Agentic AI monetization is the process of turning autonomous AI agents into revenue by charging for the work they complete, the workflows they run, or the business results they produce. Unlike basic AI tools that generate text, images, or summaries on demand, agentic AI can plan steps, use tools, call APIs, update systems, trigger workflows, and complete tasks with limited human input. That changes how you package, price, meter, bill, and prove value. IBM describes agentic AI as AI systems that can accomplish goals with limited supervision. At the same time, Google Cloud explains that agentic AI can set goals, plan, and execute tasks with minimal human intervention.
Why Agentic AI Changes Software Monetization
Traditional SaaS pricing was built around users, seats, plans, storage, and fixed feature bundles. That model works when software is mainly a tool used by humans. Agentic AI changes the model because the software is no longer only waiting for a user to click buttons. It can take action, complete work, and make operational decisions inside a workflow.
This creates a different value equation.
You are not only selling access to software. You are selling completed work. You are selling faster customer support, cleaner sales follow-up, automated research, better procurement workflows, resolved tickets, processed documents, improved reporting, or fewer manual tasks.
That is why flat-rate pricing often feels too limited for agentic AI. A small customer might use one agent lightly. A larger customer might let the same agent process thousands of requests, trigger expensive model calls, use vector search, update CRM records, and run multi-step workflows across several systems. The cost to serve both customers is not equal.
LogiSense frames this shift as part of the “Do It for Me” economy, where AI agents handle transactions, services, and business processes with less direct human input. The same source explains that SaaS and XaaS businesses are moving from subscription tiers toward usage-based, event-driven, and consumption-based billing because AI agents create variable usage patterns.
For founders, SaaS teams, product leaders, and finance teams, the main challenge is simple. Your pricing must reflect customer value without letting compute, token, integration, and support costs eat your margin.
From Seats to Work Completed
Seat-based pricing is familiar because it is easy to explain. A company pays for each user who accesses the software. That model still works for many SaaS products, especially tools used directly by employees every day.
Agentic AI often needs a different unit of value.
A support agent might resolve tickets without a human support rep opening the conversation. A sales agent might qualify leads, enrich records, write outreach, schedule follow-ups, and update CRM fields. A finance agent might review invoices, match purchase orders, flag errors, and prepare approval notes. A legal operations agent might summarize contracts, extract renewal terms, and route high-risk clauses for review.
In each case, the customer does not care only about the number of users. The customer cares about completed work, saved time, reduced backlog, lower cost per workflow, or faster response.
That is why agentic AI pricing often moves toward tasks, actions, outcomes, transactions, or digital labor units.
This shift also changes how customers compare your product with alternatives. A traditional SaaS buyer compares software against other software. An agentic AI buyer often compares your agent against human labor, outsourcing, internal operations, or agency support.
Your pricing page, sales deck, and onboarding must make that comparison easy.
Subscription-Based Pricing for Early Adoption
Subscription pricing remains useful for agentic AI, especially when the product is new, the buyer wants cost predictability, or the value metric is still being tested.
For example, a startup might charge a monthly fee for an AI research assistant with limits on searches, reports, integrations, and workspace members. An HR tech company might sell a monthly plan for an AI recruiting assistant that screens resumes and drafts candidate messages.
The benefit is simple. Subscriptions are easy to sell, easy to forecast, and easy for customers to approve.
The risk is margin leakage. If power users run high-cost workflows inside a flat subscription, the vendor pays the compute bill while the customer captures most of the value. That is why many agentic AI companies start with subscriptions but add usage limits, fair-use rules, credits, or paid overages.
A good subscription plan should define what is included, what counts as extra usage, what triggers an upgrade, and how customers can monitor usage before surprise costs appear.
Usage-Based Pricing for Variable Agent Workloads
For agentic AI, this can include conversations, agent runs, API calls, documents processed, minutes of voice interaction, records enriched, workflow steps executed, tokens processed, or automation events.
This model works well when usage varies by customer size, season, channel, or workflow complexity. It also protects your margin because revenue rises as usage rises.
Salesforce’s Agentforce pricing page describes flexible options, including consumption-based pricing through Flex Credits or Conversations, as well as per-user licensing. Salesforce Help also states that Flex Credits support a pay-per-action model for Agentforce at scale.
Usage-based pricing is fair when the usage metric is easy to understand and tied to value. It becomes difficult when the customer cannot predict the bill or connect usage to business results.
For example, charging only by tokens can confuse business buyers. Tokens are a technical cost driver, not always a value driver. A customer does not want to think about token burn. They want to know how many tickets were resolved, how many leads were enriched, or how many reports were produced.
The best usage-based models hide technical complexity behind business-friendly units.
Instead of charging only for tokens, you can charge for completed research reports. Instead of charging only for API calls, you can charge for qualified records. Instead of charging only for workflow steps, you can charge for completed cases with clear usage caps.
Action-Based Pricing for Agent Steps
Action-based pricing charges for specific tasks completed by an AI agent. This can include updating a CRM field, sending a follow-up, searching a knowledge base, classifying a ticket, generating a quote, scheduling a meeting, or triggering a workflow.
This model is useful when the agent performs many small, measurable actions across systems. It fits agent platforms, enterprise workflow tools, and API-based products.
The benefit is accuracy. You can meter each agent action and connect it to the delivery cost.
The challenge is customer perception. Too many microcharges can make customers feel like every small movement costs money. That can slow adoption because users become cautious about using the product.
To solve this, you can package actions into credits. Customers buy a monthly credit bundle, and different actions consume different credit amounts based on cost and value. Simple actions use fewer credits. Complex actions use more.
This gives you pricing control while keeping the customer experience cleaner.
Outcome-Based Pricing for Measurable Results
Outcome-based pricing charges customers only when the AI agent delivers a defined result. This model is attractive because it connects price to business value instead of access or activity.
Intercom’s Fin is a strong example. Intercom’s pricing page lists Fin at $0.99 per outcome, and Fin’s help center explains that an outcome is counted when Fin completes the configured action as part of a conversation.
Zendesk has also moved AI agent pricing toward automated resolutions. Zendesk Support states that AI agent pricing is measured in automated resolutions, replacing the earlier monthly active user pricing for older AI agent customers.
Outcome-based pricing works best when the outcome is clear, measurable, and accepted by both sides.
For customer support, the outcome might be a resolved ticket. For sales, it might be a qualified meeting. For finance, it might be an approved invoice without manual correction. For recruiting, it might be a screened candidate who has moved to the next stage. For procurement, it might be verified savings from vendor negotiation.
This model can increase trust because customers pay when value appears. It can also create risk for the vendor. The agent may use tokens, tools, and infrastructure without producing a billable result. If the success rate is low, the vendor carries the cost.
Before using outcome-based pricing, define the outcome carefully. Set rules for attribution, exclusions, disputes, human handoff, partial completion, refunds, and customer-side dependencies.
Output-Based Pricing for Deliverables
Output-based pricing charges for a finished artifact. This is useful when the agent creates something the customer can review, use, publish, send, file, or store.
Examples include a research brief, meeting summary, sales account report, contract risk memo, compliance checklist, image pack, campaign brief, data enrichment file, or monthly insights report.
This model works well for agentic AI products that produce repeatable deliverables.
It is easier to understand than token pricing because the buyer can see the output. It also gives the vendor room to price based on perceived value rather than raw compute cost.
For example, a legal AI agent that produces a contract review memo should not be priced only by token usage. The value is the reviewed contract, extracted risks, and time saved for the legal team.
Output pricing works best when each output has a consistent scope. If one “report” can be two pages or two hundred pages, you need tiers, limits, or complexity bands.
Agent-as-an-Employee Pricing
Agent-as-an-employee pricing treats the AI agent like a digital worker with a defined role. The customer pays a monthly fee for an AI sales assistant, AI support agent, AI recruiter, AI analyst, AI SDR, AI operations coordinator, or AI finance assistant.
This model is easy for business buyers to understand because it maps to existing budgets. A company already knows what it spends on a role, a contractor, or an outsourced service. Your agent is positioned as a digital labor unit with a clear job description.
The pricing can include one agent, defined skills, specific integrations, a monthly task allowance, admin controls, onboarding, and support.
This model works well for vertical products. A general AI assistant is harder to price as a digital employee. A specialized AI claims processor for insurance, an AI appointment coordinator for clinics, or an AI property listing assistant for real estate has a clearer role and stronger buyer intent.
The risk is overpromising. If you call the agent an employee, customers expect reliability, accountability, availability, and role-specific quality. Your product must define where the agent acts independently, where it asks for approval, and where a human remains responsible.
Transaction-Based Revenue and Agent Commerce
Some AI agents do not only complete internal work. They can also support or trigger transactions. These transactions can include bookings, renewals, purchases, insurance quotes, financial product comparisons, procurement orders, ticket reservations, or marketplace purchases.
In this model, the provider earns a commission, transaction fee, referral fee, or take rate.
This can become a strong revenue model when the agent sits close to buying intent. A travel planning agent can earn from bookings. A procurement agent can earn from supplier transactions. A benefits agent can earn from partner enrollment. A commerce agent can earn from purchases completed through approved partners.
Transaction revenue needs careful governance. Customers must understand when the agent is recommending based on fit, when a partner fee exists, and how approvals work before money moves.
Agent commerce also increases legal and trust requirements. Autonomous agents that transact on behalf of users raise questions about consent, authority, refunds, responsibility, and data ownership.
API Monetization for Developer Adoption
API monetization works when your agentic AI capability can be embedded into other apps or workflows. Developers pay for access to your agent, orchestration engine, reasoning layer, workflow automation, decision API, voice AI, document extraction, or tool-use system.
API pricing usually depends on volume. It can include calls, tasks, tokens, workflow executions, data processed, or result quality levels.
This model can scale fast because developers and SaaS companies build your capability into their products. It also requires strong documentation, uptime, error handling, usage dashboards, and clear rate limits.
For agentic AI APIs, customers need more than a simple endpoint. They need logs, traceability, retries, sandbox testing, permissions, usage caps, and cost controls.
A good API monetization model should separate cost drivers from customer value. You can meter technical usage internally while billing customers through simpler units such as completed classifications, verified records, processed documents, or successful workflow runs.
Marketplace Revenue for Specialized Agents
Agent marketplaces allow developers, agencies, and vendors to publish specialized AI agents for specific tasks or industries. The platform earns through commissions, listing fees, subscription fees, premium placement, enterprise distribution, or revenue sharing.
This model can work when the platform has strong distribution and trusted quality control.
A marketplace filled with weak agents will lose buyer trust quickly. Buyers need clear categories, ratings, security reviews, integration details, pricing transparency, usage logs, and support rules.
For developers, marketplaces reduce go-to-market friction. They can build a narrow agent, list it, and earn from usage. For platform owners, marketplaces increase product depth without building every agent internally.
The hardest part is quality control. Agentic AI agents can act inside customer systems. That means the marketplace must review permissions, data handling, failure modes, and compliance needs before agents reach business users.
Enterprise Licensing and Private Deployments
Enterprise buyers often need custom terms, private deployments, security controls, audit logs, service-level agreements, data retention rules, role-based access, and deeper integrations.
That is where enterprise licensing becomes valuable.
Instead of selling a public plan, you sell an enterprise agreement that includes platform access, custom agents, workflow design, integrations, deployment support, admin controls, governance, and ongoing success management.
Enterprise licensing works well for regulated industries such as finance, healthcare, insurance, government, legal, and manufacturing. These buyers often have strict requirements around data, security, review workflows, and user permissions.
The sales cycle is longer, but the contract value can be much higher. The challenge is the delivery cost. Custom work can reduce margin if every enterprise deployment becomes a one-off services project.
To protect margins, define standard implementation packages. Separate product licensing from custom development. Keep reusable components reusable. Charge clearly for integrations, data migration, workflow design, training, and ongoing managed support.
Consulting and Implementation Revenue
Agentic AI is still new for many companies. Buyers often need help finding the right use case, designing the workflow, preparing data, connecting tools, setting approval rules, training users, and measuring performance.
That creates a strong service opportunity.
Consulting and implementation revenue can include AI strategy, workflow mapping, agent design, system integration, data preparation, prompt and policy setup, testing, employee training, governance planning, and monthly optimization.
This model is especially useful for agencies, AI consultants, implementation partners, and developers building custom agents on workflow platforms.
For startups, services can fund early product development. You learn real customer problems, build reusable workflows, and discover which agent use cases customers will pay for.
The risk is becoming a services company when you planned to build a software company. To avoid that, convert repeated services into templates, product features, onboarding playbooks, and reusable integrations.
Managed AI Services for Ongoing Revenue
Many businesses do not want to manage agents themselves. They want the outcome without maintaining workflows, prompts, integrations, monitoring, and error handling.
Managed AI services solve that problem.
In this model, you charge a recurring fee to run, monitor, improve, and report on AI agents for the customer. The service can include performance review, workflow updates, model tuning, quality checks, escalation rules, compliance reporting, and cost optimization.
This model fits agencies and service providers because it combines human oversight with AI automation. It also fits enterprise buyers who need accountability.
Managed AI services can be priced as a base monthly retainer plus usage or outcome fees. The base fee covers support and management. The usage or outcome fee captures upside as the agent handles more work.
Backend Metering and Cost Control
Agentic AI monetization fails when companies cannot measure usage accurately.
You need to know what each customer used, which agent ran, which workflow steps happened, which tools were called, how many tokens were consumed, what the model cost was, what outputs were produced, what outcomes were completed, and what should be billed.
Zuora highlights the need for systems that track usage, enforce entitlements, automate billing, and handle revenue recognition for dynamic AI models. Its guide also describes the difficulty of proving outcomes, attributing value, and managing revenue timing when agents work across tools and billing cycles.
LogiSense also points to real-time rating and mediation as key needs for AI-generated transactions, especially when agents create high-volume activity that traditional billing systems cannot manage manually.
Your backend should track both technical cost and business value.
Technical cost includes tokens, compute, storage, model calls, vector search, API calls, tool usage, retries, and human review. Business value includes completed tasks, resolved issues, approved outputs, qualified leads, processed documents, saved hours, or revenue events.
If you track only technical cost, your pricing will feel too technical. If you track only business value, you may miss margin problems. You need both.
Choosing the Right Pricing Metric
The best pricing metric is easy to understand, easy to measure, hard to game, connected to customer value, and linked to your delivery cost.
A poor metric creates confusion. A strong metric makes buying easier.
For customer support, resolved conversations or automated resolutions can work. For sales, qualified leads or completed account research packs can work. For legal operations, reviewed documents or risk memos can work. For finance operations, processed invoices or approved reconciliation tasks can work. For marketing operations, completed campaign workflows or generated content packs can work.
Avoid pricing only around internal AI costs unless your buyer is technical. Tokens, model calls, and storage matter to your margins, but they are not always the best customer-facing unit.
A clean pricing model often combines three layers.
The base platform fee covers access, security, admin controls, integrations, and support.
The usage layer covers variable workload.
The premium layer covers advanced workflows, enterprise controls, human review, or guaranteed service levels.
This hybrid approach gives customers predictability while protecting your unit economics.
Margin Planning for Agentic AI Products
Agentic AI products can look profitable at low usage and become expensive at scale. That happens when agents require many model calls, long context windows, repeated tool use, data retrieval, retries, human review, or third-party APIs.
Maven’s source material highlights that agent costs can vary because agents use LLMs, communicate through natural language, access vector stores, and handle business objects of different sizes and complexities
Margin planning should happen before launch.
You need to estimate cost per action, cost per completed workflow, cost per outcome, and cost per customer segment. You also need to test edge cases. Long conversations, messy data, repeated failures, heavy users, and complex integrations can all increase cost.
Set guardrails early.
Use monthly usage caps. Add overage pricing. Limit high-cost models to high-value tasks. Use cheaper models where quality remains acceptable. Cache repeated answers. Review failed workflows. Track cost per customer. Turn your highest-cost workflows into premium features.
Pricing is not only about revenue. It is also about making sure the product can survive real usage.
Finance and IT Ownership
Agentic AI monetization cannot sit only with product marketing. It affects product, engineering, finance, legal, sales, customer success, and IT.
Product defines what the agent does.
Engineering builds the metering and system logs.
Finance defines billing rules, revenue timing, margin thresholds, and reporting needs.
Legal defines liability, data usage, service terms, and customer responsibility.
Sales explains pricing to buyers.
Customer success helps customers understand usage, outcomes, and ROI.
Zuora’s guide frames agentic AI monetization as a shared finance and IT problem because companies need systems that connect usage data, entitlements, billing, and revenue rules.
This shared ownership matters because agentic AI creates events across systems. A single workflow can start in a chat, call a CRM, use a model, search a knowledge base, update a ticket, send an email, and create a billable result. No single team can manage that alone.
Legal and Compliance Considerations
Agentic AI agents act on behalf of users. That makes contracts more complex than standard SaaS agreements.
Your agreement should define what the agent can do, what it cannot do, when human approval is required, who owns outputs, how data is used, how training data is handled, what happens when the agent makes a mistake, and how disputes are reviewed.
Paid.ai and GitLaw introduced an open-source Master Services Agreement designed for AI agent companies, with language focused on responsibility, liability, data ownership, and training rights. Paid.ai also says most agent companies still use SaaS-style contracts that do not fit autonomous systems that book meetings, write code, or make decisions.
This does not replace legal advice. It does show the direction of the market. Agentic AI contracts need to describe delegated authority, approval workflows, monitoring, audit logs, data rights, and limits of use more clearly than older SaaS contracts.
The more autonomy your agent has, the stronger your controls must be.
Building Customer Trust Through Transparent Billing
Customers will not scale agent usage if they cannot understand the bill.
Transparent billing should show what was used, what was completed, what counted as billable, what was excluded, what failed, what required human handoff, and how much usage remains in the current plan.
For outcome-based pricing, customers need a record of each billable outcome. For action-based pricing, they need a clear activity log. For credit-based pricing, they need to see how credits were consumed. For enterprise pricing, they need reports that finance and procurement teams can review.
Trust also improves when customers can set limits.
Give admins budget caps, usage alerts, approval thresholds, team-level controls, and workflow-level permissions. Let customers choose whether the agent can act automatically or wait for approval in sensitive workflows.
A customer who feels in control is more likely to increase usage.
Practical Monetization Strategy for Startups
Startups should not copy enterprise AI pricing blindly. The right model depends on the use case, buyer, margin profile, and maturity of the product.
Start with a narrow agent that solves a painful workflow. Define the business value in plain language. Choose one primary value metric. Add usage limits to protect costs. Test pricing with real customers. Review actual usage weekly. Compare revenue per customer with delivery cost per customer. Adjusting the packaging before the scale makes the problem harder.
Early-stage startups can use a simple hybrid model.
Charge a base monthly fee for platform access and support.
Include a fair amount of usage.
Charge overages for extra usage.
Offer premium implementation for complex customers.
Move toward outcome-based pricing only when you can measure outcomes with confidence.
This approach keeps pricing easy while giving you room to learn.
Common Pricing Mistakes to Avoid
Many agentic AI products struggle because pricing is decided too late.
One mistake is using flat subscriptions without usage controls. This can hurt margins when customers run heavy workflows.
Another mistake is pricing only by tokens. This exposes your internal cost structure but does not explain customer value clearly.
Another mistake is using outcome-based pricing before the outcome is well defined. That leads to billing disputes.
Another mistake is bundling services and software together without a clear separation. That makes margins hard to read.
Another mistake is hiding billing logic. Customers need to understand what they pay for before they scale usage.
A better approach is to build pricing, metering, and reporting into the product from the beginning.
How to Decide Your Agentic AI Monetization Model
Your pricing should start with the customer’s job, not your model cost.
For high-volume, low-complexity workflows, usage-based pricing can work well.
For clearly measurable business results, outcome-based pricing can work well.
For role-specific agents, agent-as-an-employee pricing can work well.
For repeatable deliverables, output-based pricing can work well.
For developer products, API pricing can work well.
For regulated or complex enterprise deployments, licensing plus implementation can work well.
For agencies and service providers, managed AI services can work well.
Most strong models will be hybrid. The base fee gives predictability. Usage pricing covers variable costs. Outcome or premium pricing captures value when the agent performs higher-impact work.
Agentic AI monetization works best when the customer understands the value, your team understands the cost, and the billing system can connect the two without manual cleanup.
The Future of Agentic AI Monetization
Agentic AI will push software pricing away from access and closer to work completed.
The market is already testing several models at the same time. Intercom uses outcome pricing for Fin. Salesforce offers Agentforce options that include Flex Credits, Conversations, and per-user licensing. Zendesk has moved AI agent pricing toward automated resolutions. These examples show that no single model has won across all use cases.
The winning model will depend on the workflow.
Customer support will favor resolutions. Sales will favor qualified pipeline actions. Finance will favor processed and approved work. Legal will favor reviewed documents and risk outputs. Developer tools will favor API usage. Enterprise operations will favor hybrid licensing with governance and support.
The companies that do this well will not treat monetization as an afterthought. They will build pricing logic, cost tracking, outcome measurement, contracts, and customer reporting into the product from day one.
Agentic AI monetization is becoming a core product discipline. It is where product value, customer trust, billing infrastructure, legal clarity, and unit economics meet.
Agentic AI Monetization: FAQs
What Is Agentic AI Monetization?
Agentic AI monetization is the process of earning revenue from AI agents that complete tasks, workflows, outputs, or business results. Instead of charging only for software access, companies can charge based on what the agent does or delivers.
Why Does Agentic AI Need A Different Pricing Model?
Agentic AI needs a different pricing model because it performs work, not just supports human users. Traditional seat-based pricing does not always match the value or cost of autonomous workflows.
How Is Agentic AI Different From Traditional SaaS?
Traditional SaaS gives users tools to complete work. Agentic AI can complete parts of the work itself, such as resolving tickets, processing documents, updating records, or generating reports.
What Are The Main Agentic AI Pricing Models?
The main pricing models include subscription pricing, usage-based pricing, action-based pricing, outcome-based pricing, output-based pricing, API pricing, enterprise licensing, and managed AI service pricing.
What Is Usage-Based Pricing In Agentic AI?
Usage-based pricing charges customers based on how much they use an AI agent. This can include agent runs, conversations, documents processed, API calls, workflow steps, or records enriched.
What Is Outcome-Based Pricing For AI Agents?
Outcome-based pricing charges customers only when the agent delivers a defined result. Examples include resolved support conversations, qualified leads, processed invoices, or completed contract summaries.
What Is Action-Based Pricing In Agentic AI?
Action-based pricing charges for specific actions completed by an agent. These actions can include sending emails, updating CRM records, classifying tickets, generating quotes, or scheduling meetings.
What Is Output-Based Pricing For AI Agents?
Output-based pricing charges for a finished deliverable created by the agent. Examples include reports, summaries, campaign briefs, compliance checklists, meeting notes, or research files.
Why Is Seat-Based Pricing Not Always Suitable For Agentic AI?
Seat-based pricing is not always suitable because agentic AI value depends on completed work, not only the number of users. One user can run thousands of workflows, while another may use the agent lightly.
How Can Startups Monetize Agentic AI Products?
Startups can monetize agentic AI through monthly subscriptions, including usage limits, paid overages, implementation fees, API access, outcome pricing, or managed AI services.
What Is The Best Pricing Model For An AI Customer Support Agent?
A customer support agent often works well with outcome-based or usage-based pricing. Common pricing units include resolved conversations, automated responses, handled tickets, or support workflows completed.
What Is The Best Pricing Model For An AI Sales Agent?
An AI sales agent can be priced based on qualified leads, account research packs, outreach workflows, CRM updates, meetings booked, or sales tasks completed.
Why Is Backend Metering Important For Agentic AI Monetization?
Backend metering helps track agent usage, workflow activity, model costs, completed tasks, customer outcomes, and billable events. Without metering, pricing, and billing, it becomes difficult to manage.
What Costs Should Companies Track In Agentic AI?
Companies should track model usage, compute, storage, API calls, vector search, workflow retries, human review, integration costs, and customer support costs.
How Can Companies Protect Margins In Agentic AI Pricing?
Companies can protect margins with usage caps, overage pricing, credit limits, workflow tiers, cheaper models for simple tasks, caching, approval gates, and premium pricing for complex workflows.
What Is A Hybrid Pricing Model For Agentic AI?
A hybrid pricing model combines a base fee with usage, outcome, or premium charges. For example, a customer may pay a monthly platform fee plus extra charges for completed workflows or high-volume usage.
Why Is Transparent Billing Important For AI Agents?
Transparent billing builds trust by showing customers what was used, what was completed, what counted as billable, and how much usage remains. It also helps customers control costs.
What Legal Issues Matter In Agentic AI Monetization?
Important legal issues include agent permissions, human approval, data ownership, output ownership, liability, error handling, audit logs, and limits on autonomous actions.
How Should A Company Choose The Right Agentic AI Monetization Model?
A company should choose the model based on the agent’s role, customer workflow, delivery cost, usage pattern, and how clearly the result can be measured. The best model should be easy to understand, easy to track, and connected to customer value.
