Product Operating Models in Banking: Identifying the Capability Gaps That Undermine Product-Centric Strategy

Why a product operating model is a strategy validation decision

Banks are increasingly adopting product operating models (POMs) to improve responsiveness, align delivery to customer outcomes, and reduce waste created by project-centric structures. The strategic appeal is clear: persistent, cross-functional teams can continuously improve products and journeys, learn faster from customer feedback, and deliver incremental value rather than waiting for large program releases.

In banking, however, a product operating model is not a simple organizational redesign. It is a target operating model change that affects governance, risk management, funding, technology enablement, and performance measurement. Executives responsible for strategy validation and prioritization must therefore test whether the bank’s current capabilities can support the POM’s core premise: empowered teams with end-to-end accountability operating within clear regulatory, security, and resilience guardrails.

What a product operating model means in a banking context

A product operating model shifts the organization from temporary project teams delivering outputs to persistent teams accountable for outcomes over the product lifecycle. In banking, “product” often means end-to-end customer propositions and journeys that span channels, operations, servicing, data, and controls. The model typically includes cross-functional teams, outcome-based metrics, iterative planning and delivery, empowered product leadership, flexible funding aligned to value streams, and continuous customer insight loops.

The practical implication is that the bank must reconcile product autonomy with regulated obligations. This requires a coherent operating model for decision rights, evidence production, and risk engagement that allows teams to move quickly without relying on exceptions or late-stage controls.

Core components of a product operating model and the capability gaps they expose

Cross-functional teams with real end-to-end ownership

Persistent, cross-functional teams are central to the POM, but banks often struggle to make teams genuinely end-to-end. Capability gaps appear when critical skills remain centralized and queue-based, such as security architecture, compliance review, data engineering, and environment provisioning. Teams may be “cross-functional on paper” but still dependent on external approvals and shared services that operate on different cadences.

To be effective, cross-functional teams need defined product boundaries, access to enabling platform capabilities, and clear interfaces with shared services. Without that, the bank risks creating a product structure layered on top of a project funding and approval model, producing frustration rather than throughput improvement.

Outcome-based metrics that can be governed and trusted

POM success depends on measuring outcomes—customer value, operational efficiency, risk outcomes—rather than outputs such as milestones and scope completion. In banks, capability gaps arise when data required to measure outcomes is fragmented, when definitions differ across functions, or when performance management remains tied to project delivery rather than product health.

Executives should expect tension here: outcome metrics are harder to define, and some outcomes are influenced by external factors. The governance requirement is to establish decision-grade measures of product performance that can be trusted, audited where needed, and used to prioritize investment across products and value streams.

Iterative planning and delivery that fits regulated change

Iterative delivery cycles allow product teams to respond quickly to customer feedback and market shifts. In banking, the gap is rarely the delivery method itself; it is the institution’s ability to integrate controls and evidence into frequent change. When auditability and policy compliance are not engineered into delivery, teams face late-stage rework, approvals become unpredictable, and iteration slows to the pace of the slowest governance mechanism.

Capability maturity depends on standardized delivery patterns, automated testing and evidence capture, and predictable engagement with risk and compliance functions. Without those, banks often revert to batch releases and program structures, undermining the POM’s value proposition.

Empowered product leadership and decision rights

The POM assumes empowered product leadership, including strong product management embedded at multiple levels. In banks, empowerment can be constrained by legacy governance structures, where decision rights remain distributed across committees and functional leaders. When product managers lack authority over prioritization, servicing design, or control trade-offs, teams cannot move quickly, and accountability becomes ambiguous.

The capability gap is decision architecture: clarity on what product leaders can decide, what must be escalated, and how conflicts between customer experience, cost, and risk posture are resolved. Without explicit decision rights, empowerment becomes rhetorical and the operating model defaults back to negotiation.

Funding models aligned to products and value streams

Flexible funding is central to the POM, shifting from temporary project budgets to sustained investment in product teams based on expected outcomes. Many banks struggle to make this shift because annual budgeting, capitalization rules, and governance practices are built around projects and initiatives. When funding remains project-based, product teams operate with unstable capacity, and long-term product health work is deferred in favor of near-term deliverables.

For executives, the key is to align investment governance with value streams while retaining financial discipline. That often requires new portfolio routines, clearer outcomes accountability, and a more explicit mechanism for reallocating capacity based on performance and strategic priority.

Data and customer insight loops that actually drive prioritization

POMs depend on continuous insight loops to keep products aligned with evolving needs. In banking, gaps appear when customer feedback is collected but not translated into prioritization, when data is siloed across channels and servicing systems, or when privacy and access controls prevent timely insight. The result is product roadmaps driven by internal assumptions rather than evidence.

A mature capability combines journey analytics, complaint and service signals, and operational performance data into actionable decisions. It also requires governance that ensures insights translate into changes across the full journey, not only in digital interfaces.

Banking-specific constraints that shape product operating model readiness

Legacy platforms and shared services can reintroduce project behavior

Even with product teams in place, banks remain constrained by core systems, tightly coupled architectures, and shared platforms that require coordinated releases. This can force product teams into program synchronization, reducing autonomy and slowing iteration. The implication is that POM adoption must be sequenced with platform engineering and modernization strategies that reduce dependency intensity and provide self-service capabilities.

Risk and compliance integration determines whether autonomy is safe

In banking, autonomy without guardrails increases risk. Product operating models therefore depend on risk and compliance being integrated into product delivery. The capability gaps most often emerge in traceability, evidence quality, and consistent control patterns across products. When these are weak, banks either slow down with manual reviews or tolerate exceptions, both of which undermine confidence.

Operating model changes must account for workforce and incentives

POM adoption changes roles, career pathways, and accountability structures. Banks often experience cultural resistance when incentives remain aligned to functional optimization or project delivery rather than product outcomes. Skills gaps in product management, design, and modern engineering also limit adoption depth. These are not “soft issues”; they determine throughput and stability, especially when delivery cadences increase.

What executives should test to identify product operating model capability gaps

To validate strategic ambition, executives should test whether the bank can sustain product accountability across four dimensions:

Decision rights: product leaders can make trade-offs within clear guardrails, with predictable escalation paths
Enablement: platforms, data, and shared services support self-service and reduce dependency bottlenecks
Assurance: security, compliance, and operational resilience are embedded into delivery with standardized evidence
Investment governance: funding and performance management are aligned to product outcomes and value streams

Where any dimension is weak, banks typically experience partial adoption: teams are renamed, ceremonies are introduced, but delivery still behaves like projects. That gap invalidates assumptions about speed, cost efficiency, and customer impact.

Validating strategic priorities by identifying product operating model capability gaps

Strategy validation and prioritization require leaders to test whether a product-centric transformation is realistic given current digital and organizational capabilities. A structured maturity assessment helps by defining what “product operating model readiness” means in measurable terms and benchmarking the bank’s current state across governance, delivery discipline, data and platform enablement, workforce capability, and control integration.

Applied consistently, this creates decision confidence: leaders can sequence the shift from projects to products, target the most material capability gaps, and avoid overcommitting to product autonomy where enabling platforms and assurance routines are immature. In this context, DUNNIXER Digital Maturity Assessment helps executives identify whether the bank has the operating model capabilities to sustain end-to-end product accountability, and where investment should focus to make product-centric strategy executable without weakening regulatory compliance, cybersecurity, or operational resilience.

Reviewed by

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.