Analytics, Augmentation, Automation and Adaptation
We need to acknowledge the differences between data science, machine learning and artificial intelligence. By understanding these differences we can clearly define and demonstrate the path from analytics to adaptive intelligence systems. While data analytics can deliver excellent insight into the underlying business dynamics, true artificial intelligence will only be demonstrated when systems can leverage the full suite of techniques and tools of AI within a framework that supports the application of augmentation, automation and finally adaptive methods.
To explore this in more detail, let us try to describe what we mean by the four ‘A’s, however, there needs to be some caution, as we note that each one has some overlap with adjacent layers of the pyramid. The top of the pyramid is Analytics, and is equivalent to the tip of the iceberg. The bottom of the pyramid, is adaptive systems, this being the most sophisticated of the four ‘A’s and the most complex to deliver, but with the most significant benefits. Augmentation and Automation are stepping stones from Analytics to Adaptive Systems, but necessary steps that each can be demonstrated to show benefit to the business while providing the opportunity for the business to gain confidence of the systems put in place.
What we are seeing across many industries, is a focus on leveraging big data platforms and data lakes by applying just data science techniques in the form of data analytics. It seems that at the moment, few if any companies realise the power of a platform that not only delivered advanced data analytics and visualisation, but can help them on the journey to produce fully automated and adaptive intelligence systems. Such systems could support entire business processes, freeing up staff to focus on more complex tasks, leaving the automated systems to cover the high volume simpler tasks.
Hopefully some of the companies that are currently building the tools and frameworks that are currently available are planning to deliver more advanced platforms in the future, However, unfortunately at the moment it appears that companies are not producing the platforms and frameworks that are needed to support the development of all four ‘A’s. This seems to be a huge missed opportunity, and should be avoided if we want to prevent the AI field stalling again (avoiding the third AI Winter).
What needs to happen next is to deliver platforms that can augment manual workflow by providing semi-automated systems that support business process and enable the subject matter experts to focus on the more involved and complex elements of the business process. Moving this platform forward to a point where the majority of the process is fully automated would add significant benefit to any business workflow. The bottom layer of the pyramid, is adaptation, and this is where we get fully enabled intelligent systems that can adapt to changes in the underlying data and continue to perform at the level of performance when the system was initially introduced.
I would like to see this pyramid referenced for the development of machine learning frameworks and artificial intelligence platforms, to guide the type of capabilities being build and deployed.
We should not just strive to build data analytics, but produce intelligent systems that can augment and automate business process in a way that adapts to changes in the data space over time providing systems that continue to deliver performant automation.