A brief note before we begin
AI presents a once-in-a-lifetime opportunity for individuals and organizations. Whether you believe that or think AI is just another bubble, this article gives you a chance to take a fresh look at the opportunity.
The primary target audience of this article is HR professionals, though much of what’s said is relevant for other functions as well. If you’re a business leader, this can serve as a reference for what to expect (and ask for) from your HR team.
I have tried to detail my views on the role HR can play as the world of work pivots around AI. The ideas in this article are based on our Let's go back to this and we'll continue to be experience working with 100+ organizations—from startups and SMEs to large enterprises—on upskilling their teams and implementing AI. My background as a hands-on AI engineer with over a decade of entrepreneurial experience in the HRTech vertical helps me empathize with both worlds and gives me the ability to translate HR problems into AI opportunities, assess feasibility, and think about what will actually work in real-world settings. That’s the lens through which this article has been written.
Also, I’ve intentionally skipped the usual “X% of companies saved Y hours using AI in recruitment” kind of stats, for two reasons:
They can be misleading. AI is still early in its journey—far behind the potential it holds for organizations. Every organization is different in its priorities, maturity, and readiness and what works for one can fall flat in another.
Most successful AI use cases today are small, task-specific, and evolve bottom-up.
Let’s begin!
Impact on the HR department and HR role
You may have heard it from others, but it's important to reiterate: the HR role as we know it today will cease to exist. AI will take away a large part of your task inventory—meaning the department size will shrink, and so will the number of jobs available in the market (as per the current role definitions). If that scares you, the fear is real. But what is also real is a huge opportunity that is up for grabs, if you decide to lead and build the right mindset and skills for it.
Many of the long-held dreams of HR leaders —such as ‘competence frameworks that are used as intended' (and stays relevant as the business evolves), ‘skill-based organizations’, ‘data-driven performance review systems’, and so on— that couldn’t be realized at scale because of the complexity of implementation and the costs involved—are now becoming possible with AI. And that’s why I see there are more opportunities than there is scope for fear.
On Mindset and Understanding the Nature of AI—Before You Dive In
Fundamentals first, before we dive deeper into topics such as ‘Future Skills for HR’ or discuss AI use-cases -
Be a Believer: I meet a lot of HR folks who still think that the hype around AI will die down soon. If you think so too, please check the following:
170 million new jobs are projected to be created globally by 2030, while 92 million existing roles face displacement. 39% of existing skill sets will become outdated between 2025–2030. ~ The World Economic Forum's (WEF) Future of Jobs Report 2025
AI leaders have seen 1.5x faster revenue growth, 1.6x higher shareholder returns, and 1.4x better return on invested capital than their less advanced peers. ~ OpenAI 2025 report on Identifying and Scaling AI Use Cases
You can make a positive difference only by being a believer!
Hype, Vision versus what is Possible today: A lot of confusion and disbelief gets created when we fail to discern hype from vision or reality. I see people engaging in emotionally charged debates around “AI employees,” “AI BDRs,” etc., pointing out that AI systems make small errors and are nowhere closer to human intelligence—such arguments won’t lead us anywhere. It’s okay if you don’t agree with the picture of the future someone is trying to paint, but don’t let emotional bias come in the way of creating value with what is available and possible today.
Don’t give up too early – be at it to get it: Quite often, people driven by all the hype around AI try their hands with a tool such as ChatGPT, only to return disappointed that it didn’t meet their ‘expectations’ or made a simple mistake. But this is where they go wrong—people who are able to get value out of AI are those who try it over a sustained period of time (and for multiple types of tasks). By doing so, they not only build a sense of what the tool can or cannot do—e.g., where human supervision would be required—but are also able to relate with every new update and even start predicting what’s coming next. The technology is evolving at a rapid pace, so approach it with a mindset of understanding rather than a mere comparison with human intelligence.
Understand the Scale and Speed of the Change: While it’s good to draw from previous technology transformations, the change brought by AI is very different in terms of both speed and scale. Why? Because while the previous generation of products were largely designed to provide infrastructure for humans to work and collaborate seamlessly, AI is about getting the work done. That changes the equation—the impact and momentum of AI development are unlike anything we've seen before. If you fail to evolve, your entire team, and eventually the organization, may become irrelevant and overtaken by new start-ups or competitors who adopt AI effectively.
AI Adoption Works Bottom-Up—One Use Case at a Time: Given the state of AI, the speed at which it is evolving, and the current state of trust in users for AI systems, the approach to building and adopting AI should be bottom-up. Identify a set of well-defined tasks, try to solve them with AI, gather user feedback, and iterate from there (relatable to Design Thinking or the Lean Startup process). AI does not give you a magical wand that you can just integrate into your workflow and expect a major improvement. And this is another point of difference from previous technology transformations. Success, then, lies in evolving your processes, your products, and your organization step by step—one iteration, one use case, one experiment at a time, always shaped by your own business context and where you stand.
Know the difference: generic AI vs vertical AI: Another thing I often notice is the mixing of expectations between ‘generic’ and ‘vertical’ AI. A tool like ChatGPT can perform a wide range of cognitive tasks, though with varied levels of accuracy. A specialized or vertical AI tool, on the other hand, is designed to be really good at just one thing—for example, summarizing a video interview and providing structured feedback to the interviewer for specific job roles. These tools are narrow in scope—often built to handle just a few scenarios initially. That leads to a key conclusion: if you want to see AI perform like an expert, start with a narrow use-case. That’s counterintuitive, especially compared to how tech adoption worked previously (more on this in the section: ‘On Implementing AI in HR’).
Six Future Skills of HR: The Way to Lead in the AI Era
I have heard a lot of HR people talking about the importance of human skills and how AI systems can never compete with human judgment, and I agree—skills such as stakeholder management, building curiosity in the workforce, etc., will become increasingly more important in the age of AI. But I strongly believe that, along with human skills, certain behavioral and hard skills are also required for HR professionals to survive and thrive in an AI-first organization.
* Start with the Basics of AI—Get Hands-on, Don’t Just Read the News
Everyone recommends upskilling yourself in AI, but given the vast amount of knowledge out there and the rapid pace of development, the problem is non-trivial. Some thoughts below:
Basics of the modern AI Stack: Learn about the key components of any modern AI application, terminologies, tools, key players, and where they fit in. You don’t need to know how to build them or understand the internals of each component, but you do need to understand what each one does and how they fit together to create and deliver an experience. Terms such as evaluation, RAG, multimodal, and agentic AI are important to get familiar with.
Get your hands dirty: The only way to learn AI is to get hands-on and try it yourself. For HR, my recommendation is not to limit yourself to HR tools alone. You should try tools from other areas as well—for example, AI sales tools, or no-code application builders such as lovable.dev that can build a complete website from natural language input.
Why It Matters: Points #1 and #2 together will form the foundation for building the future skills (listed below). They will also enable you to understand new developments—such as a new model release—and place them correctly in the broader picture. So, when you hear the news of a new development—e.g. a new model release—you can make sense of it and decide whether to dive deeper. This kind of understanding is critical.
Future Skills for HR
1. Talent Analytics or Workforce Intelligence: An area that has a tailwind due to the organizational push on AI. Every firm is working towards organizing its data, making it more accessible for AI models. As a result, a lot of data that was previously unavailable now becomes usable for deriving intelligence. Most importantly, meaningful correlations with business data and cross-departmental data are now becoming possible. Also, because of the capability of the models, tasks such as sentiment analysis, which were earlier difficult to do, are now far easier to implement, giving a strong push to this field.
Building on this shift, the focus in analytics is no longer about mere reporting or even descriptive insights. It’s now moving towards prescriptive and predictive analytics. For HR, the key lies in being able to ask the right questions, identify the data that matters, translate business challenges into analytics problems, run experiments, and interpret the results into clear, actionable insights for leaders. Being able to do this consistently and iterate based on what you learn will become a foundational skill set.
2. Iterative Working, yes! Design Thinking: As I mentioned previously, AI evolves in a bottom-up manner and HR's role becomes very important in facilitating this lean or iterative way of working across the organization—by participating in the development of right organizational structures, support systems, processes and of course, skills.
3. Change Management for AI and Building an AI-First Culture: As I mentioned earlier, the nature and scale of change brought by AI are fundamentally different from what we've seen before. This means mastering how you manage and lead through change is an essential skill.
The impact of AI on both improving productivity and eliminating jobs is real—and both realities exist at the same time. You have to reconcile them in your own context. As per EY 'AI Anxiety in Business' Survey, a staggering 75% of employees are concerned that AI will make certain jobs obsolete, and about two-thirds (65%) say they are anxious about AI replacing their own job. While upskilling, cross-skilling, and reskilling are important tools, how and what we communicate about the organization’s AI strategy—and how it aligns with individuals—becomes very important.
There has always been a debate around top-down versus bottom-up change, but in the case of AI, it is pretty obvious that bottom-up change works best. I have highlighted more on this below in the sections on 'Implementing AI in HR and other functions'. Your frontline people, those directly involved in hands-on tasks, are the ones from where innovation will bubble up in the era of AI. Empowering them is a very important part of building an AI-first culture.
4. Org. Design for an AI Future: AI is set to fundamentally change talent management and workforce planning, but it’s also changing the very nature of jobs themselves. That makes organization design a moving target—what is working today may not work tomorrow. Some parts of the organization may need to become leaner, while others might need to scale up quickly. Transitions will increasingly become more frequent, driven by or supported by underlying reskilling and cross-skilling. Also, the boundaries between departments and job roles are blurring. What used to be a separate function or a distinct role, could now be an API call, with some human supervision. It presents a great business opportunity, but at the same time, keeping pace with this is extremely challenging. There are no technologies or foolproof methods anyone can claim to have for solving this right now. We all have to learn this as we go, and that presents a great opportunity for HR to participate proactively.
It starts with really understanding the nature of different job roles in your organization and the impact AI is likely to have on them. HR should spend more time working with business leaders on deconstructing jobs into tasks; identifying which tasks are best performed by humans vs agents and reconstructing new ways of working. That’s the starting point. From there, it’s about leveraging AI to solve specific use-cases that eventually create a flywheel—one that keeps evolving with your business.
5. Facilitating AI Conversations: Learn to be better at facilitating AI conversations. HR has always had a natural strength in leading discussions, but in the context of AI, you have a much deeper role to play. I see three dimensions or angles that need to come together:
Employee Centricity: This means keeping a close eye on employee mindsets, motivations, fears, hopes, and how AI is likely to impact specific job roles. The conversations you facilitate must always account for the human side—not just the business or technology narrative.
Compliance Mindset: It’s about maintaining a compliance mindset—not just ticking boxes, but genuinely engaging with regulatory requirements, safety concerns, and the risk of bias. By putting compliance front and center, you help build trust in the organization’s use of AI—making it clear that ethical guardrails and safety are part of every conversation and gradually get built into the design.
Opportunity Focus: Finally, there’s the opportunity focus—the ability to see and articulate what’s actually possible with AI, rather than just focusing on risks. This is where HR can help the organization spot new ways of working, uncover value, and encourage experimentation—ensuring that concerns around risk don’t stall progress but that new opportunities are pursued following Responsible AI practices.
The way HR facilitates these conversations will shape whether the organization captures the opportunity of AI—or lets it slip. Play it passive or overly cautious, and you risk holding the business back. Play it well, with balance and perspective, and you can help your organization move forward—responsibly and confidently—into the future.
6. Actively participating in the Build Process: As I said above, successful AI initiatives are driven bottom-up, unlike the previous era of digital transformation and multi-year, large scale implementations. Many organizations are choosing to build and/or drive these initiatives internally rather than letting their vendors/platform providers lead it for them and rightly so! making active participation a must. If you are not fully immersed in the implementation process, your AI initiatives are bound to fail. Highlighting three areas where HR can look to develop their skills:
Developing Use-Cases and Getting buy-in from Leadership: Another skill that is—and will continue to be—critical for HR professionals is learning how to identify and develop meaningful AI use-cases — within HR as well as across other functions. It’s not just about spotting an interesting problem, but really thinking through user-adoption, roadmap and impact/ROI. Worth noting that the journey from problem identification to ROI (revenue increase / cost reduction) takes time and varies by department and organization. And that’s why being able to sell the roadmap to your CFO is key. I’ve seen, for instance, that using AI in the employee onboarding journey—however limited the scope may be —sets the tone early. It gives new employees a chance to experience AI hands-on, which builds comfort and openness. Later, these employees are more likely to experiment and adopt. So, while it may not deliver high ROI on its own, it plays an important role in driving broader adoption across teams.
Partnering effectively with IT & Vendors – It starts with building an understanding of how AI applications work under-the-hood and what really goes into building a working production system, stepwise from creating the right dataset (and understanding what 'right' means here!), to model identification, testing, evaluation till deployment. You don't need to understand the technicality or learn how to do each step on your own but a high-level understanding of the process flow, the input/output, associated challenges and limitations is important. This gives you the ability to ask the right questions and empathize with the process of your IT counterparts—whether in-house, external, or platform vendors. There is a need to skill your internal IT teams to effectively partner with every business function effectively so (I have covered under 'Implementing AI in HR and other functions') - so they too appreciate the importance of making this engagement participatory. HR, in particular, is a fertile ground for AI innovation, and the more involved you are in shaping these solutions, the more value you’ll be able to unlock for the business and for employees.
Evaluating AI Applications - This is another key skill, and you can think of it as an extension of the previous point about partnering in the build process. The ability to evaluate AI outputs and communicate actionable feedback to the builders is critical—especially as these tools are constantly evolving. It’s not just about checking if something “works” or not but really understanding how the AI behaves in real-world setting, spotting patterns, identifying potential biases, and flagging gaps where the system might be missing context. HR teams should also learn how to document test cases, simulate realistic scenarios, and assess whether an application genuinely adds value for employees and business users—not just in theory, but in day-to-day use.
In Part-2 of this article, I will dive deeper into the details of implementing AI in HR and other functions - highlighting what others are building, what's working (and what's not) and how HR can participate meaningfully.
I have added references for further exploration and invite the community to add their questions, comments, or suggestions, and share it with their peers.
AI in the Enterprise [OpenAI]
Workforce Planning in the age of AI [Mckinsey]
Future of Jobs Report 2025 [World Economic Forum]
Agentic AI in HR [Mercer]
Building trusted AI in the enterprise [Anthropic]