AI has made pricing complicated again. In SaaS, sellers could rely on predictable usage and stable costs. With AI, both sides are uncertain. Buyers don’t
Pricing AI is hard because no single metric captures how value is created and how costs behave. A per-user fee feels simple but ignores automation.
Pricing metrics shape how buyers perceive value and how companies capture it. Choosing the right one is one of the hardest decisions in any business,
When private equity firms evaluate an acquisition, they focus on metrics such as revenue growth, margin trends, and customer concentration. Pricing gets a passing glance:
Businesses love to talk about “market segments.” The term sounds tidy, strategic, even scientific, as if you could carve a market into perfectly labeled slices.
Business school professors love to preach that companies exist to maximize shareholder value. The idea is simple enough: shareholders take the risk, so the company
With SaaS, per-seat pricing was simple, predictable, and familiar to buyers. It became so common that many equated “SaaS pricing” with “seat-based pricing.” But AI
Every day a new vendor pops up promising “AI powered” this or “machine learning” that. Budgets are being carved out, boards are asking about AI
A pricing metric is really the intersection of two perspectives: how the buyer measures value (their KPIs) and what the seller can and does measure
You’re sitting in a conference room facing a software purchase decision. Two vendors, similar products. Vendor A costs 15% more but has a proven track
