The technology industry loves a gold rush. <\/p>\n\n\n\n
In the late \u201890s, companies raced to claim their piece of the \u201cinformation superhighway,\u201d stockpiling bandwidth and investing in domain names. A decade later, the mobile revolution sparked a similar frenzy, with businesses scrambling to launch apps, regardless of whether they served a true purpose. The cloud era brought a similar rush, as organisations rapidly embraced distributed computing, often before understanding its strategic value. <\/p>\n\n\n\n
Today, we\u2019re seeing a similar pattern with AI<\/a> as the models that power AI solutions are becoming increasingly commoditised. Indeed, the market is flooded with large-language models (LLMs<\/a>), specialised models, and open-source alternatives, all promising better speed, lower cost, and smarter results. New entrants like DeepSeek\u2019s R1 add to the pile, fueling hype and a fear of missing out<\/a>. If CIOs aren\u2019t buying the latest and greatest or building their own specialised model, they worry the AI revolution will pass them by while competitors race ahead.<\/p>\n\n\n\n As with past technological booms, however, the real value doesn\u2019t lie in acquiring the core technology \u2014 in this case, \u201cdo-it-yourself\u201d (DIY) AI. It lies with how you apply it to drive meaningful business outcomes. Just as the true winners of the internet era weren\u2019t those who bought the most bandwidth but those who created the most compelling digital experiences, today\u2019s AI leaders will be those who focus on deploying digital labour within a deeply unified platform that connects AI to real-time data, logic, and workflows. <\/p>\n\n\n