For early-stage technology startups navigating today’s hyper-competitive market, the pressure to achieve rapid growth has never been more intense. To survive and scale, many of these young companies face a formidable imperative: they must secure high-value contracts with large enterprises—the deep-pocketed titans of the global IT ecosystem. Yet, as any seasoned enterprise software vendor will readily confirm, breaking into the fortress of corporate procurement is notoriously difficult. Building an innovative product is merely the price of admission, not the key to victory.
According to Arsalan Tavakoli-Shiraji, co-founder and senior vice president of field engineering at Databricks, a critical mistake made by countless younger startups is the naive belief that their technology will practically sell itself. Today, Databricks stands as a towering heavyweight in the artificial intelligence and big data storage sectors, boasting a staggering valuation of $190 billion and counting more than 70 percent of the Fortune 500 among its customer base. However, the company’s current market dominance obscures its modest origins as a tiny startup. During those formative years, Tavakoli-Shiraji was personally tasked with front-line sales and the heavy lifting required to bring in the company’s first major enterprise deals.
Reflecting on those early experiences during a panel appearance at TechCrunch Disrupt, Tavakoli-Shiraji shared hard-earned wisdom with founders striving to follow a similar trajectory. When technology entrepreneurs first attempt to sell to large enterprises, he noted, the prevailing assumption is always that success hinges entirely on having the superior technical architecture. But as he discovered through trial and error, people and processes ultimately matter the most.
Recalling the sobering realities of early-stage enterprise sales, Tavakoli-Shiraji described securing a coveted meeting with a promising corporate prospect, only to arrive and discover that the decision-maker across the table was the exact same individual who had recently signed a massive, career-defining agreement with Databricks’ primary competitor. The prospective client had virtually no interest in listening to an alternative pitch because they had already staked their professional reputation on the competing platform.
On other occasions, early deals would inexplicably fizzle out late in the sales cycle simply because the startup was fundamentally unprepared to navigate the rigorous legal, contractual, and cybersecurity scrutiny demanded by corporate legal departments. Drawing from these foundational lessons, Tavakoli-Shiraji outlined essential strategies for early-stage companies aiming to successfully capture and retain enterprise-grade customers without falling into common commercial traps.
Sell under a bigger tech company’s umbrella
During its earliest days, Databricks experienced firsthand the frustration of trying to secure a meeting with premier financial institutions, such as major Swiss bank UBS, only to be repeatedly stonewalled by traditional corporate gatekeepers. The breakthrough came when the startup executed a strategic partnership with Microsoft, integrating its platform to become an official Azure cloud partner. This alliance fundamentally transformed the mechanics of their go-to-market strategy, ensuring that Databricks’ sales agreements would be seamlessly covered and validated under Microsoft’s established enterprise terms.
Crucially, the partnership meant that Microsoft’s extensive sales force actively advocated for the startup and facilitated high-level introductions. Suddenly, meetings that had once felt nearly impossible to secure became readily accessible. Tavakoli-Shiraji recalled that this alliance served as an enormous boon, granting the young company instant access to enterprise distribution channels that would have otherwise taken years to build independently. With Microsoft’s commercial machinery lending momentum, customer inquiries began surging from every direction, generating a new wave of operational challenges—such as prospective clients requesting specialized regulatory features for international jurisdictions where Databricks had not yet expanded—which the founding team ultimately welcomed as high-class problems.
This dynamic has since come full circle. Today, Databricks operates as the anchor enterprise platform partnering with a new generation of startups, extending its own rigorous cybersecurity frameworks and contracting terms to bring emerging technologies into the corporate fold. Prominent examples of this ecosystem model include Hightouch, a company delivering AI-powered marketing and customer data tools in which Databricks has also made direct financial investments; Addepar, a provider of specialized data infrastructure for investment professionals; and Tecton, a machine learning startup that Databricks ultimately acquired.
When selling AI, address the “four C’s”
As artificial intelligence dominates enterprise software budgets, pitching AI solutions to corporate buyers requires navigating a distinct set of organizational anxieties. Tavakoli-Shiraji categorizes the primary concerns and objections raised by enterprise buyers into four distinct pillars, which he refers to as the "four Cs": context, choice, cost, and control.
Context addresses the enterprise requirement for AI systems that can be trained effectively—akin to onboarding a new human employee—so that the technology absorbs company-specific terminology, internal workflows, corporate priorities, and institutional knowledge. Choice refers to the organizational demand for architectural flexibility, ensuring that buyers retain the freedom to swap underlying AI models rather than being locked into a single proprietary provider. Cost centers on predictability and financial governance, enabling IT leaders to tightly monitor and manage the operational expenses associated with running heavy AI workloads at scale. Finally, control encompasses the robust security guardrails, compliance frameworks, and governance policies required to govern AI behavior and safeguard sensitive corporate data against leaks or misuse.
Attack the expensive legacy systems enterprises think they’re stuck with
When evaluating the current state of artificial intelligence adoption across corporate America, Tavakoli-Shiraji frequently employs a lighthearted litmus test when speaking with chief information officers. Using a scale from one to five, he asks leaders to quantify their organizational AI maturity: a score of one indicates that employees merely have basic access to consumer tools like ChatGPT, while a five signifies that the enterprise has successfully embedded novel AI capabilities that fundamentally transform its core business processes.
Although corporate executives consistently lean toward rating themselves as a three leaning toward four, Tavakoli-Shiraji observes that roughly 99 percent of the executives he engages with actually occupy buckets one and two. Most large enterprises have yet to figure out how to deploy autonomous AI agents in ways that generate measurable business value extending beyond helping engineering teams write software code. Consequently, he cautions that it will take many years before organizations are able to properly and deeply integrate AI into everyday enterprise operations.
For ambitious startups, this widespread adoption lag represents a massive commercial opportunity, particularly when it comes to modernizing the expensive, entrenched legacy systems that corporate leaders often feel trapped maintaining. In many traditional institutions, such as major commercial banks, core operations still rely heavily on decades-old infrastructure like IBM AS/400 systems and mainframe computers. For years, internal IT departments avoided touching these ancient codebases because the original documentation had vanished and few employees understood what the underlying code actually executed.
Today, however, advanced AI agents possess the analytical capacity to inspect, comprehend, and systematically decompose legacy code. Tavakoli-Shiraji emphasizes that this capability opens the door for innovative startups to build products that bridge the gap—ranging from solutions that layer modern user interfaces over antiquated systems to those designed to replace them entirely.
