Before AI initiatives can take effect, clarity about the status quo is needed – technologically, organizationally, and culturally. Our assessment provides a compact, well-founded inventory.

We analyze the relevant dimensions of your AI maturity: data quality and availability, existing tool landscape, competency levels in teams, governance structures, and strategic anchoring. We combine stakeholder interviews with an AI-powered preliminary analysis that systematically evaluates available information – before the first conversation even takes place.

The AI landscape is evolving rapidly. Our impulse formats create the foundation for executives and domain teams to make informed decisions – and gain initial practical experience.

At the center: an expert talk on generative AI that communicates technical fundamentals accessibly without oversimplifying. How do Large Language Models work? What can agent systems do today – and where are their limits? Complemented by live demonstrations and initial hands-on exercises for transfer to your own work context.

The most valuable AI use cases emerge at the intersection of your teams' daily challenges and the capabilities of current technology. In structured workshops, we work with your business and IT teams to identify solutions for concrete pain points – and systematically evaluate each use case by impact and feasibility.

The top cases are qualified to implementation level: What data is available? What prerequisites need to be created? Which solution architecture fits? Typical results range from automated proposal generation to AI-powered ticket routing to semantic duplicate detection in master data – applications that address where time and quality are lost daily.

AI is fundamentally changing software development. When deployed purposefully, AI coding assistants increase developer productivity by 20–40% on routine tasks. But the productivity gain doesn't come automatically – it emerges through the right approach, appropriate tooling, and systematic capability building.

Our approach goes beyond classic tool introductions. We evaluate AI coding tools in the specific context of your codebase and architecture, define a tooling strategy including data privacy policies, and roll out tooling incrementally – with power users as multipliers and team-specific hands-on workshops.

Beyond that, we accompany the step from autocomplete to agentic development: AI agents that automatically generate service scaffolding from structured requirements, derive E2E tests from acceptance criteria, or analyze bug reports and create fix suggestions. Complemented by AI-powered code reviews that check not just style, but ensure business logic consistency across services and provide concrete improvement suggestions.

The interface between business and development is often the biggest bottleneck in complex projects. AI can serve as a bridge: Requirements agents support domain experts in structured requirement capture, automatically check for completeness and consistency, and compare new requirements against the existing inventory.

The result is an end-to-end pipeline with clear responsibilities: Business and Requirements Agent deliver complete functional requirements, Product Owners translate these into user stories with acceptance criteria, developers derive technical specifications – AI-supported at every stage, but with human responsibility for the outcome.

In parallel, we help build AI-powered knowledge management: Internal domain knowledge – process documentation, protocols, technical specs – becomes available as a searchable, queryable knowledge base. New team members ask questions from day 1 and get source-based answers.

AI Enablement becomes concrete where tools are selected, configured, and integrated into existing processes – and where the organization builds the capabilities to operate and evolve them independently.

We accompany the design and implementation of your AI toolchain: From evaluating suitable platforms and models to data privacy clarification to workflow integration. We focus on vendor independence and modular architectures that remain adaptable – because in the end, that's exactly what determines whether AI remains a short-term experiment or becomes part of value creation.

In parallel, we build the internal capabilities necessary for sustainable operation: Data governance, prompt engineering standards, evaluation and monitoring routines, clear roles and responsibilities. Including ongoing adoption measurement and regular reporting to management – so progress remains visible and manageable.