As AI models become dramatically more capable and widely accessible, we’re entering the new era of the agentic enterprise. As AI systems have been moving from insight to action, the conversations around AI transformation have evolved. They now revolve around capabilities such as faster decision-making, autonomous workflows, and machine-led execution. 

Where does this leave people? Employees grappling with this shift often feel unsure of their relevance in an uncertain future. The fears are understandable given that AI is not just improving efficiency. It is reshaping how work gets done, how performance is measured, and how value is created. 

However, one thing is clear: the success of the agentic enterprise will depend less on the intelligence of systems and more on people's readiness to handle this massive redesign of work. 

As per the Stanford AI Index 2025 report, India leads globally in AI talent acquisition, with an average annual hiring rate of 33%. India also boasts one of the most AI-literate workforces globally, second only to the U.S., supported by extensive domestic data ecosystems across sectors such as health, agriculture, finance, education, and public administration. This opens up a massive opportunity for enterprises and employees alike to take advantage of the AI opportunity. 

Humans will remain central to organisational success 

As AI takes on a larger chunk of execution, human contribution becomes more critical. Navigating ambiguity, exercising judgment, managing relationships, and making ethical decisions—these skills remain fundamentally human. These are not edge cases; they are central to leadership, trust, and organisational culture. 

In fact, as output becomes easier to generate, the ability to validate, interpret, and apply it becomes the true differentiator. Simply having a “human in the loop” is not enough. Companies need humans who can challenge systems, contextualise insights, and take responsibility for outcomes. Without this, the agentic enterprise may become efficient, but not necessarily effective. 

Understanding the impact of AI across different layers can help craft an approach for upskilling across various job roles. For instance, task automation is already transforming high-volume, rules-based work, such as classification, summarisation, and basic drafting. When these tasks dominate a role, automation can feel like displacement because it directly reduces working hours. 

In most roles, AI can act as a co-pilot to augment tasks such as writing, analysis, planning, and decision support. This also means the role of humans is substantially elevated in executing the task. 

Administrative and process-heavy functions may experience the greatest impact from consolidation or automation. Here too, the real shift lies in evolving expectations: what employers value, how outcomes are measured, and which skills signal future relevance. 

Building an agentic-ready workforce 

Enterprises must take a deliberate and structured approach to workforce enablement to ensure that people remain at the centre of this transformation. Here are some considerations for upskilling programmes: 

· Build knowledge, not just skills: Rather than focussing on technical expertise alone, organisations must invest in broad-based literacy around interpreting and questioning data, understanding AI capabilities and limitations, risk awareness, etc. This creates a workforce that can engage with AI critically, not passively. 

· Judgement and discretion: As AI-generated content scales, human judgment becomes even more valuable. Therefore, training must involve stress-test decisions and applying contextual understanding. This is particularly critical in high-stakes functions such as hiring, performance management, and strategy. In addition, skills in leadership, problem-solving, collaboration, and communication will see the greatest demand. With these, individuals are better equipped to navigate complexity, lead change, and work effectively in human-AI teams. 

· Strengthen domain expertise: A deep understanding of industry context, customer needs, and operational constraints will be one of the biggest differentiators for high performers. The ability to connect technology to real-world outcomes is most crucial. 

Leading by example 

Leaders must model the behaviour shift that they want to see. This means actively experimenting with new tools and evaluating their usefulness in real business contexts. 

While formal training is useful, a vibrant culture of learning and inquiry is even more critical. Workforce transformation cannot be treated as a parallel initiative to technology adoption. It must be embedded into it. Therefore, ensuring that AI systems are designed with human workflows in mind while also creating psychological safety for experimentation and change will be important for organisations. 

In the agentic AI era, the most resilient professionals will be those who build capabilities that travel across tools and contexts, rather than mastering a single tool. Also, the most successful organisations will not be those that deploy the most advanced AI, but those that take their people along and create environments where people can adapt, learn, and lead alongside it. 

(The author is managing director-India, Snowflake. Views are personal.)