AI at work: The need for critical thinking and responsible use

Artificial intelligence (AI) is changing how people work, make decisions and develop professional skills. As AI tools become more widely available, the focus is shifting from simply learning how to use them to understanding their outputs, evaluating their accuracy and applying them appropriately in real-world situations.

For professionals and organisations, this shift raises an important question: how can people develop the knowledge and judgement needed to work effectively with AI?

AI systems can assist with tasks such as analysing data, preparing content, identifying patterns and generating recommendations. These capabilities can support productivity across industries, but their usefulness depends on how they are applied. Understanding the context of a task, recognising limitations and checking the reliability of results remain important parts of professional work.

Access to AI is only the starting point

The growing availability of AI tools has made certain tasks easier to perform. Professionals can use them to explore ideas, summarise information, develop drafts and examine data. However, receiving an answer quickly does not necessarily mean that it is accurate or suitable for a particular situation.

Evaluating AI-generated information requires subject knowledge and critical thinking. Users need to identify assumptions, verify facts and consider whether the results reflect the circumstances in which they will be used. In areas involving financial decisions, customer information or operational planning, these checks can be particularly important.

The role of structured learning

AI can support learning by explaining concepts, generating examples and helping users explore unfamiliar subjects. However, effective learning also requires progression, practice and feedback.

Structured learning helps professionals build foundational knowledge before moving to more complex applications. It can combine theoretical understanding with practical exercises, case studies and opportunities to assess what has been learned. Regular feedback also helps learners identify gaps and improve their approach.

Without a clear learning path, users may become familiar with individual tools or tasks without developing a broader understanding of the principles behind them. Structured programmes can connect these experiences and provide opportunities to apply knowledge in different contexts.

The objective is not simply to acquire information, but to develop the ability to assess situations, solve problems and make informed decisions.

Applying AI across professional roles

AI is relevant to a range of functions beyond technology teams. Its applications differ according to the responsibilities and expertise required in each field.

Marketing professionals may use AI to analyse customer trends or develop campaign ideas while relying on their understanding of audiences and communication strategies. Finance teams may use analytical tools to examine patterns but must also assess assumptions, risks and relevant regulations. Operations managers may use AI-supported systems to improve planning while accounting for practical constraints.

In each case, AI can assist with analysis and recommendations, but domain knowledge remains important in determining how results should be interpreted and used.

Human oversight is also essential. AI-generated outputs may contain errors, reflect limitations in the available data or overlook relevant circumstances. Professionals must verify information and retain responsibility for decisions made using these systems.

Learning throughout a career

The skills required to work with AI vary according to a person’s experience and responsibilities. Early-career professionals may need to build foundational knowledge and learn how AI tools fit into their fields. Mid-career employees can explore ways to integrate AI into existing workflows, while senior leaders may need to understand its implications for strategy, risk and organisational processes.

Continuous learning can help professionals keep pace with changing tools and workplace expectations. This may involve formal courses, guided training, practical projects, peer learning or independent study using reliable resources. The emphasis should be on developing skills that can be applied to actual work rather than focusing exclusively on familiarity with particular tools.

Turning AI adoption into practical skills

As organisations introduce AI into everyday workflows, they also need to consider how employees will develop the knowledge to use it responsibly. Training can address technical proficiency alongside data awareness, critical thinking, ethical considerations and the ability to evaluate results.

(The author is Associate Director – AI/Data Science, Great Learning)

ABOUT THE AUTHOR

Dr. Pavankumar Gurazada

[email protected]