This question has been following me around for a while, but it became very real at the recent Cumulus Conference at the University of Johannesburg (UJ). I joined a panel facilitated by Frédéric Degouzon (Director of International Strategy & Development at L’École de design Nantes Atlantique), alongside Pierre-Yves Panis (Chief Design Officer at Signify) and my friend and colleague, Kgothatso (KG) Lephoko, a senior lecturer at UJ and PhD candidate in Digital Transformation at the Johannesburg Business School.

In the intimate audience were practitioners and educators from South Africa and around the world. People weren’t there for abstract theory. They wanted to know what this actually means for their work, their students, and their careers.

This article is my attempt to slow that conversation down and make sense of a few threads:

• The reality check: What is genuinely new, and what is just another tool shift.

• The talent impact: How this hits juniors, mid-levels, and seniors differently.

• The African context: Why our “missing data” is both a risk and a strategic opportunity.

• The human moat: Why our most important work now is to amplify what makes us human, not imitate machines.

What’s new, and what isn’t

Let’s start with a simple truth: concern is real, but it isn’t new.

We’ve been through disruptive tool shifts before. Many of us remember the transition from paper to tools like Freehand, to Adobe Creative Suite, and now to Figma and Miro. Each wave automated something that used to be slow, manual, and sacred to the craft.

Despite each wave, design didn’t vanish. What kept the profession relevant was not the tool, but the underlying principles: empathy, systems thinking, and a clear view of consequences.

However, AI is different in two specific ways:

1. Speed: It doesn’t just accelerate execution; it collapses the time between “thought” and “artefact” to near zero.

2. Mimicry: It generates layouts, copy, and code that look deceptively “human” at first glance.

This creates a new tension. The barrier to entry for producing design has lowered, but the barrier to entry for valuable design, work that solves real business problems without introducing risk has risen.

Who is actually at risk? (The squeeze has moved)

When I first started tracking the rise of Generative AI, my immediate worry was for the juniors. I looked at the automation of basic tasks and wondered: if the bottom rung of the ladder is gone, how does anyone start climbing?

But the more I observe teams adopting these tools, the more my perspective has shifted. I am less worried about the new entrants, and significantly more concerned about the middle layer.

Here is how I see the risk and opportunity distributing across the three tiers of our industry:

1. Juniors: The disappearing “grunt work”

Traditionally, juniors cut their teeth on low-risk, repetitive tasks: resizing assets, cleaning wireframes and churning out screens. AI now handles this execution instantly.

• The Risk: If the “grunt work” disappears, where do juniors learn the craft? How do they develop the intuition that comes from doing the work manually 100 times?

• The Opportunity: Juniors are effectively AI-natives. They have less ego tied to “how we used to do it” and are willing to experiment rapidly. If we structure their roles correctly, they stop being “production assistants” and become rapid prototypers who can stress-test ideas faster than any senior team could.

2. Seniors: Context as a moat

Senior practitioners carry something AI does not have: deep institutional memory and political context. They know why the last product failed, they understand regulatory constraints, and they can read the room during a difficult stakeholder meeting.

• The Risk: Complacency. If seniors refuse to engage with the tools, they become expensive bottlenecks.

• The Opportunity: They become the “orchestrators.” They use AI to generate options, while they focus entirely on problem framing, ethics, trade-offs, and decision-making.

3. Mid-levels: The squeezed middle

This is the group I now worry about most. They have more responsibility than juniors but less strategic leverage than seniors. Their value has often been defined by being “very good executors”, the safe pair of hands that delivers high-quality assets.

• The Risk: AI is moving hardest into the execution layer. Being a “good executor” is no longer a defensible position when a machine can execute faster and cheaper.

• The Opportunity: They must move up the stack into problem definition and facilitation. They need to stop protecting the pixels and start bridging the gap between business goals and AI-assisted delivery.

The “Power Pair” Hypothesis This creates a fascinating new dynamic. I believe we might see the emergence of a “barbell” team structure where the combination of Junior + Senior becomes the most powerful unit in the business.

Imagine a fearless, AI-native junior who can prompt, iterate, and generate at high velocity, paired directly with a seasoned senior who provides the taste, the constraints, and the strategic direction. The junior provides the speed; the senior provides the compass. Together, they can move faster than a traditional heavy team structure, bypassing the “review loops” of the middle layer entirely. The middle must evolve to facilitate this system, or risk being bypassed by it.

What I’ve learnt about how AI actually works

Through my learnings at IMD and from experts like Dr Jay van Zyl, my mental model for AI has shifted from “magic” to “mechanics.”

It is a pattern machine, not a thinking partner. AI predicts the next pixel or token based on data. It does not have lived experience, a body, or a stake in the outcome. It doesn’t lie awake at night worrying about whether a design decision will pose a reputational risk to the bank.

In Johannesburg, I framed it this way:

“Don’t ask AI for the meaning of life. Tell it the meaning of life – then interrogate it based on your input.”

This shifts the workload from creation to curation.

• If you ask AI to “fill in the gaps,” you are outsourcing thinking (high risk).

• If you feed it your context, constraints, and principles, and then critique the output, you are using it as an amplifier (high value).

The limits of compute There isn’t enough computing power in the world to replace the context and complexity of a room full of humans. AI creates connections at scale, but it operates in a bounded reality. It creates outputs; humans own outcomes.

Africa is not a footnote – it’s the missing data

If you are working in South Africa or emerging markets, you know that most Large Language Models (LLMs) are trained on Global North data.

This means our languages, financial realities, informal economies, and regulatory nuances are often treated as edge cases or missed entirely.

The Risk: We deploy tools that make confident but harmful recommendations because they don’t understand the context of a user in Soweto or a small business in Nairobi.

The Opportunity: We don’t have to accept being an afterthought. There is massive strategic value in:

1. Curating local datasets: Building the proprietary data that makes AI actually work for our markets.

2. Designing for constraints: We are experts in designing for low bandwidth, high data costs, and diverse languages.

3. Governance: We can lead the way in designing evaluation criteria that catch local harms early.

We are not just consumers of this technology; we have the opportunity to shape how it is adapted.

The Human “Unfair Advantage”

If AI handles the patterns, what is left for us?

1. Embodied Empathy AI can mimic the language of empathy, but it feels nothing. Designers see the hesitation in a client’s eyes, the tension in a user interview, the silence in a boardroom. We use that unspoken data to make decisions.

2. Sense-making in messy systems Organisations are political and emotional. A model might suggest an “optimal” process that is mathematically perfect but culturally impossible. Humans navigate the gap between “optimal” and “real.”

3. Holding Tension Leadership is often about holding contradictory truths: innovation vs. stability, efficiency vs. humanity. AI optimises for a single objective function. Humans can hold the tension between competing values.

A pragmatic path forward

We need to move from “talking about AI” to changing how we practice. Here is where I believe we should focus:

For Practice: Redesign the apprenticeship

• Stop hiding the tools: Treat AI literacy like typography, a core craft skill.

• Pairing: Pair AI-native juniors with context-rich seniors. Let the junior drive the tool; let the senior drive the critique.

• Focus on metrics: Don’t measure “AI adoption.” Measure whether it’s helping us learn faster, reduce rework, or deliver better client outcomes.

For Research: Interrogate the model

• Question the source: “Whose data is this trained on?” “Where does this break for our users?”

• Real-world loops: Don’t rely on synthetic data. Build feedback loops from real client behaviour in our specific markets.

For Education: Assess the thinking, not the artefact

• Shift the grade: If AI can produce a polished UI in seconds, the grade shouldn’t be for the UI. It should be for the problem framing, the ethical reasoning, and the justification of the solution.

• Context is king: Encourage projects rooted in African realities, where “out of the box” AI solutions fail and human ingenuity is required.

Optimism with eyes open

My stance is simple: AI will absolutely change how we work. It will reshape career paths and expose gaps in our systems.

But it does not remove the need for thoughtful, principled leadership. If anything, it raises the bar. The challenge for us in Africa is to ensure we are not passive test subjects for other people’s algorithms. We have the talent and the lived experience to co-author this story.

The real question isn’t “Will AI replace us?” It is: “Will we use this moment to amplify what makes us uniquely human or quietly outsource it?”

Call to Action

I am partnering with two industry peers to co-author a deeper dive into this topic.

If you are working at the intersection of design, AI, and business strategy, specifically in the African context and want to share your experiments or challenge these ideas, I’d love to hear from you.

Let’s write the story ourselves.

At the task level, the answer is yes. AI is dismantling traditional disciplines at speed. Product managers who spend their time grooming backlogs or compiling reports are already being edged out by smarter tools. Designers who once poured hours into wireframes and UI explorations are watching generative platforms create them in seconds. Engineers writing boilerplate code are being matched, and often outpaced, by copilots that can generate, test, and refactor faster than any human. If your value sits in repetitive, tactical execution, then you are replaceable.

But the story changes at the higher levels. Senior product managers who can craft a vision and build executive alignment still matter. Architects who design for resilience in messy, regulated environments are indispensable. And designers who can connect the dots between customer journeys, business systems, and organisational outcomes are not just safe, they’re poised to lead.

While all three disciplines require creativity, their center of gravity differs. For product managers, it often sits in logic, prioritisation, and feasibility. For engineers, it lies in precision, scale, and resilience. Design’s unique power is that its center of gravity rests in human ambiguity and intuition. We are trained to begin in the chaos of human needs and create coherence from it.

Apple’s obsession with typography and Airbnb’s reinvention of trust are familiar examples, but we need to bring this lens to today’s AI-driven world. Imagine two AI-powered financial planning tools. The tech-and-product-led version might perfectly optimise a user’s portfolio based on data. It’s efficient but cold. The design-led version first seeks to understand the user’s relationship with money, their fears, hopes, and dreams. The resulting AI doesn’t just give advice; it builds trust, educates with empathy, and frames its suggestions in a way that aligns with the user’s life goals. One manages a portfolio; the other empowers a person. That is the difference design makes.

AI makes this adaptability even more critical. Yes, the tools are automating traditional design tasks, but that only frees us to focus on what machines cannot replicate: creativity and intuition. AI is exceptional at processing what already exists. Designers, however, can imagine what has never existed. We can intuit the leap from “what is” to “what could be.” That human spark,the ability to create the unexpected is where our edge lies.

This is why unplugging matters. If all we do is lean on AI, we’ll end up regurgitating the same recycled outputs the machine feeds on. The real value comes when designers step back, breathe, and listen to what humans need, not just what the dataset suggests. Once we’ve made that leap, then we can bring AI back into the process as a multiplier, not a master. Those who don’t will drown in sameness. Those who do will shape the future.

I see design as the most human operating system for business. AI can generate, but it cannot care. It can simulate choice, but it doesn’t understand consequence. It can remix fragments, but it cannot weave them into trusted, coherent experiences. That’s our job, and it has never been more important than it is now.

The Designer’s Leadership Playbook for the AI Wave

If design is to lead in the AI era, we must lean into the traits that set us apart.

1. Championing the Human Experience

Product and tech often begin with feasibility and optimisation. Designers start with people. We ask not only can we build it or should we build it, but does it truly solve a human problem? In an AI-driven world, this instinct to anchor in the human experience is a leadership trait.

2. Connecting Strategy to Execution

Designers excel at taking a big idea and translating it into practical, testable steps without losing sight of the bigger picture. It’s this ability to make vision tangible that keeps execution tethered to strategy in the middle of chaos.

3. Integrating Across Silos

Design is a natural integrator. We sit between business, technology, and customer needs, and we speak the language of all three. While PMs optimise for delivery and developers for efficiency, designers hold the wider view and ensure coherence across silos.

4. Creating the New Through Intuition

AI can only remix what’s already there. Designers can create something truly new. We combine data with intuition, cultural understanding, and creativity to make leaps into the unexpected. This is the space where real differentiation happens.

5. Crafting the Narrative of “Why”

Design doesn’t just produce solutions, it frames them. We tell stories that help people see, feel, and believe in a future. In a world flooded with AI-generated outputs, the ability to articulate why it matters is what inspires action.

So here’s the rallying call: design doesn’t just have a chance to survive this wave, it has the responsibility to lead it. AI is changing what work looks like for all of us, but design is the discipline best placed to rise above the noise. If we lean into our humanity, our creativity, our integrative instincts, and our storytelling, we can become the operating system that makes everything else work.

The real question isn’t who’s most replaceable. The real question is who’s most ready. And in the age of AI, it has never been more important to be a designer.

So I’ve shifted my approach. I lead with the pillars and tuck the detail away in an accessible place. Everyone can pull it when they need to sit with it; nobody is forced to drink from the firehose on day one. And yes, I still do a “big-bang” strategy reveal because detail signals rigour and calms dissenting voices early. But the big-bang is not the point; it’s the starting gun. The real adoption happens incrementally, over months, through day-to-day interactions where we embed the strategy into how we think, decide, and measure progress.

What I do differently now

I separate availability from delivery.

There’s a difference between making all the detail available and delivering all the detail up front. I’ll do the hard thinking early: assumptions, options, trade-offs, risks, metrics and package it so the curious can dive in. But I present the strategy as a clear story with a handful of pillars and a crisp “why.” People need a map, not a manual.

There’s also a human factor leaders forget: a one-time briefing is a forgetting machine. Decades of learning research show we retain a fraction of what we hear once. So I treat the first strategy reveal as credibility creation (“this has been thought through”) and direction setting (“here’s where we’re going”), not as the only time people will ever hear it.

I use repetition without the eye-roll.

If I’m not slightly tired of hearing myself say our pillars, I probably haven’t said them enough. The trick is to vary the surface, keep the core: repeat the same few ideas in different forums, 1:1s, reviews, stand-ups, roadmaps, monthly/quarterly engagements, each time connecting them to real decisions. It’s not “strategy theatre.” It’s spaced reinforcement so the message actually sticks.

I embed the strategy in the work (not just the deck).

Rather than waving a flag that says “this is strategy,” I connect everyday conversations back to it. In reviews I’ll ask, “How does this move us toward our outcomes?” In planning, “What will we stop to make room for this pillar?” In 1:1s, “Where are we seeing friction against the vision?” It’s a weave of steering, coaching, evangelising, and when necessary, tearing down and redrafting. You don’t need to announce the strategy every hour; you shape the environment so it becomes the default way of thinking.

I insist on outcomes and baselines.

One of the most practical shifts I’ve driven is getting designers comfortable with quantifying their work. Not because numbers are everything, but because they focus the effort. I ask the “why, why, why, why” and then, “What’s the baseline?” If you’re redesigning a flow, what is the drop-off today? If you’re improving quality, how are we measuring errors now? Once there’s a baseline, value becomes easier to articulate, and momentum follows. I’ve watched designers who once avoided metrics start to bring them to reviews and 1:1s with pride because the numbers now tell their story.

I make progress visible, little and often.

Monthly or quarterly, I show the journey, not just the destination: where we’ve moved from the big-bang to the increments, what shipped, what changed in sentiment, where we’re behind. People begin to see the arc. Those who were unconsciously contributing start to actively contribute. Not everyone gets there at the same time, and that’s okay. As a leader, your job is to hold the tune and adjust the voices in the choir until there’s harmony.

I standardise reporting, but I don’t rush it.

Common language beats clever dashboards. We standardise how teams report progress (a small set of outcome metrics tied to pillars), and I give it six to twelve months to bed in. Start small. Be boringly consistent. You’re building muscle, not chasing novelty.

I keep the vision steady and the details agile.

Environments shift. Competitors launch. Technology surprises you. I will adjust tactics without drama, but I anchor those adjustments back to the pillars: “Same north star. New route.” People accept change when they see the through-line hasn’t moved.

What this avoids

It avoids the two traps I fell into earlier in my career:

• The under-communication trap: one impressive reveal, then silence. Leaders live with the strategy every day and assume others do too. They don’t. Without reinforcement, people fill the vacuum with their own narratives.

• The detail deluge trap: radical transparency that reads like volatility. When every brainstorm and tweak lands in the same channel as decisions, teams can’t tell what to act on. Attention splinters. Confidence erodes.

The middle path is clear, simple pillars; accessible depth; and relentless, varied reinforcement tied to real decisions and outcomes.

What it feels like inside the team:

At first, inquisitive designers test the edges (that’s good). They question definitions, push on trade-offs, and hunt for contradictions. Because the detail is accessible, you can point to the rationale without turning every meeting into a dissertation. Over time, the conversation changes. Designers begin to pre-empt the strategic questions: “We chose Option B because it advances Pillar 2 and we expect it to move metric X from A to B.” Wins get celebrated in the language of the vision. The strategy stops being your story and becomes theirs.

You’ll also notice the tempo change. Fewer pet projects sneak in under the radar. More “no’s” are easy because the framework makes them self-evident. And when you do pivot on a tactic, the team doesn’t read it as whiplash, they see it as responsiveness in service of the same destination.

A note on tone and trust

I’m careful that “incremental steering” never feels like manipulation. Transparency still matters. I’ll say explicitly: “Here’s decided. Here’s exploratory. Here’s what might change.” I invite critique at the right altitude: debate the pillar if we’re mis-aimed; debate the tactic if we’re mis-executing. People don’t need performative certainty; they need clarity about certainty.

And yes, metaphors help. I’ve used the choir one because it captures the patience required. Harmony isn’t instant. You amplify some sections, quiet others, repeat the melody until everyone’s got it, and then you invite solos at the right moments. That’s leadership.

If you’re about to reveal a new strategy, try this:

1. Tell the story, not the spreadsheet. Lead with the why and the pillars.

2. Park the proof where it’s easy to find. Make depth available, not compulsory.

3. Reinforce in every forum. Vary the wrapper; repeat the core.

4. Measure what matters. Baseline first, then design toward outcomes.

5. Show the arc. Little wins, sentiment shifts, real gaps.

6. Hold the vision, flex the route. Adapt details without wobbling the north star.

None of this is flashy. It’s boringly effective. And it respects how humans actually learn, align, and decide. Strategy isn’t what’s written in the deck you present once; it’s what your team does on an ordinary Tuesday without you in the room.

If you’ve set a three-year strategy, don’t expect comprehension in three months. Hold it. Walk people through it. Make the progress visible and the expectations repeatable. Let them figure pieces out in their context, and standardise just enough to keep the language shared.

The trouble with revealing your entire strategy too soon isn’t that people aren’t smart enough to follow it. It’s that true understanding takes time and repetition, and the day-to-day work is where people decide whether your vision is real. Your job is to keep the melody alive long enough for it to become theirs. When you start hearing your pillars echoed back to you, unprompted, in decisions and in demos, you’ll know it’s working.