Databricks' $190 Billion Playbook: How to Land Fortune 500 Buyers

Databricks is valued at $190 billion and counts more than 70% of the Fortune 500 as customers — but its co-founder says the deals that built that machine were won on relationships, not technology. Arsalan Tavakoli-Shiraji, co-founder and senior vice president of field engineering at the AI and big-data storage giant, laid out his playbook at TechCrunch Disrupt last week. His core message to founders: stop assuming great tech sells itself.

"Everybody when they're selling to enterprises at startups … we think it's all about the tech and having the best tech," Tavakoli-Shiraji told attendees. He learned the hard way, he said, that "people and processes matter the most."

Why tech alone doesn't close the deal

Startups have never faced more pressure to grow fast, and landing large enterprises — the deep pockets of the IT world — is often the only way to do it. Tavakoli-Shiraji, who was responsible for bringing in Databricks' first big deals, recalled two early failures that shaped his thinking.

In one, he finally secured a meeting with a prospect, only to discover the person had signed a mega deal with Databricks' major competitor. "He has no interest in listening to me because he bet his career on that competitor," Tavakoli-Shiraji said. In other cases, early deals fizzled because the startup wasn't prepared for legal, contractual, or security questions — the unglamorous paperwork that enterprise procurement demands.

Borrow a bigger company's umbrella

When Databricks was young, it tried and failed to get a meeting with UBS, the large Swiss bank. Then it did "a partnership with Microsoft," becoming an Azure cloud partner. Sales agreements fell under Microsoft's terms — and Microsoft's salespeople booked the meetings and advocated for the startup.

"All of a sudden, that meeting that wasn't hard to get," the founder recalled. "It was a huge boon to us that we were able to partner with somebody who had that access and distribution." With Microsoft's sales team helping, "we started getting pulled everywhere." That created new problems, like prospects wanting features in countries Databricks hadn't expanded to — but those were good problems to have.

The roles have now reversed. Databricks is the big company that sells startups' products under its own terms and cybersecurity setup. Current examples include Hightouch, which builds AI-powered marketing and customer-data tools (Databricks has also invested in it); Addepar, which offers data tools for investment professionals; and Tecton, a machine learning startup Databricks later acquired.

What does selling AI to enterprises require?

Tavakoli-Shiraji groups enterprise objections to AI into what he calls the four Cs:

• Context — AI that can be trained "like bringing on a new employee," learning the company's terminology, processes and priorities.
• Choice — the freedom to switch AI models rather than be locked to a single provider.
• Cost — control over how much the AI product costs to run.
• Control — safeguards governing the AI's behavior and protection of sensitive information.

The legacy-system opening

He jokingly asks CIOs to rate their AI maturity on a scale of one to five. A one means employees merely have access to ChatGPT; a five means the company has adopted AI capabilities that changed its business processes. "And everybody loves picking, 'I'm three leaning towards four.' But 99% of the ones I talk to are in bucket one and two."

Enterprises still haven't figured out how to deploy AI agents in ways that create value beyond helping engineering teams write code. "It's going to take many, many years before people can actually properly deploy AI in a very meaningful way in the enterprise," he said.

That gap is the startup opportunity. "If you go to any bank today, they still have [an IBM] AS/400 and mainframes," Tavakoli-Shiraji described. "Nobody wanted to touch it because nobody knew what that code did." AI agents can now "go in and understand and actually decompose what that code does," he said — opening the door to products that add modern interfaces to those ancient systems or replace them outright.

He also shared predictions for the future of enterprise AI adoption, plus an anecdote about pretending to be a lawyer to land a big enterprise contract when Databricks was young. For founders selling into the Fortune 500 today, the lesson is blunt: your product is the entry ticket, but Microsoft-style distribution and readiness for procurement are what actually close the deal.