A few days ago, I was in a classroom at Ashoka University, teaching students how to build with AI. Many of them came from psychology, literature, philosophy, and other humanities disciplines. Most had never written a line of code.

Yet within hours, they were building simple apps, workflows, and prototypes using natural language, prompts, and AI tools. It struck me, once again, that one of the defining truths of this era is this: in the age of AI, English is the new coding.That is why I keep returning to a simple idea: the real challenge of AI is not merely technological. It is human.

The people and organisations that will thrive are not necessarily the ones with the biggest models or the most elite AI scientists. They will be the ones that become AI literate and then redesign work around that literacy. That is the practical playbook.I have argued for what I call the three laws of AI literacy.

The first is that in the age of AI, everyone need not be an AI expert, but everyone needs to be AI literate. The second is that the definition of literacy itself is changing. It is no longer just reading, writing, and arithmetic; it is those things plus the ability to work with AI in what you do.

The third is that no investment in AI models, tools, or agents will truly land in an enterprise or educational institution unless the people using them are AI literate first. Those ideas are central to how I think about this moment.This matters because AI talent itself is changing. We are used to thinking of AI talent as a rarefied group: researchers, model builders, PhDs, elite engineers.

Of course they matter. But the bigger prize lies elsewhere. The most important AI talent in your organisation is often not the 1% you are trying to hire from a frontier lab.

It is the other 99%: the marketer who uses AI to generate and test campaigns faster, the HR manager who uses it to redesign screening and onboarding, the finance professional who can automate analysis and compliance checks, the operations lead who can turn domain knowledge into intelligent workflows. Real AI talent is becoming less about building the model, and more about orchestrating work with models, agents, tools, and judgment.There is hard evidence that this shift is already under way. Stanford’s 2025 AI Index found that 78% of organisations reported using AI in 2024, up from 55% the year before, and 71% reported using generative AI in at least one business function, more than double the previous year.

That tells us AI is no longer an edge experiment; it is moving into the operating core of firms. But adoption alone is not transformation. This is where businesses often get it wrong.

They treat AI as another software layer, bolt a chatbot onto an old process, run a few pilots, and then wonder why the value does not arrive. The better historical analogy is not the smartphone app. It is electricity.When factories first moved beyond the steam engine, they did not simply replace one power source with another and carry on as before.

Under steam, machines had to be organised around a central drive shaft. With electricity, power could be distributed differently, and the whole factory could be redesigned. Productivity gains came not from swapping the engine, but from rethinking the layout and the workflow.

AI is exactly like that. If steam power was about mechanising muscle, AI is about augmenting cognition. The gains will not come from inserting AI into yesterday’s work.

They will come from redesigning work itself.That is also what the latest organisational evidence suggests. McKinsey’s 2025 global AI survey found that redesigning workflows has the biggest effect on whether companies see EBIT impact from generative AI. In other words, the winners are not just deploying tools; they are rewiring how work gets done.

This is where people and business strategy come together. For individuals, the playbook is literacy: learning how to prompt, verify, collaborate with AI, and use judgment. For organisations, the playbook is workflow change: moving from isolated use cases to end-to-end redesign.

And for leaders, the challenge is cultural. AI cannot remain the domain of a tech team. It has to become a shared language.The World Economic Forum says 22% of today’s jobs will be disrupted by 2030, with 170 million new roles created and 92 million displaced.

That should worry us a little, but it should also clarify the task ahead. The question is not whether AI will matter. It already does. The question is whether people and businesses will remain spectators, or become literate participants in building the next way of working.Jaspreet Bindra is the Co-founder and CEO of AI & Beyond(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)