When Alan Turing was developing the Turing Test for determining machine intelligence—what we today refer to as Artificial Intelligence (AI)—he first posed the question, “Can machines think?” At the time, this seemed preposterous, but technology advances in recent years now have us asking a similar question: “Can machines learn?” The answer is “yes,” as long as we supply them with enough of the right kind of data.
This ability to learn is a specific, data-oriented form of AI that has the power to dramatically increase productivity and streamline business processes. Companies are increasingly looking to AI and specifically machine learning (ML) as a way to improve their businesses. While many of these applications are still in the early developmental phases, companies must prepare now in order to successfully implement AI and ML in the future and take full advantage of the power of this technology. Specifically, organizations need to establish the data ecosystems today that will become the foundation of ML applications, training models and business process development in the future.
Machine learning analyzes data flow in the enterprise. Anticipating this eventual capability, the most critical issue is how companies prepare the data that will be used as the basis for machine learning. Data scrubbing and formatting is one of the greatest challenges in implementing AI so companies should adopt clean data format methods now.
To address this challenge, IT and the lines of business need to organize around a specific business problem they are trying to solve. Defining that specific problem creates a direct correlation between the input, its format and shape, and ultimately the decision output. Therefore, understanding the outcome and the kinds of decisions that need to be made will determine which data sets you use and how you need to scrub, transform or clean them.
Looking ahead into 2018 and the next few years, there are three steps companies looking to power their process optimization efforts with AI must take:
- Look for “low-hanging fruit” opportunities where AI can make a difference and good, clean data is readily accessible.
- Build a solid technical infrastructure and invest in developing the digital assets for collecting and organizing the data needed for AI and machine learning.
- Invest in a common platform that allows IT and the lines of business to collect and clean the data that will be used to build the training models for machine learning.
A comprehensive process automation platform can help businesses address all three of these steps and businesses who use one to make sure they are ready for AI- and ML-enabled applications often fall into one of three categories – each with its unique challenges:
- New user with existing LOB data
- New user with no existing LOB data
- User with an existing platform and an existing data repository