AI & AUTOMATION · ACROSS GERMANY

INQA coaching for AI and automation.

Turn isolated AI experiments into a workflow your team can rely on. With FM24, you choose a specific bottleneck, explore suitable approaches with your employees and define how people, knowledge and systems work together.

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INQA coaching can support employee participation in shaping digital change, including AI and automation. Funding covers eligible coaching, rather than a promised software purchase or completed integration project. The responsible INQA advisory centre assesses whether your project qualifies.

Funding, eligibility and costs in the INQA guide

Not every task needs AI. Every solution needs a clear workflow.

Start with a recurring task: where do questions, duplicate entries or delays arise? Once responsibilities and handovers are understood, we decide what kind of support is appropriate.

Clarify the process

An enquiry stalls because nobody owns the next step. First, define intake, handover and responsibility.

Automate defined rules

A known event should trigger a defined action, such as a reminder or the transfer of approved data. Fixed rules are often sufficient.

Use AI purposefully

Unstructured text needs classification, information needs summarising or drafts need preparing. AI can help when sources are traceable and appropriate review is in place.

Three possible projects. Three different checks.

Illustrative examples of collaborative coaching work. These are not client case studies or a promise of included technical delivery.

From the inbox to the CRM

Starting point: Enquiries are read repeatedly and forwarded manually to different contacts.
Team experiment: The team develops categories and explores how AI can prepare a routing suggestion with relevant details.
Review and responsibility: Unclear enquiries and missing details go to manual clarification. Only authorised people approve binding commitments.
Tangible work product: An agreed target workflow with routing rules, exception handling and a test checklist for subsequent system integration.

Internal knowledge for reliable answers

Starting point: Answers depend on individual employees’ experience, and documents contradict one another.
Team experiment: The team defines a bounded knowledge collection and tests answers to typical internal questions.
Review and responsibility: Sources must be current and accessible. If reliable evidence is missing, the system should identify the gap and a responsible contact.
Tangible work product: A maintained source list, ownership of updates and assessed test questions to inform a knowledge assistant.

Product information for additional markets

Starting point: Product descriptions are translated separately for each market and copied between the shop and inventory system.
Team experiment: Sales and product owners create a glossary, required information and a workflow for multilingual drafts.
Review and responsibility: Item identifiers and technical values remain controlled. Subject matter and translations are reviewed before publication.
Tangible work product: An agreed approval workflow with a glossary, quality criteria and requirements for shop and ERP interfaces.

From a bottleneck to a working practice your team owns.

The work follows the INQA phases. Tests and tools are agreed for your project. Employees help shape the approach and need time to test it and give feedback.

Initial phase: define goals and boundaries
We describe the existing workflow, identify participants and select the pilot. This includes data sources, access rights, human approvals and an observable baseline. The result is a shared brief.
Innovation phase: test alternatives in everyday work
The team explores approaches in manageable steps. We examine normal cases as well as missing data, incorrect AI answers and broken handovers. Feedback informs rules, roles and the next test.
Learning phase: establish what works
We compare experience against the baseline and record limitations. Responsibilities, usage rules and a plan for further work are documented. Outstanding technical tasks go into a separately agreed implementation plan.

Progress is assessed. Savings are evidenced.

Before testing, we agree how to recognise an improved workflow. Without a baseline, an assumption cannot become a demonstrated improvement.

Effort and lead time

How much handling time and how many handovers does a comparable case require before and during the pilot?

Quality and exceptions

Which answers are correct? Which cases reach a person for review, and does the recovery path work when something goes wrong?

Usability and ownership

Can the team explain the workflow, maintain sources and continue working during disruption? Who approves changes?

Plan coaching, implementation and training clearly.

INQA coaching: Employees develop and test new ways of working. Potential funding applies to eligible coaching costs under the applicable conditions.
Technical implementation: Production interfaces, software licences and further development require their own scope and a separate agreement on costs.
AI training: Tool knowledge and a shared approach to AI can be developed through targeted learning. General training does not replace organisational change.

Funding, eligibility and costs in the INQA guide Explore AI training and competence development

Across Germany. Based in Berlin.

FM24 is based in Berlin and supports companies across Germany with AI-assisted business automation. This focus also serves businesses in Berlin and Brandenburg, for example when knowledge is distributed across locations or administration, sales and specialist teams need to work together more effectively.

We agree scheduling, working methods and any on-site sessions around your team and the coaching process. The programme requires, among other conditions, a registered office and workplace in Germany; the regional INQA advisory centre assesses individual eligibility. Our support is not limited to Berlin or Brandenburg.

Specific questions about your AI project

How do we keep the first pilot manageable?

We limit the test to one task, a responsible group and a manageable set of data. Further systems are involved only once subject-matter checks and handovers work. This makes it possible to see why an approach fails or improves.

Can an AI suggestion directly trigger a binding action?

There is no blanket approval. We distinguish preparatory suggestions from binding actions and define approvals according to the consequences of errors. AI might draft a response, while an authorised person reviews and sends a commitment.

How are exceptions handled in automation?

A workflow needs a clear path for missing information, technical failures and uncertain classifications. These cases are deliberately tested in the pilot. A named person needs to see what is unresolved and how to continue handling it safely.

What should we bring to the first conversation?

Describe a recurring task, the roles involved, current tools and the desired outcome. Real customer data and confidential documents are not needed for this first assessment. We agree how required data will be handled before an experiment.

How do we assess value without a blanket ROI promise?

We agree relevant comparison criteria and examine similar cases before and during the pilot. Alongside time, we consider errors, rework, exceptions and acceptance. Value is demonstrated only when the observations support it.

How do we handle conflicting knowledge sources?

Before introducing AI-assisted search, the team decides which source governs each topic and who updates it. Contradictions are made visible. An assistant should not conceal an unresolved rule behind a persuasive answer.

How does FM24 work with teams outside Berlin?

We support companies throughout Germany. Together, we plan a way of working that allows the employees involved to shape the project and carry out tests in their business. Whether and when on-site sessions help is agreed for each project.

What if the pilot is unconvincing?

A rejected approach can still provide useful insight. We record whether data quality, effort, risk or a lack of value argues against deployment. This leads to an adjustment, another approach or a deliberate decision to leave that task outside AI for now.

THE NEXT STEP

Which workflow should work better in your business?

Discuss your bottleneck with us. We will narrow down a meaningful AI or automation project and consider whether INQA coaching could be the right next step.