In the Age of AI, What It Really Means to Be a Software Professional

In the rapidly evolving world of technology, AI is rewriting the rules of the game. Given the new ways of learning and working, what it means to be a software professional? The shift is far deeper than swapping one tool for another. As automation takes over routine coding, the real currency is no longer just syntax mastery but the ability to solve problems, architect solutions, and lead execution. In this wide-ranging conversation with CiOL, Dr. Pavankumar Gurazada, Associate Director, AI/Data Science at Great Learning, unpacks how soft skills are becoming a force multiplier in an AI-first world, why relevance in tech education comes from real-world grounding rather than hype, and how learning models must adapt to the time and financial constraints of professionals outside big-city tech hubs.

From the changing nature of the developer’s role to the disciplined process Great Learning uses to integrate emerging tools like AI agents and low-code platforms, Dr. Gurazada offers an unvarnished, practical perspective on building future-ready talent. This isn’t about chasing buzzwords—it’s about blending technical rigor with the human skills that AI can’t automate. Excerpts.

The push for soft skills is being hailed as the new secret sauce for tech careers. But let’s be honest, can communication or critical thinking really offset shrinking demand for core coding roles in an AI-first world? What’s the deeper value proposition here for coders?

The growing emphasis on soft skills isn’t about replacing technical expertise; it’s about redefining what makes a technology professional effective and future-ready. As AI begins to automate routine coding tasks, the demand is shifting toward professionals who can go beyond execution—those who can understand context, solve complex problems, and contribute to cross-functional teams.

We have observed this shift closely. While coding fundamentals remain essential, what increasingly sets learners apart is their ability to think critically, communicate effectively, and make strategic decisions in real-world environments. These are not peripheral skills; they are core to thriving in modern tech roles where AI is a collaborator, not just a tool.

The deeper value proposition for aspiring coders lies in building end-to-end capability, understanding how to frame problems, architect solutions, and drive outcomes. Our programs are designed to support this through a combination of technical rigor, structured problem-solving, and collaborative learning. Learners work on capstone projects that mirror real industry challenges, applying both technical and soft skills in a setting that demands analytical thinking, team coordination, and clear communication of outcomes.

In a world where AI can generate code, the true advantage lies in knowing what to build, why it matters, and how to lead its execution—and that’s where soft skills become a force multiplier, not a fallback.

Almost every edtech platform today says it’s preparing learners for the AI era. But we also hear growing fatigue around a lot of buzzword overload. How does Great Learning ensure its courses stay relevant—and more importantly, credible—in a fast-shifting AI landscape?

You have highlighted a significant challenge in education today. The sheer volume of hype around AI can be overwhelming. To counteract this, we’ve built our curriculum design around a core principle: grounding our content in proven, real-world application.

Our in-house team of data scientists and academicians continuously maps the AI landscape, but they don’t do it in a vacuum. A key source of insight comes from our extensive enterprise training programs. By teaching the workforce of leading companies, we gain invaluable, real-time understanding of their operational needs and strategic goals. These practical learnings are systematically distilled back into our course design, ensuring our public curriculum reflects how AI is actually being applied in practice, not just in theory.

This brings me to a crucial point about credibility. While a course must be relevant, its true value is measured by its impact. That’s why we constantly solicit feedback from our alumni well after they’ve completed their courses. This helps us gauge whether our programs are delivering measurable career impact—are our graduates getting promoted? Are they leading new projects? This post-course validation is essential.

Combined with the guidance from our top-tier university and enterprise partners, these feedback loops allow us to separate the reliable trends from the hype. We don’t just teach AI; we build a learning ecosystem that evolves with it, always anchored to academic credibility and proven career outcomes.

We keep hearing that ‘continuous learning’ is the answer. But that’s easier said than done, especially for working professionals in Tier 2 or Tier 3 cities juggling jobs, finances, and family. How do we address that harsh reality?

Time constraints are a challenge for working professionals across the globe, regardless of their city or location. We recognized this even before starting Great Learning and built our entire learning model around solving this issue. Over the past six months, 54% of the demand for our courses has come from learners in Tier 2 and Tier 3 cities, underscoring the widespread need for flexible, effective learning solutions.

Addressing Time Constraints: At Great Learning, we recognized this challenge early and built our entire learning model to address it head-on. Pure self-learning often fails due to lack of structure and support, while full-time programs are simply not feasible for most working professionals. That’s why we created a mentored learning model using the flipped classroom approach: conceptual learning via pre-recorded videos from top faculty, followed by live weekend sessions with industry mentors, bringing both flexibility and personalization to even the busiest learners.

Addressing Affordability: Academic research and our own experience have consistently shown that robust learning outcomes require far more than just high-quality content. Learners also need timely doubt resolution, project guidance, hands-on support, and career assistance to truly succeed. However, delivering these essential elements at scale has traditionally been expensive, making such programs inaccessible for many.

To solve this, we’ve leveraged our AI-powered learning assistant, GLAIDE, to bring these high-impact components to learners in a more cost-effective and scalable way through our newly launched Great Learning Academy Pro.

The return on investment for upskilling is still a gray area for many learners. Beyond completion certificates, what does real career impact look like in your view, and how does Great Learning track that over time?

We design our programs with this deeper impact in mind. The rigor of our curriculum, combined with its practical, industry-aligned nature, equips professionals to contribute more effectively in their roles. For many of our learners, particularly those with a decade or more of experience in mid-to-senior-level roles, the outcome isn’t just technical upskilling but enhanced decision-making capabilities, greater executive presence, and the ability to lead teams and initiatives with confidence.

We see ourselves as career enablers, not just course providers. Over time, many of our learners have transitioned into more strategic roles, taken on leadership positions, or pivoted to high-growth domains—outcomes we track through ongoing alumni engagement and career support initiatives.

A testament to this long-term value is that many of our alumni choose to return as mentors, guiding future cohorts. It reflects both the trust they place in the platform and the tangible impact the learning experience has had on their own professional journeys.

With AI automating entire workflows, the nature of coding itself is changing, from hands-on execution to more strategic oversight. What does this mean for the traditional idea of a software developer? Are we heading toward a world where ‘basic coding knowledge’ is enough?

AI is definitely changing the way we develop software. With automation handling more of the routine coding, like generating boilerplate or writing tests, developers are spending less time typing every line and more time thinking about the bigger picture. It is becoming less about hands-on coding and more about guiding AI tools, designing systems thoughtfully, and making sure everything works well, securely, and at scale. So, rather than replacing developers, AI is shifting their role toward being strategists and problem solvers.

As for whether basic coding skills will be enough going forward, it really depends on what you want to do. For some roles, especially those outside core engineering, knowing the basics plus using AI tools can be enough to get a lot done. But for anyone building real, complex systems, deep technical knowledge is still critical. Developers need to understand how systems work under the hood and be able to spot when AI-generated code needs tweaking or a complete rethink. In the end, AI is not making coding obsolete; it is just changing what good developers focus on.

Finally, how are your own course design teams staying ahead of the curve? Do you have a built-in process to refresh content based on emerging tools and roles like prompt engineering, AI agents, or low-code architectures?

That’s a crucial question, as it gets to the heart of how to build a truly effective tech curriculum. Our process is built on the understanding that course design must be stable conceptually while remaining current tool-wise.

First, we establish a robust foundation. Our in-house team of academics and data scientists designs the core curriculum around timeless principles. This ensures that the learning is not tied to a single tool that might become obsolete.

Second, we have a structured process for tool refreshes. We don’t chase every new trend. Our teams are tasked with this as a key performance indicator—to constantly test and evaluate emerging tools, such as new AI agent frameworks or low-code platforms. We integrate them into the curriculum only after we’ve validated their stability and industry adoption. This prevents our learners from wasting time on fleeting fads.

Finally, we use our network of industry mentors as a dynamic, real-time resource. These professionals join live sessions to provide specific, on-the-ground insights. For instance, while the curriculum teaches the fundamentals of prompt engineering, a mentor can demonstrate a brand-new technique or tool they just adopted at their company. This layered approach ensures our content is always rigorous, relevant, and directly aligned with the evolving needs of the industry.

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