Cost-First CPQ: Make AI & Outcome Pricing Profitable
A century-old measurement principle still defines profitable growth in 2025. The 1922 Dayton Moneyweight Scale—built by the company that became IBM—put cost and price in the same eyeline at the moment of sale. That’s exactly what modern CPQ must do. As AI spending hits $1.3 trillion by 2029 and outcome-based pricing becomes the norm, only 13% of enterprises see real AI impact. Why? Because AI amplifies whatever economics you feed it. Without cost-to-serve visibility in your CPQ flow, you’re gambling with your margins. This is why CPQ must start with Cost—not Configure.
The Scale That Changed Everything
I’m standing in my office looking at a photograph of something that changed everything—and most people have never heard of it.
It’s the Dayton Moneyweight Scale from 1922. Two dials. One facing the customer showing weight. One facing the clerk showing weight and cost. That simple machine—built by a company called Computing-Tabulating-Recording that would become IBM—encoded a truth we’ve forgotten: if you can’t see cost at the point of sale, you’re not selling, you’re guessing.
In two days, I’ll walk onto the stage at the Usage Economy Summit in San Francisco. The conference program says I’m there to talk about “how CPQ powers usage-based models.” But what I really want to talk about is something deeper—something that connects that 1922 scale to the AI-powered, outcome-based pricing models we’re all racing to deploy.
I want to talk about the bigness of little things.
Why I’m Obsessed with a Century-Old Scale
The Pattern I Keep Seeing
Throughout my career, I’ve watched smart companies with great products destroy their margins in the name of growth.
They’d win the deal, celebrate the signature, then watch delivery costs balloon. Support tickets multiply. Renewals turn into re-negotiations. Revenue was up. Profit was gone.
The pattern was always the same: they knew their price. They didn’t know their cost-to-serve. That gap is where businesses bleed out.
Finding Value in Imperfect Information
About two weeks ago, I was at an auction at Gallery 15. I go there looking for imperfect information opportunities—I know the historical value of certain items; others don’t. That’s where opportunity lives in auctions. Not in perfect pricing, but in the gaps where information is asymmetric.
Auctions are, in theory, the perfect way to maximize value and find the highest willingness to pay. But they work best in single, discrete transactions with scarce goods. That’s not the world we’re in anymore.
That’s where I saw an IBM scale from the early 1900s. You can see one at the Smithsonian—beautiful industrial design that encodes a profound business principle.
Economic Clarity at the Point of Sale
That Dayton scale put cost and price in the same eyeline, at the same moment, facing the same decision. Not in a spreadsheet three months later. Not in a post-mortem. Now. At the point where you still have agency.
When C-T-R made those scales, they weren’t selling technology. They were selling economic clarity—a small, decisive measurement that protected both the merchant’s margin and the customer’s trust. The merchant could see immediately if they were making money. The customer could see they were getting fair weight.
IBM’s Enduring Operating Principle
Fast-forward a century. IBM sold that scale division to Hobart in 1934 and went all-in on information systems. But the operating principle never changed: measure where value is created, standardize that truth, then scale it. That’s how you sustain reinvention across a hundred years.
And that’s exactly what cost-first CPQ needs to do in 2025—not just quote faster, but measure better.
The Outcome-Based Pricing Wave (and the Hidden Rocks)
Why Everyone Wants Outcome-Based Pricing
Here’s what’s happening right now in boardrooms and pricing committees everywhere: everyone wants outcome-based pricing.
Tie what we earn to what customers achieve. Shift from inputs to impact. Align incentives. It sounds perfect. And the momentum is undeniable.
Forrester’s writing about aligning AI pricing strategies to customer outcomes. Gartner-influenced thinking positions outcome-based models as the next frontier in SaaS pricing. Boards want it. Customers demand it. Sales loves it.
Consumption models and outcome-based pricing capture some of the value maximization that auctions achieve, but they’re better for long-term relationships. They’re better for buyers who want to align cost with actual value received. They’re better for sellers who want scalable, “land and expand” revenue. But only if you know your costs.
The Missing Piece: Cost Visibility
And it can be perfect—if you know what it costs to deliver those outcomes.
But here’s what keeps me up at night: outcome-based pricing without cost-to-serve visibility is a precision instrument pointed at the wrong target. You’re optimizing for a result you can’t afford to deliver.
McKinsey’s pricing research consistently shows that pricing discipline anchored in reliable cost signals is what separates durable profitability from profitless growth.
You can have all the telemetry in the world. All the usage metrics. All the customer success dashboards showing adoption and satisfaction scores. But if you don’t know what it costs to onboard that customer, integrate their systems, staff their support tickets, handle their change requests, and manage their renewals, you’re flying blind.
The Profitless Growth Trap
I’ve seen it firsthand. A company launches an outcome-based model. Revenue climbs. The board is thrilled. Sales is crushing quota. Customer satisfaction scores are up.
And six quarters later, Finance is quietly asking why gross margin dropped 800 basis points. The CFO is looking at a revenue graph going up and to the right, and a margin graph going down and to the left, and nobody can explain where the profit went.
The answer is always the same: we priced to the outcome, not to the cost of achieving it.
Usage Models Amplify the Risk
This is especially dangerous in usage-based and consumption models. When customers consume more, revenue goes up. That sounds great until you realize you haven’t modeled the infrastructure costs, support costs, and customer success costs that scale with usage. You’re celebrating top-line growth while bottom-line margins evaporate.
And in strange markets like today—tariffs changing by the day, disrupted supply chains, input prices soaring on the back end—the sales guy is just trying to get the deal. CPQ, unless dynamic, runs the risk of selling bad or really bad revenue. Moore’s Law doesn’t always work. Layer in some bad AI, and what do you get? The servicePath™ “extra C”—Cost—becomes essential.
T&Cs vs. Trust
Yes, you can cover overages and unexpected costs in your Terms and Conditions. But here’s the truth that nobody wants to say out loud: customers hate this. They see it as sneaky. Poor planning on your part. A bait-and-switch. It instantly erodes the trust that usage-based pricing is supposed to build.
The better way? Have cost understood and built-in from the start. Still have the safe-outs in your T&Cs for truly exceptional circumstances, but not needing to use them strengthens relationships. It builds trust. It turns pricing from a negotiation into a partnership.
That’s why cost-first CPQ isn’t just a nice-to-have. It’s survival.
The AI Amplification Paradox
The Investment vs. Impact Gap
Now let’s talk about AI. Because if there’s one thing everyone agrees on in 2025, it’s that AI is going to transform everything.
The numbers are staggering. One in three companies plans to spend more than $25 million on AI this year. IDC projects global AI spending will hit $1.3 trillion by 2029, driven by agentic AI adoption.
But here’s the uncomfortable truth buried in the fine print: only 13% of enterprises report significant AI impact at scale.
That’s the gap I think about constantly. Massive investment. Marginal impact. Why?
AI Is an Amplifier, Not a Savior
Because AI is an amplifier, not a savior. It scales whatever economics you feed it.
If your unit economics are healthy—if you know your costs, if you have margin guardrails, if you understand what it takes to deliver—AI makes you faster, smarter, more profitable. It recommends better configurations. It flags risky deals. It predicts renewal risk before it materializes.
But if your unit economics are broken—if you’re over-discounting, over-customizing, under-pricing complexity, selling deals you can’t afford to deliver—AI just accelerates the damage. It helps you lose money faster.
The $70 Circle: When Speed Becomes Dangerous
A Story That Haunts Me
Let me tell you a story that haunts me.
We asked an AI model to draw a circle. A simple circle. The kind a child draws with a crayon. The task cost $70 in compute.
No one could explain it. The cost was buried somewhere in the model’s internal reasoning—”added to the model” in some emergent capability we can’t audit, can’t control, can’t optimize.
The Consumption Trap Revealed
This is the consumption trap in its purest, most terrifying form: selling outcomes but paying for inputs. The customer’s willingness to pay for a circle? Maybe a cent. Maybe nothing—it’s a throwaway task. Your cost-to-serve? $70.
The quote looks brilliant. The velocity is incredible. The economics are catastrophically broken.
Three Compounding Risks
This $70 circle represents three compounding risks that define the modern pricing crisis:
Cost-to-Value Mismatch – Catastrophic margin errors when the value you deliver doesn’t match the cost you incur. This isn’t a rounding error. This is orders of magnitude misalignment.
Black Box Behavior – Emergent AI logic without auditability. You can’t cap what you can’t see. You can’t optimize what you can’t measure. The model decides to think longer, reason harder, use more tokens, burn more compute—and you don’t find out until the bill arrives.
Speed Without Guardrails – Automation outruns human control. Velocity becomes volatility. By the time you realize the AI is burning $70 for a $0.01 output, it’s already executed that function thousands of times across dozens of customers.
Speed is dangerous.
Understanding the Velocity Gap
This is what I call the Velocity Gap—the dangerous disconnect between how fast you can sell and how fast you can verify true profitability.
The early 1900s IBM scale was deterministic, physical, safe. You put a pound of flour on it, it showed you exactly what it cost, what to charge, what your margin was. Every time. Reliably. Transparently.
The AI scale is opaque, digital, financially dangerous. You don’t know what it’s going to cost until after it’s done. And by then, you’ve already quoted the deal, signed the contract, set the customer expectation.
What IBM Gets Right About AI Value
IBM CEO Arvind Krishna framed it perfectly at Think 2025: “Unlock the full value of enterprise AI”. Note the word value. Not speed. Not automation for its own sake. Not impressive demos. Value—which means measurable, P&L-visible, margin-protected outcomes.
Jagged Economics: Why the Edges Matter Most
The Myth of Smooth Economics
Let me introduce a concept I think about constantly: jagged economics.
In a perfect world, every deal would be a clean, profitable package. Standard configuration, standard pricing, standard delivery. Smooth economics across your entire customer base.
But the real world isn’t smooth—it’s jagged.
Where Cost Spikes Hide
One customer wants a custom SLA that requires 24/7 coverage in three time zones. Another needs a regional instance that multiplies your infrastructure costs. A third has compliance requirements that triple your integration effort. A fourth wants outcome-based pricing but can’t provide the telemetry you need to measure the outcome accurately.
Each one of those “little” customizations creates a cost spike—a jagged edge in your margin profile.
If you can’t see those edges at the point of configuration, you’ve already lost. You’ll price to the smooth average and deliver to the jagged reality. That gap—between smooth pricing and jagged delivery—is where profit disappears.
Jagged Intelligence Meets Jagged Economics
Demis Hassabis, CEO of Google DeepMind, describes current AI systems as having “jagged intelligence”—they excel at incredibly complex tasks but fail at basics. They can solve advanced math problems but can’t reliably count objects in an image. They can write elegant code but can’t draw a simple circle efficiently.
As Hassabis explains: “It isn’t kind of a jagged intelligence where some things, it’s really good at, like today’s systems, but other things it’s really flawed at.”
Our businesses are starting to mirror this with jagged economics: sophisticated pricing models that can’t guarantee consistent profit. Brilliant pricing strategies that break down in the messy reality of actual delivery.
The Cure: Deterministic Economics
The cure isn’t more AI power. The cure isn’t more sophisticated pricing models. The cure is deterministic economics.
We need systems that ensure repeatable, auditable profitability floors—so that AI’s brilliance is contained and amplified by CPQ’s economic control. So that sophisticated pricing strategies are grounded in the reality of what things actually cost to deliver.
Why CPQ Must Start with Cost
This is why CPQ must start with Cost—not as an afterthought, not as a reconciliation exercise after the deal closes, but as the first “C” in the acronym.
Cost, Configure, Price, Quote. In that order.
The Extra “C”: Making Every Input Cost Visible
What Makes Cost-First Different
Here’s what makes cost-first CPQ fundamentally different from traditional systems.
One of the key things about the “Extra C” is this: the detailed, independent cost of every input is known.
Not aggregated. Not estimated. Not “we’ll figure it out later.” Known. Precisely. In real time.
Granular Cost Components
Every input—whether it’s a third-party API call, a unit of compute, network bandwidth, labor hours, support tickets, customer success time, or AI inference—can have protected margins or cost-plus hurdle rates assigned to it.
Because these key input costs and governance rules are known and codified, they can be easily reassembled into any new product structure—whether it’s consumption-based, outcome-based, hybrid, or something we haven’t invented yet.
This is the economic flexibility that usage-based businesses desperately need but rarely have.
Cost Visibility During Configuration
When cost is visible during configuration—when you can see in real time that adding a custom integration adds $40K to delivery, or that offering 24/7 support in APAC doubles your support intensity, or that promising a specific uptime SLA requires infrastructure redundancy that cuts your margin in half—you can design margin into the deal, not hope for it afterward.
Learning from the Dayton Scale
That’s what the Dayton scale did a century ago: it made economics jagged-aware. The clerk could see that not all produce had the same cost per pound. Flour was different from sugar was different from coffee. The scale didn’t smooth that away with averages and estimates. It surfaced the specific economics of each transaction so pricing could respond accurately.
Embedded Economics in Action
Embedded economics means the quote calculates profitability and payback before it leaves your CRM. Before you make promises you can’t afford to keep. Before you set customer expectations you can’t meet profitably.
It turns the deal desk from a defensive gatekeeper—saying no to risky deals after they’ve been quoted—into an offensive coach, helping sales teams see the economics of their choices in real time and guide them toward configurations that work for everyone.
Closing the Velocity Gap: Strategic Discounting vs. Gambling
CPQ as the Control Plane for Revenue
The CPQ market is about $3.14 billion in 2025 and tracking toward $6.62 billion by 2030, growing at roughly 16% annually. But the growth isn’t because companies want better quote templates or fancier proposal documents.
It’s because CPQ is becoming the control plane for revenue operations—the single system that governs commercial truth from configure through quote, contract, billing, and renewal.
When Profitable Quotes Don’t Need Approval
When CPQ is architected cost-first with embedded governance rules, something powerful happens: sales teams can generate profitable quotes without risk.
That means deals don’t have to go to a traditional deal team or deal room for every quote. Healthy deals—ones that meet your margin floors, fit within your delivery capacity, align with your strategic priorities—auto-approve. Sales moves at full velocity. Leadership focuses on true exceptions, not routine reviews.
Strategic Discounting Done Right
But here’s the critical distinction that most companies miss:
You still have the flexibility for the sales team to over-discount to win a strategic deal. But when they do, it’s instantly escalated to the right level of authority with full transparency into what you’re giving up.
Selling deals at a loss is not a good long-term business plan. Every CFO knows this. But the flexibility to do it knowingly and quickly for specific strategic reasons—landing a marquee logo that opens up an industry, entering a new market, displacing an entrenched competitor—that’s not just acceptable, it’s often essential.
The Danger of Unknowing Discounting
The problem isn’t strategic discounting. The problem is doing it unknowingly, thinking you’re selling a profitable deal when you’re actually selling at a loss. That’s the dangerous outcome of the velocity gap.
The Tariff Time Bomb
The Velocity Gap means Sales can close a three-year usage deal in an afternoon. But if that quote was generated just hours before a new 25% tariff hit on a key imported chip, or before a cloud provider raised their egress fees, or before a critical third-party API changed their pricing model, your finance team won’t discover the cost spike until the first quarterly review—100 days too late.
That three-year contract you just celebrated? It’s a tariff time bomb. A margin time bomb. A profitability time bomb that’s going to detonate in slow motion over the next 36 months.
Why Best-of-Breed Matters
Cost-First Architecture at servicePath™
At servicePath™, we’ve architected our platform cost-first from the ground up. Before a sales rep sees configuration options, before pricing engines fire, before quote documents generate, the system loads the economic truth: unit costs, delivery complexity, support intensity, lifecycle costs.
Best-of-Breed Beats Monolithic
That’s the foundation. But here’s what matters even more in the Usage Economy: best-of-breed systems that integrate seamlessly beat monolithic platforms that try to do everything.
CPQ and billing are two sides of the same coin in usage-based businesses. You can’t quote usage-based deals accurately without understanding billing complexity. You can’t bill usage-based customers profitably without the economic guardrails that should have been set at quote time.
The Power of Integration
We work with best-of-breed billing and monetization platforms like LogiSense—specialists in real-time usage metering, complex rating engines, multi-dimensional pricing, and revenue recognition for consumption models.
The integration creates a closed loop: Quote → Contract → Usage → Bill → Renew
One Economic Truth from Quote to Cash
Quote economics flow seamlessly into billing rules. No manual translation. No spreadsheet mapping. No “we’ll figure out the billing later.”
Real-time metering captures consumption as it happens. Rating engines apply the exact tiers and rules that were promised at quote time. And when renewal comes around, quotes reflect actual usage data and actual delivery costs—not optimistic assumptions from the original deal.
Closing the Quote-to-Billing Gap
Most companies in the Usage Economy have a dangerous gap between quoting and billing. Sales quotes based on competitive pressure and what it takes to win. Billing invoices based on actual consumption. Delivery costs spike in ways nobody modeled at quote time. And renewals become tense negotiations because the original economics didn’t hold up.
Best-of-breed integrations close that gap and create one economic truth from quote to cash.
The Contract as a Living Asset
Beyond the Point-in-Time Quote
Here’s something most CPQ systems fundamentally misunderstand: the CPQ quote is just the snapshot. Just the starting point.
Enterprise contracts are living assets. They persist for years. They evolve with the customer relationship. And increasingly, they’re hybrid—mixing multiple pricing models in a single agreement.
The Hybrid Reality
Your largest customers don’t buy one thing. They buy MRR (fixed-price subscriptions for baseline services), consumption tiers (usage-based pricing that scales with their growth), outcome-based guarantees (performance commitments tied to business results), and professional services (time-and-materials or fixed-bid projects)—all mixed into one complex, evolving contract.
Where Traditional CPQ Falls Short
Traditional CPQ systems miss this completely. They’re designed for point-in-time quotes. Quote economics get established at the beginning, then the contract drifts over three years as you handle mid-term revisions, co-term adds when they acquire other products, expansion into new regions, changes in compliance requirements, and renewals that look nothing like the original deal.
By year two of a three-year contract, nobody can tell you with certainty whether the deal is still profitable. The original quote is ancient history. The contract has been amended four times. Usage patterns have shifted. Delivery costs have changed. And you’re flying blind into renewal negotiations.
The Single Source of Truth for Contract Lifecycle
At servicePath™, our Service Contracts platform takes the detailed economics from CPQ and makes it the Single Source of Truth for the entire contract lifecycle—not just the initial quote.
This is how you add safety, governance, and speed across multi-year customer relationships. When contracts are centralized with their full economic context, you get real-time insights on renewals and terminations. You can handle mid-term changes while keeping financial accuracy intact. You can model “what if” scenarios for expansion or contraction.
Protecting Your Profit Floor
Most importantly, this ensures that the profit floor you carefully built into the original quote doesn’t quietly leak away over three years of contract evolution.
The Scar Tissue of Integration
The Pain Every CIO Knows
Let me be brutally honest about something that every enterprise CIO knows but nobody wants to talk about publicly: almost every company has deep scar tissue from CPQ-to-Billing integration projects.
Projects that ran over budget. Implementations that took twice as long as planned. Systems that never quite worked the way they were supposed to. Workarounds and manual processes that were supposed to be temporary but became permanent.
Not Implementation Failure—Architectural Failure
These aren’t implementation failures. They’re not vendor failures. They’re not even consulting failures.
They’re architectural failures.
The Design Debt Problem
We built these systems for a different world. Static SKUs in the 2000s. Product catalogs that changed quarterly, not daily. Quote templates and fixed-price bundles. Annual renewals where everyone got the same price increase. Simple subscription pricing where usage didn’t matter.
Those systems were never designed—never could have been designed—for real-time usage streams, dynamic pricing that changes based on consumption patterns, hybrid outcome-based deals, or AI-driven services where costs are variable and unpredictable.
I call this Design Debt—the accumulated cost of building systems for yesterday’s business model while trying to operate today’s business model and prepare for tomorrow’s.
The Event-Driven Future
But here’s the good news: we’re at an architectural inflection point. The next decade of monetization will be fundamentally event-driven—real-time integration between systems, streaming data flows, instant visibility into economics as they happen. Not nightly batch reconciliation jobs. Not monthly finance reviews where you discover margin problems after the fact.
When Architecture Matches Business Model
Event-driven architectures where signals feed directly into mediation and rating engines. Where CPQ continuously updates with real-time cost data. Where governance rules monitor boundaries automatically. Where telemetry feeds back to tune pricing models based on actual delivery costs.
When these layers align properly—when the architecture matches the business model—your business stays balanced even at high speed. Even with complexity. Even with hybrid models and outcome-based pricing and AI-driven services.
That’s the promise. And it’s achievable now in a way it simply wasn’t five years ago.
Predictability in Chaos: The Shinkansen Principle
A Lesson in Precision at 300 km/h
Last week I was in Japan. On the Shinkansen from Tokyo to Osaka, I watched something that perfectly captures what deterministic CPQ should feel like.
As the train approached Kyoto station at 300 kilometers per hour, passengers started standing up. They grabbed their luggage from the overhead racks. They put on their coats. They moved toward the doors. All of this while the train was still moving at highway speeds.
The Difference Between Chaos and Precision
In North America, that would be chaos. People stumbling. Luggage falling. Announcements telling everyone to stay seated until the train comes to a complete stop.
But on the Shinkansen, it’s precision. It’s routine. It’s predictability inside motion.
Governance by Design
Passengers know exactly when to stand. They know exactly how long they have. They trust the deceleration curve so completely that they can perform complex tasks while moving at speeds that would be reckless anywhere else.
That’s governance by design.
The technology, the coordination, the operational discipline, the cultural trust—all of it is so deeply refined that you don’t fall. You don’t stumble. The system is architected for speed and safety simultaneously. Not speed or safety. Both.
What Deterministic CPQ Should Feel Like
That’s what deterministic CPQ should feel like.
When your systems are properly architected—when cost is visible at configuration, when guardrails are embedded in the workflow, when approvals are automated for healthy deals, when contracts maintain economic truth throughout their lifecycle—speed becomes exhilarating instead of terrifying.
Speed as Competitive Advantage
Sales can move fast without breaking things. Finance can trust the numbers without auditing every deal. Customers get consistent, fair pricing without surprises. Renewals become routine instead of renegotiations.
When your systems align, speed becomes your competitive advantage, not your risk factor.
Why This Matters
Lessons from a Career in Revenue Systems
I’ve spent my career in and around pricing, quoting, and revenue systems. I’ve been inside companies at every stage—startups finding product-market fit, growth companies scaling fast, enterprises managing complexity.
And I’ve watched brilliant products fail because the business model didn’t pencil. I’ve seen sales teams demoralized because every deal required three layers of approval and still ended up underwater on delivery. I’ve seen finance teams drowning in spreadsheets trying to reconcile what was quoted versus what was delivered versus what was billed versus what was actually profitable.
The Revenue-Profit Disconnect
I’ve seen companies hit their revenue targets and miss their profit targets by embarrassing margins. I’ve seen boards celebrate growth quarters and quietly fire CFOs three months later when the margin story emerged.
Coming Back to the Scale
And through all of it, I keep coming back to that scale. That simple, humble, mechanical scale from the early 1900s.
No cloud infrastructure. No AI. No real-time data streams. Just two dials, some brass weights, and an unwavering commitment to truth.
But it worked. It worked because it honored a principle so fundamental that it’s still true a century later: put the right measurement in the right place at the right time.
The Modern Translation
That’s still the job. In 2025, the measurement is cost-to-serve. The place is the CPQ flow, at the moment of configuration. The time is before signature, not after.
Building Companies That Grow Well
If we can get this right—if we can bring the clarity and honesty and economic transparency of that 1922 scale into the complexity of modern SaaS, usage-based, outcome-driven, AI-powered business models—we’ll build companies that don’t just grow fast, but grow well.
Companies where deals are not only won, but worth winning. Where growth doesn’t come at the expense of profitability. Where promises made at sale can be kept in delivery. Where renewals are celebrations, not renegotiations.
That’s the bigness of little things. That’s why getting cost right matters more than any other metric.
Meet Us at the Usage Economy Summit
Come Find the servicePath™ Team in San Francisco
In two days, I’ll be delivering the keynote at the Usage Economy Summit in San Francisco on November 5th. But more importantly, the servicePath™ team will be there, and we want to meet you.
Event Details: Usage Economy Summit 2025 Hyatt Regency San Francisco Downtown SOMA 50 Third Street San Francisco, CA 94103 November 5, 2025
If you’re wrestling with any of these challenges:
- Revenue growing but margins shrinking
- Usage-based pricing that’s hard to quote accurately
- AI costs that are unpredictable and eating into profitability
- The gap between what sales quotes and what finance discovers three months later
- Renewals that turn into renegotiations because the original economics didn’t hold
Come find us at the Summit.
Let’s Talk About Your Jagged Edges
I’d love to hear what’s actually working in your business. Where the jagged edges are. What keeps you up at night when you think about pricing and profitability.
The servicePath™ team and I will be there throughout the conference. We’re not there to pitch—we’re there to have real conversations about the economics of growth in the Usage Economy.
Why This Conversation Matters Now
Because this stuff matters. Not in an abstract, theoretical, write-a-think-piece kind of way. In a P&L, margin-protection, company-survival kind of way.
The companies that master cost-first economics in the next three years will dominate their markets for the next decade. The ones that don’t will wonder why growth suddenly got expensive, why margins eroded, why renewals got hard.
The Bottom Line
Honor the little things. Measure accurately. See cost clearly. Build profitability into every quote, not hope for it after every signature.
Get the cost right first. Everything downstream—configure, price, quote, bill, renew—falls into place.
Just like it did a hundred years ago.
Register for the Usage Economy Summit and see you on November 5th at the Hyatt Regency in San Francisco.
— Daniel Kube, CEO, servicePath™