AI itself will increasingly become ubiquitous. Access to AI will therefore not be the differentiator. Sachin Grover, Vice President- Head of GenAI Initiatives, NIIT Ltd feels the differentiator will be the ability to translate AI into measurable human capability and business performance. In an exclusive conversation with Rajneesh De, Group Editor, APAC Media & CXO Media, Grover asserts this is precisely where NIIT’s experience becomes particularly relevant. NIIT, according to Grover, is not looking at AI as another layer of content but how can it fundamentally improve the way capability is built, demonstrated and translated into performance.
- What are the use cases of NIIT’s AI-ready L&D Enterprise series of products and services?
The opportunity around AI in learning goes far beyond introducing AI-enabled courses. As AI becomes embedded into everyday work, enterprises are rethinking roles, workflows and, importantly, the capabilities their people need to remain productive and relevant.
At NIIT, our focus is on three areas: reskilling employees whose roles are being transformed by AI, preparing talent for emerging AI-enabled roles, and enabling new talent to become productive in an AI-first workplace. Our solutions span GenAI and Agentic AI, role-based deep skilling, AI-enabled coaching, simulated practice and enterprise capability transformation.
The NIIT India Skills Gap Report 2026 also reinforces an important shift in how employability is being defined. Increasingly, it is not simply about qualifications, but about the ability to apply skills effectively in real-world situations. As roles evolve faster, learning has to become continuous, contextual and closely connected to business outcomes.
For enterprises, the real measure of an AI-ready workforce will therefore not be how many people have completed a course, but how effectively they can apply new capabilities to improve performance. That is the transition we are enabling at scale.
- What are NIIT’s tangible initiatives around AI-powered coaching and interactive simulations?
Our approach is increasingly focused on creating environments where learners can practise, make decisions, receive contextual feedback and improve before encountering similar situations on the job. This becomes particularly relevant for areas such as technology skilling, banking and enterprise transformation, where application and decision-making are critical.
We are also integrating AI capabilities across learning and assessment, including through platforms such as iamneo, to strengthen opportunities for practice and feedback.
The larger objective is not to use AI simply to automate learning. It is to create a more experiential learning environment where individuals can build proficiency through practice. Over time, AI-powered coaching can make that feedback increasingly personalised, contextual and available in the flow of work.
At NIIT, we are actively building agentic learning systems- AI that doesn’tdoes not just recommend content, but acts as a digital colleague guiding learners through complex, real-world challenges in the flow of work.
Simulations and immersive learning experiences are a core part of this shift. By placing learners inside realistic, role-specific scenarios- whether a client negotiation, a technical troubleshooting task or a compliance-sensitive decision- we allow them to practise judgement and build muscle memory in a safe environment before the stakes are real. Combined with AI-powered coaching, these simulated experiences generate the contextual feedback loops that make learning stick, and they give enterprises a much closer proxy for on-the-job readiness than a traditional course completion ever could.
- How is NIIT creating an AI-enabled platform that transforms personalized individual learning into a continuous performance improvement system?
The larger shift is from learning as an event to capability development as a continuous system. In an AI-driven environment, learning cannot remain disconnected from what an individual does at work.
An AI-enabled learning ecosystem should be able to understand an individual’s current capability, what their role requires, where the gaps exist and what intervention can help them improve. NIIT is building towards this through AI-enabled learning, assessments, deep-skilling platforms, simulated practice and data-driven learner journeys.
The next evolution is to connect these elements more intelligently so that learning becomes increasingly responsive to individual performance and changing role requirements. Rather than giving every employee more content, the focus should be on providing the right learning, practice and feedback at the right point in their development.
Our AI Horizon Model maps this progression across four stages- Literacy, Fluency, Proficiency, and Maturity while providing enterprises with a structured roadmap to evolve workforce capabilities from foundational AI awareness to strategic, organisation-wide adoption. The journey begins by establishing the workforce’s current knowledge and skill levels through the AI Readiness Index, our proprietary assessment tool, creating a clear baseline for the path ahead.
Ultimately, the value of personalisation lies not in creating a different learning journey for its own sake, but in whether that journey improves capability and performance. That is where AI can fundamentally change enterprise learning.
- What are the key pillars of NIIT’s GTM strategy and what initiatives support this strategy?
Our go-to-market strategy is increasingly anchored around AI-led capability transformation, deeper enterprise penetration and outcome-led solutions rather than standalone training interventions.
A key priority is expanding our presence across GCCs and Indian enterprises, where the need to reskill talent for AI-enabled roles is becoming increasingly pronounced. At the same time, we are strengthening our ability to address the complete talent lifecycle, from early-career onboarding to deep reskilling of experienced professionals and capability development for emerging roles.
We have recently introduced the NIIT Enterprise Tech Learning segment by integrating StackRoute and RPS, which further strengthens our ability to take deep-skilling solutions to enterprises at scale, while iamneo adds an AI-first technology layer across learning, assessment and talent workflows. We are also continuing to build sector-specific solutions across areas such as financial services, technology and other large enterprise segments.
The fundamental GTM shift is from selling learning programmes to solving capability problems. Enterprises increasingly want to know whether a learning intervention can improve workforce readiness, accelerate productivity or enable a role transformation. Our strategy is being built around those outcomes.
- Which verticals are showing NIIT maximum traction and what are their use cases?
Our Enterprise business continues to be the largest part of NIIT’s business, contributing 65% of total revenue in Q1 FY27 and growing 8% year-on-year. The Consumer business contributed the remaining 35% and grew 27% year-on-year. Within this, technology remains a significant growth area, with technology programmes accounting for 71% of revenue and growing 16% year-on-year.
From an enterprise perspective, we are seeing strong demand across technology-led organisations, GCCs, financial services and large Indian enterprises, particularly as organisations rethink roles and workforce capabilities in response to AI.
In technology and GCC environments, the focus is increasingly on GenAI, Agentic AI, data, cloud, cybersecurity, software engineering and architecture capabilities. The conversation has moved beyond creating AI awareness to building professionals who can apply these technologies within enterprise environments and solve real business problems.
In financial services, the opportunity spans technology transformation, AI-enabled coaching and building talent for increasingly digital and AI-led operating models. Across enterprises more broadly, the requirement is around reskilling existing employees, developing new digital capabilities and preparing talent for roles that are being reshaped by AI.
Across these segments, the common requirement is increasingly clear: organisations are looking for demonstrable capability, faster time-to-productivity and learning that translates into measurable performance.
- With long-term contracts focused on transformation, how does NIIT balance between greenfield and brownfield contracts?
We do not see greenfield and brownfield opportunities as competing choices. They address different stages of an enterprise’s transformation journey.
Greenfield engagements allow organisations to design new capability models, learning architectures and talent pathways around emerging technologies and roles from the outset. Brownfield opportunities are equally important because most large enterprises already have significant talent, technology and learning investments in place, and in practice these accounts tend to expand faster once the initial engagement proves value, since the relationship and context are already established. The challenge is to modernise these environments, identify emerging capability gaps and reskill people for changing roles without disrupting business continuity.
Our approach is therefore transformation-led rather than contract-type led. Whether an engagement begins with a new mandate or an existing relationship, the objective is to demonstrate measurable value and deepen the partnership as the client’s requirements evolve.
In long-term enterprise relationships, the real value is not simply the duration of the contract. It is the ability to continuously adapt the capability agenda as technology, roles and business priorities change.
- Given the number of AI-powered edtechs in the market today, what still remains NIIT’s unique differentiator?
AI itself will increasingly become ubiquitous. Access to AI will therefore not be the differentiator. The differentiator will be the ability to translate AI into measurable human capability and business performance.
NIIT brings together decades of experience in talent development, deep enterprise relationships, practitioner-led learning, technology expertise, proprietary diagnostic tools such as the AI Readiness Index, and an increasingly outcome-oriented approach to skilling. Our capabilities across deep technology learning, assessments, experiential learning and AI-enabled platforms allow us to address the capability challenge more holistically.
The skills challenge has also fundamentally changed. It is no longer simply about access to knowledge or acquiring another qualification. It is about whether people can apply what they know as roles, technologies and business requirements continue to evolve.
That is where NIIT’s experience becomes particularly relevant. We are not looking at AI as another layer of content. We are looking at how it can fundamentally improve the way capability is built, demonstrated and translated into performance. In an AI-first economy, the organisations that succeed will be those that can convert technological change into workforce capability at scale.
