# Mapping the impact of AI on roles and tasks

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# Mapping the impact of AI on roles and tasks

By Ivo Donker — compiled with AI support (Claude & Gemini) · Last updated: 6 August 2026

Organizations looking to apply artificial intelligence often start with a broad question: which roles will change or disappear? This starting point rarely leads to usable insights. A role is an administrative collection of diverse responsibilities, tasks, and interactions. Virtually no role within an organization consists solely of work that can be directly taken over by language models or automated systems.

To get a realistic picture of the operational and organizational consequences of AI, an analysis at the task level is necessary. By breaking work down into individual tasks, it becomes clear where automation is possible, where human oversight remains required, and where AI offers no added value. This approach forms the basis for a well-considered [process for prioritizing applications](https://consultancy.llmnet.nl/en/ai-usecases-prioriteren) within business processes.

## Why a focus on the task level is necessary

A job description in an HR system provides a static overview of goals and competencies, but rarely describes an employee's daily reality. An account manager handles relationship management, negotiations, drafting quotes, processing customer data in a CRM system, and solving ad-hoc problems. Only the textual and administrative parts of this role lend themselves to support from algorithms.

When an organization evaluates a role as a whole, two risks arise. On one hand, the impact of AI can be overestimated, leading to unrest among employees and unrealistic expectations of quick savings. On the other hand, the potential can be underestimated, because complex roles get labeled as "non-automatable," while specific sub-tasks can in fact be organized more efficiently.

Breaking roles down into individual tasks makes it possible to make targeted choices. In this context, a task is a well-defined unit of work with a clear input, a processing step, and an identifiable result. Only once work has been structured at this level of detail can its suitability for technological support be assessed objectively.

## Practical inventory of work activities

Mapping out tasks doesn't have to become a lengthy project. A well-known pitfall is organizing months of interview sessions or combing through outdated role profiles. This results in a theoretical overview that quickly becomes outdated.

A more effective approach starts with the organization's core processes. Instead of asking individual employees what they do in a day, the business processes that require the most time or resources are taken as the starting point. The inventory takes place in short, structured working sessions with the people who carry out the work on a daily basis. They have the best view of the actual steps, the exceptions, and the informal coordination needed to keep a process running.

Note: Never rely solely on official process maps or HR documentation for the inventory. Practice almost always deviates from the formal description. Ask employees about their actual daily schedule and the specific actions they perform.

During these sessions, tasks are categorized based on their characteristics: is the task repetitive, is the input structured, how much context is required, and what is the risk of harm if errors occur? This information forms the basis for further analysis.

## The four categories of task impact

After the inventory, each task is assigned to one of the four categories. This classification determines the strategic and operational next steps.

Category | 
Description | 
Role of the human | 

1. Take over | 
Routine, predictable tasks with low complexity. | 
No direct involvement; only periodic system checks. | 

2. Accelerate with review | 
Tasks where AI generates a draft that is reviewed by an expert. | 
Ultimately responsible for review and approval. | 

3. Support without deciding | 
Gathering and structuring information to support human decision-making. | 
Makes decisions independently based on the information provided. | 

4. Don't touch | 
Tasks that require human empathy, physical presence, or strategic insight. | 
Fully performed by humans, without the use of AI. | 

### 1. Take over (full automation)

This category includes tasks that are highly structured, follow a fixed pattern, and carry a negligible risk of harm if an occasional error occurs. Think of reformatting data from one system to another, or categorizing standard incoming messages. Here, the human role shifts from execution to monitoring process progress.

### 2. Accelerate with after-the-fact review

This concerns tasks where generative models create a first version of a product, such as drafting a standard agreement, writing a draft response to a customer question, or generating code. The employee shifts from creator to reviewer. The time savings occur in the initial phase, but quality assurance remains a human responsibility.

### 3. Support without decision-making authority

For complex assessments or strategic choices, AI can serve as an analysis tool. The technology gathers relevant information from large volumes of documents, summarizes it, or provides suggestions. However, the system doesn't make decisions or formulate final conclusions. The output supports the professional, who makes the judgment independently. This requires specific competencies, as described in the analysis of [skills for non-technical roles](https://vacatures.llmnet.nl/en/ai-vaardigheden-in-niet-technische-functies).

### 4. Don't touch

Some tasks must be explicitly kept out of the scope of automation. This applies to work in which human trust, ethical considerations, physical actions, or complex relationship-building are central. Delivering bad news in a conversation, building a personal bond of trust with a customer, or making decisions in a crisis situation are examples of tasks where technological intervention undermines the quality of the work.

## Criteria for classifying tasks

Assigning a task to one of the four categories is based on four objective criteria. These criteria prevent decisions from being made based on gut feeling or technological enthusiasm.

1. Impact of an error (risk of harm): What are the consequences if the system produces an incorrect result? In a medical, legal, or financial context, a single error can cause major damage. As the risk of harm increases, the task shifts toward support or full human execution.

2. Recognizability of errors: Can an employee tell at a glance whether the system's output is correct? If checking the result takes as much time as performing the task independently, the net gain is zero and automated support isn't effective.

3. Dependence on unwritten context: Much work relies on tacit knowledge that resides in the minds of experienced employees and isn't captured in documentation. AI systems have no access to this context. Tasks that depend heavily on it aren't easily supported.

4. Legal and contractual requirements: Privacy legislation, sector-specific regulations, or agreements with customers can impose restrictions on the processing of data by automated systems. These constraints must be mapped out in advance.

## The problem of hidden tasks

A common mistake in impact assessments is overlooking informal and invisible work. Within organizations, roles don't consist only of the main tasks listed in a process model, but also of a network of supporting activities.

Examples of these hidden tasks include:

- Handling exceptions that don't fit the standard system.

- Providing ad-hoc help to colleagues based on personal experience.

- Checking and interpreting data from other departments for logic and consistency.

- Fixing small errors made earlier in the chain.

When the standardized main tasks of a role are automated, these hidden tasks remain. This can lead to a paradoxical situation: total workload decreases in theory, but the complexity of the remaining work increases. Employees no longer have to do routine work, but are left facing only complicated exceptions and corrections. When designing [change processes within the organization](https://consultancy.llmnet.nl/en/verandermanagement-bij-ai) this must be explicitly taken into account.

## Saving time versus creating capacity

A common misconception in the business case for AI implementations is the assumption that time saved automatically translates into more capacity or lower costs. If a language model helps an employee perform a task ten times a day, each three minutes faster, that adds up to thirty minutes of gain on paper.

In practice, these scattered fragments of a few minutes rarely lead to actual redeployable capacity. The time gained disappears into the natural interruptions of the workday, such as coffee breaks, catching up on email, or informal consultation. Only when time reduction is concentrated into contiguous blocks, or when entire process steps are redesigned, can the saved time be usefully repurposed for other tasks.

## Employee participation, transparency, and psychological safety

An impact assessment of roles and tasks touches the core of employees' work. If the analysis is carried out behind the closed doors of a project group or presented as a disguised efficiency drive, resistance and distrust arise. This can seriously hinder the collection of honest data about tasks and process steps.

The process should be approached transparently from the start, with active involvement of the works council and the departments involved. As described in the analysis on [the broader societal impact of AI in the workplace](https://nieuws.llmnet.nl/en/ai-en-werk), the way change is communicated is decisive for success.

A sound approach requires clear agreements about the purpose of the analysis: improving process quality, reducing workload, and increasing effectiveness, rather than cutting jobs. To maintain support, it's essential to look at the methodology for [involving stakeholders](https://consultancy.llmnet.nl/en/stakeholders-meekrijgen-bij-ai-projecten) in every phase of the project.

## The structure of an impact assessment's outcome

The end result of the assessment isn't a general advisory report, but a detailed overview per task. This overview contains at least the following elements:

- The chosen category: Take over, accelerate, support, or don't touch.

- The substantive rationale: The specific arguments based on risk, context, and recognizability of errors.

- The preconditions for implementation: Which technical integrations, data quality, and security measures are required.

- Training and guidance needs: Which new skills employees need to work effectively with the chosen support. This ties in with the setup of targeted [training programs for staff](https://consultancy.llmnet.nl/en/ai-training-medewerkers).

## Monitoring and the baseline measurement

An impact assessment isn't a one-time snapshot. The capabilities of technology change, and the way employees perform their tasks evolves as they become more familiar with new tools.

To assess whether the intended effects are achieved, the baseline situation must be accurately documented before implementing an application (the baseline measurement). This involves measuring quantitative factors, such as turnaround times and error rates, as well as qualitative aspects, such as perceived workload and satisfaction with the quality of the work.

After implementation, the assessment is repeated periodically. Tasks initially classified in the "support" category may, over time and once reliability has been demonstrated, shift toward "accelerate with review."

## The pitfall of misdirected steering through metrics

When monitoring the impact of AI, there's a risk of unwanted behavioral effects caused by measuring the wrong indicators. When an organization holds employees accountable for the volume of generated text or the speed at which administrative tasks are handled, this can lead to a decline in substantive quality.

The focus must always remain on the final outcome of the process, not on the quantity of AI-supported intermediate steps. Metrics should be designed to encourage the desired quality and care, rather than blind production drive.

## Further reading

- [Prioritizing AI use cases in practice](https://consultancy.llmnet.nl/en/ai-usecases-prioriteren)

- [Change management when implementing AI applications](https://consultancy.llmnet.nl/en/verandermanagement-bij-ai)

- [Setting up AI training for employees](https://consultancy.llmnet.nl/en/ai-training-medewerkers)

- [Bringing stakeholders on board with AI projects](https://consultancy.llmnet.nl/en/stakeholders-meekrijgen-bij-ai-projecten)

- [Developments around AI and the labor market](https://nieuws.llmnet.nl/en/ai-en-werk)

- [Essential AI skills for non-technical roles](https://vacatures.llmnet.nl/en/ai-vaardigheden-in-niet-technische-functies)

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