At first glance, it might seem amusing, or even trivial, that artificial intelligence can stumble over the simple task of counting the number of R’s in the word "strawberry."
Go on… try it now for yourself - ask your favourite AI how many R’s are in the word “strawberry” and you’ll probably get the answer “2”.
Yet, this small hiccup is symbolic of a much larger challenge: how businesses must navigate AI's limitations with careful change management.
AI’s brilliance in pattern recognition, predictions, and automation is undisputed. It’s reshaping industries at lightning speed. But every AI system, no matter how sophisticated, has its quirks. Take, for instance, an AI Large Language Model (LLM) that, despite all its algorithms and vast datasets, struggles with the basic task of counting the letters in “strawberry.” It might offer variations like "strawbery" or "strawbrry," leaving us humans scratching our heads. It seems like a simple issue, right? But in the business world, oversights like this can have serious consequences. And that’s where the importance of managing AI adoption and implementation comes in.
When organisations introduce AI into their processes, whether it’s a customer service chatbot, an automated reporting tool, or predictive analytics software, they’re doing more than just installing a fancy piece of tech. They’re shifting how decisions are made, how data is interpreted, and even how teams collaborate. And this shift requires more than just flipping the “on” switch. It demands meticulous planning, training, and most importantly, change management and organisational wide adoption.
The "R" Factor: Why Small AI Mistakes Can Snowball
Let’s return to our strawberry problem. Imagine your AI model misinterpreting crucial data points in financial reports because it misreads or miscounts elements. The potential for errors, from minor irritants to significant business impacts, grows exponentially if left unchecked. Without proper oversight, these “R” mistakes—those little miscalculations—can snowball into full-fledged business risks.
Here’s where change management comes in. Introducing AI into a workflow isn’t just about teaching the system to count correctly; it’s about ensuring your teams know how to interact with these systems and understand why they make certain decisions. Are they designed to handle edge cases? How should employees address AI’s limitations? Managing this change ensures that small issues like the strawberry "R" problem are caught early and corrected before they lead to bigger issues.
Managing AI: Awareness, Desire, and Reinforcement
Effective change management, using frameworks like ADKAR (which, fun fact, isn’t an AI miscount of "radar"), helps businesses plan and implement change in a structured way, making sure that everyone in the organisation has:
Take, for example, a finance department introducing AI-driven analytics. While the system might do 90% of the heavy lifting, the team still needs to know where the system might falter (like with our friend, the strawberry). They need to be trained to spot anomalies, understand why they happen, and know how to fix them.
Embracing AI While Respecting Its Limitations
AI is a powerful tool, but it’s not infallible. The excitement of automation, speed, and efficiency can sometimes overshadow its current limitations. It’s critical for organisations to remember that AI is still dependent on data, algorithms, and human oversight.
AI’s role should not be to replace the human touch but to complement it. In fact, the strawberry counting conundrum is a reminder that human oversight is still necessary. If your AI can’t reliably tell you how many r’s are in “strawberry,” it certainly can’t handle your most complex tasks without some degree of guidance.
As AI technology continues to evolve, organisations must be vigilant in how they integrate these tools into their processes. Change management helps ensure that employees are not only aware of AI’s capabilities but also of its shortcomings.
In the end, successfully navigating AI’s quirks and ensuring it operates smoothly within your business boils down to one simple thing: the right balance of human expertise and AI capability—where both know how to count the r’s in strawberry and manage the nuances of change.
If AI can fumble with strawberries, imagine what it can do with your sensitive data. Make sure your AI deployment is as sweet as it can be—with the right management to smooth over the bumps.