Traceable editorial approval
How can you allocate editorial approvals without increasing back-and-forth exchanges or losing accountability for publication?
3500 € per session, excl. VAT
In short
Over two days, totalling 14 hours excluding breaks, design a prototype editorial workflow combining responsibilities, checks and evidence of approval. Generative AI helps prepare reviews and summarise comments. You test exceptions, measure decision quality and prepare a limited pilot, without a full-scale rollout.
Why this training?
Approvals scattered across messages, files and meetings create uncertainty about which version has been approved and who made the decision. An explicit workflow distinguishes contributions, specialist checks, adjudication and authorisation to publish. AI can suggest a checklist or group comments, but it does not replace human approval. Its contribution must remain identifiable, verifiable and separate from decisions that commit the organisation. Running case study: The fictitious organisation Éclat Boréal Éditions is preparing a professional newsletter and a page presenting an offering. Sales feedback contradicts editorial corrections, while an unsourced claim requires adjudication before publication. Application at work: Propose a pilot for a newsletter, appoint its owner and compare turnaround times, feedback and defects against a documented baseline. Then decide on adjustments before any expansion.
Objectives of this training
- Mapping editorial stages and bottlenecks
- Assigning responsibilities and adjudication rights
- Prototyping a workflow with statuses and evidence of approval
- Testing rejections, urgent cases and changes after approval
- Measuring detected defects and preparing a pilot
Who is it for and what are the prerequisites?
Roles and tasks concerned
- Communications managers and editorial managers
- Content project managers coordinating multiple approvers
- Subject matter representatives taking part in editorial reviews
Prior skills
- Be familiar with a content production and publication process.
- Be able to use a generative AI assistant and verify its suggestions.
What you will work on
Diagnosing workflows
Objective: Locate bottlenecks. Requests, contributions, approvals and publication.
Allocating responsibilities
Objective: Identify the final decision-maker. Writer, reviewer, expert, adjudicator and deputy.
Defining checks
Objective: Tailor the review to the risk. Accuracy, tone, sources, confidentiality and specialist checks.
Formalising transitions
Objective: Make transitions explicit. Draft, review, corrections, approval; deadlines and escalation.
Building the prototype
Objective: Make decisions traceable. Identifier, version, comment, owner and evidence of agreement.
Setting boundaries for AI assistance
Objective: Separate suggestions from approvals. Feedback summary, references and human approval.
Simulating a review
Objective: Test the standard workflow. Handover, correction and agreement on a specific version.
Handling exceptions
Objective: Test adjudication routes. Absence, urgency, disagreement and changes after agreement.
Measuring quality
Objective: Establish objective evidence for the test results. Simulated turnaround times, feedback, detected defects and missed defects.
Preparing the pilot
Objective: Define the scope of the trial. Scope, support, acceptance thresholds and stopping.
What you take away
- Workflow map and responsibility matrix
- Prototype approval register
- Editorial checklist and AI instructions
- Test report and pilot plan
Method and assessment
- Brief explanations followed by immediate practice using the case study that runs throughout the course.
- Individual work followed by work in pairs, with participants reviewing each other’s outputs.
- Guided group discussion: what works, what needs correcting and what remains a human decision.
- Preparation: Describe an existing workflow and anonymise its examples; the exercises use only fictional or anonymised cases. Identify the tools authorised by the organisation and the applicable confidentiality rules.
Observable criteria: Each transition has an owner and an explicit condition. ; Each approval identifies a version, a decision-maker and a date. ; Every disputed claim is assigned a source or a decision to withdraw it. ; The exceptions tested trigger a traceable human adjudication. ; The indicators specify the calculation, source and review frequency..
Frequently asked questions
Can AI approve content?
Not within this framework: publication authorisation remains a human decision.
Is specialist software required?
No, an authorised shared register is sufficient for the prototype.
How should you handle an urgent publication?
Provide a streamlined workflow with a designated adjudicator and essential checks.
Will the workflow be deployed in two days?
The training produces a tested prototype and a pilot plan, not a production deployment.
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