Databricks Co-Founder Reveals Strategies for Winning Enterprise Customers

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Databricks co-founder Arsalan Tavakoli-Shiraji shares lessons on securing large enterprise deals, emphasizing partnerships, understanding client concerns, and modernizing legacy systems with AI.

Startups often struggle to win large enterprise customers, mistakenly believing that superior technology alone will secure deals. Arsalan Tavakoli-Shiraji, co-founder and senior vice president of field engineering at Databricks, explained at TechCrunch Disrupt that while having robust technology is essential, successful sales also depend heavily on people and processes.

In its early days, Databricks found it difficult to even secure meetings with major companies like UBS. The breakthrough came through partnering with Microsoft as an Azure cloud partner, which allowed Databricks to leverage Microsoft’s established sales channels and contractual frameworks. This partnership was vital in gaining trust and opening doors that had previously been closed.

Tavakoli-Shiraji advises startups to consider selling under the umbrella of larger tech companies to benefit from their sales reach and reputation. As Databricks grew, it transitioned into enabling startups such as Hightouch, Addepar, and Tecton, either by selling their products under Databricks’ infrastructure or through direct investments and acquisitions.

Addressing enterprise hesitations about AI adoption, Tavakoli-Shiraji identified four main concerns, termed the “four Cs”: context, choice, cost, and control. Enterprises want AI that can be customized to their unique environments (context), prefer flexibility to switch between AI models (choice), demand transparent and manageable expenses (cost), and require safeguards for security and data privacy (control).

He also pointed out that most enterprises are still in the early stages of AI maturity, often only utilizing basic tools like ChatGPT. There is significant opportunity for startups to help enterprises modernize costly and opaque legacy systems, such as banks’ IBM AS/400 mainframes, by deploying AI to analyze, understand, and either modernize or replace outdated codebases.

Tavakoli-Shiraji’s insights highlight the importance of strategic partnerships, deep customer understanding, and innovation in legacy system modernization as keys to winning and growing enterprise relationships through AI and data technologies.