The Rising Floor of Human Contribution

Clients are increasingly asking us about the future of work: What will work look like in three years? Which behavioral competencies will matter most? What kind of people should we hire today, and how should we prepare them for future work?

At present, there are no clear answers to these questions. The public debate moves between two poles. One view sees AI mainly as another tool in people’s hands, much like the internet or the smartphone. The other sees AI as something that may, within a few years, significantly automate large parts of the work people perform today.

Reality will probably be less simple than either extreme. AI is uneven: It can perform some types of work far better than others. Its real impact will also depend on regulatory and human preference constraints, organizations’ ability to implement AI, the value AI creates after deployment, and how surrounding workflows and external factors influence demand for the work.

We do not have a crystal ball, but we can already see useful signals in the data. One of them is how AI impact connects to behavioral competencies.

How AI Connects to Human Competencies

Korn Ferry’s AI Impact Score measures the potential impact of AI at the level of individual job responsibilities, which are the tasks and duties that must be performed to achieve the results of a role successfully. Responsibilities are a core component of Korn Ferry’s Success Profiles, which define what “great” looks like in a given role. And because we know which competencies are most needed to perform each responsibility successfully, we can examine how AI impact connects to different competencies. That is, we can evaluate how the impact of AI relates to the 38 behavioral competencies—the observable behaviors and skills that matter most for success at work—comprising the Korn Ferry Leadership Architect™ (KFLA) library.

It’s important to note that the AI Impact Score does not measure whether a human competency itself is becoming more or less important. It measures the extent to which AI may affect the work in which that competency is demonstrated.

Some Competencies Are More Often Linked to Less Impacted Work

Overall, our data suggests that AI does not affect work connected to all 38 KFLA competencies in the same way.

Some responsibilities have the potential to be more affected by AI than others. Looking across responsibilities, we have identified the competencies we estimate will be least affected by AI through AI’s expected impact on the responsibilities to which they are connected. They include Global Perspective, Strategic Mindset, and Persuades.

These competencies are typically connected with strategy, taking a broad lens on issues, and persuading people. AI can support the surrounding work: preparing materials, analyses, summaries, scenarios, or communications. But the distinctive human value in how these competencies are demonstrated often lies beyond where today’s AI is naturally strong.

Other Competencies Are Linked to More Impacted Work

On the other hand, some competencies are connected to responsibilities with a higher average AI Impact Score in our dataset. These include, for example, Self-Development, Demonstrates Self-Awareness, and Develops Talent.

This should not be read as a warning that these competencies will become less important. A higher AI Impact Score means that AI may change the work in which these competencies are demonstrated more strongly. It may also point to greater opportunity: When people use AI well, they can raise the speed, quality, and scale of that work.

For example, Self-Development will not become irrelevant to success at work. On the contrary, a person with a strong ability to learn, relearn, and unlearn can use AI to significantly accelerate their own development, such as by getting immediate explanations and feedback, personalized learning paths, or simulations of various situations. In many situations, however, human involvement will remain essential to turning that learning into real growth.

These competencies should therefore not be described as “at risk.” They are connected to work where AI may raise the performance bar. As that work changes, people may need to demonstrate the same competency in a more complex way: faster, with greater independence, and with a stronger ability to use AI as part of how the work gets done.

Competencies Are Not Flat; Competency Level Matters

We see another signal in the data at the more detailed competency levels.

In Korn Ferry’s Success Profiles, each competency has eight levels of complexity, beginning with contributing dependently (requiring direction from others) at levels 1 and 2, continuing on to contributing independently at levels 3 and 4, then contributing through others (management) at levels 5-6, and finally, contributing strategically at levels 7-8. Develops Talent level 1 and Develops Talent level 8 are not different competencies. They are different levels of the same competency; they differ in context, scope, autonomy, level of ambiguity, impact, and influence on others.

In our data, AI impact is highest for work that requires lower levels of competency complexity. This matters because lower-complexity work is often where people enter a functional career ladder, build experience, learn the standards of the profession, and develop the judgment needed for more complex work later.

The strategic question, therefore, is not only whether AI can make this work more productive. It is what happens to the work, and to the people doing it, when productivity increases?

There Are Two Broad Scenarios

In the first scenario, demand grows with productivity. Organizations may continue to need a similar number of people doing this work, but the performance bar will rise. Employees will be expected to produce more, improve quality, work faster, and use AI effectively as part of how the work gets done.

In the second scenario, productivity grows faster than demand. The same work can be done with less effort, but there may not be enough additional demand to use all the capacity that AI creates. In that case, organizations may face pressure to reduce the number of roles needed for this type of work in its current form.

This is where the rising floor hypothesis comes in: We anticipate that the baseline for the level of contribution will increase, although in different ways for different scenarios. In the first scenario, the pressure is mainly upward within the work. People need to deliver more, better, or faster with AI. In the second scenario, the pressure is both upward and outward. The people who remain in the work may need to use AI to deliver more value at the same competency level, while others may need to move into higher-complexity work, adjacent areas where demand is stronger, or work that is less directly affected by AI.

This risk will not appear equally across all types of work. It will be most acute where AI can realistically take on a large share of the work after human, regulatory, and organizational constraints are considered, and where demand is unlikely to grow enough to absorb the capacity AI creates.

In these areas, the issue is not only potential job reduction. It is also the possible loss of work that has historically provided learning and development opportunities at lower levels of competency complexity, especially for entry-level and junior employees.

Organizations therefore need to know where this risk is material and where it is not. In some areas, AI-enabled capacity may be absorbed by higher demand, better quality, or more valuable work. In others, the traditional learning ladder may need to be deliberately redesigned.

What Does This Mean For People?

It will probably not be a simple either/or. For many people, relevance in AI-augmented work will depend on one or more of three moves. Not everyone will need to act on all three with the same intensity. Where AI affects the work less, these moves are more about long-term direction. Where AI affects the work strongly, they become more urgent and more concrete.

The first is to grow toward higher levels of competency complexity within one’s field: work with greater scope, ambiguity, independence, stakeholder exposure, and impact.

The second is to extend one’s current level of competency with AI. A person may not need to demonstrate every relevant competency at a higher level of complexity, but they will need to use AI effectively so that the quality, speed, or scale of their work meets higher expectations.

The third is to move toward work where humans hold a structural advantage over AI. For some, this means leaning into the human core of their current role: building trust, navigating ambiguity with others, and creating meaning. For others, it may mean a more fundamental shift into work that AI cannot easily do at all, such as work that requires physical presence, hands-on skill, or deep human interaction.

Relevance will not only depend on whether a person demonstrates a given competency. It will depend on what level of complexity they can demonstrate, how well they can extend their capability with AI, and how strongly they can contribute in the human spaces where AI remains constrained.

What Does This Mean For the Organization?

A few key actions can help organizations navigate this transition even as ambiguities remain.

Map work underneath the level of job titles.

A role is too large a unit for understanding the impact of AI. Organizations need to map work at the level of responsibilities and workflows, and connect those responsibilities to the competencies and competency levels required to perform them well. They also need to assess how changes in the responsibility mix due to AI impact may shift the competencies needed for the role.

Look at how AI changes the mix of responsibilities inside a role.

AI may not affect all responsibilities in a role equally. Some activities may take less time, while others may become more central. For example, if AI reduces the time a sales professional spends searching for client information or preparing materials, the role may shift toward more time with clients, more stakeholder conversations, and more judgment in how opportunities are pursued. In that case, the competency model for the role may also shift: Some requirements may become less central, while others become more important.

Ask which competency level will be important.

Think beyond simply which competencies will be needed. Competencies are not flat. A lower level of competency complexity may be more AI-supported or more exposed to automation, while higher levels may require more human judgment and experience.

Distinguish between the competencies connected to more and less AI-impacted work.

Organizations will continue to need the full range of competencies. But as AI affects some types of work more than others, it may change the relative weight, required proficiency level, and mix of competencies needed in specific roles.

Identify where AI takes away learning work.

Organizations should track not only which tasks can be automated or augmented, but also which tasks have historically helped junior people learn the work. They will need to ask what will replace this and what the new ways of learning will be, and in which areas this problem may be more acute.

Treat AI transformation as a redesign of capability architecture.

It is not enough to buy tools and train prompting. Organizations need to decide what work they will deploy via AI, what work they will deliver with people, and how people will develop the skills and competencies they will need for the future.

Conclusion

The future of work is not only a question of automation. It is a question of people development. AI is raising the floor of valuable human contribution, and organizations will need to understand not only what work AI can take on, but what levels of human contribution they will need, and how their employees will develop the capabilities to deliver them.

Every organization will have to answer one question, sooner or later. When AI raises the floor, will you raise your people with it?

Learn more about Korn Ferry Architect and AI Impact Score capabilities.

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Methodology:

For this article, we looked broadly across responsibilities. We examined the pattern of competencies associated with responsibilities more and less affected by AI. The impact of AI on the competencies most relevant for any specific role will depend upon multiple factors, including the combination of responsibilities associated with the role. The article also uses demand elasticity as an interpretive lens. The effect of higher AI-enabled productivity will depend partly on whether demand for the work grows enough to absorb the additional capacity. We did not estimate elasticity directly in this analysis, but the distinction informed the two scenarios described in the paper and our interpretation of how AI impact may translate into changes in work and roles. Korn Ferry’s AI Impact Score is built on the Korn Ferry Success Profiles: a structured, calibrated library of profiles that describe what success in a particular role looks like when done well. This library spans industries and job functions. Rather than scoring profiles as a whole unit, the AI Impact Score decomposes each profile into its component responsibilities and assesses each one individually for its exposure to automation, augmentation, and transformation by AI. Large language models are used to assess impact, using a consistent responsibility taxonomy and calibrated scoring criteria applied across profiles. Because scoring happens at the most granular levels, the findings make it possible to see AI’s impact at whatever level of work a given decision requires.