By Artem Korsakov ·
A neural network helps quickly prepare a draft post, and a video cutting service offers short clips. Yet if the publishing schedule still slips, it is worth checking the other stages: sourcing, fact-checking, approvals, corrections, and publishing itself. Automating a draft solves only part of the task.
Where time is actually spent
Let us take one piece: a post and a short video based on an interview with a client expert. The path looks like this:
- The editor receives the interview recording and decides what to write about.
- They prepare a draft post and a video script.
- The draft is sent to the expert for fact-checking.
- The expert responds two days later and corrects one number.
- The editor edits the post, but forgets about the video script.
- The client approves the post; the video goes to editing with the old number.
- The post is scheduled, but publication fails due to a platform error. This is noticed only a week later.
Generation speeds up the second step. The other six remain the same, and some become more complex: there are more drafts, and all of them must be checked.
Подпись: An educational example from the article. The draft is ready in an hour, while the rest of the time goes to waiting, reworking, and unnoticed publishing failures.
A fact lives in multiple materials
The main reason for errors is that one fact appears in several pieces. A number from an interview ends up in the post, in video subtitles, and in a carousel caption. When the expert corrects the number, it must be corrected everywhere. If the link between the fact and the materials is not recorded anywhere, the editor must remember it by heart.
A neural network exacerbates this problem. It creates more variants from the same source, and each variant carries facts. The more variants there are, the more places where the old version remains.
Approving a specific version
The second reason is related to approvals. The expert approved the text on Tuesday. On Wednesday, the editor edited the introduction. What exactly did the expert approve: the text on Tuesday or the text with the introduction? If approval is attached to the entire piece, without a version, it is impossible to answer.
Services for teams working with social media solve this through multi-level approvals. For example, Planable describes multi-step approval, where a piece passes through designated approvers in sequence. Thus, there is already a ready tool for such a process. The question is whether approval is tied to a version and withdrawn when a fact changes.
Publishing and errors
The third reason arises during publishing itself. Scheduling tools exist, and some combine video cutting with scheduling. For example, OpusClip has a publishing calendar for videos obtained via cutting. Besides scheduling, it is necessary to check how the selected service reports errors and retries sending.
For regular publishing, you must see errors: a piece failed to publish, the platform did not confirm publication, or a retry sent a duplicate. And you need a rule: if a fact is corrected after scheduling, the old version must not go live.
A separate case that is easy to miss: a platform accepted the publication, but confirmation did not arrive. To the system, this looks like a failure, although the post is already live in the channel. If you correct a fact after this, you must remember that the old version is published and requires correction. A retry in such a case must check whether publication was already accepted; otherwise, a duplicate will appear in the channel.
Check: where your publishing gets stuck
Answer the questions based on materials from last month:
- How many pieces were published late, and at what step did they stall?
- Was there a case when a fact was corrected in one piece and forgotten in another?
- Can you say which exact version of the text the expert approved?
- Do you learn about failed publications on the same day?
- Does the scheduler know that a piece was changed after approval?
- Are costs for models and editing visible per client?
If the answer to questions two and three is "yes, it happened" and "no, it cannot be determined," speeding up generation is pointless. The bottleneck lies in the links between facts, versions, and approvals.
Why this cannot be solved by hiring
A common reaction to missed deadlines is to hire another editor. This helps with the volume of drafts, but links between facts and materials still live in people's memories. With each new employee, there are more such memories, making them harder to coordinate. If one editor corrects a fact, the second will not know that their video is also outdated.
The same applies to costs. When multiple people create materials for multiple clients, costs for models, editing, and storage get mixed up. Without accounting per operation, it is difficult to understand how much each client costs in production.
Подпись: Hypothetical costs, not service tariffs. The majority of the cost is editor time.
What changes when a process is recorded
When facts, materials, and approvals are linked, correcting a fact immediately shows which pieces are outdated. Approval is withdrawn only from them. Scheduled publishing of an old version is cancelled. The editor reassembles the piece, the expert confirms the new version, and the scheduler places it in the queue.
Generation remains useful in such a system, but ceases to be the center. The center becomes accounting of what has been checked and what has not.
In a demonstration scenario, you can trace the chain: source, post and script, corrected fact, outdated versions, reassembly, expert approval, and a local publishing queue with a failure and retry.
Related article: From one interview to posts and videos: working a content factory.
Sources and limits of the example
Sources confirm the described features of ready-made products, not demand or the profitability of developing one's own product. Scenarios and amounts are educational. Demonstrations work with fictional data; real integrations, AI, payments, and external publishing are not connected in them.