What IT Leaders Must Rethink About Talent in the AI Era

Sundar Ramachandran, Head of IT, Tata Elxsi in an interaction with CIOTechOutlook shared his views on what fundamental shifts IT leaders must make in their talent strategy to stay competitive in a rapidly automating enterprise landscape, how organizations should redesign workforce models to balance AI-driven automation with high-value human capabilities such as problem-solving, creativity, and systems thinking and more.

Sundar Ramachandran is Head of IT at Tata Elxsi, driving enterprise-wide technology strategy and digital transformation initiatives across multiple sectors. He has previously held leadership roles at Tata Motors, JSW Group, and Wipro, delivering large-scale IT and digital programs. With deep expertise in IoT, AI, enterprise systems, and data centers, he has led high-value accounts and built high-performing teams at scale. Sundar continues to drive innovation through AI-powered service transformation and operational excellence.

In the AI era, what fundamental shifts must IT leaders make in their talent strategy to stay competitive in a rapidly automating enterprise landscape?

As AI adaption increases, IT leaders need to anchor talent strategy in continuous reskilling of the existing workforce; ensuring people grow with AI rather than treating skills as a one-time hiring decision. Industry research indicates that organizations that invest in workforce upskilling are more likely to realize measurable business value from AI adoption. Because AI capabilities change frequently, organizations must ensure teams remain updated on emerging tools, models, and practices. This transformation cannot be limited to technical teams alone; AI adoption affects functions such as finance, legal, and business operations. Companies increasingly need AI literacy across the entire workforce rather than a small group of AI specialists. Therefore, the strategic priority for IT leaders is building an enterprise-wide AI-ready workforce through ongoing learning and experimentation.

How should organizations redesign workforce models to balance AI-driven automation with high-value human capabilities such as problem-solving, creativity, and systems thinking?

Organizations must redesign workforce models around a “human-in-the-loop” framework where AI accelerates processes while humans validate outcomes. In regulated sectors such as healthcare, AI can support testing and regulatory tasks, while human oversight remains essential to validate outputs and ensure compliance. Many studies on generative AI suggest the same idea: AI works best as a helper, not a replacement for experts. It can speed up tasks and suggest options, but real-world judgment still belongs to people. Consequently, future workforce models must combine automation efficiency with human creativity, systems thinking, and accountability.

With generative AI accelerating productivity, what new technical and cross-functional skills will define high-performing IT teams in the next five years?

Generative AI is reshaping the skills required for IT teams, shifting the focus from manual development toward AI-assisted creation and rapid prototyping. Many applications can be generated through prompt-based interactions rather than traditional development workflows. Hence, prompt engineering is a critical competency. Teams must learn to use precise prompts to extract accurate outputs from large language models.

Another critical skill of the future is the ability to understand token usage, cost optimization, and infrastructure efficiency when deploying AI models. Organizations are also building internal tools to monitor GPU utilization and optimize AI workloads dynamically. For instance, we have built internal tools such as our Demeter application to monitor LLM usage and GPU utilization, and to optimize AI workloads dynamically. The roles combining generative AI expertise, cloud infrastructure knowledge, and system optimization will define high-performing teams. Therefore, the future IT workforce must combine AI engineering capabilities with operational efficiency and cost awareness.

How can IT leaders future-proof their talent pipeline amid rising demand for AI architects, data engineers, cybersecurity specialists, and cloud-native professionals?

Demand for AI architects, data engineers, cybersecurity specialists, and cloud-native experts are growing quickly in India’s competitive talent market. Traditional employee retention methods are no longer enough. Organizations are now focusing on building future-ready talent by investing in continuous learning and giving employees’ real exposure to new technologies. Instead of limiting development to training programs, many companies are creating opportunities for teams to work with emerging technologies in real-world situations. This approach especially appeals to younger professionals, who prefer roles that let them explore, experiment, and innovate rather than repeat routine tasks.

What role should continuous learning, internal mobility, and AI-led upskilling platforms play in building an adaptive and resilient IT workforce?

Continuous learning is very important for building an IT workforce that can adapt to the AI era. Companies need to create safe systems and clear rules that let employees try new technologies without risking sensitive company data. When people feel safe to experiment, they learn faster and come up with better ideas. Training programs should also give employees chances to use the skills they learn. Instead of only learning theory, they should be able to test ideas, build small solutions, and improve them through practice. Many companies are also creating internal marketplaces for employees to share the AI tools or solutions they build. We are building a similar internal marketplace at Tata Elxsi. Such platforms allow teams to reuse useful tools instead of building everything from scratch. Instead of relying only on hiring new talent, most companies plan to meet the growing demand for AI skills by retraining their existing workforce at scale. This encourages employees to move into new roles, learn new skills, and contribute to innovation within the company.

As AI becomes embedded into enterprise workflows, how must leadership evolve to manage ethical AI adoption, governance, and responsible innovation within IT teams?

As AI becomes part of daily work in many companies, leaders need to set clear rules to make sure it is used in a safe and responsible way. Companies can add simple safety steps, such as secure browsers and limits on what data AI tools can access. These steps help stop sensitive company information from being shared by mistake when employees use AI tools. It is also important to have clear guidelines that explain what employees are allowed to do with AI and what they should avoid. This will help them use AI with more confidence and fewer risks.

We have set up an AI governance council has been created to guide how AI is used at Tata Elxsi. This group includes IT leaders, members from the AI Center of Excellence, CTOs from different business units, and quality leaders. The council meets regularly to review how AI is being adopted and to set the right safeguards. Their goal is to support new ideas while also protecting security and meeting compliance requirements. With this kind of structure in place, companies can introduce AI in a careful and responsible way across the whole organization.

In a hybrid and globally distributed work environment, how can IT leaders foster collaboration, culture, and innovation while integrating AI into everyday operations?

In today’s workplaces, many teams work from different locations and even different countries. Because of this, companies need safe digital spaces where employees can work together, test ideas, and build new solutions using AI tools. Online collaboration platforms help teams share the AI solutions they create inside the company.

At the same time, companies must watch their systems carefully. They need strong monitoring and incident-management processes to quickly detect policy violations or possible security risks. Cybersecurity experts also highlight the need for proactive monitoring and AI-based threat detection. These tools help organizations spot unusual activity early and respond before problems grow bigger.

By combining strong collaboration platforms with clear governance and security practices, companies will build an environment fostering innovation and growth while company systems stay secure.