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From Shared Services to Global Business Services in Banking

How COOs turn enterprise digital ambitions into controlled execution through an AI era operating model

InformationFebruary 17, 2026

Reviewed by

Ahmed AbbasAhmed Abbas

At a Glance

Banks evolving from shared services to global business services centralize end-to-end processes, standardize platforms and controls, embed data and automation, and align governance and KPIs to deliver scale, cost efficiency, resilience, and consistent customer outcomes.

Why the shared services operating model is now a strategy execution instrument

In 2026, the Shared Services Operating Model has become a principal mechanism for banks to convert digital strategy into operational reality. The shift is structural, not cosmetic: shared services are increasingly expected to industrialize automation, standardize controls, and create measurable capacity for change, rather than simply reduce unit costs. This is the point where strategy validation becomes operational: strategic ambitions only become credible when the operating model can prove it can absorb new demand, maintain control effectiveness, and sustain resilience under higher automation and faster release cycles.

As banks mature, many are moving toward an integrated Global Business Services model that unifies finance, risk, and technology service delivery under a more coherent governance framework. The practical appeal is executive-grade: a single model can make priorities explicit, concentrate scarce capabilities, and reduce duplication in controls design, data handling, and platform stewardship. For COOs, the relevant question is not whether GBS is “better,” but whether the bank’s current capabilities can support it without compounding operational and compliance risk.

Key trends reshaping banking shared services in 2026

Agentic AI moves from pilots to production control

Banks are increasingly treating agentic AI as an operating capacity embedded inside shared services rather than a standalone innovation track. The execution challenge is governance as much as technology: when AI agents are expected to complete end-to-end activities, the bank must define accountable ownership, evidencing standards, and exception handling that aligns with existing first and second line expectations. Without those disciplines, automation can accelerate error propagation, obscure audit trails, and create concentration risk in a small set of automation patterns.

COOs should treat industrialization as a controlled expansion of scope. The bank’s operating model must define what an AI agent is permitted to decide, what it must escalate, and what constitutes a “material” failure state. This framing shifts AI from a productivity narrative to a controllable service with measurable risk boundaries.

AI ready data foundations become a binding constraint

Shared services frequently expose the most stubborn data realities in banks: inconsistent definitions, fragmented lineage, and uneven quality controls across processes and legal entities. In 2026, these issues are no longer merely analytics problems. They determine whether scaled automation is feasible and whether supervisory confidence can be sustained once decisions are delegated to machines. Data mesh and data fabric approaches are increasingly used as organizing patterns to reduce duplication and make ownership explicit, but they only deliver value when data controls, stewardship, and access entitlements are enforced as operating model disciplines.

From a COO perspective, “AI ready” is best understood as a threshold condition for execution. If upstream data reliability and lineage are not strong enough to support automated decisioning and monitoring, then strategic ambitions that assume scaled agentic workflows should be treated as high execution risk until the data foundation is stabilized.

Nearshoring rises as work becomes more judgment intensive

As automation absorbs routine transactional work, shared services work shifts toward exceptions, investigations, and control execution that require contextual judgment. This has increased the attractiveness of nearshoring, particularly to regions offering time-zone proximity and tighter coordination with onshore process owners. The operational insight is that location strategy is now part of risk and resilience strategy: it affects handoffs, incident response speed, knowledge retention, and the ability to maintain stable operations while changing processes.

Hybrid delivery becomes the default architecture

In 2026, many banks are converging on hybrid delivery models that combine captive centers for strategically sensitive work with specialist providers for standardized transactional services. The governance implication is that hybrid delivery is only as strong as its boundary management: clear control ownership, consistent evidence standards, and disciplined vendor oversight. Where these are weak, banks can end up with fragmented control execution and unclear accountability precisely where automation increases the speed and impact of failures.

Core service categories and what changes when they become digital first

Finance and accounting

Finance shared services remain anchored in high-volume processes such as accounts payable, accounts receivable, and record-to-report, but the digital shift changes how value is measured. Consolidation is no longer only about throughput and error rates; it is also about how effectively the function can support faster close, real-time reconciliations, and consistent policy application across entities. When agentic automation is introduced, controllership priorities should drive the design, especially evidence generation, segregation of duties, and exception workflows.

Risk and compliance

Risk and compliance services are increasingly operationalized as continuous monitoring and case management platforms, including KYC, AML, and real-time risk sensing. The execution burden for COOs is to ensure that the move toward AI-enabled surveillance strengthens defensibility rather than weakening it. Models and agents must be integrated into established governance routines for tuning, validation, and operational testing, with clear controls for escalation, documentation, and reviewer independence.

IT and digital infrastructure

Shared services for IT and digital infrastructure increasingly act as the enabling substrate for product and channel modernization, including cloud platforms, API management, and cyber operations. The operating model question becomes whether infrastructure shared services can provide standardized patterns that are secure by default while also supporting rapid change. In practice, this requires consistent platform engineering, reliable service catalogs, and explicit operational resilience objectives integrated into day-to-day delivery.

Implementation roadmap for COO led operationalization

Define the strategic posture and test its feasibility

A useful early step is to align the shared services mission to enterprise goals and explicitly choose whether the model is expected to be primarily cost focused or value focused. This is not branding; it determines investment posture, talent profile, governance intensity, and how benefits will be evidenced. Strategy translation should begin with feasibility tests: process standardization potential, control criticality, data readiness, platform dependencies, and change capacity in the first line.

Select a delivery model that preserves accountability

Captive, outsourced, and hybrid models can all succeed, but each changes how accountability is enforced. Captive models favor tighter control and institutional knowledge retention; outsourced models can accelerate access to capacity and specialized skills; hybrid models concentrate control-sensitive work while externalizing repeatable activities. COOs should treat the delivery choice as a control design decision: who owns outcomes, who owns evidence, and how operational resilience is maintained during incidents and changes.

Establish governance for scaled automation

Industrializing agentic AI typically requires a governance mechanism that can operate across finance, risk, and technology rather than within a single function. Whether framed as an AI Center of Excellence, an AgentOps capability, or an integrated operating committee, the goal is the same: ensure that automation is released with explicit risk boundaries, monitored against defined performance and control indicators, and retired or modified without creating new operational exposure.

  • Define accountable owners for automated processes and their control outcomes
  • Standardize evidence expectations for audits, model governance, and supervisory reviews
  • Instrument end-to-end monitoring for drift, exceptions, and operational failure states
  • Embed segregation of duties and approval controls into workflows, not policy documents

Continuously optimize using operational telemetry

Once shared services become the execution engine for strategy, static SLAs are insufficient. Process mining, event logs, and control telemetry allow banks to identify bottlenecks, rework drivers, and control failure patterns in near real time. Updating SLAs to reflect operational reality is less about renegotiation and more about governance maturity: ensuring that service performance, risk indicators, and change throughput are managed together rather than in separate forums.

How executives validate strategy realism and prioritize execution sequencing

Strategy validation and prioritization in shared services depends on proving that the bank can execute change without compromising control effectiveness. For COOs, the critical trade-offs are usually hidden in operational details: how much standardization can be achieved, where exceptions will accumulate, whether data quality can sustain automation, and how quickly control owners can adapt. A credible operating model makes these constraints explicit and quantifies the execution risk of each strategic ambition.

In this context, digital maturity is not an abstract scorecard. It is a governance tool to reduce decision risk by clarifying capability gaps and sequencing options. Used properly, it helps leadership distinguish between initiatives that are strategically attractive and those that are operationally executable within the bank’s control and resilience tolerances.

Validating strategic ambition through digital maturity evidence

Execution confidence improves when shared services modernization is evaluated against the bank’s actual capabilities across process discipline, data foundations, automation controls, platform resilience, and governance cadence. Those dimensions determine whether the bank can move from shared services to an integrated GBS model without creating uncontrolled complexity, especially when agentic AI is expected to operate across functional boundaries. By mapping these capabilities to the operational risks already in view such as weak lineage, fragile handoffs, inconsistent evidence, and uneven vendor oversight executives can test whether the strategic target state is realistic and where it must be staged.

Used as an executive decision discipline, a digital maturity assessment helps leaders prioritize which service towers to industrialize first, what prerequisites must be satisfied, and which risks require explicit mitigations before scope expands. Sequencing becomes defensible when it is tied to readiness signals rather than optimism. Within that framing, the DUNNIXER Digital Maturity Assessment can be used to benchmark readiness across the specific operating model constraints that determine success, supporting more confident trade-offs between speed, control strength, and operational resilience.

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Reviewed by

Ahmed Abbas
Ahmed Abbas

The Founder & CEO of DUNNIXER and a former IBM Executive Architect with 26+ years in IT strategy and solution architecture. He has led architecture teams across the Middle East & Africa and globally, and also served as a Strategy Director (contract) at EY-Parthenon. Ahmed is an inventor with multiple US patents and an IBM-published author, and he works with CIOs, CDOs, CTOs, and Heads of Digital to replace conflicting transformation narratives with an evidence-based digital maturity baseline, peer benchmark, and prioritized 12–18 month roadmap—delivered consulting-led and platform-powered for repeatability and speed to decision, including an executive/board-ready readout. He writes about digital maturity, benchmarking, application portfolio rationalization, and how leaders prioritize digital and AI investments.

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