AI & CREATIVE THINKING
AI should
expand creative possibility, not reduce creative responsibility
AI is changing the creative process quickly, and I am actively trying to understand what that means in practice.
I am curious about where it can genuinely help across the creative process, including much earlier than production. I am more interested in where AI actually improves the work. That starts with understanding the challenge and testing the brief, then carries through into development, production and review.
For me, using AI well means finding the right role for it at each stage while keeping people responsible for what the work means and whether it is good enough.
Efficiency is
only part of it
Earlier this year I built a working framework for my own practice and workflow, with help from an engineer friend. It sets out how I would use AI at each stage of a creative project, from understanding the subject through to review.
Building it meant deciding, stage by stage, where the tool helps and where the judgement has to stay with a person. Some of those calls were easy.
Shiny objects are easy to make now. The harder calls were about where AI belongs earlier on, and what the work should be judged against at the end. That standard has to come from the people who make the work and care what it is for.
It is still being put through its paces and has not been used on live work yet. Most of what follows on this page comes from thinking it through.
Efficiency is only part of it
Creative teams are being asked to do more with tighter budgets and timelines. AI clearly has a role in that. It can remove repetitive effort and help teams organise complex information, creating more space for the work that needs human attention. Efficiency measures the process. It does not tell us whether the work is any good. A faster workflow is not much use if the work becomes generic or loses touch with the people it is for.
The useful gain is the space AI can create for better judgement and craft, including the decision about what is actually worth making.
Move AI upstream
A lot of the conversation around generative AI begins with output. Can it write the copy or make the image? Can it build the deck or create the video?
I think some of its most interesting creative potential happens earlier. AI can help a team understand an unfamiliar subject, organise complex information and test different ways of framing a problem before anyone starts making the final thing.
That is particularly interesting to me in healthcare and science communication, where understanding the facts is only part of the challenge. We also need to understand what those facts mean in somebody’s life. AI can widen the information and possibilities available to the team. People still need to decide what is relevant, truthful and worth pursuing.
Help me understand what is worth making before you help me make it
The human standard still matters
The easier it becomes to generate polished output, the more important judgement becomes. AI-supported creative work still has to earn its way forward. Does it answer the real brief? Is it clear, accurate and appropriate for the people it is meant for? Those standards do not come from the tool.
AI may help with the making. People are still responsible for the meaning.
I am still exploring
The technology is changing too quickly for me to claim I have a finished answer. I am testing tools and workflows and paying close attention to what actually happens in the work. Some uses are genuinely helpful. Others create more output without making anything better. I want to understand the difference.
I would rather stay close to the change, question it and learn from using it.