AI is not simply changing jobs. It is changing the definition of what it means to be skilled. It’s workforce transformation.
Jagdish Sharma, Co-Founder & Director of Cedro, explores why enterprises need to move beyond course completion and conventional training towards measurable capability, continuous learning and AI-enabled workforce transformation.
The discussion examines AI literacy, human judgment, skills intelligence, learning ROI, employee capability and the connection between workforce transformation and customer experience.
Editorial Positioning
The enterprise learning conversation is undergoing a fundamental shift.
For years, organisations largely approached learning through courses, certifications, training programmes and compliance requirements. The emergence of generative AI is challenging that model.
AI is changing not only what employees need to learn, but also how organisations define skills, identify capability gaps, measure learning outcomes and prepare their workforce for changing roles.
Jagdish Sharma brings a distinctive perspective to this transformation. His career spans IT and telecom, including organisations such as ECI Telecom and Ciena, followed by work in workforce skilling and entrepreneurship development, before co-founding Cedro.
This interview will explore the evolution from training to skills, from skills to capability, and ultimately from capability to business performance.
The conversation will also examine whether AI literacy alone is sufficient, what happens to human judgment and decision-making in an AI-assisted workplace, how enterprises can measure the ROI of learning, and what Indian organisations need to do to prepare their workforce for the next phase of digital transformation.
From Technology to Workforce Transformation
Q1. You have spent more than two decades across IT, telecom, networking and workforce development before co-founding Cedro. What did you learn from the technology industry that eventually shaped your thinking about enterprise skills and capability building?
JS: Having spent more than two decades in technology, one thing I have seen consistently is that technology changes much faster than people and organisations can adapt to it.
From networking and telecom to cloud, digital transformation and now AI, every major technology shift creates a new capability gap. Organisations usually don’t struggle to invest in technology the bigger challenge is ensuring people have the skills to actually use it and create business value from it.
That experience shaped my thinking around workforce transformation and upskilling and eventually Cedro.
For me, workforce transformation goes beyond training or giving employees access to a learning platform. It is about understanding where the business is going, what skills will be required, and how we continuously prepare people for that change.
I also strongly believe that today we don’t have a shortage of learning content, we have an adoption challenge. So the focus has to move from courses and completions to capability, application and business impact.
Ultimately, technology transformation and workforce transformation have to move together.
Why Workforce Transformation?
Q2. What convinced you that workforce capability had become as important to enterprise transformation as technology itself? What gaps were you seeing in the way organisations were preparing their people for technological change?
JS: What convinced me was seeing that organisations were making significant investments in technology, but the same level of attention was not always going into preparing people to work with that technology.
The gap was quite visible. Technology decisions were often made with a transformation roadmap, while learning was still treated as a separate HR or L&D activity. People would attend programmes, complete courses, but the connection between learning and actual business capability was sometimes missing.
It is about identifying which skills will matter for the business, understanding where the gaps are, and most importantly, driving adoption and application.
With AI, this has become even more critical. Technology can be deployed quickly, but building the skills, confidence and new ways of working across an organisation takes time.
For me, real enterprise transformation happens when technology, people and capability move together.
What Enterprises Really Need Today
Q3. How has the enterprise learning conversation changed in recent years? Are organisations still primarily buying training, or are they increasingly looking for measurable business outcomes through skills and capability?
JS: The conversation has definitely changed. A few years ago, much of enterprise learning was about providing access to content and measuring completions or learning hours. Today, organisations are asking much more focused questions — what skills are we building, who needs them, and what impact are they creating?
We are also seeing learning becoming much more closely connected to business priorities. Whether it is AI adoption, leadership, digital skills or role-specific capabilities, organisations want learning that is relevant and can be applied at work.
So the shift is clearly from training to capability building. Content is still important, but simply providing content is not enough. The real value comes from driving adoption, building measurable skills and connecting learning to business outcomes.
From Training to Capability
Q4. There is a significant difference between completing a course and actually acquiring a capability. How should enterprises make that transition from training completion to demonstrable skill acquisition and business impact?
JS: I think the first step is to move beyond measuring learning only through course completions and learning hours. These are useful indicators, but they don’t necessarily tell us whether someone has actually developed a skill.
Enterprises need to look at the complete journey learn, assess, practise and apply. Can employees benchmark their skills? Can they practise in real-world scenarios? Can managers see progress, and can those skills ultimately be applied in the workplace?
This is where learning platforms can play a much bigger role. Today, platforms can provide personalised learning journeys, skill assessments, hands-on practice and much better visibility into skill development.
So the platform should not just be a place to consume content; it should help organisations understand whether learning is actually building capability.
The Role of Learning Platforms
Q5. What should a modern enterprise learning platform deliver beyond a large library of courses? Should the focus now move towards skills intelligence, personalised learning, capability assessment and workforce planning?
JS: Absolutely. A large content library is useful, but today enterprises need much more from a learning platform. It should help organisations understand what skills they have, what skills they need, and where the gaps are.
The platform should then help employees with personalised learning, skill assessments, practice and clear development paths based on their role and future requirements.
AI has made this even more important because the definition of a skill itself is changing. It is no longer only about what you know, but also how effectively you can use technology and AI to do your job better.
So I see modern learning platforms evolving from content platforms to skills and capability platforms, giving organisations better visibility into their workforce and helping people continuously adapt as roles and skills change.
Has AI Changed What a “Skill” Means?
Q6. Has generative AI fundamentally changed the definition of a workplace skill, or are organisations simply adding AI skills to existing competency frameworks?
JS: I think generative AI is doing much more than simply adding another skill to the competency framework. It is starting to change how many existing skills are actually applied at work.
For example, communication, research, analysis, coding or problem-solving are not new skills, but AI is changing how people perform each of them.
That is why I see a clear difference between AI literacy and AI capability. AI literacy is understanding what AI is, what it can do and how to use it responsibly. AI capability is being able to apply AI effectively in your specific role to improve productivity, decision-making and outcomes.
For organisations, the real opportunity is not just to make everyone AI-aware, but to build role-specific AI capability across the workforce.
AI Literacy vs AI Capability
Q7. There is now considerable emphasis on AI literacy. But what is the difference between being AI-aware, AI-literate and genuinely AI-capable? What should enterprises expect from employees at each level?
JS: I see it as three levels.
AI-aware means you understand what AI is and where it can be useful.
AI-literate means you know how to use it responsibly and effectively.
Finally, AI-capable means you can actually apply AI in your role to improve the way you work and deliver better outcomes.
But the human advantage remains very important. AI can help with speed, analysis and automation, but judgement, creativity, critical thinking, empathy and decision-making still require people. Enterprises should not look at AI as replacing human capability. The real opportunity is to combine human strengths with AI capability and make the workforce more effective.
The Human Advantage
Q8. If AI increasingly provides information, analysis, recommendations and even decisions, which human capabilities become more valuable rather than less valuable?
JS: I think the more AI takes over information, analysis and routine decision-making, the more valuable human judgement becomes. Skills like critical thinking, creativity, problem-solving, communication, empathy and leadership will become even more important. The ability to ask the right questions, challenge what AI is telling us, understand the business context and make the final judgement will remain very human. So I don’t see AI reducing the importance of these capabilities. I believe it will actually make them more valuable.
Q9. Should organisations be putting greater emphasis on critical thinking, judgment, creativity, contextual understanding, communication and ethical decision-making?
JS: Yes defiantly, these capabilities are becoming more important, particularly as AI takes over more routine and analytical work. But the important question for enterprises is how we connect these capabilities to business outcomes. Learning should not be measured only by participation or completion. We need to look at whether it improves productivity, decision-making, innovation, leadership effectiveness or other measurable business outcomes.
For me, the ROI of learning comes from connecting skills to business performance and that is where organisations need to focus more.
Measuring the ROI of Learning
Q10. Enterprise learning programmes have historically struggled to demonstrate direct ROI. How should organisations measure the impact of learning in an AI-driven workplace?
JS: The organisations need to move beyond traditional learning metrics like completion rates and learning hours. In an AI-driven workplace, we should look at what changed after the learning whether employees are more productive, making better decisions, using AI more effectively, or performing their roles differently.
The measurement also needs to be linked to the business objective from the beginning. If the objective is productivity, measure productivity. If it is AI adoption, measure adoption and application.ROI is not about how much learning happened; it is about what business impact happened because of that learning.
Q11. Should the metrics move from course completion and certification towards productivity, performance, innovation, employee retention or revenue impact?
JS: Yes, absolutely. Course completion and certification are still useful, but they should be leading indicators, not the final measure of success. The real question is whether the skills developed are improving productivity, performance, innovation, employee growth or ultimately business results.
For example, if an organisation is investing in AI learning, we should be able to see whether employees are actually using AI in their roles and whether that is improving efficiency or outcomes.
I believe the metrics need to move from activity to impact from what people learned to what they are able to do differently and what the business gains from it.
Skills and Business Performance
Q12. How can organisations connect skills development with actual business outcomes? What does a mature skills-to-performance framework look like?
JS: The connection starts with a simple question: what business outcome are we trying to improve, and what capabilities do our people need to deliver it?
Once that connection is clear, organisations can measure whether the development of those skills is improving performance whether that is productivity, quality, innovation or customer experience.
For example, if employees are more capable and confident in using technology or AI, that should ultimately reflect in how quickly, efficiently and effectively we serve our customers.
So a mature skills-to-performance framework should connect business goals → required skills → employee capability → performance → customer outcomes.
Ultimately, the customer should feel the impact of a better-skilled workforce.
From Employee Capability to Customer Experience
Q13. Ultimately, workforce capability affects the customer. How can organisations connect employee skilling and AI adoption with outcomes such as customer experience, service quality, productivity and customer loyalty?
JS: I think one of the biggest mistakes enterprises make is looking at skilling and AI adoption as an HR or technology initiative, rather than connecting it directly to business and customer outcomes.
If we are building a particular capability, we should be clear about what it is expected to improve productivity, service quality, response time or customer experience—and then measure that impact.
For example, AI adoption should not be measured simply by how many employees have access to an AI tool. The real question is how they are using it and whether it is helping them serve customers better and work more effectively.
What Are Enterprises Getting Wrong?
Q14. What are the biggest mistakes organisations are making when they launch AI-skilling initiatives?
JS: Many organisations start with generic AI awareness sessions and focus on how many people completed them. But the real question is what does AI mean for each role, and how can employees actually use it in their day-to-day work?
The second mistake is trying to roll it out without a clear business objective. AI adoption should be connected to something tangible productivity, quality, decision-making or customer experience. And finally, organisations sometimes underestimate the human side people need confidence, the right guidance and an environment where they can experiment and adopt AI responsibly. I would say the shift needs to be from AI awareness to role-based AI capability and real-world adoption.
Q15. Are they focusing too heavily on teaching tools and not enough on changing workflows, decision-making and organisational behaviour?
JS: I have seen there is a tendency to focus heavily on the tools, but from a learning perspective, the bigger opportunity is to help people understand how AI can be relevant to their role and how they can use it effectively. A common AI awareness and learning foundation can be created across the organisation, but beyond that, learning should become role- and skill-specific.
A finance professional, a salesperson, an HR professional or a technology professional will have very different learning needs. So, I would say one common AI learning direction, supported by multiple role-based skill tracks.
The objective is to move people from awareness and experimentation to confidence and practical application. That is where AI simulators can play an important role they give employees an opportunity to practise realistic, role-specific situations and build confidence before applying AI in their actual work.
One AI Strategy or Multiple Skill Tracks?
Q16. Should every employee receive broadly similar AI training, or should organisations create different AI capability pathways for different functions, roles and levels of responsibility?
JS: I don’t think every employee needs the same level or type of AI training. There should be a common foundation of AI awareness and responsible use, but beyond that, the learning should be relevant to the employee’s role, function and level of responsibility.
More importantly, AI learning cannot be a one-time programme. The technology is evolving continuously, so organisations need to create continuous learning pathways where employees can keep building their skills as their roles and AI capabilities evolve.
Making Learning Continuous
Q17. “Continuous learning” has become a familiar corporate phrase. What does it actually take to create a culture where learning becomes part of everyday work rather than an activity employees perform separately from their jobs?
JS: Employees should not have to think, “Now I have to go and do some training.” Learning should be available when they need it, whether they are trying to solve a problem, learn a new technology or improve a particular skill.
India has a huge advantage because we have a young, ambitious and technology-oriented workforce that is generally willing to learn and adapt.
The opportunity for organisations is to build on that mindset and make learning a part of everyday work available anytime, anywhere, and relevant to what employees need at that moment, rather than treating it as a separate training activity.
India’s Workforce Advantage
Q18. India has a large technology-enabled workforce and a rapidly expanding digital economy. Are Indian enterprises adequately prepared for AI-driven workforce transformation?
JS: Indian enterprises are reasonably well positioned, particularly because we have a large, technology-enabled and relatively young workforce that is comfortable adapting to new technologies.
The bigger challenge is not awareness of AI. It is building AI capability at scale across different roles and levels of the organisation. Some organisations are already moving quite fast, while others are still at the awareness and experimentation stage. The opportunity now is to make AI learning more continuous, practical and role-specific, so that employees can confidently apply it in their day-to-day work.
We have a strong workforce advantage. The next step is to convert that advantage into AI capability across the workforce.
Q19. Where do you see the biggest capability gaps?
JS: From what I see, the biggest gap is not necessarily a lack of technical knowledge. It is how quickly people can learn, adapt and apply new technology in their work. AI and digital skills are obviously important, but at the same time, I think skills like critical thinking, problem-solving, communication, creativity and adaptability are becoming equally important.
Because technology will keep changing. The people who can continuously learn and adapt will be the ones who stay relevant. So, for me, the biggest capability is really the ability to learn, unlearn and relearn as the workplace changes.
The Skills That Will Matter Most
Q20. Looking towards 2030, which skills do you believe will become most important for knowledge workers?
JS: Looking towards 2030, I think the most important skill will be adaptability the ability to continuously learn and work with new technology.
Along with that, I see critical thinking, problem-solving, creativity, communication and collaboration becoming even more important. AI will increasingly take care of many routine tasks, so the value of people will come from how they think, make decisions, solve problems and work with others.
For me, the winning combination for 2030 will be human skills + AI fluency
Q21. Conversely, which conventional skills or approaches to work are likely to lose value as AI becomes embedded in everyday enterprise workflows?
JS: I think it is less about particular skills becoming irrelevant and more about certain ways of working losing value.
Routine, repetitive work, spending too much time searching for information, preparing standard reports or doing tasks that AI can do faster will gradually reduce in value.
From a CEO’s perspective, I think the focus should be on helping employees move up the value chain using AI to take care of the routine work and allowing people to spend more time on customers, innovation, problem-solving and strategic thinking.
The CEO’s Workforce Agenda
Q22. What do you believe CEOs are still underestimating about AI and workforce transformation?
JS: Most CEOs understand that AI is important. What is sometimes underestimated is how much effort it takes to get people ready for that change. You can bring in the technology quite quickly, but getting people comfortable with it, changing their mindset and helping them actually use it in their day-to-day work takes time.
And this is not a one-time exercise. AI will keep changing, so people will have to keep learning. For me, the real challenge for CEOs is not just “How do we bring AI into the organisation?” but “How do we make sure our people are ready to work with it?”
Q23. If you had to give an enterprise CEO three priorities for preparing their organisation for the next five years, what would they be?
JS: If I had to give a CEO three priorities for the next five years, they would be:
First, build AI capability across the workforce. Not everyone needs to become an AI expert, but everyone should understand how AI can support their role.
Second, make learning continuous. Technology will keep changing, so organisations need to create a culture where people continuously learn, adapt and reskill.
Third, connect skills to business outcomes. Don’t measure learning only by courses completed. Look at whether the workforce is becoming more productive, innovative and capable of delivering better customer outcomes.
Ultimately, the organisations that will be ready for 2030 are the ones that invest in both technology and the people who use it.
Looking Towards 2030
Q24. What will the enterprise workforce look like by 2030?
JS: By 2030, I see the enterprise workforce becoming much more AI-enabled, flexible and continuously learning. People will work alongside AI much more naturally. Many routine tasks will be automated, while employees will spend more time on problem-solving, creativity, decision-making, collaboration and customer relationships.
I also believe the traditional idea of a fixed skill set will change. People will need to continuously learn and update their capabilities as technology and roles evolve.
So, the workforce of 2030 will not necessarily be about AI replacing people. It will be about people who know how to work effectively with AI.
Q25. And what will successful organisations do differently in the way they hire, develop, deploy and continuously reskill their people?
JS: Successful organisations will treat their workforce as something they need to continuously develop, not just manage. They will hire for the ability to learn and adapt, develop people based on the skills the business actually needs, and give employees opportunities to apply those skills in their roles.
Reskilling will also become a continuous process rather than something organisations do only when a new technology arrives. Ultimately, the organisations that do well will be the ones that can keep their people learning, keep their skills relevant and move talent to where the business needs it most.
Rapid-Fire
1. One skill every employee will need:
Answer: The ability to continuously learn, unlearn, and relearn.
2. One skill AI will make more valuable:
Answer: Knowing not just what AI can do, but when and how to use it.
3. One misconception about AI and jobs:
Answer: That AI will take away jobs. I believe it will change how we work, not end work.
4. One mistake enterprises should avoid:
Answer: Investing in technology without investing in people
5. One capability every CEO should build:
Answer: The ability to listen, learn, and adapt quickly
6. One prediction for the workplace in 2030:
Answer: AI will become a part of almost every job, just like the internet is today.
Closing Editorial
As AI becomes embedded across enterprise workflows, the workforce conversation is moving beyond the question of which jobs technology might replace.
The more consequential question may be which capabilities organisations need to build in humans working alongside increasingly capable AI systems.
For enterprises, this makes learning less of a support function and more of a strategic capability. The organisations that can continuously identify skills gaps, develop relevant capabilities and connect those capabilities to business outcomes may have a significant advantage in the AI-driven economy.
Jagdish Sharma’s perspective offers an important lens into that transition — from training employees to continuously transforming organisational capability.
Editorial Takeaways
During editing, ideally extract strong, quotable observations around these themes:
– AI is changing the definition of workplace skills.
– Training completion is not equivalent to capability.
– AI literacy must evolve into AI-enabled performance.
– Human judgment becomes more important as AI-generated recommendations increase.
– Learning needs to move closer to the employee’s workflow.
– Skills intelligence can become part of workforce strategy.
– Learning ROI needs to be connected to business performance.
– Workforce transformation ultimately affects customer experience.
– Indian enterprises have an opportunity to turn workforce scale into a genuine skills advantage.
– The future belongs to organisations capable of continuously reskilling their workforce.
Technology transformation cannot succeed without workforce transformation. And workforce transformation ultimately influences the customer experience.
