It’s undeniable that artificial intelligence is a big part of doing business in 2026. Given this, it is not surprising that many products are being developed to push the technology into areas of business it hasn’t touched. Today, we are going to tell you about the difference between AI models and why one man’s great idea could be the thing that set AI back.
In its current state, artificial intelligence takes whatever you tell it very literally. As such, it is very easy to misdirect it into digital rabbit holes… which is the last thing you want, when time is very much money to your business. This is precisely why it is so crucial that we become adept at properly prompting the AI models we use. Too many hallucinations (responses that share inaccurate or unreliable information) simply waste time and money, but the better the prompt, the less prone the AI will be to hallucinate. Let’s go over some of the best practices to keep in mind as you draft your prompts.
As an IT service provider, our techs spend their days at the intersection of cutting-edge and business-critical. In 2026, the conversation about each has shifted. It is no longer about whether you should use AI, because everyone is, but about the risks of trusting it blindly. We have seen it firsthand: companies that treat AI like a set-it-and-forget-it solution often end up calling us for emergency damage control. Here are the major pitfalls of over-trusting AI and how to keep your business from becoming a cautionary tale.
There are two types of digital transformation. There’s the kind that streamlines a business into a powerhouse, and there’s the kind that turns into a ghost ship; perfectly automated, technically efficient, and completely devoid of life. Right now, we are witnessing a massive shift in the way people do things. While your competitors are busy bragging about replacing their support staff with agentic AI, what they are often doing is building a wall between themselves and their customers.
One of the most common criticisms of generative AI tools is that they often “hallucinate,” or make up information, making them somewhat unreliable for certain high-stakes tasks. To help you combat hallucinations, we recommend you try out the following tips in your own use of generative AI. You might find that you get better, more reliable outputs as a result.