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How to start and win the AI race

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How to start and win the AI race

by Jennifer Johnson


A rulebook for organizational change

Auto racing is great global pastime, and so I would like to start this article about Generative AI with an analogy: 

Imagine you are the crew chief of an auto racing team that is preparing for the Daytona 500, a grueling contest that demands peak performance from all team members – the drivers, engineers, technologists, owners, and pit crew. 

(FYI. For our AI analogy, the crew chief is a company’s technology officer who is charged with leading his organization through the complicated topic of Generative AI and AI in general. Roll with me, here.) 

As crew chief, you know in your gut that a new engine (artificial intelligence) will help your car run faster and better but your team is still stuck in the garage. Your pit crew (your developers) is crankily wondering whether Pit Row will ever be the same. Your team’s sponsors (your marketing team) are only interested in the car’s branding and promotion (the promise of AI) and not the details of the race (the downsides of AI). The driver (your CEO) is untrained on how engines (the AI models) really work or if the car is safe to drive. And then there are your team owners (your shareholders). They just want to see your team cross the finish line first, regardless of how you get it there. 

You need a plan to move forward if your team is going to win.  

1. The formula of Engine + Fuel = Success  

Data is the fuel for the engine that is artificial intelligence. The quality and quantity of data used to train an AI model will have a direct impact on the model’s performance. That is because AI models learn from data. The more data that an AI model is trained on, the better it will be able to learn and perform its tasks. 

The quality of the data is also important. The data should be accurate, complete, and representative of the real world. Otherwise, the model will suffer. In addition to the quantity and quality of data, the type of data that is used to train an AI model is also key. Different types of data are suited for different tasks.  

For example, image data is well-suited for tasks such as image classification and object detection, while text data is well-suited for tasks such as natural language processing and machine translation. 

There are about a dozen subtopics around data that you will have to consider as part of a comprehensive AI strategy, making it critically important for you to establish an internal process to guide the conversation as soon as is reasonably possible. You need to decide what you are going to do, and then do it – not the other way around.  

2. Safety is largely determined by the quality of the track 

When a major accident happens at the Daytona 500, it can take a long time to clean up the track. When you are racing at speeds more than 190mph, any single screw left on the track can cause catastrophic damage. 

The same can be true of AI. If the models aren’t monitored properly, your organization will be “against the wall” with no way to recover. Any organization plan around AI should include the creation of an AI Advisory Board to help steer the conversation and create an AI priorities roadmap.  

Consulting firms who deal with multiple accounts should consider AI Client Advisory Boards to provide an organized forum for AI discussions and concerns and the technology and the use of the technology moves forward. 

As important will be the creation of a Risk Mitigation strategy to help teams navigate what will be murky waters around data quality, privacy, and ethics. Teams will require guidance and training if they are going to move forward with AI initiatives. Considering what is at stake in the race should be a worthy investment, not a worthless expense.

3. Warm up the engine 

In NASCAR, it’s critical to get the engines and tires up to temperature before the race.  If you don’t do that, the mechanics of the motor and fuel consumption are thrown off. 

With AI, you also need to warm up before you start in earnest. Starting with low-effort, high-reward proof of concept projects from the ground up can build the kind of foundation that organizational growth can be built on. 

Start slow, keep it simple and steer with intention. That’s the way to win the AI race.  

4. Team safety on pit row 

Pit Row at the Daytona 500 is a dangerous place. It can be especially scary for newcomers to racing. 

Similarly, AI is frightening because the concepts and terms around AI are so new. It seems like a lifetime ago, but ChatGPT is only six months old. And although the industry is abuzz with words like transformers, encoders, and decoders, hardly anyone outside a small technology circle has heard of those terms before.  

As you chart your company’s path with AI, it is important to remember that team upskilling and training will have to come first. Sure, you will have to hire new roles to compete in the AI economy. But you won’t know what those roles are, or their job responsibilities, until you dig into the new technologies and terminology yourself. 

AI is going to affect different teams differently. Creative teams are going to be impacted differently than finance teams and development teams. Therefore, it is important that you adopt a training and upskilling program that recognizes that not everyone needs to know the same things. 

5. “Tires win races” 

In the movie, “Days of Thunder,” pit chief Harry Hogge (Robert Duvall) tells driver Cole Trickle (Tom Cruise) that “tires win races.” He meant that control is part of what a driver needs to succeed. If tires get greasy because of a driver’s oversteering, the car will end up slipping all over the track, potentially causing a wreck. 

Likewise, with AI, problems with data quality, infrastructure and ethics can quickly put you in the wall. These elements are essential for the system to function optimally. In short, they are your tires. You will need to keep an eye on them and be prepared to change them if they show wear and tear. It's the only kind of forethinking AI strategy that can land you in the Winner’s Circle race-after-race. 

Want to learn about how to create a winning AI strategy? Contact us.  


Jennifer Johnson works in digital strategy at Horizontal Digital.   

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