AI is nothing new. Humans began researching AI in the 1940s, and computer scientists like John McCarthy looked at the potential for this technology to be achieved. But what’s relatively new is the amount of hype. It feels exponential. ChatGpt was released on Great Fanfare in 2022, and now Deepseek and Qwen 2.5 have taken the world by storm.
The hype is understandable. Increased computational power, access to larger datasets, improved algorithms, and training techniques make AI and ML models virtually double in their effectiveness every few months. Every day, we see major leaps in areas such as inference and content generation. We live in an exciting time!
However, hype can backfire, and it can be suggested that there is more noise than material when it comes to AI. We’ve all become very used to the information overload that often comes with these groundbreaking developments. In doing so we lose sight of the incredible opportunity before us.
Perhaps because “noise” around generative AI is dominant, some leaders may think that technology is immature and uninvested. They may want to wait for an important adoption before they decide to jump into themselves. Alternatively, they want to play it safely and use generated AI only for the lowest impact area of their business.
They’re wrong. Experimenting with Generating AI and potentially failing faster is better than not starting at all. Being a leader means taking advantage of opportunities to change and rethink. AI moves and advances very quickly. If you don’t ride the waves, if you’re sitting pretending to be careful, you’ll completely miss it.
This technology will be the foundation of tomorrow’s business world. Those who jump in now decide what the future looks like. Do not use the generated AI to create progressive gains. Use it to jump. That’s what the winner is trying to do.
The adoption of generator AI is a simple problem in risk management. Executives need to be well versed. Treat technology like any other new investment. Find a way to move forward without exposing yourself to the extent of excessive risk. Just do it something. You will quickly learn whether it is working or not. AI will improve the process or not. Make it clear.
What you don’t want to do is the victim of the paralysis of analysis. Don’t think too much about what you are trying to achieve. Like Voltaire said, don’t let it perfection Become the enemy of good. The first creates a variety of results you want to accept. Then grab it, repeat for the better, and continue moving forward. The perfect use case, the perfect time to experiment, waiting for the perfect opportunity, will do more harm than good. The longer you wait, the more chances you have to sign.
How bad is that? Choose a few trial balloons, activate them and see what happens. If it fails, the organization is better.
Tell your organization I’ll do it Generation AI experiment fails. How about that? There is great value in organisational learning. Try, pivot, see how your team is struggling. Life is learning and overcoming after the next obstacle. How else do you decide on your organization’s limitations if you don’t push your team or tool to the point of failure? How else would you know what is possible?
If you have the right people in the right role, and if you trust them, there’s nothing to lose. Stretching your teams with real, impactful tasks will help them grow as professionals and gain more value from their work.
If you try to fail in one generative AI experiment, you’ll be in a much better position when it’s time to try the next one.
To get started, identify areas of business that create consistent bottlenecks, unforced errors, inappropriate expectations, and the biggest challenges that have left opportunities revealed. Activities or workflows with tricky challenges that seem to be solving or incredible time-consuming data analysis can be a great candidate for AI experiments.
With my industry, supply chain management, there are opportunities everywhere. Warehouse Management, for example, is a great launchpad with generation AI. Warehouse management often involves adjusting a large number of moving parts in real time. The right person should be in the right place to process, store and retrieve the product. This requires special storage needs, as with refrigerated foods.
Managing all these variables is a massive venture. Traditionally, warehouse managers don’t have time to review reports of countless labor and goods to match stars. It takes quite a while, and warehouse managers often fry other fish, such as dealing with real-time confusion.
However, the Generated AI Agent can view all the reports being generated and create an informed action plan based on insights and root causes. Identify potential problems and build effective solutions. This will not exaggerate the time to save managers.
This is just an example of a critical business area that can be optimized using generated AI. Time-consuming workflows, especially those that involve processing data or information before making decisions, are great candidates for AI improvements.
Just choose a use case and leave.
Generated AI remains here, moving at the speed of innovation. New use cases appear every day. Every day, technology is getting better and more powerful. The benefits are abundantly clear. The organization has changed from within. Humans who use data on that side to operate at peak efficiency. Faster, smarter business decisions. I was able to continue many times.
The more you wait for the so-called “perfect conditions” to occur, the further you will be behind you (and your business).
With a great team, a healthy business strategy and a real opportunity to improve, there’s nothing to lose.
What are you waiting for?
