What does AI for companies mean in practice?

AI for companies means giving employees secure, central access to generative AI — connected to internal systems, with custom assistants for recurring tasks and accompanying training. The difference from private AI accounts lies in data protection, governance, and the ability to build and evaluate productive use inside the process in a controlled way.

Can AI be used in a GDPR-compliant way in companies?

Yes, provided the platform is operated in the EU, a data processing agreement (DPA) is in place and company input is not used for training. Central permission management, SSO, least-privilege scopes and logged access to internal data are further prerequisites. meinGPT is operated by SelectCode GmbH in the EU and is ISO 27001 certified; its security is reviewed regularly through independent penetration tests.

What should you look for in an AI provider from Germany?

When choosing an AI provider from Germany or Europe, objectively verifiable criteria beat gut feeling: operation of the platform in the EU, a data processing agreement, no training on company input, independent evidence such as ISO 27001 certification, access to several models, depth of integration through connectors and an API, and accompanying enablement. meinGPT is operated by SelectCode GmbH in Germany and is ISO 27001 certified.

Which AI tools do companies need?

Rather than many individual AI tools, most companies are better served by one central platform that bundles the key functions: chat across several models, document and knowledge search over connected sources, meeting summaries, image generation, translation, and self-built assistants for specialist tasks. The advantage of a platform over scattered individual tools lies in consistent governance, data protection and shared enablement.

How do you introduce AI in a company?

One possible framework has four steps: first a pilot with a secure group and clear use cases, then governance (roles, permissions, approvals), followed by enablement through training and champions, and finally evaluation through usage reporting. Metrics and measures are set for the specific rollout; no single measure guarantees adoption.

What does AI for companies cost?

Costs depend on the model and the scope. In self-service, meinGPT starts at €29 per user per month including a usage credit; the guided rollout combines a monthly platform base fee with a licence tiered by headcount and a one-off setup. AI usage runs on a shared credit balance rather than flat rates — so costs stay predictable and scale with usage.

Which AI models should companies use?

It makes sense to have access to several leading models (such as GPT, Claude, Gemini) instead of lock-in to a single vendor, complemented by European and open-source models for particularly sensitive applications. That way you can pick the right model per task and avoid vendor dependency — without changing the platform, the governance or the data-protection level.

What is the difference between an AI platform and individual AI subscriptions?

Individual AI subscriptions give each person an isolated chat — without shared governance, without connections to internal systems and often without robust data-protection commitments. An AI platform for companies bundles several models behind one interface with central permission management, connects the AI to permitted company data, allows self-built assistants and comes with enablement. The practical difference shows less in the chat window than in data protection, control, and whether the AI really gets used day to day.

How long does it take to introduce AI in a company?

A 90-day plan can structure a pilot, the first departmental assistants, governance, and preparation for broader adoption. It is not a promise of company-wide usage within that period: data approvals, technical connections, internal decisions, and enablement determine the specific timeline.

How do you make sure the AI actually gets used?

Missing adoption is a risk in AI rollouts. Training, champions, and usage reporting can help make usage visible and adjust enablement deliberately; they do not guarantee a particular usage rate. meinGPT therefore combines the platform with optional guidance whose effect must be evaluated in each rollout.

Practice

Whole company · Use cases

AI for companies: choosing a platform, rolling it out GDPR-compliantly & measuring real use

AI for companies in Germany: what makes an AI platform, objective selection criteria (GDPR, integrations, roles & permissions, cost, enablement), a four-step rollout model and answers to the most common questions — with real workflows, an example prompt and honest limits.

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Who it is for: Managing directors, IT and digital leads, and business departments in mid-sized companies
Impact: Observe usage during the rollout and identify unused licences early
Task: Evaluate, choose and roll out an AI platform company-wide

What it is about

What this use case delivers.

An AI platform for companies gives employees secure, central access to generative AI — several leading language models behind one GDPR-compliant interface, connections to internal systems, custom assistants for recurring tasks, and accompanying enablement. What matters is not model access alone, but the combination of EU operation, central permission management, integration into existing processes, and the ability to support and evaluate usage deliberately. The difference from private AI subscriptions lies in data protection, governance, and a shared framework for practical use.

How it works

A company AI platform bundles leading language models (GPT, Claude, Gemini as well as European and open-source models) behind a single interface with central user and permission management. Employees chat, generate images, summarise meetings and search internal documents — without their input being used to train the models. Connectors (MCP) and an API link internal systems, so the AI accesses permitted company data on a least-privilege basis and answers with sources from your own context instead of general internet knowledge. Departments build assistants for recurring tasks and share them across the team; some workflows and connected systems require additional technical configuration. Administrators manage roles, permissions and approvals centrally and keep an overview through audit logs of who accesses what. The academy, training, and champions can support the rollout, while usage reporting shows where the platform is being used and where further enablement may be useful. These elements support adoption but do not guarantee a particular usage rate or the success of a specific rollout.

Concrete workflows

These steps are part of the implementation.

These recurring tasks can be covered with the same underlying pattern.

Secure access for every employee

Instead of private AI subscriptions, the whole team gets central, GDPR-compliant access to several leading models — with single sign-on, roles and permissions, and without input being used to train the models. Shadow AI, where employees use their own accounts unchecked, is replaced by a vetted platform whose access is logged and reviewed regularly. Company knowledge no longer ends up scattered across private accounts, but in an environment whose data protection the company can guarantee contractually.

Make internal documents and systems searchable

Connectors (MCP) and an API link the AI to permitted data sources — file shares, wikis, ticket systems, ERP. Employees get answers backed by sources from their own company knowledge instead of general internet answers. Access follows the permissions granted on a least-privilege basis and is limited to permitted scopes, so each person only sees what they are cleared for — traceably, via logs.

Build your own AI assistants without development

Departments create their own assistants for their routines without programming — quotes, application screening, reporting, support replies. The assistant encapsulates context, tone of voice, knowledge base and working steps, and is repeatable for the whole team. Everyone works with the same quality-assured tool instead of inconsistent one-off prompts, and the effort invested once in a good assistant pays off with every repetition.

Automate meetings, research and translation

Notetakers minute meetings including action items, research functions gather sources as they go, and translations run GDPR-compliantly inside the platform. Tasks that used to take hours become a matter of minutes — the professional review of the result stays with a human. Because these functions run in the same environment, no new sprawl of individual tools with their own data-protection status appears.

Steer roles, permissions and governance centrally

Administrators manage users, groups and permissions centrally, release assistants and data sources deliberately, and keep an overview through audit logs of who accesses what. Control over data, models and permissions stays inside the company — the basis for auditable, GDPR-compliant operation and for evidence towards data protection officers, the works council and auditors.

Roll out in a structured way and measure adoption

A rollout can be structured in four steps: a pilot with clear use cases, then governance (roles, permissions, approvals), enablement through training and champions, and finally evaluation through usage reporting. Reporting shows where the platform is being used and where further enablement should be considered. Which measures work and what usage rate is realistic must be measured for the specific rollout.

Example

Input and result side by side

Input

We are a mechanical engineering company with 400 employees and want to introduce AI. Create a pragmatic 90-day rollout plan: phase 1 a secure pilot group, phase 2 the first departmental assistants (sales, service), phase 3 a company-wide rollout with training. For each phase, name the goal, the people involved, the risks and one measurable metric.

Result

Phase	Goal	People involved	Risk	Metric
1 · Pilot (day 1–30)	Secure access + first use cases	IT, 15 power users	Shadow AI continues in parallel	Record weekly usage in the pilot
2 · Assistants (day 31–60)	Sales & service assistant live	Departments, one champion per team	Assistants too generic	2 assistants in productive use
3 · Rollout (day 61–90)	Prepare expansion + academy	Participating departments, HR	Adoption remains below the target	Record usage rate by participating department

Next step

Implement it in your company

In a short demo, we clarify data, ownership and the right workflow for this use case.

Book a live demo

Or download the Choosing the right AI platform — the requirements catalogue (PDF, German) as a PDF:

Choosing the right AI platform — the requirements catalogue (PDF, German)By email

Security and selection

The platform is operated in the EU, a data processing agreement (DPA) is standard, and company input is not used to train the models. It is operated by SelectCode GmbH, which is ISO 27001 certified and has its security reviewed regularly through independent penetration tests (most recently SySS, 2025). Access runs through central user and permission management with SSO; calls to internal systems are limited by least-privilege scopes, logged, and subject to regular access reviews. The information security management system governs access control, logging, the handling of personal data and the secure deletion of information no longer needed; the corresponding policies and evidence are available through the Trust Center. Control over data, models and permissions therefore stays inside the company — instead of scattered across private AI accounts (shadow AI) — and can be evidenced to data protection officers, the works council and auditors.

What to check when choosing a solution

Data protection & hosting: Is the platform operated in the EU, is there a data processing agreement (DPA), and is it contractually guaranteed that input is not used to train models?
Certification & evidence: Is there independent evidence such as ISO 27001 certification and regular penetration tests?
Model choice: Are several leading models available — instead of lock-in to a single vendor — complemented by European and open-source models for sensitive cases?
Depth of integration: Can internal systems be connected via native connectors, MCP and an API, or does it stay an isolated chat window?
Roles & permissions: Is there central user and permission management, SSO, least-privilege scopes and audit logs?
Custom assistants: Can departments build and share their own assistants and workflows without development work?
Enablement & adoption: Is there training, a champion programme and usage reporting — or does the service end at access?
Cost & transparency: Are licence and usage costs traceable and predictable (e.g. tiered pricing by headcount, usage credits instead of a flat rate)?

Known limitations

What needs to be clarified before rollout.

These points need to be clarified professionally or organisationally before rollout.

01
AI does not replace professional review — generated content (quotes, contracts, calculations) must be approved by the responsible people before use.
02
The value drawn from internal data depends on clean connections and permissions — without maintained sources and scopes, answers stay generic.
03
Access alone does not demonstrate adoption. Whether training, champions, or other measures improve usage must be measured in the specific rollout.
04
Models can be wrong or out of date; legally and safety-critical statements need source grounding and human control.
05
A platform does not replace governance decisions: which data is released and which use cases are permitted is for the company itself to decide.

Frequently asked questions

An AI platform for companies is central, GDPR-compliant access to generative AI for employees — several leading language models behind one interface, connections to internal systems, custom assistants for recurring tasks, and accompanying enablement. It differs from individual AI subscriptions through central governance (roles, permissions, logs), EU operation without training on your input, and tools for observing usage and planning enablement deliberately.

Sources

Last verified: 2026-07-02T00:00:00.000Z