We are getting new insights into our business.

In the next step, we have to “productionize” the insights. The productive IT systems have to be changed to make decisions and run automated processes based on these insights.

In the last step, the decisions and processes based on the data insights are executed. This has an impact on the real world. It creates new data which is again collected and the whole cycle starts again.

In most companies, this circle is broken and full of friction: Teams are working in isolation, data is managed in silos, most of the described steps are done manually and ad hoc. Running through the whole circle can take months or even years. AI-driven enterprises have automated the whole circle with techniques like CD for Machine Learning (CD4ML). They are able to change their business processes based on new data and insights in hours and several times a day.

Four Steps to Become an AI-Driven Company

Becoming an AI-driven company requires some fundamental changes in the culture of the company. Four steps, which are based on each other, are mandatory for this journey.

Defining a Data Governance

Companies have to agree on how to use their data. They have to define who is responsible for the data, who has access, what are the rights and duties of data suppliers and consumers and how to describe the data in a data dictionary. In larger organizations, defining data governance can be a very time-consuming process.

Setting up a Data Platform

The data must be easily accessible and discoverable for everybody involved. Many companies are setting up so-called “Data Lakes” which are (usually cloud-based) storage locations to store all kinds of data. Please take a well-meant advice: Big IT-driven company-wide data infrastructure projects without clear business values are very often doomed to failure.

Create Data Science and Machine Learning Capabilities

To create Data Science and Machine Learning capabilities, teams of data scientists, data analysts, and machine learning experts are setup. It is important that these teams do not work in isolation and develop a “throw-over-the-fence” mentality. Building mixed-teams together with the business on one side and IT, software development and operations on the other side is usually a good idea to prevent this. It is also important to keep in mind that data scientists have a different way of working than developers.

Build Intelligent Applications and Products

Building productive intelligent applications and products is the place where all comes together. Data governance and the data platform ensure that the right data is at the right time, at the right place, and in the right format. Data Scientists and Machine Learning experts are working hand in hand with developers and IT Ops in mixed teams to include machine learning models into productive applications and products. A frictionless automatically working CD4ML framework is managing the versioning of the different artifacts (source code, data, models and model parameters), the automatic testing and releasing of the software and the monitoring of the software “in the wild”. New machine learning models trained on new incoming data can be brought into production in hours.

If you have reached this point, you are a truly AI-driven company! If you don’t care, your competitors might follow this path, leaving you eventually in the quadrant of “ripe for disruption”.