When it comes to AI, bigger isn’t always better
Enterprise AI can’t afford hallucinations
While consumer AI is typically optimized for scale and creativity, enterprise AI must optimize for consistency and precision.
There are many high-stakes industries where getting the answer wrong can have detrimental and long-lasting effects. For example, in healthcare, legal and finance, the margin for error is zero, and one single hallucination can lead to serious consequences. A wrong supplier name, a misread total, or a compliance misstep isn’t a quirky model behaviour, it’s a liability. Typically, in business, it’s not just the one issue that is the concern, it’s the compounding of issues and scale.
In enterprise AI tools, just bolting a general-purpose LLM onto a workflow and hoping for accuracy is a dangerous gamble. Frontier models can change overnight, resulting in a workflow that was 92 percent accurate on Monday but, by Tuesday, produces entirely different results, may be under export controls, or may refuse to process some items. When your product has dependencies on something not designed for the job, you may not get the accuracy you need or the cost you expect because you’re effectively renting your house, and the cost of the rent can change at any time. It’s fast to build with frontier AI models, but you can usually tell when there is no accuracy claim: ‘AI can make mistakes, we may or may not train on your data…’
