AI and the future of work
AI has the numbers. Judgment is still the scarce thing.
Not a fourth thing he sells. It is the question that keeps turning up inside the other three, and it has been the subject of his published research since 2022. What follows is the position, where he has argued it, and what it looks like when it reaches an actual brief.
The short version
- AI brings precision.
- HR brings perception.
- Leaders bring courage to connect the two.
What actually changes
AI brings precision. It does not bring perception.
Five things that move when AI enters the work. Each is drawn from published research or from what he argues in public, not from a vendor’s roadmap.
Which roles hold, and which quietly stop making sense
His own framing, from the first post in his numbered series on HR: artificial intelligence can file the reports and draft the appraisal, and what that frees people for is the part the technology cannot do: “coaching teams, shaping culture, building trust.” The work does not disappear. It moves, and job descriptions are usually the last thing to notice.
Where employee trust starts to break
This is the part his research is actually about. Four papers since 2022 examine what AI-enabled systems do to employee privacy, where monitoring becomes surveillance, and where a system that is defensible on paper is experienced as something else entirely by the people inside it.
Whether your managers can actually use it
The finding from the published paper, in its own words: “Training managers and employees to understand the potential and limitations of AI tools is pivotal.” Adoption is not a licensing decision. A tool nobody can interrogate produces confident answers that nobody can challenge.
What becomes scarce when answers arrive instantly
He puts it this way: “In today’s AI-enabled workplace, where information is abundant and answers are generated almost instantly, the differentiator is no longer access to knowledge but the ability to think rigorously.” He cites the World Economic Forum’s Future of Jobs Report 2025, which finds analytical thinking the most essential core skill of the coming workforce, with 69 per cent of employers ranking it critical.
Who answers for it when the model is wrong
From his article for managers in Business Manager: automation genuinely helps with tasks like screening, and it can reduce bias rather than add to it, but only alongside “ethical AI practices and maintaining open communication with employees.” His argument is that organizations have to rethink AI’s role “beyond automation and efficiency creation”, because that is the point at which someone has to own the decision the system made.
In public
The same argument, outside the journals.
Peer review is where the work gets tested. These are where it has been put in front of the people who have to act on it.
- Forbes India: his first solo piece for the masthead, on how India’s workforce can align “speed with sensibility” in its AI journey. His framing: it is “about how we prepare our people to prompt, learn, adapt, and lead in a world being redefined by machines.”
- The Hans India: Mastering AI for Workplace Success, taking the Communications of the AIS research and turning it into something a workplace can act on.
- Business Manager: what managers need to know about AI inside HR systems: automation and bias in screening, where AI genuinely improves talent decisions, and why ethics and transparency decide whether any of it is trusted.
- An open programme on AI in HR: Artificial Intelligence in Human Resource Management, ten hours across four sessions, co-taught through SPJIMR Executive Education in December 2024.
The peer-reviewed work underneath all of it is a study of what AI-enabled systems do to employee privacy.
Where it lands
The same question, three different briefs.
It almost never arrives labelled as an AI problem. It arrives as one of these, and which of the three it becomes follows from the decision underneath it.
“We are automating, and we cannot tell what it is doing to the people system.”
It arrives as a question about structure and reward: which decisions are still human, what the policy has to say now, and who owns the outcome when a system gets it wrong.
“Our leaders need to be able to challenge what the model tells them.”
It arrives as a cohort that can read a dashboard and cannot yet interrogate one. That is a teachable skill, and it is taught at the seniority of the room.
“We need the room to leave thinking differently about this.”
It arrives as a session at a summit or an offsite, where the job is to move a large group from anxiety about the technology to a usable view of it.
Next step
Start with the decision, not the tool.
The decision your organization is actually facing, and who it affects. Whether AI is the answer to it, or a distraction from it, follows from that, and it is a reasonable outcome of the first conversation.