Making AI Come to Life in Heavy Industry – with Nikunj Mehta of Falkonry

As artificial intelligence makes its way into more industries and workflows, more and more non-technical team members will be charged with leading AI projects. The next wave of AI catalysts will be familiar with AI at a conceptual level (read: executive AI fluency), but will mostly be expert in bridging AI's capabilities to important business workflows and objectives.

In the classic business book Good to Great, author Jim Collins talks about the different approaches for technology adoption between high-performing and average companies. Collins' research indicated that high performers tend to adopt technology as an accelerant to an existing, working strategy - while underperformers tended to adopt technology in an attempt to jumpstart a change in direction or strategy that they haven't yet undertaken.

Many books could be written on the subject of AI strategy, and we've seen that "strategy" means something different from one enterprise to the next. This article is a brief overview of the common steps in creating an AI strategy - in roughly the order that the steps are usually executed.

This is a contributed article by Ian Wilson, Founder at Strategy 4 AI - learn more about Ian's online AI strategy courses here. Ian is also the former Head of AI for HSBC, one of the largest financial institutions in the world. To inquire about contributed articles from outside experts, contact [email protected]
As the use of AI to support business operations moves through its maturity cycle we have passed a number of key milestones along the way. However, following a pattern reminiscent of previous emerging technology introductions, organizations initially hired experts only to balk when they used blasphemous words like "infrastructure," "industrialize" or "strategy" rather than soothing words like "use case," "quick win" or "easy ROI." 
Unfortunately, instead of sacrificing their misaligned expectations, many businesses sacrificed their experts... 
Fast forward a couple of years and many of those businesses, having attempted to cut corners and seeing mainly failure, now have a visceral understanding of what their experts were advising and are looking, with more experienced eyes, at how to move forward from this point.
However, many businesses are still making avoidable mistaken assumptions when it comes to the use of AI Capabilities to support business objectives. I like to call the most egregious of these assumptions The 4 Horsemen of the AI-Pocolypse:

This is the second article in our "AI for Career Acceleration" series - be sure to read the first installment (and watch the video on that page).