Blogs

As a business leader in the tech industry, you’ve probably heard of CI/CD and DevOps. Many companies claim to be adopting these practices, but what does it really mean to have a successful CI/CD pipeline, and how is it different from DevOps?

In this article, we’ll explore where most companies go wrong when implementing CI/CD and DevOps, and provide insights on how to get it right.

The Confusion between CI/CD and DevOps

CI/CD (Continuous Integration/Continuous Deployment) and DevOps are often used interchangeably, but they serve different purposes. CI/CD is primarily focused on automating the build, test, and deployment process of software applications. This ensures that changes to codebase are integrated and deployed quickly and reliably.

In today’s rapidly evolving technological landscape, artificial intelligence (AI) has become an indispensable asset for businesses striving to stay competitive. However, building and maintaining a self-sufficient AI team is no small feat—it requires strategic planning, robust training programs, and ongoing support mechanisms. This article outlines a comprehensive plan that decision-makers such as CTOs, COOs, and HR managers can implement to build a formidable AI team from scratch.

Step 1: Assessing Your Organization’s AI Maturity

Before embarking on the journey of building an AI team, it is crucial to assess your organization’s current standing in terms of AI readiness. This involves evaluating whether you have clean, labeled data ready for AI training or analytics. Additionally, identifying business processes that could benefit from automation or prediction helps prioritize efforts effectively.

People-first cloud and AI transformation.

Why Cloud Migrations Fail — And Why It’s Not About the Tech

Cloud promises speed, scale, and efficiency. But for many businesses, the reality is frustratingly different:

Inflated cloud bills Broken workflows Overwhelmed teams And no clear ROI

Why does this happen? Because most cloud migrations aren’t truly transformational — they’re logistical. And logistics don’t win the future.

The 5 Most Common Pitfalls

1. Lift-and-Shift Syndrome

Simply moving legacy systems into the cloud (without rethinking them) often results in:

When most companies think of “digital transformation,” they picture new tools, dashboards, and systems.

But here’s the truth:

You can’t automate what you don’t understand.

And no tool will fix a team that isn’t ready to use it.

At rg elevate technology, we’ve seen the same pattern again and again:

• Businesses invest in AI before they understand their own data

• Cloud migrations happen without internal champions

• DevOps pipelines are built — but no one knows how to maintain them

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In today’s high-velocity digital world, businesses run on distributed systems, microservices, and AI-powered automation. But as complexity grows, so does the risk of performance issues, security breaches, and downtime. That’s where Observability steps in—not just as a buzzword, but as a strategic capability that ensures your systems are healthy, scalable, and future-ready.

At rg elevate technology, we believe observability isn’t optional—it’s foundational to digital transformation.

What Is Observability and Why Does It Matter?

Observability is more than monitoring. While monitoring answers “Is the system working?”, observability digs deeper: “Why is it not working?”

If your software feels more like a bottleneck than a business enabler, you’re not alone.

Outdated systems, inefficient processes, and slow deployment cycles are silently draining productivity and revenue. But forward-thinking companies are breaking out of that cycle — and they’re doing it with a few strategic moves that are both practical and high-impact.

Here’s how you can do the same:

Empower Your Internal Teams

The most sustainable digital transformations are the ones that don’t just bring in new tools — they elevate the people who use them.