AI for mid-sized companies

Where mid-sized companies stand on AI today

AI has arrived in mid-sized companies – though often in a disorganised way. According to a representative Bitkom survey of 604 companies with 20 or more employees, 36 per cent of companies were already using AI in summer 2025, compared with 20 per cent a year earlier. A further 47 per cent are planning or discussing its use. The companies cite legal uncertainty (53 per cent), a lack of technical know-how (53 per cent) and a lack of staff (51 per cent) as the biggest obstacles.

These figures describe a typical situation: individual employees use private chat accounts, IT does not know the data flows, and management wonders whether experiments will turn into measurable value. This guide sets out where AI reliably helps mid-sized companies today, which prerequisites matter and how to get from the first idea to productive use in manageable steps.

What AI can concretely do in a company

Modern AI systems are based on Large Language Models (LLMs). They understand and generate language, read documents, write code and increasingly operate tools. For mid-sized companies, the fields of application fall into four groups.

1. Finding and using knowledge

Quotes, project files, manuals, contracts and emails contain a company's experience. An AI knowledge base answers questions from these sources and names the documents it relies on. Technically, this is usually done via RAG (Retrieval-Augmented Generation): the system searches for matching passages and formulates the answer from them. The benefit is especially great when knowledge is spread across many systems or experienced employees are retiring.

2. Creating texts, spreadsheets and presentations

Draft quotes, minutes, job adverts, product copy or analyses are produced much faster with AI. What matters is that the AI works with the company's real templates and data and that results land directly in Word, Excel or PowerPoint – not as text that someone has to laboriously reformat.

3. Automating processes

AI agents take over recurring tasks: pre-sorting emails, creating reports at fixed times, merging data from several sources or preparing follow-up questions. Unlike classic rule-based automation, they can cope with unstructured input. Good agents work with clear permissions and request approval for critical steps.

4. Communication and meetings

Meeting transcripts with speaker attribution, automatic summaries and prepared follow-up tasks save time after every appointment. Special requirements apply here regarding informing participants and obtaining their consent – more on this in the article GDPR-compliant meeting transcription.

Typical use cases by department

The following examples show where companies typically start. For the start, choose a case that occurs frequently, is clearly defined and whose result a specialist can check quickly.

• Management: Summarise decision papers from reports, contracts and key figures; ask questions of the company's knowledge instead of gathering information.
• Sales: Prepare the customer history before a meeting, create draft quotes from templates, structure call notes. You will find seven concrete examples under AI in sales.
• Service and back office: Answer recurring customer questions with reference to manuals and product data.
• HR: Write job adverts, onboarding plans and internal policies. Caution with automated candidate selection: under the EU AI Act it is classed as a high-risk application.
• Controlling and accounting: Merge exports from different systems, explain variances, prepare monthly reports.
• Engineering and production: Make maintenance manuals, inspection reports and standards searchable; summarise fault reports.
• IT: Maintain documentation, write scripts, pre-qualify tickets.

Prerequisites: what should be clarified before you start

Technology is rarely the problem. Projects more often fail because of unclear responsibilities, missing data or uncertainty about data protection. You should answer five questions in advance.

1. Which data may the AI use? Define which repositories, systems and document types are included – and which are not.
2. Who may see what? The AI must not show anyone content they would not be allowed to see without AI. Roles and permissions must therefore also apply within the AI.
3. Which models and providers are permitted? Clarify with your data protection team where data is processed, whether a data processing agreement is in place and whether inputs are used for training.
4. Who is responsible? Appoint a person who prioritises use cases, collects feedback and maintains the rules.
5. How will you measure success? Record how long a task takes today before you start. This is the only way to demonstrate the benefit later.

Solving data protection and regulation pragmatically

Legal uncertainty is the most frequently cited obstacle. It can be resolved with a clear structure. For most use cases in mid-sized companies – knowledge search, text creation, summaries – two sets of rules are central.

GDPR

As soon as personal data is processed, the General Data Protection Regulation applies. Key elements are a legal basis, a data processing agreement with the AI provider, transparency about sub-processors and third-country transfers, and an effective permissions concept. The guide GDPR-compliant AI walks you through the assessment step by step and shows what matters in a ChatGPT alternative for businesses.

EU AI Act

The EU's AI regulation applies to all companies that use AI. Since February 2025, measures to ensure employees' AI literacy have been mandatory; since August 2026, transparency obligations have applied, for example for AI-generated content. The strict obligations for high-risk systems were postponed to December 2027 by the "Digital Omnibus on AI". The guide EU AI Act: obligations for companies explains which obligations concretely affect you.

Individual tools or a platform?

Many companies start with individual tools: a chat subscription here, a transcription service there, an automation tool for IT. That is quick, but leads to scattered data, multiple contracts and a lack of overview of costs and access. A platform – sometimes called an AI operating system – brings models, knowledge, agents and integrations together under one permissions management system.

Pay attention to these criteria when choosing:

• Model choice: Can you choose between several models and providers instead of being tied to one vendor? Is BYOK possible, i.e. using your own API keys?
• Permissions: Do roles and sharing apply per document, agent and chat – including when searching knowledge?
• Sources: Does the AI show what an answer is based on?
• Integrations: Can email, calendar, file storage and CRM be connected? Are there open interfaces such as the Model Context Protocol (MCP)?
• Contract and hosting: Are the DPA, technical and organisational measures and a list of sub-processors openly available?
• Cost control: Can usage be analysed and limited by person or cost centre?

You will find a structured comparison of common solutions on the Comparison page.

Five steps to productive use

1. Choose a use case (week 1): A frequent, easily verifiable case with measurable time required, such as answering internal questions from manuals.
2. Connect data (weeks 1–2): Connect the relevant folders and systems, adopt permissions, weed out outdated documents.
3. Pilot with one team (weeks 2–6): Ten to twenty people work with it daily. Collect examples of good and bad answers.
4. Rules and training (in parallel): Publish an AI policy, run and document a short training session on capabilities, limits and hallucinations.
5. Expand and automate (from month 2): Connect further teams, implement recurring tasks as agents or routines, assess the benefit using the before-and-after measurement.

Assessing costs and benefits realistically

The costs of an AI platform typically consist of per-user licences and the consumption of the AI models. Calculate the benefit conservatively: how many hours per week does a team save on a concrete task, and what does that time cost? You will find a detailed calculation scheme in the article Calculating AI ROI for mid-sized companies. Also plan time for data maintenance and rollout – it is the most frequently underestimated item.

Common mistakes and how to avoid them

• Starting too big: A company-wide project without a concrete case quickly loses momentum. Start small and measurable.
• Connecting knowledge without permissions: An AI that shows everyone everything becomes a security risk and loses the trust of the workforce.
• Adopting results unchecked: AI answers remain drafts. Professional responsibility lies with people.
• Tolerating shadow AI: If you do not offer an approved tool, you get private accounts with unclear data flows.
• Underestimating acceptance: Involve the works council (Betriebsrat) and departments early and show that AI takes routine work off people's hands.

Bringing the workforce and works council on board

AI changes work – and with it employees' expectations and concerns. Companies that introduce AI successfully talk early and openly about what they want to use AI for and what not. A works council (Betriebsrat) must be involved, because AI systems are regularly capable of monitoring behaviour or performance. A works agreement that sets out purposes, data types, analyses and rights builds trust and speeds up the rollout.

Internal champions are particularly effective: employees from the departments who try out use cases, share experiences and support their colleagues. Short formats – a weekly drop-in session, a collection of good prompts in a shared library, examples from your own day-to-day work – have a more lasting effect than one-off large training sessions.

How AIPE supports mid-sized companies

AIPE is an AI platform for mid-sized companies that is hosted in Germany. Teams work in an AI chat with freely selectable models, use their company knowledge with source citations and permissions per organisation and resource, create documents in the canvas and hand over recurring work to agents and routines. Systems such as Gmail, Google Drive, Nextcloud or HubSpot are connected via connectors; with the AIPE Worker, a Mac or Windows PC takes over tasks involving local files and programs. The data processing agreement can be viewed online. The All features page provides an overview of all functions, and you will find the plans under Pricing.