Original equipment manufacturers (OEMs) have largely already embedded some form of artificial intelligence into the software they license or white-label. What has not kept pace is how that capability gets priced. Traditional OEM licensing models were designed for static software capabilities, not AI systems whose cost, usage, and customer value can vary significantly across partners. As AI becomes a larger share of what a product actually does, bundling those capabilities into a flat license increasingly creates a mismatch between the value generated, the cost incurred, and the revenue captured.
AI monetization, the practice of pricing AI capabilities based on the value or usage they generate rather than folding them into a flat fee, is rapidly moving from a niche consideration to a baseline expectation across software markets. For OEMs, however, the challenge is more complex than it is for software vendors selling directly to end customers. The value created by an embedded AI capability is often realized by the channel partner reselling it, not by the OEM itself. That makes AI monetization as much a channel economics question as a pricing one.
In this article, we explore:
- How OEMs are monetizing AI capabilities today and the four pricing models gaining traction across the market.
- The key shifts likely to reshape AI monetization strategies for OEMs through 2027.
- What OEMs should prioritize now to build the telemetry, governance, and architecture foundations required for scalable AI monetization.
Why Traditional OEM Pricing Models Break Down for AI
Traditional OEM licensing models were designed for software capabilities whose cost and value remained relatively predictable across customers. AI fundamentally changes those economics. Three characteristics make AI difficult to monetize using conventional licensing approaches:
- AI introduces variable costs. Unlike traditional software features, AI capabilities generate ongoing expenses through model inference, compute consumption, third-party model access, and continuous model improvement.
- AI usage varies significantly across partners. Two partners licensing the same product may consume AI capabilities at dramatically different levels, making flat pricing increasingly difficult to justify.
- AI often becomes the primary source of customer value. As copilots, automation, and predictive capabilities mature, AI increasingly drives adoption, retention, and premium pricing. OEMs that continue bundling these capabilities risk giving away value they are funding and maintaining.
As AI capabilities become more central to software products, monetization models must evolve alongside them.
Where AI Monetization Stands Today
OEM agreements tend to fall into one of two patterns at present. The more common pattern is full bundling, where AI features sit inside the base license at no incremental cost. It is simple to administer, but it leaves whatever value those features create entirely uncaptured. A second, less common but growing pattern is a flat AI add-on fee: a fixed incremental charge for AI access, regardless of how heavily a given partner actually uses it. Neither pattern ties price to the value or usage AI capability genuinely produces, so an OEM monetizing this way is, in effect, guessing at a number rather than measuring one.
Four Monetization Models OEMs Are Testing
As that gap becomes harder to ignore, a clearer set of alternatives is emerging. The table below sets out four models OEMs are currently testing, ordered roughly by how much measurement infrastructure each one requires.
| Model | How It Works | Where It Fits |
| Bundled inclusion | AI features included in the base license at no extra charge | Early-stage AI features still proving value to partners |
| Flat AI add-on | Fixed incremental fee for AI access, regardless of usage | Simple to administer, works when usage is fairly uniform across partners |
| Usage-based metering | Price scales with AI-driven actions, such as API calls or automated workflows completed | AI features with highly variable usage across partners |
| Outcome-based pricing | Price tied to a measurable business result the AI feature produces | Mature AI capability with a clear, attributable outcome |
| Increasing Maturity, Telemetry & Trust Key Takeaway Most OEMs will progress in this order—building telemetry, governance, and partner trust at each stage to unlock more value from AI. |
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What Changes by 2027
The four models above describe where OEMs stand today. What follows is a view of where that picture is headed, based on three shifts already visible in how channel agreements and partner negotiations are being structured. Together, they point toward a market where unpriced AI capability becomes a competitive liability rather than a neutral default.
The first shift is that bundled inclusion stops being a permanent state and becomes a deliberate, temporary one. Bundling will likely remain the sensible choice for new, unproven AI features still building partner trust. What changes is that OEMs will increasingly treat it as a starting phase rather than a default they never revisit, graduating a feature to metered or outcome-based pricing once its value to partners becomes clear and measurable.
The second shift follows directly from the first: usage metering moves from a competitive advantage to a baseline requirement. An OEM without accurate, partner-level usage data will find itself unable to participate in the pricing conversations its better-instrumented competitors are already having, regardless of how strong the underlying AI capability actually is.
The third shift extends that same logic into the channel relationship itself. Revenue-sharing arrangements between OEMs and channel partners will increasingly be priced around AI-driven value specifically, separated out from the base product, since AI features are frequently what allow a partner to charge its own end customers more in the first place. An OEM that cannot isolate and price that incremental contribution enters the negotiation with less leverage than one that can point to a specific, metered number.
What OEMs Need to Measure to Monetize AI Successfully
Usage-based and outcome-based pricing models depend on one capability above all others: measurement. OEMs cannot monetize what they cannot observe, attribute, or audit.
To support future AI monetization models, OEMs should begin instrumenting:
- Model inference volumes to understand AI workload consumption.
- API and transaction volumes to support usage-based pricing models.
- AI feature adoption rates across customers and channel partners.
- Automated workflow execution volumes to measure AI-driven activity.
- Partner-level usage segmentation to distinguish consumption patterns across reseller ecosystems.
- Cost-to-serve metrics to align pricing with infrastructure and model costs.
- Business outcome metrics to support future outcome-based pricing models.
OEMs that establish this telemetry foundation early will be significantly better positioned to move beyond bundled pricing as AI capabilities mature.
What This Means for OEM Roadmaps Now
None of the three shifts above are pricing decisions an OEM can make on the day a partner asks for them. Each depends on instrumentation built well before that conversation starts: telemetry granular enough to separate AI-driven usage from base product usage, and governance clear enough to support an auditable usage claim inside a partner agreement. Both belong on a product roadmap as near-term engineering priorities, not as items to revisit once a specific deal requires them. An OEM that waits for a partner to request usage-based terms before building that instrumentation will be negotiating from a materially weaker position than one that built it in advance, simply because the second OEM can produce evidence the first one cannot.
Where TechBlocks Fits In
Across the OEM engagements we support at TechBlocks, we see the same pattern repeatedly: organizations want to move toward usage-based or outcome-based AI pricing, but discover that the telemetry, attribution, and governance needed to support those models were never designed into the product in the first place.
That is why we encourage OEMs to think about monetization much earlier in the product lifecycle. Decisions around instrumentation, partner-level usage tracking, API architecture, and governance directly influence which commercial models become feasible later. Retrofitting those capabilities after a product has already scaled across multiple partners is significantly more difficult.
Through our AI-Native ISV & OEM transformation approach, we help organizations build these foundations as products evolve. Using TechBlocks AiDE and the Enterprise Data Organization (EDO) operating model, OEMs can instrument AI-driven usage, establish auditable governance, and create the operational visibility required to support usage-based and outcome-based monetization strategies with confidence.
For OEMs, that often means moving AI monetization from a pricing assumption to a measurable, defensible, and scalable business capability.
Is Your Product Architecture Ready for AI Monetization?
Many OEMs discover too late that their products lack the telemetry, instrumentation, and governance required to support modern AI pricing models. We, at TechBlocks, can help you evaluate your current platform, identify monetization constraints, and build the foundations required for usage-based and outcome-based commercial models.
FAQs on Future of AI Monetization
The right answer depends on how proven the feature is. Bundling remains reasonable for new AI capability that is still building partner trust and accumulating usage data. Once usage patterns become clear and the value is demonstrable, separating the pricing typically captures value the Original Equipment Manufacturer is otherwise giving away for free.
Telemetry is the most common blocker. Usage-based pricing only works if the product can accurately separate AI-driven actions from base product usage, and legacy products were rarely instrumented finely enough to do that reliably from the start.
In practice, it tends to strengthen the OEM’s negotiating position, provided the OEM can isolate and measure the AI-driven portion of the value being shared. Without that measurement, revenue-sharing conversations default to whatever the partner is willing to concede rather than to what the underlying data actually supports.
For mature, well-instrumented AI features with a clear and attributable business outcome, yes. For the broader population of OEMs, usage-based metering is the more realistic near-term step, since it asks for less complex attribution work while still moving the business away from flat bundling.
It does, and the underlying monetization principles hold regardless of whether the software ships inside a physical product or as a standalone platform. If anything, the telemetry requirement matters more in hardware-adjacent contexts, since usage visibility is typically harder to obtain once a product is deployed out in the field.
