Nowadays we see that technology alone is rarely enough to unlock sustainable business growth. When a new technology is combined with a ‘new ways of doing business’, true value is created. This article identifies a number of emerging trends in artificial intelligence for 2018. Executives should learn to shape the outcome rather than just react to it.
1. Artificial Intelligence seen as commodity
In recent years, many tech giants (Google, Microsoft Azure, IBM) invested heavily in the general-use of Machine Learning and Deep Learning. In 2018, more SME businesses will learn how to use their solutions and full service platforms. They have managed to optimize Computer Vision and Natural Language Processing in such a way that it will most likely outperform any other (smaller) player in this field. With help of API’s they will take over (market share up to 85%) the general-use machine learning industry in 2018.
2. Democratization of ‘Click’ Machine Learning
In 2017 there has been an exponential use of so called ‘click – drag and drop’ tools. Usually highly specialised solutions are able to generate business value without any hassle of interference of writing code. It also looks amazing and is very visualized.
These tools allow users to model a solution without writing code (such as Orange, Dataiku, Exploratory). The threshold to start having fun with machine learning just decreased…
3. “Guys, these geeks are infiltrating”
Many organizations started in 2015 and 2016 ‘AI labs’ & ‘Advanced Analytics’ teams. These teams were build in a highly centralized set up in order to ensure that they could learn from each other and operate across different departments. However, organizations learned that the actual best practice is to embed data scientists and analytic team members within individual business units.
4. Predictive Analytics on the loose
As mentioned, many organizations invested heavily in new ways to apply advanced analytics and predict certain outcomes. In 2018 we will see more and more autonomous agents that will use the outcomes of these predictive analytics tools to plan and interact directly with customers.
In simple terms, The data and tools now available will not just convince someone to do something, but will actually do it themselves! Two examples,
- Customer data is used to identify which customers are most likely to unsubscribe and will be instantly offered personalized deals in order to keep them.
- Products that are connected to the internet, such as tires in the future, will not only provide the car and garage with information on their wear and tear, but also make and set appointments for maintenance and replacement.
About the Author
Robin Martinus van Ittersum is specialized in achieving operational efficiency and process improvement. Started his career as an IT consultant. Robin has a business (Master in Business Administration) management oriented background but started working in Advanced Analytics teams in his role as a consultant. Robin would describe himself as a ‘Sunday’s Data Scientist’. Nowadays the quantity of tooling that is out there and that can help you implement machine learning solutions are enormous (and scattered). Therefore he decided to help managers and business analysts, mainly within Banking & Insurance domains to understand what implications AI can have on their businesses, but also what are all of the best practices seen across several industries.
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