The most immediate operational risk is the hallucination. To prevent this, organisations need to impress it upon their staff, to never use AI for final-mile decisions. They have to implement a Human-in-the-Loop policy where high-risk tasks like customer communications require human sign-off.

Another issue will be the reliance on free public models which can create a data poisoning problem. Free models are free to users by mostly using the user conversations as training data to finetune those models. Pasting Q4 financials or employee reviews into a free chat window effectively hands that data to the model provider, who can distribute to other users using their models, albeit not directly. This will most likely be avoided by spending more on business tiers buying vital data privacy clauses that exclude your inputs from model training.

This connects directly to the risk of data privacy breaches, which remain a persistent threat whenever sensitive information is processed without adequate safeguards. Regulatory non-compliance is a close companion, as AI usage can easily outpace awareness of data protection and sector-specific laws, leaving small organisations exposed to fines and legal action.

Taken together, these risks are the major part of the core of what a small organisation must manage when adopting AI. None of them require a dedicated AI department to address them. What they require is clear policies on what never goes into a chatbot, deliberate spending on business tiers with proper data clauses, and keeping an eye on costs. AI can let an organisation punch above its weight, but only if the risks are managed before they become failures.