As generative AI (GenAI) rapidly evolves, industry leaders are emphasizing the need for companies to focus on targeted, manageable goals rather than attempting large-scale AI deployments. During a session at TechCrunch Disrupt 2024, Chet Kapoor, chairman and CEO of data management firm DataStax, underscored the critical role of data in AI, stating, “There is no AI without data, there is no AI without unstructured data, and there is no AI without unstructured data at scale.” However, Kapoor and other panelists cautioned against being overwhelmed by vast amounts of data and instead urged companies to build with precision and clear objectives in mind.
Joining Kapoor were Vanessa Larco, a partner at venture capital firm NEA, and George Fraser, CEO of Fivetran, a data integration platform. Together, they discussed strategies for companies looking to leverage GenAI effectively. Kapoor noted that while data is foundational, early AI projects are about experimentation and learning rather than scaling up. “The teams that develop these first few projects are writing the manual on how to do generative AI — they’re the pioneers,” he explained.
The panelists emphasized a streamlined approach: companies should focus on solving specific, immediate problems rather than attempting to integrate GenAI across entire organizations. Larco, who advises startups across sectors, advocated for working backward from the desired outcome. “What are you trying to solve for, and what is the data you need for that? Start by identifying that data, then build around those specific goals,” she advised, cautioning against the temptation to “dump” all available data into a large language model, which can lead to bloated, inaccurate outcomes.
Fraser, who has led Fivetran in serving clients such as OpenAI and Salesforce, echoed the advice to focus on immediate, tangible needs. “Only solve the problems you have today; that’s the mantra,” he said, explaining that most costs in innovation arise from features that ultimately go unused. Companies often regret not planning for scale retrospectively, but Fraser pointed out that the primary expenses come from unsuccessful developments, not from lack of scalability.
Comparing this period of GenAI to the early days of the internet and mobile technology, Kapoor described it as “the Angry Birds era of generative AI.” He explained that, while current AI applications are promising, they have not yet reached their full potential in transforming industries. “This year, companies are rolling out small, internal GenAI applications to work out the kinks. Next year will be the year of transformation, with applications that actually start changing a company’s direction.”
In short, as AI technology accelerates, industry experts are advising businesses to remain grounded, taking manageable steps towards GenAI adoption. By focusing on specific use cases, companies can refine their approach, build strong data foundations, and lay the groundwork for a future where AI will drive meaningful change.
