When to Rebuild an Approval Workflow in Make

What reporting drift means in an approval workflow

Reporting drift is the gap between the state shown by a workflow and the state of work in the business. An item may be marked approved even though the delivery team has not received the required handoff. A request may remain pending because a decision was made in an inbox but never recorded in the system. An owner field may show the person who submitted the request rather than the person responsible for the next action.

The key distinction is between a technical event and a business event. A Make module completing successfully proves that an automation step ran. It does not prove that the intended approval, decision or handoff occurred.

Each workflow state should describe a meaningful business condition. For example, awaiting finance review should mean that finance still needs to make a defined decision. It should not merely mean that a record was sent to a finance inbox.

Why approval workflows become unreliable

Most approval workflows do not fail all at once. They drift as the business adds request types, approval levels, teams, exceptions and connected systems. The original scenario may have been designed for one straightforward path. Later changes are added as small patches, each of which seems reasonable in isolation.

Common causes include:

• Several systems can update the same status without an authority rule.
• Approval decisions are captured in email or chat instead of structured fields.
• New exception paths are added without revisiting the standard process.
• The meaning of an owner field changes between systems.
• Reports are created after the automation rather than used to define it.
• Team changes leave old routing rules, approvers or escalation paths in place.
• Retries and duplicate triggers create repeated tasks or contradictory updates.

Complexity alone is not the issue. The more serious problem is ungoverned complexity. If nobody can explain which system owns the current state, what each status means or who is accountable for the next action, the workflow has become difficult to trust.

The operational cost of unreliable approval data

Unreliable approval data creates work outside the scenario itself. People ask for updates, inspect message threads, compare records and correct reports before meetings. This manual effort is often distributed across operations, sales, delivery, finance and leadership, so the cost is easy to underestimate.

Decisions take longer

Approvers may not know whether a request is waiting on them, another team or missing information. Work remains open while people search for context that should have been attached to the record.

Handoffs become informal

An approval is not operationally complete simply because a status changed. The next owner needs a clear signal, the relevant context and a defined action. If the workflow updates a record without transferring responsibility, it has moved data without moving work.

Reports lose decision value

A report is useful when it helps someone decide what to do. If every dashboard requires manual interpretation, leaders are not looking at the operation directly. They are looking at a partial record that must be explained by the people closest to the process.

Downstream automation becomes unsafe

Notifications, task creation, synchronisation and AI-assisted processes depend on consistent states and fields. Ambiguous approval data can cause later automations to act too early, too late or on the wrong records.

Optimize, rebuild or replace the workflow

The decision should be based on the condition of the process, not on the age or apparent size of the Make scenario.

Replacement may be appropriate when the business now operates differently from the way the workflow was designed. A rebuild keeps the platform but redesigns the process around clearer rules. Replacement changes the underlying approach because the existing structure cannot support the required governance or handoffs.

Use this diagnostic question: Could a new team member identify the current state, next owner and required decision from the system without asking someone for clarification? If not, the issue is probably broader than a single broken module.

Define the operating model before changing Make

Make should execute agreed decision logic. It should not be the place where undocumented business rules accumulate. Before rebuilding the scenario, define the process that the automation must represent.

Business states

List the states that matter, such as submitted, awaiting review, returned for information, approved, rejected and handed off. Give each state a precise definition, an entry condition, an expected owner and a valid next action.

Decision ownership

Identify the role accountable for each decision and the fallback if that person is unavailable. The owner should represent responsibility for the next action, not simply the person who created or last edited the record.

Source of truth

Choose which system owns the current approval state, outcome and active owner. Other systems may display or receive those values, but they should not independently overwrite them without an explicit rule.

Evidence and timestamps

Define what must be recorded when a decision is made. Depending on the process, this may include the decision, decision-maker, timestamp, reason, required conditions and resulting owner. A timestamp for a message sent is not necessarily a timestamp for an approval completed.

Exception handling

Separate the standard path from genuine exceptions. Decide what happens when information is missing, an approver changes, a deadline is missed, a request is withdrawn or a decision is reversed.

Reporting questions

Design reporting around decisions the business needs to make. Useful questions include: What is waiting for action? How long has it been waiting? Who owns the next step? Which requests were rejected or returned? Where are handoffs being delayed?

A practical sequence for rebuilding

Rebuilding in the right order reduces the risk of recreating ambiguity in a cleaner-looking scenario. The process should be clarified before the automation is redesigned.

This sequence keeps automation in its proper role. The scenario should move records between defined states, enforce agreed conditions and create visible ownership. It should not decide what an approval means while the process is running.

Example: a multi-team approval process

Consider a hypothetical service business where a new project requires commercial approval, delivery review and finance confirmation. The original workflow sends a message to each team and updates a shared status after each response.

Over time, delivery starts work after receiving an informal message, while finance relies on a separate record. The central status remains awaiting approval because no system owns the final outcome. Leadership sees a queue of delayed projects even though some are already active.

A rebuild would define the approval states, capture each decision as structured data, make one system authoritative and create an explicit handoff to delivery only after the required conditions are met. Reporting could then distinguish requests waiting for a decision from requests approved and ready for work.

The example is hypothetical, but the design lesson is practical: the workflow should represent the business gate, not merely the communication surrounding it.

How Make should fit into the rebuilt process

Make can be useful when several systems need to participate in a controlled process. It can route records, transform data, create tasks, update systems and send action-oriented notifications. Its flexibility also makes governance important.

A scenario with many routers and exception branches may continue to run while becoming difficult to inspect or change. A rebuild should therefore include clear module boundaries, meaningful names, documented ownership, deliberate error handling and a defined approach to retries and duplicate events.

Notifications should prompt action, not serve as the approval record. The decision and outcome should be captured in the authoritative system so reporting and downstream logic can use consistent data.

Where an approval leads into delivery work, the connected workspace also needs to reflect the handoff. A task should have the correct status, owner, due information and context required for the next team to act. This may require considering ClickUp workspace architecture and workflow design alongside the automation itself.

A broader review of systems, CRM, automation and AI implementation services may also be useful when the workflow crosses several tools and no single team owns the complete operating model.

How to measure whether the rebuild worked

The outcome should be measured through operational reliability, not simply by counting modules or reducing scenario length. Ask whether:

• The current approval state can be identified without checking multiple tools.
• The next accountable owner can be found for every open request.
• Approval timestamps reflect actual decision events.
• Reports separate waiting, approved, rejected and returned requests.
• Exceptions are visible rather than hidden in messages or spreadsheets.
• A change can be made without putting unrelated workflow paths at risk.
• The process can explain why an item is waiting and what must happen next.

The central test is simple: does the workflow give the business a dependable answer to what is happening, who owns the next action and what decision is required? If yes, targeted optimization may be enough. If no, another patch is unlikely to restore trust. Rebuild the operating logic first, then make the automation express it.