Decision summary
A simulated distributor wants a shared view of delayed orders and missing information. The proposed solution is a dashboard and a small action-management application connected to its existing order and customer systems. Conventional integrations and rules handle the core workflow; AI is optional. No client delivery or measured results are claimed.
Business problem
Operations managers need to know which orders require attention, who is handling them and what is preventing delivery. The information exists, but no single view connects the exception with its owner and next action.
Current process
A coordinator exports order data, checks delivery updates, compares spreadsheets and sends emails to colleagues. Each department maintains its own notes. The weekly report is already out of date when the meeting starts.
Why the current process is inefficient
People repeat reconciliation work and discuss the same unresolved orders without a shared record of decisions. Missing updates can look like healthy orders. A dashboard alone would expose the problem without necessarily helping anyone resolve it.
Could this be solved without AI?
Yes. First assess the reporting, alerting and task features of existing software. A configured dashboard and deterministic rules may cover the whole need. Custom development is justified only if the missing workflow and integration cannot be addressed adequately with current capabilities.
AI opportunity
An optional later experiment could summarise long exception notes or propose categories for free-text updates. It must not invent delivery dates or decide customer commitments. Add it only if the baseline reveals a worthwhile burden that rules and templates do not address.
Proposed workflow
- Read approved order and delivery fields from existing systems.
- Reconcile records using stable identifiers and display refresh status.
- Flag overdue orders and missing information using agreed rules.
- Show the source, exception, owner and next action in a dashboard.
- Let an authorised person assign, comment on or resolve an exception.
- Write approved updates through supported interfaces and log the outcome.
- Reconcile failed updates and escalate unresolved exceptions.
Solution architecture
Read-only connectors initially feed a normalised reporting store. A dashboard displays current exceptions and data freshness. A lightweight application manages assignments and action history. A queue handles approved writes to existing systems with duplicate protection and retry limits. Original systems remain authoritative for orders and customer records. Javier González leads the solution architecture; specialist delivery roles are defined for the engagement.
Data requirements
Confirm order IDs, status definitions, timestamps, delivery events and responsible departments. Agree which system owns each field. Limit personal data to what is necessary for the task. Review access, retention, processing locations and provider arrangements with the organisation’s relevant specialists before using live data.
Human, AI and automation responsibilities
Operations owns exception definitions and priorities. Staff validate ambiguous records and customer commitments. Rules calculate alerts; integrations move authorised information. Any AI summary remains a draft that the responsible person checks against its sources.
Human-in-the-loop
Start with manual approval of changes to operational records. Show the affected fields before confirmation. Escalate conflicting sources to a named owner. Preserve a manual fallback when data is stale or a connector is unavailable.
Implementation approach
Map the current process and measure reporting effort first. Pilot a read-only view for one workflow with representative users. Check reconciliation and permissions before adding assignments or supervised writes. Prepare operational ownership, training, monitoring and recovery before production use. Maintain the solution from launch and expand only after the first workflow proves useful. Documentation and paired work support an internal handover if desired.
Technology used
Existing system APIs or approved exports, a reporting store, a dashboard or reporting tool, and an application for exception handling. Prefer configuration of current tools where it meets the need. No language model is required for the core solution.
Cost considerations
Include connector availability and licence limits, data preparation, reporting licences or hosting, application development, maintenance and time spent resolving data discrepancies. Compare these costs with configuring the existing software. Budget optional AI evaluation separately rather than including it by default.
Measurement
Measure reporting preparation time, record reconciliation accuracy, data freshness, unassigned exceptions and time to resolution. Track duplicate writes and failed synchronisations. Agree a representative sample, baseline and acceptance criteria with the process owner before the pilot. Faster reporting is not enough if users still cannot resolve exceptions.
Expected business impact
The hypothesis is less manual reconciliation and clearer ownership of operational exceptions. No savings, error reduction or delivery improvements have been observed. Replace this section with measured findings only after a real evaluation, including its scope and limitations.
Risks and limitations
Conflicting definitions can produce a misleading dashboard. Stale data must be visible. Incorrect permissions can expose customer information. Uncontrolled writes can duplicate tasks or change the wrong record. If existing software already meets the need, another application may add unnecessary maintenance and complexity.
Design considerations
Connect visibility with an action and an accountable owner. Keep the source of truth clear. A useful solution may require integration and process design without AI. These are design considerations for a simulated example, not lessons from a delivered project.