In this Digital era, getting your own hands into every operational activity is not the right thing to do. There are IT transformation consulting experts whose experience and skill set can be leveraged to reduce time, effort, and cost for your organization. In that context, today’s organizations are delivering more and more into cloud-based infrastructure.

How are AI governance and data governance related? Better still, what’s more important for an organization to focus on, AI-powered data governance or AI data governance? These are important questions, but before we answer these, let’s understand how AI and data governance are related to each other.

Version managers, in general, are hacks. But they are useful hacks, as they solve a real problem: we all work on multiple projects at the same time, and each project needs a different version of tooling like Go, Python, or Ruby. It’s not enough to install a single version system-wide and hope for the best. Even if we can get away with using a single version across all projects, it would only be temporarily and accidentally working.

The journey from a great idea for a Generative AI use case to deploying it in a production environment often resembles navigating a maze. Every turn presents new challenges—whether it’s technical hurdles, security concerns, or shifting priorities—that can stall progress or even force you to start over.

When faced with new innovations like GenAI, every company must avoid pitfalls that result in unscalable and unsustainable solutions.

There’s no question which technology everyone’s talking about in retail. Generative AI continues to promote incredible levels of interest with its promise of next-level productivity and new kinds of employee and customer experience. It’s all happening at light speed. When ChatGPT burst onto the scene, it gained hundreds of millions of users in a matter of months.