Thought Leader Q&A: Discussing How To Effectively Scale Learning With Dr. RK Prasad

Scaling Learning: What Is Working Inside Global Enterprises

Dr. RK Prasad has nurtured CommLab India from concept to commercial success and is responsible for formulating the business strategy. He is also responsible for nurturing customer relationships. An entrepreneur at heart, RK has 35+ years of experience in sales, corporate training, university teaching, and eLearning. He regularly conducts seminars and webinars for customers across the world on various topics of technology-enhanced learning. RK holds a Ph.D. in Mobile Learning from Lancaster University, UK, and an MBA. A good teacher, strategist, futurist, and an engaging trainer, RK helps people learn and bloom. His priorities are his employees, his customers, and his community.

Today, we get an inside look at LearnFlux 2026, CommLab India's virtual thought leadership summit for L&D leaders, where Dr. RK Prasad, CEO & Co-Founder of CommLab India, moderated a panel discussion with James Gage, Vice President, Digital Business Readiness at Nalco Water, an Ecolab company; Alicia Pazelt, Head of Portfolio Strategy and Commercial Operations for Intuit Academy, Intuit; and David Aparicio Aylward, Head of Online Learning and Platform Management at DHL Supply Chain.

The panelists share how their organizations scale learning as the business grows, and what they have seen work across large, global workforces. They also talked about the role AI plays in helping learning scale faster, and why too much content can slow adoption.

L&D teams have always found it hard to measure and prove ROI. As business leaders, what signals tell you training is working? Is it speed, consistency, customer value, or something else?

The Head of Portfolio Strategy and Commercial Operations at a fintech company's learning academy said it is all of the above. When learning works, you see speed, greater customer value, and faster adoption, helped by the shift to microlearning that reaches employees just in time.

Whereas the fintech leader sees the results in speed and adoption, the Head of Online Learning and Platform Management at a global logistics company looks for L&D indicators, such as LMS completions and course ratings, to connect with business value: lower turnover, faster upskilling, and faster career progression.

On the other hand, the Vice President of Digital Business Readiness at a global water, hygiene, and sustainability services company looked at it from a different angle. In that business, no one notices learning unless it is missing. Then field teams create workarounds; customer onboarding slows down, and digital tools go unused. It reminded me of the hygiene factors we studied in business school: no one talks about them until they are gone.

In the same way, when learning works, the business sees the results but does not call it learning. Learning counts only when it changes behavior, moving an employee from "I completed this training" to "I'm using the tool" to "my customer is realizing value."

The signals are clear, even without empirical numbers:

• Speed: faster onboarding, faster upskilling, and quicker adoption of digital tools.
• Consistency: more competent customer conversations across the field.
• Customer value: a smoother customer journey from awareness to adoption to value.
• Workforce: lower turnover and faster career progression.

We may never prove ROI with complete precision, but when these signals improve, training is working.

Models like Kirkpatrick and Phillips make the first two levels easy to measure, but behavior change and business impact are harder. Do you think AI can help us measure those better?

Reaction and learning are easy to measure. Behavior change and business results are harder, because it is difficult to apportion a business result to training when so many other factors play a part. AI can help, but only once you know what you are measuring. The panel's answers came down to the below points:

• Start with what you measure. AI will process data better, but the focus has to come from you. As the Head of Online Learning and Platform Management at the logistics company pointed out, in a large workforce you can compare sites and see whether higher completion of core programs, such as onboarding and supervisor training, goes along with better retention and engagement.
• Keep humans in the loop. According to the Head of Portfolio Strategy and Commercial Operations at the fintech company, AI tools track more and process it faster, which can create an overabundance of data that tells no clear story.
• Accept that some outcomes are still hard to capture. The Vice President of Digital Business Readiness at the water services company admits measurement is not solved yet. One signal of success is a rep saying, "I got it from a peer," and finding a clearer way to measure that is the next challenge.

With heavy workloads, it's tempting for Instructional Designers and developers to accept what AI produces as it is. How can teams prevent that, and can AI tools themselves be designed to keep people involved?

I have come across this situation. When the volume of work is high, it is tempting for a designer to take what the AI produces.

The panel agreed that AI is a great accelerator, but it is designed to please the user, which is not the same as a good learning design. Without a subject matter expert or Instructional Designer applying solid criteria, the content turns into fast food. One test that came up: few professionals would let Copilot reply to their CEO unchecked, even at 90% accuracy. Training deserves the same check.

The fix lies in the process. The Head of Portfolio Strategy and Commercial Operations at the fintech company's learning academy suggested agreeing as a team where AI fits, such as storyboarding or QA checks, and where a human must review, then building that into the workflow. As the Vice President of Digital Business Readiness at the water, hygiene, and sustainability services company put it, AI should be your copilot, not your designated driver.

The tools can help, but only so far. We discussed this while developing an AI agent at CommLab India. AI can be trained on Instructional Design principles, so the first draft has the right structure. Still, my colleagues were clear: they face the customer, and the customer is the final judge. The decision to send it out stays with them.

When you start a new learning initiative, how much time do you give a pilot to prove itself before you scale it, and what criteria do you use?

Every organization is trying something new, whether in onboarding, sales, or leadership training, and none of the panelists gave a fixed timeframe for a pilot.

The Head of Online Learning and Platform Management at the logistics company separates two types. A pilot that solves a specific problem runs long enough to show results, and if they do not come, enough is enough. An "I have a dream" project, driven by a long-term vision, can run three to five years, though many end close to where they started after years of investment.

Either way, the criteria have to be set before launch. The Head of Portfolio Strategy and Commercial Operations at the fintech company's learning academy stressed defining what good looks like and when the decision will be made. Once a pilot has resources and leadership backing, it becomes hard to let it fail. So be honest about the results: pivot if the outcome is not there, and plan for full production if it is.

Adoption decides whether a pilot is ready to scale. At the water, hygiene, and sustainability services company, the Vice President of Digital Business Readiness has seen pilots prove the software works without proving that employees will change their habits. The ones that scale invest as much in adoption as in functionality, with clear business value, field champions, leadership support, and a reinforcement plan. The clearest signal comes when employees recommend the tool to each other instead of headquarters promoting it. That is when a pilot becomes the way the organization works.

Most large companies work in quarterly or annual cycles and expect quick results. What support do you get for a promising long-term idea that may take years to pay off?

Support depends less on the idea than on the company and the timing. The panel pointed to three factors:

• Culture: Some companies value outcomes like engagement and retention and have the size and resilience to wait three to five years for a return. Others are focused on next quarter's numbers.
• Timing: When a business problem puts training in the spotlight, investment follows quickly. At other times, you are selling the vision to anyone willing to listen.
• Attribution: Even when the return comes, the runway is long, and it is hard to show whether the result came from training or from something else.

Does the nature of your industry shape your organization's culture of innovation and change in learning?

Industry sets the pace of change. Culture decides how a company responds to it.

An L&D head at a cement manufacturer once told me that little had changed in her business in a hundred years. The panel described the other extreme. At the water, hygiene, and sustainability services company, part of the business serves data centers, where cooling technology for AI chips changes almost weekly. Traditional eLearning cannot keep up there, so learning has moved to weekly interventions. The same company also serves chemicals, paper, and primary metals, which move far more slowly. The panel takeaway was that the rate of failure you accept has to match the rate of change.

Industry also shapes who you train. Field teams need a different approach from IT engineers working on software release cycles. But the panel agreed that industry is not the whole story. Companies in the same sector, even of similar size, take very different approaches to learning. Meanwhile, logistics and technology companies share more than you might expect on engagement and talent development.

Culture is what makes the difference. Companies that build innovation into how they work keep growing, even after a century in business. Learning works the same way. Whatever the industry, L&D has to keep pace with how work is changing, and that is what we will explore in Corporate L&D Trends 2027: The View from the Trenches, a live webinar on AI-assisted learning, workforce readiness, and connecting training to everyday performance.

When you try to scale a program that has passed its pilot, or because your workforce has grown, what breaks first: content, platform, processes, resistance from people, or the sheer load of training?

Scaling exposes whatever was already weak. Stretch a system, and you find the weak link. The panel pointed to three areas:

• Process, content, or adoption: Whichever gap already exists shows up first when a program grows.
• How learning reaches employees: Mandatory training needs strong processes and clear communication. Self-directed learning needs relevant content at the right moment, and managers who give employees time to learn.
• Adoption in large field organizations: This is often the hardest part, so reinforcement gets the most investment, through in-platform guidance, peer coaching, and managers' reinforcing expectations.

L&D teams are small compared with the workforces they serve, which is why many work with partners like CommLab India. Where does a partner support you so that scaling doesn't break down?

The panel described four ways a partner keeps scaling on track:

• Content creation: A partner accelerates content development, so the internal team can focus on the last mile, making sure content reaches employees at the right moment. That last mile is where scaling most often breaks down.
• Localization and translation: A partner adapts and translates content so it can reach a global workforce.
• Seasonal peaks: During busy seasons, a partner helps the team update content and get new experts ready on fixed dates, without missing deadlines.
• Thought leadership: As things change fast, a partner acts as a force multiplier, bringing an outside view of where the learning market is heading. Learning faster than competitors is hard to do in a silo.

In each case, the partner takes on the work that stretches internal capacity, so L&D can focus on what only it can do. If that balance is something your organization is working through, connect with CommLab India.

If you could change one thing so learning can scale faster, what would it be: funding, governance, measurement, or something else?

No one on the panel chose funding. Their answers pointed elsewhere:

• Culture: Employees need space to learn, ownership of their development, and managers who coach them.
• A frictionless experience: Learning should meet employees where they work, instead of a three-hour course triggered by a notification.
• Alignment: Business leaders and L&D must agree on the critical, non-negotiable behaviors to develop.

As one panelist put it, you can have a shelf full of the best books, but it is of no use if no one is interested in reading. In other words, learning scales faster through pull: when employees want it, when it fits into their day, and when the business agrees on what it should achieve. It was a great discussion indeed.

Wrapping Up

A big thanks to Dr. RK Prasad for walking us through how businesses can effectively scale learning through the insights of celebrated and experienced professionals. If scaling learning within your organization is high on your priority list, make sure to connect with CommLab India to discuss the challenges you should have in mind and how to best address them.