Operationalising Artificial Intelligence and Machine Learning

The returns from using AI and machine learning alongside any organisation’s data are potentially limitless. But for data to deliver value, it needs to be usable.

Quality matters. It is not enough for organisations to just collect information, they need to properly categorise, cleanse and manage it appropriately, ensuring data uniformity on an ongoing basis. Many organisations think AI is a ‘plug and play’ technology that will deliver immediate returns right from deployment. It is not as easy as this, and in reality, it all depends on which type of AI an organisation is using and the type of challenge it is looking to overcome by implementing it.

The most important thing is ensuring any data fed into the AI is accurate and of high quality, and so the first step for organisations embarking on an AI strategy is operationalising the data within their own organisation. It is critical to feed AI with the right information, as, for example, ML models learn solely based on data that is presented to them. Organisations that improperly train their data models run the risk of receiving inaccurate predictions or confusing conclusions. It is best to use ‘representative’ data that reflects the characteristics of the real world and real people, as this gives the model the context it needs to recognise human patterns and make informed predictions.

Essentially, AI technologies can be used to deliver operational benefits that shine a light on inefficiencies within an organisation, and highlight problems that may have previously not been known about, let alone attempted to be solved. If used correctly, the possibilities are endless.