In enterprise sales, the user and buyer are different people. While the user needs a problem solved, the buyer needs a business outcome that advances their career. Product managers must identify and build for the metric that makes their buyer look good—like cost savings or productivity gains—to secure the sale and ensure product success.

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.

While preventing a single multi-million dollar mistake is a product's biggest value, it's easier to sell based on quantifiable time savings. The justification "this costs one-fourth of a new hire" is a straightforward business case for a budget holder, making the sale simpler.

Traditional "value-based selling" is obsolete. In an AI-driven market, customers demand tangible, immediate results, not buzzwords. A sales rep's only true value is their deep product expertise—the ability to deploy the tool, troubleshoot, and demonstrate ROI firsthand. Reps who lack this are being bypassed in favor of those who can actually deliver.

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.

The bar for new AI products is exceptionally high. Customers expect transformative results, like replacing multiple hires or generating six-figure revenue on day one. Products offering only incremental productivity gains will be ignored by a market flooded with high-ROI options.

The future of technology sales, particularly AI, is not about selling infrastructure but about solving specific business problems. Partners must shift from a tech-centric pitch to a consultative approach, asking 'what keeps you up at night?' and re-engineering customer processes.

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.

The trend is shifting from simply adopting AI to proving its ROI with specific metrics. As industry leaders publicly share their AI-driven gains, it creates a competitive necessity for all other companies to follow suit and quantify their own benefits, making it 'table stakes' for all.