The Two Worlds of Artificial Intelligence: Why the biggest challenge in AI is not technology, but translation
- Prof George Batsakis
- 3 days ago
- 4 min read
Artificial intelligence is everywhere. It writes emails, recommends what to watch, helps doctors diagnose diseases, and increasingly supports decision-making within organizations. For many, AI feels like a single, rapidly advancing technology reshaping the world. But that impression is to a great extent misleading. Behind the scenes, AI is evolving in two very different worlds that rarely interact. One is highly technical, focused on algorithms, models, and computational performance. The other is managerial and practical, focused on how AI is used in organizations, how people react to it, and whether it creates value. The problem is not that both worlds exist. The problem is that they are largely disconnected. This AI divide between management and technical disciplines is what a recent research paper by my coauthors and me, published in the International Journal of Innovation Management, documents systematically (Batsakis et al., 2026).
The two sides of the same (AI) coin
If you talk to engineers or data scientists, AI is about how systems are built. The conversation revolves around models, optimization, prediction accuracy, and computational efficiency. Progress is measured in performance improvements: faster, more accurate, more scalable. On the other hand, when you talk to managers, employees, or users, AI is about what systems do. The focus shifts to questions such as:
Can we trust it?
Will it replace jobs?
Does it improve decisions?
How do customers react to it?
These are not just different perspectives; they are different languages. Research shows that technical work focuses heavily on developing AI capabilities, while management research focuses on understanding how organizations actually use AI and whether it creates value (Batsakis et al., 2026). The two sides rarely connect.
Organizations are still figuring it out
Despite the hype, most organizations are still in the early stages of using AI effectively.
Research suggests that many firms are still experimenting with AI rather than fully integrating it into their operations (Davenport & Ronanki, 2018). There is a clear gap between having access to AI and using it in a way that creates real value. This gap becomes clearer when we look at actual adoption rates. As Figure 1 shows below, even among advanced economies, the share of firms adopting AI remains relatively modest. While large firms are significantly ahead, adoption among smaller firms is still limited in most countries. This highlights a key point: AI may be advancing rapidly, but its diffusion into everyday business practice is far from complete.
Why the disconnect matters
At first glance, this may seem like an academic issue. It is not. When the technical and business sides of AI are misaligned, organizations face very real problems. These can be summarized as:
Overpromising and underdelivering: AI systems are often presented as more capable than they actually are. This happens because those implementing them do not fully understand their technical limitations.
Powerful tools that no one uses: Highly advanced systems are developed but never adopted, because they do not fit how people actually work.
Decisions without understanding: Many systems produce outputs that users rely on without understanding how they were generated.
The global divide in AI
The divide in AI is not only between engineers and managers. It is also geographical.
Different countries are not just adopting AI at different speeds; they are operating at very different levels of readiness and capability. As Figure 2 illustrates, countries such as the United States, China, and the United Kingdom operate at high levels of both AI intensity and readiness, while many others lag significantly behind. This uneven distribution reinforces an important insight: AI is not spreading evenly; it is creating new layers of inequality across countries, organizations, and individuals.
Why this matters for our day-to-day work
This divide is not something happening “out there.” It directly affects how we use AI in everyday situations. Think about how you interact with AI. We are writing emails or reports, analyzing data, using recommendations, or even relying on automated suggestions. In each case, we are using tools built in the technical world, but interpreting them in the human one. The challenge is not just to use AI, but to use it critically and effectively.
A simple way to think about AI
If there is one practical takeaway, it is that you do not need to understand how AI works to use it effectively, but to understand what it can and cannot do.
That means questioning outputs, recognizing limitations, and applying judgement. The difference between effective and poor use of AI is rarely technical. It is behavioural.
Bridging the gap
The future of AI will not be determined by better algorithms alone. It will depend on whether we can bridge the gap between the technical and human sides of the technology.
This requires:
closer collaboration between developers and users
better communication of what AI systems actually do
more realistic expectations about their capabilities
AI should not replace human thinking. It should support it.
A more grounded view of AI
Much of the current conversation around AI swings between extremes, such as hype or fear. The reality is more nuanced. AI is powerful, but uneven. Advanced, but not fully adopted. Available, but not equally accessible. Understanding this is essential. Because in a world increasingly shaped by AI, the real advantage will not come from having access to the best technology. It will come from knowing how to use it better than others.
References
Batsakis, G., et al. (2026). Bridging the divide: An interdisciplinary systematic literature review on the evolution of artificial intelligence in management and technology literature. International Journal of Information Management.
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
