Allow us to charge you based on outcomes! This is the cry of AI providers these days. It is high time we switch from seat-based pricing to outcomes-based models, the experts urge. Because according to them, AI doesn’t just tap software budgets; it also taps labor budgets. If deploying the tool results in direct headcount reduction for the customer, shouldn’t the vendor be allowed to capture some of the value? If customers win, vendors gotta win, has become the new rhetoric.
However, is the customer truly ready for outcomes-based pricing? Handing a variable, unpredictable bill to the CFOs who are used to per-user/per-month pricing is bound to invite resistance.
Before determining if AI pricing must be based on outcomes, work-done or simply usage or seat-based, let’s be cognizant of four conditions that influence the monetization of a product.
Factor A: Customers desire measurable ROI
Factor B: They also demand predictability in pricing
Factor C: Vendors wish to capture the upside created by their AI tool
Factor D: They are wary of the potential margin pressure from token costs. Nearly every AI tool is built on top of a third-party model provider, creating constant pressure to maintain roughly 5X markup over the underlying token cost.
Based on the above four factors, we at Trigent have developed a concise framework that will enable both enterprises and vendors to gain better clarity on product monetization.
Figure 1: AI Monetization Matrix (generated with AI)
As the graph in Figure 1 shows, any AI tool may invariably fall into one of the four categories. Before moving further, let’s understand the two primary variables here.
| On the X-axis, AI Output has two categorical values: Assistive and Agentic. On the Y-axis, Business Impact has two categorical values: Hard-to-Measure and Easy-to-Measure |
Let’s quickly dive into the individual categories:
Quadrant 1: Assistive Intelligence (Assistive AI/Hard-to-Measure Impact)
Chatbots, drafting assistants, and autocomplete coding assistants fall into this category. Here, the AI output is mostly suggestive with humans still in control as they decide the final output. The challenge lies in the tangible impact: how do you, for instance, assess the impact of a blog created with a tool such as ChatGPT?
These are personal productivity tools whose impact varies widely by the user, with little direct causal linkage between the suggestive AI output and the ensuing business outcomes. Make no mistake, these assistants might catalyze a collective business transformation. But quantifying the enterprise-wide impact is hard as it largely relies on how effectively users leverage these tools for favorable business outcomes.
So, if our AI tool functions primarily as an assistant, where the output is suggestive and the measured impact is low, what is the most appropriate pricing model? ChatGPT, for instance, offers a seat-based subscription, so do Grammarly, Notion, and Jasper that rely on either seat-based or per-user subscriptions. These tools offer tiered plans, encouraging users to pay more for higher usage limits, advanced capabilities and features. But the pricing is always tied to the number of users or seats—not work done or outcomes achieved.
Quadrant 2: Operational Automation (Agentic AI/Hard-to-Measure Impact)
How would you price the AI tool when its output is agentic and measured impact is hard to attribute? We can draw inspiration from existing tools. AWS Lambda, for example, is priced on usage, with costs calculated based on two primary components: the number of times the function is requested and compute duration. This is a classic consumption-based pricing model.
RPA bots offer another parallel, typically charged per task execution. Is it based on work done? The RPA bots complete discrete units of tasks within a larger workflow, they rarely automate the entire workflow. Take the case of web scrappers. Their job is to visit websites and collect data, but stop short of inferring or acting on the business meaning of that content. Usage-based method is the go-to pricing for this quadrant.
What about outcomes-based pricing? It may not be accurate because the business value per execution is often small or hard to isolate. Seat-based pricing is even less practical as agentic systems do not require a human user to operate them. The pricing anchor, therefore, shifts to the volume of resources consumed or how much the system has been utilized, making consumption-based models the most defensible approach.
Quadrant 3: Decision Intelligence (Assistive AI/Easy-to-Measure Impact)
Some assistive tools go beyond merely offering suggestions. They provide highly contextual answers that save significant time and effort. Now the question is, if your tool is an answer engine used in high-value business decisions, how should you price it?
Trigent’s Rick is a classic example. It answers live queries on warehouse capability, dock availability, and inbound and outbound schedules, effectively removing the need for phone calls and manual follow-ups. Unlike generic assistants, its impact is measurable: you can track query volumes, response times, and cycle-time reduction. If previously it took 5–15 minutes to obtain an answer, Rick answers in seconds.
However, pricing based on work done is still premature because Rick remains assistive. It does not execute transactions autonomously (for example, it does not reschedule operations on its own). A human is still in the loop.
Consequently, decision intelligence tools, like operational automation tools, are best priced on usage. But note the subtle difference: operational automation tools are charged in terms of system execution (how much the system has run on its own). Whereas, decision intelligence tools like Rick are priced in terms of human interaction volume (how much the system has been used by the humans).
Quadrant 4: Autonomous Business Agents (Agentic AI/Easy-to-Measure Impact)
As you may have guessed, the tools in this quadrant are perfect candidates for outcomes-based pricing, but with a caveat. Let’s unwind them through one AI tool, Jake. Trigent’s Jake is a frontline ops agent used by 3PLs and brokers to streamline communication with carriers. Jake alleviates 3PLs’ biggest pain: the sheer volume of manual calls and follow-ups that lead to disputes and operational chaos. Jake operates autonomously on behalf of the 3PL. He calls drivers and captures real-time updates on check-ins, ETAs, and departure times. He also accelerates carrier onboarding by sharing tender rates with carriers and collecting the responses through calls or messages.
So, how do we price Jake?
Right from floating the tender to tracking detention, Jake manages the entire operational loop for a load. Should we then price Jake based on the work done, say per load handled? Conceptually, yes. Work-unit pricing becomes intuitive when an agent autonomously executes an end-to-end operational cycle.
Today, however, Trigent prices Jake based on consumption: the number of calls multiplied by the call duration, measured in minutes. As Jake evolves to close the loop autonomously, we can naturally move towards work-unit pricing, for example, per load or per carrier onboarding.
How about outcomes-based pricing for Jake? Not quite, at least not yet. Outcomes in logistics are shaped by multiple factors beyond Jake including the warehouse team or the customer. For example, Jake informs that the driver’s ETA is 10.00 a.m. but delays can still occur due to labor shortage or dock unavailability. Outcomes are distributed across the ecosystem, making attribution – and therefore outcome-based pricing – structurally complex.
So, when would outcomes-based pricing really apply? When your tool not only completes the full workflow but owns the outcome end-to-end. This is the single most critical determinant to opt for outcomes-based pricing.
A customer service agent that resolves queries is an ideal example. The agent can be priced based on the number of resolutions. Similarly, sales agents that directly drive more product sales can command a share of the revenue generated. However, attribution is rarely perfect. What if the product falls below customer expectations? That’s not an agent problem, yet it directly affects the outcome metric. Hence, for tools living in quadrant 4, a hybrid pricing model is prudent: a base fee to cover guaranteed system value and a performance-linked bonus tied to realized business outcomes. This balances risk, attribution ambiguity, and incentives across both vendor and customer.
| AI Pricing Models: What to Use, WhenSeat-Based Pricing Best for assistive AI tools where humans stay in control and business impact is diffuse or hard to measure (e.g., chatbots, writing assistants, coding copilots). Usage / Consumption-Based Pricing Ideal for agentic or automation-led AI where value scales with system execution or interaction volume, but outcomes are shared or hard to attribute (e.g., RPA bots, AI workflows, decision intelligence tools). Outcomes-Based Pricing Works only when AI owns the workflow end-to-end and directly controls a measurable business result (e.g., autonomous agents resolving tickets or completing transactions). Often best paired with a base fee + performance bonus. |
Bottom line
Outcomes-based pricing is bold and forward-looking, and it will likely become the preferred pricing model over the next decade. However, until an AI tool completely owns the outcome end-to-end, consumption-based or seat-based pricing remains the more pragmatic choice. In practice, pricing should track autonomy and attribution: charge for usage or seats when humans remain in the loop, and transition to outcome-linked models only when the system can demonstrably control results.
1 When should enterprises avoid outcomes-based pricing for AI tools?
Outcomes-based pricing should be avoided when AI does not fully control results end-to-end. This is common in enterprise solutions built through custom cloud application development where humans, partners, or legacy systems still influence outcomes.
2 Why is usage-based pricing so common for enterprise AI platforms?
Usage-based pricing aligns well with how value is consumed in AI platforms delivered via Custom cloud application development. Here costs scale with execution, interaction, or compute rather than with fixed business results.
3 How does AI maturity influence pricing strategy?
As AI systems evolve through iterative custom cloud application development, pricing typically shifts from seats to usage and only later to outcome-linked models once autonomy and attribution become clear.
