Databricks Co-Founder Says Enterprise Wins Depend on People and Process, Not Just Better Technology
Arsalan Tavakoli-Shiraji, co-founder and senior vice president of field engineering at Databricks, told TechCrunch Disrupt attendees that startups selling to large enterprises often assume the best technology wins. He argued that people and processes matter more. The remarks were reported by TechCrunch on October 26, 2026.
Databricks helps businesses store and analyze large volumes of data. According to the same report, the company is valued at $190 billion, and more than 70% of the Fortune 500 are Databricks customers. Those figures describe a company that grew through enterprise sales, not only through individual engineers adopting the product on their own.
Tavakoli-Shiraji recounted that Databricks first tried and failed to secure a meeting with UBS, the Swiss bank. The company's route into enterprise accounts changed when it became an Azure cloud partner through a partnership with Microsoft. Sales agreements were covered under Microsoft's terms. After that, Microsoft sales staff booked meetings for Databricks and advocated for the startup inside their own customer relationships. The sequence is a useful reminder that a cloud marketplace listing, which is a storefront inside a cloud provider's platform where customers can buy software, works as a distribution channel only when someone on the partner's side is willing to carry the product into a conversation.
On the objections enterprises raise to AI projects, Tavakoli-Shiraji groups them into four categories, which he calls the four Cs: context, choice, cost, and control. Each category maps to a different buyer concern, from how models are grounded in proprietary data to who decides which models and vendors are used, and what run-rate economics (the recurring cost of running workloads once they scale) look like. The framework moves the conversation away from benchmark performance and toward the questions a CIO's procurement and security teams will ask.
Databricks also works with startups, including Hightouch, Addepar and Tecton, by selling their products under Databricks' own terms and cybersecurity setup. That arrangement lets a smaller vendor pass through the governance review, the internal approval process for security and data risk, that a large bank or insurer would otherwise require it to clear separately. Databricks later acquired Tecton, a machine learning startup, so the partnership ultimately became an ownership relationship.
For context, Matei Zaharia, Databricks co-founder and CTO, received the 2026 ACM Prize in Computing, a recognition reported by TechCrunch on April 8, 2026. In May 2026, TechCrunch Disrupt also featured a session with Tavakoli-Shiraji on what kills enterprise AI deals, a topic the October remarks return to.
In my view, the most transferable point is the one about sequencing. Many infrastructure startups build for the engineer who adopts a tool first, then discover that the buyer who signs the contract works on a different timeline, with different risk tolerances and a different set of approvals. The UBS anecdote, as Tavakoli-Shiraji described it, shows how a closed door at a large institution can say little about the product and a great deal about the route in.
The broader context here is that enterprise AI purchases now sit where data governance, model choice and cloud commitments meet. Vendors that reduce friction in those areas, through hyperscaler partnerships, packaged security controls or acquisitions that bring a capability in-house, may find the path to production shorter than a feature comparison would suggest. Whether that holds across the market is an open question.
