Overview
An Odoo AI project often starts with an idea but not enough information to approve it. “Use AI to improve customer service” does not define a workflow, data source, owner or measurable result.
An Odoo + AI project brief template converts that idea into a one-page decision document covering current pain, workflow boundaries, required records, ownership and success.
The brief decides whether an idea deserves discovery or a controlled pilot. This guide includes a blank template, completed example and review checklist.
What an Odoo AI Project Brief Should Achieve
A useful brief lets business, process, data, technical and security owners assess the same proposal without a demonstration.
The brief should answer four core questions:
What measurable workflow problem are we solving?
Which Odoo data and approved external sources will the solution use?
Who owns the business result and who controls the data?
Which metric will decide whether the pilot succeeds?
It should also define prohibited actions, exception routing and included costs. If these points cannot fit on one page, the idea may be too broad.
The One-Page Brief Structure
The following fields create a practical minimum brief. They are ordered to keep technology from being chosen before the problem and control boundary are clear.
| Brief field | Question to answer | Evidence to include |
|---|---|---|
| Project name | What short name identifies the workflow? | Process plus AI role |
| Problem | What delay, error or manual effort exists today? | Baseline volume, time or correction data |
| Business outcome | What should improve if the idea works? | One operational outcome |
| Workflow boundary | Where does the process start and end? | Trigger, final state and excluded cases |
| AI role | Will AI classify, extract, summarise, draft or recommend? | Named output and prohibited actions |
| Required data | Which records, fields and documents are needed? | Source, owner, freshness and access |
| Accountable owner | Who approves scope and owns the result? | Named role with decision authority |
| Human control | Who reviews the AI output and when? | Accept, edit, reject and escalation actions |
| Exceptions | What can go wrong or fall outside scope? | Manual queue and response owner |
| Success metric | Which primary measure decides value? | Baseline, target, method and period |
| Guardrail metrics | What must not become worse? | Quality, compliance, access and service measures |
| Cost boundary | What build and operating costs are included? | Configuration, usage, testing and support |
| Pilot decision | What happens after measurement? | Scale, revise or stop gates |
Avoid general statements. “Reduce handling time while keeping material misclassification below the approved limit” is testable. “Improve efficiency” is not.
Step 1: Define the Problem in Workflow Terms
Start with the current workflow. Name the trigger, users, steps, end state and exceptions then observe a representative sample.
For example, support agents read email tickets then identify the customer, category, team and priority. Reassignments occur when classification is wrong. The problem is intake time and inconsistency rather than missing AI.
Use this problem formula:
[User group] spends [measured time or effort] completing [workflow step] for [volume and case type], which causes [verified delay, error or cost].
Keep one intervention. Classification, response drafting and automated communication are different risk decisions.
Step 2: Set the Workflow Boundary
Define the event that starts processing and the state that marks completion. Name eligible users, company, team, language and record type.
Name exclusions such as safety claims, legal complaints, refund demands, unidentified customers and unsupported languages. Keep their manual path.
The target flow could be:
Customer email → Odoo Helpdesk ticket → eligibility rules → approved context retrieval → AI classification suggestion → rule validation → agent review → confirmed category and team → normal SLA workflow → KPI record
This separates interpretation from action. Odoo documents deterministic automation rules plus AI server actions that read record context and interpret prompts. Use each only where needed.
Step 3: Describe the AI Role Precisely
Use a precise verb: classify, extract, summarise, draft or recommend. Avoid “AI manages the process.”
Here AI suggests a category and team from the ticket plus named customer and product fields. It cannot reply, close tickets, change SLA promises or access private finance notes.
Odoo documents AI fields using record context plus agents operating through defined topics and tools. Keep the brief independent of component choice because standard rules may solve part of the problem.
Step 4: List the Required Data
An AI-ready ERP gives required records consistent meaning, ownership, access and freshness.
List each model or document at field level. Ticket classification may need subject, body, language, customer, product, team and approved category definitions. Historical labels must be trusted.
For each source, record:
Why the workflow needs it
Which role owns its quality
How current it must be
Which users and services may access it
Whether it is authoritative or contextual
Which known quality problems exist
Whether it contains personal or confidential information
Do not write “all CRM and Helpdesk data.” Use the minimum information then sample missing links, duplicates, inconsistent categories, outdated documents and sensitive notes. Include cleanup in project cost.
Step 5: Name One Accountable Owner
One business owner must approve scope, assign users, resolve policy and decide whether the outcome is valuable. Coordination does not replace accountability.
The support lead may own ticket-classification value while administrators configure fields and data owners define category quality. IT owns relevant integration and security.
Use roles publicly and names internally. Identify who can pause the pilot after access, customer or quality failure.
Step 6: Choose One Primary Success Metric
Choose one primary measure that works for both baseline and pilot. A long KPI list does not define success.
Classification may use median handling time as primary. Acceptance, reassignment and material errors protect quality. Faster routing fails if sensitive requests reach the wrong team.
| Metric type | Example for ticket classification | Measurement rule |
|---|---|---|
| Primary value metric | Median active classification time | Compare equivalent eligible cases before and during pilot |
| Quality guardrail | Material misclassification rate | Count wrong category or team changes that affect service |
| Control guardrail | Sensitive-case bypass count | Critical exclusions should normally have zero tolerance |
| Adoption metric | Eligible tickets reviewed through the pilot | Divide pilot-processed cases by all eligible cases |
| Exception metric | Percentage sent to manual classification | Separate correct escalation from technical failure |
| Economic metric | Cost per valid classified ticket | Include usage, review, monitoring and support |
Define each source, calculation, sample, period and owner before launch. Do not change definitions after seeing results.
Step 7: Add Human Control and Exceptions
Name the reviewer who sees the input, proposal and evidence. Give that role authority to accept, edit, reject or escalate.
Define low confidence, missing identity, conflicting data, multiple issues, unsupported language, sensitive content, timeout and permission failure. Each needs a queue, owner and response time.
Keep the manual path. Preserve the original, AI result, configuration version and reviewer action so errors are diagnosable.
Step 8: Set Pilot Scope and Decision Gates
Limit duration, case count, team, language and transaction type. Include enough normal and exception cases for a decision.
Set three possible outcomes before work begins:
Scale: The primary metric improves and all guardrails remain within approved limits.
Revise: Value remains plausible but data, prompt, rule or training changes need another test.
Stop: Value does not justify cost or a critical control cannot be maintained.
A stopped pilot can prevent weak automation from reaching production. Name the gate owner and evidence.
Copy-Ready Odoo + AI Project Brief Template
Copy the structure below into a project document, Odoo Knowledge page or discovery worksheet.
Decision owner and review date:
Completed Example: Helpdesk Classification Pilot
This fictional example shows the required detail. Any numbers are illustrative.
| Brief area | Completed example |
|---|---|
| Project | Helpdesk ticket classification assistant |
| Problem | Agents manually categorise delivery and product questions. Baseline time and reassignment will be measured from a representative sample before approval. |
| Outcome | Reduce classification effort without increasing material routing errors. |
| Boundary | One support team, English email tickets and two request categories. Sensitive, legal, safety and refund cases are excluded. |
| AI role | Suggest category, priority and team. It cannot reply, close the ticket or change a customer commitment. |
| Data | Subject, body, customer, product, language, approved category definitions and labelled historical tickets. |
| Owner | Support operations lead owns value. Helpdesk administrator owns configuration while the service-data owner owns labels. |
| Human control | An agent confirms or changes every suggestion. Low-confidence and excluded cases enter manual classification. |
| Primary metric | Median active classification time for eligible tickets compared with the approved baseline. |
| Guardrails | Material misclassification, sensitive-case bypass, reassignment and unauthorised-access events. |
| Cost | Discovery, cleanup, configuration, test preparation, model usage, training, monitoring and support. |
| Gate | Scale, revise or stop after the approved case count and review period. |
Model and architecture decisions follow data, version, security and standard-rule assessment.
Cost and Dependency Questions
Ask these questions before approving discovery or a pilot:
Can standard Odoo configuration or automation rules solve the problem?
Which Odoo version, edition, modules and hosting environment are involved?
Does the design require an external model or integration?
What data cleanup and labelling effort is required?
Which cost changes with users, calls, documents or tokens?
Who monitors failures and supports the workflow after launch?
How will upgrades trigger regression testing?
Who owns prompts, mappings, test cases and custom components?
Include employee review time. Use measured net effort rather than gross AI output.
Final Project-Brief Review Checklist
Check the brief before sending it for review:
The problem describes a workflow pain rather than a missing technology.
The baseline uses a named source and calculation.
The workflow has one clear start and end.
Eligible and excluded cases are explicit.
The AI role uses a precise action verb.
Prohibited actions are documented.
Every required data source has an owner and access rule.
One business owner can approve, pause and stop the pilot.
Human review occurs before any material action.
Normal, ambiguous, sensitive and failure cases are included.
One primary success metric controls the value decision.
Quality and control guardrails prevent false success.
One-time and recurring costs are in scope.
Scale, revise and stop criteria are approved in advance.
If several items remain blank, complete discovery before requesting an estimate.
Connect the Brief to Odoo AI Discovery
The brief is discovery input. Discovery validates evidence, samples data, tests access, compares standard and custom options then builds a test plan.
Connect the template with Odoo AI and automation services when specialist assessment is needed. Discovery should return a confirmed workflow, source register, ownership, controls, estimate and gates.
Frequently Asked Questions
1. What is an Odoo AI project brief?
It is a short decision document that defines the workflow problem, desired outcome, required data, ownership, AI role, controls, pilot scope and success measures before solution design begins.
2. How long should an Odoo AI project brief be?
The core brief should normally fit on one page. Supporting evidence such as data samples, process maps, risk reviews and test cases can sit in separate attachments.
3. Who should own an Odoo AI project?
One business process owner should be accountable for scope and value. Data, technical and security owners support the project but should not replace business accountability.
4. What data should be listed in the brief?
List the specific Odoo records, fields, documents and external sources required by the workflow. Include ownership, purpose, freshness, access, retention and known quality problems.
5. How many success metrics should the brief include?
Choose one primary value metric then add a small number of quality, control, adoption and cost guardrails. Every metric needs a baseline, source, calculation and review period.
6. Should the project brief name a specific AI model?
Only when an approved constraint already requires it. In most cases the brief should define the problem and requirements first so discovery can compare standard Odoo, external services and custom options.
7. When is an Odoo AI project brief ready for discovery?
It is ready when the workflow boundary, owner, required data, human control, important exceptions and measurement approach are clear enough for a consultant to validate assumptions and estimate a pilot.
Conclusion
An Odoo AI project brief makes one idea testable. It names the workflow problem, minimum data, accountable owner and success metric.
Workflow boundaries control scope. Prohibited actions and review control risk. Exceptions keep work moving while guardrails stop speed from hiding quality or access failures.
Complete the template before choosing a model or requesting development. Missing evidence signals the need for discovery and a clearer ERP automation decision.
