Get your company ready for artificial intelligence

It looks like artificial intelligence (AI) has outranked all other technologies in popularity in 2016. It was a year where a broad audience became aware of its potential and risk. There are thought leaders who compare AI to innovations like electricity and the internet. The thinking is that AI is augmenting people in executing tasks. With that in mind, how does a company get ready for artificial intelligence?

AI has entered our work and personal lives in different formats and pace. For a company it is important to understand that AI is an "enhancer" that is dependent on other elements. Compare it to a domino stone track. AI is one of the stones, or perhaps many of the stones, but it needs other stones to fall first.

Companies struggle to understand what those stones are and in what order they need to be placed? Or even worse, there can be parallel tracks of domino stones that rally at the same time and draw on the same pool of resources. Yet AI continues to evolve rapidly and a situation of "being stuck in the middle" puts industry peers ahead of your company. What should you do?

The first step is to understand the common denominator of all AI technologies. They all rely on massive amounts of data. The good news is that companies collect data at a rapid pace and in amounts that are growing year over year. The bad news is that the quality of data is not meeting the needs of most AI technologies. Accuracy, completeness, relevance, consistency, reliability and accessibility are aspects of data that are a huge challenge for any company. 

You see that some companies appoint a Chief Data Officer to deal with "the data problem." That is a good step forward, however it is not fixing the root cause. If you peel the onion, you will find a number of reasons that pollute quality of data: poor data definitions, inconsistent and sub-optimized business processes, and under-utilized core business applications like enterprise resource planning (ERP) are the prominent ones.

Data quality is one of the domino stone tracks that a company has to set up and rally, but there are more. At a certain point in time the data track has to collide with the "operating model" track.