You have made a step on AI solutions, but are in the initial phase of AI platform solutions



Your organisation has some success cases with data and AI. Some AI solutions are built end to end with data, algorithm and application integrated. They have some level of automation in scoring the algorithm and updating the data and application. However, operationalization of AI use cases is not performed best practice yet and many PoCs have not been operationalized at all. Enthusiasm in the business for AI is growing as first impact is proven and some applications/dashboards are being used regularly. However, the AI solutions are not fully embedded and hardly challenge the status quo in the ways of working in those business processes.


One or more experiments with modern (often cloud-based) infrastructure may be employed somewhere in your organisation. However, even new data and advanced analytics initiatives typically depend on legacy infrastructure and data mostly resides in silos. A lot of unclarity and disagreement on target infrastructure typically exists when operationalizing new data analytics/AI use cases. Standards are uncommon and non-uniform. Infrastructure for data management probably exists, but scalable operationalization of AI is new or only just starting. Models are (up until now) developed in sandbox-like environments that are suitable for development but lack flexible and scalable production pipelines.