AI is changing jobs. But have we actually stopped to define what those jobs are becoming?
I have been thinking a lot about that question lately. As organizations rapidly adopt AI, there is tremendous focus on the technology, the tools, and getting employees to use them. But I think there is another issue that deserves just as much attention. If AI changes what people do every day, doesn't it also change what they need to be good at?
And if that's true, we may need to take another look at the competency models we use to hire, develop, evaluate, and prepare our people for what's next.
Consider a software developer. For years, a significant part of a developer's value came from the ability to write code. They understood programming languages, wrote thousands of lines of code, debugged problems, tested solutions, and ultimately turned an idea into a working product.
Today, AI can write a lot of that code. So, what does the developer's job become? Are they still primarily a coder? Or are they becoming more of an architect who determines what needs to be built? Are they directing AI, evaluating its output, identifying problems, understanding how different pieces fit together, and making sure the technology actually solves the business problem?
The developer is still critically important. But the job is changing. And if the job is changing, the competencies required to be great at it are changing too. It certainly isn't just developers.
In marketing, AI can create content, analyze data, develop campaign ideas, and produce dozens of variations in minutes. The marketer's value increasingly shifts toward understanding the customer, developing strategy, directing AI, experimenting, and deciding what actually goes into the market.
In sales, AI can research accounts, prepare meeting questions, summarize calls, draft follow-ups, and recommend next steps. That potentially puts even greater value on discovery, relationships, problem solving, negotiation, business acumen, and commercial judgment.
The same thing is happening in finance, customer service, HR, R&D, operations, and just about every other function.
AI Is Creating Role Ambiguity
Most organizations have spent decades defining jobs around responsibilities, activities, skills, and expected outcomes. AI is disrupting all four. Some activities are disappearing, while others take a fraction of the time. Decisions can be supported by analysis that once took days to produce. Employees can create, analyze, research, code, and problem-solve in entirely new ways.
But that raises a bigger question than simply what work people will do. What do people now need to be good at?
If a developer spends less time writing code and more time directing, evaluating, and integrating AI-generated code, the competencies required to be a great developer begin to change. If a marketer can generate content in seconds, perhaps content creation becomes less differentiating while customer insight, strategic thinking, experimentation, and judgment become more important. If AI can give a salesperson an incredibly detailed analysis of a customer before a meeting, perhaps research becomes less important while discovery, business acumen, relationship building, and the ability to interpret that information become more important.
This is where the impact of AI gets much bigger than learning how to use a new tool. AI isn't simply changing the tasks within our jobs. It may be changing the competencies required to be great at them.
Are Our Competency Models Keeping Up?
Organizations have invested enormous amounts of time and money developing competency models. They define what great leadership looks like, what great selling looks like, what great marketing looks like, and what a high-performing manager or individual contributor should know and be able to do.
Those models influence hiring, development, performance management, succession planning, and career progression. But many of them were developed for a world before generative AI became part of everyday work. The obvious response is to add AI proficiency or digital fluency to the competency model.
I don't think that's enough.
AI may be changing the relative importance of the competencies already there. We are moving from doing the work to determining what work should be done, from producing to evaluating what was produced, and from analyzing information to interpreting what it means. We are also moving from following a process to exercising judgment, from completing a task to understanding its business impact, and from knowing the answer to knowing how to ask the right questions.
That potentially elevates a different set of competencies: critical thinking, judgment, strategic thinking, business acumen, systems thinking, problem solving, communication, collaboration, and decision making.
Perhaps one of the biggest changes is that we need these competencies much earlier in people's careers. A relatively junior employee equipped with AI may suddenly have access to research, analysis, ideas, recommendations, and capabilities that previously required years of experience or support from multiple people. But having access to those capabilities and knowing what to do with them are very different things.
AI can generate ten strategies, but someone still needs to determine which one makes sense. It can analyze a market, but someone needs to decide what matters. It can recommend an investment, but someone needs to understand the assumptions and risks. It can produce an answer in seconds, but someone needs to know whether it is a good answer.
As generating answers becomes easier, judgment about those answers may become more valuable.
So perhaps the question organizations should be asking is: Is our competency model still describing the workforce we need next, or the workforce we needed before AI?
And That Changes Training
If the competencies required for success are changing, training and development need to change with them. Teaching people how to use AI is certainly part of the equation. Employees need to understand the tools, how to work with them, where they are useful, and where they can fail. But AI training alone isn't enough. You don't develop judgment by watching a video about judgment. You don't become a strategic thinker by reading about strategy. And you don't build business acumen by memorizing financial terminology.
These capabilities are developed through practice and experience. This is where business simulations and other forms of experiential learning can play an increasingly important role. They give people opportunities to confront ambiguity, analyze information, work with AI, question its recommendations, make trade-offs, make decisions, see the consequences, receive feedback, and try again in a safe environment before they have to do it on the job.
That means organizations may need to rethink not only what they are teaching, but how they are teaching it. As the work becomes more about judgment, interpretation, decision-making, and problem-solving, development needs to give people more opportunities to actually practice those capabilities.
The goal shouldn't simply be creating employees who are better at using AI. It should be developing employees who are better at thinking, deciding, and performing in a world where AI is always available.
Four Things to Think About
AI is moving quickly. Job descriptions, competency models, performance expectations, and development programs may not be moving at the same speed. For HR, Talent, and Learning leaders, that creates an opportunity to step back and ask some fundamental questions:
AI may not eliminate jobs, but it may change enough of the work inside those jobs that it's time to define them differently. And perhaps the most important question isn't, "How do we get our people using AI?" It is, "What do our people need to be great at now?"