By Alistair Barr
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- AI is shifting software valuations away from annual recurring revenue toward new metrics.
- Traditional ARR is losing relevance as AI-driven usage and outcome-based business models emerge.
- Investors now prioritize AI leverage, operational efficiency, and customer productivity gains.
Startups and other tech companies love to boast about "annual recurring revenue." AI could make this metric obsolete, though.
According to a new report by consultant AlixPartners, investors are on the cusp of abandoning the traditional ARR-multiple playbook that defined the SaaS era. In its place will emerge hybrid valuation models that reward companies not for the size of their subscription base but for how effectively they use AI to elevate customer outcomes.
For decades, ARR served as the bedrock for valuing enterprise software firms. It measures revenue from subscriptions by taking the value of current contracts and extrapolating that out over a full year.
AlixPartners now argues that ARR is becoming increasingly "meaningless" in an AI-first economy, especially as usage- and outcome-based business models replace the per-seat licenses that have dominated the SaaS industry.
The big change is related to how expensive AI models are to run. Every time a new AI software service taps into this intelligence, the provider has to pay a per-token price. That makes fixed, per-seat SaaS subscriptions tougher to offer.
This means revenue could fluctuate more in the future, because it will be based on consumption rather than fixed contracts. This makes the "recurring" part of the ARR equation a much less reliable proxy for durable value.
AlixPartners says investors are already shifting focus toward a hybrid valuation approach in the AI era:
• AI leverage ratios — These measure how effectively companies convert AI investments into revenue and margin gains. Rather than rewarding scale for its own sake, investors will reward operational efficiency and automation-driven profitability.
• Outcome-based performance benchmarks — Metrics such as customer margin expansion, reduced task completion time, or increased throughput will matter more than raw seat-based user counts.
• Traditional ARR multiples — Still relevant but no longer sufficient on their own.
New forecasting metrics, such as "time to usage," "usage ramp rate," and "usage volatility," are emerging to help investors gauge how quickly customers adopt AI features and how stable their consumption patterns are over time.
The message is clear: In the AI era, value follows impact. Companies that can demonstrate real productivity gains for customers and operational leverage for themselves will earn premium valuations. Those clinging to legacy ARR-driven models risk being left behind as investors pivot to frameworks that better capture the economics of intelligent software.
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