The Hidden Cost of Bad Make Design in Approval Workflows

Why approval workflow design becomes an operational issue

An approval workflow is a chain of business decisions, not just a sequence of app actions. A request may need budget review, legal input, manager approval and a final handoff to delivery or finance. Each stage changes the business state of the request and may create work in another system.

Make can coordinate these movements, but it cannot decide what an approval means unless the business has defined it. The scenario needs to know which record is authoritative, which fields must be present, who owns each state, what happens when a request is rejected, and how exceptions become visible.

This is why a technically successful scenario can still produce an expensive operating model. When staff do not trust the status shown in the CRM or project tool, they check multiple systems. When a handoff is unclear, they send a message. When a failed run is invisible, they create a manual backup. The business then pays for both the automation and the work required to supervise it.

What bad Make design looks like in an approval process

Bad design is often cumulative. No single filter or field mapping causes the entire problem. Instead, several small decisions create a workflow that is difficult to operate and maintain.

Statuses describe activities instead of business states

A status such as “email sent” or “manager notified” records an automation event, not the state of the request. It does not tell the team whether the request is awaiting a decision, returned for changes or ready for the next department.

A useful approval state should answer three questions: what has happened, what is expected next, and who is responsible for that next action.

Several systems act as competing sources of truth

If the CRM says approved, the project tool says pending and a spreadsheet contains the latest note, people cannot reliably determine which state controls the process. Synchronisation may be working exactly as configured while the operating model remains contradictory.

Routing depends on brittle values

Hardcoded names, inconsistent labels and one-off filters can route work correctly today and incorrectly after a team change. A new department, approver or service line can expose assumptions that were never documented.

Errors disappear into the background

A failed module is not just a technical inconvenience. If no one can see the failure, the request may stop moving without an owner being alerted. A reliable workflow needs a retry approach for temporary failures and a visible exception path for issues that require human review.

Manual work exists outside the scenario

Regular spreadsheet reconciliation, direct messages to approvers and repeated record corrections are evidence that the workflow boundary is incomplete. Manual work is not automatically a problem, but hidden manual work makes ownership, capacity and reporting difficult to assess.

The hidden costs of weak approval automation

Longer approval cycle times

Every unclear state or missing notification creates waiting time. A request may be ready for review, but the responsible person does not know it, or the request may be blocked while the team investigates which system is correct.

Duplicate records and rework

When a scenario cannot reliably identify an existing request, it may create another record instead of updating the original. The resulting correction work spreads across the CRM, project management tool, reporting layer and communications history.

Unreliable operational reporting

Approval reporting is only useful when the underlying states have consistent meaning. If “approved” is written before the final review, or if rejected requests remain in active pipeline reports, leaders cannot distinguish throughput from incomplete work.

Unclear ownership

An approval can be visible without being owned. The record may show a department but not a person, or show the original requester after responsibility has moved to finance or operations. This creates follow-up work and makes delays difficult to assign.

Delayed downstream work

A project, contract, purchase or onboarding task may depend on approval. If the handoff is triggered by the wrong event, the next team either starts too early or waits for a manual confirmation. Both outcomes create avoidable risk.

Lower confidence in the operating system

Trust is an operational asset. Once people expect automation to be incomplete, they stop treating system data as current. That reduces the value of dashboards and encourages side processes that are harder to govern.

A practical model for designing approval workflows in Make

Before changing a scenario, define the workflow as a set of meaningful business states. A simple operating model is:

This sequence separates the decision logic from the tool actions. Make can then implement the agreed process instead of becoming the place where the process is invented through filters and routers.

Decision rule: patch, rebuild or redesign?

Not every issue justifies a full rebuild. The right response depends on whether the defect is isolated or whether the workflow model itself is unstable.

A rebuild sits between these options. It is appropriate when the process is sound but the existing scenario is undocumented, difficult to test or dependent on brittle filters. The scenario can be reconstructed around the same approved business rules with clearer modules, error handling and documentation.

Ask these diagnostic questions before choosing:

• Can the team define every approval state in plain language?
• Is there one authoritative record or a clearly governed record model?
• Does each state have a visible owner and next action?
• Can someone identify failed, overdue and rejected requests without searching several tools?
• Would a team change or new approval category require editing many hardcoded routes?

If the answers are mostly no, the problem is structural. A Make automation service can help assess the scenario, but the assessment should begin with process and data design rather than module count.

Hypothetical examples of design failure

Example: commercial approval

Suppose a sales team submits a discount request through a form. Make creates a CRM task and sends an email to a manager. The manager approves by replying to the email, but the reply does not reliably update the original request. Sales then changes the CRM stage manually. The workflow has notifications, but it does not have a controlled decision record or a reliable transition into the approved state.

A better design would capture the decision in a defined approval interface or structured record, preserve the approver and timestamp, and update the CRM only after the authoritative state changes.

Example: operational handoff

Suppose an approved client request creates a project in a work management tool. A duplicate project appears when a scenario is retried after a temporary connection failure. Without an idempotent record key or a check for an existing project, the delivery team receives two versions of the same work.

The issue is not simply that the connection failed. The design lacked a safe rule for retrying the action.

What a reliable Make approval workflow should make visible

A strong workflow should make the following information easy to find:

• The current business state of the request
• The authoritative record and related records
• The current owner and next required action
• The decision history, including timestamps and reasons where needed
• Failed actions, retries and unresolved exceptions
• The downstream systems updated by the approval

These requirements are as important as the scenario itself. If the workflow changes CRM records, pipeline structure and reporting, the data model must support the process. Where the approval process depends on CRM ownership, lifecycle stages or reporting definitions, HubSpot consulting may be relevant alongside the automation work.

Documentation should explain the trigger, required inputs, routing rules, state transitions, connected systems, failure paths and ownership. This allows the workflow to be tested when the business changes instead of being rediscovered through incidents.

How to reduce the risk before adding more automation

Start by mapping one approval from request to completion, including the manual actions that happen outside Make. Identify where a decision is made, where the decision is stored and which event authorises the next handoff.

Next, define the minimum data required at each transition. Do not allow the scenario to advance when the approver, related record or decision context is missing unless there is an intentional exception route.

Then test the non-happy paths. Consider rejected requests, returned requests, duplicate submissions, changed approvers, missing fields, temporary connection failures and approvals that remain open too long. These cases reveal whether the workflow represents the real process or only the ideal one.

Finally, connect reporting to a decision. A dashboard should help someone identify overdue approvals, workload, blocked work or data quality issues. If a metric does not support an operational response, it may not belong in the first version of the reporting model.

For examples of connected Make work across automation, CRM and operations, see the Make projects portfolio. The useful lesson is not a particular module pattern. It is the relationship between process definition, data structure and automation behaviour.

Final perspective

Bad Make design becomes expensive when an approval workflow is treated as a technical chain rather than a business control. Delays, duplicates and unreliable reporting are symptoms of unclear states, weak ownership, competing sources of truth or missing exception handling.

The practical response is to define the process first, then decide whether the existing scenario should be patched, rebuilt or redesigned. More routers, notifications and connected tools will not create clarity by themselves. A well-designed workflow makes the current state, next action, owner and failure path visible to the people responsible for the work.