GenAI: Thinking harder

Until the early 2020s, most creatives in architecture and design were relaxed, even optimistic, about the advance of AI. Robots had disrupted factory work in the 1980s, but the received wisdom was that the next wave would target white-collar jobs — lawyers, accountants — not us ‘no-collar’ creatives. What, after all, could be more innately human than creativity, innovation and taste?

That ambivalence evaporated in 2021-22. Image generators such as DALL·E, Midjourney and Stable Diffusion landed in 2021. Then, in November 2022, ChatGPT-3.5 shook even the sleepiest corners of designland awake. The pace of AI innovation – in what often seems like the only dynamic sector of the economy – has since provided regular aftershocks. The text-to-video tools like Google’s Veo and OpenAI’s Sora that put polished video production within reach of anyone who can write a sentence, being one of the latest.

AI, GenAI and AGI

First, some distinctions. AI terminology is riddled with anthropomorphism. Besides the slippery idea of ‘intelligence’, the leans heavily on human analogies — ‘neural’ networks, ‘learning’, ‘memory’, ‘reasoning’, even ‘hallucination’. These terms invite a false equivalence between silicon systems and our human ‘wetware’ (as some techies insist on calling our brains). Modern systems do not think as we do; they simulate some outputs of our thinking, but by very different means.

Impact on the creative process

GenAI has already reverberated through photography, illustration, graphic design, post-production, copywriting and content marketing. The work most vulnerable to disruption tends to be:

• Well-defined: low ambiguity with clear briefs and outputs, often constrained by frameworks such as templates or guidelines.
• Execution-focused: emphasis on producing artefacts, not on strategy, research or stakeholder engagement.
• Training data availability: outputs are widely represented in public training data and legible to machines (e.g. portrait photography, product shots, marketing copy).
• Siloed workflows: tasks are performed at arm’s length from teams or organisations (e.g. some freelance illustration).
• High volume, low differentiation: repetitive, template-based outputs where speed and cost dominate.

As with earlier automation waves, it is easier to see which jobs get displaced than which new jobs get created – either by that technology or by adjacent developments. The desktop-publishing (DTP) revolution of the 1980s and 1990s initially hurt typesetters and paste-up artists, but then led to a net expansion in opportunities for graphic designers as creative demand exploded and the web appeared and needed designing.

Paradoxically, historically, automation has tended to coincide with employment growth, because productivity gains have driven economic growth. Some argue that ‘this time it’s different’, that AI is qualitatively different from previous general-purpose technologies, such as steam power, electricity and computing. While this is still a moot point, one reason why today is different is that the GenAI disruption is happening during a prolonged period of economic stagnation, which is generating few new jobs.

Outside the most exposed disciplines, GenAI is aiding Architects, Industrial and UX designers across the creative process:

1. Discovery & research
   AI tools can conduct wider literature reviews, summarise markets, mine reviews, draft research plans and generate interview guides. They can also simulate ‘synthetic users’ to pressure-test early ideas when budgets are tight.
2. Concept generation & development
   Depending on the domain and brief, GenAI can significantly accelerate brainstorming, producing hundreds of divergent – and often crazy – options in seconds. Today, we direct through text prompts, reference images, and rough sketches; these are blunt instruments for communicating precise intent. To provide greater control, tools are evolving to accept more ‘multi-modal’ inputs, like voice gestural sketching, spatial constraints, style locks and parametric levers that let designers steer with far finer granularity.
3. Visualisation & prototyping
   Translating ideas into mock-ups and prototypes can often be done faster. High-fidelity visuals, storyboard frames and interface screens can be produced in minutes. Convincing apps and websites can now be scaffolded from natural language or ‘vibe coding’. Expect deeper integration with production tools, like 3D CAD and BIM-compatible models.
4. Content creation & production
   GenAI shines at the grind. It resizes assets, localises copy, produces variants, and helps keep campaigns coherent across channels. As more tools add AI co-pilots, maintaining conceptual consistency across platforms, formats, products, and regions will become less tedious.
5. Testing & iteration
   In packaging, interfaces and content, GenAI can simulate user feedback, uncover potential issues, and propose variations for A/B tests. Teams will increasingly test with ‘synthetic users’ first, then validate with real users.
6. Presentation & communication
   From first-draft decks to theatrical ‘vision films’, GenAI expands how we sell our ideas. It can draft rationales, generate supporting visuals and help stitch them into fluent narratives.

All of this streamlines the toil, but also creates new work. Because models are fluent, their mistakes can be subtle: factual errors, misattributions, logical gaps or fabricated details can slip past a quick skim, so more time needs to be given to rigorous checks. Creative leaders remain accountable for truth, taste and fit, and need to protect time dedicated to creative reviews. Design operations must evolve too: prompt libraries, model and asset governance, decision logs, redesign of workflows, and clear guidance for when and how to use GenAI at each stage. Not a quick job, and as the technology evolves, these frameworks will need to be rapidly updated.

More compelling than efficiency gains are the early signs that GenAI can enable entirely new products and services — such as mass-personalised media. During the 2024 Paris Olympics, NBC and Peacock launched ‘Your Daily Olympic Recap’, a customised highlights service that let users select up to three of their favourite sports. Each day, they received a bespoke 10-minute video summarising the action, narrated by an AI-generated version of sportscaster Al Michaels — with human’s checking for accuracy and tone. NBC anticipated delivering over seven million unique variations of these personalised recaps during the Games — a scale that would have been impossible without AI.

The human role in creativity

That frame still fits. Irrespective of technical developments in AI or the efficiency demands from management, we should insist on playing these eight fundamentally human roles in the creative process:

1. Framing
   If we don’t define the problem, scope, context, and goals – GenAI will do it for you, quietly based on generic patterns that are unlikely to align with your brand, client, or strategy. AI productivity gains are useless if your team is racing in the wrong direction.
2. Attunement
   AI can synthesise interviews or fabricate ‘synthetic users’, but nuanced insight is social and situational. Attunement is the human skill of sensing what is said and unsaid: noticing tensions, reading the room, spotting behaviours that do not fit the script, and reconciling conflicting stakeholder needs. It combines empathy with a holistic search for the truth.
3. Critical thinking
   When AI can produce confident and fluent BS, being able to think critically is more important than ever. This includes interrogating sources, triangulating claims, looking for AI bias, an insistent push to get as close to the reality of a situation as possible, and evaluating both the objective and subjective elements of a concept.
4. Direction
   Direction is the hard work of articulating a creative vision, then refining it until it is clear, coherent and appropriately ambitious. This requires a creative struggle to resolve competing requirements and constraints in a particular context. Humans author the vision and fight for it, AI can help expedite its execution – but possesses zero understanding or intent.
5. Judgement
   Design judgement is partly explicit craft and partly tacit knowledge — ‘we can know more than we can tell’. It is the cultivated sense of what ‘good’ looks and feels like, why a proposal fits a brand, why a timing is right for a market, and how to strengthen the perception of quality. AI may catch technical flaws; only humans can decide whether the work sings.
6. Agency
   Ideas do not move by themselves. Very little happens in the real world without human charm, guile, grit and hustle. As the tech sage, Kevin Kelly put it: ‘There are lots of very smart people who think that the most important thing in the universe is intelligence. (And let’s remember we don’t have a widely agreed-upon definition of intelligence). They vastly overrate the importance of intelligence. To get things done in the world, you need much more – including empathy, vision, persuasion, enthusiasm, determination, grit and many other things.’
7. Storytelling
   Even stellar work needs selling. Storytelling is how ideas survive contact with organisations. We craft stories that grab the head and heart, and flex them to different audiences and situations. We read the room, sense resistance, and pivot the arc in real-time.
8. Accountability
   Whatever the number of AI agents in a team, accountability remains human. Someone must own the rationale, the quality and the consequences — including legal and reputational risks. Systems do not have skin in the game; people do.

Today’s AI tools are remarkably capable. But humans are even more so. Our creative strengths are not just different from AI’s — they are also more consequential. In an age that routinely overestimates technology, underestimates humanity, and blurs the line between the two, we must not only uphold human values and capabilities, but also strengthen them to master this new technology to deploy it in ways that enhance our creativity.

We must master GenAI in both senses, to become proficient with it and be in the driving seat. Evidence is building that the early use of AI can ‘flatten’ our thinking and produce competent work that is uniform, clichéd and safe. This should not surprise us. GenAI is a technology of averages: it leans toward the mean, reinforces mainstream patterns, and smooths the edges off outlier ideas and perspectives. It raises the floor and lowers the ceiling of creative work.