Could the way we teach AI today be detrimental to fostering long-term innovation within companies?

A C-Suite client frustrated about the level of investment in data once asked me, "When will we be finished?" I replied, "The day your competition stops." As long as you are in business, you need to keep innovating. You can take a break when you go bankrupt! 

After taking a few AI courses the main issue I find with is their failure to recognize the value of long-term thinking. The courses tend to prioritize project-based approaches, which limit innovation to a specific timeframe before moving on to a new project. Consequently, this approach has the potential to favour "Quick Wins" over long-term evolution.

An example of this can be found in a case study of a hotel striving to recreate the feeling of "coming home". Although they had lofty aspirations, the project faced limitations in terms of data availability and time. Consequently, they settled on implementing an expedient check-in process, which certainly enhances the customer experience. However, it is important to note that this check-in process is just the first stage. The true challenge lies in consistently delivering the essence of "coming home" and determining which future actions will have the most significant impact on customers. While the project demonstrated innovation, it did not set the company on a route to being more innovative.

By identifying not only what the data tells us but also what it fails to reveal, organizations can proactively seek knowledge gaps and actively use AI to pursue solutions that will enrich customer engagement. By developing a long-term strategy to provide AI with the context it requires, companies can create stronger connections between their brand and consumers. This approach enables organizations to differentiate themselves in a highly competitive environment, leveraging the transformative power of AI while others are limited to superficial solutions.