How can AI support in the content creation process?

With all the hype around AI tools these days, I’d like to add some thoughts on how this technology can pragmatically be used in the content supply chain. The outlined ideas below are specifically aimed at sports and esports organizations.

To start with, I’ve divided the content creation process into five different steps. For each respective step, I’ve included a few suggested AI use cases and tools. This is obviously by no means a scientific paper nor an approach to cover the entire possibilities with this technology but should provide a first useful overview to start with the integration of AI into day-to-day tasks across the content supply chain.

1)   Content Ideation

The constant pressure of coming up with new innovative ideas which resonate with the audience and current trends is a key challenge, especially for smaller content teams. Tools such as ChatGPT can easily be used as a source of inspiration. Start with identifying topics of interest for your channel, define keywords and use them to ask the AI various questions to identify content ideas for your niche with the help of AI.

2)   Content Sourcing

This step is often a challenge for larger organizations that barely have any own ongoing competitions throughout the year and function as governing bodies by focusing on enabling clubs, leagues and member associations to compete in a defined structure. By offering tools such as AI-assisted live clipping tools like WSC Sports or Levuro to stakeholders, organizations can support stakeholders while being able to access their content and promote the sports across the entire year.

3)   Archiving & Tagging

Quite a pain point for everyone working with large amounts of content: It’s archiving and tagging to easily identify archived content whenever needed. AI automated picture and video recognition of team crests, sponsor logos or player (faces) can help to ease some pressure on the operational teams and allow easier production in the next steps. Content Management tools like ScorePlay can already assist with built-in AI recognition features. 

4)   Production & Creation

This step will probably be impacted the most in the near future. ChatGPT offers excellent opportunities to support with AI-written articles. Those can further be turned into podcasts or into videos by using text-to-video applications. The graphic department can be supported by tools like AutoDraw, NightCafe Studio, NVIDIA Canvas, Dall-E and other tools that create their own graphics with textual or visual-based input. Another use case specifically in sports is AI-created cutouts for green screen photos - e.g., ClipDrop.co.

5)   Publication, Distribution and Community Management

The final step which will – for time being – require the most review before its usage: AI automated social media copies, re-sizing or tagged-based distribution to stakeholders is a first step towards the automation of publication and distribution of content. Various content management tools already offer some of these options. Content X developed by Infront can be named specifically for the sports industry. Community management is another field in which AI can be of help, assisting to aggregate and responding to various messages and comments across multiple platforms. Predictive content performance is another field in which AI can support identifying the most relevant topics, assets, headlines or similar ahead of publication.

Worth noting that you should understand each AI tool before its usage, especially if the tools are using live databases such as Google or are trained on a database with a specific cutoff date – the latter is currently applicable for ChatGTP.

I’m always curious to discuss this field in more detail or provide additional input if needed – feel free to reach out! (David Huber)

If you want to explore and discover AI tools yourself, I recommend Futurepedia.io which features hundreds of useful tools.