Introduction: When answers become widely available
A few years ago, preparing a professional report, building a business plan, or analyzing a dataset often required considerable time, effort, and specialized knowledge. Today, AI tools can help employees complete significant parts of these tasks in far less time.
This shift creates real opportunities for individuals and organizations. Tasks that once consumed hours can be completed faster. Ideas that previously required a full team can be tested with limited resources. Access to knowledge has become easier than ever.
But these possibilities raise a more important question: when producing answers becomes easier, what distinguishes someone who can make a sound decision?
I believe this is one of the central questions leaders and organizations should be asking. Professional value will increasingly depend on the ability to think critically, verify information, understand context, anticipate consequences, and take responsibility for decisions made with the help of technology.
AI has changed how work gets done. Are we developing our thinking just as quickly?
The impact of AI tools on everyday work is easy to see. Employees can draft reports, summarize long documents, propose project plans, and develop training content in a fraction of the time. Yet faster output does not automatically mean better quality, nor does it guarantee that the result fits the context in which it will be used.
Imagine a manager asking an AI tool to create a plan to improve team performance. The tool may produce a structured plan with objectives, performance indicators, training programs, and a timeline. On the surface, it may look complete. But does it address the team’s actual problem? Is it based on reliable data? Does it reflect employees’ capabilities, the nature of the work, and the organization’s culture? Is it realistic given the available resources?
These questions require more than the ability to generate text. They require someone who understands the organization, knows the problem, and can assess proposals against reality.
This is the difference between using AI to complete a task and using it to improve the quality of work. In the first case, the priority may be speed. In the second, the focus shifts to the value of the result and its relevance to the objective.
Speed gives us an advantage. Good judgment helps us direct that advantage toward a useful outcome.
What does research tell us about the skills needed in the AI era?
In September 2026, the IBM Institute for Business Value published a global study examining the priorities of HR leaders and employees as AI adoption grows. The study included 1,500 HR leaders and 8,800 employees worldwide.
The findings pointed to a notable gap between what organizations consider important and what employees prioritize. Seventy-one percent of participating HR leaders identified the ability to supervise, verify, and override AI outputs when necessary as an important skill, while only 29% of employees ranked professional judgment among their priorities. Sixty percent of employees also expressed concern that some of their skills could weaken with AI use, with critical thinking among the concerns highlighted.
These findings point to a challenge that goes beyond learning to use new tools. If organizations expect employees to review AI outputs and make sound decisions about them, capability-building programs need to give critical thinking and professional judgment as much attention as technical skills.
Source: IBM Institute for Business Value, September 2026:
https://newsroom.ibm.com/2026-09-21-new-ibm-chro-study-ai-puts-critical-thinking-at-the-center-of-workforce-priorities
The World Economic Forum’s Future of Jobs Report 2025 supports this broader direction. Analytical thinking ranked first among the core skills identified by participating employers, with 69% considering it essential. Resilience, flexibility, agility, leadership, and social influence also featured among important workplace skills.
Source: World Economic Forum, Future of Jobs Report 2025:
https://www.weforum.org/publications/the-future-of-jobs-report-2025/in-full/3-skills-outlook/
This does not mean technical skills are becoming less important. The picture is one of complementarity: organizations need people who understand technology, think analytically, adapt to change, and use knowledge to make appropriate decisions.
The problem is not using AI. It is how we use it.
AI can improve the quality of work, but it can also amplify mistakes when users treat its outputs as ready-made facts. Three patterns of use are worth distinguishing.
First, task execution. An employee asks a tool to prepare a report or plan and accepts the result with limited review. This may save time, but it leaves room for errors and unsupported assumptions.
Second, reviewing and improving outputs. The employee checks information, verifies sources, tests assumptions, and requests revisions when weaknesses appear. AI becomes part of the production and review process.
Third, using AI to expand thinking. The employee defines the problem, then uses AI to explore alternatives, compare scenarios, test ideas, and identify risks that might otherwise go unnoticed. The final decision is then grounded in evidence, context, and real objectives.
The third pattern gives people a more active role. The tool can expand the space for thinking, while the user remains responsible for asking the right question, evaluating the answers, and choosing a course of action that serves the goal.
Not every task requires an extensive analysis. The appropriate level of review depends on the task and the consequences of an error. Summarizing an internal note is different from preparing a financial recommendation or making a decision that affects employees or beneficiaries. The more sensitive and consequential the decision, the more important verification and human review become.
Why leadership needs to rethink competence
In traditional workplaces, competence was often associated with completing many tasks, meeting deadlines, and producing good outputs. These factors still matter, but they need to be reconsidered in an environment where AI can perform an increasing share of the work.
If two employees can prepare a report quickly using the same tool, their real difference may not be writing speed or formatting. It may lie in one person’s ability to identify the right problem, spot misleading information, connect findings to organizational goals, and make a recommendation that can be acted upon.
This also changes the manager’s role. Leadership needs to go beyond encouraging employees to use new tools and create an environment in which they can use them responsibly and thoughtfully.
In my view, it is not enough for an organization to tell employees to use AI and then measure success by the number of tasks completed or hours saved. It also needs clear standards for output quality, guidance on when human review is necessary, and opportunities for employees to learn how to identify and address errors.
Technology transformation does not succeed by providing tools alone. It requires changes in the practices, skills, and working systems around those tools.
How can we develop thinking skills in the age of AI?
Through my interest in training and leadership development, I see an important challenge in moving from delivering knowledge to designing real opportunities to practice it. The same principle can be applied to AI through a practical four-step framework.
1. Define the problem before writing the prompt.
Before opening an AI tool, ask: What problem am I trying to solve? What outcome do I need? What information is required for the solution to be appropriate? The clearer the problem, the better equipped you are to assess the answer.
2. Treat the answer as a hypothesis to evaluate.
Do not stop at how convincing a result sounds. Ask what evidence supports it, what assumptions it relies on, and what information may be missing. Check primary sources when factual or current information matters, and distinguish facts from recommendations and predictions.
3. Test alternatives before deciding.
Ask the tool to present different options and explain the benefits and risks of each. Then evaluate them against your actual circumstances, available resources, and the people affected by the decision.
4. Review the outcome and learn from the process.
After completing the task, consider what worked and what needs improvement. Was the information accurate? Was the recommendation suitable? Did you need to make substantial changes? What would you do differently next time?
These four steps do not require a complex training program to get started. They can be applied to writing a report, designing a presentation, analyzing a problem, or making a management decision. The important part is to make them a repeated practice and a normal part of everyday work.
What does this mean for training and leadership development?
If the skills required at work are changing, the way we develop those skills should change too. Employees may need to learn AI tools, but they also need opportunities to practice critical thinking, analysis, communication, problem-solving, and decision-making.
This raises an important question for organizations investing in training: do we measure a program’s success by what participants learned during the sessions, or by their ability to apply that learning in real work situations?
A leadership development program may include sessions on AI, but it becomes more valuable when participants face practical situations that require them to evaluate AI outputs, identify weaknesses, discuss alternatives, and defend their decisions with evidence.
Responsibility for development does not rest with employees alone. Organizations need clear policies for responsible use, access to appropriate tools, quality standards, time for experimentation and learning, and clarity about accountability when AI outputs inform important decisions.
Leaders also need to model these practices. When a manager reviews AI-generated work openly and explains why a recommendation was accepted or rejected, the team sees that effective technology use requires judgment and responsibility, not technical knowledge alone.
Conclusion: Human value shows up in the quality of our decisions
AI will continue to change how many tasks are performed, and its ability to support analysis, writing, planning, and execution will keep expanding. The wider adoption of these tools also gives us greater responsibility for how we use them, evaluate their results, and develop the skills that make their use valuable and safe.
I do not believe the real challenge for employees and leaders is keeping up with every new tool entering the market. The greater challenge is building a lasting ability to learn, think, verify, and decide in an environment where tools change quickly.
Producing an answer may become easier, but identifying the right question still requires understanding. Completing a task may become faster, but deciding which task matters most requires judgment. Options may multiply, but choosing among them still depends on context, objectives, and responsibility.
For this reason, I believe the organizations that benefit most from AI will be those that invest in both the technology and the capabilities of the people using it.
Technology may help us produce more. Our ability to think well will continue to determine the value of what we produce.
I will leave you with a question: when you use AI at work, do you focus more on getting an answer quickly, or on making sure the answer is worth building a decision on?
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