Why Winning Big Enterprise Customers Is Harder Than It Looks
Startups face intense pressure to grow quickly, and landing large enterprises is often the fastest route to serious revenue. But to win enterprise customers is rarely simple. One critical mistake younger startups make is believing their technology will practically sell itself.
Arsalan Tavakoli-Shiraji, co-founder and senior vice president of field engineering at Databricks, warns that this assumption can be costly. Databricks helps businesses store and analyze huge amounts of data. Today it is an AI and big data storage giant valued at $190 billion, with more than 70% of the Fortune 500 as customers. But it was once a tiny startup, and Tavakoli-Shiraji was responsible for helping bring in its first big deals.
“Everybody when they’re selling to enterprises at startups … we think it’s all about the tech and having the best tech,” he said at a recent tech conference. But he learned the hard way that “people and processes matter the most.”
He remembers securing a meeting with a prospect in the early days, only to discover that it was the same person who signed a mega-deal with his major competitor. “He has no interest in listening to me because he bet his career on that competitor,” Tavakoli-Shiraji said. He also remembers early deals fizzling because the startup wasn’t prepared for legal, contractual, or security questions.
Here are his top tips for startups looking to win enterprise customers.
Partner With a Bigger Tech Company to Open Doors
When Databricks was young, it tried and failed to score a meeting with UBS, the large Swiss bank. Then it did “a partnership with Microsoft,” becoming an Azure cloud partner. That meant sales agreements would be covered under Microsoft’s terms, and Microsoft’s sales team actually booked the meetings and advocated for the startup.
“All of a sudden, that meeting 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 for countries Databricks hadn’t expanded to, but they were good problems to have.
Today, Databricks is the big company that works with startups, selling their products under Databricks’ own terms and cybersecurity setup. Examples include Hightouch, which provides 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 that Databricks later acquired.
Address the “Four Cs” When Selling AI
Tavakoli-Shiraji groups enterprises’ main concerns and objections into four categories he calls the four Cs: context, choice, cost, and control.
Context means an AI that can be trained “like bringing on a new employee,” he says, learning the company’s terminology, processes, priorities, and so on.
Choice is the freedom to switch AI models rather than be tied to a single provider.
Cost is about controlling how much the AI product will cost to run.
Control involves the safeguards that govern the AI’s behavior and protecting sensitive information.
Understanding these concerns can help startups position their products in ways that resonate with enterprise buyers. When you can speak directly to each of the four Cs, you show prospects that you understand their real-world constraints, not just your own product features.
Target the Expensive Legacy Systems Enterprises Rely On
Tavakoli-Shiraji jokingly asks CIOs where they stand with AI on a scale of one to five. A one means employees merely have access to ChatGPT, and a five means the company has adopted new AI capabilities that have 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 allowing engineering teams to 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.”
One big opportunity for startups to bring that value is to modernize expensive legacy systems. “If you go to any bank today, they still have [an IBM] AS/400 and mainframes,” he described. “Nobody wanted to touch it because nobody knew what that code did.”
But today, AI agents “can go in and understand and actually decompose what that code does.” That, he says, opens the door to products that do everything from adding modern interfaces for those ancient systems to replacing them altogether. For startups trying to win enterprise customers, this is a rare opening where genuine technical value meets urgent business need.
Key Takeaways for Startups
Winning big enterprise customers takes more than great technology. It requires partnerships, preparation, and a deep understanding of what large organizations actually need. By addressing the four Cs, leveraging the reach of larger partners, and targeting outdated systems that enterprises are desperate to modernize, startups can improve their odds of closing major deals.
Tavakoli-Shiraji’s experience at Databricks shows that the path from tiny startup to enterprise powerhouse is built on people and processes as much as product. Founders who want to win enterprise customers should treat sales, legal readiness, and partner relationships as core parts of their strategy, not afterthoughts. The technology matters, but the ability to navigate real enterprise concerns is what turns a promising product into a signed contract.
