AI Should Not Simply Provide Answers: It Should Encourage Curiosity, Reflection, and Continuous Improvement

Artificial intelligence has the potential to transform the way organisations learn and develop, but only if it is guided as a learning partner rather than a replacement for human expertise. AI can analyse data, identify patterns, suggest solutions, and automate repetitive tasks, but specialists must continue to apply critical thinking, experience, and professional judgement.

From my experience in engineering and offshore projects, AI can support quality inspections, analyse test results, identify trends before failures occur, summarise complex technical documentation, and make lessons learned from previous projects immediately accessible. It can also strengthen communication by helping specialists translate complex technical information into clear, audience-appropriate messages while preserving technical accuracy. Instead of spending valuable time searching for information or refining communication, specialists can focus on solving technical challenges, improving quality, collaborating effectively, and making better-informed decisions.

To strengthen learning and development, organisations should encourage employees to question AI's outputs, validate them against standards, procedures, and practical experience, and actively share knowledge across teams. AI should not simply provide answers—it should encourage curiosity, reflection, and continuous improvement. It can support specialists in structuring ideas, explaining complex concepts, and communicating lessons learned more effectively, making knowledge easier to understand and transfer across multidisciplinary teams. By asking better questions and communicating more clearly, specialists develop a deeper understanding of their work, making learning a natural part of everyday practice.

Equally important is the responsible use of AI. Clear organisational guidelines should ensure that confidential information is protected and that AI is used ethically, securely, and transparently. Employees should also understand both the capabilities and the limitations of AI, recognising that it is a tool to support—not replace—professional judgement. Trust is essential if AI is to become an effective partner in learning and collaboration.

Ultimately, the greatest value comes when AI and human expertise work together. AI contributes speed, data analysis, knowledge retrieval, and communication support, while specialists contribute judgement, ethics, creativity, collaboration, and accountability. Used in this way, AI does not replace expertise—it strengthens it, creating a culture where people continuously learn, improve, communicate effectively, and make better decisions together.

Institute's Reflection: The Future of Expertise Is Human with AI

The contributor highlights an important distinction that is becoming increasingly evident across organizations: the greatest value of AI lies not in replacing expertise but in enhancing it. Research supports this perspective. Studies show that AI can improve productivity and output quality, particularly for less experienced workers, while also helping them learn from more experienced ways of working (Brynjolfsson et al., 2025). However, these benefits are greatest when people remain actively engaged rather than accepting AI-generated answers without evaluation. AI is therefore most valuable when it complements professional judgment.

This emphasizes the importance of questioning AI outputs and validating them against practical experience. This aligns with recent research showing that deliberately engaging with AI through structured questioning and reflection promotes deeper reasoning and critical thinking compared to simply using AI to generate answers (Gerlich, 2025). In practice, asking better questions may become as important as finding better answers.

Another important insight concerns communication and knowledge sharing. Research on organizational learning shows that organizations create value when experience is transformed into knowledge that others can understand, share, and apply (Argote & Miron-Spektor, 2011). AI can accelerate this process by helping specialists organize ideas, communicate complex concepts more clearly, and make valuable experience easier for others to learn from.

Our own Sense-Making Labs reached similar conclusions. Participants consistently described AI as most valuable when it functions as a thinking partner that stimulates reflection, improves communication, and supports collaborative problem-solving. Rather than replacing expertise, AI appears to amplify it by creating more opportunities to question assumptions, connect knowledge across disciplines, and learn continuously from everyday work. Organizations that intentionally cultivate these practices are likely to strengthen not only AI adoption but also long-term learning, collaboration, and sustainable performance.

References:

• Argote, L., & Miron-Spektor, E. (2011). Organizational learning: From experience to knowledge. Organization Science, 22(5), 1123–1137.

• Brynjolfsson, E., Li, D., & Raymond, L. R. (2025). Generative AI at Work. The Quarterly Journal of Economics, 140(2), 889–943.

• Gerlich, M. (2025). From offloading to engagement: An experimental study on structured prompting and critical reasoning with generative AI. Data, 10(11), Article 172.