Since the launch of tools such as ChatGPT, interest in generative AI (GenAI) has been enormous. The applications seem endless: from content generation and software development to strategic analysis. The rise of generative AI (GenAI) and large language models (LLMs) has fundamentally changed the playing field of data and technology. But where buzzwords flourish, unrealistic expectations also arise. And that’s exactly where the risk lies. For no matter how impressive the technology, without proper expectation management, even the best AI approach leads to disappointment.
What do we see as data & AI consultancy in practice? And how do you ensure that GenAI does not remain hype, but actually delivers sustainable value?
| Forecast | Reality |
| GenAI replaces work | GenAI speeds up and supports work |
| GenAI fits everywhere | GenAI works well in some places, is inappropriate in others |
| GenAI works by itself | Without people, GenAI makes no impact |
| GenAI realizes business goals | Business goals remain leading. AI supports them |
1. GenAI accelerates, but does not replace
GenAI is a powerful assistant, but it does not own the outcome.
It helps structure information, generate initial versions and make knowledge accessible. Yet we see organizations getting in without a clear use case or evaluation criteria. There is a lot of testing, but little implementation. The result? Trust crumbles.
Our advice: start small and concrete. Choose processes where human review makes sense, such as documentation analysis, content creation or internal Q&A. Measure value at three levels: efficiency, quality and adoption.
2. Strategy is also daring to limit
A data strategy is not just about seeing opportunities; it’s also about making choices. Not every AI application will fit within your culture, processes or compliance frameworks. Those who deploy AI strategically must also have the courage to say, “We’re not doing this (yet).” That requires leadership and technical direction.
Our advice: ask these questions:
- What are our guiding principles for AI?
- How do we ensure transparency, ownership and data security?
- Who is involved in review and prioritization?
3. The impact is in the organization
LLMs are getting better and better, but technology by itself is not the deciding factor. The value is in how you deploy it.
- How good is the input data?
- Are processes set up for cooperation between humans and machines?
- Is there room for feedback and adjustment?
- How quickly do you learn as an organization?
Technology accelerates, but people and processes determine success. GenAI delivers lasting value only when it is embedded in your data strategy, including governance, change management and talent development.
Our advice: Link GenAI initiatives directly to concrete goals. Get domain experts, IT and data science working together from the beginning. Without collaboration, it will remain an experiment.
Conclusion: Realism is not a brake, but the engine
GenAI is not hype; it is here to stay. But getting value out of it requires sharp choices. You need to have your expectations in focus and embed technology in the broader strategy. That requires vision, ownership and the courage to make choices. Not everything has to be done now. But what you do now must provide direction for tomorrow.
Our advice: Start with your business goals. See where GenAI adds value, not the other way around.
Okay but how do you do that?
We help organizations make GenAI part of their strategy in a thoughtful and workable way. Not a blueprint, but advice that fits your goals, processes and people. With our experience in AI in practice, we ensure that technology not only remains experimental, but contributes to real business value.
Wondering where opportunities exist for your organization? We are happy to think with you.
