Startups today find themselves under unprecedented pressure to scale rapidly, driven by the relentless demands of investors and a hyper-competitive tech landscape. To achieve this crucial momentum, many early-stage companies realize they must transition from selling to small businesses and land large enterprise customers—the deep pockets of the global IT economy. Yet, as any veteran software founder or enterprise sales leader will readily attest, securing lucrative contracts with major corporations is notoriously difficult, filled with bureaucratic hurdles, lengthy sales cycles, and fierce competition.
According to Arsalan Tavakoli-Shiraji, co-founder and senior vice president of field engineering at Databricks, one of the most critical and common mistakes younger startups make is operating under the comforting illusion that their technology will practically sell itself. Databricks has grown into an artificial intelligence and big data storage behemoth, currently valued at a staggering $190 billion, with more than 70 percent of the Fortune 500 utilizing its platform. However, the company’s current market dominance stands in stark contrast to its humble beginnings as a tiny, resource-strapped startup. During those formative years, Tavakoli-Shiraji was personally responsible for helping secure the company’s first major enterprise deals, learning valuable lessons the hard way.
Speaking to attendees at TechCrunch Disrupt, Tavakoli-Shiraji reflected on the early misconceptions that plague so many newly minted software ventures. While acknowledging that foundational technology must function reliably and effectively, he warned that product quality alone is never enough to close enterprise-level agreements.
"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 the audience. Over years of trial and error, however, he discovered that human relationships, corporate dynamics, and established business processes ultimately matter the most.
He vividly remembers the sting of early missteps, such as securing a hard-fought meeting with a prized corporate prospect only to discover that the individual across the table was the exact person who had recently signed a multi-million-dollar mega-deal with Databricks’ primary competitor. The prospect had no interest in listening to a pitch because his professional reputation and career trajectory were already tied to that rival vendor. In other instances, promising early sales cycles completely fizzled out simply because the startup was entirely unprepared to answer complex legal, contractual, or rigorous cybersecurity questions posed by corporate procurement teams.
Drawing from his extensive journey from a startup engineer to an executive at a multi-billion-dollar enterprise giant, Tavakoli-Shiraji outlined essential strategies for early-stage companies attempting to break through the enterprise barrier and secure transformative accounts.
Sell under a bigger tech company’s umbrella
In the early days of Databricks, the company struggled significantly to gain traction with traditional corporate giants, trying repeatedly and failing to secure a standard introductory meeting with UBS, the prominent Swiss banking institution. Realizing that cold outreach to risk-averse financial institutions was hitting a brick wall, the startup changed its strategy by forging a strategic partnership with Microsoft, integrating its platform to become an official Azure cloud partner.
This alliance meant that Databricks’ sales agreements would be vetted and covered under Microsoft’s widely trusted corporate terms and compliance frameworks. More importantly, it meant that Microsoft’s extensive, established sales force actively booked meetings and advocated for the startup’s capabilities to their massive roster of enterprise clients.
"All of a sudden, that meeting wasn’t hard to get," the co-founder recalled, emphasizing the immediate shift in market dynamics. "It was a huge boon to us that we were able to partner with somebody who had that access and distribution."
With Microsoft’s formidable sales team helping grease the wheels, Databricks experienced a rapid acceleration in customer acquisition, noting that they "started getting pulled everywhere." While this sudden influx of enterprise interest created entirely new operational challenges—such as prospective clients demanding compliance features and localized support for countries where Databricks had not yet established a physical presence—they were ultimately the kind of high-stakes problems that fast-growing startups need to survive and scale.
Today, Databricks has evolved into the established industry giant that actively works with and nurtures smaller startups, offering them a similar pathway to market by selling their specialized products under Databricks’ own trusted corporate terms and robust cybersecurity infrastructure. Notable examples of this ecosystem model include Hightouch, a company that provides advanced AI-powered marketing and customer-data tools and in which Databricks has made strategic financial investments; Addepar, which offers sophisticated data analytics and management tools tailored for investment professionals; and Tecton, a specialized machine learning startup that Databricks later acquired to strengthen its own platform offerings.
When selling AI, address the four Cs
As the technology landscape shifts toward generative models and automated solutions, selling artificial intelligence to enterprise buyers introduces an entirely new layer of complexity. Tavakoli-Shiraji groups the primary concerns, hesitations, and objections voiced by chief information officers and corporate buyers into four distinct categories he refers to as the "four Cs": context, choice, cost, and control.
Context refers to an enterprise’s urgent need for artificial intelligence systems that can be trained effectively, functioning much like onboarding a new corporate employee. Enterprises want AI that can learn the company’s internal terminology, proprietary processes, operational priorities, and unique workflows rather than relying solely on generalized public models.
Choice represents the enterprise’s desire for operational flexibility—specifically, the freedom to switch between different underlying AI models and providers over time rather than being locked into a single proprietary vendor ecosystem that could leave them vulnerable to pricing shifts or technological stagnation.
Cost centers on predictability and financial management, addressing corporate anxieties regarding how much an AI product will actually cost to run at enterprise scale, especially as computational workloads fluctuate.
Finally, control involves the critical safeguards, guardrails, and governance policies required to govern AI behavior, prevent hallucinations, and ensure rigorous protection for sensitive corporate and customer data.
Attack the expensive legacy systems enterprises think they’re stuck with
To illustrate the current state of enterprise AI adoption, Tavakoli-Shiraji frequently asks chief information officers where their organizations stand on a developmental scale of one to five. On this hypothetical index, a rating of one signifies that employees merely have basic access to public tools like ChatGPT, while a rating of five indicates that the corporation has successfully integrated advanced AI capabilities that have fundamentally transformed its core business processes.
"And everybody loves picking, ‘I’m three leaning towards four,’" he noted jokingly. "But 99% of the ones I talk to are in bucket one and two."
Despite the massive corporate hype surrounding artificial intelligence, most large enterprises have yet to figure out how to deploy autonomous AI agents in ways that generate genuine business value beyond allowing internal software engineering teams to write code more efficiently. Tavakoli-Shiraji cautioned that it will take many years before organizations can properly and securely deploy artificial intelligence in a truly meaningful, enterprise-wide capacity.
This cautious adoption curve, however, represents a massive market opportunity for nimble startups capable of delivering immediate, tangible value by helping corporations modernize their expensive, cumbersome legacy systems. He pointed out that if you walk into almost any major traditional bank today, their core financial infrastructure still relies heavily on decades-old IBM AS/400 systems and aging mainframes. For years, corporate IT leadership deliberately avoided touching or modifying these critical systems because the original documentation was lost and nobody truly understood what the underlying legacy code actually did.
Today, however, modern AI agents possess the analytical capability to inspect those aging codebases, understand their architecture, and actually decompose what the legacy code does line by line. Tavakoli-Shiraji argues that this breakthrough capability opens the door wide for innovative startup products that can do everything from wrapping modern, user-friendly interfaces around ancient legacy systems to replacing them entirely, offering enterprise buyers a viable path out of the technological traps they thought they were permanently stuck in.
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