Reshaping finance operations

There is a shift in priorities—and expectations

Data from a recent Deloitte Global Business Services (BPO) survey reflects this shift. Nearly 50% of surveyed GBS organizations are prioritizing next-generation capabilities, while ~35% are focused on improving customer experience—and both signal a move beyond traditional efficiency metrics. At the same time, more than 55% of GBS leaders report achieving ~20% cost savings, showing that efficiency remains important but is no longer the sole objective.

AI is a catalyst

AI is a major catalyst in this evolution, but outcomes have been uneven. Despite strong interest, less than 10% of surveyed organizations have realized meaningful financial benefits from GenAI. The primary barriers—data quality, cybersecurity, and governance—highlight that the challenge is not just adopting AI, but operationalizing it effectively.

Finance remains the anchor

Perhaps most notably, finance continues to anchor this transformation. With ~90% of surveyed GBS organizations including finance in scope, it remains the leading domain for expansion—often setting the pace for broader enterprise change.

As BPO and GBS models mature, finance leaders are being forced to rethink long-standing assumptions and answer a new set of strategic questions.

How can organizations optimize cost without eroding service quality or business value? How should they balance outsourcing, offshoring, and internal capabilities? What role should AI play in transforming service delivery—and how can that transformation be achieved without introducing new risks?

These questions are becoming more urgent as the gap between expected and realized outcomes widens. Many organizations anticipate significant gains from AI and outsourcing, yet actual results often fall short. While a large portion of those surveyed expect 20–60% efficiency improvements, a meaningful share report achieving less than 10%.

This disconnect points to a deeper issue—execution readiness. There are barriers between ambition and reality.

Data: the critical constraint

Data challenges are a primary barrier, with ~59% of organizations citing data quality and governance as key obstacles to GenAI adoption. Without a strong data foundation, even the most advanced AI solutions will struggle to deliver value.

Talent as a limiting factor

Talent continues to be a defining constraint. 41% of surveyed organizations report difficulty recruiting the right skills, while roughly a third cite retention challenges, capability gaps, and rising labor costs. However, this also presents an opportunity. Organizations that invest in upskilling, culture, and flexible career pathways are better positioned to unlock value from both BPO and AI.

The shift toward integrated value creation

Priorities are also evolving. While cost and efficiency remain relevant, organizations are increasingly focused on end-to-end process ownership, cross-functional value delivery, and digital capability building.

Building the capabilities that matter

Closing the gap between ambition and results requires more than incremental change. It demands a deliberate focus on capabilities. Leading organizations are investing in process excellence and continuous improvement, alongside data strategy, analytics, risk and compliance, and digitalization. Capabilities like end-to-end process ownership and process mining are becoming essential for visibility and control.

Evolving the operating model

Operating models are transforming in parallel. Traditional hierarchical structures are giving way to more agile, networked organizations. Work is increasingly organized around value streams rather than functions, with dynamic teams that flex based on demand and required skills. This enables better integration of human talent, AI, and external partners.

Redefining roles in an AI-enabled world

The role of talent is also shifting. Advantage now comes from how quickly skills can be deployed and not just where they sit. (2025 Deloitte Global Business Services (GBS) Survey). Yet fewer than 30% of responding organizations are actively redesigning work, jobs, and careers for AI, highlighting a significant gap. (Deloitte State of AI in the Enterprise, 2026).

Managers are also evolving from task supervisors to orchestrators—coordinating work across humans and machines while ensuring quality, ethics, and accountability. (Deloitte Humans x Machines report: Work design essential, 2025).

Trust and governance as enablers

As AI adoption scales, trust becomes critical. 64% of surveyed workers report declining trust in agentic AI, underscoring the need for clear governance, accountability, and human oversight. (Deloitte Trustworthy AI, 2025). Organizations need to define decision boundaries, embed controls, and ensure transparency to build confidence in AI-enabled operations.

Rethinking location as a strategic lever

Location strategy remains important—but with a broader lens. It’s no longer just about cost arbitrage. It’s about accessing talent, managing risk, and enabling global collaboration in a more distributed, digital-first environment.

The BPO-enabled organizational structure also needs to evolve

A flatter, networked structure means GBS organizations will need to shift to agile, cross-functional teams organized by value streams. Matrixed reporting enables individuals to span both value stream teams and discipline groups, driving swift knowledge-sharing. Evolving to dynamic resourcing and teams creates flexibility with workloads, which means focusing on the right skills at the right time instead of fixed roles.

1. Be explicit about value

Define where value will come from—cost efficiency, improved insights, or enhanced service delivery—before scaling BPO or AI initiatives.

2. Invest in talent and work redesign

Build digital and AI capabilities while redesigning work to fully leverage human and machine collaboration.

3. Scale governance and process ownership

Strengthen governance frameworks and clarify accountability to protect value as operations grow more complex.

BPO and GBS are entering a new phase—one defined not just by efficiency, but by capability, intelligence, and adaptability. The shift from transactional execution to AI-augmented judgment is already underway. Organizations that embrace this shift—grounded in strong data, talent, and governance—should be better positioned to unlock value, manage risk, and lead in an increasingly complex business environment.