One of the questions I get asked the most, especially when speaking at conferences is a version of the following:
I am not Google or Facebook or Tencent or Alibaba. So what can AI do for me?
The implied follow-up question is that given we know AI is driven by data and these organizations have massive data sets of various types, how can my organization possibly compete? The answer is that although those companies have great data – and with great data comes great responsibility, as some of these companies are learning, albeit slowly – they do not have deep troves of verticalized data specific to your company and your industry (unless of course, you happen to be a direct competitor of them, in which case good luck!)
I’ve explored elsewhere the rise of AI platforms and there are quite a few cross-industry AI use cases that apply to almost any company beyond those with only a handful of employees, such as customer service in the form of chatbots or virtual assistants, content and document processing and personalization.
But a lot of the use cases I see are specific to particular industries. That’s because AI and machine learning is driven by data. It’s also because it is not possible to wave a magic wand and transform an entire organization using AI, so for the vast majority of organizations implementing some form of AI it is about identifying specific use cases and applying the technology to those narrower problems, driven from the data.
For example, here are a few of the top use cases we see in financial services, both now and in two years’ time.
And I’m pleased to say that in the next 451 Research Voice of the Enterprise AI & Machine Learning use case survey, we have expanded the list of vertical industries we cover to six, adding energy and media/entertainment to financial services, healthcare, manufacturing, and retail.
Look for that brand new data in January 2020 and you can learn more about our survey-based AI/ML research here.