Artificial intelligence is everywhere right now, on slides, in pitches, and lining the pages of design strategy decks. But most organisations aren’t transforming with AI. They’re still figuring out how to build with it.
In a recent post by the Design Executive Council, the distinction is made clear: what businesses are calling “AI transformation” is currently just “AI construction.” For design leaders and educators, that distinction isn’t critical. And overlooking it is what keeps us from leading the AI conversation with the clarity it demands.
You can’t transform with AI until you build for it
Many people talk about AI transformation as though it’s a switch you flip. But this transformation is the outcome. What’s often overlooked is that it can’t happen without construction. And that misunderstanding is what keeps organisations stuck.
Businesses want the increased productivity, better decisions, and competitive edge that AI promises. But they often underestimate that transformation requires groundwork: systems, workflows, team fluency, and integration. Because AI tools are widely available (many are just a subscription away), it’s easy to assume that infrastructure isn’t needed. But tools alone aren’t transformation.
What’s missing is the deliberate effort to construct an AI ecosystem: a set of tools, workflows, and behaviours designed to integrate AI into the fabric of practice. It’s the same at the individual level. I’ve built my own AI ecosystem: Midjourney for images, RunwayML for video, Krea for 3D, ChatGPT for everyday writing, Anara for academic research, to name a few. These tools are a part of my system that accelerates my workflow. That’s construction. That’s infrastructure. And without it, transformation can’t take root.
Design must be offensive, not decorative
One line from the article hit me hard:
“Design can be one powerful lens through which companies anticipate change, and the discipline that ensures AI solutions are not just technically sound, but behaviorally wise, emotionally intelligent, and competitively sharp.”
This is a rallying cry.
Too often, design is invited into the AI conversation after the infrastructure is in place, brought in to make the interface pretty or the chatbot less robotic. But if we want human-first systems, design has to be there from day one. Design is not the final flourish; it’s the compass. That means design leaders can’t wait for the invite. We must take the offensive and actively shape how AI is imagined, integrated, and scaled. Not just for aesthetics, but for ethics, experience, and emotional intelligence.
Teaching AI to designers to stay ahead of the curve
This isn’t just a challenge for professionals. It has deep implications for design education and research.
If we want designers to have a seat at the AI strategy table, we need to equip them with more than just tool skills. We need to train them to question how AI is used, what it means, and how it shapes behaviour. That starts by keeping our own understanding current and not letting AI become someone else’s domain. Otherwise, we risk design being seen as the final touch, superficial and aesthetic, rather than a method of shaping meaning from the start. Staying ahead of the curve means treating AI like a design material, not a bolt-on feature.
Most organisations haven’t built yet
Here’s what I’m noticing on the ground: universities and organisations want to claim they’re in the “AI transformation” phase. But when I look at their capabilities, the reality is closer to the early construction stage and sometimes not even that. They’ve skipped over the foundational work: investing in AI fluency, testing workflows, building guardrails, or refining outcomes. In Blackrock’s AI evolution framework, these are Phase 1 Build Out and Phase 2 Adoption. But everyone wants to jump to Phase 3 Transformation without doing the work.
It’s not just an organisational issue. For individuals, too, there’s a case for taking AI construction seriously. Think of it as building your personal AI ecosystem:
- Start with the tools and infrastructure (Build)
- Test how it fits your workflows (Adopt)
- Reflect and use feedback to improve your processes and outcomes (Transform)
This building your personal AI ecosystem deserves a post of its own (and it’s coming soon).
I’m somewhat transforming? But with scaffolds
Personally, I’m somewhere between adoption and transformation. I’ve built my own ecosystem of tools, workflows, and prompts. I’m experimenting and evaluating constantly. But even so, transformation isn’t a destination, it’s a cycle of testing, refining, and rethinking.
Working with others, I’ve seen how seductive the word “transformation” is. But real transformation is hard. It takes infrastructure, clarity, and above all, time. My job often becomes helping people recognise what phase they’re actually in and what’s missing to move forward.
So where are you in your AI transformation journey?
Think about your own practice, organisation, or classroom. Are you constructing the foundations? Adopting and testing workflows? Or already transforming how you design and deliver?
And what’s the one thing you need to build today to move forward?
