AI isn’t just changing what companies sell. It’s changing how value is priced, delivered, and owned.
That tension first surfaced at ELEVATE, the Customer Value Community’s annual gathering of senior GTM leaders from companies like ServiceNow, Salesforce, Adobe, and HP. At ELEVATE, J.B. Wood, President & CEO of TSIA, introduced new research from TSIA on AI Economics to a room of leaders navigating these shifts in real time. The conversation continued in our recent TSIA & Ecosystems webinar, AI Economics & the Future of Outcome-Based Offers, where J.B. Wood was joined by Chad Quinn, CEO & Co-Founder of Ecosystems, to expand on that research with practical observations from the field.
Informed by live polling and practitioner examples, the discussion reinforced a clear reality: AI is forcing a structural shift in how value is defined, delivered, and governed.
Below are five key takeaways drawn directly from the discussion.
1. Traditional Pricing Assumptions No Longer Hold in an AI-Driven Market
A foundational insight from the session was that long-standing pricing models, particularly per-seat and user-based pricing, are becoming misaligned with how value is actually created. Companies can no longer assume that customer growth automatically leads to more licenses or users. Many organizations are seeing revenue grow while customer headcount stays flat or declines. In that environment, relying on legacy pricing models becomes:
“A path to destruction, not a path to growth.” — J.B. Wood, President & CEO of TSIA
This is why live polling during the session showed that most organizations are actively exploring hybrid, value-based, or outcome-based pricing, even if they are still early in maturity. Pricing is increasingly viewed not as a commercial afterthought, but as a core design decision tied to outcomes.
Ecosystems’ platform helps address this gap by providing a closed-loop way to define outcomes with customers, quantify value using real data, and track progress across the lifecycle, giving teams the evidence they need to support pricing decisions grounded in demonstrated results.
2. AI Will Create a Services-Led Era, Not a Services-Free One
According to recent TSIA research, AI does not eliminate the need for services. Instead, it reshapes where and how services show up.
While AI can automate portions of traditional service delivery, J.B. Wood cautioned against assuming those efficiencies translate directly into higher margins:
“This whole thing is going to be a services-led offer.” — J.B. Wood, President & CEO of TSIA
He emphasized that any labor savings achieved through AI must be reinvested into new capabilities that help customers succeed:
“Whatever savings you get, you have to quickly reinvest in new capabilities.” — J.B. Wood, President & CEO of TSIA
The implication for the community is clear: organizations will need services that support outcome design, activation, realization, and ongoing evolution, not just implementation.
3. AI Shifts Outcome Ownership Closer to the Provider
One of the most consequential shifts discussed was how AI changes accountability. Historically, providers delivered insights while customers owned decisions and outcomes.
“Now, not only are we going to generate the data and the insights, but we're going to have our AI capabilities look at that data and insights and actually make a decision and take an action.” — J.B. Wood, President & CEO of TSIA
When AI takes action, providers are inherently closer to owning the outcome. For community members, this reinforces why outcome-based offers raise both opportunity and risk. Higher upside comes with greater responsibility for governance, transparency, and shared accountability with customers.
4. Outcome-Based Models Require Closed-Loop, Governed Value Systems
A critical warning surfaced around the use of public LLMs for value estimation without governance.
Chad Quinn shared research showing that identical prompts can yield very different results, which is a phenomenon known as structural randomness. In the context of value commitments, you cannot have structural randomness when it comes to value.
“It has to be closed loop. It has to be secure. And it has to be a system of record.” — Chad Quinn, CEO & Co-Founder of Ecosystems
This underscores a key requirement: outcome-based pricing and value storytelling only work when outcomes are defined, quantified, and tracked in a consistent, governed way across the lifecycle.
This is the problem ViViEN™ was designed to address. Rather than relying on one-off prompts or moment-in-time outputs, ViViEN™ operates as a closed-loop system of record for value, providing a consistent, secure environment where outcomes are defined, quantified, and tracked using governed logic that can be trusted across sales, customer success, and executive conversations.
5. Go-to-Market Roles and Motions Will Be Fundamentally Reshaped
Finally, the session made clear that AI Economics is not just a pricing or services issue. It is an organizational one.
TSIA introduced a shift away from traditional lifecycle thinking toward a Design → Activate → Realize → Evolve motion.
This reframes how sales, services, customer success, and value teams interact with customers. Forward-deployed roles and value-focused teams are moving closer to the customer and closer to the outcome itself.
For many in the community, this reflects what they are already experiencing: blurred lines between functions and a growing need for shared value language and shared systems.
The shift to AI Economics is already underway. As this conversation showed, the question is no longer if pricing, services, and GTM models will change, but how quickly organizations can adapt with discipline and governance.
For teams looking to take the next step, Ecosystems’ platform, including ViViEN™, provides a way to operationalize these principles by helping organizations define, quantify, and track customer outcomes in a closed-loop system of record. Contact us here.
