The Initial Reactions
In the face of significant new developments, a few mechanisms often come into play: possibilities are overestimated, and due to fear of the unknown, there's initially an excessive focus on potential 'dangers'. This was no different with the invention of the knife, computers, or the blockchain. Of course, discussions on the ethical future of artificial intelligence need to take place, and policy makers need to establish frameworks. This is already in progress. The contours of European policy are becoming visible. Thus, we can now examine how we can harness AI.
What's the Impact?
In the workplace, AI is increasingly aiding people by performing or supporting parts of their tasks. This creates more room for genuinely human tasks. Artificial intelligence is already present in our smartphones and other 'smart' devices, allowing them to recognize behavioral patterns and adapt to your actions. In the work environment, we already see robust analytical tools for vast amounts of data, images, or text. And more possibilities are emerging.
AI isn't here to take over human intelligence; it's here to primarily provide support. For instance, we developed a much more efficient planning system with self-learning effects, supported by AI, for a client. For another customer, we used data analysis to significantly enhance the effectiveness of procurement, waiting times, and supply. This was achieved using AI and the Azure platform. Read more about this example here.
Tips for Practical AI Implementation
At Nimble, we assist organizations with process optimization through automation and digitization, where artificial intelligence can also play a role. We achieve this by sharing knowledge, brainstorming smart applications, and bringing them to fruition. For several years, we've collaborated with AI students at the University of Groningen. We experiment with AI ourselves and do so collaboratively with clients. We often receive inquiries from organizations wanting to know how to navigate this development. Here's our advice for the initial phase:
1. Embrace Openness
You can only leverage new solutions by first being open to them. Don't let anyone convince you that 'robots are taking over the world', but instead look at existing examples. Consider which specific tasks in your organization could benefit from AI support. Think of repetitive tasks, (data) analysis, or planning that could become smarter and more efficient. Also, explore how existing tools (like ChatGPT) could better assist your employees or customers. We aid organizations in this thought process based on their organizational strategy and optimization needs.
2. Dare to Experiment
Many successful applications stem from experiments. Dare to experiment. Existing AI platforms (such as Azure or Google) already offer significant computational power to perform initial experiments with ease. We also experiment with clients, sometimes making upfront investments in solutions we believe in to realize improvements. That pragmatic attitude is simply part of our DNA.
3. Start Small
New developments become less 'daunting' and more manageable when you start small with initial experiments and steps. This allows you to gain manageable and secure first experiences. Often, this leads to new ideas and insights that naturally encourage broader adoption of the new technological assistance. This way, development progresses step by step, and it turns out not to be as 'monumental' as it might seem at first.