Having worked at the intersection of IT and education for more than 20 years, I can see how two fundamentally different learning models coexist today: one is based on the gradual accumulation of knowledge and experience, while the other relies on instant access to answers through AI. Yet technology evolves much faster than the way our brains work. It is precisely within this gap between the speed of obtaining information and the depth of assimilating it that a new competitive advantage for both individuals and companies is emerging.
How AI Has Already Changed Learning
The Illusion of Competence: A New Risk
How Knowledge Is Actually Formed
What Really Works: How Companies Should Approach Learning in the AI Era
How AI Has Already Changed Learning
Over the past two years, I have observed a very clear shift in people’s behavior. We are far less likely to view learning as a long-term process and increasingly approach it as a way to solve a problem here and now. Instead of taking a course or systematically exploring a subject, we ask a question and immediately receive an answer.
Learning has become shorter, more targeted, dynamic, and personalized: everyone receives explanations tailored to their level and context exactly when they need them. This significantly increases the speed of work, but at the same time, it changes the very way we acquire knowledge.
AI has effectively removed several important stages from this process: searching, comparing sources, questioning, and verification. We are becoming more inclined to trust ready-made answers than to formulate them ourselves. In practice, this often appears as increased productivity, but not necessarily as deeper understanding. Working with teams, I regularly see tasks being completed more quickly, while understanding of the underlying mechanisms often remains superficial.
“Learning has accelerated. Thinking has not always kept pace.”
This directly aligns with what Barbara Oakley, author of the bestseller Learning How to Learn and one of the world’s leading researchers of how people actually learn, has been emphasizing. Learning is not merely about access to information; it is a process that involves effort, repetition, reflection, and mistakes. These are the mechanisms through which knowledge becomes firmly embedded.
AI does not replace this part of the process. More often, it bypasses it. As a result, what matters most today is not the volume of information available to us, but our ability to work with that information and transform quick answers into deep understanding.
The Illusion of Competence: A New Risk
When answers are available instantly, it is easy to feel as though you already know the subject. In reality, however, the most important parts of learning often never happen: there is no deep engagement, no mistakes, and no time devoted to true understanding.
This is clearly visible in practice. A specialist may efficiently complete a task with the help of AI, but when asked to explain the logic behind the solution or reproduce it without the tool, they often experience difficulties. As a result, we begin to confuse access to knowledge with knowledge itself. This is the illusion of competence.
In my view, this is a greater risk than automation itself. We start mistaking access to answers for genuine expertise. AI can create a sense of competence, but learning still requires practice, personal interpretation, and independent decision-making. Even in intellectual work, “the hand must remember” through note-taking, explanations, and the development of one’s own approaches to solving problems.
How Knowledge Is Actually Formed
Learning can be explained through two modes: focused thinking and diffuse thinking.
In focused mode, we consciously work on a task, analyze information, and try to understand it. Diffuse mode works differently. During this state, we are not actively thinking about the problem, but the brain continues to process information in the background.
This may happen during a walk, a break, or while switching to another activity. It is during these moments that knowledge begins to integrate, connections between ideas emerge, and a more holistic understanding develops. This process may not appear productive, but it is often what leads to insights and unexpected solutions.
AI significantly enhances the focused mode by helping us quickly find, explain, and structure information. However, it cannot replace the diffuse process. If this element is missing from learning, speed cannot compensate for the loss of depth, and knowledge remains fragmented.
What Really Works: How Companies Should Approach Learning in the AI Era
In my experience, effective learning today is not about banning AI or allowing its unrestricted use. It is about creating the right balance between speed and depth. Technology makes knowledge more accessible, but it does not automatically create expertise. Therefore, companies need to do more than implement AI tools. They need to also preserve and strengthen the mechanisms that support critical thinking, analysis, and professional judgment.
Develop AI Literacy and Teach Effective AI Collaboration
Providing employees with access to AI tools is no longer sufficient. Companies should intentionally develop AI literacy: an understanding of the capabilities, limitations, and risks of artificial intelligence.
This includes the ability to formulate high-quality prompts, provide context, critically evaluate outputs, and verify information. In the coming years, AI literacy will become as fundamental a business competency as digital literacy or data skills.
Develop Thinking, Not Just Tool Skills
AI is rapidly changing the tools we use, but businesses still need people who can analyze situations, draw conclusions, and make decisions under uncertainty. That is why learning programs must cultivate not only technical capabilities but also logical reasoning, critical analysis, and the ability to solve complex problems.
Create Space for Knowledge Reflection
Access to information has become instantaneous, but understanding does not emerge the moment an answer is received. Knowledge requires time for integration and reflection.
Alongside training programs, companies should create opportunities for reflection through post-training discussions, case reviews, lessons learned sessions, and experience-sharing initiatives across teams.
Preserve Space for Deep, Focused Learning
In an environment of constant notifications, instant responses, and multitasking, concentration has become a strategic resource. Organizations need to create conditions where employees can not only obtain information quickly but also engage with it deeply.
Emphasize Practice and Reinforcement
Regardless of technological advances, people learn through repetition and application. The most effective learning programs do not end with a training session. They include practical assignments, peer learning, mentoring, and regular opportunities to revisit and apply acquired knowledge.
Align Learning with Long-Term Business Objectives
In the AI era, learning is no longer simply a response to immediate skill gaps. Companies increasingly need to understand which competencies will determine competitiveness in the years ahead and make talent development an integral part of business strategy.
As a result, the role of corporate learning is changing. Its purpose is no longer merely to help employees perform tasks faster. It must develop the ability to think critically, adapt continuously, and make high-quality decisions in an environment where access to information is no longer a competitive advantage.
What This Means for Business
These changes are already having a very tangible impact on business. Through my work at Sigma Software and as the Head of Sigma Software University, I see organizations facing not only technological disruption but also a transformation in how expertise is developed.
In the past, access to knowledge was a competitive advantage. Today, knowledge is available to nearly everyone. What increasingly matters is the ability to analyze information, evaluate it critically, and make sound decisions based on it.
Businesses’ demands for learning are changing. Organizations are no longer focused solely on teaching employees new tools. They are asking how to develop professional judgment, maintain high-quality expertise, and build a culture of learning in a world where answers are available instantly.
Because of this, the role of learning is undergoing a fundamental shift. It is no longer a standalone HR or L&D function. It is becoming an essential element of business strategy. The objective of learning today is not simply to transfer knowledge, but to create an environment where critical thinking, professional judgment, reflection, and problem-solving capabilities can thrive.
These are precisely the challenges we address at Sigma Software University. We help organizations go beyond implementing AI tools by rethinking how people development works through corporate learning programs, AI upskilling and reskilling initiatives, leadership and team development, learning ecosystem consulting, and learning culture transformation.
In the age of AI, the key competitive advantage is no longer access to technology. It is people’s ability to use that technology consciously and effectively.
AI does not reduce the expectations placed on people. On the contrary, it increases the value of the competencies that technology cannot automate: the ability to think, learn, adapt, and make responsible decisions. In my view, these are the qualities that learning should be designed to develop in the years ahead.
