Mixing metaphors to build an AI strategy (Part 1 of 2)
The train has not left the station
The majority of organizations we’re working with at Google are just beginning their AI journeys. They are either dabbling or are starting proofs of concept. A few are advancing their POCs into production. Despite all the hype about AI, market research shows that it’s still early days. It’s the perfect time to start planning.
Google and Atos recently commissioned a study by IDG Research and CIO Magazine on the state of enterprise AI readiness. The resulting analysis reveals the most important aspects of an AI strategy based on responses from organizations at various waypoints (more on that research here). For this blog post, I’ve organized that strategic insight into four To-Do’s before you board the train. We’ll cover two in this post. And the other two in the following post.
1. Find a champion, or several
Two-thirds of AI leaders say organizational inertia is a barrier. It’s just a fact. Status quo is easier. My recommendation is to find an executive champion, or several, to inject an AI-mindset into your company. AI needs to be led from the front.
Your champions’ first job is to lead the charge by encouraging the organization to embrace new technologies.
We see this in every organization, in every sector. There’s always a reluctance, especially if your company is doing ok. You don’t realize the need to change because you’re still making money, you’re sitting on some cash. In reality, you have more opportunity than other companies to advance to the next level. But you may not get there because you’re not aware of your rapidly changing competitive landscape.
The champions’ second responsibility is to educate themselves on the technology so they can lead their CIOs, CTOs and IT teams to think about what kinds of problems AI can solve. They’re the ones who are best positioned to see where AI will have the biggest impact. An executive at a utility company, for example, may have a team that physically checks trees growing near powerlines every day. His mindset should be to ask, “How can AI help us do that more efficiently?” This is actually happening, by the way. AI is perfectly capable of tracking trees growing near power lines.
Perhaps most importantly, your executive champions need to be able to answer basic employee questions like, “Is my job being replaced by AI?” The point of AI is to make their jobs better and make them better at their jobs. So, the organization must embrace potential instead of fearing the unknown.
In my own experience, Google leadership constantly communicates the need to embrace change and innovation — on internal and external forums. It helps employees overcome the organizational inertia that blocks innovative thinkers from disrupting the status quo.
2. Bake the cake before icing it
People always think of AI as the icing on the cake. Data is the cake. There’s no AI without it. Ironically, while data is the foundation that makes artificial intelligence and machine earning so powerful, it’s also the most underutilized asset of businesses. Everyone’s sitting on a goldmine of data, whether it’s structured or unstructured. The leaders will be the ones who figure out how to use it. This point is proven time again by, for example, the masterminds at Amazon.
Another example is Waze. Initially it’s done a good job at rerouting users around accidents and traffic because it uses all of the navigation data generated by people going from point a to point b on their daily commutes.
It’s a matter of mis en place. Everything in place. Get your ingredients (data) ready. Capture it properly. Process it properly. Clean it. Understand it properly to apply analytics. This is a team activity; the size of a football team if not larger. From your engineers and IT department, to business analysts and marketing, make sure you have your data ready. Then you can think about the core capabilities you want to gain from AI.
We’ll go into the other two To-Do’s in my next post. In the meantime, you can review the market research on this very topic from IDG Research and CIO Magazine at https://atos.net/en-na/lp/ai-readiness.