Jesse Sumrak

Most businesses make the same mistake with AI: they pick one tool and try to force it to do everything. Microsoft Copilot becomes their solution for coding, content creation, data analysis, and customer support. Or they subscribe to ChatGPT Plus and expect it to handle every business need with equal excellence. Really, there’s no single

AI implementations are failing at staggering rates.  Recent studies show that 80% of AI projects never make it to production, and those that do often get abandoned. The culprit isn’t the technology, though. It’s the strategy (or lack thereof). Organizations are treating AI like any other software deployment: pick a vendor, run a pilot, roll

AI adoption is accelerating. Companies are deploying machine learning models, generative AI tools, and automated decision systems at breakneck speed. They ultimately promise unprecedented efficiency, deeper insights, and competitive advantages that seemed impossible just years ago. That’s not the complete story, though. AI introduces risks that most organizations aren’t prepared to handle. We’re talking about:

Using AI in your small business isn’t just about implementing the latest large language model (LLM) and calling it good enough. Almost half of all SMBs have started using AI, but over 80% of these AI projects eventually fail.  That’s not a problem with the technology, though—it’s an issue with the approach.  Companies jump straight