Why works councils need to understand AI

Artificial intelligence rarely enters a workplace under one large banner. It arrives as a new button in office software, a ranking function in recruitment, an automatic translation on a building site or an optimisation feature in a rota. A supplier may call it analytics or automation. Employees can still find that it changes how work is assigned, how performance becomes visible and which skills count.

That makes AI literacy a practical requirement for employee representatives. In Germany, a works council, or Betriebsrat, has statutory information, consultation and co-determination rights. Those rights are useful only when the council can identify the system in front of it. If management and the vendor understand the purpose, data and account structure while the council is still learning the vocabulary, the negotiation starts unevenly.

No works council needs to build a language model. It does need enough knowledge to separate a writing assistant from a scoring tool, a forecast from a decision and a genuine human review from a quick click that confirms whatever the software recommends. These distinctions affect employees and determine which questions, safeguards and legal provisions belong in the discussion.

The difficult part begins after the product demonstration

AI looks tidy in a sales presentation. A meeting assistant produces a neat summary. A scheduling system removes idle time. Recruitment software finds the strongest candidates. Real workplaces contain incomplete records, unusual career histories, machine breakdowns, informal teamwork and exceptions that never reached the process manual.

A council that knows only the demonstration will debate features. A council that understands the workplace asks what happens around the feature. Was a safety instruction summarised from an approved source? Did a worker receive a lower score because a faulty machine reduced output? Does the system retain prompts or editing times? Can an update change the model, the data source or the administrative reports without another local implementation project?

Employee representatives bring information that a software supplier cannot provide. They know where the formal process differs from the work actually done. Technical literacy lets them turn that experience into testable questions. General enthusiasm produces weak scrutiny. General suspicion produces weak alternatives. Both leave the vendor's description largely intact.

Basic literacy means knowing what to ask

A first assessment can begin without code or advanced mathematics. For every proposed system, the works council should be able to obtain clear answers to questions such as these:

• What workplace problem is the system meant to solve, and how will success be measured?
• Which data does it receive, where does that data come from and does it relate to identifiable people?
• What does the system produce: text, a prediction, a score, a ranking, an alert, a recommendation or a decision?
• Who sees the output, and how does it affect hiring, shifts, tasks, appraisals, promotion or dismissal?
• Which prompts, clicks, corrections, response times or other actions are logged and available for later analysis?
• Which errors, biases and fabricated results are known or reasonably foreseeable?
• Who can disregard, correct or contest an output, and is that intervention recorded?
• What happens when the supplier changes the model, connects another data source or adds a reporting function?

Simple questions often expose the real design. Management may say that a person always makes the final decision. The next question is whether that person has the time, evidence and authority to disagree. Ten seconds to approve an automatically generated ranking gives the process a human participant. It does not provide meaningful human judgement.

German law provides tools, not a completed assessment

The German Works Constitution Act, the Betriebsverfassungsgesetz, addresses AI through several provisions. Section 80 requires the employer to give the works council complete information in good time, and to provide documents needed for the council's work. Where the council has to assess the introduction or use of AI, the Act treats the involvement of an expert under Section 80(3) as necessary. An expert can examine contracts or data flows. The council still has to commission the right examination and understand its consequences.

Section 90 covers plans for technical installations, work processes, workflows and workplaces. Information must arrive early enough for concerns and proposals to influence the plan. Section 87(1)(6) addresses technical facilities intended to monitor employee behaviour or performance. Section 95 expressly extends the rules on selection guidelines to cases where AI is used. Sections 96 and 97 bring vocational training into the discussion when jobs and required capabilities change.

No single provision applies to every AI feature. A tool that drafts an internal note differs from a camera system, an employee score or software that allocates work using personal characteristics. Calling all of them “AI” does not settle the legal analysis. Calling them ordinary software does not settle it either. Purpose, data, technical design and actual use matter.

A ChatGPT case from Hamburg shows why architecture matters

In January 2024, the Hamburg Labour Court considered a company policy that allowed employees to use ChatGPT and similar services. Access was voluntary, through a browser and private accounts. The employer could not inspect the chat histories. In that specific interim proceeding, the court rejected the claimed co-determination right under Section 87(1)(6), among other claims. The account and access arrangements were central to the reasoning.

The decision is sometimes shortened to “no co-determination for ChatGPT”. That reading loses the useful lesson. A company account with central login, usage records, administrative dashboards or employee-level reporting presents a different set of facts. The same branded product can have a different workplace effect depending on its configuration. A council that does not ask about logs, identity, interfaces, storage and administrator access may treat two different systems as though they were identical.

Technical literacy here is modest and concrete. Ask who owns the accounts, where the prompts go, which records remain, whether the supplier trains on the material and who can retrieve usage data. A statement that the supplier is GDPR-compliant does not answer those questions.

A building site makes the range of workplace AI visible

Many people associate AI with office jobs. On a construction site it may sit inside translation, safety reporting, materials planning, maintenance and crew scheduling. An app translates the morning briefing. A camera identifies missing protective equipment. Scheduling software assigns teams according to availability and qualifications. A prediction indicates when equipment may fail. A voice assistant turns a spoken note into the daily site report.

These uses share a fashionable label and little else. Translation may help a multilingual team work safely, but technical terms must be accurate. Camera analysis may prevent accidents while creating the capacity for constant observation. Automated scheduling can reduce waiting time yet overlook accumulated physical strain, practical experience or the value of a stable crew. A drafted report saves paperwork only if invented details do not enter the official record.

The useful question is therefore narrower than “Should construction use AI?” For each application, the council needs to know which task changes, which data appears and who carries the cost of a wrong result. That is the basis for negotiating access, training, review and limits.

Consultation can improve the implementation

Technology projects sometimes describe employee consultation as delay. OECD workplace surveys offer a different perspective. In its 2022 survey, workers were more likely to report positive effects on performance and working conditions when workers or their representatives had been consulted about new workplace technologies. Employers that consulted also reported positive effects more often. The report describes an association and warns against treating it as simple proof of causation. Even with that caution, the mechanism is credible. Employees know the workarounds, dependencies and exceptions missing from a process map.

An informed works council can make a pilot more useful to the employer as well as safer for the workforce. Time saved is one measure. Error rates, rework, stress, safety, distribution of demanding tasks and the ability to challenge a recommendation are measures too. A trial that records only speed can reward a system that moves hidden work onto employees.

Training deserves the same scrutiny. The OECD found that trained AI users were more likely to report improvements in working conditions, although training was also associated with greater concern about job stability. Teaching people where to click is insufficient. They need to recognise unreliable output, know which data must stay out of the tool and have somewhere to report a failure. Managers need rules for cases in which an AI output must not become evidence for a personnel decision.

The EU AI Act turns literacy into an organisational matter

Article 4 of the EU AI Act requires providers and deployers to take measures, to the best of their ability, to ensure an appropriate level of AI literacy among staff and others operating AI systems on their behalf. The European Commission says that this obligation has applied since 2 February 2025. Its official guidance even addresses employees using ChatGPT for advertising copy or translation: they should be informed about risks such as hallucinated content.

The rule does not prescribe one identical course for everyone. Knowledge should reflect the system, the person's role and the people affected. Someone reviewing draft translations needs different preparation from a person supervising an employment system. The Act classifies certain systems used in recruitment, personnel management, task allocation based on personal characteristics, and performance or behaviour monitoring as high-risk. Article 26 also provides for worker representatives and affected workers to be informed before a high-risk AI system is put into use at work.

Parts of the high-risk timetable are moving through the EU simplification process. Following the political agreement reported in May 2026, the Commission's current implementation page gives 2 December 2027 for high-risk systems in employment and other listed areas. Organisations should check the consolidated legal position when a deployment takes place. That uncertainty does not remove the operational question facing a works council today: what does the system do, and are the planned controls credible?

Build a map before the next formal AI project arrives

Many councils begin with the tool that management has just submitted for consultation. A short internal inventory provides a better starting point. Which AI features are already enabled in office platforms? Which services are employees using on their own? Which systems process HR records, customer conversations, safety information or performance data? Which suppliers can change a model or add analytics without a conventional local software upgrade?

The result does not need to be a complex register. It should help the council distinguish low-impact assistance from systems that shape decisions or expose employees. A translation aid for an informal draft deserves a different level of attention from software that filters applicants. A chatbot with no personal data is different from a platform that publishes per-user activity. This map directs scarce council time towards health, rights and consequential decisions.

It should also include unsanctioned use. When employees turn to private AI accounts because the organisation offers no approved option, commercial secrets and personal information may leave controlled systems. A total ban can push use further out of sight. A secure alternative with understandable rules will often produce better behaviour than a policy nobody can apply to real work.

A useful works agreement can survive a product update

AI services change more often than traditional workplace equipment. A supplier replaces the underlying model, connects a new source or introduces employee-level reports. A works agreement tied only to a product name and version can become outdated while the interface still looks familiar.

The agreement should state the permitted purpose and excluded uses. It should cover data categories, retention, access, logs, human review, complaints and the handling of material changes. For employment decisions, it should say what role an AI output may play and which judgement remains with a person. A pilot needs measures agreed in advance and a real decision point at which the parties can continue, revise or stop it.

Employee feedback belongs in that design. The workforce will notice when a translation repeatedly mishandles a trade term, when reporting creates extra work or when one group receives poorer recommendations. A simple reporting route gives both employer and council evidence that will never appear in the vendor brochure.

Outside expertise works best with an informed client

Specialists can be indispensable. They can inspect a data protection assessment, review a contract, trace information flows or explain the legal position. German law's explicit reference to an expert for AI assessments helps councils obtain that support. The expert cannot decide which workplace concern matters most.

A prepared council can commission a focused review: determine whether logs can be attributed to individual workers; identify transfers outside the European Economic Area; assess whether a ranking becomes the effective personnel decision; list changes the vendor may make without fresh approval. Those instructions produce evidence that can be used in negotiation.

Some members may develop deeper specialisation, while every member should understand the basic questions. Section 37(6) of the Works Constitution Act provides paid release for training where the knowledge is required for council work. Whether a particular AI course meets that test depends on the situation in the establishment and the tasks approaching the council. A current or planned AI deployment creates a concrete connection that can be assessed and documented.

AI literacy gives the council something useful to negotiate with

A works council is neither the sales team for workplace technology nor a committee formed to stop it. Its job is to help shape good work and to set limits where monitoring, discrimination, loss of professional judgement or unclear responsibility threaten employees. A product name provides too little information for that task.

A capable council can enter before the design is fixed. It can tell whether a pilot measures the right outcomes, distinguish a drafting tool from an evaluation system and test claims of human control against the actual workflow. It can also propose workable alternatives: a smaller data set, narrower access, a training plan, a limited trial, an appeal route or removal of a problematic function.

Basic AI knowledge has therefore become part of institutional capacity. Representatives asked to shape tomorrow's work need to understand how today's software prepares decisions, records behaviour and distributes tasks. They do not need every technical answer in advance. They need enough command of the subject to recognise when an answer is missing.

Sources and further reading

German Works Constitution Act: Section 80 on information and AI experts

German Works Constitution Act: Section 87 on technical monitoring facilities

German Works Constitution Act: Section 90 on workplace and process planning

German Works Constitution Act: Section 95 on selection guidelines and AI

German Works Constitution Act: Section 96 on vocational training

European Commission: questions and answers on AI literacy under Article 4

European Commission: current AI Act implementation timeline

EUR-Lex: Regulation (EU) 2024/1689, the Artificial Intelligence Act

OECD: workplace AI surveys on consultation, training and outcomes

Hans Böckler Foundation: understanding and assessing algorithmic systems

Haufe: commentary on the Hamburg ChatGPT decision 24 BVGa 1/24

When management, a works council and employees need a shared starting point for an AI programme, a keynote can establish the vocabulary before the negotiation begins. I would be pleased to develop a talk that connects technical basics with workplace examples, employee participation and the limits of automated judgement.