60% of CEOs say AI will grow headcount - so why are teams still underperforming?

For a sector that prides itself on precision, BFSI is entering an unusually ambiguous phase.

On paper, the signals look strong. Hiring has not slowed. Investment in artificial intelligence is accelerating. And a growing number of CEOs now believe AI will expand, not shrink, their workforce.

But step inside organisations, and the picture is less straightforward.

Teams are staffed, yet not always effective. AI tools are deployed, yet not fully embedded. Decision-making is richer in data, yet often slower in execution.

The question is no longer whether AI will change the workforce. It already has. The more pressing issue is why, despite that shift, team performance is not improving at the same pace as investment and intent.

Confidence without clarity

A global survey by Cisco captures the contradiction succinctly. Nearly 97% of CEOs say they plan to integrate AI into their businesses, driven by goals such as improving operational efficiency (69%), driving innovation (68%), and staying ahead of competitors (54%).

Yet the same study reveals that 74% of CEOs admit that gaps in their AI knowledge limit their ability to ask critical questions in the boardroom.

This is not just a leadership issue - it is an organisational one.

Because when strategic direction is shaped by partial understanding, execution tends to become uneven. Priorities shift quickly, expectations are not always clearly defined, and teams are left navigating a moving target.

The hiring–performance disconnect

This helps explain the apparent disconnect between rising headcount and inconsistent outcomes.

AI is widely expected to create new roles—data specialists, model validators, AI governance leads, and hybrid business-technology functions. In BFSI, where risk, compliance, and customer trust are paramount, this expansion is particularly pronounced.

But hiring alone does not translate into capability.

According to the Cisco findings, skills and knowledge gaps (40%) remain the most significant barrier to AI progress, followed by infrastructure limitations (35%) and security concerns (34%).

In practice, this means organisations are often adding talent into systems that are still evolving—without fully addressing how that talent will operate within redesigned workflows.

The result is a familiar pattern: roles are filled, but ramp-up is slow; teams are larger, but output is not proportionately higher.

Understanding the execution gap

At the heart of the issue is a gap between adoption and application.

Most BFSI organisations have moved beyond the exploration phase. AI is already being used in areas such as fraud detection, underwriting, customer engagement, and risk modelling.

However, embedding these capabilities into everyday decision-making remains a work in progress.

Part of the challenge lies in the nature of BFSI work itself. Decisions are increasingly shaped by both human judgement and machine-generated insights, but accountability remains firmly human. This creates a layer of complexity that many organisations are still learning to manage.

At the same time, leadership confidence in AI’s potential does not always extend to operational clarity. While 82% of CEOs believe they understand the benefits of AI, fewer are confident in their grasp of how the technology works in practice, according to Management Today.

That distinction matters. Because without a clear understanding of how AI integrates into workflows, organisations struggle to move from isolated use cases to consistent, scalable performance.

When urgency outpaces readiness

Another factor shaping current outcomes is the pace of investment.

The Cisco survey highlights that 54% of CEOs say fear of missing out is influencing their AI spending decisions. This urgency is understandable, particularly in a sector where competitive advantage is closely tied to technology adoption.

However, FOMO-driven investment can lead to a mismatch between ambition and readiness.

Organisations move quickly to deploy tools and expand teams, but slower to build the underlying systems—capability frameworks, governance structures, and workflow redesigns—that enable those investments to deliver value.

In BFSI, where regulatory oversight and risk management are critical, this imbalance can be particularly constraining. AI cannot simply be adopted; it must be integrated within clearly defined guardrails.

The cascading impact on teams

These structural gaps manifest most visibly at the team level.

Employees are expected to work across traditional domain expertise and emerging technological capabilities. A credit analyst today must interpret model outputs alongside financial data. A relationship manager must balance customer insight with algorithm-driven recommendations.

This hybridisation of roles is not inherently problematic—but it does require deliberate capability-building.

Without it, teams operate in a state of partial fluency: familiar with the tools, but not fully confident in applying them. This leads to slower decision cycles, increased validation layers, and, ultimately, inconsistent performance.

Compounding this is the challenge of leadership alignment. If senior leaders are still developing their understanding of AI, middle management often lacks clear direction on how to translate strategy into execution.

The result is not failure, but friction—persistent, cumulative, and difficult to resolve without systemic intervention.

A sector at an inflection point

For BFSI, the stakes are particularly high.

The sector is not only adopting AI for efficiency but embedding it into core functions—risk assessment, compliance monitoring, customer engagement, and portfolio management. At the same time, competitive pressure from fintechs and platform-led ecosystems is accelerating the need for transformation.

India’s position adds another layer of complexity. As a global hub for BFSI operations, including AI, cloud, and cybersecurity functions, the country is central to how these shifts play out at scale.

This creates both opportunity and pressure. Organisations must not only adopt AI, but do so in a way that aligns talent, governance, and execution.

Moving from intent to impact

What emerges from the data is not a lack of ambition, but a need for sharper execution.

Closing the gap between headcount growth and team performance will require:

• Stronger leadership fluency in AI, enabling more informed decision-making

• Structured capability-building, aligned to evolving role requirements

• Clear governance frameworks, particularly in high-risk environments like BFSI

• Redesigned workflows, where human and machine contributions are clearly defined

These are not isolated initiatives. They are interconnected shifts that determine whether AI becomes a performance driver or remains an underutilised investment.

The conversation BFSI leaders need now

As organisations move beyond early adoption, the focus is shifting—from whether to invest in AI to how to make it work.

This is where the next phase of dialogue becomes critical.

At the People Matters BFSI Talent & Tech Summit 2026, the emphasis is firmly on execution. The conversations are designed to move past broad narratives and into the realities leaders are grappling with—building capability at scale, aligning talent with technology, and ensuring that AI delivers measurable outcomes.

For leaders navigating the gap between hiring and performance, these discussions are not abstract. They are immediate.

Because the competitive advantage in BFSI will not come from adopting AI first, but from operationalising it effectively across teams.

If your organisation is working through these challenges—balancing hiring with capability, and investment with outcomes—this is a conversation worth being part of.

Join peers who are tackling the same questions. Exchange what is working—and what is not. And leave with a clearer view of how to turn AI ambition into team performance.

Registrations are now open.