Enterprise AI: Transitioning to Outcome-Based Pricing

Enterprise AI is witnessing a pivotal change this year, focusing more on what buyers are willing to invest in rather than just the price per token. The CFO of OpenAI has shifted dialogue towards the value of “useful intelligence per dollar,” suggesting AI should be evaluated based on the tasks it accomplishes instead of mere usage statistics. By August 2026, leadership communicated to investors that the era of “tokenmaxxing” was over, as companies began directing routine tasks to the most cost-effective models while saving advanced models for complex jobs that require their capabilities.

The Shift from Effort to Value

The underlying reason for this change was that effort and value were never closely linked. Experts observing the industry highlight that an agent that completes a fraction of deals while utilising significantly fewer tokens remains the one deserving to be compensated. Counting tokens measures output but not the journey taken. Sierra, a customer service agent firm, already charges based on this principle, setting a fixed fee for successfully resolved interactions while incurring no cost when a matter escalates to human intervention. Salesforce has also begun reporting remarkable figures in Agentic Work Units, defined by the tasks accomplished rather than the user licenses occupied.

Value-Based Pricing: A Dual Perspective

Value-based pricing serves both sellers and buyers, and analysts from Constellation Research have noted that in the past, it has often benefitted vendors more than clients. The critical inquiry goes beyond whether the measurement is evolving; it is about whether the results manifest effectively.

Interface Evolution into Agents

As pricing shifts towards outcomes, products are being redefined. Instead of focusing solely on the interface, the emphasis is now on the agent that generates results. Founders in AI describe the essential component not as the API or a button’s action, but as the system’s framework—the skills, documentation, and rules that encapsulate how experts effectively engage workflows and derive value. In this context, future applications focus on this underlying framework rather than just on visual interfaces.

Claudeforce: A Case Study

Claudeforce embodies this trend as a revolutionary product. Announced on 26 August 2026, it positions Anthropic’s Claude as the primary reasoning engine within Salesforce, integrating the platform’s data, actions, and regulated workflows into Claude through a layer termed AIforce. This comes equipped with 37 preconfigured sales skills. Sellers can conduct pipeline reviews or prepare deals without ever needing to access Salesforce’s user interface. Observers noted that clients might never again need to engage with the Salesforce app directly. The market perceived this as a strength rather than a challenge, with Salesforce announcing that Agentforce’s annual recurring revenue had surpassed $1.5 Bn, up more than 240% year on year, as the CEO asserted that discussions of a software apocalypse should be put to rest.

The Evolution of Billing

Founders should recognise how resilient billing practices have become. The framework effectively transforms a client’s accumulated organisational knowledge into transferable skills that disseminate throughout the entire enterprise. This encoded knowledge, as significant as model weights, is what becomes difficult to replicate. Interestingly, today’s agents, often characterised by disorganised notes instead of structured databases, highlight how messy accumulated context contributes to preserving institutional memory.

Challenges with Departmental Boundaries

The aspiration for a singular framework encompassing an entire organisation grapples with a longstanding challenge; centralising knowledge within a single database has historically faltered at enterprise scale. Record systems have dominated individual domains—Salesforce in sales, SAP in procurement, Workday in HR—but none have evolved into the company’s central brain. Consequently, agents reflect these departmental boundaries. Claudeforce epitomises the agent-driven expression of CRM, but not of the entire enterprise, and the same is likely true for every system designed to assist agents.

Barriers to Universal Solutions

The persistence of these boundaries is rooted in organisational structures as much as in technological limitations. Companies’ organisational charts dictate that no single person oversees the entire process, leading AI to be integrated into silos rather than restructuring the workflows connecting them. Thus, a truly universal enterprise framework remains contingent on a reorganisation most companies have yet to pursue. This is why immediate victories are more likely to arise not from a universal agent, but from teams that manage a defined framework and the outcome unit within a well-defined, high-value domain. This represents a more achievable goal and is where new entrants are gaining market share before established companies can realign themselves around agents.

The Case for Frontier Agents

Despite this, there is merit in exploring the potential of frontier agents, which can already operate across disparate systems, extracting data from multiple sources without a unified database. This capability tends to challenge rigid barriers. However, the trust, governance, and audit trails required by regulated enterprise buyers still reside within individual record systems, thereby prolonging these boundaries beyond what raw capabilities might indicate.

Monetising Outcomes and Their Impact

Outcome-based pricing and deep integration represent the same concept viewed from differing perspectives. When a platform is embedded sufficiently to price results, it simultaneously becomes capable of measuring potential gains. OpenAI has articulated that as intelligence permeates fields like scientific research and financial modelling, licensing agreements and outcome pricing will be tied to “sharing” the value generated. A platform significantly contributing to a breakthrough is likely to transition from charging for service to claiming a portion of the achieved result.

The Deepening Dependency

Dependency grows more profound as an agent becomes increasingly embedded within a workflow, making it challenging to extricate. The more closely a supplier can link their outcomes to the agent, the stronger their position upon contract renewal. This lesser impact is associated with agents that manage continuous relationships, evolving beyond singular interactions. The ability of an agent to maintain a territory strengthens the argument for sharing the value produced within that territory.

Balancing Attribution Concerns

Attribution poses a complex challenge, impacting both ends of the discussion. A successful sale or cost-saving results from a multitude of factors—including marketing efforts, seasonal influences, and human judgement—not solely the model used. This complexity currently safeguards the customer, but the embedded platform retains the most comprehensive record of the process, positioning it advantageously for future attribution debates.

Reasserting Customer Control

In response to these challenges, the notion of governance over business models has gained prominence, emerging as a stronger concept than previously discussed data sovereignty. Even clients without specific regulatory demands find valid reasons to retain AI models within their control, as this prevents suppliers from unilaterally altering their financial benefits. Ownership now serves as a legitimate commercial safeguard, moving beyond only compliance.

The Practical Shift in Deployment

The practical realities now align with this logic. Open models are increasingly capable of managing substantial volumes of enterprise tasks, and on-premises or private cloud solutions have achieved cost efficiency at stable volumes through hybrid frameworks that allocate advanced models for high-value tasks. Prosus has publicly revealed reductions in inference costs by as much as 26 times by transitioning suitable workloads to open models. A new class of service providers is already emerging to cater to this demand—specialists dedicated to hosting AI workloads within a client’s own infrastructure, catering specifically to regulated enterprises.

India’s Emerging Opportunity

This landscape presents a unique opportunity for India, building on a service export industry by accommodating various clients’ software needs. The AI iteration of this industry surpasses its predecessors in breadth, as implementation efforts now extend into model selection, private adjustments, assessment, integration, and compliance documentation—all executed on infrastructure determined primarily by clients. The Global Capability Centers already established within these enterprises are well-positioned to leverage this opportunity, alongside existing frameworks designed to support scalable AI operations.

Clarifying Human vs. Agent Comparisons

As previously discussed, this ongoing dialogue fills an essential gap. Enterprises should move towards compensating outcomes rather than efforts, defining such outcomes based on their own criteria. This transitional economy dictates who retains the value generated from these outcomes. Significant advancements arise as enterprises pivot to paying for delivered results instead of consumed tokens, facilitating direct comparisons between a single agent and a human regarding the same outcome metric.

The Nuance of AI Productivity

However, a caveat exists to temper this enthusiasm. The basic unit of AI productivity revolves around processes rather than individual tasks, a distinction that may be complex. Outcome-based pricing enables precise task comparisons but does not allow for a straightforward one-to-one substitution, providing a more valuable understanding than both simplistic assessments of workforce size and exaggerated claims of stagnation. The opportunity for Indian founders lies in creating a framework that allows businesses to accomplish their objectives without relinquishing primary control to machines, while also enabling rapid scaling in line with shifting paradigms.

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