AI&AutomationYourOrganizationActuallyUses
I help organizations design and implement AI and automation systems that generate measurable adoption, efficiency, and business impact.
Experience from IT transformation, CRM migration, and AI implementation in enterprises and mid-sized companies.
Enterprise AI adoption rarely fails because of technology. It fails because of adoption. Employees bypass new tools, processes aren't followed, and the promised ROI never materializes. My approach addresses exactly this: The people who are supposed to work with the AI.
Why AI Projects Fail
The reality in many organizations
of AI projects fail to achieve their goals
According to recent studies, over 70% of AI projects don't achieve their planned objectives. The reason rarely lies in the technology itself, but in the lack of consideration for human factors during enterprise AI implementation.
AI tools are deployed but bypassed in daily work
Employees find workarounds because the AI solution doesn't fit their daily workflows. The investment evaporates because workflow automation wasn't aligned with real usage scenarios.
Processes exist but aren't followed
Documented processes and actual work reality diverge. Without understanding this gap, sustainable AI process optimization cannot succeed.
Decisions remain manual or inconsistent
Despite implemented AI systems, teams rely on manual decision paths. Human-centered AI adoption is missing – nobody defined when AI decides and when humans do.
Employees don't trust or understand the AI
Without transparent communication and training, mistrust develops. AI is perceived as a threat rather than support. Successful AI adoption requires change management from the start.
There's a better way.
The Human-Centered Approach
Why traditional AI consulting often fails
Traditional AI projects focus on technology and neglect the human factor. My Human-Centered AI approach reverses these priorities: First we understand how people actually work – then we design the AI solution accordingly.
Behavioral analysis before technology selection
Before discussing tools, we analyze real workflows. Where does friction occur? How are decisions made? These insights determine the technical solution.
Adoption as success criterion
An AI project's success isn't measured by technical implementation, but by actual usage. We define adoption KPIs from the start and track them continuously.
Iterative rollout instead of big bang
Instead of months of development followed by go-live, we opt for gradual introduction. Each iteration is tested with real users and optimized.
Enablement instead of training
Traditional training is quickly forgotten. Instead, we develop playbooks, decision aids, and contextual help that support employees in their daily work.
Human-Centered AI Adoption Framework
3 Phases for Sustainable AI Implementation
The framework combines behavioral analysis, technical implementation, and change management into an integrated approach. Each phase builds on the previous one and creates the foundation for measurable AI adoption.
Adoption Audit
Analysis of behavior, processes, and decision logic. Interviews, system usage, data, and real workflows.
In the Adoption Audit, we examine how your teams actually work – not what's documented in process guides. Through interviews, observations, and data analysis, we identify friction points, untapped potential, and true decision paths. The result is a detailed Adoption Map showing where AI and automation can have the greatest impact.
Workflow Design
Designing clear, human-usable AI workflows. Roles, decision rules, and interfaces are clearly defined.
Based on audit insights, we design AI workflows that integrate seamlessly into existing work processes. We clearly define when AI decides autonomously, when it makes suggestions, and when humans take over. Workflow automation is designed to support employees rather than add complexity.
Implementation & Enablement
Technical implementation with n8n, Python, CRM, and Analytics. Complemented by playbooks, guidelines, and decision logic for teams.
Technical implementation happens iteratively with continuous user feedback. In parallel, we develop enablement materials: playbooks for typical scenarios, decision aids for edge cases, and dashboards for success measurement. This ensures that enterprise AI adoption not only works technically but is actually used.
What You'll Receive
Tangible results, not PowerPoint slides
Every deliverable is designed for practical applicability. You receive no theoretical concepts, but deployment-ready tools for successful AI adoption in your organization.
Adoption & Friction Map
Visualization of all adoption blockers and quick wins in your processes. The map shows at a glance where employees struggle, where processes are bypassed, and where the biggest improvement levers lie.
Prioritized Use Cases
Evaluated AI and automation use cases by ROI and feasibility. Each use case includes effort estimation, expected benefit, required resources, and dependencies on other initiatives.
Workflow Blueprint
Technical specification for your automation and AI workflows. The blueprint defines data flows, decision logic, interfaces, and fallback scenarios – ready for implementation.
Implemented Workflows
Fully implemented and tested automations in n8n, Python, CRM, or analytics tools. Including documentation, monitoring, and alerting for production operation.
Enablement Playbooks
Practical guidelines for your teams: When do I use the AI? How do I handle edge cases? What do I do with unexpected results? The playbooks aren't training materials but work aids for everyday use.
Adoption KPIs
Dashboard with measurable metrics on usage and success of the AI implementation. You see at a glance whether adoption is proceeding as planned and where adjustments are needed.
Is This Right for You?
This approach isn't for everyone. Here's an honest assessment of whether we're a good fit.
Suited for
- Mid-sized companies
with 50-500 employees who want to implement AI strategically rather than just buying tools
- Enterprises
with complex processes and the realization that previous AI initiatives haven't achieved hoped-for adoption
- Product, IT, and Digital teams
who are responsible for successful AI implementation and need to deliver measurable results
- Consultancies (white-label)
who want to offer AI implementation to their clients without building technical expertise themselves
Not suited for
- Tool installation without change
If you just want an AI tool installed without preparing the organization, I'm the wrong partner
- AI projects without ownership
Without an internal sponsor with decision authority and budget, AI projects cannot succeed
- Low-budget or rush projects
Sustainable AI adoption takes time and resources. Quick fixes don't work here
Frequently Asked Questions
Answers to the most important questions about enterprise AI adoption
Request AI Adoption Audit
Start with a free initial consultation. Together we analyze whether and how the Human-Centered approach can work for your organization.