Path-to-Scale Operating Model Blueprint

Why Growth Exposes, Not Creates, Problems

When leaders say, “scaling is breaking us,” it’s tempting to assume growth caused the issues. In practice, growth is usually the first time the organization experiences enough volume, variability and decision velocity to reveal what was previously handled through informal coordination.

Early-stage operations can run on proximity and heroics: someone notices an exception, walks over to another team and gets it resolved. The cost is real, but it’s hidden because it’s absorbed by individuals. Once volume rises, those same exceptions become a queue. Once teams distribute, the same fixes become escalated. Once product lines multiply, the same decisions become governance of debates.

At that point, adding headcount may increase throughput in isolated areas, but it often increases coordination cost faster than it increases output, more people, more handoffs, more alignment work. Teams become busy, yet cycle time worsens. That is a design signal, not a staffing signal.

A useful diagnostic question is:

What is slowing down execution or coordination?

If execution is slow, capability or capacity may be the limiter. If coordination is slow, the operating model is the limiter.

McKinsey’s recent work on operating models makes a related point: many leaders default to reorganizing structure first, but structure alone doesn’t create value—operating model performance comes from interlocking design choices, not just boxes and lines.
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What an Operating Model Really Is

For scale, an operating model is not an org chart and it is not a collection of tools.  

It’s the system that determines:

McKinsey’s explainer definition is straightforward: an operating model is the “backbone” that outlines how the company delivers value, operates day-to-day and achieves objectives.  

For leaders trying to scale, the practical point is this:

If your operating model is implicit, it is already inconsistent.

Inconsistency is manageable on a small scale because people compensate. At a higher scale, inconsistency becomes visible as rework, escalations, manual intervention and reporting mistrust.

A diagnostic way to structure operating model clarity is to ask whether five core areas are explicitly defined and consistently understood across functions. Think of these as operating model documentation areas-not legal “contracts,” just the minimum definition set required to scale without relying on tribal knowledge.

• Flow defination: what are the standard paths work takes and where are handoffs?
• Decision defination: who decides what, with what inputs and what cadence?
• Exception defination: what counts as an exception and who owns resolution end-to-end?
• Data defination: Who owns each critical data domain and changes rights?
• System boundary defination: What is the boundary between ERP and surrounding systems?

If leaders can’t answer these consistently across functions, scale will surface the mismatch.

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Common Scale Failure Patterns (with diagnostic signatures)

Below are the patterns leaders recognize in hindsight - written as diagnostic “what it looks like / why it breaks / symptoms you feel.”
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Pattern 1: Scaling people instead of stabilizing flow

What it looks like: headcount increases, but “work about work” increases faster (status syncs, handoff clarifications, re-approvals).
Why it breaks at scale: more volume creates more coordination points; without stable flow, staffing adds interfaces.
Symptoms: cycle time worsens even as staffing rises; leaders escalate more often “just to move things.”
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Pattern 2: Undefined decision rights (decisions travel upward by default)

What it looks like: decisions routinely escalate because teams aren’t sure who can decide or fear downstream consequences.
Why it breaks at scale: decision velocity becomes a constraint; escalation queues form.
Symptoms: senior leaders become bottlenecks; teams stall waiting for approval; “we need alignment” becomes a default phrase.
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Pattern 3: Function-level tuning creates global friction

What it looks like: Each function refines  its own KPIs (procurement, finance, operations) in ways that push cost or exceptions into adjacent teams.
Why it breaks at scale: every local workaround becomes someone else’s exception queue.
Symptoms: rising conflict across teams; “handoff ping-pong”; growing reconciliation work.
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Pattern 4: ERP configured around legacy behavior

What it looks like: the system is shaped to preserve historical workflows instead of stabilizing new standard paths.
Why it breaks at scale: legacy variants multiply; reporting and controls degrade; customization becomes a permanent tax.
Symptoms: upgrades are feared; “we can’t change that because the system…” becomes common.
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Pattern 5: Data ownership fragmentation (no one owns the record)

What it looks like: customer/product/vendor/material data is corrected in multiple places; disputes occur about which value is “right.”
Why it breaks at scale: integrated systems amplify bad data; errors propagate across modules and reports.
Symptoms: reporting debates; manual cleanup; distrust in dashboards; operational delays from validation failures.

Data governance sources consistently frame the core requirement here as explicit roles, policies and decision rights for data changes.  
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Pattern 6: Exception handling is tribal knowledge

What it looks like: “ask John, he knows how this works” becomes the operating procedure for edge cases.
Why it breaks at scale: edge cases become frequent; experts become bottlenecks; quality becomes variable.
Symptoms: inconsistent outcomes; slow onboarding; spikes in escalations during peak periods.
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Pattern 7: Governance added too late (after the damage is visible)

What it looks like: governance appears as extra meetings and approvals once failures become painful.
Why it breaks at scale: late governance often adds friction without restoring clarity; it becomes a layer on top of ambiguity.
Symptoms: meeting load increases; decisions slow further; teams route around governance to “get work done.”
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Pattern 8: Automation multiplies instability

What it looks like: automations are built on inconsistent inputs and unclear exception paths, so failures create more manual cleanup than before.
Why it breaks at scale: automation amplifies whatever it touches—good flow becomes faster; broken flow becomes higher-volume breakage.
Symptoms: silent failures, reconciliation work, “automation babysitting,” brittle integrations.

Thoughtworks describes “spotting dysfunctional red flags” in operating models and notes that piecemeal change without understanding how the organization delivers value often creates predictable dysfunctions.  

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Section 4: The Stability Before Scale Principle

This brief uses one sequencing principle, because leaders need a shared language for what comes first:

This is not a methodology. It’s a constraint.

• Observe where work slows, where exceptions spike, where escalations cluster
• Measure cycle time, rework, decision latency, manual intervention in ERP
• Stabilize remove ambiguity in ownership, exception paths and decision rights
• Optimize then improve flow, reduce handoffs, automate the repeatable
• Scale only after the model absorbs variability without heroics

Scaling an unstable operating model doesn’t just preserve the instability—it increases the cost of living with it. Every new region, product line, integration and workflow adds more surfaces for that instability to show up.

A useful diagnostic statement is:

If you can’t hold stability at today’s volume, you won’t hold it at double the volume—unless the operating model changes.

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Section 5: Why ERP Alone Cannot Fix a Broken Operating Model

ERP is not where operating model problems go to die. ERP is where they become permanent. These systems reflect operating model decisions: how you define work, approvals, ownership and data. If those decisions are unclear, the ERP becomes a mirror of the confusion-often through customizations, manual controls, and shadow processes.

This is why ERP leaders often experience a specific trap:

• The business wants the system to match how work is currently done (including inconsistency).
• IT tries to satisfy the business by embedding that behavior into configuration or customization.
• Exceptions remain unmanaged, so manual intervention becomes “normal.”
• Reporting becomes less trustworthy because the underlying data and process paths are fragmented.

Gartner’s ERP guidance is blunt: a large share of ERP initiatives fails to fully meet business-case goals and some fail catastrophically - often because alignment to goals and execution model are weak.  

Panorama’s ERP reporting similarly emphasizes that when organizations don’t optimize processes and prepare employees, even sophisticated technology fails to deliver benefits.  

This is not an ERP critique. It’s a design reality: ERP operationalizes whatever governance and ownership model you give it.

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Section 6: What a Path-to-Scale Blueprint Addresses (scope, not steps)

A Path-to-Scale Blueprint is not a roadmap. It is not a catalog. It is a definition of the operating model conditions required for scale to be absorbed without coordination cost exploding.

At blueprint level, the focus areas are:

1) Work segmentation
How work is separated into stable streams (by product, customer type, geography, business unit) without creating fragmentation.

2) Decision rights and escalation paths
Which decisions are made where and what escalation is “exceptional” vs. routine. (If escalation is routine, decision rights aren’t working.)

BCG’s operating model discussion for tech companies highlights how governance choices (central vs. local) connect directly to the ability to scale critical processes.

3) System boundaries
Where ERP is the system of record vs. where surrounding systems legitimately own the workflow. (Boundary ambiguity produces duplication and manual reconciliation.)

4) Data ownership
Clear ownership for key domains (customer, product, supplier, material, chart of accounts), including who can approve changes and how quality is enforced.
Authoritative definitions of data governance consistently emphasize policies/procedures plus identified individuals with authority and responsibility.  

5) Exception management
Defined classes of exceptions, owners for resolution and a visible mechanism for learning (exceptions shrink over time rather than becoming permanent).

6) Governance cadence
A cadence is not “more meetings.” It is the minimum set of recurring decision forums required to keep ownership and priorities stable as volume changes.
Even in technical governance contexts (e.g., large migration programs), the role of governance is described in operational terms: how decisions are made, how issues escalate and how progress is measured.  

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Section 7: Questions That Reveal Scale Readiness (diagnostic set)

Use these questions to locate the fault lines, especially before ERP modernization, reconfiguration, automation programs, or shared-services scaling.

Flow and friction

• Where does work slowdown as volume increases at execution points or at handoffs?
• Which teams spend the most time waiting for someone else’s input?
• Which work types have the highest rework rates and why?

Decisions and escalation

• Which decisions escalate most often and what triggers escalation (risk, ambiguity, politics, unclear data)?
• Where do decisions get revisited multiple times because inputs aren’t trusted?
• What decisions are made “by meeting” because no one has clear authority?

Exceptions and manual intervention

• What is the highest-frequency exception class today?
• Who owns cross-functional exceptions end-to-end (not “their part”)?
• Which exceptions are effectively permanent policy gaps?

Data ownership and reporting confidence

• For each critical data domain, who can approve changes—and is that consistent across regions and functions?
• Where do teams maintain parallel spreadsheets because the ERP data isn’t trusted?
• What percentage of time in monthly close is spent reconciling data vs. executing close?

Gartner notes that many organizations still don’t measure data quality, which makes it hard to understand the cost of poor data and the impact of fixes—an avoidable blind spot at scale.  

ERP friction (the “scale tax” inside the system)

• Where does ERP require manual intervention to process volume (approvals, workarounds and data correction)?
• Which transactions fail most frequently and what is the root cause: data, configuration, ownership, or exception design?
• Which reports trigger debates rather than decisions?

If leaders can answer these with specifics (not generalities), they’re already closer to operating model clarity.

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Section 8: ERP Readiness for Scalable Growth

Scale is a design problem. When the operating model is implicit, people compensate until growth makes compensation impossible. At that point, organizations often respond by adding tools, adding headcount and adding layers of governance. Those moves can increase activity while stability deteriorates.

A Path-to-Scale Blueprint starts earlier. It makes the operating model explicit, like how workflows, how decisions are made, how exceptions are handled, how accountability is assigned and how systems support execution, so growth increases output without multiplying coordination cost.

If you want a fast way to see where your ERP environment is already compensating for operating model gaps, take the ERP Health Assessment. It translates operational symptoms into measurable exposure and helps prioritize what must be stabilized before automation and system changes amplify the damage.