Generative AI (GenAI) can accelerate processes, increase productivity, and unlock new value. Yet many companies focus only on short-term efficiency gains: they test individual tools and measure their benefits in terms of time saved - without pursuing an overarching strategy. However, GenAI only unfolds its full potential when it is purposefully integrated into business processes.
Based on our experience in numerous GenAI projects, we have developed a proven three-step framework. It helps companies not only to deploy GenAI selectively, but also to scale it and integrate it strategically into their business processes. The three stages - Individual Impact, Functional Impact and Business Impact - help make initial applications quickly usable, optimize processes in a targeted manner and establish AI as a long-term growth driver.
The framework in practice - three stages of GenAI integration
Stage 1:
→ Individual Impact - the first step to change (6-8 weeks of use)
Many organizations start with GenAI tools, such as Microsoft 365 Copilot, to help employees with everyday tasks. GenAI facilitates document creation, meeting summaries, and structured information searches. However, without targeted control, the benefits are often limited to individual applications.
Key actions:
- Identify initial use cases in departments with high GenAI potential.
- Measure ROI by analyzing time savings.
- Training and change management to establish new ways of working in the long term.
Example:
A company uses M365 Copilot to automatically generate meeting minutes. This saves employees valuable time when preparing for meetings and client calls. Instead of wading through notes, they receive structured summaries of relevant content - allowing them to focus more on strategic decisions. The focus is on saving individual time and increasing productivity.
Stage 2:
→ Functional Impact - Targeted Integration of GenAI into Processes (2-12 months of use)
While Stage 1 focuses on the individual benefits of GenAI for individual employees, Stage 2 is about systematically optimizing entire processes. The transition from individual applications to process-based automation enables significant efficiency gains.
In this phase, GenAI is no longer used only for support, but is actively integrated into existing workflows and systems. GenAI offers great potential for optimization, especially in data-intensive and repetitive processes.
Key actions:
- Identify business processes that can be made significantly more efficient through automation.
- Integrate AI into IT systems and workflows to ensure smooth operations.
- Establish clear governance structures to ensure accountability and compliance.
Example:
A company automates the bidding process in purchasing. Generative AI creates RFP documents, analyzes incoming bids, and assists in supplier selection. As a result, processing time is reduced, internal resources are used more efficiently, and time-to-market is significantly accelerated.
Stufe 3:
→ Business Impact – GenAI als strategischer Wachstumstreiber (mehr als 12 Monate Nutzungsdauer)
Erst wenn GenAI strategisch in die Unternehmensprozesse eingebettet wird, entfaltet sie ihr volles Potenzial. Während Stufe 1 auf den individuellen Nutzen und Stufe 2 auf die Prozessoptimierung fokussiert, geht es in Stufe 3 um die Skalierung und Verankerung von AI als Wachstumstreiber.
Unternehmen, die GenAI gezielt zur Optimierung von Geschäftsmodellen, zur Förderung von Innovationen und zur datenbasierten Entscheidungsfindung einsetzen, sichern sich langfristige Wettbewerbsvorteile. KI wird nicht mehr nur als Automatisierungstool betrachtet, sondern als integraler Bestandteil der Unternehmensstrategie.
Schlüsselmaßnahmen:
- Definition klarer KPIs, um den langfristigen Erfolg messbar zu machen.
- Nutzung von OKR als Steuerungsinstrument, um AI-Transformation gezielt zu lenken.
- Skalierung der AI-Strategie über einzelne Abteilungen hinaus auf die gesamte Organisation.
Example:
A company sets itself the goal of developing a compact, enterprise-wide AI strategy within one year and implementing at least 10 relevant AI use cases in different business areas. These could include the automation of financial processes, AI-assisted optimization of customer interactions, or data-driven product development.
This structured approach ensures that AI not only delivers selective efficiency gains, but also actively contributes to the company's innovation and scalability. Regular evaluation of OKR-based goal achievement and continuous optimization of AI-enabled processes turn AI into a sustainable competitive advantage.
Conclusion: AI as a continuous success factor
Integrating GenAI is not a one-time IT project, but a dynamic, ongoing process that requires constant adaptation and strategic development. Companies that view AI as a short-term efficiency tool are wasting valuable potential. Only through a targeted and gradual implementation - from initial use cases to process optimization and strategic scaling - can AI achieve its full business impact.
The framework presented here helps companies not only to deploy GenAI selectively, but also to embed it sustainably in business processes and corporate strategies. This helps to avoid wrong investments, leverage synergies across teams and departments, and secure long-term competitive advantage.
Those who develop a clear AI strategy early on will benefit twice: from short-term productivity gains and a long-term transformation that enables new business models. Companies that take a structured approach to AI now are not only actively shaping their own future - they are setting the standard for their industry.
How IT becomes a growth engine?
