AI is not new. Humans began to study AI in the 1940s, and computer scientists like John McCarthy opened their eyes to understand the possibilities this technology could achieve. However, relatively new is the volume of the hype. Feeling index. Chatgpt released a big fanfare in 2022, and now DeepSeek and Qwen 2.5 are taking the world.
The hype is understandable. Due to increased computing power, access to larger data sets, improved algorithms and training techniques, AI and ML models actually double their power every few months. Every day, we see major leaps in areas such as reasoning and content generation. We live in exciting times!
But the hype may backfire, which may indicate that when it comes to AI, there is more noise than substance. We are all used to overloading information, which often accompany these groundbreaking developments that we can watch unintentionally. In this way, we ignore the incredible opportunities before us.
Perhaps due to the large amount of “noise” in generating AI, some leaders may think the technology is immature and not worth the investment. They may want to wait for an important adoption volume before they decide to sneak into themselves. Or maybe they want to play a role safely and use generative AI only in areas with the lowest impact in their business.
They were wrong. Experiments and potentially fail quickly when generating AI is better than not starting at all. Being a leader means taking advantage of opportunities for change and rethinking. AI moves and advances rapidly. If you don’t ride the waves, you’ll miss it completely if you sit down with caution.
This technology will become the foundation of tomorrow’s business world. Those who dive now will decide what that future looks like. Don’t just use generative AI to grow gradually. Skip with it. That’s what the winner has to do.
Generated AI adoption is a simple risk management issue – supervisors should be very familiar with it. Treat technology like any other new investment. Find ways to move forward without putting yourself at excessive risk. Just do something. You will immediately learn whether it works; AI can improve the process, or it doesn’t improve. It’s obvious.
What you don’t want to do is become a victim of analytical paralysis. Don’t spend too long thinking about what to achieve. As Voltaire said, don’t let Perfect Become an enemy OK. First, create a series of results that you are willing to accept. Then stick with it, iterate better, and keep moving forward. Waiting for perfect opportunities, perfect use cases, ideal timing for experiments, does more harm than good. The longer you wait, the more chances you will have to sign.
How bad is it? Choose some test balloons, launch them and see what happens. If you do fail, then your organization will be better.
Assume your organization Do Failed in the AI experiment it generates. What? Organizational learning has great value – try, rotate and understand how the team struggles. Life is about life that learns and overcomes the next obstacle. If you don’t push teams and tools to failure points, how will you determine organizational limitations? What else can you know?
If you have the right role (if you trust them), you will have nothing. Putting your team in real, impactful challenges will help them grow as their professionals and gain more value from their work.
If you try to pass one generated AI experiment, you will be better positioned when trying the next one.
First, identify areas where your business area poses the biggest challenges: consistent bottlenecks, unmandatory errors, inconsistent expectations, and remaining opportunities. Any activity or workflow with a lot of data analysis and tricky challenges to solve or seems to take too much time can be an excellent candidate for AI experiments.
There are opportunities everywhere in my industry, supply chain management. For example, warehouse management is an excellent launch pad for generating AI. Warehouse management involves planning numerous moving parts, often in almost real-time. The right person needs to process, store and retrieve the right location of the product at the right time – there may be special storage needs, and so do refrigerated foods.
Managing all these variables is a difficult task. Traditionally, warehouse managers don’t have time to review countless labor and commodity reports to keep the stars consistent. This takes a lot of time, and warehouse managers usually have other fish to fry, including adapting to real-time interference.
However, the generated AI agent can review all generated reports based on insights and root causes and develop an informed action plan. They can identify potential problems and build effective solutions. The time savings of the manager cannot be exaggerated.
This is just one example of how generative AI can be used to optimize key business areas. Any time-consuming workflow, especially the workflow that processes data or information before making a decision, is an excellent candidate for AI improvement.
Just select a use case and start.
The generated AI will remain here, it moves at an innovative speed. New use cases appear every day. Every day, technology is getting stronger and stronger. The benefits are clear: organizations are transforming from the inside out; humans run at peak efficiency and make data on their side; faster, smarter business decisions; I can keep moving forward.
The longer you wait for the so-called “perfect conditions”, the further you (and your business!) will be.
If you have a good team, a sound business strategy, and a real opportunity for improvement, you won’t lose anything.
What are you waiting for?
