--- title: "Enterprise Startups Face Challenge Winning Over Big Clients" url: https://noti.group/enterprise-startups-face-challenge-winning-over-big-clients/ language: en publisher: "Noti Group" section: "Technology" published: 2026-10-26T22:00:00.000Z updated: 2026-10-27T00:24:49.136Z id: 6c6bad2f-846b-4f19-878f-b006ad89c397 source: "TechCrunch https://techcrunch.com/2026/10/26/databricks-co-founder-shares-his-secrets-for-winning-big-enterprises-customers/" attribution: "Link to https://noti.group/enterprise-startups-face-challenge-winning-over-big-clients/ and name Noti Group when you quote or summarize this story." --- # Enterprise Startups Face Challenge Winning Over Big Clients Securing contracts from large IT clients is notoriously difficult for startups. Winning over big enterprises has become a crucial challenge for startups looking to rapidly expand their customer base. Securing contracts from these deep-pocketed IT clients is notoriously difficult, and companies that sell to enterprises can attest to this. Arsalan Tavakoli-Shiraji, co-founder and senior vice president of field engineering at Databricks, warns against a common mistake made by younger startups: underestimating the importance of relationships in winning large enterprise customers. While having cutting-edge technology is essential, it's not enough on its own to secure deals. Databricks' success story is a testament to this. The company has grown into an AI and big data storage giant, valued at $190 billion, with over 70% of the Fortune 500 as clients. However, Tavakoli-Shiraji recalls his early days at Databricks when he was responsible for securing its first major deals. Tavakoli-Shiraji shared a valuable lesson learned from his experiences: people and processes matter just as much as technology in winning over enterprise customers. He emphasized that startups often focus too much on showcasing their tech, but neglect the importance of building relationships with potential clients. In his early days at Databricks, Tavakoli-Shiraji encountered situations where prospects were already heavily invested in rival solutions, making it difficult to get a foot in the door. He also experienced deals falling through due to unpreparedness for complex questions around legal, contractual, and security issues. When trying to win over large enterprises as customers, startups often face significant hurdles. One strategy that can help is partnering with a larger tech company, which can provide access to valuable resources and expertise. This approach was employed by Databricks when it first attempted to secure a meeting with UBS, the Swiss bank. Databricks eventually formed a partnership with Microsoft, becoming an Azure cloud partner in the process. As part of this arrangement, sales agreements were covered under Microsoft's terms, which simplified the process and provided a level of credibility that was previously lacking. With Microsoft's sales team advocating on their behalf, Databricks found it easier to secure meetings and establish relationships with potential clients. This partnership had an immediate impact on Databricks' fortunes, as they began to receive invitations to meet with numerous large enterprises. At first, this created new challenges, such as dealing with requests for features that were not yet available in certain regions. However, these issues were seen as a positive problem to solve, rather than a barrier to success. Today, Databricks itself serves as the larger partner, working with startups and providing them with access to its own sales channels and resources. This approach has allowed Databricks to expand its offerings through partnerships with companies like Hightouch, Addepar, and Tecton, which provide AI-powered marketing tools, data solutions for investment professionals, and machine learning capabilities respectively. Databricks' co-founder has identified key concerns that enterprises have when it comes to adopting AI solutions. He groups these concerns into four categories: context, choice, cost, and control. Context is about an AI system's ability to learn and adapt to a company's unique terminology, processes, and priorities, much like hiring a new employee. This requires the AI to be able to understand the nuances of the business. The second concern is choice, which involves giving enterprises the freedom to switch between different AI models without being locked into a single provider. This flexibility is crucial for companies that need to adapt quickly to changing market conditions. Enterprises are often hesitant to adopt new AI solutions due to concerns about cost and control. Cost refers to the expense of implementing and running an AI system, while control involves ensuring that the AI behaves safely and securely, protecting sensitive information from unauthorized access. Bringing value to large enterprises is a significant opportunity for startups, and one way to do this is by modernizing expensive legacy systems that are still in use today. Legacy systems, such as those found in banks using IBM's AS/400 and mainframes, can be daunting due to their complex code. However, AI agents have made it possible to understand and decompose the functionality of these systems, opening up new possibilities for innovation. This breakthrough allows startups to offer products that not only modernize interfaces but also replace legacy systems altogether. By leveraging AI, companies can breathe new life into outdated technology and provide a more streamlined experience for users. The potential for enterprise AI adoption is vast, and startups are well-positioned to capitalize on this trend by providing innovative solutions that address the needs of large organizations. Databricks co-founder shares his secrets for winning big enterprises customers To succeed in selling AI solutions to large organizations, it's essential to focus on providing value through data and analytics rather than just pushing a product. This approach requires understanding the specific needs of each enterprise customer and tailoring solutions accordingly. Startups can differentiate themselves by offering innovative and scalable products that meet these needs. By doing so, they can establish long-term relationships with their clients and stay ahead in a competitive market. The potential for enterprise AI adoption is vast, and startups are well-positioned to capitalize on this trend by providing cutting-edge solutions that address the complex needs of large organizations. This approach will ultimately lead to a more widespread and effective use of AI technology across various industries. --- Source: [TechCrunch](https://techcrunch.com/2026/10/26/databricks-co-founder-shares-his-secrets-for-winning-big-enterprises-customers/) Published by Noti Group: https://noti.group/enterprise-startups-face-challenge-winning-over-big-clients/