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Organizations that succeed today not only use artificial intelligence but also transform their entire work processes. The introduction of AI technology enables companies to enhance their talent acquisition processes and workforce management systems, improve operational efficiency, and create adaptable business solutions.
The Analytics Insight Podcast episode investigates the impact of AI technology on workforce management, talent acquisition, and business operations. Varun Sachdeva, who serves as the APAC talent acquisition and workforce strategy leader at NLB Services, explains to organizations the best methods for implementing AI into their operations while developing teams that can succeed in the future. The interview includes the following selected quotations:
Ans: NLB Services is a global technology and business transformation organization that helps enterprises scale with speed, resilience, and digital intelligence. We started our journey back in 2007. Today, we partner with organizations across India and international markets, primarily in North America and EMEA. India is a very strong geography for us; we have an integrated portfolio of solutions spanning AI, data engineering, talent solutions, digital operations, automation, workforce skilling and upskilling, and end-to-end GCC build-and-managed services.
Ans: I have been a talent acquisition HR professional for over 23 years and currently lead the organization's Asia Pacific talent acquisition and workforce strategy. My role basically focuses on building and delivering scalable hiring frameworks that balance speed, quality, and cost. I work with GCCs and multinational clients to optimize the workforce planning and delivery models. I absolutely believe in the concept of tech and touch, where technology acts as an enabler and human intelligence focuses on the experience and engagement.
Ans: Brilliant question, and, you know, in my opinion, when AI becomes a coworker, it changes how teams organize work and make decisions. It allows humans to focus on judgment, creativity, and complex problem-solving rather than on routine tasks. So all the repetitive, routine tasks are taken care of by AI, and human intelligence can actually focus on creativity, innovation, and the parts that were never part of the ecosystem earlier.
So, you know, today we need people who can understand the AI insights, while the tool can share, you know, data recommendations in real time, but we also need people who can interpret them into business outcomes.
It is not an IT infrastructure as such, and that's where a lot of organizations get confused, you know, where to start, what to implement, and how the business outcomes will be defined. And that's where we feel that, you know, it's a buzzword, but people don't know what value it brings to, uh, the work ecosystem.
Ans: So being AI-ready means being able to adapt quickly as tools and processes evolve. You know, it requires the willingness to learn continuously and engage with new ways of working. Understanding what AI can do and how it complements human decision-making is extremely critical. Various studies have actually shown that 30 to 40% of the tasks in most roles can be automated or augmented by AI.
So the workforce models must treat AI, as I mentioned earlier, as an operating team, not as an IT infrastructure. Collaboration changes when AI is involved, so working alongside AI responsibly and effectively is extremely important. Organizations, you know, in the last couple of years have actually shown that they value people who are flexible, who can embrace change, and who can translate AI capabilities into real business impact.
That's where organizations need to define employees' career growth through continuous learning and align it with business outcomes, so it becomes a win-win for everyone. That's what truly being AI-ready means to me from a workforce perspective.
Ans: We do this regularly within our organization, and I'm gonna quote some examples. We'll share what we are doing internally, and many other organizations are likely following the same path. Reskilling and upskilling are most effective when they are directly tied to business outcomes rather than treated as a separate HR initiative.
A large organization needs a blend of structured programs and on-the-job learning so people can apply new skills as they practice them. Historically, you know, peer learning and mentorship have proven to play a critical role because knowledge transfer happens fastest when employees learn from colleagues who have applied those skills in real situations.
Leveraging technology is important, but it works best when it supports human engagement rather than replacing it. So a continuous upskilling or learning requires flexible modular approaches that allow teams to learn at their own pace while keeping the organization goal-aligned.
