At a Glance
The 2026 bank product operating model blueprint defines end-to-end journey ownership, integrated data and systems, embedded controls, decision rights, and KPI-linked outcomes, aligning products, technology, and governance for scalable, accountable, and customer-focused execution.
Why the product operating model becomes a TOM decision in 2026
By 2026, the product operating model (POM) has moved beyond a digital delivery preference and into the core design of the bank’s target operating model (TOM). The driver is execution pressure: customers expect continuous service improvement, competitors iterate faster, and agentic AI compresses the cycle between insight and action. When operating rhythms remain project-based, banks can deliver change, but they struggle to sustain value, scale controls, and keep ownership clear once programs close.
For COOs, the practical question is not whether to “adopt agile,” but whether the bank’s structure can continuously deliver outcomes while meeting supervisory expectations for accountability, resilience, and evidence. POM is the action blueprint because it hard-codes ownership, funding, decision rights, and control design into enduring value streams rather than temporary initiatives.
Core pillars of the 2026 product operating model
From projects to products
Transitioning from projects to products means shifting accountability from delivery milestones to lifecycle performance. A product (for example, “Mortgages” or “Mobile App”) is owned end-to-end: customer experience, process performance, control effectiveness, and run-cost. This closes a common execution gap where benefits were assumed at project approval but were never operationalized or measured once teams dispersed.
Cross-functional squads with embedded control expertise
POM squads are designed to remove handoffs that create latency and operational risk. In mature implementations, risk, compliance, fraud, and marketing specialists are embedded into the product lifecycle so that design choices are evaluated with control implications in mind, not after build completion. This is particularly important as AI-enabled automation increases the speed and scope of change, making late-stage review both slower and less effective.
Outcome-based funding by value stream
Outcome-based funding reallocates investment toward value streams rather than fixed-scope projects. This improves adaptability when market signals change—for example, accelerating BNPL propositions for younger segments or re-prioritizing onboarding controls in response to fraud trends. For COO governance, the advantage is traceability: funding aligns to measurable outcomes, making it easier to connect investment decisions to capacity, risk appetite, and performance evidence.
Autonomous engineering enabled by decoupled architecture
Independent delivery depends on technical decoupling. API-first and modular architectures allow product teams to deploy updates without waiting for enterprise release trains, reducing coordination overhead and speeding remediation. From an operational perspective, this also supports clearer blast-radius management: when a change fails, containment can be faster and accountability more precise.
Strategic impact in 2026: what executives should measure
Many organizations describe POM benefits in qualitative terms. In 2026, executive teams increasingly need a measurement logic that links POM maturity to outcomes that stand up in board and supervisory discussions: shareholder value, customer engagement, and brand trust—while also demonstrating that risk and resilience are improving, not deteriorating.
| Metric | Mature Product Model | Traditional Project Model |
|---|---|---|
| Shareholder Returns | 60 | 0 |
| Customer Engagement | 38 | 0 |
| Brand Awareness | 37 | 0 |
COOs should extend these high-level indicators with operational measures that directly reflect TOM health: time-to-remediate incidents, change failure rate, exception volumes, control evidence completeness, and capacity released from manual work. Without this “operational proof layer,” product teams can appear faster while risk and cost accumulate in the background through rework, fragmented controls, or brittle integrations.
Sector-specific transitions shaped by POM
Retail banking: toward invisible banking
Retail banks are moving toward experiences where AI-enabled pods manage automated savings, proactive coaching, and timely interventions. The operational implication is that customer outcomes are driven by continuous adjustments—requiring durable ownership, tight control instrumentation, and rapid learning loops that project structures rarely sustain.
SME banking: from discrete products to orchestrated workflows
SME propositions increasingly compete on workflow integration, embedding banking into accounting, invoicing, and cash-flow management. A product operating model helps coordinate capabilities across channels, payments, lending, and servicing so that SMEs experience a coherent workflow rather than separate products with inconsistent data and policy rules.
Wealth management: hyper-automation with scalable personalization
Wealth organizations are adopting automation to extend “white-glove” servicing to mass affluent segments. The key execution risk is suitability, advice governance, and explainability in AI-assisted decisioning. Product teams must own not only the digital experience but the supervisory evidence and escalation paths that protect clients and the institution.
Implementation challenges COOs must solve
Legacy constraints and responsiveness limits
Monolithic cores and tightly coupled integration patterns constrain how independently product teams can operate. Even when teams are reorganized, they remain dependent on shared release cycles, manual data reconciliations, and fragile middleware. The near-term execution objective is often architectural enablement: defining stable APIs, isolating change domains, and simplifying the application estate so that teams can own outcomes without hidden dependencies.
The accountability gap in human-and-agent operations
As agentic AI scales, the organization must specify where human judgment remains in-the-loop, who approves high-stakes decisions, and how responsibility is assigned when outcomes are produced by a combination of people, models, and automated agents. Ambiguity here creates operational and regulatory exposure: faster execution with weaker accountability is not a maturity improvement.
Governance-first design embedded in the product lifecycle
Successful banks are treating governance as a build-time capability. That includes model and agent change controls, documented decision rights, test and monitoring standards, and audit-quality traceability of automated actions. Embedding governance into product lifecycle events—discovery, design, build, release, and run—reduces the need for late-stage “compliance reconciliation” and helps teams scale safely across multiple products and jurisdictions.
Validating POM ambition into an executable priority sequence
Strategy validation and prioritization is where many product operating model programs fail quietly: the bank adopts the language of products, but its capabilities cannot yet sustain the operating cadence, control requirements, or architectural independence that the model assumes. A digital maturity assessment provides the evidence needed to test whether strategic ambitions are realistic given current digital capabilities—especially across architecture decoupling, delivery reliability, data integrity, control automation, resilience, and AI governance.
For COOs, the assessment should be used to turn the TOM into a sequenced action blueprint. It highlights where capability gaps will block outcome-based funding (for example, weak value-stream measurement), where squads will remain dependent on enterprise release trains (for example, tightly coupled cores), and where agentic automation will outpace accountability design (for example, unclear human-in-the-loop thresholds). This supports decisions that are inherently trade-offs: speed versus control evidence, autonomy versus platform standardization, and rapid personalization versus data governance and model risk.
When the assessment is mapped to a product lifecycle, executives can define “ready to scale” criteria that reduce decision risk: which domains must be improved before expanding autonomy, which controls must be instrumented before increasing agentic execution, and which architectural simplifications unlock the largest capacity release. Applied as a governance input rather than a scorecard, the DUNNIXER Digital Maturity Assessment helps leadership validate priorities, set realistic pacing, and increase confidence that product-model execution will improve resilience and regulatory posture alongside performance.
Reviewed by
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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