AI readiness isn't about tools. It's about building critical thinkers.
Banpu Public Company Limited, an integrated energy company, is approaching workforce transformation differently than most organizations racing to become "AI-ready." Rather than stacking new tools and technical certifications, the company is betting on something harder: teaching employees to question what they know.
Sundaram Iyer, Head of Banpu Academy, frames the challenge plainly. "An AI-ready workforce is defined not by what people know - but by how willing they are to rethink it."
For a company with decades of operational expertise and established practices, this means holding confidence in that knowledge while staying genuinely open to having it challenged. The balance between confidence and curiosity now underpins how Banpu is reshaping its people strategy.
The real risk: uncritical adoption
Iyer identifies a danger many organizations overlook. The problem isn't low adoption of AI tools-it's blind trust in them.
"An operation manager who blindly trusts an AI-generated forecast is more dangerous than one who doesn't use AI in the decision process at all," he said.
This is why Banpu is building AI literacy as a baseline across all functions, from project development to HR. The goal isn't job titles like "AI specialist." It's producing critical thinkers who can interrogate AI outputs and use them effectively.
The company has built this strategy on real-world use cases-optimizing mining supply chains, predictive maintenance on machinery, price forecasting in energy trading, and talent analytics. Adult learners engage when content connects directly to their work.
Learning requires more than "flow of work" moments
Iyer pushes back on a popular corporate learning idea: that learning happens "in the flow of work," squeezed between meetings and emails.
"Real learning - the kind that rewires thinking and builds lasting capability - requires dedicated focus, reflection, and the deliberate discomfort of questioning what you thought you knew," he said.
Banpu is creating space for deeper learning instead. Not microbursts of content, but meaningful programs where people step back from routine work to engage deeply and surface assumptions they've stopped questioning.
The company also established a Strategic Capability Cluster in 2018, integrating people, academy, and digital capabilities to accelerate adoption of AI and data analytics across operations.
Addressing fear and false confidence
Two obstacles stand in the way. First is fear about job displacement. Banpu addresses this directly through its Hi-AI program-a reminder that AI augments human intelligence rather than replaces it. All employees and executives experience firsthand what AI can and cannot do.
The second obstacle is trickier: what Iyer calls the "illusion of familiarity." People who casually use generative AI tools assume they're AI-literate.
"Getting someone to move from comfortable hobbyist to critical, skilled practitioner requires them to first acknowledge there's a gap. That's a hard conversation with smart, experienced people," Iyer said.
Banpu uses peer influence to drive change. When a respected site engineer or trading lead says they've changed how they work using AI, it carries more weight than any top-down mandate. The company runs quarterly AI showcases to recognize people who've applied AI effectively and helped others in their business process chain adopt it.
What HR and L&D leaders should do
Iyer offers three pieces of advice for HR professionals redesigning learning strategies.
First, design for personal transformation, not just upskilling. Future-proofing means addressing the emotional and psychological aspects. Help people reimagine how they add value to the business and their lives.
Second, return to adult learning fundamentals. Treat employees as experienced, intelligent adults. People learn best when they understand why something matters, can connect it to their experience, and have agency over their learning journey. Most corporate programs ignore this.
Third, model the behavior yourself. L&D leaders who aren't personally grappling with AI and learning in public have a credibility gap.
The measurement challenge remains
Banpu tracks completion and satisfaction easily. Measuring what actually matters-changed behavior and better decisions-is harder.
"Measuring impact remains the honest frontier challenge," Iyer said. "We're building towards that, but I won't pretend we've solved it."
Time constraints also persist. Operational pressure is real and business environments don't pause for learning. Banpu's answer isn't fragmenting learning into smaller pieces but making a genuine organizational commitment that protects learning time as non-negotiable.
Learning in public
Iyer practices what he advocates. He deliberately uses AI in his own work as a thinking partner he pushes back against, rather than as a shortcut.
He protects time for reading, reflection, and peer conversations with people who challenge his views. He practices not being busy-stopping occasionally to think and try new approaches.
"By staying honest about what I don't know - which, in this space, is still plenty. Intellectual humility isn't a virtue; in this fast-moving field, it could be a survival skill," he said.
For HR professionals looking to build AI-ready workforces, the takeaway is clear: it's a human challenge first, a capability challenge second. The belief that people, given the right conditions, learn, adapt, and thrive is where transformation begins.
For those responsible for learning strategy and talent development, resources like AI for Training & Development Managers and AI for CHROs (Chief Human Resources Officers) provide structured approaches to building these capabilities within your organization.
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