This article was written by Dave R. Messinger, Senior Managing Director and Daniel Blacklock, formerly Director, Enterprise Transformation & Strategic Transformation Governance at Citi. The original article was published by FTI Consulting. You can find the article here.  

Banking transformations have evolved, but governance capabilities have not. With significant enterprise-wide investment at stake, defining the target operating model is not enough; banks must also demonstrate whether the model is delivering results.

In areas such as financial reporting, established accounting, control, and review frameworks provide a high degree of discipline. For strategic commitments, however, equivalent rigor is often lacking.

As banks invest in multi-year transformation programs for regulatory remediation, core system modernization, data governance, and artificial intelligence (AI)-driven operating model changes, oversight and governance must evolve to ensure that planned outcomes are achieved. Boards and bank leaders want clarity on where the operating model is driving efficiency gains, for example, and how those improvements are felt in day-to-day execution.

With such high stakes, banks must ask a crucial question: How is the organization defining its target operating model, and what evidence is being used to validate it? While this may seem obvious, many organizations struggle to answer it objectively. Why? Intended outcomes are likely not explicitly defined, and the indicators needed to measure progress are often misaligned or inconsistently applied.

Here, we explore how organizations can strengthen governance of enterprise commitments.

The Structural Gap

Enterprise transformation failures are often attributed to execution gaps, misalignment, or cultural resistance. The adage “measure twice, cut once” helps ensure downstream mistakes are avoided before execution. For transformation efforts, this exposes a fundamental gap: If the target state isn’t clearly defined (measure twice), the bank cannot determine whether that state has been achieved.

It’s important to note that transformation projects are typically linear, with a defined set of deliverables that may or may not address the needs of the bank. But, focusing on delivery versus achieving a target state may lead to trouble. Consider a newlywed couple building a house. They may find their plans inadequate when they find out they are expecting triplets. The target state of having enough room for their family can change, and so it is with organizations.

While it’s easy to say that projects will be adapted while underway, that tends to result in a narrowing of scope, rather than preserving the desired end state.

Execution signals (milestones, dashboards, metrics, and narrative reporting) tell you the progress of the project, but not whether the project will actually meet your needs when it’s done. At the same time, accountability is distributed across functions, programs, committees, and executive roles. Without a single point of accountability such as a Project Management Office (PMO), building a holistic, evidence-based view of transformation success is simply not possible.

The Oversight Void

When large transformation programs are underway, oversight bodies and senior management rely on structured reporting to understand progress. Portfolio and PMO systems provide the most organized view available. They maintain current data, track delivery status, and support regular reporting across milestones, budgets, issues, and execution posture.

While these systems are effective at managing work, they are not designed to measure outcomes. Understanding the difference between execution and outcomes is critical to gauging operating model performance. As transformations cut across business units, individual unit-level red/yellow/green tracking no longer provides a complete view of performance or progress.

Execution monitoring assesses whether the work is on track and well-resourced, and identifies any issues for escalation.

Governance oversight seeks to answer an entirely different set of questions including:

• What is the intended operating condition?
• How can someone evaluate its effectiveness once in place?
• Does the commitment remain achievable?
• Has the intended condition been established?

For many institutions, no single enterprise capability is explicitly designed to answer those questions. Finance governs financial truth. Risk governs exposure. Organizations are forced to make interpretative rather than determinative evaluations regarding success.

Not being able to determine whether a target state has been achieved is a structural governance gap that must be addressed. While organizations manage work, they often fail to consistently define or govern the operating models they aim to achieve. The target state must be a set of objective conditions that can be validated firsthand. The target state can then serve as the basis for answering the questions oversight bodies are asking.

The Questions Oversight Bodies Actually Ask

Across institutions, governance discussions usually converge on a familiar set of questions posed by boards, executive operating committees, steering groups, or other transformation oversight bodies:

• What is the intended target state?
• Is the target state still achievable?
• Has the target state been achieved?
• Is the achieved target state durable, and can oversight step down?

These questions are reasonable and expected, but difficult to answer within typical oversight structures.

Financial reporting provides a useful contrast. Financial outcomes are determined under defined frameworks, supported by controls, and subject to independent review. Material changes are reconciled and disclosed through established processes.

Transformation commitments aren’t held to the same rigorous standard. Oversight relies on aggregated reporting and interpretive assessment rather than rule-based evaluation of operating conditions.

Even with robust reporting in place, reporting alone cannot confirm whether transformation goals have been met. Here are a few reasons why:

Performance indicators fluctuate for many reasons. For instance, milestones might be completed while underlying exceptions remain unresolved. New systems may be deployed while legacy processes persist in parallel, masking gaps in the new environment. Data may be migrated while control environments remain incomplete.

Consider a regulatory remediation program intended to establish a governed data environment.

A typical formulation might state:

This provides direction and investment but does not define conditions for evaluation, leaving room for interpretation.

The following defines more specific conditions for an optimized target state:

Each element is observable in the operating environment; it either exists, or it does not. The conclusion does not depend on subjective interpretation. Organizations cannot definitively answer whether a transformation has been achieved without observable evidence.

The Precision Required for Reliance

Many digital transformation projects still fail in 2026.¹ While the reasons vary, we have observed several instances in which failure of the project can be traced back to leadership’s failure to communicate and manage toward a unified vision.

Without concrete definitions of intention and performance, progress remains interpretive. This pattern is visible in regulated environments. Programs can demonstrate strong execution based on reporting over time, even when operating conditions are incomplete or structurally fragile. Oversight based on reporting alone does not reveal this gap.

Regulatory assessment focuses on whether the required operating condition functions in practice. For transformations, the difference between execution evidence and operating-state reality is where ongoing oversight and rework originate.

Determination Capability Vs. Interpretation Flexibility

Addressing this gap does not require expanding execution oversight. It requires a focused capability for determining whether reliance on a major commitment is justified.

Such a capability focuses on a small set of core commitments. For each, it maintains:

• a defined target state expressed as an evaluable operating condition
• the conditions required for that state to exist
• the current evidence relevant to those conditions
• an evidence-based assessment of whether the commitment remains achievable and whether the state has been established

This is not an additional reporting layer. It is an entirely different function. Its job: establish whether the proposed target state is observable in a way that’s not open to subjective interpretation or fuzzy logic.

Conclusion

Transformation activity continues to expand in scale and consequence, yet organizations tend to focus on execution oversight rather than on measuring outcomes. This disconnect makes it difficult to address regulatory oversight questions regarding major transformative commitments. Existing systems and reporting tools are not built for or aligned to outcomes, making it difficult for organizations to support outcomes with evidence that truly reflects the operating-state reality.

Despite significant investment and exposure, organizations that continue to rely on transformation outcomes based on interpretation rather than real evidence risk wasting precious resources on expensive and ineffective transformation programs. This fundamental paradigm shift requires a new organizational discipline and a single point of accountability that works across the enterprise.