Data & AI
UCD
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AI is transforming the way content designers work, creating new opportunities to improve how content is researched, designed, managed and maintained. In this blog, Content Design Lead Laurna Robertson explores how AI can support each stage of the content lifecycle, from discovery and planning through to content design and maintenance, while highlighting the importance of human judgement, accessibility and user focus.
Introduction
Over the last few months, I’ve been delivering content design training for one of our client's associate content designers. The training covers best practice across the content design lifecycle, from discovery through to maintenance.
When designing the materials, it was critical to reflect how AI is already impacting every stage of the content design lifecycle and how it can be applied in practice.
A 2024 industry survey by UX Content Collective found that over 80% of content designers were already using AI in their day-to-day work. This number has likely only increased as AI becomes more embedded in the tools we use and the processes we follow.
AI has a role to play at every stage of the content design lifecycle, from discovery through to maintenance, but it should be used with careful consideration.
Content discovery
In content discovery, AI can be used to:
speed up discovery activities, but managing stakeholder expectations and assumptions with this will be important
provide a wider range of data/insights
enable you to do a quick discovery at any stage of a project/piece of work.
My personal experience of using AI for this phase is that it’s most effective at analysing and summarising the significant volumes of data that content discoveries usually involve (using locked-down tools for the organisation/business area). Examples of this include:
prompting for keyword research and recommendations to improve findability and usability of content from this
suggesting recommendations based on content audit data
providing analysis of intent and behavioural drivers.
Careful prompting (and thorough review of outputs) is needed for this, however, to avoid the risk of false certainty.
AI can also be used to help to turn those insights and recommendations into a report and/or slide deck that summarises findings. Again, careful review is required of these, and it can be beneficial to take time to think about, and provide AI with, the structure and key messaging.
Activity | How AI can be used |
Finding content issues | AI highlights patterns, duplication, gaps. |
Identifying user needs | AI can cluster needs faster, but with less nuance. |
Understanding intent | AI improves intent detection through language analysis. |
Synthesis | AI speeds up synthesis, risks oversimplification. |
Prioritisation | AI suggests priorities, humans must justify them. |
Content planning
How to use AI in content planning
In the content planning phase, AI can be used to:
support faster sense-making by turning discovery findings/recommendations into a draft plan
create plans quickly and reduce manual effort to allow you to focus on the important decisions.
Specifically, I have found AI helpful for:
helping you to move from discovery outputs to planning by asking it for help with recommendations and turning those recommendations into a draft plan
identifying gaps to add to the plan – however, AI cannot assess when content is ‘enough’ and requires interrogation about whether ideas are unnecessary
identifying patterns that can be reused for components and pages
sense checking proposed KPIs and performance metrics for pages.
Content design and checking
Ensuring your content is scannable for AI (content structure)
Like humans, AI scans for information and looks for consistent, logical structures. So, the good news is, all the content structure best practice principles that content designers already use as part of their design work make content more scannable for AI too.
There are a couple of additional considerations when it comes to how to structure content:
use headings that answer real user questions
ensure each chunk of content makes sense on its own
use common questions in subheadings (for example ‘what is…’).
Content style and AI
Again, good news – following content style best practice helps content be found and prioritised by AI.
Some additional considerations for content style to consider specifically for AI include:
How you name things matters: be consistent and use language of your users, otherwise your content won’t get picked up when users are searching for things.
Use clear, explicit language with low inference: AI systems work best when meaning is explicit, not implied. Clear nouns and verbs reduce ambiguity and improve retrieval.
Content accessibility and inclusivity with AI
This is a well-documented weak spot for AI currently, so it’s just as well content designers are accessibility and inclusivity ninjas!
It’s important to take care when using AI because:
it learns from biased data
content that sounds inclusive doesn’t always mean it is inclusive
it does not take responsibility for harm.
How to use AI safely for inclusive content
AI can be helpful when used deliberately for:
drafting, sense checking, or widening options (not final decisions)
reviewing outputs with consideration for, or research with, people who have lived experience and accessibility needs.
The key message here is you should treat inclusive guidance from AI as hypotheses, not truths.
Content findability and AI
When considering how findable a content item is for AI, ask yourself the following questions:
Does the content item clearly answer a real user question?
Is the purpose obvious in the title and summary?
Would AI confidently extract a correct answer to the most common questions users ask about this topic?
Is the language user-centred, not organisational?
Is it connected to related content meaningfully?
Again, these are similar considerations that content designers have been asking for years when trying to make their content findable in search engines.
Content maintenance
What happens when content is not maintained?
If content is not actively maintained, the risks get bigger with AI (a recent blog by the Department for Business and Trade ‘How we’re preventing AI misinformation’ captures well the scale of the problem the UK government faces).
So, targeted content maintenance becomes even more important in the age of AI – as content is scraped for learning models, out-of-date content that is contradictory, poorly structured, or just doesn’t meet your users’ needs, may well end up in AI responses.
Both internal and external content has a role to play here and having well-governed content means the information provided is more likely to be trustworthy for users.
How to use AI for content maintenance
In the maintenance phase, when used well, AI can help reduce repetitive work and make it easier to spot potential risks. It can do this by:
identifying content that needs maintenance
prioritising what needs maintenance
ensuring consistency at scale.
However, it’s important not to treat AI as a replacement for content judgement and outputs at this phase will require review. It is also important to have other mechanisms to catch out-of-date and risky content, for example review flags.
Key takeaways
At this stage in its maturity, AI can help accelerate some key tasks across the content design lifecycle. It is particularly useful for data analysis, identifying patterns and making recommendations.
Some tools are becoming increasingly effective at supporting the writing and editing of content. But as content designers know, writing is only a small part of the role (research suggests anywhere between 10% and 20%).
Content designers must remain accountable for delivering user-focused products, services and guidance that meet user needs, and provide the right information at the right time in the right channels.
OpenPerspectives is our platform for Opencast people to share their thoughts and perspectives on modern digital delivery. It offers practical insight into user-centred design, engineering excellence, product leadership, data-driven decision making and building expert capabilities, grounded in real-world experience.
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