AI vendors love to talk about data, but most of that talk misses the point. When AI takes over tasks that humans used to perform, the traditional signals we relied on to understand value disappear. Clicks vanish. Workflows disappear. Time-on-task becomes meaningless. AI often hides value more than it reveals it.
Value visibility doesn’t come from smarter models. It comes from integration. You only understand the value an AI creates when it operates inside systems that already measure business outcomes.
In practice, AI products fall into one of three levels of value visibility.
- Level 1: No integration. You see activity, not impact.
- Level 2: Shallow integration. You see product performance, not business performance.
- Level 3: True integration. You see measurable outcomes because the system records them.
To illustrate these levels, we will use Grammarly. Not because it is flawed, but because it is familiar, beloved, and perfectly illustrates how value visibility expands with integration. Grammarly today lives at Level 1. With performance analytics, it moves to Level 2. Only deep integration into business systems unlocks Level 3, where outcomes are observable and pricing can finally align with value.
Grammarly Today: Zero Integration, Zero Outcome Visibility
Grammarly is wildly popular. Millions of people, including me, use it every day. But from a pricing perspective, Grammarly is flying blind.
What Grammarly can see is trivial:
- How many suggestions it offered
- Which suggestions were accepted
- How often a user opens the app
- Which features get clicked
None of this tells Grammarly whether the writing improved anything that matters.
Grammarly has no visibility into:
- Whether a clearer email closed a deal
- Whether a more professional message influenced a candidate
- Whether a better support reply reduced churn
- Whether a corrected document saved meetings, rework, or frustration
In other words, Grammarly sees activity, not impact.
This is exactly the position most AI products are in today. The vendor can observe the tool performing tasks, but cannot observe whether those tasks changed the business. Without that connection, outcome-based pricing is impossible. You charge a subscription because you have no choice.
Shallow Integration: More Data, but Still No Value
Now imagine Grammarly integrated just enough to measure its own performance. Lets’ say then that the system could report:
- How many errors were fixed
- How severe they were
- How readability scores improved
- How consistency changed over time
It’s more data. It looks impressive. It feels closer to value.
But it’s still useless for pricing.
Why? Because shallow integration measures tool performance, not business performance. It tells you Grammarly works. It does not tell you Grammarly matters.
A sales email with fewer grammar mistakes might still fail. A well-written support reply might still escalate. A polished proposal might still lose. A refined job posting might still attract nobody.
You can have perfect writing and terrible outcomes. Or terrible writing and great outcomes.
Shallow integration doesn’t solve the fundamental problem. It still doesn’t tell Grammarly whether its contribution produced measurable economic benefit. It is telemetry dressed up as insight.
This is where many AI vendors will get stuck. They think that having more data means they understand value. But if the data isn’t tied to a business result, it’s just product analytics.
True Integration: Connecting AI to Systems That Measure Outcomes
To actually see value creation, Grammarly would need to be embedded inside the systems where writing influences measurable business results. That means operating inside ecosystems that already track KPIs.
Imagine Grammarly integrated into systems such as:
- Salesforce measuring deal velocity and win rates
- Zendesk measuring ticket resolution time
- Applicant tracking systems measuring candidate response rates
- Marketing platforms measuring engagement
- Customer success systems measuring renewal outcomes
Now Grammarly can observe the before/after gap: Without Grammarly vs with Grammarly.
This gap is the economic value. It’s measurable. It’s priceable.
If integrating Grammarly increased candidate acceptance rates by five percent, that is real value. If it reduced support ticket resolution time by ten minutes, multiplied across thousands of tickets, that is real value. If it improved close rates in enterprise sales, that is real value.
For the first time, Grammarly would see outcomes instead of activities. Outcomes are the raw material of value-based pricing.
This is the transformation AI needs to escape commodity pricing. Not model improvements. Integration improvements.
Why Integration Determines Pricing Power
The Grammarly examples illustrate a universal truth about AI systems:
No integration – You can’t see value. You charge access fees. Most AI is stuck here.
Shallow integration – You can measure product performance, but not business performance.
Better dashboards, same pricing model.
True integration – You can see the business impact. You can price to outcomes because the outcomes are observable.
Sellers desperately want outcome-based pricing. Buyers want it too, at least in theory. But neither side gets what they want until the AI operates in a system where the consequences of its work are measured.
AI doesn’t automatically reveal value. In fact, AI hides value unless it is integrated where value occurs.
Integration turns invisible value into observable value. Observable value into measurable value. Measurable value into monetizable value.
AI vendors who understand this will design their products around integration, not isolation. They will become part of the customer’s operational backbone. And once you’re part of the backbone, your value stops being hypothetical.
It becomes measurable.
It becomes defensible.
And finally, it becomes priceable.
Now, go make an impact!
Tags: ai pricing, pricing, Pricing AI, pricing foundations, pricing metrics, pricing skills, pricing strategy, pricing value, value
