Leading AI Transformation Through Talent Development

The AI Skills Imperative: Why Role-Based Learning Is the New Standard

Across industries, AI adoption has moved from experimentation to execution. Yet a critical challenge remains: while organizations are investing in AI technology, many have not yet closed the gap between the tools available and the talent capable of using them effectively.

According to IBM AI Upskilling Insights, while 89% of business leaders say their workforce needs improved AI skills, only 6% report having begun meaningful upskilling.

For Birlasoft, the response has been a deliberate, structured approach to AI capability-building,  one that starts not with technology but with roles. As Deepak Kumar Arora puts it: "The idea is to look at roles and look at what capabilities are needed, and then put it across levels.”

This role-based framework spans multiple layers: foundational AI literacy and prompt engineering for all employees; productivity-enhancing tools for domain-specific roles such as developers, testers, and business analysts; and deep technical pathways for those building generative AI and agentic AI solutions for clients.

Scaling AI Learning Across Business Functions, Without Losing Focus

One of the most common pitfalls in enterprise AI learning programs is breadth without depth,  training everyone on everything, with outcomes that are hard to measure and even harder to sustain. Birlasoft's approach deliberately avoids this.

Only 35% of business leaders report having a mature, organization-wide AI upskilling program, with most training described as fragmented, optional, and disconnected from actual job tasks. — AI Skills Gap Report 2026

Rather than a one-size-fits-all curriculum, Birlasoft developed function-specific learning journeys. For HR, the organization built an 80-hour structured learning program. For delivery teams, the focus shifted toward productivity gains through GitHub Copilot, code-generation tools, and AI-assisted testing frameworks. For business leaders and sales functions, the emphasis was on using AI to sharpen client proposals and solution design.

Leadership alignment was foundational. The organization cascaded learning from the top down, with C-suite executives, including the CFO and CHRO, completing AI courses and visibly championing adoption. "When leaders are learning alongside their teams," Deepak noted, "it signals that this is not optional, it's strategic."

Beyond Tools: The Mindset Shift That Determines AI Success

Technology access is not the bottleneck. The real challenge in scaling AI capabilities, as Deepak identified, is behavioral, specifically, how employees think about and interact with AI in their daily workflows.

A 2025 workforce study found that only 49% of employees feel equipped for their current roles, down from 59% in 2024, even as organizations increase investment in AI training programs. — TriNet AI Upskilling Report

Deepak described a dual risk: employees either over-rely on AI, eroding their own cognitive sharpness, or resist adoption out of fear of displacement. Both extremes undermine enterprise value creation.

The antidote, he argued, is structured thinking, a power skill that becomes more valuable, not less, as AI handles routine tasks. "With AI coming in, you have to understand the length and breadth of any area. Structured thinking is going to be very important."

This perspective aligns with a broader market signal: power skills, including structured thinking, systems reasoning, stakeholder communication, and ethical judgment, are reemerging as differentiators in the age of AI. Enterprise learning programs that prioritize these alongside technical skills are better positioned to drive sustained adoption and measurable outcomes.

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Outcome-Driven Learning: Moving Beyond Completion Rates

One of the most persistent gaps in enterprise learning is the disconnect between training activity and business outcomes. For L&D leaders, this is not just a measurement challenge; it is a credibility challenge.

Research suggests that roughly 1 in 50 enterprise AI investments produce meaningful ROI, largely because organizations invest in technology without adequately upskilling the people who use it. — Digital Applied AI Upskilling Guide

Deepak's approach is to invert the conventional training design model, which he calls "inverting the Kirkpatrick model." Rather than designing programs and then asking what outcomes they might produce, Birlasoft starts with the business outcome, aligns with functional leaders on what success looks like, and then designs the learning intervention backward from that point.

For the L&D function specifically, the key metric is deployment: how many capability-ready employees are placed in billable roles to solve real client problems. For business functions, the metrics shift toward delivery velocity, quality improvement, and cost efficiency. Critically, both sets of metrics are agreed upon before programs are designed, not retrofitted after the fact.

This outcome-first orientation is what separates high-impact workforce transformation programs from well-intentioned but poorly measured learning initiatives.

Agentic AI and the Future of Human-AI Collaboration

As generative AI matures into agentic AI, systems capable of autonomous decision-making and multi-step task execution, the demands on workforce capabilities are evolving again. Birlasoft is already building internal agents for HR and L&D functions, with plans to expand across business processes.

But Deepak is clear that agentic AI is not a panacea. The value lies in identifying where agents can genuinely replace repetitive, decision-bound tasks within end-to-end workflows, not in deploying agents for their own sake. "The agent should bring in some outcomes, which is very important. First, it is very important to understand what the art of the possible is."

On the concern most employees carry, that AI will replace their jobs, Deepak offered a grounded perspective: the nature of roles is shifting, not disappearing. New roles such as prompt engineers, agentic AI developers, and CXO-level heads of AI are emerging faster than traditional roles are being automated. The imperative is for employees to evolve alongside these changes, not resist them.

Building the AI-Ready Enterprise with Simplilearn SkillUp+

The conversation between Simplilearn and Birlasoft reflects a broader market reality: AI transformation is as much a talent challenge as a technology one. Organizations that will lead in the next decade are those investing now in structured, outcome-driven enterprise learning that scales with their business.

Simplilearn SkillUp+ is an AI-first enterprise learning platform designed to help organizations build AI-capable, future-ready workforces.

The platform combines 700+ monthly live expert-led sessions, 1,000+ async content modules, hands-on projects, labs, and role-based learning paths across AI, digital, and business skills. Built on an outcome-driven learning model, Simplilearn SkillUp+ helps organizations move beyond content consumption to real-world skill application and measurable business impact.

Whether you are building foundational AI literacy across your workforce, creating deep technical pathways for agentic AI developers, or designing function-specific learning journeys for HR, finance, or sales, Simplilearn SkillUp+ gives L&D leaders the infrastructure, content, and analytics to drive results at enterprise scale.

Trusted by leading global enterprises, Simplilearn is the partner organizations choose when workforce transformation is not a training exercise; it is a business imperative.

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