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As businesses race to adopt artificial intelligence (AI), one truth is becoming impossible to ignore: successful AI adoption is not just a technology challenge — it’s a people challenge. Too many organizations still approach AI with a “plug it in and watch the magic happen” mindset. In my previous blog post, I concluded that it isn’t what can AI do for you, but rather how can you apply […]

Artificial intelligence has evolved from experimental novelty to indispensable business utility. According to the 2025 World Economic Forum’s Future of Jobs Report, 50% of employers globally are planning to reorient their business to target new opportunities resulting from AI technology. Therefore, the conversation for small and midsize businesses (SMBs) is no longer if you’ll use AI — it’s how you’ll do it securely, affordably, and at scale.  That’s where a multi-model Large Language Model (LLM) strategy comes in. It’s the next

Uncontrolled software usage — from personal Dropbox accounts to unapproved AI tools — is one of the fastest-growing cybersecurity threats facing small and midsize businesses today. Known as Shadow IT, these unauthorized tools create blind spots for IT teams, expose sensitive data, and increase the risk of compliance violations.  But as AI becomes woven into

Artificial intelligence has officially arrived in the workplace — and its impact could rival the Industrial Revolution. According to McKinsey, AI has the potential to unlock up to $4.4 trillion in annual productivity gains for businesses worldwide. With large language models (LLMs) now available from leading developers like Anthropic, Cohere, Google, Meta, Mistral, and OpenAI,

AI tools like ChatGPT™, Copilot®, and Gemini™ are already being used by employees in businesses of every size. But while these tools can drive efficiency, they also introduce risks when used without oversight — from accidental data leaks to compliance violations.  According to recent research highlighted by Forbes, while employers are rapidly investing in AI