Do You Need a Chief Artificial Intelligence Officer?

Well, if you ask me, the short answer is – why not? After all, companies around the world have a long history of employing people with weird titles ranging from “Chief Happiness Officer” to “Galactic Viceroy of Research Excellence”. A more reasonable response, however, would need to take one important thing into consideration – what a CAIO’s job in your organization would be?

There is no doubt that “Artificial Intelligence” has already become an integral part of our daily lives, both at home and at work. In just a few years, machine learning and other technologies that power various AI applications evolved from highly complicated and prohibitively expensive research prototypes to a variety of specialized solutions available as a service. From image recognition and language processing to predictive analytics and intelligent automation - a broad range of useful AI-powered tools is now available to everyone.

Just like the cloud a decade ago (and Big Data even earlier), AI is universally perceived as a major competitive advantage, a solution for numerous business challenges and even as an enabler of new revenue streams. However, does it really imply that every organization needs an “AI strategy” along with a dedicated executive to implement it?

Sure, there are companies around the world that have made AI a major part of their core business. Cloud service providers, business intelligence vendors or large manufacturing and logistics companies – for them, AI is a major part of the core business expertise or even a revenue-generating product. For the rest of us, however, AI is just another toolkit, powerful and convenient, to address specific business challenges.

Whether your goal is to improve the efficiency of your marketing campaign, to optimize equipment maintenance cycle or to make your IT infrastructure more resilient against cyberattacks – a sensible strategy to achieve such a goal never starts with picking up a single tool. Hiring a highly motivated AI specialist to tackle these challenges would have exactly the opposite effect: armed with a hammer, a person is inevitably going to treat any problem as if it were a nail.

This, of course, by no means implies that companies should not hire AI specialists. However, just like the AI itself was never intended to replace humans, “embracing the AI” should not overshadow the real business goals. We only need to look at Blockchain for a similar story: just a couple years ago adding a Blockchain to any project seemed like a sensible goal regardless of any potential practical gains. Today, the technology has already passed the peak of inflated expectations and it finally seems that the fad is transitioning to the productive phase, at least in those usage scenarios where lack of reliable methods of establishing distributed trust was indeed a business challenge.

Another aspect to consider is the sheer breadth of the AI frontier, both from the AI expert’s perspective and from the point of view of a potential user. Even within such a specialized application area as cybersecurity, the choice of available tools and strategies can be quite bewildering. Looking at the current AI landscape as a whole,  one cannot but realize that it encompasses many complex and quite unrelated technologies and problem domains. Last but not least, consider the new problems that AI itself is creating: many of those lie very much outside of the technology scope and come with social, ethical or legal implications.

In this regard, coming up with a single strategy that is supposed to incorporate so many disparate factors and can potentially influence every aspect of a company’s core business goals and processes seems like a leap of faith that not many organizations are ready to make just yet. Maybe a more rational approach towards AI is the same as with the cloud or any other new technology before that: identify the most important challenges your business is facing, set reasonable goals, find the experts that can help identify the most appropriate tools for achieving them and work together on delivering tangible results. Even better if you can collaborate on (probably different) experts on outlining a long-term AI adoption strategy that would ensure that your individual projects and investments align with each other and avoid wasting time and resources. In other words: Think Big, Start Small, Learn Fast.

If you liked this text, feel free to browse our Artificial Intelligence focus area for more related content.


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