Strategic prudence in AI: Experts advise incremental approach for meaningful advancements
Generative AI requires a gradual, data-focused approach to avoid costly missteps, data leaders advised at TechCrunch Disrupt.
At TechCrunch Disrupt 2024, data management leaders advised AI-driven businesses to focus on incremental, practical applications rather than expansive, large-scale projects. Chet Kapoor, CEO of DataStax, stressed that AI’s effectiveness relies heavily on having robust, unstructured data at scale, but warned companies against rushing into overly ambitious initiatives. The discussion featured insights from Kapoor, Vanessa Larco of NEA, and Fivetran’s CEO George Fraser, all of whom advocated a targeted approach to data application in generative AI.
Rather than applying AI across all company functions immediately, Larco suggested that firms begin with well-defined objectives. Identifying relevant data is key, she said, and applying it selectively can avoid the pitfalls of costly errors. Companies looking to capitalise on AI should ‘work backwards’, focusing first on the issue to be solved and gathering the specific data required, Larco added.
Fraser underscored the importance of addressing current needs before planning for broader scaling. Many innovation costs, he pointed out, stem from projects that fail rather than those that succeed. His advice: ‘Only solve the problems you have today’.
Kapoor likened today’s generative AI era to the early days of mobile apps, emphasising that most AI projects are currently in exploratory stages. He believes next year will see transformational AI applications begin to shift company trajectories.
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