Creating AI roadmaps
We live in extremes and in a tangible hype around AI. Expectations often swing between “LLMs are the new machines of truth” and “they are useless”. When creating roadmaps, it is crucial to ignore these extremes.
Instead, the focus must consistently be on business and use cases: How is money made, which process is supported, which concrete problem is solved, and can customers really be excited by it? It is not about using AI for its own sake, but about measurably supporting the business with AI and – looking further ahead – perhaps even reinventing it.
Of course, AI can also lead to new products and processes. But then the question comes up: What added value does this create for the customer, and can the customer credibly claim this market for themselves? Technological progress can certainly be reflected in a roadmap, but it must never be assumed as guaranteed.
Role of LLMs in the roadmap
LLMs are particularly strong at data extraction, relationship analysis, and generating summaries. In a Graph RAG solution, they form the central language interface, and their multimodal capabilities open up additional potential. Generating high-quality texts or content is more demanding, but can be implemented very effectively with clear requirements, good prompts, and human review.
These capabilities lead to a recurring solution pattern: capturing, structuring, and assessing information, then feeding it into decisions and processes. This exact pattern must be explicitly embedded in an AI roadmap.
From use case to AI roadmap
A viable AI roadmap or AI strategy is always derived from the business and the process, not from the technology. As a solution provider, the key contribution is to clarify which use cases are realistic, what is possible today, what requires additional data or infrastructure, and where the architectural limits lie.
The task is to create clarity about where a genuinely new productivity gain arises and what can be implemented concretely – for example in the form of automated analyses, assistant systems, or new self-service offerings.
The final design and prioritization, however, can only be done by the owner of the respective process, because that is where responsibility for business impact and implementation resides. This perspective is self-evident for sol4data – and it is the foundation for turning AI hype into a robust roadmap with real business value.
