You want a smart decision making system? OK, it goes like this. Start with a body of good solid data. Not necessarily big data but data that is comprehensive in a particular important aspect of your business. Then, identify the business decisions that are prime subjects for automation. Add advanced data analytics that can be used to identify trends and dominant features of the data. Blend in some AI, that uses machine learning to identify the trends, anomalies and other characteristics and spit out optimum courses of action to advance business interests. Finally, identify the business processes that are used to make those decisions. Integrate the technology with the business processes, adjusting and modifying the processes where necessary.
Right away, we see some of the challenges. How good, really, is the data. Is it readily available, balanced, nuanced, rich? Is it in usable format and platform independent? Are the data analytics reliable, accurate and consistent? Will the decisions reached with the AI be consistent with business policy and culture? Fundamentally, how well can the data, analytics, AI and processes be integrated? What if any will be the role of people? How will we manage the changes in people activity?
With this quick glance at the landscape, it's easy to see why AI implementation is proceeding slowly. Management can envisage the benefits. But the devil is in the details.
Nevertheless, there is a lot of activity in the business world developing smart decision making systems. We can expect to see a lot more over the next few years.