AI opportunities are well developed, but AI platform capabilities are lacking behind



Your organisations runs algorithms in fully automated pipelines, batchwise or even real time. Data flows are automated and model predictions feed front-end applications or dashboards to aid human decision making. These operationalized solutions are often embedded in key processes to augment or fully automate decisions. However, it is still a challenge to continuously scale and optimize existing use cases towards their full potential and set priorities for the ever growing backlogs.


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.