Blogs

AI adoption has moved beyond experimentation. Making AI work inside real organisations now requires navigating real-world constraints: messy data, legacy systems, and regulatory requirements. Many companies have invested heavily in tools and pilots, yet turning that effort into lasting business value remains difficult.  At AIDVICE, we’ve observed that the bottleneck isn’t the budget or the technology—it’s organisational readiness. Success depends on how clearly a company alig

“Vibe coding” is the fastest way to build an app today. But the true competitive edge in 2026 is system depth. While Generative AI captures headlines, it is really an amplifier of a much larger foundation.  According to McKinsey , GenAI could add $4.4 trillion in annual value once fully adopted. But when one looks at the total AI value pie, broader AI such as analytics, predictive systems, and deep learning is expected to add far more: between $11 trillion and $17.7 trillion

A mid-career professional sits at their desk, watching an AI tool draft a 10-page report, write complex code, or design a marketing plan in seconds. That cold feeling hits the stomach: “Is my job gone?” The headlines certainly want you to think so. But experience teaches one to be skeptical of the doom-mongering.  The “AI will take your jobs” narrative feels familiar to anyone who has watched technology evolve over time. Similar fears surfaced when the Internet arrived, and a

AI may look complex, but everything about it rests on four simple cornerstones: Ethics, Literacy, Technology, and Tools. If we get these right, everything else falls in place.If we miss even one, the impact can be severe at a personal, organisational, or global level. These four cornerstones might seem deceptively simple. One may ask why only these four, especially for a technology as ground-breaking as AI. These four encompass every dimension we need to worry about , fully

Will SDLC be called AIDLC in the AI world? Here is my AIDVICE. As AI becomes part of almost every digital initiative, a natural question arises: what are the key segments of an AI project, and how do they fit into a traditional SDLC? Or should AI have a lifecycle of its own, something we may soon call AIDLC ? In one of my previous write-ups, I mentioned that AI is also “just”  software, only this time, data drives it end to end, and the algorithms are rooted in probability an