There was a time when knowing how to use a computer was considered a differentiator. Then came digital literacy, which gradually moved from being a useful skill to an unstated expectation across most jobs. AI appears to be following a similar trajectory.
But there is a more complicated question underneath this shift. If AI is changing how work gets done, should employees who do not embrace it risk being left behind in career progression? Or should organisations allow employees to choose whether and how deeply they want to engage with AI?
Kashish Kapoor, Global Head – HR, Newgen
Yes, but organisations must enable employees before evaluating them.
AI fluency will increasingly become what digital literacy became a decade ago: a professional expectation rather than a specialist skill. That said, organisations have a responsibility to make that transition inclusive.
Every new capability follows a similar journey. It begins as an advantage, becomes a differentiator and eventually becomes the baseline. AI is on that path.
But organisations must enable people before they evaluate them. That means investing in learning, creating opportunities for hands-on application and giving employees the confidence to experiment before AI proficiency becomes an expectation. Career growth should not be determined simply by whether someone uses AI. It should be influenced by whether they are willing to learn, adapt and use it responsibly to create better outcomes.
AI adoption should be value-driven, not compliance-driven. When people experience how AI helps them make better decisions, solve problems faster and create greater impact, adoption becomes organic. That is how organisations build a workforce that does not merely respond to change, but anticipates it.
Only then can accountability be fair, transparent and aligned with business outcomes.
Takeaway: Make AI fluency an expectation gradually, but first give employees the tools, exposure and confidence to acquire it.
Shalini Modi, Senior Vice President and Global Leader – Employee Learning and Skilling, Genpact
Yes, but the depth of fluency must be tied to the role, not imposed uniformly.
AI fluency is gradually moving from being a “nice to have” to becoming a baseline capability. But that does not mean every employee needs the same level of technical expertise.
The expectation should reflect the role and how AI is changing the work itself. For some employees, that may mean using AI tools effectively. For others, it may involve supervising AI-enabled workflows, questioning outputs or making decisions using AI-generated insights.
That shift also means organisations need to look beyond learning participation when making talent decisions. AI capability will increasingly sit alongside functional expertise, business judgment and leadership capability in decisions around career progression, internal mobility and succession planning.
At Genpact, this is reflected in our talent architecture, which connects our skill taxonomy and role-skill framework with career progression and succession planning. We are also working towards having 100 per cent of our workforce operate as AI Practitioners by 2027.
The broader point is that AI fluency is becoming less about whether an employee chooses to take an AI course and more about whether they can remain effective as their role evolves. It should not become a blanket technical requirement. The expectation should be relevant to the work employees do and the value they are expected to create.
Takeaway: AI fluency should influence career decisions, but its depth must be determined by the role rather than imposed as a one-size-fits-all requirement.
Salil Chinchore, Group Chief Human Resources Officer, Rustomjee
Yes, AI literacy will increasingly become a workplace expectation—but it should not mean the same thing for everyone.
AI has already moved beyond being a technology organisations are simply experimenting with. As early use cases show gains in productivity, speed, decision-making and problem-solving, AI is beginning to reshape not just how work is done, but how roles themselves are designed. This makes AI literacy less of a learning opportunity and more of a basic professional capability.
But AI literacy should be understood in practical, rather than technical, terms. Employees need to know how to use AI effectively, ask the right questions, validate outputs, recognise bias and errors, protect confidential information and exercise human judgement. Organisations, meanwhile, need to provide the tools, guardrails, training and practical use cases that make responsible adoption possible.
A blanket mandate, however, is unlikely to work. The AI capabilities required by a software engineer will differ significantly from those needed by an HR professional, marketer or frontline employee. Organisations should therefore establish role-based expectations, ranging from basic awareness and responsible use to advanced application, workflow redesign and AI-enabled problem-solving.
There is also value in identifying digitally curious employees as ‘AI Champions’ who can experiment with use cases, build AI-enabled solutions and help colleagues adopt new ways of working. This can make AI adoption more organic and practical rather than another top-down learning mandate.
Because excessive pressure to “learn AI” can be counterproductive, the principle should be simple: accountability follows enablement. Employees can be expected to use AI where it is relevant and beneficial only when organisations have equipped them with the right tools, training and support.
Takeaway: The goal is not to make everyone an AI expert. It is to build a workforce that understands where AI can augment its work, knows how to use it responsibly, and can recognise where human judgement must remain firmly in charge.