Organisations have never had more learning content at their disposal. Digital platforms, on-demand courses and AI-generated resources have made knowledge easier to access than ever before. Yet capability gaps persist. Employees complete programmes, earn certifications and return to work without necessarily changing how they perform.
The problem is no longer simply one of access or content availability. It is the disconnect between what employees learn and what they are expected to do differently.
AI is bringing this challenge into sharper focus. When content can be created, translated and updated in minutes rather than weeks, the value of L&D must extend beyond producing and distributing learning materials. The priority shifts to identifying the right capabilities, embedding learning into everyday work and measuring its impact.
These questions shaped the People Matters webcast, Content Isn’t Capability: How L&D Must Rethink Learning at Speed, held in partnership with Workday. Moderated by Cheshta Dora, Head, Research & Content, People Matters, the discussion featured Mohammed Imran Khan, Head of Learning and Development, Piramal Finance; Anila Nair, Solution Consulting Leader, Workday India; and Rahul Nayar, Head of Learning & People Strategy Retail, Tata 1MG.
More content isn't closing the capability gap
For years, L&D functions have focused on expanding learning access, building content libraries and increasing participation. But the availability of learning does not automatically translate into improved performance.
Rahul Nayar identified a fundamental disconnect: organisations may define business outcomes clearly at an enterprise level, but struggle to translate them into specific expectations for functions, roles and individuals.
“As a result, training programmes can appear strategically aligned without addressing what employees actually need to do differently. A successful intervention must go beyond transferring knowledge to reinforcing behaviours consistently. It has to be a consistent, constant process that builds a habit," Rahul explained.
This challenge becomes more pronounced as business priorities evolve. Skills that were relevant when a programme was designed may no longer reflect the demands employees face when they complete it.
Mohammed Imran Khan argued that the abundance of content makes human judgment more important, not less. AI can instantly recommend courses and generate learning resources, but determining which intervention is relevant requires understanding an employee's role, challenges, existing capabilities and preferred learning methods.
"It's not about how many programs I give to you. It's about how many capabilities I can get in one individual," Imran said.
The implication is that L&D must become more selective about what it creates and delivers. Instead of responding to every business request with another programme, learning teams need to diagnose the underlying performance problem.
Not every operational challenge requires training. Some demand changes to processes, systems or managerial practices. Identifying that distinction is essential to avoiding unnecessary learning interventions.
The priority is to shift from content volume to capability relevance: defining the performance gap first, then determining whether learning is the right solution.
Capability develops where learning meets work
Even relevant content has limited value if employees cannot apply it. Traditional learning models often separate development from daily responsibilities, requiring employees to attend programmes and subsequently translate what they learned into practice. The speakers advocated a different approach: embedding learning into workflows, supported by practice, coaching and real-time feedback.
At Piramal Finance, Imran described how learning is increasingly being integrated into the systems employees use. Rather than removing employees from their responsibilities for lengthy programmes, the organisation is using shorter interventions, mentoring, managerial support and contextual guidance.
For example, system prompts can flag missing information while employees process files, helping them correct errors before submission. Learning becomes part of completing the task accurately, rather than a separate activity intended to prevent future mistakes.
"We are moving away from pulling people out and calling them into a classroom program, and we are trying to make learning and development more like flow of work," Imran explained.
At Tata 1MG, the emphasis is on demonstrating competence in real situations.
Rahul described how pharmacists entering the retail business possess technical knowledge but must also develop customer engagement and communication capabilities. For operational processes, employees progress through theoretical instruction, supervised practice and real customer interactions before being considered fully ready.
This staged approach recognises that understanding a process and executing it independently are different levels of proficiency. Anila Nair reinforced the importance of practical application, particularly in customer-facing roles where employees encounter unpredictable situations.
"Practice is absolutely important more than ever, and the value of simulations and real-life role plays over one-way knowledge sharing is immense,” Anita noted. Technology can support this transition through personalised learning, simulations and contextual recommendations. But managers remain critical to sustaining it.
As the discussion highlighted, business leaders must co-own capability development from the outset, helping define expected outcomes, creating opportunities for practice and reinforcing behaviours after formal interventions conclude.
Learning effectiveness, therefore, cannot end at course completion. It must be assessed through employees' ability to apply skills consistently and perform more effectively in real situations.
AI is shifting L&D from content production to capability architecture
AI is changing how learning teams spend their time. Content generation, assessments, translations and video production can increasingly be automated, reducing the effort required for routine development activities.
But the bigger opportunity is not simply faster production. It is redirecting L&D's attention towards identifying future skill requirements, designing effective interventions and demonstrating business impact.
Anila argued that this begins with re-examining roles as AI changes work. Some skills are becoming less relevant while new capabilities emerge, requiring organisations to continuously reassess what employees need to know and do.
"We need to know what that role is now asking for. What skills does it really need? Some skills would have become obsolete. There are new skills that are coming up," Anila explained.
This positions L&D as an architect of capability development, connecting evolving role requirements with learning, mentorship, coaching and performance expectations.
It also demands a different approach to measurement.
Instead of reporting primarily on training volumes, attendance or trainer evaluations, organisations need to establish whether interventions are producing the intended behavioural and business outcomes.
Imran recommended evaluating learning programmes through outcome-based scorecards, assessing whether they solve the problems they were designed to address. "Instead of measuring a trainer's success, we need to measure program success," he said.
However, faster content creation also introduces governance responsibilities. Imran highlighted the need for human validation of AI-generated learning materials, particularly in regulated financial services. At Piramal Finance, content undergoes established review processes to ensure accuracy, regulatory compliance and organisational relevance.
Rahul described similar safeguards at Tata 1MG, where protecting sensitive customer and health information is central to technology adoption. The lesson is that AI should accelerate learning delivery without compromising quality, contextual relevance or information security.
The new mandate for L&D is to combine technological speed with stronger human judgment and business accountability.
From learning delivery to business impact
The conversation underscored a fundamental shift in how L&D must define success.
AI can help organisations create more content, reach more employees and respond faster to changing requirements. But these advantages matter only when they translate into stronger capabilities.
For learning leaders, the priorities are clear: diagnose capability gaps before designing interventions, integrate learning into everyday work, establish shared accountability with business managers and measure outcomes beyond completion.
Ultimately, the question is no longer how much learning an organisation delivers, but what employees can do differently because of it.
Even relevant content has limited value if employees cannot apply it. Traditional learning models often separate development from daily responsibilities, requiring employees to attend programmes and subsequently translate what they learned into practice. The speakers advocated a different approach: embedding learning into workflows, supported by practice, coaching and real-time feedback.
At Piramal Finance, Imran described how learning is increasingly being integrated into the systems employees use. Rather than removing employees from their responsibilities for lengthy programmes, the organisation is using shorter interventions, mentoring, managerial support and contextual guidance.
For example, system prompts can flag missing information while employees process files, helping them correct errors before submission. Learning becomes part of completing the task accurately, rather than a separate activity intended to prevent future mistakes.
"We are moving away from pulling people out and calling them into a classroom program, and we are trying to make learning and development more like flow of work," Imran explained.
At Tata 1MG, the emphasis is on demonstrating competence in real situations.
Rahul described how pharmacists entering the retail business possess technical knowledge but must also develop customer engagement and communication capabilities. For operational processes, employees progress through theoretical instruction, supervised practice and real customer interactions before being considered fully ready.
This staged approach recognises that understanding a process and executing it independently are different levels of proficiency. Anila Nair reinforced the importance of practical application, particularly in customer-facing roles where employees encounter unpredictable situations.
"Practice is absolutely important more than ever, and the value of simulations and real-life role plays over one-way knowledge sharing is immense,” Anita noted. Technology can support this transition through personalised learning, simulations and contextual recommendations. But managers remain critical to sustaining it.
As the discussion highlighted, business leaders must co-own capability development from the outset, helping define expected outcomes, creating opportunities for practice and reinforcing behaviours after formal interventions conclude.
Learning effectiveness, therefore, cannot end at course completion. It must be assessed through employees' ability to apply skills consistently and perform more effectively in real situations.
AI is shifting L&D from content production to capability architecture
AI is changing how learning teams spend their time. Content generation, assessments, translations and video production can increasingly be automated, reducing the effort required for routine development activities.
But the bigger opportunity is not simply faster production. It is redirecting L&D's attention towards identifying future skill requirements, designing effective interventions and demonstrating business impact.
Anila argued that this begins with re-examining roles as AI changes work. Some skills are becoming less relevant while new capabilities emerge, requiring organisations to continuously reassess what employees need to know and do.
"We need to know what that role is now asking for. What skills does it really need? Some skills would have become obsolete. There are new skills that are coming up," Anila explained.
This positions L&D as an architect of capability development, connecting evolving role requirements with learning, mentorship, coaching and performance expectations.
It also demands a different approach to measurement.
Instead of reporting primarily on training volumes, attendance or trainer evaluations, organisations need to establish whether interventions are producing the intended behavioural and business outcomes.
Imran recommended evaluating learning programmes through outcome-based scorecards, assessing whether they solve the problems they were designed to address. "Instead of measuring a trainer's success, we need to measure program success," he said.
However, faster content creation also introduces governance responsibilities. Imran highlighted the need for human validation of AI-generated learning materials, particularly in regulated financial services. At Piramal Finance, content undergoes established review processes to ensure accuracy, regulatory compliance and organisational relevance.
Rahul described similar safeguards at Tata 1MG, where protecting sensitive customer and health information is central to technology adoption. The lesson is that AI should accelerate learning delivery without compromising quality, contextual relevance or information security.
The new mandate for L&D is to combine technological speed with stronger human judgment and business accountability.
From learning delivery to business impact
The conversation underscored a fundamental shift in how L&D must define success.
AI can help organisations create more content, reach more employees and respond faster to changing requirements. But these advantages matter only when they translate into stronger capabilities.
For learning leaders, the priorities are clear: diagnose capability gaps before designing interventions, integrate learning into everyday work, establish shared accountability with business managers and measure outcomes beyond completion.
Ultimately, the question is no longer how much learning an organisation delivers, but what employees can do differently because of it.
