Startups are currently facing immense pressure to rapidly scale their businesses. This pursuit frequently involves acquiring large enterprises—those vital financial powerhouses within the tech sector—as clients. However, companies aiming to secure enterprise contracts often encounter significant challenges.
Arsalan Tavakoli-Shiraji, co-founder and senior vice president of field engineering at Databricks, highlights a common misconception among startups: the notion that their technology will automatically attract customers. While the functionality of the tech is crucial, Tavakoli-Shiraji emphasizes that other factors, such as the people and processes involved, are equally essential.
Databricks, now an AI and big data leader valued at $190 billion and boasting over 70% of Fortune 500 companies as clients, began as a modest startup. Tavakoli-Shiraji recalls the effort it took to secure initial significant contracts.
“Many startups mistakenly focus solely on having superior technology when selling to enterprises,” he remarked at TechCrunch Disrupt last week. His experiences taught him that “the human element and effective processes are what truly matter.”
One memorable instance from his early days involved arranging a meeting with a potential client who had just signed a major deal with a rival. “He wasn’t interested in what I had to say because he had already committed to that competitor,” Tavakoli-Shiraji noted. He also recognized that early deals often fell through due to the startup’s lack of preparation around legal, contractual, or security inquiries. He then shared strategic advice for startups hoping to attract large enterprise customers.
Partner with Established Tech Giants
In its early days, Databricks struggled to secure a meeting with UBS, the prominent Swiss bank. The situation turned around when Databricks formed a partnership with Microsoft, becoming an Azure cloud partner. This collaboration allowed Databricks’ sales agreements to be covered under Microsoft’s terms.
Furthermore, with Microsoft’s sales team advocating for Databricks, the once-challenging meetings became more accessible. “Suddenly, we found ourselves getting invited to meetings that we previously couldn’t reach,” he recounted, noting the positive impact of collaborating with a company with established connections.
With support from Microsoft, Databricks expanded its reach, leading to new opportunities—even if they occasionally brought unexpected challenges, like requests for features not yet available in regions where Databricks had yet to launch.
Today, Databricks is a key player collaborating with startups, offering their products under its own framework and security protocols. Partnerships with companies such as Hightouch, which provides AI-driven marketing and customer data solutions; Addepar, known for data tools tailored for investment experts; and Tecton, a machine learning firm acquired by Databricks, illustrate this trend.
Address Key Business Concerns in AI Sales
Tavakoli-Shiraji categorizes the primary concerns enterprise clients have into four areas he refers to as the four Cs: context, choice, cost, and control. “Context” refers to ensuring that an AI system can be trained like a new employee, comprehending the organization’s language, procedures, and priorities.
“Choice” emphasizes the ability for organizations to switch AI models instead of being tied to a single vendor. “Cost” focuses on managing the operational expenses related to AI technologies. Finally, “control” pertains to the measures in place to oversee the AI’s operations and protect sensitive data.
Tackle Legacy System Challenges
Tavakoli-Shiraji humorously questions CIOs about their organization’s AI maturity on a scale of one to five, noting that most identify their readiness as low. “While they often claim a score of three or four, I find that most are actually closer to one or two.” He points out that many companies have yet to effectively utilize AI in ways that positively impact their business processes.
A significant opportunity for startups lies in modernizing outdated systems that enterprises believe cannot be changed. “Visit any bank today, and you’ll find relics like the IBM AS/400 and mainframes,” he revealed, noting the reluctance to alter such systems due to uncertainty about their underlying code.
However, advancements in AI allow for deeper insights and potential solutions for these legacy systems. “AI can analyze and break down those outdated codes, enabling the development of modern interfaces or even complete replacements for those systems,” he explained.
Tavakoli-Shiraji concluded by sharing his insights on the future of AI adoption in enterprises, along with an entertaining story from his startup’s past where he impersonated a lawyer to secure a major contract.
