Efficiency vs. Complexity: How AI is actually used
On the surface, AI tools seem like huge time savers. They can write code or process data in no time. But as with any new technology, there are limitations. Artificial Intelligence systems can “hallucinate”—producing inaccurate or nonsensical information—so human oversight is still necessary. Plus, the “garbage in, garbage out” principle is alive and well. If the data going in isn’t good, the results will be flawed, creating a cycle where human intervention is critical.
In some cases, AI even complicates processes. For example, recruiters are finding that AI-generated résumés flood the system with high-quality applications. That might sound good, but it makes it harder for hiring managers to separate the truly exceptional from the average. The result? More complexity, not less.
AIQ and the importance of skills
Another major challenge? A lack of employee skills to use AI effectively. AIQ—or the ability to skillfully handle AI—is crucial for unlocking AI’s true potential. Right now, many workers lack these skills, and it’s hurting productivity. Worse, there’s often a disconnect between what companies think they’ve done to provide AI training and what’s really happening on the ground.
A recent study found that while 59% of executives claim their companies offer AI training, only 45% of employees agree. Given that most companies have more employees than executives, this suggests a much bigger problem in communication or access to training.
Data literacy—a key part of AIQ—is critical for making AI work. Generative AI models, like the ones used today, don’t “understand” information like humans do. They rely on statistical patterns in huge amounts of data. This makes AI great at mimicking human behavior, but without actual understanding. That’s why employees need to know how these systems work, so they don’t just blindly trust AI outputs. Understanding AI’s limitations and knowing how to assess its results are vital skills.
Stalled productivity growth
Across advanced economies, labor productivity growth has slowed, despite the rise of AI. The promise was that AI would supercharge productivity, but so far, the results have been mixed. In contrast, countries like South Korea and Germany are seeing more significant gains thanks to advancements in sectors like manufacturing and pharmaceuticals.
The issue isn’t just about adopting new technology—it’s about overcoming the complexities of implementation. Many countries are already highly efficient, making further productivity growth harder to achieve. Still, it’s clear that AI’s full potential hasn’t been tapped, and there’s plenty of room for improvement. That’s why we need to continue exploring how to fully integrate AI into the global economy.
Solutions: Investing in education and data literacy
Closing the AIQ gap requires big investments in education and training. And this isn’t just about teaching technical skills. Workers need to develop broader data literacy to understand AI’s complexities. Companies need to roll out accessible training programs focused on understanding and critically evaluating data. That’s how employees can move from just using AI to using it responsibly and innovatively.
Governments and businesses both have a big role to play here. Public and private sectors need to partner in investing not just in AI-specific training but in strengthening entire education systems to build the knowledge economies of the future. Lifelong learning is critical in a world where technology evolves rapidly. To stay competitive and fuel economic growth, continuous education is non-negotiable.
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The future of business and innovation
Beyond education, countries need to commit to a future-proof approach to tech development. Staying at the cutting edge means creating environments where both local and international businesses can thrive. This requires a willingness to innovate, collaborate, and embrace diverse perspectives.
Countries that invest in a forward-looking strategy will not only lead in technology but will also strengthen their economies and tackle global challenges head-on. The goal isn’t just to be ahead in tech—it’s to make sure economies and societies reap the full benefits of innovation.
Conclusion
AI has the potential to unlock massive economic growth and bring about broader societal benefits. From automating routine tasks to addressing major challenges like climate change through data analysis, the opportunities are huge. But we’re at a pivotal moment. The future of work is on the line, and AI can transform it—if we start using it effectively today. By investing in both people and an innovative business environment, we can ensure AI isn’t just a passing trend but becomes a real driver of long-term growth.
Daan van Beek MSc, speaker, trainer and author of the BI & AI book 'Data Science for Decision-Makers and Data Professionals'. He is the CEO of the Passionned Group and you can follow Daan on Twitter.
