Only 8% of organizations fully understand the true cost of serving AI features, as forecasting and billing pressures mount.
Companies are racing to turn artificial intelligence (AI) into revenue, but many are doing so without a clear view of what AI actually costs to deliver.
SWEDEN, May 26, 2026 /PRNewswire/ — DigitalRoute’s new global study, AI State of Monetization 2026: The Year Pricing Broke, finds that only 8% of organizations are extremely confident they understand the true cost-to-serve of their AI features, while almost half (47%) cite rising AI-related costs as a top challenge.
The findings reveal a widening gap between AI ambition and commercial readiness. Only 23% say their organization is highly accurate at forecasting AI-related usage, cost and revenue fluctuations, and 61% report that revenue predictability has gotten harder.
This suggests that AI monetization has reached a new phase. In 2025, the central question was whether AI should generate revenue. In 2026, the challenge is whether businesses can price it, measure it, bill it and forecast it well enough to scale without eroding margin.
That shift is reflected in the strategic drivers behind AI monetization. Margin protection is now the leading priority, cited by 35% of respondents, ahead of customer pressure, simplicity, speed to market and competitive parity. Yet only 9% of organizations say nothing needs to change before they can scale AI-driven revenue with confidence.
“AI has moved from innovation budget to commercial reality, but monetization models haven’t caught up,” said Ari Vanttinen, CMO at DigitalRoute. “Every AI interaction creates value for the customer, and a share of that value belongs to the seller as price. The catch: unlike SaaS, where margins scale with users, AI costs scale with usage. And if a business can’t track AI usage and costs with high fidelity, they cannot price it. That’s scaling AI revenue on fragile economics.”
There is no consensus on how AI should be monetized. Fully bundling AI into the existing offer is the largest single approach at 25%, but almost as many organizations (23%) remain experimental or undecided. Paid add-ons account for 18%, API charging for 15% and outcome-based pricing for 12%. The market is active but still fragmented.
This fragmentation creates a further challenge for finance, product and technology leaders. Without a settled pricing logic, organizations must decide which unit of value they are charging for, how usage should be metered and how variable AI costs should be reflected in the customer offer.
High-fidelity usage data is becoming central to that shift. Three-quarters of respondents (76%) say real-time usage data is materially important to AI monetization, yet 38% say managing and leveraging AI usage data is one of their biggest challenges. A further 45% say they need a better data foundation before they can scale AI-driven revenue confidently.
Taken together, the findings suggest that businesses are short on the commercial infrastructure needed to turn AI usage into predictable, profitable revenue. As AI becomes embedded into every digital product, workflow and customer experience, the organizations that succeed will be those that can connect usage, cost, pricing, billing, and forecasting into one coherent operating model.
You can read the full report here: AI State of Monetization 2026: The Year Pricing Broke, or listen to the audiobook version on Spotify, Apple Books, Audible and Google Play Books.
About DigitalRoute
DigitalRoute is the company behind UsageCloud™, the Agile Monetization Platform built to master the full usage‑to‑revenue chain. From raw, messy, multi‑source usage data to recognized, reconciled, and settled revenue, UsageCloud turns every usage moment into trusted, finance‑grade value. More than 400 enterprises across SaaS, AI, cloud infrastructure, mobility, media, and telecom rely on DigitalRoute to power usage‑based, hybrid, and outcome‑based models without ripping and replacing their existing billing or ERP systems. Learn more at www.digitalroute.com.
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SOURCE DigitalRoute
