How to Build an AI-Assisted Matchday Content Workflow

Matchday is where AI either pays for itself in a single afternoon or quietly gets abandoned by your social team. The clubs winning with it have stopped trying to use ChatGPT during the match — that always fails — and have started using it as a draft engine that wraps around the match, with the live window kept entirely human.

Here is the four-stage workflow we deploy for clubs from National League up to the Championship. It runs on any of the major LLMs (Claude Sonnet 4.5, GPT-5, Gemini 2.5 Pro). Tools are interchangeable. The structure is not.

Stage 1: pre-match brief (T-24 hours)

The single biggest mistake clubs make is firing up ChatGPT for the first time at 4:55pm on a Saturday. By then it is too late — there is no context loaded, no tone calibration, no storyline.

The night before the match, the social lead spends 15 minutes writing a match brief document and pasting it into a fresh chat with the LLM. The brief contains:

• The 11–14 line tone-of-voice block (see the system prompt in our 27-prompt library).
• The fixture: opponent, kick-off, venue, stand-out injury news, ticket allocation.
• The week's 2–3 storylines: "Adekoya's first start since the calf injury", "150th derby", "Stand renaming announcement at half-time".
• Three example captions from your last 5 matches as tone anchors.
• A "do not say" list specific to this fixture (e.g. avoid mentioning the 5-0 reverse fixture loss).

This single document is the most valuable artifact of the entire workflow. Save the chat window — you will reuse it through Stage 4.

Stage 2: pre-match content (T-24 to T-15)

With the brief loaded, generate the deterministic content first:

1. Tomorrow's IG hype Reel caption (T-24).
2. Lineup announcement post template (T-90, fill scorers later).
3. Half-time fallback posts: one for winning, one for level, one for losing. Pre-generated.
4. Three full-time long-caption templates: win, draw, loss, with [SCORER] and [MIN] placeholders.

This entire batch takes 8–12 minutes inside the same chat that has your brief loaded. The LLM stays consistent because it has the context. None of these posts are auto-published — they go into your draft queue (Buffer, Later, Sprout, or just a shared Google Doc).

Stage 3: live match (kickoff → full time) — humans only

Do not use the LLM during the match.

Goal posts, line decisions, sub announcements — all human. The penalty for an AI-generated post that gets a fact wrong (a wrong scorer, an injured player named) on a live feed is reputational damage that takes a month to undo. The lineup template, half-time fallbacks and full-time templates from Stage 2 are exactly what give you the speed you used to need an LLM for.

A single dedicated person on X for live updates, plus your photo/video team capturing for the post-match window. This is the matchday minute-by-minute playbook we already publish — the AI workflow wraps around it, not through it.

Stage 4: post-match drafting (full time + 0 to +90 min)

The hot 30 minutes after full time is where the workflow earns its keep. The same chat window with your match brief is reopened. You paste in:

• Final score and goalscorers with minutes.
• One sentence on the standout performer.
• One sentence on the key tactical moment.
• A link or paste of the live X feed from the match.

Then in a single message you ask for the full output set:

Using the match brief from earlier in this chat, draft: (1) the 90-word IG long caption, (2) the post-match X thread (4 tweets), (3) the YouTube highlight description, (4) the LinkedIn post for the commercial director, (5) a 25-second TikTok voiceover script.

A good model returns all five in 30–45 seconds. A junior on the team reviews and posts within 5 minutes. Total elapsed time from full-time whistle to Instagram drop: 6–8 minutes, against a typical 22–28 minutes without the workflow.

Stage 5: T+24 — the recap loop

The morning after, the same chat is used one more time:

• Long-form Reel script (60–90 sec) for the recap.
• YouTube highlight package description.
• Photo gallery captions for the website.
• A "what we learned" article-length post for the club website (great for SEO; helps fill the international-break content gap).

This stage is where most clubs leave value on the table. It takes 25 minutes and meaningfully increases organic search traffic to the club site.

What this workflow costs (in 2026)

Running this end-to-end through Claude Sonnet 4.5 or GPT-5 costs a club roughly £0.80–£2.40 per matchday in API/usage fees — usually less than a single iced coffee. The labour saving is between 45 and 90 minutes of senior social team time per match, every week, all season.

Common failure modes

• Skipping Stage 1. The brief is the workflow. Without it the model produces generic content and the team blames the model.
• Trying to use the LLM live. Always fails. Pre-generate templates instead.
• Auto-publishing. Even the best output needs 60 seconds of human review. Always.
• Using a different chat each time. Tone drifts. Use one persistent thread per matchday.