AI as a Learning Partner: Insights from Nandita Giri on the Future of Enterprise Learning

This month’s WomenTech spotlight featured Nandita Giri, Senior Software Engineer at Microsoft, who led a thought-provoking session on how AI, large language models (LLMs), and autonomous agents are reshaping enterprise learning. Drawing on her deep experience as both a technologist and a mentor, Nandita guided attendees through the rapidly shifting learning landscape—highlighting the opportunities, challenges, and future possibilities of AI-powered upskilling.

She opened by underscoring the urgency of reskilling in today’s fast-changing workplace. “The World Economic Forum predicts that over 50% of employees will need reskilling by 2027,” she noted, pointing to AI as not just a tool, but “an intelligent collaborator—your co-worker who never takes a break.”
 

Why Traditional Corporate Learning Falls Short

Nandita highlighted the limitations of traditional training programs: one-size-fits-all modules, outdated content, and low engagement rates. Annual compliance trainings, for example, often see completion rates under 40% without repeated reminders. In contrast, AI-powered learning adapts to each individual, integrates into daily workflows, and offers more dynamic, personalized experiences.

How AI and Agents Transform Learning

LLMs and autonomous agents are at the core of this transformation. While LLMs like ChatGPT can understand context and generate meaningful responses, agents can go further—planning tasks, searching resources, and proactively offering training before performance gaps widen.

Imagine, Nandita explained, an AI system that detects when a sales representative’s metrics are slipping and immediately recommends a targeted learning module before results decline. Or a conversational co-pilot embedded in productivity tools, ready to generate a risk-mitigation plan or guide an engineer through writing code in real time.

The Four Pillars of AI-Powered Learning

To structure this transformation, Nandita introduced a four-pillar framework:

1. Intelligent, scalable ecosystems that curate and deliver continuously updated learning content.

2. Personalized upskilling that identifies gaps at both individual and team levels and recommends just-in-time training.

3. Immersive simulations and co-pilots that provide hands-on, experiential learning through VR, AR, and real-time AI role-play.

4. Responsible AI governance, ensuring transparency, fairness, and data privacy in every learning interaction.

The last pillar, she emphasized, is the most critical. “AI in learning is not just about capability—it’s about responsibility. Personalization must be transparent, content must be checked for bias, and governance has to build trust.”
 

Case Studies in Action

Nandita illustrated the impact of AI with real-world examples. A financial services company replaced long compliance training sessions with AI-driven microlearning modules, boosting engagement by 70% while reducing compliance incidents. Another tech firm created an AI mentor network that paired employees with mentors based on skill gaps and career goals—doubling mentorship participation and accelerating promotion readiness.

Challenges and Opportunities Ahead
 

Despite the promise, challenges remain. Employees may resist new methods, AI systems must be explainable to build trust, and governance around data privacy and ethics is non-negotiable. But for organizations willing to embrace experimentation, the rewards are significant: faster skill acquisition, higher engagement, productivity gains, and fewer errors.

Looking ahead, Nandita predicted that AI-driven skill passports—digital records of skills that follow employees across roles—will become the norm. Mentorship powered by AI will become standard, and learning will increasingly integrate seamlessly into enterprise systems. “The future is about delegating routine tasks to AI co-pilots so we can focus on complex decision-making and innovation,” she said.
 

Watch the recording below:
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