The latest wave of artificial intelligence arrived with a very familiar promise: greater efficiency, higher productivity and lower costs. Whether those promises will ultimately be realized is still unfolding. But one thing is clear: AI is here to stay.
I think we are focusing too much on whether AI will make organizations more productive.
What makes me curious is what happens to the organization when the work itself starts to change.
Because this is not simply a technology transformation. It is a transformation of work. AI is changing how work is imagined, how work is designed, how decisions are made and, increasingly, what organizations consider valuable human contribution.
Lessons from 4 waves of disruption
I have spent more than 20 years working through several waves of disruption, giving me a front-row seat to how change can reshape the nature of work.
I remember a time when the internet was not something you assumed everyone had access to. If you wanted to send something online or print a CV, you might have had to find a cybercafé. I saw digital access, once relatively new, become embedded in work.
Then came the financial crisis and recession in 2008. The recession taught me that organizations can fundamentally change with the economy. The digital transformation wave that followed showed me that technology can fundamentally change how work is performed.
Then came COVID-19. Almost overnight, organizations had to reimagine where and how work happened. Things that organizations had spent years discussing suddenly happened within weeks.
Then came AI. What struck me about these different moments was that each changed something different.
The recession changed the economics of work.
Digital transformation changed how work was performed.
COVID-19 changed where work happened.
But AI is beginning to challenge something deeper. The leadership conversation needs to change. And it begins with asking ourselves: What work actually needs to be performed by humans?
AI adoption is not AI transformation
There is a temptation for leadership teams to treat AI the same way they have treated other technology initiatives: Select the technology, create the policy, roll out the tools, train employees, measure adoption, move on.
But AI does not simply sit alongside the existing organization. It has demonstrated potential to change the work within it.
Recent research from McKinsey makes a similar point. Its 2026 research found that many organizations are using AI to accelerate existing activities while leaving the underlying operating model, workflows, governance and capabilities largely unchanged. Only a minority of organizations surveyed had fundamentally redesigned their operating models around AI.
That distinction matters.
If you give 1,000 employees access to an AI tool and they each save an hour a week, you may have created individual productivity. But if the workflow, decision rights, customer journey, measures of performance or the way teams interact have not changed, you have not necessarily transformed the organization.
You may simply have made the existing system slightly faster. True transformation comes when leaders zero in on redesigning how work gets done.
Leadership teams today tend to focus on how to get their teams to adopt AI. Instead, they should be asking, “If AI changes this work, what does the organization need to become?”
That means looking beyond adoption to how AI will reshape work, people, customer outcomes and future organizational capabilities.
Leadership development needs to move beyond existing roles
For decades, organizations have built leadership pipelines around existing roles.
We identify high performers, we give them more responsibility, we develop them against a competency framework, we prepare successors for roles that already exist. But what happens when the role itself is changing?
I don’t think the future leader necessarily needs to be the best version of today’s leader.
They may need to understand systems rather than simply manage functions. They may need to understand technology without being technologists. They may need to make decisions with incomplete information and understanding of the impact of those decisions across people, customers, technology and economics.
In other words, leaders need to understand the work before they decide what workforce they need. That is a significant shift.
The question moves from: “Who do we need for this role?” to: “What work needs to be done, how should it be organized and what combination of human capability and technology is required to do it well?”
That is a workforce strategy, but it is also leadership.
Leaders also need to be accountable for the risks alongside the gains and the outcomes. That requires greater tolerance for ambiguity, because not every decision will come with certainty.
The expertise problem nobody is talking enough about
Another consequence of AI that concerns me even more: Technology has always changed jobs. That is not new.
It is the speed. If the nature of work changes faster than people, organizations and education systems can adapt, then the biggest risk is not simply that some jobs disappear.
It is that the pathways through which people become capable are starting to disappear too.
Think about entry-level work. Some of the repetitive, administrative, analytical and drafting tasks traditionally given to early-career employees are exactly the tasks AI increasingly can perform.
Those tasks may not have been particularly exciting, but they served a purpose. They allowed people to observe, to practice, to make mistakes, to understand context, to gradually move from doing the work to understanding the work.
So, if AI performs that work, where do future experts come from?
The World Economic Forum estimates that 39 percent of workers’ core skills are expected to change by 2030. It also identifies skills such as creative thinking, resilience, flexibility and agility as increasingly important alongside technology skills. For leaders, the challenge is not only to identify those skills, but to create the experiences through which learners can develop them.
This cannot just become another conversation about adding skills to a competency framework. We need to think about how capability is built. Perhaps we need to redesign apprenticeship rather than simply eliminate the work that used to provide it.
A junior employee using AI should not simply accept an answer because the machine produced it. They need to learn how to question it, validate it, improve it and understand when it is wrong.
That is how judgement develops. And judgement is becoming one of the most important leadership capabilities of all.
Leaders need to know when not to trust the machine
We talk a lot about leaders needing to become more data-driven and AI literate. I agree.
But I think there is another capability that matters just as much: knowing when the answer is not good enough. It is also knowing how much data is enough to decide. More data does not necessarily mean better judgment. AI can generate an answer, it can analyze information, it can identify patterns and it can produce a recommendation.
But being a leader is not about accepting the fastest available answer. It is about understanding context, timing, consequences and accountability.
Anthropic’s Economic Index, for example, found that AI use in its dataset leaned toward augmentation rather than full automation, with people and AI often working together through validation, learning and iteration.
That tells us something important. The future is not simply about human versus machine.
It is about how deliberately we design the relationship between the two. Leaders will need to decide what AI should do, what humans should do, where human judgment is essential and where automation genuinely improves the outcome.
That is not an IT decision, nor is it an HR decision. It is a leadership decision.
So, what does future-ready leadership mean?
That is the leadership challenge I think we are still underestimating. We spend a lot of time asking which jobs AI will create and which jobs it will remove. I am less interested in predicting the exact job titles of the future, and more interested in whether organizations are capable of adapting when the future arrives.
Because we have been here before.
AI is now challenging what work needs to look like in the first place. The fastest adopters will not necessarily be the organizations that navigate this AI wave successfully. They will be the ones whose leaders understand that AI adoption is not the destination.
The real transformation happens when leaders redesign the work, the organization and the capability around it. And perhaps that is what future-ready leadership really means.
Future-ready leadership is not about having all the answers about what work will look like. It is about building organizations capable of answering the question when it changes.
