Abhik Bhattacharyya on Beyond Automation: How HR Leaders Are Building Agile, AI Enabled Workforces
HR leaders, therefore, need flexible learning pathways that meet employees where they are, ranging from foundational digital literacy to advanced AI applications. Short, practical modules work better than one-size-fits-all training. The aim is to expand access to capability building across locations and backgrounds so workforce transformation remains inclusive, scalable, and aligned with long-term employability outcomes.
As organizations integrate AI into core HR functions, the focus is shifting from experimentation to practical, outcome-driven adoption. The challenge is no longer about introducing technology, but embedding it seamlessly into existing workflows without disrupting people, processes, or culture. This marks a critical transition where HR must balance efficiency gains with the preservation of human judgment, empathy, and trust.
At the heart of this transformation lies a fundamental shift in how work and skills are evolving. With roles rapidly changing, HR leaders must take a data-led approach to decide between upskilling employees for evolving roles and reskilling them for entirely new ones. This requires moving beyond intuition toward structured workforce analytics, skill adjacency frameworks, and continuous learning models that align talent capabilities with future business needs.
Equally important is addressing the human side of AI adoption. Employee concerns around job displacement and fairness remain significant, making transparency, inclusion, and hands-on learning essential. Organizations that actively involve employees in the AI journey and provide clear growth pathways are better positioned to build confidence and reduce resistance, positioning AI as a productivity partner rather than a threat.
As automation takes over routine tasks, HR’s role becomes more human-centric than ever. The emphasis shifts toward coaching, employee well-being, and culture-building—areas where empathy and contextual judgment are irreplaceable. Success, therefore, is not just measured by efficiency, but by how effectively AI enhances trust, inclusion, and employee experience.
However, scaling AI in HR comes with real challenges, from algorithmic bias in hiring to uneven digital readiness across regions and complex data privacy requirements. Addressing these requires strong governance frameworks, inclusive learning strategies, and close collaboration across HR, technology, and compliance teams to ensure ethical and equitable implementation.
In this conversation with People Manager, Abhik Bhattacharyya, CHRO at Univo Education, highlights, organizations that succeed will be those that use AI not just to automate processes, but to build agile, inclusive, and future-ready workforces—where technology drives efficiency, and people remain at the center of progress.
Q.: How are HR leaders practically embedding AI into core processes like recruitment, onboarding, and performance management without disrupting existing workflows?
Ans: We’re not replacing processes, we’re layering AI into existing workflows. Start with low-risk areas like CV screening or onboarding queries, integrate into current systems, and scale. For instance, in recruitment, it supports faster CV screening, skill matching, and candidate engagement. In onboarding, it improves access to information and resolves routine queries more efficiently. In performance management, it helps identify patterns, capability gaps, and development needs. Many organizations are starting with low-risk use cases, integrating AI into current systems, and scaling only after adoption stabilizes.
This shift is already mainstream. 75% of knowledge workers globally now use AI at work. The goal is to make HR more agile and outcome-driven, while ensuring judgment, coaching, and culture-building remain firmly human-led. The ultimate goal is to transform HR into a more agile, outcome-driven department that prioritizes results over administrative drudgery. However, this technical evolution does not sideline the human element. While automation handles repetitive transactions, core responsibilities such as professional coaching, ethical judgment, and culture-building remain firmly human-led. By streamlining the “paperwork,” personnel can dedicate their energy to the moments that matter most, ensuring that empathy and intuition remain the heart of the workplace.
Q.: In the face of AI adoption, how do HR leaders decide whether to reskill employees for new roles or upskill them to work alongside AI in their current roles?
Ans: It comes down to role adjacency and shelf-life. If the role evolves, we upskill. If it becomes redundant, we reskill. The decision is data-led, based on role disruption risk and future capability demand, not intuition.
To make this decision effectively, HR leaders need a structured, data-led approach. The process involves three steps which include role task analysis, identification of automatable tasks and development of existing skills into upcoming job requirements. The use of skill adjacency frameworks together with workforce analytics allows organizations to assess their capacity to develop new capabilities or their need to eliminate existing skills.
This urgency is only growing, with 39% of workers’ core skills expected to change by 2030. Organizations need flexible, outcome-driven learning models that build industry-relevant capabilities and long-term employability, rather than relying on one-time interventions after disruption has already taken hold.
Q.: What practical steps can HR leaders take to overcome employee fears of job displacement and build confidence in AI as a supportive tool?
Ans: Transparency is key. We should position AI as a productivity partner, not a replacement. Involving employees in the journey will reduce anxiety far more than top-down messaging.
Involving employees in the AI adoption journey is equally important, as participation reduces anxiety far more effectively than top-down messaging. The second is access to hands-on learning so people build confidence through use, not theory. The third is visible investment in future-ready skill development. When organizations create clear growth pathways, AI feels less like a threat and more like an enabler. That matters because 77% of employers already plan to upskill their workforce, signaling that adaptation, not avoidance, is becoming the dominant response to change.
The second important factor is also hands-on learning opportunities. Employees need the opportunity to voice their ideas, feelings, and opinions about the use of this technology. Holding workshops, roundtables, or periodic check-ins to consult with individuals and teams can be useful to uncover and address issues. HR leaders need to provide training programs that teach employees about different topics which include understanding industry impacts and solution implementation and solution usage. This approach builds confidence, reduces anxiety, and shows how AI can support their day-to-day work rather than replace it. By combining open dialogue with experiential learning, organizations create a culture of collaboration and trust, ensuring that employees feel valued, informed, and prepared to adopt new technologies effectively.
Q.: With AI automating routine HR tasks, how can leaders ensure that human interaction and empathy remain central to workplace culture?
Ans: AI handles transactions; humans handle moments that matter. We have included this in our way of working. AI should automate repetitive tasks, not the human essence of HR. AI handles transactions; humans handle moments that matter, and this principle is increasingly shaping how organizations design their ways of working.
The organization uses this principle to develop its operational systems because AI performs transactions while humans manage important life events. HR teams spend their time on coaching employees, conducting career discussions, supporting employee well-being, and building organizational culture when administrative work decreases. Organizations become more focused on their people when they use that approach. The actual measurement of success depends on whether AI usage increases trust and empathy, which helps people to make better choices. Technology should enhance speed and responsiveness, while human leadership abilities determine how people feel included, stay motivated, and develop their skills.
Q.: How do HR leaders address the uneven readiness of workforces across regions where some employees are digitally fluent while others struggle with basic tech adoption?
Ans: Uneven readiness requires a learner-centric approach. We should segment the workforce, not standardize it. Digital natives will move fast but others should get structured handholding.
HR leaders, therefore, need flexible learning pathways that meet employees where they are, ranging from foundational digital literacy to advanced AI applications. Short, practical modules work better than one-size-fits-all training. The aim is to expand access to capability building across locations and backgrounds so workforce transformation remains inclusive, scalable, and aligned with long-term employability outcomes.
This shift is becoming urgent. According to the World Economic Forum, nearly 44% of workers’ core skills are expected to change by 2027, making inclusive upskilling a business priority, not just an HR initiative. To respond effectively, organizations must also invest in skill assessments, localized content, and peer-led learning to ensure accessibility across regions.
For instance, companies like Amazon have adopted tiered upskilling programs that cater to both entry-level and advanced talent, ensuring broader participation.
Ultimately, the goal is not just to upskill a few but to expand access to capability building across the workforce. When done right, this approach drives inclusion, improves retention, and ensures transformation is both scalable and aligned with long-term employability.
Q.: What are the real challenges HR leaders face when using AI for global hiring, such as bias in algorithms or cultural nuances in candidate evaluation?
Ans: HR leaders embedding AI in global hiring are increasingly encountering challenges around bias, contextual understanding, and trust. Recent data shows that over 88% of companies now use AI for initial candidate screening, yet concerns around fairness remain significant. Tools like HireVue and LinkedIn Talent Insights rely on large datasets, and without careful calibration, may unintentionally replicate historical biases. In fact, 67% of organisations report ongoing challenges in managing AI bias despite advancements in ethical AI systems.
Cultural nuance is another critical challenge. AI systems often struggle to interpret region-specific communication styles, language subtleties, and behavioural cues, which are essential in global hiring. This limitation becomes more pronounced as organisations scale across geographies, where context matters as much as competence.
At the same time, trust remains a significant barrier. A 2025 report by Greenhouse highlights a stark disconnect: while 70% of hiring managers trust AI to make faster and better hiring decisions, only 8% of job seekers perceive these systems as fair. This widening trust gap reinforces the need for human oversight, greater transparency, and clearer communication in AI-led hiring processes. Organisations like IBM are addressing this by combining AI-driven insights with human judgement and continuous model audits to ensure fairness and accountability.
Q.: How are HR leaders managing the practical risks of employee data security when deploying AI tools across jurisdictions with different legal frameworks?
Ans: We should treat AI like any other critical infrastructure, with strict governance, clear data boundaries, and compliance with local regulations. Employee data includes personal, behavioural, and performance information, so organizations need clear safeguards, strong compliance processes, and responsible oversight across jurisdictions. This means close collaboration between HR, legal, compliance, and technology teams. It also requires transparency with employees about what data is collected, how it is used, and what protections are in place. In an AI-driven environment, credibility depends on ethical, compliant, and employee-first implementation.
Approximately 60 percent of data breaches are attributable to insider threats, and the organization needs to protect itself from insider threats. Professionals can use automation to handle their administrative duties, which enables them to focus on important tasks such as career coaching and cultural development work. The new direction requires HR to develop more flexible systems that focus on learning while maintaining their main purpose of helping employees. The organization achieves success through its ability to transform complex information into improved worker outcomes while establishing trust and data protection and developing employee skills for the future.
Q.: From a practical HR perspective, how do leaders justify the investment in AI tools to boards and executives, especially in cost-sensitive industries?
Ans: Pilots with measurable ROI build credibility faster than large upfront investments. The business case for AI has to be outcome-driven. HR leaders should connect investment to measurable improvements in hiring speed, workforce productivity, employee experience, retention, and learning effectiveness. Boards also want to see how AI improves scalability and decision-making. The strongest case is not based on automation alone, but on how AI helps build a more agile workforce. That conversation is becoming easier because one in four workers globally is already in occupations with some exposure to generative AI, making preparedness a strategic necessity, not an optional experiment.
According to Gartner, AI is driving new business models and operational efficiencies, but its real value emerges when strategy and talent are in sync. This conversation is becoming a strategic necessity rather than an optional experiment, as nearly 30 million jobs each year will be redesigned, not eliminated by AI-driven innovation.
Q.: How can HR leaders use AI to personalize learning and development pathways while ensuring inclusivity across diverse employee groups?
Ans: AI can help tailor learning paths, but we need to ensure the inputs are diverse and bias-checked. Personalization shouldn’t create exclusion, it should widen access and relevance. This is where skills-first thinking matters. By focusing on capabilities rather than narrow credentials, organizations can significantly expand access to learning and future opportunities.
That’s why many organizations are shifting toward a skills-first approach. Instead of relying heavily on degrees or past job titles, AI can map what people are actually capable of, identify adjacent skills, and recommend realistic upskilling pathways. This opens up learning opportunities to employees from non-traditional or underrepresented backgrounds, making development more accessible and relevant.
At the same time, inclusivity has to be designed in, not assumed. This means training AI systems on diverse, representative datasets and continuously auditing them for bias. The concern is real recent 2025–2026 data suggests that nearly 40% of employees believe AI-driven HR tools can replicate or even amplify existing biases if left unchecked. That perception alone makes transparency critical.
HR leaders also need to ensure that AI-driven recommendations don’t operate like a black box. Employees should have visibility into why certain courses or paths are suggested, along with the flexibility to explore beyond them. Human oversight remains essential here not to override AI, but to question, validate, and refine it.
Q.: What new skills must HR professionals themselves acquire to remain effective in an AI-driven workplace?
Ans: HR teams need data literacy, tech fluency, and stronger business alignment. We should invest in building HR Teams who can connect technology, talent, and business outcomes seamlessly. To remain effective, HR professionals must bridge the gap between technical governance and human advocacy. Mastering data interpretation and digital fluency is now a prerequisite for making informed, evidence-based decisions that drive the responsible adoption of modern tools. However, these capabilities must be balanced with irreplaceable human strengths: empathy, ethical judgment, and complex change management.
By automating administrative burdens, leaders can pivot toward high-value interactions like career coaching and cultural development. This shift requires HR to become more agile and learner-centric, ensuring that technological progress never outpaces the core mission of supporting people. Ultimately, success in a digital-first workplace depends on translating sophisticated insights into better workforce outcomes while maintaining a foundation of trust, inclusivity, and long-term talent growth. For further insights into the evolving workplace paradigm, visit
