Human judgement in an algorithmic world

By: Radhika Nair

We have spent years asking whether AI will replace humans. Perhaps the more pertinent question is this: When machines can do more of what humans once did, what must we preserve about the way we think, decide and lead? 

The answer lies not in resisting technological progress, but in redefining the relationship between human and machine intelligence. For organisations, the real opportunity lies in understanding what becomes possible when the strengths of both are brought together. 

AI can process vast amounts of information and data, identify patterns, automate repetitive tasks, and generate insights at a speed and scale that humans cannot match. Humans, meanwhile, bring context, curiosity, empathy, ethics and the ability to exercise judgement when situations are ambiguous or the consequences extend beyond what the data can capture.

The future of work, therefore, is unlikely to be a contest between the two. It will increasingly be about how cleverly organisations combine them.

The World Economic Forum’s Future of Jobs Report 2025 estimates that 39% of workers’ existing skill sets will be transformed or become obsolete between 2025 and 2030. At the same time, analytical thinking remains the most sought-after core skill among employers, while creative thinking, resilience, leadership and social influence continue to be critical. AI and big data are among the fastest-growing skills, but they are developing alongside human capabilities.

This is an important signal. The future workforce will not simply need to know how to use AI. It will need to know how to think with AI.

The first wave of automation was largely about taking over predictable, repetitive tasks. AI is different. It can increasingly interpret information, generate recommendations and insights, identify anomalies and, in some settings, act with a certain degree of autonomy.

Deloitte’s State of AI in the Enterprise research reflects this acceleration, with organisations moving from experimentation towards embedding AI into core business processes. Yet Deloitte also highlights a significant governance challenge: organisations are adopting increasingly autonomous AI systems faster than they are building the governance mechanisms required to oversee them.

This raises an important leadership question: If a machine can make a decision, does that mean it should make the decision on its own?

The answer should depend not only on what technology can do, but also on the consequence of making a wrong decision.

Consider the strengths of each side – AI brings speed, scale, consistency, pattern recognition and the ability to work across enormous volumes of data.

Humans bring context, experience, ethical reasoning, empathy, intuition and the ability to understand circumstances that may not be represented in the data.

The opportunity lies in the intersection.

AI can expand and augment what humans can see. Human judgement determines what should be done with what we see.

For an organization like ours operating at the intersection of transportation, technology, manufacturing, safety and sustainability, this distinction is particularly relevant.

At Volvo Group, technology is already transforming how products are designed, manufactured, operated and serviced. AI and autonomous technologies are creating newer horizons across transportation and industrial applications.

But the ambition of intelligent technology should not simply be to remove people from the system.

It should be to determine where machines can perform better, where humans can perform better, and where the combination of the two creates a fundamentally better outcome.

In a manufacturing environment like ours, for instance, AI can analyse thousands of data points and identify a potential deviation in a production process far earlier than a person might, especially in a shopfloor set-up. Predictive analytics can identify patterns that signal a maintenance requirement before even the equipment fails.

Yet an experienced operator may notice something that the data does not capture: an unusual sound, a change in operating conditions or a contextual factor that has not appeared in historical datasets.

The strongest system is therefore not necessarily the one that gives the machine the final word. It is the one that allows machine intelligence and human experience to challenge and complement each other.

The same principle applies beyond manufacturing. AI can identify patterns in workforce data, help managers synthesise information or highlight areas that warrant attention. But a people leader still needs to understand the individual and organisational context before acting on that insight.

Technology can enable leaders to take informed decisions. 

This requires organisations to rethink what it means to keep humans “in the loop”.

Human involvement should not become a ceremonial approval step at the end of an automated process.

Organisations need to deliberately design the operating model between people and AI, right from the beginning.

Three questions could help.

First, where should AI lead?

For high-volume, predictable and lower-risk processes, organisations can allow AI to automate and execute with limited human intervention.

Second, where should AI augment?

For decisions requiring analysis, interpretation or multiple data points, AI can provide recommendations while humans retain responsibility for evaluating the context and making the final call.

Third, where must humans remain in command?

Decisions involving safety, ethics, significant consequences or fundamental human interests require clearly defined human accountability.

This is not about limiting AI.

It is about matching the level of human judgement to the level of consequence.

The new leadership capability thus becomes knowing when to trust AI. As AI becomes more capable, one of the most important capabilities for leaders may be knowing when to trust it, when to question it and when to override it.

That requires a different kind of AI literacy.

It is not enough for employees to know how to prompt a system or use an AI application. They need to understand its limitations, recognise potential bias, question its assumptions and interpret its outputs critically.

The World Economic Forum’s emphasis on analytical thinking, creative thinking, resilience and leadership alongside AI and big data skills, therefore, has become much more significant.

We will need engineers who can challenge an algorithm, managers who can recognise when data does not tell the whole story, and employees who feel empowered to say, “The system recommends this, but the context tells me we need to look again.

That ability to challenge technology constructively may become one of the most important forms of human capability in the algorithmic workplace and certainly a key differentiator, in the way we operate in a collaborative Human + AI world.

There is another dimension that becomes increasingly important as AI systems become more autonomous: accountability.

An algorithm can produce an output. It cannot carry organisational accountability. The responsibility ultimately remains with the people and institutions that design, deploy and authorise the technology. This is why responsible AI cannot be treated solely as a technology agenda. It needs to be part of leadership, governance, risk management and organisational culture.

At Volvo Group, where safety and responsible business are fundamental to how we think about our products and our impact, this principle has particular relevance.

The question is not simply whether an AI system works. It is whether it works reliably, transparently and responsibly, and whether there is clarity about who is accountable when circumstances fall outside the model's expectations.

This also changes how we think about talent. India's GCC ecosystem is increasingly moving beyond traditional delivery models towards global ownership of technology, innovation and business outcomes.

Research from NASSCOM and Zinnov points to the growing importance of AI capabilities and large-scale workforce upskilling within this transformation.

But technical capability alone will not be enough. The organisations that create the greatest value from AI will be those that build people who can combine technological fluency with judgement, creativity, collaboration and purpose.

They will redesign jobs rather than simply automate tasks. They will measure not only how much AI is being used, but whether it improves the quality of decisions.

And they will create cultures where people are encouraged to question both human assumptions and machine recommendations.

The defining question of the AI era will not be how much work we can hand over to machines.

It will be how intelligently we can combine human and machine capability. AI can help us process more, see further, move faster and discover patterns that would otherwise remain invisible. Humans bring something different: context, conscience, curiosity, empathy and judgement.

Neither is sufficient on its own. The opportunity lies in the “+”.

The organisations that thrive will be those that understand what to automate, what to augment and what must remain deeply human.

The future thus is not Human versus AI. It is Human + AI.

And the organisations that learn to make that partnership work, with clarity about the strengths, limitations and responsibilities of each, will be the ones best equipped to shape the future of work.

About the author: Radhika Nair is the Head of People and Culture at Volvo Group. She is a strategic HR leader, she brings extensive experience across diverse industries, including banking, retail, telecommunications, ITES and product organisations. With a strong understanding of people dynamics and business strategy, she focuses on driving organisational success, shaping positive workplace cultures, strengthening business partnerships and fostering talent development through people-centric HR practices.