Your employee may be doing the work of three different roles — and you may not even realize it.

Artificial intelligence is changing much more than the speed at which tasks are completed.

It is also changing who performs those tasks.

A new study from OpenAI reveals an increasingly evident phenomenon in the labor market: professionals are using AI to take on activities that traditionally belonged to other professions.

In an analysis of more than 800,000 work-related messages, OpenAI found that 43.5% of profession-specific messages involved tasks associated with another occupation, after generic activities were excluded from the analysis.

The result is a quiet transformation in the structure of jobs.

A marketing professional can analyze data.

A salesperson can explore a customer database.

An HR professional can perform tasks that previously depended on specialists.

And this raises an important question for companies:

If jobs are changing, shouldn't the way we measure productivity change too?

What Are Hybrid Jobs?

For a long time, companies organized work around relatively well-defined roles.

Marketing handled marketing.

Sales handled sales.

HR handled people.

Technology handled systems.

Finance handled numbers.

When a task went beyond the boundaries of a particular department, there was usually a handoff: the work was passed on to another professional or department.

Artificial intelligence is reducing some of this need.

With AI tools, a professional can perform activities that previously required the involvement of another department.

OpenAI calls this phenomenon “task crossover”: activities historically associated with one profession appear in the AI usage of people from other professions.

This does not necessarily mean that one person is replacing three professionals.

It means that the scope of work is expanding.

 

43.5%: A Number Worth Paying Attention To

The study's main finding is striking.

Among the profession-specific messages analyzed by OpenAI, 43.5% were related to tasks outside the user's profession, after generic activities were removed.

In other words, a significant portion of professional AI use is happening precisely at the intersection between different roles.

The study found this behavior even more strongly in some areas:

• 77% among customer experience professionals;
• 75% among designers;
• 69% among human resources professionals;
• 56% among legal professionals;
• 53% among marketing professionals.

This shows that AI is not simply accelerating tasks that were already part of a job.

In many cases, it is enabling professionals to take on tasks that previously would have been handed off to someone else.

 

What Is Actually Happening in Practice?

Imagine a marketing professional.

Previously, they might have depended on an analyst to interpret certain data, a developer to solve a simple website issue, or another specialist to structure an analysis.

Today, with the help of AI, they may be able to handle some of these activities themselves.

The same thing is happening in other areas.

Sales + Data Analysis

A salesperson can use AI to explore a customer database and identify patterns that previously might have been analyzed by a data professional.

Marketing + Technology

A marketing professional can solve simple website problems without having to wait for a developer to step in.

HR + Specialized Activities

Human resources professionals can use AI to perform tasks that previously required specialized support.

Small Businesses

This phenomenon may be even more relevant in smaller companies.

OpenAI's research found a higher share of tasks from other professions among users in smaller workspaces. Among average users, this share was 18.9% in environments with 2–5 users, compared with 16.3% in environments with more than 100 users.

This makes sense.

In a small company, there isn't always a specialist available for every need.

When a problem arises, the person who identifies the need is more likely to try to solve it themselves.

And AI makes that much easier.

 

Is AI Creating More Generalist Professionals?

This may be one of the biggest changes driven by artificial intelligence.

For decades, the market increasingly valued specialization.

Now, we are seeing a complementary movement:

specialists who are capable of performing tasks outside their own area of expertise.

This does not mean specialization has become less important.

Quite the opposite.

Specific knowledge remains fundamental.

But AI can allow that knowledge to be combined with skills that previously required other people.

The result is a more multidisciplinary professional.

And for companies, this can represent a significant change in how teams are structured.

 

Marketing Is One of the Best Examples

OpenAI's research shows that marketing is particularly interesting when it comes to this crossover.

Marketing professionals devoted 24.3% of their messages to tasks associated with other professions.

At the same time, marketing tasks appeared in 8.9% of messages from professionals in other fields, the highest level of task diffusion among the occupations analyzed.

This helps explain a reality that many managers are already noticing.

Today, someone in sales can create content.

A product professional can analyze campaigns.

A founder can produce marketing materials.

A designer can perform technical tasks.

And a marketing professional can work with data, technology, and automation.

The boundaries between roles are becoming less rigid.

 

The Problem: Productivity Metrics Also Need to Evolve

Here is one of the most important consequences of this transformation.

If jobs are changing, the metrics used to evaluate those jobs also need to be reconsidered.

Imagine a professional who previously spent most of their day performing tasks specific to their role.

Now they use AI to automate some of those activities and spend their time on:

• analysis;
• decision-making;
• strategy;
• review;
• creation;
• problem-solving;
• collaboration with other departments.

If the company continues to evaluate productivity based solely on traditional metrics, it may end up misinterpreting the work being done.

Time spent on an activity is not necessarily synonymous with value delivered.

Likewise:

being busy does not necessarily mean being productive.

 

More Activity Doesn't Mean More Productivity

This is a fundamental point for managers.

When a role becomes more hybrid, the number of activities performed by one person can increase significantly.

But that doesn't mean all of those activities have the same impact.

One professional may spend two hours on an operational task and generate little value.

Another may spend 30 minutes analyzing a problem and make a decision that saves the team dozens of hours.

That's why companies need to start looking at productivity more broadly.

Not just:

“How long did this person spend working?”

But also:

“Where was that time spent?”

“Which activities take up the most time?”

“Are there bottlenecks?”

“Which tasks could be optimized?”

“How is the work actually distributed?”

 

What Does This Change for Managers?

The transformation of jobs brings at least four important challenges for companies.

1. Job descriptions can become outdated

The role described during the hiring process may not accurately represent what that professional does a few months later.

AI makes it possible for new activities to be incorporated into a person's routine very quickly.

2. Teams can become more multidisciplinary

A professional can take on tasks that previously depended on other departments.

This can reduce bottlenecks and increase autonomy.

3. Productivity can become harder to interpret

If activities change, comparing professionals based solely on the time spent on specific tasks can lead to inaccurate conclusions.

4. Data becomes even more important

The more complex the work routine becomes, the harder it is to understand productivity based solely on perception.

You need to observe patterns.

 

How Can Companies Keep Up With This Change?

The first step is to move away from the idea that productivity simply means being busy during working hours.

A more comprehensive analysis needs to consider how time is being used.

For example:

Productive time: activities directly related to deliverables and goals.

Meeting time: how much of the routine is being consumed by meetings.

Time spent in tools: which applications and systems account for most of the work.

Idle time: periods when no activity is recorded.

Time distribution: how different types of activities occupy the workday.

These data points don't need to be used to control every movement of an employee.

The goal should be to understand the operation.

 

That's Where Monitoo Comes In

Monitoo helps companies turn computer usage data into productivity insights.

The platform allows companies to analyze, for example, how much time employees spend on specific websites and programs, as well as providing dashboards and reports to make analysis easier.

This gives managers a clearer view of how time is being distributed.

And this perspective becomes even more important as jobs become less predictable.

Because if someone is taking on tasks from different departments, you may need to understand not only how much they work, but how their work is changing.

Technology can help answer questions such as:

• Where is the team's time being concentrated?
• Which activities take up the most time?
• Are there periods of idle time?
• Which tools are part of the daily workflow?
• Are there processes that could be improved?
• Has the distribution of activities changed over time?

Data doesn't replace management.

But it can make management decisions much more precise.

 

The Future of Jobs May Be Less About Roles and More About Capabilities

OpenAI's research points to a transformation that could go far beyond artificial intelligence as a tool.

If professionals can take on tasks from different areas, perhaps the future of work will be defined less by:

“What is your job title?”

and more by:

“What can you do?”

This shift could create more flexible teams, more autonomous professionals, and faster processes.

But it also requires companies to rethink how they structure roles, distribute responsibilities, and evaluate productivity.

OpenAI itself highlights that AI usage patterns can serve as an early signal of changes in occupations before those transformations formally appear in job descriptions or titles.

 

Conclusion: Your Employee May Be Doing Much More Than Their Job Title Says

Artificial intelligence is making it easier for professionals to move beyond the traditional boundaries of their roles.

And OpenAI's data shows that this is already happening.

43.5% of the profession-specific messages analyzed involved tasks associated with another occupation.

This means work is becoming more hybrid.

And perhaps the biggest challenge for managers isn't figuring out which jobs AI will replace.

Maybe it's understanding:

“How are the jobs we already have changing?”

Because your employee may be doing marketing, analysis, technology, and strategy work at the same time.

And you may not even realize it.

The question isn't just how much your team works.

It's how work is changing and where time is being invested.

 

Frequently Asked Questions

What are hybrid jobs?

They are roles that combine activities traditionally associated with different professions. Artificial intelligence can facilitate this crossover by allowing professionals to perform tasks that previously depended on other departments.

What did OpenAI's research discover about AI and professions?

The analysis of more than 800,000 messages from ChatGPT users in the United States indicated that 43.5% of profession-specific messages, after generic tasks were excluded, involved tasks associated with another occupation.

Which professions show the most task crossover?

In the study, the phenomenon was particularly high among customer experience professionals, designers, HR professionals, legal professionals, and marketers. Among profession-specific messages, tasks from other occupations represented 77%, 75%, 69%, 56%, and 53%, respectively.

Is AI eliminating specialization?

Not necessarily. The data points to an expansion of the activities performed by professionals. Specialists can continue performing their core role while taking on additional tasks with the help of AI.

How should productivity be measured as jobs become more hybrid?

It's important to analyze more than simply hours worked. Data on time distribution, tools used, activities, idle time, and processes can help managers better understand how work is actually being performed.

 

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