Why some leaders make better workforce decisions than others: Deloitte's Deepan Dasgupta examines

Not very long ago, workforce decisions were largely shaped by experience, judgement and managerial intuition.

Leaders hired based on potential they believed they saw. They invested in learning programmes they assumed would work. They made retention bets based on what they thought employees valued most. Sometimes they were right. Often, they were not.

Today, however, organisations have access to a level of workforce intelligence that previous generations of leaders could only imagine. Skills data, workforce analytics, AI-powered insights and predictive models are changing how organisations understand talent, productivity, engagement and workforce risk.

Yet a striking reality remains: while some leaders are using these capabilities to make significantly better workforce decisions, others continue to rely primarily on instinct.

According to Deepan Dasgupta, Partner, Deloitte India, the difference increasingly comes down to an organisation's willingness to challenge assumptions and act on evidence.

The shift from intuition to evidence

One of the biggest changes taking place inside organisations is the growing move away from intuition-led workforce planning.

"The workforce-related questions being asked by leaders in organisations that are keeping pace with workforce technology are increasingly skill-centric," says Dasgupta.

As AI becomes more deeply embedded across business functions, workforce discussions are becoming less about roles and headcount and more about capabilities.

"With the growing dominance of AI, leaders are focusing on building skill-based organisations and enabling more seamless reskilling pathways for talent."

This shift is also influencing how leaders evaluate workforce investments.

"They are also becoming more inclined to make talent and workforce decisions based on objective data-backed insights rather than intuition, particularly for investments aimed at improving employee productivity and well-being."

Deloitte's latest People Analytics findings suggest this transition is already underway.

Key findings include:

• 76% of organisations surveyed are actively identifying skill gaps
• 59% are investing in cross-learning, personalised development pathways and internal mobility

Why assumptions are becoming a leadership risk

The most effective workforce decisions are often made when leaders are willing to question what they believe to be true.

Dasgupta says this is becoming more common among organisations with higher levels of People Analytics maturity.

"Leaders are increasingly willing to challenge long-held assumptions around what drives productivity, which employees create the most value, how careers should progress, and whether traditional performance indicators remain relevant in a rapidly changing world of work."

The implications extend well beyond analytics.

For years, talent management was built around attracting, developing and retaining employees. Today, many organisations are reconsidering those models.

"Organisations are shifting away from traditional talent models of attract, develop, and retain toward access, curate, and engage, with greater emphasis on continuous upskilling and agile talent development."

Deloitte's research points to a broader workforce rethink:

• About 80% of organisations surveyed are strengthening workforce planning and contingent talent strategies
• About 81% are rethinking traditional retention approaches by strengthening employee experience, well-being and career development

The leaders making stronger workforce decisions are often those willing to accept that yesterday's assumptions may not solve tomorrow's workforce challenges.

When workforce data tells leaders something they do not expect

Workforce intelligence often creates uncomfortable moments.

Many leaders enter workforce discussions with strong views about talent shortages, employee satisfaction, learning effectiveness or the reasons employees leave. Data does not always validate those beliefs.

According to Dasgupta, one of the most common areas where workforce insights challenge organisational thinking is workforce planning itself.

"Contradictions most commonly emerge when identifying true workforce gaps, whether in the form of deficits or surpluses."

Learning and development is another area where perception and reality frequently diverge.

"Another major area is the gap between training investments and the actual effectiveness of those programs, which often provides valuable insights."

Employee sentiment can reveal similar disconnects.

"An external perspective can also provide a reality check on actual employee satisfaction levels versus organisational assumptions."

Workforce analytics also regularly uncovers differences between perceived and actual drivers of productivity, engagement and attrition.

"We also frequently see differences between perceived and actual drivers of attrition, productivity, and engagement. While leaders may have strong hypotheses about what employees value most, workforce data often reveals a more nuanced reality."

For organisations willing to confront these findings, uncomfortable insights often become valuable strategic advantages.

The organisations pulling ahead are acting earlier

Workforce intelligence is not valuable simply because it generates insights.

Its real value lies in helping organisations act before challenges become crises.

"Workforce insights are not solely about highlighting conclusions or actions that leadership may find uncomfortable; they are equally important in enabling proactiveness and organisational readiness."

This capability is becoming particularly important as AI reshapes jobs and skills requirements.

"The rise of AI and its ability to replace routine work is one such example, where workforce insights may indicate a surplus in certain employee groups while simultaneously recommending reskilling opportunities."

Rather than reacting after disruption occurs, organisations can identify workforce risks earlier and create transition pathways for employees.

"Organisations that consistently act on data-driven workforce insights are better positioned to respond to undesirable situations in a way that protects both the organisation and its employees."

Better decisions require better governance

As organisations increase their use of workforce data and AI, questions around bias, governance and responsible use become more important.

Dasgupta believes workforce intelligence can improve objectivity, but only if organisations put the right controls in place.

"People Analytics, and more broadly any data and AI-driven analysis, must be applied responsibly. Coupled with Responsible AI, it helps minimise associated risks."

He points to the ability of workforce analytics to surface hidden patterns and inequities.

"From a bias-reduction perspective, People Analytics can identify unconscious bias across talent processes, helping organisations detect affected employee groups and take appropriate corrective actions."

However, technology alone is not enough.

"Without strong controls, there is a risk of reinforcing existing biases rather than reducing them."

As a result, governance, transparency and human oversight are becoming essential components of workforce intelligence programmes.

The future belongs to leaders who combine judgement with intelligence

The rise of AI has prompted widespread debate about whether algorithms will eventually replace human decision-making.

Dasgupta sees a different future.

"Leadership judgment will remain critical regardless of how decisions are made."

He describes organisational AI adoption as progressing through three stages:

• AI-assisted
• AI-augmented
• AI-powered

Most organisations remain in the first stage, where AI supports decisions but does not make them.

Over time, however, AI is expected to play a larger role in generating insights, identifying patterns and recommending actions.

Yet leadership judgement remains irreplaceable.

"Over time, AI will play a larger role in generating insights, identifying patterns, and recommending actions, while leadership judgment remains essential for interpreting context, balancing priorities, and addressing ethical, cultural, and strategic considerations."

The leaders making the strongest workforce decisions are unlikely to be those who choose between human judgement and data.

They will be the ones capable of combining both.

As Dasgupta puts it: "The future is not human versus AI, but human judgment enhanced by AI-driven intelligence."