Blogs

The slides were ready, the agenda was set, but the mood in the room told a different story. A group of Digital Operations professionals had been pulled from their daily work for an AI workshop. Some had signed up willingly. Others had been told to attend. All of them had an opinion about what they were walking into. Before we started, we asked one question: what do you think about AI? We are deliberate about asking it this way. What people think tells you more than what they

Generative AI has captured the enterprise imagination like few technologies before it. The investment is real, the use cases are ambitious, and the pace of adoption has been unlike anything the business world has seen in recent memory. Alongside that excitement, a question has been growing louder: are we watching the early stages of a bubble? At the market level, that debate is open. Some point to overinflated valuations and capital expenditure that has yet to translate into

The project manager's role is evolving. As AI takes on more of the day to day work, what remains is surprisingly, more human. The tasks that have historically consumed the majority of a project manager's time, scheduling, reporting, risk tracking and resource forecasting, are increasingly being handled by AI with greater speed and consistency than any manual process can match. For project managers, this shift opens up a more expansive version of the role. One where the focus

Project management has always evolved with the tools of its time. AI is the latest shift, and by most accounts, the most significant one yet. AIDVICE explores what it looks like to embrace AI as a capability that improves outcomes and where the AI journey for a project manager really begins. For most project managers, that journey begins in a very familiar place - often with overwhelm. If you're a project manager who has been delaying AI upskilling because you don't know wher

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