Will Outcomes-Based Models Accept the Blame?
Limits to paying only for the results that count
Imagine signing a contract that promises “no results, no bill,” only to discover later that “results” were defined by someone else, measured with flawed data, and delivered through a system you barely control. Outcomes-based models promise perfect alignment: you pay only for success, and your solution provider is fully on the hook. But what happens when the lights go out, the dashboard goes dark, or the market shifts? Who’s left holding the bag?
We’ve received a great deal of inquiry on the topic of outcomes-based or results-based business models since the advent of “agentic AI.”
In this post, we’ll strip away the slick marketing and ask the uncomfortable questions lurking behind every “outcomes guarantee.” We’ll explore why attribution remains a tug-of-war game, how enterprises reconcile budget certainty with variable returns, and why the very design of these models may bake in a premium that feels more like a penalty. Ultimately, we’ll ask: can outcomes-based contracts truly absorb blame, or do they simply repackage risk in ways that leave both parties blaming someone else?
Complexity of Attribution and Measurement:
The "Who Gets the Credit?" Problem: In complex enterprise environments, many factors contribute to a business outcome (e.g., increased sales). How much of that is due to the solution versus, say, a new marketing campaign, a strong sales team, or market conditions? Clearly defining and attributing the deep learning or so-called agentic AI’s direct impact can be incredibly challenging and often leads to disputes.
"Success" is Contested: What one company considers a "successful resolution" or "efficiency gain" might be different for another, and codifies a worldview. Who decides what types of gains count? When? Under which constraints? Pre-defining granular, traceable metrics is essential for outcomes-based models, but if every company or situation is different, it will be hard to achieve scale outcomes.
Data Quality and Granularity: To accurately measure outcomes, you need a robust data infrastructure and high-quality, granular data. Many organizations, even those that call themselves AI-native, still struggle with data silos, inconsistent data quality, and a lack of real-time data capture, which makes outcome measurement tenuous.
Designing attribution isn’t simply a matter of better instrumentation; it’s a socio-technical choreography. Whose voices shape the measurement logic? How might that logic shift as relationships evolve?
Risk Aversion and Control for Enterprises:
Budget Predictability: CFOs and procurement teams often prioritize predictable costs. Outcome-based models, by their nature, can introduce cost variability if the outcomes fluctuate. Large enterprises, with their complex budgeting processes, often prefer fixed or usage-based pricing that offers more certainty, even if it means less direct linkage to a specific outcome.
Shared Accountability, but Unequal Control: While outcomes-based models encourage shared accountability, the client still often holds significant control over factors influencing the outcome (e.g., change management, data integration, internal processes, user adoption). If the client doesn't fully enable the outcomes-based solution, the vendor might not achieve the outcome and thus not get paid, even if their technology is sound. This risk profile is the biggest reason why vendors fail to launch an outcomes-based model.
Why Outcomes Models are Often Priciest and Not Price Disruptive
Risk Premium for the Solution Provider:
When a solution provider takes on the risk of delivering a specific outcome (and not getting paid if they don't), they bake that risk into their pricing. This "risk premium" means that unless they are subsidized to under-price their offerings (via an aggressive winner-take-all VC-funded strategy), outcome-based models are inherently more expensive than traditional time-and-materials or fixed-price contracts, where the client assumes more of the performance risk.
The solution provider is effectively guaranteeing a return on investment (ROI), which is a high-value proposition.
Complexity of Implementation and Monitoring:
Implementing an outcomes-based contract requires significant upfront work: detailed discovery, precise outcome definition, agreement on measurement methodologies, and often custom integration and dashboards to track performance. This adds to the solution provider’s operational costs, which are then passed on to the client.
Ongoing monitoring, reporting, and potentially re-negotiating if conditions change also contribute to the higher cost, or providing “true-ups” to calibrate what happened vs. what was planned.
Limited Market Readiness:
While appealing in theory, many enterprises are still not fully equipped with the internal processes, data infrastructure, or cultural mindset to engage effectively in pure outcomes-based contracts. This limits widespread adoption and means that the early adopters willing to take on these models are often larger, more mature organizations with deeper pockets. Instead, they remain a specialized offering for clients seeking maximal alignment and ROI assurance.
To Be Sure, there’s Paid
An emerging ecosystem of VC-backed, so-called AI-native companies is racing to deliver outcomes and attribution solutions without relying on external consultants or complicated maneuverings of finance teams. Take Paid, for example: it claims its AI can pinpoint margin-improvement opportunities and assign credit for results end-to-end. Yet even tools built for narrow, predictable use cases will still run into the strategic and tactical roadblocks we’ve outlined here.
In essence, outcomes-based models powered by various machine learning and deep learning systems represent a potential progression to measure what counts in the interactions between a solution provider and customer. However, their current position as a "premium tier" or worse “surprise, a higher bill” rather than a "disruptive low-cost option" is a direct reflection of the inherent complexities, risks, and high-value proposition they entail within the current enterprise landscape. These challenges, from the complexities of attribution and measurement to the realities of risk aversion and limited market readiness, underscore the need for a more evolved approach.
This is where Contribution Design emerges as a critical and transformative pre-sales activity to align your solutions with strategic customer priorities. By shifting the focus from mere transactional engagements to a deliberate and upfront effort to define how key participants and processes will collaboratively contribute to and benefit from the desired outcomes, we can actively mitigate the very issues that keep standard contracts and business models from greater coherence towards shared outcomes.
If we accept that every aspect of an outcomes-based model is a deliberate design choice of measurement logic, risk allocation, and collaboration workflows, then Contribution Design invites us to:
Surface Power Dynamics: Who decides what counts?
Co-create Measurement Frameworks: Evolve them as relationships mature.
Design Shared Governance: Build living contracts that adapt to emergent realities.
By reframing “shared accountability” into a concrete, participatory process, we move from transactional supplier–buyer encounters to generative partnerships. That’s where outcomes-based models can transcend “premium surprise” and become sustainably transformative.
Curious how this looks in practice? Reach out to explore how Reason Street can help you design your next business model as a shared journey rather than a predetermined curve.