When I think of AI, things that come to mind are often from movies like Minority Report (2002), where predictive data puts people in jail, or I, Robot (2004), where humanoids serve us. 

It might sound scary, but this isn't the first time technology has triggered fear of change. Back in the 1980s, when the IT boom happened, many feared a massive loss of jobs. That wasn't true then, and it isn't true now.

In this article, you'll discover how to shift your focus from IT problems to business challenges, see real-world use cases where generative AI is driving massive savings, and understand why the human element remains vital in the age of automation. 

Understanding the generative leap

The preceding generation of AI was focused on learning from data points. Today, the Large Language Models (LLMs) used across social media platforms are learning on their own and generating content, which is why they push certain types of content to you. 

This is a massive leap in data amalgamation. Data is the new gold. Every time you use your phone or an Alexa, data points are being captured.

From tech problem to business solution

Today's AI can execute almost anything complicated. For instance, Continuous Integration/Continuous Delivery (CICD) pipelines can now be linked with LLMs – and this is happening for major manufacturers, airlines, and retailers I work with. 

However, the key isn't just the tech itself; it's about thinking of the business challenge first, rather than just the tech problem.

  • In the primitive world, a techie would ask a CIO, "What are your IT problems?"
  • Today, we ask, "What are your business problems?"

For a retailer like Tesco (a UK supermarket chain), that might mean figuring out how to get a delivery truck out of the depot earlier to avoid losing money on rebates for delayed shipments. For an airline, it's ensuring a plane is off the tarmac within five minutes to save fuel. We understand the business problem, convert it into a tech solution, and execute it through generative AI.

Generative AI has also become far more interactive and customized to the user’s needs. While some primitive chatbots still frustrate users with limited responses, better platforms like Walmart's offer drop-down selections, using cognitive search to rapidly find and present solutions.

This personalized interaction is already happening in retail. For example, when I walked into a Prada store in Paris, the system detected my credentials and immediately showed me the new set of bags that matched the kind I’d bought on my last visit. That kind of customized interaction is only possible because we are keying in those data points.