Many companies are misusing artificial intelligence by focusing solely on cost reduction, rather than leveraging its transformative potential, according to Nigel Vaz of Publicis Sapient. Vaz argues that this approach fails to recognize AI as a "once-in-a-generation opportunity" akin to the internet, capable of fundamentally reshaping business models.
The implementation of AI requires a comprehensive reimagining of existing processes, Vaz explained. He cautioned against viewing AI as a monolithic entity, instead advocating for a two-step approach. First, businesses must establish a robust infrastructure, beginning with their data. "Is your data connected? Is it organized? Is it in a format that can be leveraged?" Vaz questioned, emphasizing the "garbage-in, garbage-out" principle of AI.
Second, companies should select one or two high-value projects that are neither too large to deliver value quickly nor too small to be applicable across the business. Many CEOs, Vaz noted, have been drawn to cost-based AI applications due to their perceived ease of implementation.
The industry impact of AI is expected to be significant, with potential applications spanning various sectors, from healthcare to finance. However, realizing this potential requires a shift in mindset, moving beyond simple automation to embrace true transformation. This transformation, Vaz analogized, is about "caterpillars becoming butterflies," not just creating "faster caterpillars."
Experimentation is a necessary part of AI implementation, but many experiments fail due to a lack of foundational understanding and strategic focus. Establishing a solid data foundation is crucial, ensuring that data is connected, organized, and readily accessible for AI algorithms. Without this foundation, AI initiatives are likely to produce unreliable or irrelevant results.
Currently, many businesses are in the early stages of exploring AI applications. The next developments will likely involve a greater emphasis on data infrastructure and a more strategic approach to project selection, focusing on areas where AI can deliver significant and lasting value.
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