AI & data-driven decision support

Large enterprise data repositories contained operational, procurement, warehousing, asset and facilities information that could be used more effectively for forecasting and decision support.

Advise on how machine learning and generative AI could be applied to historical enterprise data to derive practical decision-support models.

Advised on the use of ML and generative AI for financial forecasting, business acquisition, resource requirements and investment decisions. Framed use cases, prerequisites and organisational implications rather than claiming production deployment.

Established a considered view of where ML and generative AI could contribute to enterprise decision-making, and where organisational, data or governance prerequisites needed strengthening first.