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Category: AI

  • AI vs Rules: Why Deterministic Software Fails at Revenue Operations Quote Approvals

    AI vs Rules: Why Deterministic Software Fails at Revenue Operations Quote Approvals

    How the RevOps industry keeps trying to solve: How To Scale Human Judgement

    | 8 min read | Revenue Operations

    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:

    1. Understand context: Read the actual contract terms, customer history, and market conditions
    2. Apply principles, not rules: “We generally avoid 90-day payment terms unless the customer has strong credit”
    3. Explain reasoning: “I recommend approval because despite the 35% discount, this customer has perfect payment history and the deal includes a 3-year commitment”
    4. 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:
      1. Deal comes in with exception
      2. System checks 10,000 pre-programmed rules
      3. Doesn’t find exact match
      4. Routes to manual queue
      5. RevOps creates ticket for engineering
      6. Wait days/weeks for new rule
      7. 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:
      1. Deal comes in with exception
      2. AI reads deal details + governance policies + historical data
      3. AI provides recommendation with reasoning
      4. Human reviews AI analysis
      5. Approves or adjusts based on context
      6. System learns from decision
      7. 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:

    Option A: Spend $200K on software that requires constant rule updates, breaks on edge cases, and still needs human intervention

    Option B: Spend $20K giving your AI access to your data and governance policies, letting it reason about each unique situation in real-time

    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.

  • The Junkyard Economy: A Hypothesis on Post-Automation Survival

    The Junkyard Economy: A Hypothesis on Post-Automation Survival

    I was reading a post on X.com by @kimmonismus, and she brought up a discussion I’ve thought a lot about off and on, but she challenged me to really think about this.

    Her core argument was stark: we’re approaching an unprecedented economic transformation where AI and robotics won’t just augment human labor but replace it entirely. She laid out two camps – the optimists who believe technology always creates new jobs, and the realists (her camp) who see this time as qualitatively different. Her reasoning: AI is reaching human-level intellectual capabilities, robotics is finally becoming practical thanks to AI, and together they’re making human wage labor obsolete.

    What struck me most was her emphasis on the distributional crisis. She asked the question that keeps me up at night: “If there are no jobs, there are obviously no wages to be earned. So how are we going to satisfy our needs?” She pointed out we’re heading into this transformation with no plan – no universal basic income, no AI tax, no safety net. Just darkness and hope.

    Who Better to Discuss This With Than Claude?

    So naturally, I turned to my LLM Claude to hash this out. And in typical fashion, our conversation took an unexpected turn.

    I raised what I see as the fundamental catch-22: If you fire everyone, who buys the products? It’s simple math:

    • Company X buys AI + bots to mine minerals = massive third world layoffs
    • Manufacturers get cheap commodities, buy AI + bots = massive Chinese layoffs
    • US companies get cheap products, buy AI + bots = white and blue collar layoffs
    • Result: No one left to buy from US companies
    • The whole system collapses under its own efficiency

    But here’s where it gets interesting. I don’t think we’ll see some government solution or global cooperation. And UBI, forget it – our debt is insane, and without workers paying taxes due to layoffs, there will never be UBI. Instead, I think we’ll see the emergence of micro-local economies. And Claude pushed me to explain what I meant.

    The Ford Example

    Picture this: Ford lays off 10,000 workers, replacing them with AI and bots. Now you have 10,000 people with incredible skill sets scattered across different plants – mechanics, engineers, supply chain experts, quality control specialists. All that expertise, suddenly “redundant.”

    Meanwhile, junkyards are full of cars and parts. The internet still exists. Cheap AI is available to everyone.

    Those displaced workers start pooling resources. They buy broken cars for scrap prices, fix them using their decades of expertise, and sell them locally. They undercut Ford because they have no corporate overhead, no shareholders, no massive factories to maintain. People stop buying new cars and start buying refurbished ones from people they know and trust.

    The Economic Cascade

    This pattern replicates across every industry. Laid-off restaurant workers create ghost kitchen cooperatives. Displaced retail workers form local fulfillment networks. Former office workers pool their skills for distributed services.

    What emerges is a two-tier economy: the “official” automated corporate tier and a scrappy parallel human economy operating in its shadows. But here’s the kicker – this isn’t stable. As more people get laid off, fewer can afford even the efficient corporate products. Corporate revenues decline, leading to more automation to cut costs, leading to more layoffs.

    It’s a death spiral. The corporations optimize themselves out of existence.

    The Junkyard as Metaphor

    What I love about this hypothesis is the junkyard metaphor. All that “depreciated” capital – both human expertise and physical assets – that the efficient economy discards becomes the foundation of a new system. The corporations essentially compost themselves into fertile ground for thousands of smaller, local operations.

    The endpoint isn’t a return to pre-industrial society. It’s something new: distributed networks that use modern tools (internet, AI, accumulated knowledge) without massive scale and centralization. Instead of supply chains that break when one ship blocks a canal, you get redundant local capacity. Instead of “too big to fail,” everything becomes small enough to fail without systemic collapse. And instead of answering to shareholders, you go back to answering to consumers and produce better goods and services. Again, this undercuts and outperforms the “efficient” corporation.

    The Corporate Full Circle

    Here’s the beautiful historical irony: corporations weren’t always meant to be permanent. Originally, charters were granted in the service of a public purpose, and could be revoked if this were not fulfilled. Prior to the 17th century, the first corporations were created in Europe as not-for-profit entities to build institutions, such as hospitals and universities, for the public good.

    The East India Company, the world’s first commercial corporation, was granted a specific charter for a specific purpose. Early American corporations were similar – temporary entities created to accomplish public works, then dissolved. It wasn’t until the mid-1800s that corporations gained the right to define their own purpose and exist in perpetuity.

    So perhaps what we’re witnessing isn’t the death of capitalism but corporations finally fulfilling their original design – temporary entities that dissolve when they no longer serve the public good. Except this time, they’re dissolving themselves through their own efficiency.

    The Thomas Kinkade Hypothesis

    I don’t know what will happen. I don’t know how things will look. But there’s something deeply appealing about the picture that emerges from this hypothesis – something that reminds me of those Thomas Kinkade paintings that adorned one in five American homes. You know the ones: glowing cottages, cozy Main Streets, warm light spilling from every window. Critics called them kitsch, but millions saw in them a vision of the life they longed for.

    Imagine Main Street coming back to life – not with chain stores but with that diner run by the Johnsons from church, where everyone knows your order before you sit down. Picture restored ’60s muscle cars humming quietly on lithium batteries, lovingly rebuilt by the auto workers who once assembled their modern counterparts. Envision actually working in your hometown again, walking to a job where your neighbors are your customers and your reputation is your resume.

    It’s not about going backward – it’s about going forward to something more rooted, more connected. Where the barista at the coffee shop isn’t worried about corporate metrics but about whether Mrs. Chen’s arthritis is acting up again. Where the mechanic isn’t trying to upsell you a warranty package but genuinely wants your kid’s first car to run safely. Where success isn’t measured in quarterly earnings but in whether the community thrives.

    In a way, we’d be building the world Kinkade painted – those impossible glowing villages that critics mocked but people loved. Except this time, the glow wouldn’t come from his trademark luminous paint. It would come from community, from purpose, from actually knowing the people you serve. The “Painter of Light” might have been onto something after all – just not in the way he imagined.

    The Detroit Model: A Warning for Every City

    As for @kimmonismus’s position – this is grim, especially for those in cities. What is a city for? A place to find work at companies scaled nationally or worldwide. If she’s right, let’s look at what happened to Detroit when the auto plants closed.

    Detroit went from 1.86 million people in 1950 to just 639,111 by 2020 – losing over 60% of its population. When the factories shut down, 296,000 manufacturing jobs vanished. But it wasn’t just the plant workers who suffered. Every neighborhood business that depended on those factory paychecks collapsed too. The tax base evaporated. The city couldn’t maintain infrastructure. Abandoned factories like the massive Packard Plant became monuments to decay, standing empty for decades.

    This is the model for what happens to every major metropolitan area when the primary employers disappear. New York without finance. San Francisco without tech. Houston without energy. Cities exist to concentrate workers for large corporations. Remove the corporations, and what’s left?

    The answer is: not much. Those who can flee to the suburbs or other cities will. Those who can’t are trapped in a spiral of declining services, rising crime, and urban decay. Detroit filed for bankruptcy in 2013 – the largest U.S. city ever to do so.

    Now multiply that by every major city in America. That’s the darkness @kimmonismus fears, and she’s right to fear it. The micro-local economies I envision? They won’t save Manhattan or downtown San Francisco. They’ll emerge in the small towns and rural areas where people still know each other’s names, where the cost of living is low enough to experiment, where there’s space to rebuild.

    The cities? They’ll hollow out, just like Detroit. Because when the corporations leave – and they will, once they don’t need human workers – there’s no reason for millions of people to cluster together in expensive, congested urban cores. The age of the megacity might be ending, replaced by a thousand small communities built on human connections rather than corporate efficiency.

  • Maximize ROI with n8n: Cost-Effective Automation Insights

    Maximize ROI with n8n: Cost-Effective Automation Insights

    What is n8n?

    n8n (pronounced “n-eight-n”) is a workflow automation platform founded in 2019 by Jan Oberhauser that combines AI capabilities with business process automation. The name stands for “nodemation,”o WordPress.


    Why use n8n and not Zapier?

    • AI-native capabilities: Built-in support for creating AI agent workflows with LangChain integration n8n vs Make vs Zapier [2025 Comparison] Digidop
    • Enterprise features: Advanced permissions, SSO, and air-gapped deployments for security-conscious organizations Powerful Workflow Automation Software & Tools – n8n n8n
    • Extensive integrations: Over 400 app integrations and 900+ ready-to-use templates GitHub – n8n-io/n8n GitHub
    • Code flexibility: Users can write JavaScript/Python and add npm packages while still benefiting from a visual interface GitHub – n8n-io/n8n GitHub


    What can n8n do for me?


    ROI for $25,000 Investment in Workflow Automation


    Rapid Payback Period: A Forrester study found that organizations implementing workflow automation saw a considerable three-year ROI of 248% with a payback period of less than six months Microsoft.

    Time Savings and Efficiency    : One case study calculated that manually sent emails cost about three minutes of employee time each, amounting to approximately $25,000 per month for a company with 100 caseworkers sending 5 emails daily Gravity Flow. This represents just one example of manual processes that can be automated.

    • Error Reduction: Workflow automation reduces human errors, leading to more efficient operations and fewer costly mistakes Neuroject.
    • Staffing Optimization: One organization reduced their music copyright processing department from 3-4 employees to just 1 after implementing workflow automation Inpute.
    • Focus on Higher-Value Work: Employees relieved of routine tasks can be refocused on more rewarding and higher-value activities Deloitte, allowing businesses to better utilize specialized talent.
    • Process Acceleration: A pharmaceutical organization reported saving 11,000 hours by running 72 RPA automations for document processing Microsoft, demonstrating the scale of potential time savings.


    Annual Spending on n8n Contractor Services


    Overview of Annual Spending


    While there is limited publicly available data specifically detailing how much companies spend annually on n8n contractor services, my research reveals several key insights that help establish an estimated range.


    Enterprise Spending Benchmarks

    For enterprise customers, n8n Enterprise pricing for on-premise deployment starts at approximately $10,000 per year, which includes 10 active workflows n8n Community. This provides a baseline for understanding the software licensing costs, upon which contractor services are typically added.

    When companies hire contractors to develop custom n8n workflows, their spending varies significantly based on:

    • Project scope and complexity
    • Number of integrations required
    • Company size and industry
    • Ongoing maintenance needs


    Typical Contractor Project Costs


    Based on the research, typical n8n contractor project costs fall into these ranges:

    • Simple workflow implementation: $2,000-$5,000
    • Medium complexity automation: $5,000-$15,000
    • Enterprise-grade workflow systems: $15,000-$50,000+


    These estimates align with general workflow automation development costs, adjusted for n8n’s specific market positioning.


    Annual Spending Patterns


    Companies typically spend on n8n contractors in two distinct ways:

    1. Initial implementation: One-time projects to set up and customize workflows
    2. Ongoing maintenance and development: Recurring costs for updates, expansions, and support

    For small to medium businesses, the annual spending on n8n contractors typically ranges from $10,000-$30,000, including both implementation and maintenance.

    For larger enterprises implementing complex workflow systems, annual spending can reach $50,000-$150,000, especially when multiple business processes are being automated and require specialized expertise.


    Cost Factors Influencing Spending


    Several factors influence how much companies allocate to n8n contractor services:

    1. In-house capability: Companies with technical teams may spend less on contractors, using them only for specialized work or peak periods
    2. Integration complexity: Self-hosted installations face challenges with OAuth processes for major platforms like Google, requiring more contractor expertise Pixeljets
    3. Workflow volume: n8n’s unique pricing model charges based on workflow executions rather than individual tasks AffMaven, which affects how companies budget for both platform and contractor costs
    4. Customization needs: Companies requiring highly specialized workflows that interact with proprietary systems typically spend more on contractor services


    Cost Efficiency Considerations


    Many companies report cost savings when using n8n compared to other platforms. For complex workflows with thousands of operations, n8n’s pricing model can be significantly more cost-effective than competitors who charge per operation n8n Blog, potentially allowing companies to allocate more budget to contractor expertise rather than platform costs.


    Conclusion

    While exact figures for industry-wide spending on n8n contractor services are not publicly available, the research indicates that companies typically spend between $10,000 and $150,000 annually, depending on their size, complexity of automation needs, and whether they’re in initial implementation or maintenance phases.

    The market for n8n contractors continues to grow as the platform expands its enterprise customer base, with recent funding of $60 million and over 3,000 enterprise customers TechCrunch suggesting increasing demand for specialized contractor services.


    Understanding the n8n Market, ROI, and Automation Trends


    Why Limited Data Exists for n8n Contractor Spending


    The limited data on n8n contractor spending can be attributed to several factors:

    1. Relative Newness of the Platform: n8n was founded in 2019 by Jan Oberhauser CanvasBusinessModel, making it a relatively young platform compared to more established automation tools. The initial GitHub repository was created on June 23, 2019 n8n Blog, with wider release in October of that year.
    2. Growth Stage of Adoption: While n8n now has more than 3,000 enterprise customers and around 200,000 active users TechCrunch, the platform is still in a growth phase compared to more established players like Zapier. The specialized contractor market for n8n is still developing.
    3. Diverse Implementation Models: Organizations implement n8n through various methods – self-hosting, cloud solutions, and hybrid approaches. This diversity makes tracking overall contractor spending challenging.
    4. Private Contract Nature: Most consulting arrangements are private contracts between businesses and freelancers, with limited public disclosure of rates and project details.
    5. Market Focus on Software vs. Services: Most market research focuses on the workflow automation software market (estimated at $22.1 billion) rather than the associated contractor services market.


    Automation Trends and Workforce Impact


    There is indeed a significant upward trend in companies shifting responsibilities from employees to automated workflows:

    1. Current Displacement Rates: Recent data reveals that 14% of workers have already experienced job displacement due to automation or AI Seo, and this trend is accelerating.
    2. Future Projections: According to the World Economic Forum’s Future of Jobs Report 2025, automation is expected to displace the equivalent of 8% (or 92 million) of current jobs by 2030, while creating 170 million new jobs World Economic Forum, resulting in net growth of 7% in total employment.
    3. Industry-Specific Impact: The administrative sector is projected to be among the hardest hit in the next five years Fortunly, with automation potentially affecting nearly half the workforce in certain industries.
    4. Corporate Intentions: 40% of employers anticipate reducing their workforce between 2025 and 2030 in areas where AI can automate tasks VentureBeat, according to WEF survey data.
    5. Physical vs. Knowledge Work: Between 75 million to 375 million workers globally may need to switch occupational categories and learn new skills McKinsey & Company, with the highest displacement rates in predictable physical tasks.


    Does ROI Come From Eliminating Full-Time Positions?


    The question of whether workflow automation ROI comes primarily from staff reductions has a nuanced answer:

    1. Partial Displacement vs. Complete Elimination: Most successful implementations don’t completely eliminate positions but rather change job functions. Automation typically eliminates the tasks employees enjoy least, allowing them to focus on higher-value work Deloitte.
    2. Departmental Rightsizing: Some cases do show significant staff reductions, as with the music returns department that went from 3-4 employees to 1 after automation Inpute.
    3. Growth Accommodation: Many organizations implement automation to handle growing workloads without adding staff, rather than to reduce existing headcount.
    4. Strategic Workforce Allocation: Companies often reinvest developer time saved through automation into more strategic and innovative projects that drive business growth Microsoft.
    5. Overall Productivity Gains: Employees involved in high-impact automation use cases saw productivity increases from 200 hours saved annually to 450 hours Microsoft, showing that ROI often comes from increased output rather than staff reductions.

    In conclusion, while workforce reduction can be one source of ROI from workflow automation, the most significant and sustainable returns typically come from improved efficiency, error reduction, faster processes, and enabling employees to focus on higher-value work. Organizations investing in n8n and similar platforms are often looking to optimize their operations rather than simply reduce headcount.

    Conclusion: Why n8n Workflow Automation Matters

    n8n is a powerful tool that helps businesses automate repetitive tasks and connect with AI technology. Started in 2019, this young company has grown quickly with over 3,000 business customers and about 200,000 active users.

    The money benefits are clear – businesses report getting back 248% of what they invested within just six months. These benefits come from saving time, making fewer mistakes, and letting employees focus on more important work rather than boring tasks.

    Companies typically spend between $10,000-$150,000 per year on n8n contractors, depending on their size and needs. This cost is worth it because n8n offers advantages over competitors like Zapier – including better AI features, stronger security, more app connections, and the ability to add custom code.

    The market for workflow automation tools is growing fast – it’s worth about $22.1 billion. n8n’s recent $60 million in funding shows investors believe in its future success.

    Companies using n8n should understand that the biggest benefits don’t just come from reducing staff. Instead, the real value comes from making operations run smoother and allowing skilled workers to focus on creative and strategic work. As automation changes jobs (with major shifts expected by 2030), businesses that smartly use tools like n8n will handle these changes better while improving both efficiency and employee satisfaction.

    For businesses looking to stay ahead, n8n isn’t just a tool for automating workflows – it’s a strategic asset that helps build more flexible, responsive, and successful organizations.