Welcome To Our Blog

The right kind of training can reduce revenue leakage in IT services companies. Here’s how.

CEO, Nuvepro

The number of open positions in IT companies is perhaps the largest in the history of the industry. A significant chunk is led by the demand for expertise in cloud technologies, such as Microsoft Azure, AWS and GCP. Several clients have these open positions, and it is easy revenue for IT companies if they can cater to their needs.

However, not many of these positions are being filled. While the recruitment teams are spending sleepless nights to get a good resume, the training teams are working hard to upskill the employees. In this blog, let us focus on how the training (or lack of it) is leading to revenue leakage.

Failing Certifications

Almost all IT companies, depending on their size, have invested millions of dollars on training their employees and getting them ready for the cloud technologies. Most of the focus was on the employee getting certified than building real-world execution skill. This tactic worked for a while and clients were happy to hire the certified individuals. After a while, clients have realized that the certification is not a guarantee to the ability of the individual to execute the project. This made the clients to insist on interviewing the candidate and testing their execution knowledge leading to increased rejections. These rejections are directly impacting the fulfilment and hence the revenues. What could have been easy revenues is now being a face-saving effort.

Missing the plot

So, where did the IT companies miss the plot? Yes, the temptation of vanity certifications is far too greater than the desire for practical knowledge. I don’t have anything against the certifications. It is just one part of the equation and gives a good base of theoretical knowledge. The other part is to instill the confidence through continuous practice and the ability to execute it right the first time. Just stopping at certifications is as dangerous as showing an individual some training videos on how to ride a bike, giving license and leaving them on any of the Bangalore roads.

What must be done now?

In short, complete the cycle of learning. Add the playbook type of practical exercises and assessments based on real-world configuration. Case in point – Here’s how some of our clients have done it:

Nuvepro has an offering called, Challenge Labs. It has a pre-configured lab environment that is as close to the environment that an engineer works in real work. The engineer is given a bunch of practical challenges to solve using the labs. It is like the test that happens before the license is given. Our clients have set up these challenge labs and assess the individuals’ ability to execute before certifying them or placing them on a project.

The result? Our clients increased the success rate of customer interviews so much so that customers waived off the mandatory interview process. The revenues are back and the engineer works with high confidence and engagement.

What next?

Challenge your employees. Talk to our team if you want to set up a pilot of the challenge labs. It just takes a few minutes. The point is, whether you come to Nuvepro or not, do challenge your employees on their practical knowledge. You should do it than allow them to fail in the real world. Of course, you don’t have to let go of any revenue, instead improve your brand as experts.

Sign up for Newsletter

Our Latest Posts

Agentic AI

Agentic AI Training: Building AI Agents that Enhance Human Potential, not replaces it 

Artificial Intelligence (AI) has moved beyond buzz. It’s no longer just about automating repetitive tasks; it’s about creating intelligent, decision-making agents that collaborate with humans to achieve better outcomes. This new paradigm is called Agentic AI—an AI that doesn’t just “do” but can “act,” “decide,” and “learn” in context.  The future of work, learning, and business lies not in machines taking over but in humans and AI working together—side by side.  In today’s fast-paced digital world, artificial intelligence (AI) is no longer a futuristic concept—it’s an everyday reality. We see AI in the recommendations we receive while shopping online, in the chatbots that answer our queries, and even in the smart assistants that help manage our schedules. But as we stand at the edge of the next major shift in technology, a new kind of AI is emerging: Agentic AI.  So, What is Agentic AI?  To put it simply, Agentic AI refers to AI systems that don’t just sit passively waiting for instructions. Instead, these AI systems—or AI agents—can actively take decisions, plan actions, and execute tasks autonomously. They are designed to think, learn, and act in ways that resemble human decision-making.  Imagine an assistant that doesn’t just provide you with information when you ask but can also suggest the best course of action, take that action, and adapt its approach based on the outcome. This is what Agentic AI brings to the table.  How Does Agentic AI Differ from Generative AI?  Generative AI, like ChatGPT or DALL·E, creates content—text, images, audio—based on the prompts it receives. While this is incredibly powerful, it is inherently reactive. It needs human direction to function.  Agentic AI, on the other hand, is proactive. It doesn’t just create—it understands goals, makes decisions, executes tasks, and learns from the results.  Traditional AI vs. GenAI vs. Agentic AI: What’s the Difference?  The world of Artificial Intelligence has seen a rapid transformation over the years, moving from simple automation to content generation, and now to intelligent action. To truly understand where Agentic AI fits in this evolution, it’s essential to differentiate it from Traditional AI and Generative AI (GenAI).  Traditional AI was built to automate repetitive, well-defined tasks. These systems operate by following pre-programmed rules, making them highly reliable in structured environments. Think of early chatbots, fraud detection models, or robotic process automation (RPA). They work well for what they were designed to do, but they lack adaptability and struggle with handling complex or ambiguous situations.  Then came Generative AI (GenAI)—the type of AI that captured global attention. GenAI models like ChatGPT or Midjourney are trained on vast amounts of data to generate creative outputs—be it text, images, music, or even code. These systems are excellent at mimicking human creativity and providing interactive, human-like responses. However, they remain reactive—they can only respond based on the prompts they receive. They don’t pursue goals or make independent decisions.  Now we’re entering the age of Agentic AI—a transformative leap where AI is not just generating content but actively working toward achieving specific outcomes. Agentic AI is capable of decision-making, adapting to different environments, and learning from the results of its actions. Unlike GenAI, which waits for a prompt, Agentic AI can take the initiative, set priorities, and collaborate deeply with humans to meet business objectives. For instance, AI agents are already being used in customer support, healthcare diagnostics, and adaptive learning platforms—helping businesses not just save time but actually drive measurable outcomes.  The key difference lies in how these systems operate: Traditional AI is rule-based, GenAI is creative and predictive, and Agentic AI is autonomous and outcome-driven. While traditional systems help with repetitive tasks and GenAI assists with content creation, Agentic AI focuses on taking actions that move the needle—whether it’s improving customer satisfaction, reducing operational costs, or accelerating workforce readiness.  Ultimately, Agentic AI doesn’t aim to replace human potential; it aims to amplify it. It’s where autonomy, intelligence, and human partnership come together to create value in ways we’ve never seen before.  Why is Agentic AI Gaining Traction?  Agentic AI is rapidly gaining traction because today’s business environment has become far too complex, fast-paced, and data-driven for traditional systems to keep up. Organizations are facing massive amounts of data, shorter decision-making windows, and mounting pressure to innovate and stay ahead of the competition. Relying solely on manual processes, static automation, or even conventional AI models is no longer enough.  This is where Agentic AI comes in. By bringing autonomy, intelligence, and adaptability together, Agentic AI helps businesses make quicker, smarter decisions while significantly reducing the risk of human error. It enhances efficiency, boosts productivity, and enables organizations to respond to market shifts in real time—something that’s becoming essential in today’s volatile economy.  Industries such as finance, healthcare, manufacturing, and retail are already seeing the impact. From automating complex workflows to delivering personalized experiences and optimizing operations, Agentic AI is not just a buzzword—it’s becoming a strategic necessity for businesses that want to stay competitive, resilient, and future-ready.  Agentic AI helps businesses:  The Inner Workings of Agentic AI:  While the technical side of AI can sound complicated, the way AI agents actually work is pretty easy to understand when we break it down into simple steps. Think of an AI agent as a super-efficient virtual employee that not only gets things done but also learns and improves over time.  Here’s how it works:  Perception: First, the AI gathers information from different sources. This could be anything—text, images, voice commands, or real-time business data. It’s like the AI “listening” or “observing” what’s going on.  Thinking: Next, it processes this information using pre-trained models, built-in logic, or sometimes even symbolic reasoning. This is where the AI analyzes what it has seen or heard and makes sense of it.  Planning: Once it understands the situation, the AI figures out the best possible action to take. It’s like drawing up a quick plan of what needs to happen next.  Execution: With the plan ready, the AI takes action. This could be something as

Read More »
Skilling

How Leading Enterprises are Redefining Skilling ROI Through Project-Ready Execution with Agentic AI 

Having a skilled workforce isn’t your competitive edge anymore—having a workforce that’s ready to deliver from Day Zero is.  Enterprises are spending millions on various skilling platforms, technology skills training, certifications, and content libraries. Yet project delays, missed KPIs, and bloated bench time continue to bleed margins. Why? Because knowing something doesn’t guarantee doing it, especially when delivery demands speed, precision, and accountability from day one.  This is where the game changes.  Agentic AI is redefining how enterprises validate, deploy, and trust skills—not by tracking learning paths, but by measuring real execution inside real-world hands on learning environments. It’s not assistive AI. It’s autonomous, outcome-linked intelligence that sees, scores, and scales what your business needs most: project-readiness solutions that moves the needle.  If you’re still skilling for completion rates and hoping it translates into delivery, you are already falling behind. It’s time to flip the model.  Agentic AI Is Quietly Reshaping How Enterprises Work—And It Shows in the Numbers  For years, AI investments have hovered in the realm of “innovation budgets” and experimental pilots. But now the conversation has shifted—from potential to proof. Agentic AI is now delivering measurable ROI across the enterprise workforce stack: in bench cost reduction, faster deployment cycles, real-time resource optimization, and improved project margins.  And unlike traditional upskilling or automation tools, Agentic AI isn’t just an assistant—it’s an active agent in execution.   It doesn’t just suggest, it acts. It doesn’t just train, it validates. It doesn’t just track progress, it drives outcomes.   That shift—from passive to proactive—is exactly why enterprises are now seeing tangible business value. Agentic AI is quietly reducing waste, increasing agility, and freeing up millions in hidden productivity losses.  If you’ve been wondering whether Agentic AI justifies the investment—the numbers now speak for themselves. Here’s a breakdown of where the ROI is showing up, and how it’s redefining workforce transformation at scale:  Realizing Business Outcomes with Agentic AI: What Enterprises Must Understand  The evolution of artificial intelligence has moved far beyond automating simple tasks. Today, enterprises are stepping into a new phase with Agentic AI—AI systems that can independently plan, make decisions, and act in complex environments with minimal human guidance. While this concept may sound futuristic, it’s already becoming a practical priority for businesses focused on productivity, scale, and intelligent operations. Most enterprise wide workforce skilling solutions stop at learning. Agentic AI, however, enables intelligent action — making decisions, adapting to workflow changes, providing AI powered skill mapping and executing project-aligned goals autonomously.  According to recent projections by Gartner, the adoption curve for Agentic AI is steep and undeniable.  These are not just hopeful numbers. They reflect a growing need among organizations to move past isolated automation and toward something more holistic—systems that don’t just support work but actually carry it forward.  Agentic AI enables this by introducing a layer of autonomy into workflows. It’s no longer about training a model to respond to prompts—it’s about deploying AI agents that can monitor AI-powered learning environments, interpret changes, take action, and continuously optimize their performance. This capability makes them far more adaptable than traditional rule-based automation or even virtual assistants.  However, unlocking the value of Agentic AI requires careful planning. Gartner cautions that organizations should not rush into adopting agents across the board. Instead, enterprises should start by identifying clear, high-impact use cases where the return on investment is measurable—whether that’s in reducing operational overhead, improving speed of execution, or enabling decisions that were previously bottlenecked by manual processes.  One of the biggest barriers to adoption is legacy infrastructure. Many current systems were never designed to support autonomous agents, which makes integration costly and complex. In some cases, businesses may need to rethink and redesign entire workflows to accommodate the level of independence Agentic AI brings. This redesign, while effort-intensive, is often necessary to realize the full benefits of intelligent automation.  Gartner’s guidance emphasizes the importance of focusing on enterprise-wide productivity rather than isolated task improvements.   Agentic AI should be positioned where it enhances business outcomes through tangible metrics—reducing cost, increasing quality, accelerating delivery, scaling operations and also act as a skill assessment platform. Organizations can take a phased approach: use custom AI assistants for simple data retrieval, automation for repeatable tasks, and build AI agents for decision-making and goal-oriented execution.  Agentic AI isn’t just about making systems smarter—it’s about making businesses faster, leaner, and more resilient. The potential to drive meaningful change is here. But to turn that potential into measurable business value, enterprises must adopt with clarity, strategy, and the willingness to reimagine how work gets done.  Rethinking Skilling in the Age of Agentic AI: Why Nuvepro Delivers What Enterprises Truly Need  Over the last decade, AI has slowly become embedded into the learning and skilling ecosystem—recommending courses, analyzing assessments, or helping L&D teams map career paths through Generative AI learning paths. But a major shift is now underway.  We are moving into the era of Agentic AI—a phase where AI systems are no longer passive assistants, but proactive agents capable of reasoning, acting, and adapting based on real-world goals. And in the world of workforce readiness, this shift calls for something more than traditional assessments or generic training paths.  Enter Nuvepro.  While many platforms are evolving to keep pace with AI trends, Nuvepro was built from the ground up with one core belief: skills only matter when they translate to delivery. That’s why Nuvepro has positioned itself not as another content provider or skill validation assessment engine, but as a full-fledged platform to create project-readiness solutions through AI-driven, real-world skilling experiences. Nuvepro transforms enterprise wide skilling solutions into an active, measurable, and delivery-ready model. This isn’t theoretical AI — it’s AI that builds AI agents and deploys AI agents for enterprise that understand your workflows and accelerate project readiness and business outcomes.  From Skill Awareness to Project Readiness  A lot of learning platforms focus on skill visibility. They provide assessments, benchmarks, and dashboards that tell you what your employees might know. But knowing is only half the equation.

Read More »

GenAI Adoption Maturity: Bridging CTO Innovation and CIO Integration Through Skilling – Insights from Nuvepro’s COO

Generative AI (GenAI) is reshaping how organizations think about automation, creativity, and productivity. Yet, despite its promise, GenAI adoption remains fragmented – largely driven by CTO-led experimentation, with CIOs cautiously observing from the sidelines. The missing link? Skilling. Without a skilled workforce and a culture of responsible innovation, GenAI risks stalling before it reaches enterprise maturity. The GenAI Adoption Maturity Curve  To understand the dynamics of GenAI adoption, we can visualize three overlapping trajectories:  Skilling: The Strategic Enabler  Skilling is not just a support function – it’s a strategic enabler that:  Creating a Conducive Environment for Skilling  To accelerate GenAI maturity, organizations must invest in:  Skills Validation: The Fail-Safe for Enterprise Readiness  Skilling alone isn’t enough – skills must be validated in real-life scenarios. This ensures:  Real-world simulations, hands-on labs, and scenario-based assessments are essential to move from learning to readiness.  Real-World Lessons from Early Failures  Early adoption has shown that enthusiasm without structure can lead to missteps: These failures underscore the need for skilled, validated, and responsible adoption.  Skilling as the Bridge – Enabled by Nuvepro  GenAI’s journey from innovation to enterprise integration hinges not just on technology, but on capability building. Organizations must empower their teams to experiment responsibly, build confidently, and scale sustainably.  This is where Nuvepro plays a pivotal role. With its hands-on skilling solutions, Nuvepro provides:  By partnering with Nuvepro, enterprises can bridge the gap between CTO-led innovation and CIO-led transformation, ensuring GenAI adoption is not just fast – but also safe, scalable, and sustainable. 

Read More »
Categories