Gaurav Dhillon is the Chairman and CEO of SnapLogic, overseeing the company’s strategy, operations, financing, and partnerships. Having previously founded and taken Informatica through IPO, Dhillon is an experienced builder of technology companies with a compelling vision and value proposition that promises simpler, faster, and more cost-effective ways to integrate data and applications for improved decision-making and better business outcomes.
M.R. Rangaswami: As we’re heading into the new year, how can leaders begin to make room in budgets to take advantage of AI?
Gaurav Dhillon: As generative AI continues to be the topic of conversation in every boardroom, the question board members are asking leaders is not whether they can afford to invest in generative AI but what they will lose if they don’t. As AI-driven technologies continue to expand in reach,, there is a new baseline for business operations, which includes evolving customer expectations. Any sophisticated task with the potential to be automated will be automated.
Companies must adapt to maintain a competitive edge, and until a company strategically harnesses AI, it will struggle to meet the industry’s new productivity standards. As organizations begin to prepare for AI implementation, it’s important for them to prioritize reducing their legacy debt—or what is commonly known as technical debt.
The challenge with legacy tech stacks is that they are built around older and outdated languages and libraries, which inhibit an organization’s ability to successfully integrate new applications and systems, including GenAI tools. Modernizing infrastructure is key to ensuring enterprise data is ready for widespread AI adoption and use across the business. AI adoption is increasingly becoming integral to a company’s relevance, efficiency and effectiveness.
M.R.: What do you believe are the biggest inhibitors to AI adoption in the workplace?
Gaurav: The biggest inhibitors of AI adoption in the enterprise are rooted in the fact that people look at consumer AI tools like ChatGPT and make comparisons to their own products. AI is fueled by data, and enterprise AI needs to have guardrails on what type of data it can access. Today, we are still at the hunter-gatherer stage with business data.
Another inhibitor to AI adoption for organizations is security. Ideally, businesses want to leverage AI tools to ask questions about customers, but in order to get to this stage, organizations first need guardrails to ensure that the data is handled and accessed securely. The stakes for consumer AI are low because if you ask ChatGPT to write you a recipe for dinner and it turns out bad, you lose a meal. The bar for enterprise AI is much higher; if a customer looks to your business for answers and solutions, people’s jobs can be at stake.
M.R.: As a two-time founder, what key lessons have you learned that you believe every leader should be aware of, especially in the midst of today’s AI revolution?
Gaurav: The hardest lesson I’ve had to come to terms with is that product market fit is a scientific art. Companies can do and build amazing things at scale, but that alone won’t determine or define its success. Closely engaging with and listening to early adopters and customers is the only way successful business leaders can discern and establish what the ideal product-market fit is. As a founder and entrepreneur, it’s critical to be a part of this exploration from the very start. While motions like scale can be delegated, product market fit cannot.