The article begins by acknowledging the current surge of hype around artificial intelligence (AI) in the workplace: from claims that “you will be replaced by AI” to the promise that simply using AI will automatically boost productivity. However, Procopio argues that for many organisations, this push may be premature: “you still don’t need AI.” He emphasises that the foundational elements—data, institutional knowledge, workflows—are often missing, making the deployment of AI a risky bet rather than a guaranteed win.
He lays out the idea that autonomous or predictive AI systems—ones that act on data rather than merely generate content—require far more than just access to a large language model (LLM). These systems demand high-quality institutional data, digitised processes, well-structured workflows, and experienced humans to interpret outputs. Without these pre-conditions, AI investments may yield little real value, mirroring recent research showing many AI pilots fail to generate measurable returns.
Procopio cautions against jumping into AI because “everyone else is doing it” or because of fear of missing out (FOMO). He argues that the correct approach is to build the data and process foundation first. Then, and only then, should companies entertain AI initiatives. Until then, organisations may be better off optimising existing systems rather than chasing the AI buzz.
In conclusion, the article serves as a reminder that AI is a tool—not a panacea. For many businesses, the immediate priority isn’t adopting AI but ensuring that their data, workflows, and human expertise are strong enough to support it. Once those fundamentals are in place, AI becomes a force-multiplier rather than a leap into the unknown.
