Enterprise software companies report huge AI revenue growth, but this is often a sales tactic. Systems like Workday's 'flex credits' are packaging innovations designed to capture AI budget from CIOs, not fundamentally new, agentic experiences that transform how work gets done.

While 2023 was a grace period for AI adoption, the tools matured significantly in 2024. Companies that failed to leverage agentic AI products to re-accelerate growth are considered to have fundamentally underperformed, as the opportunity was clear and present.

Sridhar Ramaswamy suggests software valuation multiples are contracting because investors see through the strategy of just adding an 'AI SKU.' The market believes this approach won't win, favoring integrated, consumption-based models where customers only pay for demonstrated value from AI.

For established software companies, simply integrating AI is not enough. Investors are looking for a clear signal that AI is a true growth catalyst, not just a feature enhancement. The key question investors ask is whether AI will re-accelerate the company's growth. Without tangible proof in sales numbers, investor sentiment will remain neutral or bearish.

In a market where customers eagerly pay for valuable AI tools, an inability to monetize new AI features is a major red flag. It indicates the product lacks sufficient value. A key test is whether AI can drive average revenue per user (ARPU) up by 50% or more; anything less is just a feature, not a transformation.

The stock market's enthusiasm for AI has created valuations based on future potential, not current reality. The average company using AI-powered products isn't yet seeing significant revenue generation or value, signaling a potential market correction.

The traditional SaaS "Rule of 40" (Growth + Margin) is insufficient for the AI era. A better heuristic to gauge a company's AI leadership is to combine the percentage of its sales derived from AI with its market share in that specific AI category.

Despite the hype, the vast majority of companies are applying AI to secondary operational tasks, like automating support tickets. Very few have use cases that directly drive core business KPIs like revenue, retention, or win rate. The focus is on automating existing processes rather than enabling entirely new, revenue-generating capabilities.

Simply incorporating AI features is "performative." The true measure of being an AI company is whether the technology has tangibly re-accelerated revenue growth. Without that lift, the AI label is meaningless to investors and the market, as demonstrated by Meta's successful turnaround.