Enterprise organizations are not rejecting AI.
Glean, a company often described as the Google for enterprise, said it has reached $300 million in annual recurring revenue (ARR), a three-fold increase from the $100 million milestone it reached just 15 months ago.
While many AI startups are growing at a blistering pace, Glean’s progress is particularly remarkable. After years of essentially being the only player in the category, the seven-year-old startup is accelerating its growth as tech giants enter the enterprise AI search market with rival products.
“The first four or five years of our existence, we had no competition,” Glean CEO Arvind Jain told TechCrunch. “Given how important search is to make AI work in the enterprise, every single company in the world wants to be in this space.”
Tech heavyweights building Glean-like tools include Google, Microsoft, OpenAI, Anthropic, Salesforce, and Atlassian.
Jain maintains that there’s value in being a first mover in the space, but that it’s also equally important to offer a better product.
What Glean does better than its competition, according to Jain, comes down to the deep understanding that its AI tools have of customers’ business needs. Glean’s AI achieves this knowledge — a concept captured by the new, popular term “context graph” — by connecting to and learning from enterprises’ internal software systems.
Jain claims that Glean’s context graph also helps enterprises cut AI computing costs.
“If you connect your AI to Glean, it gives you all the information that you need to do your work, and that results in AI consuming far fewer tokens compared to if you unleash AI onto your systems directly,” Jain said. That’s because with Glean, AI ends up performing fewer operations, he added.
At a time when many companies are blowing through their AI budgets, those token cost savings have become a major selling point for the company.
“One of the things you know our customers really like about Glean is the fact that we can reduce your AI bill significantly,” he said.
The company, which was last valued at $7.2 billion when it raised a $150 million Series F last June, offers various pricing structures to its customers, which include Databricks, Reddit, Pinterest, and Samsung.
According to Jain, Glean offers both a consumption-based model, where clients pay per use, and a hybrid model that combines a fixed monthly fee for active users with separate usage fees for model consumption.
Glean is definitely not the first company to do this, but it’s worth pointing out that the company’s $300 million milestone cannot be fully described as traditional ARR, because a consumption model by definition doesn’t have a strictly recurring component.
Pure consumption pricing models depend on fluctuating user activity rather than predictable subscription renewals, therefore a portion of Glean’s topline is more accurately described as an annualized revenue run rate.
Glean did not immediately respond to a request for comment; this post will be updated if the company replies.
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Marina Temkin is a venture capital and startups reporter at TechCrunch. Prior to joining TechCrunch, she wrote about VC for PitchBook and Venture Capital Journal. Earlier in her career, Marina was a financial analyst and earned a CFA charterholder designation.
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Enterprise organizations are not rejecting AI. They are rejecting operational instability.
That is the shift many founders still misunderstand — and it is becoming one of the defining realities separating enterprise AI companies that scale from the ones that stall after early momentum.
For the last several years, AI startups benefited from a market driven by experimentation. A strong demo, an impressive model, and a powerful vision were often enough to generate enterprise interest, pilot programs, and investor enthusiasm.
But enterprise AI is entering a different phase now, one where enterprises are no longer evaluating whether AI is exciting. They are evaluating whether it is safe to deploy broadly.
At TechCrunch Disrupt 2026, taking place October 13–15 at Moscone West in San Francisco, Arsalan Tavakoli-Shiraji, co-founder and SVP of field engineering at Databricks, will unpack that shift during his AI Stage session, “The Enterprise Isn’t Broken. Your Assumptions About It Are.”
Image Credits:TechCrunch
Disrupt will bring together 10,000+ founders, investors, and operators to explore the technologies and operational pressures changing how companies are built and scaled. The three-day event will feature 250+ sessions across six stages, led by tech leaders directing the industry today.
Explore the sessions appearing on the Disrupt AI Stage. Ticket savings of up to $410 end on May 29 at 11:59 p.m. PT. Register here.
The pilot was never the hard part
The enterprise AI market is full of successful pilots that never became real deployments. Not because the technology failed. But because the organization could not absorb the operational consequences of adopting it.
Now the reality founders need to face is that startup AI deals rarely die because the model underperformed. They die because the enterprise lost confidence in what the deployment would require.
That is the gap Tavakoli-Shiraji’s session is designed to explore. Most enterprises are not simply evaluating whether an AI product works. They are evaluating:
Implementation risk.
Governance complexity.
Workflow disruption.
Infrastructure strain.
Compliance exposure.
Organizational trust.
An AI product can perform exceptionally well in a controlled environment and still fail commercially if its deployment creates instability within the business.
That distinction is important to founders because many AI startups are still optimizing for the wrong outcome. They are building for initial excitement rather than long-term operational adoption. And enterprises are becoming far more disciplined about recognizing the difference.
Register for Disrupt to hear how enterprise AI leaders evaluate what actually survives beyond the pilot phase. Lock in your ticket savings of up to $410 when you register by May 29 at 11:59 p.m. PT.
Enterprise AI is becoming an operational trust problem
The AI startups gaining traction inside large organizations increasingly share one thing in common: They reduce uncertainty.
They integrate more cleanly into existing systems. They create less workflow friction. They are easier to govern, easier to explain internally, and easier for organizations to trust over time.
That sounds less exciting than breakthrough demos or model benchmarks. But it is quickly becoming the difference between AI startups that generate attention and those that generate durable revenue.
The market is maturing. Enterprise buyers are asking different questions now:
What happens after deployment?
How much operational change is required?
How does this affect governance?
Can teams realistically adopt this at scale?
What happens when the model fails?
Those concerns are no longer secondary. In many organizations, they have become core to the buying decision itself. For AI founders selling into the enterprise, this session breaks down what actually drives adoption after the pilot phase ends. Check out the session details and get your $410 ticket savings to learn what to
VCs predict strong enterprise AI adoption next year — again TechCrunch
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