AI Lab

Rethinking business processes – clear, structured, and agentic.

The AI Lab is an ongoing working format where we analyze, simplify, and develop real processes together with your teams – step by step, in 8-week sprints. Instead of hypothetical exercises or isolated prototypes, we work on your actual workflows.

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Was ist das AI Lab?

A workspace for real process improvement with AI

In the AI Lab, we reflect on, standardize, and improve real workflows with your teams. The focus is always on clarity, simplification, and structure before technology is applied.

How we work with you

Not the focus of this format

No pure tool training
No playful AI experiments
No isolated prototypes without a path to adoption

Why clear processes are the foundation for agentic work

In the AI Lab, we create the structural basis for agentic ways of working: We make processes transparent, decisions comprehensible, and roles clear. Only when workflows are clearly defined can humans and agentic systems work together reliably.

Clarity & Orientation

We document workflows in a way that roles, steps, and decision points become unambiguous.

Reduced Complexity

By simplifying real workflows, we create improvements you can feel immediately in daily work.

Agentic Connectivity

We identify tasks where agentic systems make sense – and create stable decision logic.

What a sprint delivers in practice

Why the AI Lab runs in 8-week sprints

In the AI Lab, we evolve real workflows. So teams can implement these changes alongside day-to-day operations, we use 8-week sprints. They provide structure, reduce complexity, and enable clear, traceable progress.

Each sprint produces a usable outcome

Strategy Workshop

Shared starting point: goals, context, roles, bottlenecks – tied directly to your real workflow.

Training

Fundamentals of agentic work so everyone operates from a shared understanding.

Use Case Refinement

Based on real workflows, we identify meaningful and valuable starting points – not hypothetical ones.

Implementation in Co-Creation

We redesign the selected process together: clearer, simpler, and ready for agentic support.

Presentation & Success Measurement

Concrete improvements, quick wins, documentation – plus prioritization for the next sprint.

Show example outcomes

What we improve in the AI Lab

In the AI Lab, we don’t work on hypothetical examples. We work on your real workflows. Our goal: make processes so clear, logical, and adoptable that teams feel immediate relief.

Make processes visible

We capture your real workflows step by step: roles, decisions, bottlenecks.

Extract decision logic

We analyze how decisions are made, which criteria apply, and where information gaps occur.

Agentic Redesign

We redesign your workflows so they become simpler, more explicit, and agent-ready.

Immediately effective improvements

We derive concrete actions that relieve your team right away.

More about our enablement formats

What capabilities teams develop in the AI Lab

The AI Lab combines process work, enablement, and agentic fundamentals. Teams don’t just learn to improve workflows – they also build the skills to use AI and agentic systems effectively in the organization.

Compliant with the EU AI Act

People

Mindset & Fundamentals

Prompting & Operational Use

Creativity & Quality

Collaboration & Culture

Organization

Use Cases & Prioritization

Roles & Operating Model

Governance & Standards

Knowledge Base & Workflows

Technology

Tools & AI Stack

Agent Development

Testing & Debugging

Mini-Automations

Data Protection & Data Sovereignty in the AI Lab

In the AI Lab, we work only with data you explicitly approve – and always under clear data protection and security standards.

No external tools without your approval

We only use systems that you have approved.

Data stays in your environment

If desired, we work entirely within your internal tools & storage.

Transparent data flows

You always know where data lives and how it is processed.

Data economy

We use only the information required for process work.

Legal & Compliance Security

The AI Lab follows data protection and risk standards common across the DACH region.

For teams that want clarity

The AI Lab is designed for organizations that want to shape change deliberately – not as a large AI project, but as a concrete evolution of how they work.

Suitable for organizations that...

want to analyze and standardize real workflows in a structured way
need fast, tangible improvements
are looking for a low-risk, pragmatic, GDPR-compliant path into AI
want to make roles & decisions clearer, more binding, and more traceable
want to shape change step by step rather than in one big leap

Is the AI Lab a fit for you?

Schedule a conversation

How the AI Lab changes workflows

Every AI Lab works on real processes that shape daily work. The examples below show how diverse the topics can be – and how strongly clarity, structure, and agentic thinking affect everyday operations. All examples share one thing: less complexity, clearer decisions, and more stable workflows.

Marketing – Briefing Clarity

Starting Point

Incomplete briefings, many queries.

Improvement

Clear structure, mandatory fields, defined roles.

AI outcome

Agent can automatically check briefings and make suggestions.

Impact

Fewer loops, faster production.

Sales – Lead Qualification

Starting Point

Subjective evaluation, high variance.

Improvement

Clear decision criteria, uniform data points.

AI outcome

Agent can automatically pre-sort and evaluate leads.

Impact

Reliable pipeline, less manual coordination.

Backoffice – Document Processing

Starting Point

Different paths, many exceptions.

Improvement

Structured flow, defined decision paths.

AI outcome

Agent can pre-qualify and extract documents.

Impact

Fewer errors, less rework.

Marketing – Review Loops

Starting Point

Many iterations, unclear criteria

Improvement

Defined quality and approval criteria.

AI outcome

An agent can pre-evaluate content based on defined quality logics and generate optimization suggestions.

Impact

Fewer follow-up questions, stable quality, shorter project timelines.

Sales – Proposal Process

Starting Point

Unclear responsibilities, missing structure, excessive proposal lead times.

Improvement

Defined steps, clear roles, templates for proposal components.

AI outcome

An agent generates proposal drafts with suitable components and suggests variations.

Impact

Faster proposals, fewer errors, reduced coordination effort.

Back Office – Invoice Approvals

Starting Point

Slow approvals, unclear thresholds, lack of transparency.

Improvement

Clear approval logic, defined roles and thresholds.

AI outcome

An agent automatically checks invoices and suggests appropriate approvals or escalations.

Impact

Faster throughput times, fewer questions, more reliable decisions.

Marketing – Campaign Preparation

Starting Point

Each team works differently, lack of structure, many clarifications needed.

Improvement

Checklists, defined process paths, clear roles.

AI outcome

An agent automatically creates campaign setups (briefings, to-do lists, timelines).

Impact

Greater planning reliability, less chaos, faster start times.

HR – Talent Matching

Starting Point

Inconsistent evaluations, subjective decisions, long selection times.

Improvement

Structured requirement profiles and evaluation logics.

AI outcome

An agent automatically compares profiles and generates meaningful shortlists.

Impact

More accurate decisions, reduced bias, faster selection process.

Operations – Escalation Logics

Starting Point

Decisions depend heavily on individuals, unclear triggers.

Improvement

Defined escalation paths and thresholds.

AI outcome

An agent automatically detects escalation scenarios and suggests the right decision or forwarding path.

Impact

More stable processes, less uncertainty, faster response times.

HR – Onboarding

Starting Point

Different processes across teams, lack of consistency.

Improvement

Standardized onboarding paths, roles, and information flows.

AI outcome

An agent creates individual onboarding plans, assigns tasks, sends reminders.

Impact

Faster integration, less coordination effort, higher quality standards.

Operations – Task Allocation

Starting Point

Unclear capacities, many ad-hoc decisions, inefficiency.

Improvement

Skills and role models, clearly defined assignment logic.

AI outcome

An agent suggests responsible owners based on roles, skills, and capacities.

Impact

More efficient resource use, less duplicate work, clearer responsibilities.

Discuss more examples

Client voice on the AI Lab

“We participate in the AI Lab and have already done many trainings. Every hour is an enrichment and it is amazing how far we have already come in AI application.”

Veith SemrauDirector Operations Parfümerie Becker

What you have in hand after every sprint

Each sprint in the AI Lab delivers a concrete outcome – regardless of topic or team. The components below are the shared pattern across all sprints.

Process Clarity

Your real workflows are transparently documented: steps, roles, decision logic.

Simplified Processes

A workflow with clear decision points, less complexity, and unambiguous ownership.

Identified AI application points

Concrete tasks where agentic systems can support.

Identified agentic capabilities

A list of capabilities an agent can take on in the process.

Immediately effective improvements

Clearly defined actions that relieve teams right away.

Documentation

A clean, easy-to-understand preparation of results.

AI Outputs

Concrete artifacts from the sprint, e.g., prompts, customGPTs.

A simple entry point

A short orientation call is enough to define the goal, process, and team.

Schedule a conversation

Ready to rethink your processes step by step?

Request AI Lab