AI enables a fundamental shift in business models away from selling access (per seat) or usage (per token) towards selling results. For example, customer support AI will be priced per resolved ticket. This outcome-based model will become the standard as AI's capabilities for completing specific, measurable tasks improve.

Customers now expect DaaS vendors to provide "agentic AI" that automates and orchestrates the entire workflow—from data integration to delivering actionable intelligence. The vendor's responsibility has shifted from merely delivering raw data to owning the execution of a business outcome, where swift integration is synonymous with retention.

The initial enterprise AI wave of scattered, small-scale proofs-of-concept is over. Companies are now consolidating efforts around a few high-conviction use cases and deploying them at massive scale across tens of thousands of employees, moving from exploration to production.

The market is rejecting 'lame co-pilots' that provide minor workflow improvements for an extra fee. Successful AI products create entirely new, powerful use cases and deliver substantial, tangible value on day one, justifying their place in the budget.

High-ROI AI products are changing B2B buyer expectations. The old model of signing a contract before a long, uncertain implementation is dying. The new standard, which even Salesforce's CEO envies, is for customers to go live and experience the product's value *before* committing to a purchase.

KPMG's survey shows a decline in reported AI agent deployment (from 42% to 26%). This counterintuitive drop likely reflects a more sophisticated enterprise understanding of what constitutes a 'true' agent versus a simple automation. Companies are becoming more realistic about agentic complexity and implementation challenges.

Reporting from Davos reveals a disconnect between public AI hype and private executive sentiment. Tech leaders see enterprise AI adoption as "early and slow." The focus is moving from "panacea" solutions towards targeted, vertically-focused agents that can deliver measurable results, indicating a more pragmatic market phase.

Beyond upfront pricing, sophisticated enterprise customers now demand cost certainty for consumption-based AI. They require vendors to provide transparent cost structures and protections for when usage inevitably scales, asking, 'What does the world look like when the flywheel actually spins?'

AI companies are pivoting from simply building more powerful models to creating downstream applications. This shift is driven by the fact that enterprises, despite investing heavily in AI promises, have largely failed to see financial returns. The focus is now on customized, problem-first solutions to deliver tangible value.