"Artificial intelligence (AI) I hasn't just hit the big leagues; it is the big leagues. Over the course of 2025, AI was embedded into every workflow, leveraged across IT operations, and relied upon to build out a wide variety of content touching every corner of the web. In essence, large language models (LLMs) have taken centre stage, with businesses investing heavily in them to further autonomous gains."
"Large models, such as general purpose LLMs, aren't specialists; they generalise. They link disparate data points together to provide answers, sifting through vast datasets to do so. While broad knowledge is helpful in many areas, including research and content generation, it also leads to a lot more room for error. Hallucinations on these tools are common and often baffling. While these errors might be trivial in day-to-day life, they have the potential to create nightmarish scenarios if integrated into broader business workflows."
By 2025, AI became embedded across workflows, IT operations, and web content production, with businesses investing heavily in large language models to gain autonomy. Users remain wary of AI power and limitations. Organisations face management challenges, including risky trends like vibe coding that threaten data leakage and software supply chain security while delivering uneven returns. Generalist models tend to overreach, make critical errors, defend incorrect outputs, and add governance complexity. Smaller, specialist models present a potential alternative focused on safety and reliability. Hallucinations and rogue agent behaviour pose material risks when integrated into business workflows.
Read at ComputerWeekly.com
Unable to calculate read time
Collection
[
|
...
]