HCLTech's AI Growth Raises an Important Question for Software Engineers in India

HCLTech's latest quarterly results have brought fresh attention to the growing role of AI in the Indian IT industry. The company reported stronger-than-expected quarterly profits, supported by increasing demand for AI-related services. Following the announcement, its shares rose by 8.4%, reflecting a positive market response to its performance.

While financial results are important for investors, they also raise a bigger question for students, freshers, and working software engineers: How is the growing adoption of AI changing the skills companies expect from engineers?

Why Does HCLTech's Performance Matter?

Indian IT companies have traditionally built large businesses around software development, IT services, application maintenance, cloud migration, and enterprise solutions. AI is now becoming an increasingly important part of this landscape.

Companies are exploring ways to use AI to automate repetitive tasks, improve productivity, modernize existing systems, and deliver solutions more efficiently. As demand for these services grows, software engineers may find new opportunities in AI integration, backend development, data engineering, cloud infrastructure, and automation.

However, the impact may not be the same across every role. While some areas could create new opportunities, others may see changes in the type of work engineers perform and the skills employers prioritize.

What Does This Mean for Freshers Preparing for Software Engineering Roles?

For students preparing for placements, the question is no longer just about learning a programming language. It is also about understanding how to apply technical knowledge to solve real-world problems.

Here are a few areas worth focusing on:

1. DSA and Problem-Solving

Data Structures and Algorithms remain important for many software engineering interviews. A strong understanding of arrays, trees, graphs, dynamic programming, and problem-solving techniques helps candidates build logical thinking and prepare for coding assessments.

2. Development and Real-World Projects

Solving coding problems is useful, but building applications helps demonstrate how well you can apply your knowledge. Working on projects involving APIs, databases, authentication, deployment, and debugging can help you understand the complete development process.

3. AI Tools and AI Integration

Understanding how AI coding assistants work and using them responsibly can help improve productivity. Beyond generating code, engineers should learn to review outputs, identify errors, test solutions, and understand the underlying implementation. Building applications that integrate AI APIs can also provide practical experience.

4. Core Computer Science Fundamentals

Operating Systems, DBMS, Computer Networks, and Object-Oriented Programming continue to provide the foundation for understanding how software systems work. These concepts remain valuable even as development tools evolve.

5. Adaptability and Continuous Learning

The tools and technologies used by engineering teams will continue to change. Being able to learn new frameworks, understand unfamiliar codebases, and adapt to different project requirements can be just as important as knowing a particular technology.

Is AI Creating More Opportunities or Increasing Competition?

There are two sides to this discussion.

On one hand, AI adoption may create demand for engineers who can integrate AI into existing products, automate workflows, and build new applications. It may also allow smaller teams to develop solutions that previously required more time and resources.

On the other hand, automation could reduce the amount of time spent on certain repetitive development tasks. This may change entry-level responsibilities and increase expectations around productivity, practical knowledge, and independent problem-solving.

The overall impact will depend on the company, role, and type of work involved. Growing AI revenue at an IT services company does not automatically mean that fresher hiring will increase or decrease.

What Should Students Prioritize in 2026?

For someone targeting a software engineering role, a balanced preparation strategy could include:

• Building strong DSA and problem-solving fundamentals.

• Learning at least one programming language thoroughly.

• Developing and deploying practical projects.

• Understanding core CS subjects.

• Learning to use AI tools without depending on them blindly.

• Practising technical interviews and explaining project decisions clearly.

The goal should not be to chase every new AI tool. It should be to develop the fundamentals needed to understand, build, debug, and improve software while learning how new technologies can make that work more effective.

With companies such as HCLTech expanding their AI-related business, the expectations for software engineers may continue to evolve.

What do you think will matter most for freshers entering the software industry?

1. Strong DSA and competitive programming skills.

2. Development skills and real-world projects.

3. AI tools, AI integration, and automation.

4. Core CS fundamentals and system design.

5. A combination of all these skills.

If you are currently preparing for placements or already working as a software engineer, share your perspective. Are you changing your preparation strategy because of AI, or do you believe the fundamentals of software engineering will remain the same?