How Udemy Is Powering India’s Shift to Continuous AI Learning

In India, 93% of business leaders intend to use AI agents to extend workforce capabilities in the next 12–18 months. Although this shift opens a wealth of opportunity for innovation and performance, simply introducing AI tools into workflows doesn’t guarantee results.

When everyone is leveraging AI to innovate, improve efficiency, and elevate performance, the real question is: what can leaders do differently to stay ahead?

In an interaction with CiOL, Vinay Pradhan, Country Manager & Senior Director, India & South Asia at Udemy, shared insight into how Indian enterprises can reimagine learning and workforce development to unlock the real potential of AI. He emphasised that organisations must move beyond tools to design continuous, contextual, and role-based learning ecosystems that enable both human and AI collaboration in real time.

With 93% of Indian business leaders planning to integrate AI agents in the next 12–18 months, what does this shift reveal about the future relationship between human talent and AI in enterprise workflows—and how can learning systems evolve to support that balance?

As organizations gear up to embed AI agents into core workflows, we are entering an era of enhanced human-AI synergy. In this dynamic, the future of enterprise success will depend on how well organizations balance this partnership, using AI to augment human potential to the fullest.

This shift calls for a fundamental evolution in how businesses design learning. Technical proficiency will remain critical, but the real differentiator will be adaptive skills—the soft skills to collaborate with AI, think critically, and lead through change. In India, this transition is already underway, with a 90% surge in upskilling around relationship-building and a growing emphasis on skills related to communication, leadership, and change management.

In order to have a competitive edge, organizations must adopt role-based, personalized learning programs that help employees supervise, interpret, and guide AI outputs responsibly. Skills mapping can help identify emerging hybrid roles and recommends targeted personalized learning paths, assisting organizations to bridge skills gaps and build a collaborative work environment.

The next phase of enterprise learning will revolve around helping humans and AI learn, adapt, and evolve together.

Most organisations still treat learning as a one-off intervention rather than an ongoing process. How can enterprises operationalise “just-in-time learning” so that it becomes a natural extension of everyday work rather than an additional task?

In order to make learning a natural part of their everyday work life, organizations must move from scheduled programs to learning in the ‘flow of work’ – continuous, contextual, and role-specific. This means weaving learning moments directly into employees’ daily workflows, so development happens where performance happens.

Businesses can turn to AI-driven skills development tools for scenario-based challenges tailored to each employee's role. They enable employees to apply and practice technical skills in labs and sandboxes, while adaptive skills can be honed through simulations and role-playing exercises that mirror real-world situations. These practical learning environments give employees a safe space to experiment, make mistakes, receive feedback in real time, and continuously refine their abilities.

Cross-functional projects can further expand employees’ perspectives, encouraging collaboration and innovation across disciplines. When learning is personalized, experiential, and embedded within everyday workflows, it stops feeling like an extra task and becomes an instinctive, ongoing habit that drives both individual growth and overall business performance.

As generative AI continues to automate parts of work, what are the most critical new skills that employees need — not just to survive, but to lead in AI-augmented roles?

As AI becomes an inevitable part of business, the real differentiator will be how well people adapt alongside it. The organizations that lead will be those that invest equally in adaptive and technical skills, building a workforce that can think critically, collaborate effectively, and make sound decisions in partnership with AI.

Adaptive skills such as communication, emotional intelligence, problem-solving, and strategic thinking prepare employees to leverage AI tools to guide them in interpreting outputs, challenging assumptions, and connecting insights to business impact. These skills allow employees to translate AI’s potential into meaningful outcomes tailored to their industry, customers, and organizational goals.

In this next chapter, the winners will be those companies that align AI with purpose, ensuring that humans continue to drive the “why” behind the “what.”

How can companies ensure that their AI upskilling initiatives don’t deepen the digital divide within their workforce — where some employees advance rapidly with AI tools while others are left behind?

Upskilling for AI can’t be treated as a one-size-fits-all exercise. It must be designed to match every employee’s goals, role, current skill level, and learning progress. Organizations can rely on AI-based skill acceleration platforms to map skills, identify where employees stand in their learning journey, and define the best path forward for each.

To make learning equitable and continuous, organizations need to bring learning closer to where work happens. When employees can practice and apply new skills in real time, they build confidence faster and stay ahead of change.

Additionally, companies must focus on monitoring actionable analytics and improving workforce-wide program designs. By measuring uptake, completion, and skill outcomes, companies can identify pockets where employees are lagging and intervene proactively to establish a fruitful learning experience.

From your perspective, how should organisations measure the true ROI of AI-driven learning — beyond completion rates or training hours — to link it with tangible business outcomes like productivity, retention, or innovation velocity?

Recent studies show that companies investing in AI-enabled upskilling report up to a fivefold increase in skilled employees and an average productivity boost of 12%. Companies that invest in AI-driven upskilling see clear and measurable gains across multiple dimensions of performance. Employees apply new skills directly to projects, enhance technical support, and adapt quickly to emerging challenges. These improvements in employee performance act as direct indicators of learning impact, showing tangible progress in daily business outcomes rather than serving as a checkbox metric.

Internal mobility provides another indicator of ROI. When employees upskill, companies can fill roles from within rather than hiring externally, saving recruitment costs and minimizing downtime. These savings show that learning programs do more than increase knowledge, driving operational efficiency and continuity.

Adaptive skills are harder to quantify, yet they play a crucial role in sustaining business impact. Improved communication, empathy, leadership, and team resilience strengthen decision-making, collaboration, and the speed of execution, directly influencing innovation velocity.

By tracking applied skills, leadership development, internal mobility, and innovation outcomes, organizations can link learning directly to tangible business impact and overall growth.

In an era of constant disruption, how can corporate learning move beyond tools and platforms to build a learning culture — one that empowers employees to learn, unlearn, and adapt continuously?

In times of rapid change, leadership becomes the foundation of a true learning culture. Leaders must create environments where employees can experiment, make mistakes, and learn from them, turning failures into opportunities for responsible innovation.

A strong learning culture gives employees the freedom to shape change—to adapt new technologies, redefine processes, and build solutions that serve both personal growth and business outcomes. Transformation ultimately moves at the speed of trust, and that trust begins with great leadership.

Modern leaders must act as coaches, catalysts, and role models, encouraging continuous learning and bold experimentation. When leaders champion curiosity and reward adaptability, learning becomes a shared mindset that fuels resilience and reinvention across the organization.

Looking ahead to 2030, what might the ideal “AI-powered learning ecosystem” look like — where technology, data, and human curiosity coexist to future-proof both people and organisations?

By 2030, the most effective learning ecosystems will blend technology, data, and human curiosity to create a continuous cycle of growth. AI will help personalize every learner’s journey by analyzing skills gaps and role requirements in real time to deliver learning solutions that are tied to specific company goals.

Learning will continue to lean more toward active skill application through immersive simulations, interactive labs, and AI Role Plays that mirror real-world challenges. Integrated directly into daily workflows, learning prompts will appear just in time when employees face new tasks or challenges, while having background info on learning goals achieved previously. This will make upskilling a natural part of work rather than a separate activity.

In this future, human talent and AI-powered solutions will work together as true collaborators. Along with technical know-how, adaptive skills such as critical thinking, creativity, ethics, and leadership will define and propel success.

Analytics insights can help leaders align learning with business and employee strategy, track progress, and ensure every employee grows together. With this ecosystem in place, organizations will future-proof their people and business against constant change.

Beyond Tool Deployment

Enterprises often approach AI adoption with a tool-first mindset—deploying agents and automation platforms—but overlook the human layer. Vinay Pradhan’s insights suggest a shift toward systemic, contextual learning, where every AI deployment is matched with human skill readiness.

This evolution reframes AI not as a replacement for human talent but as a co-pilot for human growth. The companies that thrive will be those that treat learning as a continuous, embedded process—tied to performance, productivity, and innovation velocity.