Price the Job, Not the Tool
Part 4 - Architecting the Outcome: New Models for a New Era
Chapter 8: The Profit Model Revolution: Beyond Price-Per-Outcome
We have successfully diagnosed the patient. The Socratic scalpel and the Five Whys drill have cut through the symptoms, exposed the root cause, and allowed us to define the true job. Our Customer Success Statements (CSS) have given us a set of hard, contractual metrics. We have, in essence, created the “what”—the specific, measurable outcome we are agreeing to deliver.
Now we must architect the “how.” How, precisely, do we get paid?
This is not a trivial question. It is not the last line item on a proposal. In this new economy, the way you structure the price is just as innovative—and just as valuable—as the AI agent itself. A brilliant AI agent shackled to a stupid, misaligned pricing model will fail, guaranteed. A revolutionary “Profit Model,” by contrast, can itself be the source of a profound and defensible competitive advantage.
For decades, the technology industry has been stunningly un-innovative in how it makes money. We have obsessed over product features while treating the pricing model as an afterthought. It was either a one-time license, a per-seat subscription, or a per-API-call usage fee.
The innovation consultancy Doblin famously mapped the ten distinct types of innovation that can create a competitive advantage. Most companies focus all their energy on just one: Product Performance (better features). But the most disruptive and defensible innovations often come from the “Configuration” of the company—its internal workings and business model. And the most powerful of these is innovating the Profit Model itself.
Outcome-based pricing is the ultimate Profit Model innovation. It is a strategic act of re-architecting the entire commercial relationship around value. It is not a single model, but a new philosophy of pricing that opens up a vast, creative landscape. Let’s explore the new commercial structures that this landscape makes possible.
1. The “Pure” Outcome Models: The High-Alignment, High-Risk Pact
These are the most direct, elegant, and radical expressions of outcome-based pricing. They represent a 100% alignment of interests and require the absolute highest degree of confidence from the vendor.
The Model: Percentage of Revenue (”We win when you sell more”) This is the holy grail for any AI agent working on the “demand” side of a business. The agent is hired to “generate qualified leads” or “increase e-commerce conversion rates.”
The Pitch: “Our AI agent will run your entire email marketing campaign. We will not charge you a subscription. We will not charge you per-email-sent. We will take 1% of all revenue that is verifiably attributed to our agent’s campaign. We get paid only when you get paid.”
Why it Works: It is the definition of a partnership. The vendor is now obsessively, financially motivated to make their agent as effective as humanly (and non-humanly) possible. Every incentive is aligned toward the customer’s ultimate goal: revenue.
The Risk: Attribution is everything (as we’ll explore in Chapter 10). This model requires deep, trusted integration and a “single source of truth” to prevent endless arguments over who really caused the sale.
The Model: Percentage of Savings (”We win when you spend less”) This is the mirror image, perfect for any agent working on the “cost” side of the business—in operations, logistics, or procurement.
The Pitch: “Our ‘Delivery Profitability Agent’ will be deployed to ensure you stop losing money on bad B2C orders. We will establish a baseline of your current ‘last-mile’ losses. For every dollar of that loss our agent verifiably eliminates, we will take two cents.”
Why it Works: It directly solves the CFO’s root-cause objection from our Five Whys example. It transforms an “unaffordable” cost-center project into a self-funding profit center. The vendor has 100% of the performance risk.
The Risk: The vendor must have total confidence that its agent can outperform the customer’s existing, chaotic process. The customer must be willing to open its financial books to establish a clear, honest baseline.
The Model: Per-Outcome Fee (”We win when the job is done”) This is the cleanest model for discrete, high-volume, transactional jobs. The value of each “job” is known, fixed, and easily countable.
The Pitch: “Your human agent costs you $10 to resolve a password reset ticket. Our agent will do it autonomously for $1 per successful resolution. We will send you a simple, itemized bill at the end of the month. If the agent fails and the ticket goes to your human team, you pay us nothing.”
Why it Works: It is simple, transactional, and transparent. The customer is not buying a “chatbot” subscription; they are buying “resolved tickets” as a utility. It transforms a fixed internal cost (a support team’s salary) into a 100% variable, and massively lower, operating expense.
The Risk: The vendor must be able to handle the job at a cost-per-execution that is significantly lower than their $1 price, or they will lose money at scale.
2. The “Hybrid” Models: The Practical On-Ramp to Value
For many organizations, a pure outcome-based model is a bridge too far, too fast. The risks of attribution, runaway costs (for the customer), or zero-revenue (for the vendor) are too high. The “Hybrid” models are the practical, transitional structures that help de-risk the journey for both parties. They are the on-ramps to a fully aligned economy.
The Model: Access + Performance (”The Subscription-plus-Bonus”) This is the most logical first step for a traditional SaaS company. It blends the old world with the new.
The Pitch: “We will provide our AI Sales Agent for a small, fixed subscription of $5,000 per month. This covers our base costs for compute, availability, and human support. This is your ‘Access Fee.’ However, the real price is tied to a $10,000 performance bonus that is paid only when the agent verifiably hits the CSS metric of ‘generating 100 qualified leads’ this month. If we miss the goal, you pay no bonus.”
Why it Works: It creates a “cap and collar” that protects both parties. The vendor’s base subscription covers their infrastructure costs, preventing them from going bankrupt on a string of bad months. The customer’s risk is capped, but the vendor’s real profit is now 100% aligned with the customer’s success, creating a powerful incentive.
The Model: Tiered Outcome-Based Subscription (”The Aligned Utility”) This model looks like a familiar SaaS subscription, making it psychologically easier for a CFO to buy, but its mechanics are based on outcomes, not seats.
The Pitch: “We are not selling you seat licenses. We are selling you ‘Resolved Ticket’ packages. Our ‘Pro’ plan is $10,000 per month and includes up to 15,000 autonomous password resolutions. Our ‘Enterprise’ plan is $20,000 per month for up to 50,000. You are buying a pre-set capacity for outcomes.”
Why it Works: It’s predictable. The CFO can budget for it. The customer is still, in a clear way, paying for results, not access. It’s less purely aligned than a per-outcome fee, but it’s a massive improvement over a per-seat license that charges the same price whether 10 tickets or 10,000 are resolved.
The Model: Capped Outcome-Based Fee (”The Safety Net”) This model is designed to overcome the customer’s single biggest fear of a pure outcome model: “What if this is too successful?”
The Pitch: “Our price is $100 for every qualified lead our agent generates. However, to give you budget predictability, we will place a monthly cap of $50,000. Once we hit 500 leads, the agent will continue to work for you for the rest of the month at no additional charge.”
Why it Works: It removes the customer’s fear of a “runaway success” that results in a million-dollar variable bill they can’t pay. It builds trust. In exchange, the vendor often gets a longer-term contract, as this “bonus” value ensures the customer will never churn.
Innovating the Profit Model is not a one-time decision; it is a creative act of design. The right model for a high-volume, low-value transactional job (password resets) is completely different from the right model for a low-volume, high-value strategic job (margin protection).
By choosing to architect a new model, the vendor is making a profound statement. They are saying, “We are so confident that our agent can get this job done that we are willing to stake our own revenue on it.” This moves the relationship from a simple transaction to a true partnership. It is a new commercial pact, one based on shared risk and shared reward.
This “Risk-Reward Pact” is the new foundation. But it requires more than a clever pricing sheet. It requires a new, deeper, and more trusting relationship between the vendor and the customer. It’s a joint venture. And as with any venture, it requires both parties to align on how they will build the solution and de-risk the journey together.
Chapter 9: The Risk-Reward Pact: Aligning for the Win-Win
The innovative profit models we’ve designed are not just clever contracts; they are the constitution for a new kind of commercial relationship. The old SaaS subscription was a simple, cold, and distant transaction. The customer paid a fee, the vendor granted access. It was a rental agreement. If the customer failed to get value from the tool, that was the customer’s problem. The vendor’s only obligation was to keep the tool online.
An outcome-based model is the polar opposite. It is an intimate, high-stakes, and deeply entangled partnership. It is not a rental; it is a joint venture. When a vendor says, “We will only get paid if you save money,” they are, in effect, becoming a business partner. They are making a capital investment in the customer’s success, staking their own revenue, resources, and reputation on the outcome.
This new relationship, this Risk-Reward Pact, fundamentally rewrites the rules of engagement for both parties. It demands a level of trust, transparency, and collaboration that would be unthinkable—and, frankly, unnecessary—in the old transactional world.
The New Vendor: From Pitching to Performing
In the old model, the vendor’s work was front-loaded. The entire business was optimized to win the sale. Sales and marketing were the engines of growth. They demoed the product, negotiated the contract, and secured the renewal. The product itself just had to be “good enough” to not get canceled.
In the outcome-based model, the sale is just the starting line. Winning the contract means nothing. It’s an empty victory, a commitment to do the work that has not yet begun. The vendor doesn’t get paid for signing the deal; they get paid for executing it.
This changes everything about the vendor’s organization:
Confidence Becomes the Core Asset: A vendor cannot fake an outcome-based model. They cannot “growth hack” their way into a performance-based contract. They must possess a deep, terrifying, and verifiable confidence in their agent’s ability to deliver. This confidence has to be built on a foundation of data, rigorous testing, and a profound understanding of the customer’s job.
Sales & Engineering Merge: The old wall between “sales” (who promise) and “engineering” (who build) collapses. The sales team can no longer sell “vaporware” or “roadmap features.” They can only sell what the agent can provably do right now. The engineering and product teams, in turn, are no longer isolated; they are now, in a very real sense, part of the sales and customer-success organizations. Their code is the company’s P&L.
Customer Success Evolves: The “Customer Success” team is no longer a churn-prevention squad focused on “engagement metrics.” They are a performance optimization team. They are partners, embedded with the customer, whose sole job is to ensure the AI agent has the data, access, and permissions it needs to get the job done. They are co-pilots, not cheerleaders.
The New Customer: From Purchaser to Partner
The burden of change is just as heavy on the customer. The old model was passive. The customer was a “purchaser” who bought a tool and then “owned” the problem of implementation and value extraction.
In the new model, the customer becomes an active partner in the joint venture. They, too, have skin in the game. The AI agent cannot succeed in a vacuum. It is not magic. It needs data, access, and integration.
Transparency is Non-Negotiable: The customer can no longer hide their internal data. To hire an agent to “reduce margin loss,” they must be willing to open the books and show the vendor the margin data. To hire an agent to “generate leads,” they must be willing to share the sales data to close the attribution loop. This requires a new level of trust.
Implementation becomes a Shared Burden: In the SaaS world, a customer could buy a tool and let it rot on the shelf. In an outcome-based world, this is impossible. The customer is now a co-owner of the agent’s success. If the customer’s IT team blocks the necessary API access, or their legal team holds up data sharing, the agent fails—and the vendor gets paid nothing. The customer must do their part to enable the agent’s success.
This new, high-trust, high-risk relationship can be terrifying for both sides. A customer’s CFO might love the idea of a no-risk contract, but their IT department will balk at the deep integration required. A vendor’s CEO might love the idea of a massive performance-based upside, but their CFO will panic at the thought of a zero-revenue quarter.
This is why you cannot, and should not, try to sell this new model as a single, monolithic “big bang” decision. The risk is too high. The trust is not yet earned.
The solution is to not treat it as a single “sale” at all, but as a staged investment process, just like a venture capitalist would approach a new company. We can use a Real Options Approach (ROA) to systematically de-risk the journey for both parties. This framework reframes the innovation from a single, high-risk bet into a series of small, gated investments. Each stage buys the option to proceed to the next, with the investment increasing only as uncertainty is reduced.
De-Risking the Pact: A Real Options Framework for Outcomes
Instead of asking the customer to sign a massive, three-year outcome-based contract from day one, you invite them on a three-phase journey.
Phase 1: Purchasing the “Option to Explore”
JTBD Activity: This is the diagnostic phase we covered in Part 3. It consists of initial, qualitative research—a small number of interviews with the customer’s key stakeholders.
Investment: A small, fixed fee for a 2-week “Problem Deconstruction” workshop. This is the only “fee for service” in the entire relationship.
Business Case Question: “Is this a valuable job with enough observable struggle to justify a deeper look?”.
The Option: At the end of this phase, the partners have a shared, deconstructed understanding of the true job (e.g., “profitability,” not “routing”). This small investment buys the customer the option to validate this hypothesis with hard data. If the hypothesis is weak, both parties can abandon the idea with minimal loss.
Phase 2: Purchasing the “Option to Validate”
JTBD Activity: If the option to explore is exercised, the vendor conducts a full Job Map and quantitative research. They survey the customer’s data and systems to quantify the size of the prize.
Investment: A more significant, but still contained, research-focused investment. Crucially, no development is funded. The customer’s investment is their data and access. The vendor’s investment is their time and expertise.
Business Case Question: “Where is the greatest, most quantifiable opportunity for value creation?”. (e.g., “We have validated that 30% of your B2C deliveries are unprofitable, representing a $1.2M annual loss. This is the size of the prize.”).
The Option: This phase produces the hard metrics and the Customer Success Statements. It buys the right to invest in a specific solution concept. The data now creates new options: to target a large, underserved segment or to pivot to a different job step with a better opportunity.
Phase 3: Purchasing the “Option to Build & Test”
JTBD Activity: Armed with quantitative data, the partners co-develop a Minimum Viable Product (MVP). This is not a small version of a final product; it is a targeted experiment designed exclusively to test whether an AI agent can satisfy the top unmet needs.
Investment: This is the first real capital, but it’s a fraction of a full commercial launch. The customer provides a sandboxed environment and live data. The vendor deploys the agent.
Business Case Question: “Does our agent actually get the job done better in a real-world context?”.
The Option: This MVP provides hard evidence. If it succeeds, it grants both parties the right to expand and scale. This is the moment the full-scale, outcome-based profit model kicks in. Both parties can now sign that contract with total confidence because they have validated every assumption. If the MVP fails, the project is abandoned, having saved both sides millions in a failed market launch.
This “Real Options” approach is the practical, operational framework for building the Risk-Reward Pact. It is the “how” of the joint venture. It manages the single biggest barrier to adoption—fear—by replacing it with a structured, data-driven process for building mutual confidence.
This process builds the foundation of trust. But trust, on its own, is not enough. This new model only works if the outcome is measurable. We must now confront the single hardest, most complex, and most contentious problem in this new economy: attribution.
I make content like this for a reason. It’s not just to predict the future; it’s to show you how to think about it from first principles. The concepts in this blueprint are hypotheses—powerful starting points. But in the real world, I work with my clients to de-risk this process, turning big ideas into capital-efficient investment decisions, every single time.
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