Have you embraced AI as a powerful editing tool? Or do you find yourself shying away from using AI for one reason or another?
AI is not just for enhanced spell-checking, it can be used for a lot of the challenges modern editors face in digital, multichannel, and multilingual publishing. (Our AI in publishing guide here).
AI is a multifaceted tool in any editor’s arsenal, so here are some (possibly new) ways for you to think about AI and editorial work.
Editorial support & process improvements
AI can (and should) be used as a content creation asset: see it as an addition to your editorial team. (Peep our post on AI and content production for more ideas).
Using AI in the editorial process is all about freeing time up for creativity, not replacing people with bots or machines.
Leverage AI tools to speed up and refine editorial processes.
How?
- Idea generation
- Idea validation
- Drafting
- Training
- Explaining ideas
- Faster copyediting
Like how keyword research, listening, or social media have been used for content ideation or generation, AI is a continuum of that. It’s about working smarter and leveraging data to improve your content.
Generative AI is especially useful for providing outlines, content structures, and thematic frameworks when you want to break down complex topics. Use it as a “first take”.
We recently wrote a blog about AI and content production.
Bringing data into it
AI can be part of your editorial ecosystem in more ways than one. Here are the ways in which collecting, collating, and curating data can help boost your editorial processes:
- In a busy editorial team, machine learning powered content curation can help you collate information faster and more efficiently. AI-powered synopses, reading lists, and analyses can save you time.
- Curation can also be used customer-side: use AI to create better online content resources. Curation is a powerful tool.
- AI can gauge sentiment at a glance and can be used to validate text and manuscripts. It can even give you a quick view into reviews and content performance.
- Recommendations engines are powerful content ecommerce tools and can help you distribute your content more widely.
- Personalisation is another way in which AI is changing the way we create content, as you now have the opportunity to create even more interactive and engaging content.
Editing and proofing phase
This is probably the big one that everyone already associates with AI, but there is no denying that AI-powered editing and proofing tools are incredibly useful!
- Grammar and spell check tools are lifesavers, but a word of warning: they are not always 100% accurate and can lead to standardisation (in a bad way). Junior editors should especially approach with caution.
- NLP models can help you quickly analyse large amounts of text and spot trends, sentiments etc. This is especially helpful for high-volume editors.
- Fact checking and sourcing can be helped by AI tools, as they can very quickly find references and summarise arguments or academic papers.
- Task management and editorial operations can be smoothed out with automations and hooks powered by machine learning. You can automate processes such as file naming, document creation, uploads/downloads etc. Content flagging and approval is another thing that can be easily automated.
Top tip: Shop around until you find a spellchecker that works for you. You can train spell and grammar checking tools to take your own context into account, so it’s worth working with one for a while. You don’t have to stick with in-built tools either, there are lots of add-ons and integrations to take advantage of.
Optimisation and automation
AI can be incredibly helpful when it comes to optimisation and standardisation. When you are dealing with a large amount of information or text, use AI to help you label, parse, and categorise content.
- Cataloguing and analysing large content data sets is something that machine learning is a natural at. Use AI to help you speed up time-consuming editorial processes: we have been using machine learning to help with large content libraries for a long time. Different types of automations are available – figure out what works for your processes.
- Content categories and tagging can be made a lot easier with AI tools.
- We have also been experimenting with creating better, automated metadata using generative AI. Metadata created with AI is a gamechanger. For accessibility reasons, this is great. (More about our TimeAI products here).
- Multilingual publishing can benefit from automated translation.
- Automated SEO advice and keyword optimisation can help with digital publishing, however, always take this advice with a grain of salt.
AI editorial ethics broken down into four themes
Editors and editorial staff have a big role to play when it comes to implementing ethical AI practices.
It is obviously a massive topic, but here are the key things to keep in mind:
- Transparency: communicate openly about how and why you use AI.
- Disclosure: have a clear editorial disclosure strategy.
- Bias: AI data models have been outed as biased on numerous occasions. How are you addressing that?
- Compliancy: data protection, information security – are you on top of these?
Editor’s AI tips
So, what are some of the top tips for editors and editorial teams wanting to embrace AI:
- Be strategic with it – do not try to do everything at once. Really look at your processes and be creative in how you can automate and streamline.
- Research the different tools and platforms and do your homework and due diligence before jumping in.
- Human oversight and intervention are key parts of responsible AI usage.
- Write your own policies as well as following others: take control of your AI usage.
- Seek feedback, keep improving, and keep learning.
Like other editorial tools at your disposal, AI is just another set of tools powered by modern computing power. Work with a responsible tech team to help you improve your editorial processes. We have our own AI services for publishers, including our TimeAI products.
