Transformation fails less because the target is wrong and more because the current state was never truly understood. Leaders sketch a Target Operating Model, launch initiatives, and only later discover that the organization’s reality—its habits, constraints, and unwritten rules—doesn’t match the slideware. A disciplined current state assessment is how you close that gap between what you think is happening and what is actually happening. One practical way to do this is the Hear it – See it – Prove it methodology:
Hear it: Talk to people across the operating model and listen deeply.
See it: Go where the work happens and observe how value really flows.
Prove it: Test every insight against data so you’re working from evidence, not anecdotes.
Used well, this approach doesn’t just produce a diagnostic report—it builds trust, surfaces hidden constraints, and gives your transformation roadmap a solid foundation.
Why current state assessment matters before defining the TOM
A Target Operating Model describes how the organization will work once transformation succeeds: roles, processes, governance, technology, and how value flows from customer need to outcome. But a TOM designed in isolation from the current state is just an aspiration. A rigorous current state assessment exposes real constraints, reveals hidden strengths, clarifies the true baseline, and aligns people around reality. It reduces the “we’re fine” vs. “we’re broken” debate by grounding discussion in shared evidence. The Hear it – See it – Prove it methodology turns that assessment into a structured, human-centered, and data-backed exercise.
Hear it: listening across the operating model
“Hear it” is about collecting perspectives from the people who live the operating model every day. It is not a survey or a tick‑box exercise; it is a deliberate effort to understand how different parts of the system experience work, value, and pain. To understand the current state, you need voices from across the operating model: customers and front‑line staff who experience demand and friction directly; process owners and middle managers who understand how work is supposed to flow and where it actually bottlenecks; support functions like HR, Finance, IT, and Risk who shape the enabling environment; and executives who articulate strategic intent and assumptions about how the organization works.
The goal is not to validate hierarchy; it is to build a multi‑angle view of the same operating reality. Effective listening requires structured but open conversations. You use a consistent set of themes—strategy, roles, processes, tools, metrics, pain points—but allow people to tell stories, not just answer questions. You maintain a neutral, non‑defensive stance, making it clear you are not hunting for blame. You treat contradictions as signals, not problems. When different groups describe the same process in different ways, that misalignment is data. You pay attention to language: phrases like “we always,” “we never,” “we used to,” and “we’re not allowed to” often point to unwritten rules and cultural constraints.
From this phase, you emerge with themes of pain and value, perception gaps between leaders and front‑line staff, and hypotheses about root causes. These are not conclusions yet, but informed guesses—approval delays might be driven by unclear thresholds; data quality issues may stem from manual handoffs; risk escalation might be a cultural reflex rather than a structural requirement. These hypotheses set up the next step: going to see whether the operating model behaves the way people say it does.
See it: observing how work really gets done
“See it” is about direct observation. You go where the work happens—branches, plants, call centers, project rooms, digital workflows—and watch how value flows from input to output. People’s descriptions of work are shaped by memory, identity, and incentives. Observation cuts through that by showing the actual process versus the documented process, real handoffs, workarounds and shadow systems, and behavior under pressure. You are not there to police compliance; you are there to understand how the operating model behaves under real conditions.
Good observation is intentional. You follow end‑to‑end flows, selecting representative journeys such as customer onboarding, incident resolution, or product launch, and trace them from start to finish. You watch multiple instances because one example can be an outlier. You ask clarifying questions in the moment: what made someone choose a particular route, who else needs to approve a step, where data goes next. You note delays and decision points, capturing where work waits, who decides, and what information they use. You collect artifacts—screenshots, forms, templates, and tools that shape how work is done.
From this phase, you gain concrete examples of friction, patterns of variation across sites or teams, evidence of cultural norms, and candidate metrics that will matter later. You begin to see where the operating model is brittle, where it is resilient, and where it is quietly reinvented by people who have figured out better ways to work. These observations refine your hypotheses and point directly to where data can either confirm or challenge what you have heard and seen.
Prove it: turning insight into evidence
“Prove it” is where the methodology earns its credibility. Whatever you have heard and seen, you now test against data. The aim is not to drown the organization in dashboards; it is to build a small, sharp set of evidence that shows where the operating model is working and where it is not. You start from the hypotheses generated earlier and identify what to measure: bottlenecks and delays, quality and rework, demand and capacity, compliance and risk, and outcome metrics such as customer satisfaction, revenue leakage, cost‑to‑serve, and service‑level adherence. You do not need perfect data; you need data that is good enough to show direction and magnitude.
Most organizations do not have clean, ready‑made data for every question. That is fine, as long as you are transparent. You use multiple sources—system logs, spreadsheets, manual samples, and interviews with people who manage the numbers. You sample intelligently, picking representative periods, products, or locations. You document assumptions, being explicit about what you are inferring and where the data is thin. You triangulate, checking whether different data sources tell the same story. If they do not, that is a finding, not a failure. The goal is to move from “we think” to “the evidence suggests,” even if the evidence is partial.
Turning data into insight requires narrative discipline. You quantify the pain: approval delays add an average of three days to onboarding, with 40 percent of cases waiting on a single role. You show variation: one site completes the process in half the time of another, with fewer errors, using the same tools. You connect to outcomes: rework in a particular step drives a measurable increase in cost‑to‑serve and a drop in customer satisfaction. You highlight leverage points: removing a redundant approval could cut cycle time by 20 percent without increasing risk. Now your current state assessment is not just descriptive; it is actionable.
Connecting the methodology to the Target Operating Model
Once you have heard it, seen it, and proved it, you have a grounded view of the current operating model. The next step is to connect that view to your Target Operating Model. You can now articulate gaps with precision. Structural gaps appear where roles do not exist but need to, or where roles exist but are misaligned with accountability. Process gaps emerge where steps add no value, controls are missing, ownership is unclear, or execution is inconsistent. Capability gaps show up in skills, tools, and data that are insufficient for the desired way of working. Governance gaps reveal decision rights that are unclear, duplicated, or misaligned with risk and value. Cultural gaps surface where behaviors and norms conflict with the TOM’s principles—for example, risk aversion in a model that requires experimentation.
Not all gaps are equal. Evidence allows you to prioritize based on impact, feasibility, and sequencing. Impact measures how much the gap affects outcomes such as customer experience, cost, risk, or strategic goals. Feasibility considers investment, complexity, dependencies, and political resistance. Sequencing identifies which gaps must be closed first to enable others—data quality before advanced analytics, role clarity before accountability shifts, foundational process redesign before automation. This becomes the backbone of your transformation roadmap: a sequenced set of changes that move the current operating model toward the TOM in manageable steps.
Making the methodology stick
A methodology is only as good as the way it is applied. To make Hear it – See it – Prove it a disciplined practice rather than a one‑off project, you treat it as a loop, not a one‑time diagnostic. Operating models evolve. New products, regulations, technologies, and leaders all change how work gets done. You build a cadence of periodic reassessment, continuous listening, and embedded measurement. You are not freezing the current state; you are continuously recalibrating it.
Psychological safety is essential. People will only tell you the truth if they believe it will not be used against them. You separate diagnosis from performance management, making it clear that the assessment is about the system, not individual blame. You share findings transparently, letting people see how their input shaped the conclusions. You invite co‑design, involving staff in designing solutions to the gaps you have identified. Trust becomes both an input and an output of a good current state assessment.
Finally, you keep a clear line of sight to outcomes. It is easy for assessments to become inward‑looking. You anchor everything in strategic goals—growth, resilience, customer intimacy, efficiency. You tie every gap to a consequence, showing how each issue affects those goals. You measure improvement, tracking the impact on outcomes, not just on process metrics. This keeps the work relevant to leaders and meaningful to teams.
Conclusion: understanding before changing
The temptation in transformation is to jump straight to solutions: new structures, new systems, new slogans. But without a disciplined understanding of the current state, those solutions rest on assumptions. The Hear it – See it – Prove it methodology forces you to slow down just enough to listen to how people experience the operating model, observe how work actually flows, and prove your insights with data. From there, the gaps between current and target become visible, discussable, and actionable. You are no longer transforming an imagined organization—you are transforming the one that really exists. And that is the only one that can change.
