How to convey generative AI product value with outcome-based pricing and messaging – News

In the rapidly advancing world of technology, effectively conveying the value of generative artificial intelligence products demands a shift in traditional pricing and messaging strategies.

Some background: Despite the growing trend of pricing software-as-a-service products based on capability and customer value, per-seat pricing remains dominant, and outcome-based pricing is rare. Successful marketing and selling hinge on value stories and economic value assessments, yet widespread adoption across tech providers and sales teams remains limited. However, gen AI products, which inherently deliver direct outcomes, necessitate a change in approach.

To convey the value of gen AI solutions effectively, tech providers must transition to outcome-based pricing models. Traditional pricing structures, such as onetime purchases or subscriptions, fail to capture the true potential of gen AI, which is inherently designed to deliver outcomes that were previously unattainable or time-consuming. As venture capital fuels innovation and the demand for profitability increases, some forward-thinking vendors are already shifting toward pricing strategies that reflect the value of the outcomes delivered.

In the realm of gen AI, predicting the exact resources needed to achieve a specific outcome is challenging, as the requirements can fluctuate. This unpredictability underscores the inadequacy of per-seat pricing models, which struggle to convey the overall value and return on investment across numerous individual users.

With the rise of agentic AI capabilities, the focus will shift from individual seats to the outcomes achieved, rendering traditional seat-based pricing irrelevant. In some instances, AI agents will replace human tasks, further complicating pricing by agent as a measure of value delivered.

Stages of gen AI pricing

The transition to outcome-based pricing will occur in stages. Many gen AI products and add-ons are already adopting consumption-based models, where pricing is determined by the credits used or the functions performed, such as minutes of video processed or tasks completed.

For instance, some companies have already implemented consumption-based pricing for its AI capabilities, targeting specific outcomes like conversations. Other companies are charging based on the percentage of chargebacks recovered by its product, while others are pricing services based on successful resolutions achieved through automation. These examples illustrate the shift toward pricing models that align with the tangible benefits gen AI solutions provide.

Ultimately, the value of a gen AI solution lies in the outcomes it delivers, even if differentiation among offerings is found in the nuances of their models, training and tuning. Much like quantum computing, the value of gen AI is determined by its ability to achieve results that are otherwise nearly impossible with standard methods.

Consider the immense value of saving tens of millions of dollars through contract review automation or synthesizing a novel protein. The marketing and selling of gen AI solutions will naturally gravitate toward the outcomes desired by customers, focusing on the end results rather than the tools and components required to achieve them.

Also focus on value propositions

Shifting to outcome-based pricing models necessitates a parallel evolution in value propositions and narratives. Marketers and sellers will need to deepen their understanding of their customers, their processes and the value associated with desired outcomes.

This approach requires engaging in conversations about the outcomes that gen AI products deliver, rather than focusing solely on the methods of delivery. Though technical details may still be important for certain buyers, such as compliance teams, the emphasis will be on the value of the outcomes themselves.

Proficiency in value assessment and calculation will become essential, as the value of the outcomes sold will be the key factor in persuading buyers. Fortunately, value management tools can assist product marketers in four crucial areas: discovery, identifying value levers, articulating value and realizing value.

As agentic AI becomes more prevalent, gen AI-driven outcomes will often replace other resources such as people, applications and time. This shift requires evaluating the value of outcomes and the ROI compared with traditional methods.

When gen AI achieves tasks that were previously impossible, discussions will center on how much of the outcome value should be captured by the vendor. The focus will be on the outcome and its business value to the customer, making value calculations and discussions more concrete. Unlike traditional value hypotheses or business cases, where the link between technology and outcomes is speculative, gen AI products and services start with the outcome, simplifying the value proposition and enhancing its clarity.

The emergence of “value agents” capable of reporting real-time value realization will further streamline this process, drawing parallels with existing technologies like robotic process automation that already demonstrate value through efficiency gains. By focusing on outcomes, tech providers can offer compelling narratives that resonate with customers, highlighting the concrete business value gen AI solutions provide.

David Yockelson is a distinguished VP analyst, research vice president, and Gartner Fellow within the Product Marketing Practice team in the Technology and Service Provider Research organization. He wrote this article for News. Gartner analysts will provide additional analysis on how technology service providers can accelerate growth, drive product innovation and leverage emerging technologies at the Gartner Tech Growth & Innovation Conference, taking place March 10-11 in Grapevine, Texas. Follow news and updates from the conference on X using #GartnerTGI.

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