Nobody phrases it this way, but I think that artificial intelligence is almost a humanities discipline. It’s really an attempt to understand human intelligence and human cognition.” Sebastien Thrun
I remember getting very frustrated as a child trying to complete jigsaw puzzles and spending a long time trying to work out where to fit the next piece – I lacked patience and interest. Later, I found biology lessons in school and college fascinating, especially applied genetics, as we pored over patterns and cellular generation, and I found the patience to look, enquire, question and repeat.
My journey with the human dimension of transformation has been characterised by this patience I find for enquiry. As I embark on my partnership with AI, I constantly remind myself to check on the patterns I’m seeing and what AI is playing back to me, and where the reality and opportunities lie in that landscape.
An organisational pattern I’ve watched on repeat for two decades, most recently with AI in the leading role, is that organisations invest serious money in new tech, the pilots or proof of concepts go well, and leadership announces the transformation plan. Then between month six and month eighteen, everything quietly slows down, or nothing has fundamentally changed. The World Economic Forum’s latest playbook puts the number at 75%: three quarters of companies haven’t redesigned their workflows around AI at all, they’ve just layered intelligence on top of old architecture and old systems and wondered why it didn’t transform anything.
This is the most frequent situation I see with clients currently as the race for AI productivity gains continues full speed ahead. And it is a finding that should stop us in our tracks as we hear the echo of years gone by – successful transformation is rarely down to the technology. It is about the system in which the technology sits, and not just the organisational system, but the human one too. We are always talking about legacy IT systems and infrastructure but what about the legacy human systems and relational infrastructure – or lack thereof?
Every organisation runs on an invisible architecture. Not the org chart, but rather how decisions are actually made, who actually has authority, who is present in which forums, how information actually moves and what happens when people actually speak up. When you deploy AI into that architecture without changing it, people can think differently with AI as a partner, but the meetings, the incentives and the unwritten rules still reward the same things they did 10 years ago. The question isn’t how smart your AI is, it is still whether you’re designing systems – both inner and outer – that allow people to actually do their best work.
The system nobody redesigned
Donella Meadows, the systems thinker whose work has shaped everything from environmental policy to organisational design, ranked the places you can intervene in a system from the weakest to the most powerful. At the weak end are adjusting numbers, changing targets, and buying new tools, and at the powerful end are changing the information flows, the rules of the game, and ultimately the paradigm – the mental model the whole operating system runs on. Most AI adoption currently lives at the weak end, and for good reason, as this is our organisational comfort zone in which we can “get stuff done” – new software, new dashboards, re-engineered processes, new budget lines, but still with the same old budget governance structures and, more importantly, the same old architecture of decision-making, trust, authority and belonging.
The fix is to intervene further upstream: redesign who sees what information and when; redesign where decisions are made; and do the harder, slower work of shifting how leaders see their own role – from the person with the answers to the person who designs the conditions. That last one matters most, and it’s the one no software purchase can deliver. If your leadership identity hasn’t changed, your transformation hasn’t started.
We must start building around intelligence – not just cognitively or around artificial intelligence – but around systemic layers of intelligence (including the layered intelligence onion that is a human being both individually and collectively). The WEF tells us in their latest report that leading organisations are rebuilding around intelligence. They’re designing “intelligence engines” that learn from every interaction, redesigning operations end-to-end, and more particularly rethinking what human-AI collaboration looks like in practice. We are back to the age-old discussion that resonates but doesn’t necessarily translate into practice – effectiveness over efficiency. Give every employee a personalised coach or democratise access to basic coaching skills, and you change the quality of the conversations, and therefore the quality of collaboration and how an organisation works, not just its speed. If your AI is only trained on the data of the people already in the room, you’ve just automated the status quo.
I agree with the direction but want to push it further because if we’re going to redesign systems around intelligence, we need to ask: intelligence in service of what?
Design for thriving, not just performance
The regenerative leadership movement comprising thinkers like Giles Hutchins and Kate Raworth, and the practitioners behind the Next Economy Enterprises principles, offers a different starting premise. Instead of asking “how do we optimise?”, they ask “how do we design systems where life can thrive?” And not just survive and produce, but genuinely flourish and regenerate in terms of energy, business results and people. This is not a soft idea. It’s a design principle.
In living systems, the healthiest ecosystems aren’t the most efficient, they’re the most diverse, adaptive and interconnected. Look at the coral reefs of our warming oceans or the way our own human bodies adapt to our changing environments, learning, unlearning, and relearning constantly. The same is true of organisations. When you design for thriving, when you build trust into the architecture, distribute decision-making to where the information is, listen to the voices on the edges and create genuine conditions for people to contribute their full intelligence (cognitive, somatic, emotional and spiritual), performance follows, not the other way around. This hasn’t changed with the onset of AI.
And so the question then becomes: are we designing our AI-human workplace systems with the same intentionality or are we just making old, extractive systems faster?
Inclusion as a design decision
I truly believe that you cannot have innovation and high collective performance without conscious inclusion. This is an intentional and conscious skill that as leaders, we hone if we want to survive. It is what separates us from mere machines. In my recent podcast conversation with Stephanie Sylvestre (listen here), co-founder of Avatar Buddy, an AI company that builds digital twins – digital versions of real people that capture their personality, experience and voice – specifically designed for communities that have been locked out of access to mentorship and opportunity, what resonated with me most was her framing: inclusion isn’t an add-on, a policy or a training programme, it’s a system design decision. You either build it in from the beginning, or you end up with AI that replicates the same patterns of exclusion it inherited from the data and the existing system.
Stephanie’s work with Mosaic Commons, a virtual space where black and brown people can see themselves reflected in positions of leadership and access through digital twins of real C-suite leaders and mentors, is a brilliant example of how we can leverage technology to enable what we humans cannot do in our human systems due to our own egos, limiting beliefs and systemic biases. The premise is both simple and profound: everyone can overcome if they have one person who believes in them more than they believe in themselves. AI, designed responsibly, can scale that belief.
But here’s the critical point: responsible AI requires clean, validated data, human oversight at every stage, and small, focused datasets that reduce the risk of hallucination. You cannot afford to just plug in and hope. In the same way, designing cultures of care forces us to first look at ourselves, our own triggers and patterns and our own emotional regulation before we can attend to others. This also requires the same level of focus and care. You design with care, you involve the people the system will serve, and you build guardrails that keep it ethical. This is what “intentional design” actually means in practice – constantly questioning the status quo, who you have in the room, what comes out of the machine, and what humans (and businesses) need to thrive.
The inner system matters too
It’s not just the outer systems that need redesigning. All leadership ultimately starts wth ourselves, understanding our inner game and the different parts of self. Wendy Kendall recently reframed imposter syndrome not as something to “overcome” but as an inner signal worth listening to, and effectively a culture problem. Drawing on Internal Family Systems (IFS) thinking, she argues that imposter syndrome is often an accurate misalignment detector – a part of us recognising that the environment we’re in doesn’t actually fit who we are (read here).
This hit home for me. We have spent decades and trillions of euros on framing imposter syndrome as a problem, a confidence problem, a women’s problem at that – which of course rings true as women spend 90% of their time working in systems that were not built for them, trying to fit in.
This is really about inner system design. We’ve been taught to override our internal signals in the name of professionalism – push through, get another certification, perform harder. But the research backs her up: 71% of CEOs experience imposter syndrome while simultaneously rating themselves as fully competent. This is a direct outcome of the compete and compare culture that is prevalent in so many organisations, and breeds this performative model which amplifies the phenomenon of both wearing a mask and feeling misaligned with the culture. Cooperative, learning-oriented and care-based cultures buffer against it.
If we’re serious about designing systems where intelligence – both human and artificial – can actually produce something worthwhile, we need to include the inner architecture. Leaders need to understand their inner systems, their nervous systems, their patterns and their triggers to effectively show up. Leaders who haven’t done their own inner work will reproduce the same extractive patterns, regardless of how good the technology is. This is a design invitation to use our agency and curiosity to redesign the workplace systems we’re working and quite frankly surviving in.
What regenerative system design actually looks like
So what does it look like when you bring all of this together – the systems thinking, the intentional AI design, the inclusion lens, the inner work, the commitment to thriving? It starts with three clear moves.
First, design for the invisible layer. Every organisation runs on an architecture you can’t see on the org chart: who actually has authority, how information really flows, what happens when someone tells an uncomfortable truth, what keeps the communities going. This is where transformation lives or dies and we don’t spend any time mapping it, understanding it or leveraging it. Before you deploy a single AI tool, map the system as it actually operates and the informal networks that weave the tapestry of influence and change across the organisation.
Second, build inclusion into the intelligence. Conscious inclusion is the prerequisite for innovation and performance. AI has not changed this foundational and very human building block. Involve the people the system will serve from the very beginning. Ask whose voices are missing. Design your digital tools to level the playing field, not to replicate existing hierarchies.
Third, do the inner work. Get to know your inner leadership team and use the signals your own system is sending you. If your leaders are burning out, if imposter syndrome is rampant, if people can’t be themselves at work there is an issue bigger than one person, and it’s a culture design problem. Start by asking, as Wendy Kendall suggests, what is this signal trying to protect me from? Where is the misalignment here? The answer is the trailhead for real change.
The choice in front of us
We’re at an extraordinary and watershed moment. We have tools of unprecedented intelligence at our disposal, and yet we are still not looking at this challenge systemically. The gap between AI investment and AI impact is a systems design failure, but the fix isn’t just a better operating system for the enterprise. It’s a deeper, more human question about what kind of systems we want to build, who they’re built to serve and what human skills we need to build them effectively.
The old paradigm from extractive systems tells us to optimise, extract and scale. The new, regenerative systems tell us to design intentionally for people to thrive, then build the intelligence to support that. The technology is ready. The question is, are we?
Thank you for reading.
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You can also find more subjects like this in my podcast, Let’s talk Transformation, available on Apple Podcast and Spotify and youtube.
If you’re looking to bridge the digital gap and lead your ecosystems differently, check out our Human Systems Practitioner course : https://transformforvalue.com/human-systems-practitioner/
