How the RevOps industry keeps trying to solve: How To Scale Human Judgement
I recently came across an insightful article from Nue titled “Why Every Quote Exception Comes at a Cost” that articulates the challenges RevOps teams face daily. The authors nail the problem: quote exceptions aren’t actually exceptions—they’re the rule. Every deal is unique. Every customer negotiates differently. Every quarter brings new edge cases.
But then, in a twist of irony that would make Alanis Morissette proud, they propose solving this inherently human, nuanced problem with… more deterministic software.
Let’s unpack why this approach is fundamentally flawed—and I’ll use a classical logical technique to demonstrate exactly where it breaks down.
The Article Gets the Revenue Operations Problem Right
The Nue piece brilliantly captures the daily challenges in revenue operations:
- Every deal is an exception: “Buyers will negotiate almost anything in a contract—based on past experiences, internal politics, or just to feel like they ‘won.’”
- Context switching kills productivity: Legal, CFOs, and VPs constantly interrupted for “urgent” approvals
- The quarterly scramble: “All-hands-on-deck” situations when every deal needs special handling
- Hidden costs multiply: Each stakeholder added creates exponential drag on the sales approval workflow
They even acknowledge that rigid rules don’t work: “If your $5K startup deal gets the same legal review as your $500K enterprise contract, something’s broken.”
So far, so good. The diagnosis is spot-on.
Where the Logic Falls Apart in Quote Exception Handling
But then comes the proposed solution: build “tiered approval paths” with “automated routing based on logic and data.” In other words, create more complex deterministic rules to handle non-deterministic situations.
This is where we hit a logical wall. If, as the article states:
- Every buyer negotiates based on “internal politics”
- Sales reps “push boundaries to save deals”
- Even leadership lets things slide “when quarters look grim”
Then how can pre-programmed rules possibly handle this complexity in RevOps automation?
Applying Reductio ad Absurdum to Revenue Operations Software
Let me demonstrate why this logic breaks down by using reductio ad absurdum—a classical logical technique where we follow an argument to its natural conclusion to reveal its inherent contradictions. By taking the proposed solution to its logical extreme, we can see why it fails to address the core problem.
Day 1: “If discount > 25% AND customer_size = ‘Enterprise’, route to CFO”
Day 2: “Wait, unless they’re a strategic account… add that rule”
Day 3: “But what if they’re strategic AND in financial services? Different approval”
Day 4: “Oh, and if it’s end of quarter, lower the threshold to 20%”
Day 365: You now have 10,000 rules, 500 edge cases, and a system so complex that everyone just Slacks the CFO directly anyway.
Day 366: You realize you need to add drag to every sale because you have to have a software engineer code a solution for every use case. (reductio ad absurdum)
The Real Problem: Deterministic Solutions for Probabilistic Decisions in Sales Operations
Here’s what the article inadvertently reveals: approval decisions aren’t binary. They’re probabilistic assessments based on hundreds of factors:
- Is this customer likely to pay on time?
- Will this precedent hurt us in future negotiations?
- Can we afford this margin hit given our quarterly position?
- Is the sales rep’s judgment trustworthy here?
No deterministic system can encode this kind of nuanced reasoning. You’d literally need a new feature for every possible combination of circumstances in your CPQ automation.
What RevOps Teams Actually Need: AI + Human In The Loop
What I learned from the article, and what we all knew already, at least intuitively: human judgment CANNOT SCALE.
What can scale, thanks to LLMs, is analysis of large datasets at record speed.
The solution isn’t more complex routing rules. It’s intelligent systems that can provide AI sales operations capabilities:
- Understand context: Read the actual contract terms, customer history, and market conditions
- Apply principles, not rules: “We generally avoid 90-day payment terms unless the customer has strong credit”
- Explain reasoning: “I recommend approval because despite the 35% discount, this customer has perfect payment history and the deal includes a 3-year commitment”
- Learn from outcomes: Track which approvals led to problems and adjust recommendations
Comparing Approaches: Rules vs AI in Revenue Operations
Which would you rather…
Their Way: Traditional Revenue Operations Software
- Spend $200,000 on custom software that takes 6-12 months to implement
- Months to learn the system
- Continuous training for new hires
- Send tickets to software vendors that cost more money, create more drag
- Workflow:
- Deal comes in with exception
- System checks 10,000 pre-programmed rules
- Doesn’t find exact match
- Routes to manual queue
- RevOps creates ticket for engineering
- Wait days/weeks for new rule
- Deal stalls or gets approved outside system
Our Way: AI-Powered Revenue Operations
- Connect all your data into database tables, vectorize it, connect to LLM with MCP
- Spend a month creating pipelines and governance in-house with YOUR data, CFO, and legal teams
- Input the governance into the same database, connect that table to an LLM with MCP
- Workflow:
- Deal comes in with exception
- AI reads deal details + governance policies + historical data
- AI provides recommendation with reasoning
- Human reviews AI analysis
- Approves or adjusts based on context
- System learns from decision
- Deal moves forward in minutes, not days
The $200K Question in Sales Approval Workflow Automation (Literally)
The article ends by promoting Approvals Pro, likely another $200K+ investment (including implementation) in deterministic complexity. But consider:
Which sounds more aligned with the problem the article describes?
The Path Forward for Modern RevOps Automation
The Nue article deserves credit for articulating the problem clearly. RevOps teams are overwhelmed by exceptions, context-switching, and manual approvals. But the solution isn’t more sophisticated approval matrices or parallel routing workflows.
The solution is acknowledging that every deal is unique and building systems that can reason about that uniqueness—not try to categorize it into predetermined buckets.
When your entire article is about how every quote is an exception, maybe—just maybe—the answer isn’t more rules. Maybe it’s intelligence.
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