Talent management is changing so fast that it is easy to mistake disruption for decline. The technology is evolving. Many of the practices we have relied on for years are shifting. The lines between learning, performance, career development, succession, workforce planning and talent acquisition are becoming less distinct.
But none of that means talent management is disappearing.
I think we need to be careful not to confuse the traditional talent management technology category, or the way organizations have historically practiced talent management, with the discipline itself. They are very different.
Talent management exists because organizations must make decisions about the people and capabilities they need to execute their business strategy. They must understand the talent they have, what people can do, where capability gaps exist, how people should be developed and where talent should be deployed. They need to think about succession, performance, careers, leadership and the capabilities they will need in the future.
AI doesn’t make any of those things less important. In many respects, it makes them more important and certainly more complex.
What is changing is how we do the work.
For years, talent management was built around a set of relatively independent processes. We had a performance-management process, a succession process, a learning process, a workforce-planning process and so on. Technology largely mirrored that structure. We bought systems and modules to automate each activity.
The problem is that the business doesn’t experience talent that way.
A capability gap doesn’t necessarily mean we need to train someone. We may need to hire someone. We may already have someone elsewhere in the organization who could do the work. We may need to redesign the job. We may need to develop someone for the capability over time. And now we have another possibility: some portion of that work may be better performed or augmented by AI.
That changes the conversation considerably.
This is where I believe talent management is headed. It is becoming less about administering a series of talent processes and much more about understanding and managing the capabilities the business needs.
AI is accelerating that shift because we can now see and understand far more about the workforce than we could before. We can bring together information about skills, learning, performance, experience, career interests and work itself. We can identify relationships in that information that would be difficult, if not impossible, to detect manually.
But having more intelligence about the workforce doesn’t eliminate the need for talent management. What would be the point of having all that intelligence if we aren’t going to do something with it?
If we identify a skills gap, we still have to decide how to close it. If we identify someone with potential, we still have to decide how to develop them. If we know someone has adjacent skills that could make them a candidate for another role, someone still has to create the opportunity. If AI tells us that a job is changing, we still have to determine what that means for the person doing the job today.
Those decisions are talent management.
There is another aspect of this that I think deserves more attention. We are entering a period when organizations will need to manage human and AI capabilities simultaneously.
That is a significant change.
Workforce planning can no longer be only about how many people we need and what skills they should have. Organizations increasingly need to understand the work itself: What work needs to get done? What should people do? What can AI do? Where should AI augment people rather than replace an activity? And what new capabilities do people need now that AI is part of how the work gets done?
That doesn’t make talent management obsolete. It expands its mandate.
It also means some traditional boundaries within talent management probably won’t survive. Learning can’t operate independently of skills. Skills can’t be disconnected from workforce planning. Workforce planning can’t be disconnected from the changing nature of work. Performance can’t be separated from development. Career mobility can’t work well if we don’t understand people’s capabilities.
We have spent years connecting these concepts while often managing them separately in practice. AI gives us an opportunity, and I would argue a business requirement, to finally bring them together.
That is why I see what is happening as a metamorphosis of talent management rather than its demise.
Some of the technologies associated with traditional talent management will undoubtedly change or disappear. Some longstanding practices should be retired. I don’t think anyone will miss cumbersome annual processes that produced more administration than insight.
But the discipline isn’t going anywhere because the business need remains.
In fact, the opposite may hold.
As work changes faster, skills have shorter shelf lives, career paths become less predictable and AI takes on a larger role in getting work done, organizations need a much better understanding of their people and capabilities than they have today.
That is a talent management challenge.
Talent management is alive and well. It is simply becoming something different from what we have known before.
And I think that is very good.
