Many companies are currently looking into using artificial intelligence (AI), especially generative AI. But rising expectations for efficiency, quality, and speed often clash with limited resources, a lack of experience, and unanswered questions about data protection, IT security, and regulation. Against this backdrop, the question arises as to whether having your own ‘AI lab’ would be a suitable approach.
What is meant by an “AI lab”?
An AI lab is not necessarily a physical entity, but rather a working structure that systematically prepares and tests the use of AI. The core elements are:
- Identification and prioritisation of relevant use cases
- Rapid prototype development in a few weeks
- Accompanying review of data protection, IT security and compliance
- Planned transfer of successful prototypes into regular operation
It is crucial that the lab does not stop at prototypes, but consistently works towards operationally usable results.
Why an AI lab can be useful for companies
Avoiding shadow AI and untested AI use
In many organisations, employees use AI tools informally and without coordinated rules. This increases risks to confidentiality, personal data, contract content and intellectual property. An in-house AI lab can create a controlled environment in which tools, data categories and rules of use are clearly defined.
Structured proof of benefit
Generative AI often produces quick ‘aha’ moments, but the transition to measurable productivity or quality gains is not a given. An AI lab tests hypotheses, defines measurable targets and enables early decisions on whether to continue or discontinue.
Limiting bad investments
A lab enables cost-effective learning through prototypes and pilot projects before integration, licensing and operating costs arise. Support formats and transfer offers for companies are available and can be used to carry out tests in a structured manner.
Better preparation for regulatory requirements
The EU AI Act is in force and is gradually taking effect. Depending on the context of use, companies must comply with obligations regarding risk management, transparency, documentation and, where applicable, quality assurance. An AI lab is a suitable place to integrate these requirements into processes, tool selection and project methodology at an early stage.
When an AI lab is not effective
An AI lab is not very useful if:
- No resources (time/responsibility) are allocated for regular work
- Use cases are not prioritised and too many topics are running in parallel
- Data and security requirements are not bindingly regulated
- Prototypes cannot be transferred into operation
In these cases, there is a greater likelihood that activities will generate attention but have no lasting effect.
Model: ‘AI Lab Light’ for companies
Step 1: Lean start and clear responsibilities
A practical way to get started is with a small core team (e.g. 3–6 people) with participation from:
- Specialist departments (process and benefit responsibility)
- IT / Data / Automation (integration, operational perspective)
- Data protection and information security
- Legal/compliance, if applicable (depending on industry and risk profile)
It is important to establish a fixed rhythm that generates continuous results.
Step 2: Prioritisation through clear evaluation
One option is to evaluate according to three criteria:
- Value potential (time, costs, quality, turnover)
- Feasibility (data availability, process stability, system access)
- Risk (GDPR, IP, customer impact, AI Act relevance)
This ensures that the use cases that are realistically feasible and make a relevant contribution are dealt with first.
Step 3: Prototyping with measurable targets
Prototypes should have measurable success criteria, e.g.:
- Processing time per operation
- Error rate / rework rate
- Response time in customer service
- Throughput time in quotation, purchasing or approval processes
- Quality and consistency indicators (e.g. standardisation of texts)
Step 4: Transfer to operation as a mandatory part of the project
The transition to regular operation typically requires:
- Role and rights concept
- Quality and plausibility check (including handling of incorrect outputs)
- Monitoring and readjustment
- Integration into existing systems
- Training and process adaptation
If this step is not included in the planning, the benefits often remain at the pilot level.
Step 5: Establish binding guidelines
A pragmatic definition includes:
- Permitted tools and terms of use
- Permitted/prohibited data categories
- Rules for external communication (e.g. AI-generated content only after review)
- Documentation of use cases (purpose, data, responsible parties, risks)
This creates a robust balance between speed and control.
Conclusion
Having your own AI lab makes sense for companies if it is set up as a lean, methodical structure that consistently aligns AI use with measurable benefits, controlled tool use and regulatory safeguards. In practice, it reduces risks (including shadow AI), accelerates learning and helps focus investment on use cases that actually create value.
If you are curious and see the potential for an AI lab in your company, I would be happy to hear from you.
