Outcome-Based Pricing, AI and the Future of Experience Management | CRTV Episode 141

The enterprise technology industry has a new favorite promise: outcomes. Software vendors want to tie pricing to business value, AI is changing how companies understand and serve customers, and enterprise platforms are racing to make agent development easier.

But the big promises raise practical questions. How do you measure an outcome? Can AI predict what customers will want next? And how will platform changes reshape the enterprise software market?

On CRTV Episode 141, Constellation analysts explore these questions across three conversations.

Outcome-based pricing sounds good. The details get complicated.

In this week’s Great Debate, Larry Dignan, Martin Schneider, and Holger Mueller challenge the growing enthusiasm for outcome-based software pricing.

The appeal is obvious. Customers want technology investments to deliver measurable business results, and vendors want compensation that reflects the value they deliver. The difficulty lies in agreeing on what counts as an outcome and determining how much credit software deserves.

For example, a higher volume of sales quotes is an output. Winning more profitable deals is an outcome. Many variables shape the path between the two, including customer behavior, deal complexity, and the broader business environment.

The panel argues that measuring transformation requires more than digitizing an existing process. Organizations need baselines, relevant business metrics, and a way to connect technology changes to measurable results. Even then, customers may prefer predictable costs over sharing a portion of their business outcomes with software vendors.

The takeaway: outcome-based pricing may work in specific situations, but it is not a simple replacement for traditional software pricing.

Qualtrics sees experience management shifting from hindsight to prediction

Ray Wang’s conversation with Qualtrics CEO Jason Maynard explores a bigger change: AI could move experience management from explaining past behavior to predicting future outcomes.

Traditional experience management has focused on collecting feedback and interpreting metrics such as Net Promoter Score and customer satisfaction. The emerging opportunity is to simulate experiences, test alternatives, and anticipate customer responses before problems occur.

Maynard describes a future in which organizations can run more simulations, test ideas at greater scale, and reduce the cost of experimentation. Synthetic data and synthetic personas could expand that capability, while human behavioral data and organizational context help make predictions more useful.

Context matters. AI predictions need to work alongside governed systems and business rules rather than operate in isolation. The goal is not simply to generate another dashboard or insight. It is to put useful intelligence into the systems where people make decisions and act.

That could help organizations identify when to intervene, personalize interactions, and prevent frustrating customer experiences before they escalate.

The competitive opportunity goes beyond a better customer survey. It is the ability to build trusted experiences that strengthen customer relationships and lifetime value.

Workday Rising puts the platform in focus

Larry Dignan and Holger Mueller close the episode with a look at Workday Rising and the company’s evolving enterprise platform strategy.

Their discussion covers Workday’s new entry points, data foundation, expanded APIs, and the opportunities these capabilities create for agent development. They also examine monetization, expansion, and Workday’s ambitions around identity for both employees and AI agents.

The broader question is how quickly customers will build and deploy agents on the platform, and how Workday’s approach will compete and intersect with adjacent enterprise software markets.

For enterprise buyers, platform openness and choice matter as organizations continue experimenting with AI. For vendors, the challenge is turning platform investments into customer adoption, growth, and sustainable monetization.

From software pricing economics to AI-driven customer experiences and the next phase of enterprise platforms, this episode examines the gap between technology promises and business results.