We are entering a new age where many organisations will need to incorporate AI in order to remain competitive. The tricky part for many owners and managers in knowing where to start.
It turns out the starting place has nothing to do with AI or machine learning and instead involves what should be familiar areas. You should instead start by looking at your current processes.
Most organisations have significant investment in legacy hardware, software and processes that can’t be replaced overnight. AI machine learning can significantly improve key aspects of your legacy systems. Once other stakeholders see the gains in efficiency, reduced cost and increased competitiveness you will be able to propose more far-reaching changes.
So how do you improve key aspects of your legacy systems? The secret is to start thinking about what things cost your organisation the most. These could be physical things, processes or even use of people.
Some simple examples. While cargo ships are expensive, the largest running cost is fuel and the large financial ‘losses’ are caused by downtime due to preventative maintenance. In health services, we spend a considerable amount on reducing symptoms rather than illness prevention. In the finance industry, many people use primitive ‘gut feeling’ approaches for investing money that can be costly.
The next step is to create some goals. Continuing the examples, we need to reduce the use of cargo ship fuel. We might lengthen the time between preventative maintenance if we can better predict when things are likely to fail. In some cases we might even replace preventative maintenance with prognostics (condition based maintenance). In health, we need to concentrate on early detection and illness prevention. In finance, we need to invest in approaches we better understand and hence have a better risk.
Now think about what data describes what affects the outcomes of these scenarios. For the cargo ship case, the use of fuel might be affected by routes and speeds. Sensors might detect vibration to aid ship machinery prognostics. In health we might have medical instrument data. In finance, we might have weather data that might, for example, affect investments in (grown) commodities.
The key thing is that, in the past, it has been very difficult for humans to use this data to derive insights. The combinations of data and possible methods are huge. This is where machine learning excels.
In very very simple terms, we pass this (past) data into a neural network during a process called learning. This creates a model, which when fed current data, might for example give ship efficiency, predict machinery failure, assess health or tell you when to buy or sell shares.
In summary, AI machine learning doesn’t require a big bang approach to change in your organisation. Concentrate on the costly problems in your organisation rather than letting the technology lead the innovation.