The Current & Future State of AI & Machine Learning

My new report from 451 Research – ‘The Current and Future State of AI and Machine Learning‘ brings together the key points about AI & ML I’ve been writing, speaking to clients and presenting about for the past two years.

If you agree that technology adoption – like so many other trends – takes the form of an S curve then I think it’s worth asking ourselves, where are we on the S curve of adoption for AI and machine learning? It’s impossible to know for certain of course, but my somewhat educated guess is that we are very early. Something like this, with the orange bar indicating our current progress:


Now the point of this bit of cod-science isn’t to spark a debate as to whether we should be a few millimetres to the left or right. Rather, it serves to demonstrate that we’re early in the evolution of machine learning and its use may be barely perceptible to some – even those in the technology industry. That’s because a lot of use cases of machine learning are very narrow.

For machine example, machine learning is used to improve the accuracy of look-alike modeling in customer journey analytics. It is used to analyze user behavior for information security purposes. And it also performs automatic password resetting in customer service situations. None of those is earth-shattering and none of them also mean we’re just one algorithm or model from the so-called singularity when ‘the machines’ supposedly take over.

So at first on an S curve, things happen slowly often change (or adoption) may be imperceptible. But then things start to change quickly, then very quickly, then it slows down until it becomes almost constant.

An S curve can describe how ice melts, water evaporates, the expansion of the early universe, the fall of empires and yes, the spread of new technologies, as Everett Rogers demonstrated with his theory of the diffusion of innovations which described how and at what rate new ideas and technology spread.

I believe we’re at the very early stage of adoption and development of practical AI and machine learning and that there is so much more to come.

The majority of the report’s focus is on use cases, such as customer experience, supply chain analytics, information security, human capital management, Internet of Things (IoT), marketing automation and application performance management. And in vertical markets, we look at machine learning’s use in:

  • Financial services
  • Healthcare
  • Manufacturing
  • Retail
  • Travel & Hospitality
  • Agriculture

And to round it off we have machine learning-focused profiles of 12 major software vendors, plus mentions of numerous startups throughout the report and a section on some of the latest innovations in machine learning.

To download an executive summary of the report and to find out more, click here.

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