changes how we treat this kind of claim starting now.”
AI only sees the old pattern until you deliberately retrain it or hard-code new rules.
If you treat your AI like an all-seeing referee, it will confidently call the game based on last season’s playbook.
A fictional story: When “set it and forget it” backfires
Meet Evergreen Pantry, a fictional mid-sized food brand. (This is a hypothetical example, not an actual HRG client.)
Evergreen is drowning in deductions:
Short-pays
Compliance fines
Pricing and promotion disputes
Post-audit claims showing up 18 months later
Leadership invests in an impressive automation platform. It promises to:
Pull claims from all the retailer portals
Auto-approve low-dollar claims under a certain threshold
Surface the “most disputable” claims first using AI
Reduce handling costs dramatically
Six months later, the dashboards look great:
Claims are processed faster
Backlogs are down
Manual touches have been cut significantly
But when the finance team takes a step back, they notice a few red flags:
A key retailer quietly rolled out a new shortage policy and recalibrated their chargeback math.
The AI did not “notice.” It kept treating those debits as low-risk noise.
A wave of “low-dollar” compliance charges was being auto-approved.
Each one was small, but there were thousands of them.
A new type of post-audit claim started showing up under an old code.
The system assumed this was just the same old pattern. It was not.
No one had told the system the rules changed.
Because no one realized just how much the retailer’s new playbook mattered.
The automation did exactly what it was told—based on yesterday’s reality.
Three traps of “automation first, humans later”
If you lead with technology and sprinkle in human review only at the end, you run into some predictable problems.
1. Auto-approvals built on outdated rules
Auto-approving claims under a certain dollar amount sounds reasonable… until a retailer shifts to lots of small, recurring fines instead of a few big ones.
Without a human noticing the policy shift, you can end up rubber-stamping a new revenue stream—for the retailer.
2. Prioritizing by amount, not by winnability
Many systems rank claims by size and age:
“Highest dollar amount first.”
“Oldest claims first.”
But deduction experts tend to think more like this:
“Which claim types does this retailer usually reverse if we build a strong file?”
“Where did they recently tighten rules, and what is now harder to win?”
“Which patterns will hurt us in a line review if we ignore them?”
Those are judgment calls you make by combining:
Policy knowledge
Relationship history
Real-world experience
AI does not naturally understand that nuance on day one.
3. Missing new retailer behavior entirely
When a retailer introduces:
A new events calendar
A modified audit window
A reclassification of certain fines
…your data history does not contain any examples of the new world yet.
Someone has to read the announcement, interpret it, and translate it into:
“Here’s how our logic needs to change.”
“Here’s where our historical patterns no longer apply.”
This is exactly the kind of work human experts—like the team at HRG—do well.
Why human experts plus technology is a real advantage
So where does this leave you?
The answer is not to throw the tools away and go back to spreadsheets. The answer is to let each side do what it does best.
Technology is great at:
Collecting claims and documents
Applying consistent, well-defined rules at scale
Surfacing patterns that look statistically interesting
Reducing the time and cost of basic processing
Human deduction experts are great at:
Reading and interpreting retailer policies that just changed
Spotting when a “valid” claim is actually inconsistent with the retailer’s own rules
Understanding how today’s disputes affect tomorrow’s line review
Rewriting the rules when retailer behavior shifts
When you put those together, you get something powerful:
Clean, consistent data
Smart, retailer-aware rules
Automation that reflects today’s playbook, not last year’s
That is the model behind HRG’s deduction recovery solutions: experienced people who live in retailer policies every day, supported by technology that scales what they already know works.
A simple playbook for smarter automation
If you are in the middle of an automation project—or about to start one—here is a straightforward way to keep humans in the driver’s seat.
1. Clean up the inputs first. Before you flip the “auto” switch, make sure you have:
A clear mapping of deduction codes by retailer
Agreement on which claim types are:
Always valid
Always invalid
Needing human review
One reliable place for supporting documents to live
2. Capture the human rules. Sit down with your deductions, accounts payable, sales, and compliance folks and ask:
“If we had unlimited time, which claims would we always look at?”
“Which ones do we rarely win?”
“Where have retailer policies shifted this year?”
Write those answers down in plain language. That becomes the logic your system follows.
3. Let technology scale, not replace, human judgment. Once the rules are clear:
Let the tool gather everything and attach documents
Let automation do the routing and tracking
Let AI suggest which claims might be worth a closer look
But keep experts in the loop for:
High-dollar claims
New or unusual chargeback types
Cases where the retailer's policy recently changed
You are not trying to remove humans. You are trying to make sure they spend their time where it matters most.
A quiet next step
If you are looking at your deduction environment and thinking, “We want automation, but we don’t want to lose control,” that is a very healthy instinct.
HRG's deduction recovery solutions are built around that balance: human experts who understand retailer behavior today, plus tools that help them move faster and go deeper.
Sometimes the best first move is simply to ask:
“If we automated exactly what we’re doing now, would we just get the wrong answer faster?”
If that question nags at you, it might be time for a conversation—with your own team, or with a partner who lives in this world every day.
No pressure. Just a chance to see what your deductions look like through a fresh, human-plus-tech lens.
