Why Many Generative AI Consulting Projects Fail – and How We Do It Differently
The boom around Generative AI promises enormous benefits – yet many consulting firms fail to deliver the expected value. The Wall Street Journal highlights this gap: ambitious promises often fall short of tangible results.
Typical reasons AI projects fail:
- Choosing the wrong project focus – prioritizing growth topics instead of efficiency use cases that align with AI’s current capabilities
- Lack of deep AI expertise – many consulting firms have little hands-on experience with Generative AI
- Gap between promise and actual value – appropriate pilot projects are lacking or often cannot be scaled effectively
- Poor integration into existing processes – AI solutions are not effectively embedded in day-to-day operations
- Insufficient support for organizational change – transformation and cultural shifts are often neglected
How we do it differently:
- Access to some of Germany’s most renowned AI experts
- Projects with ROI < 1 year, regardless of investment size
- Linking technological expertise with process and transformation skills.
- Focus on previously unattainable automation potential, with process quality at least on par with human performance
👉 Our approach: Combining technology, processes, and transformation – for measurable results, real value, and sustainable change.
What’s your experience with generative AI consulting so far? Share your insights with us!