Driving or Driven by Disruption: The AI Maturity Model

Microsoft passed the trillion-dollar market cap threshold on April 25 2019, and in doing so, it overtook Apple as the most valuable company in the world.

Satya Nadella, Microsoft’s CEO, almost a year before passing that threshold, talked about a new world vision that would help propel the organization’s cloud and revenue growth. “It’s amazing to think of a world as a computer,” Nadella said, referring to a planet filled with smartphones, Internet of Things devices and cloud computing.

In a world that is a computer, Nadella put AI at the heart of Microsoft’s business strategy: “AI is the run-time which is going to shape all of what we do going forward in terms of applications as well as the platform.”

There are three dominant cloud vendors – Microsoft, Amazon and Google. All are aggressively selling AI offerings to enterprises today, acting as the weapons providers for a technology arms race. Also, when you look at Microsoft’s second quarter 2019 earnings report, the strategy is paying off, bolstered by sensational revenue growth of 76% for Azure.

In today’s environment, product teams can quickly leverage natural language processing (NLP), image recognition, machine learning, deep learning and a range of other AI services available in all the major clouds. Enterprises can add these technologies to their websites, internal operations, applications and products, all imbued with the limitless speed and scalability of modern clouds.

With this rapid growth of AI technologies available, it’s important to understand how enterprises are positioned to take advantage of AI – perhaps the most disruptive technology development since the internet itself.

Companies embarking on AI projects and opportunities can be classified according to an AI maturity model.

  • At Level I, enterprises use AI programs to drive operational efficiency. These businesses can be considered the ‘dabblers’ – organizations driving tens of billions in revenues a year but only saving a few million using AI to automate tasks previously done by humans. Level I enterprises generally apply AI to internal opportunities using a straight cost-benefit analysis (such as call center automation) and use AI services like NLP along with robotic process automation (RPAs) to eliminate manual repetitive work.
  • At Level II, enterprises deploy AI programs to drive significant earnings or revenue impact. These businesses are the ‘practitioners’ – layering machine learning through their organizations and using AI to transform user experience and customer value. They reimagine digital and even physical products with AI services, adding value and improving interactions at every opportunity.
  • At Level III, enterprises leverage AI programs to drive industry change and transformation. This is often the domain of big tech – the ‘experts.’ Facebook chooses what we see in our feeds with AI. Apple utilizes AI and AI chips to drive iPhone features like Face ID and Siri. And of course, Microsoft, Amazon and Google sell their AI services to other enterprises around the world.

But enterprises in every industry have an opportunity to remake their worlds with AI technologies. When looking at your organization’s internal AI initiatives to determine the level of AI maturity, ask these questions:

  • Is the enterprise applying AI to a practical, internal project, with a clear target benefit? Then the business is operating at Level I.
  • Is the enterprise layering AI throughout your business, making a significant difference in user experience, growth, revenues, or earnings? That is operating at Level II.
  • Is the enterprise designing products that could redefine the future of its industry? That’s Level III.

Of course, if your enterprise hasn’t started using AI programs at all, the business is at Level 0 – falling fast behind the rest of the world.

In 10 years, the leading companies in nearly every industry will have taken full advantage of AI technologies to redefine their industry and solidify their positions. Enterprises need to use AI to drive innovative disruption, or else competitors will drive the enterprises to existential disruption.

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