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Establishing a 2026 Transformation Baseline in Banking

Leader-ready baseline language and evidence conventions for agentic AI, real-time payments, and digitally enforced regulation

InformationFebruary 2, 2026

Reviewed by

Ahmed AbbasAhmed Abbas

At a Glance

Establish a transformation baseline by defining scope, taxonomy, ownership, metrics, costs, risks, controls, and current performance, creating a fact-based starting point to set targets, sequence initiatives, and track accountable value realization.

Why 2026 baselines are moving from “current state” to “evidence-ready starting point”

In 2026, transformation baselining in banking is less about producing a snapshot and more about establishing a repeatable reference state that can survive rapid change. Leaders increasingly use baseline language to test strategic ambition against what the bank can execute safely and at scale: whether agentic AI can move beyond pilots, whether real-time payment expectations can be met end-to-end, and whether regulatory digital frameworks require more continuous, machine-verifiable evidence.

The practical implication is that “baseline” must be defined in operational terms. A baseline is not just the first value in a chart; it is a controlled measurement system: scope boundaries, KPI definitions, data lineage, validation checks, and governance over how measurement changes when platforms, vendors, and processes change.

Baseline language leaders use to drive clarity under scrutiny

Transformation programs derail when leaders use baseline terms interchangeably, creating hidden disagreements about what is being measured and what counts as improvement. In effective governance forums, baseline language converges on a few patterns that signal intent and evidential strength.

“Starting point”

Signals urgency and pragmatism: “We need a reference to prioritize investment now.” The governance requirement is to attach boundaries: which business lines, which journeys, which channels, and which observation period make the starting point representative.

“Current state”

Signals operational reality and constraints: legacy stack brittleness, manual workarounds, control friction, and skills capacity. To be decision-useful, “current state” must be converted into measurable statements with sources of record.

“Reference period”

Signals comparability: leaders want a time window that reflects seasonality and portfolio mix. “Reference period” language forces explicit choices about normalization, anomaly handling, and how future measurement will remain like-for-like.

“Measurement system”

Signals maturity: leaders recognize that tooling changes, process redesign, and AI adoption can change what is observable. “Measurement system” language implies defined operational definitions, lineage, validation routines, and change control over KPI logic.

Audit current capabilities and infrastructure: the baseline discovery discipline

Establishing a transformation baseline starts with discovery that is broad enough to avoid blind spots, but structured enough to produce decision-relevant outputs. The goal is not to inventory everything; it is to identify where the bank’s current capabilities constrain strategy execution, especially where operating risk and regulatory expectations are highest.

Technology audit: identifying brittle constraints, not just legacy presence

Leaders increasingly ask for a “brittleness map” rather than a systems list: where are the hard coupling points, batch dependencies, manual reconciliations, and vendor lock-ins that will slow delivery or raise resilience risk? Core modernization discussions often stall when this is not explicit, because delivery timelines assume flexibility the estate does not have.

Data readiness: AI-ready is a risk-and-controls question, not a tooling question

In 2026, “AI-ready data” language is common, but its practical meaning is evidence of integrity, timeliness, access governance, and traceability. If model features cannot be tied back to controlled sources, or if lineage cannot be explained, the bank will struggle to operationalize agentic AI in higher-risk domains such as servicing decisions, fraud triage, or compliance investigations.

Process review: baseline the manual work that creates hidden operating risk

Manual workflows in back-office tasks, data entry, and customer support are often where transformation benefits are claimed. Baselining those workflows requires documenting both volumes and exception paths, because exceptions typically drive cost-to-serve, error rates, and customer harm. The baseline should explicitly distinguish “standard flow” performance from “exception flow” performance.

Define 2026 baseline metrics: moving from productivity theater to measurable outcomes

Leaders in 2026 are increasingly skeptical of activity metrics that look impressive but do not translate into business outcomes or risk reduction. Baseline scorecards perform better when they are small (typically 10–20 measures) and structured across four lenses: operational efficiency, financial performance, risk and compliance, and execution resilience.

Operational efficiency: cost-to-serve and time-to-outcome

For servicing and operations transformations, leader language typically focuses on “time-to-resolution,” “repeat contact,” “straight-through processing,” and “cost-to-serve.” Developer productivity claims should be anchored to outcomes that are hard to game: lead time for change, change failure rate, and incident impact. Where organizations cite large improvements (for example, double-digit percentage gains), the baseline should include the measurement method and the pre-existing variance to avoid attributing normal fluctuation to transformation impact.

Financial performance: benefits with defensible causality

Financial baselines should distinguish metrics the transformation plausibly influences from those driven primarily by macro conditions. For example, net interest income sensitivity to rate changes can inform strategy discussions, but should not be treated as a direct transformation outcome. Non-interest income growth from wealth solutions or emerging digital asset propositions can be included where product scope and adoption mechanics are explicit and where Finance agrees the attribution logic.

Risk and compliance: continuous verification over static controls

Risk baselines are increasingly framed in time: time-to-detect, time-to-triage, time-to-contain, and time-to-remediate. For fraud and scam pressure (including deepfakes and AI-enabled social engineering), leaders often seek baselines for detection latency, false-positive burden, and case cycle time across onboarding, payments, and servicing. Compliance baselines should include error rates and rework, but also evidence quality: whether controls are consistently verifiable and whether exceptions are governed.

Real-time payments: end-to-end performance and exception handling

Real-time rails and “always-on” customer expectations make batch-era metrics insufficient. Baselines should cover end-to-end authorization and posting latency, availability by component, reconciliation timing, and the operational playbooks used when downstream dependencies fail. Leaders should explicitly baseline exception volumes (repairs, reversals, disputes) because they are often where cost, customer harm, and regulatory attention concentrate.

Strategic vision and ownership: baseline governance that prevents fragmentation

Baseline discussions often expose a deeper issue: unclear ownership over outcomes, data, and decision rights. Leaders increasingly respond with governance language that makes accountability explicit, especially where AI and platform modernization cut across business lines and control functions.

Unified strategy: agree the dominant intent before measuring

Baselines are more coherent when leadership explicitly states the primary intent: efficiency, growth enablement, operational resilience, or risk reduction. Without this, scorecards become sprawling and contradictory. A focused intent does not exclude other outcomes; it prioritizes which trade-offs must be surfaced in decision forums.

Governance model: “control tower” language and hub-and-spoke realities

Many banks use “hub-and-spoke” language to balance enterprise standards with domain autonomy. In practice, the baseline needs a central owner for metric definitions and comparability rules, while benefit owners validate operational plausibility. Where banks adopt a modernization “control tower” construct, its value depends on whether it controls measurement standards, evidence quality, and exception escalation—not just reporting cadence.

AI ownership: from POCs to governed lifecycle accountability

In 2026, leaders increasingly frame AI adoption in terms of lifecycle ownership: who is accountable for model and agent behavior, policy compliance, monitoring, and change management. Baselining AI readiness should therefore include data controls, model governance workflows, and the operational capacity to handle incidents such as model drift, hallucination risk in customer interactions, and abuse of AI tooling in fraud scenarios.

Competitive benchmarking: where “peer comparison” is no longer enough

Before prioritizing initiatives, leaders want to understand whether the bank is behind on capabilities that customers now treat as baseline expectations. In 2026, benchmarking discussions often split into two tracks: peer-bank benchmarks for risk and regulatory comparability, and cross-industry benchmarks for experience expectations.

Customer experience: benchmarking against experience leaders without importing their risk model

Leaders frequently reference consumer experience patterns set by large digital platforms to frame expectations for onboarding speed, personalization, and biometric convenience. The useful translation is not “be like a tech platform,” but rather “baseline the friction and failure points that customers can no longer tolerate,” and then test how risk, privacy, and fairness constraints shape feasible design choices.

Ecosystem readiness: APIs as capability, not compliance

Open banking and API maturity are increasingly framed as a revenue enablement question rather than a compliance question. Baselining ecosystem readiness requires measuring API reliability, developer experience, consent and data-sharing controls, and third-party risk management effectiveness. This evidence is often decisive in determining whether embedded finance partnerships are realistic in the near term.

Establishing an objective baseline to validate strategic ambition and prioritize with confidence

When executives test whether strategic ambitions are realistic given current digital capabilities, the baseline must function as a shared reference state across technology, operations, risk, and finance. That reference state is built from leader-aligned language (“reference period,” “like-for-like,” “measurement system”), governed metric definitions, and evidence that remains comparable as platforms change.

Assessment dimensions that evaluate baseline governance, data lineage, control evidence, operational resilience, and AI lifecycle accountability directly strengthen the quality of the starting point. Used in this way, the DUNNIXER Digital Maturity Assessment supports an objective baseline that improves sequencing decisions for agentic AI, real-time payments modernization, and digital control frameworks—while reducing the risk that strategic plans outpace what the bank can execute and evidence under supervisory scrutiny.

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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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