What’s the Point of AI without Design and Systems Thinking?
The power of interdisciplinary collaboration with AI to understand the human experience and societal complexities.
A few decades ago, imagine a bustling Copenhagen, with its streets choked by vehicles, air quality dwindling, and the city’s famed quality of life threatened by rising vehicular pollution and congestion. Residents longed for open spaces, cleaner air, and a healthier lifestyle, while city planners grappled with the spiraling challenges of urbanization. But instead of just expanding roads or intensifying public transportation, city officials employed systems thinking. Recognizing the intertwined nature of urban living, health, and transportation, they championed cycling. This wasn’t just about laying down bike lanes; it was about reshaping urban infrastructure, fostering a cycling culture, and prioritizing eco-friendly mobility.
Today, Copenhagen is often hailed as the ‘Cycling Capital of the World.’ Streets once dominated by cars are now filled with cyclists. Not only has this eased traffic and reduced emissions, but it’s also promoted better public health, reinforced sustainable practices, and restored the city’s cherished life quality.
In parallel, AI has reshaped how we communicate, work, and even think. If there’s anything we can learn from Copenhagen’s transformation, it’s that isolated solutions can not only be limiting, but potentially unhelpful. Imagine deploying AI to solve the traffic congestion problem without any human insights or acknowledgement of interrelated issues in a system. Creating a solution based on historical traffic data doesn’t necessarily account for unpredictable human behavior, like confusion and disorientation due to frequent rerouting suggestions. This might cause people to ignore these “technically efficient” suggestions and just revert back to their old routes and habits. So much for technological sophistication.
Rather than tackling these challenges in silos, what if we intertwined the brightest spots from multiple disciplines? Imagine the power of interdisciplinary collaboration, where the confluence of AI, design thinking, and systems thinking offers a powerful lens to navigate, understand, and innovate for the human experience while understanding societal complexities.
You’re probably wondering: How do these three pillars converge to solve society’s challenges? By harnessing the iterative nature of design — a discipline that’s rooted in empathy and adaptability — and pairing it with the holistic insights of systems thinking, we can create solutions where AI can assist with addressing complex societal challenges without falling into the trap of techno-solutionism. Together, these disciplines offer a unique lens, illuminating a way forward to navigate the multifaceted challenges of our world.
The Pillars of an Interdisciplinary Exploration
As the first of the three pillars, AI acts as a transformative force, redefining our experiences, expectations, and interactions. From reshaping urban environments to curating personal experiences, of course AI has so much promise. Yet, alongside its numerous advantages, we should tread thoughtfully, addressing inherent biases, safeguarding privacy, and ensuring its decisions remain transparent, to name a few issues. Without design or systems thinking, we question an AI solution’s relevance or ability to create beneficial feedback loops that avoid long-term harm to the human condition. As researcher John Seely Brown previously said, “The technology is the easy part. The hard part is figuring out the social and institutional structures around the technology.”
One of the pressing concerns with AI is its potential to inadvertently reinforce societal biases. Consider the rise of AI in recruitment; it promises efficiency and objectivity, but if it learns from historical data tainted with biases, it could perpetuate them. A candidate might find themselves overlooked not for lack of skills, but because of an AI’s unintentional bias.
As AI systems become even more deeply integrated into our daily lives, drawing insights from vast amounts of data to enhance our experiences, we also grapple with the privacy implications. The convenience of a voice assistant, for example, comes with the underlying concern: how much of our personal life are we willing to share for the sake of convenience? As they say, we either pay with dollars or data. On top of that, we need to consider how these large swathes of data are being trained by low-paid gig workers.
As AI shapes our interactions and choices, we might find ourselves in an echo chamber, seeing only what an algorithm thinks we want to see. The challenge then becomes ensuring that while we embrace AI’s conveniences, we don’t lose sight of the diverse and serendipitous experiences that enrich our lives. Part of the charm of going to the Strand Bookstore in NYC, for example, is the experience of sifting through 18 miles of new and used books. Maybe you have to really be a book lover to appreciate something like that, but do we really want an Amazon algorithm dictating what we should read all the time?
Design Thinking
At the heart of design thinking (also widely known as human-centered design) lies a profound respect for the human experience. It’s not merely about coming up with solutions; it’s about understanding the people for whom these solutions are intended. By prioritizing empathy, design thinking champions the notion that truly transformative solutions arise when we connect with the emotions, needs, and aspirations of the end-users. Let’s not forget that these “end-users” are human beings. But while we’re remembering that we’re designing solutions for humans, we also need to remember that we’re not all equally affected by technological interventions. Especially when we’re designing solutions utilizing AI, we need to be aware of the inequalities that biases may exacerbate if we’re not careful, and if we lack understanding of its long-term effects.
Central to design thinking is the belief of continuous improvement. Ideas aren’t static; they evolve. By cultivating a feedback-driven environment, solutions can be fine-tuned, ensuring they remain relevant and effective. Yet, in our accelerated age of exponential technologies, designers face the task of harmonizing rapid innovation with genuine human-centric solutions. It becomes less about being the first to launch a new feature and more about launching something that truly matters to the user.
While appeal to the user is undoubtedly important, design thinking teaches us to look beyond the surface. It’s not just about creating something delightful, but also functional, meaningful, and socially relevant. In a world inundated with technological solutions for almost everything we can imagine, true design success isn’t measured by pixel perfection, or (on the extreme end of the spectrum) how addictive it is. It’s gauged by how well a solution fits into the fabric of people’s lives, addresses their pain points, and how it brings about positive societal change.
Systems Thinking
Systems thinking doesn’t merely suggest that everything is connected; it underscores the profound implications of these connections. Instead of isolating problems or solutions, it encourages a holistic exploration, ensuring we recognize and respect the myriad interrelationships at play.
Picture this: a company introduces a cutting-edge technology to enhance user experience. While the immediate goal is achieved, this change may inadvertently shift user behaviors, influence related markets, or even reshape societal norms. These cascading effects exemplify why a linear mindset — where A leads to B — can sometimes be limiting. In contrast, systems thinking prompts us to ask: If A leads to B, how might that impact C, D, or even Z?
In a world that’s more intertwined than ever, the challenges we face aren’t standalone issues; they’re nodes within vast networks. Whether it be the complexities of climate change, where a temperature shift in one region can influence weather patterns globally, or the intricacies of our digital economies, where a single app’s popularity can redefine market trends — these aren’t puzzles with a single solution. They’re dynamic scenarios, constantly in flux. To navigate this landscape, an understanding rooted in systems thinking becomes not just beneficial but essential.
Unraveling Complexity Through Real-world Applications: Systems Thinking at Play
Yellowstone’s Wolves: Nature’s Ripple Effect
Travel back in time, and you’d find Yellowstone National Park grappling with an ecological imbalance. Overpopulated elk herds were overgrazing, leading to the degradation of landscapes and habitats situated along rivers and streams. Birds, beavers, and other wildlife suffered due to this domino effect, and the once-thriving ecosystem began showing signs of strain.
Instead of looking for quick fixes, park officials and ecologists turned to systems thinking. Recognizing that nature’s balance hinges on intricate interdependencies, they made the bold decision to reintroduce wolves, a natural predator of the elk, back into the park.
What followed is a testament to nature’s resilience (when allowed to recalibrate). Wolves controlled the elk population, halting overgrazing. Gradually, streamside vegetation flourished, stabilizing riverbanks and attracting diverse wildlife. The park witnessed an ecological cascade, where one positive change led to another. Yellowstone today stands as a vivid example of the power of systems thinking in restoring natural balance.
Interweaving AI, Design, and Systems Thinking
AI has a gift — a predictive ability to sift through colossal data to identify patterns, probabilities, and potentials. However, while AI can predict, it’s design thinking that tests these predictions in the real world, refines them, makes them more human-centric.
Bringing back our previous example of voice assistants, which have now become an integral part of our digital ecosystem. Voice assistants had humble beginnings mired in imperfections — misunderstandings, incorrect responses, and at times, comical misinterpretations. But here’s where the magic happens: each user interaction, each voice command, and each feedback is a design iteration in itself. AI leverages this feedback, refining its algorithms, while design thinking ensures this refinement truly aligns with human nuances and preferences.
Today, these voice assistants seem almost intuitive, predicting our needs, understanding diverse accents, and integrating seamlessly into our routines. It’s a testament to how AI’s data-driven insights, combined with the iterative, feedback-centric nature of design, can yield systems that learn, adapt, and evolve with us.
Holistic Problem Solving
The contemporary world, with its intricate networks, doesn’t operate in isolation. Problems aren’t stand-alone; they’re part of a larger web of interrelated issues.
Imagine a city grappling with air pollution. AI could predict pollution peaks, and design thinking could prototype solutions like green walls or purifying installations. But, without systems thinking, one might overlook broader factors like urban traffic patterns, industrial outputs, or local climate conditions. By integrating all three pillars, one could devise a comprehensive strategy: AI-driven traffic management to reduce congestion, design-centric urban green spaces for public engagement, and systemic policies targeting pollution at its sources.
This integrated approach doesn’t just tackle the immediate concern (air pollution) but reshapes the urban ecosystem for long-term sustainability, better health, and enhanced quality of life.
Collaboration & Integration
In the multifaceted nature of challenges today, isolated expertise often falls short. The key? A coordination of diverse voices, experiences, and skills.
Take, for example, the Smart City initiatives getting traction globally. In Singapore, an interdisciplinary team — comprising data scientists, urban planners, and designers — joined forces. AI experts wrangled data to understand urban mobility patterns, designers prototyped user-friendly public spaces, while systems thinkers ensured every solution fit within the larger urban narrative, from waste management to energy consumption.
The outcome? A city where technology serves its residents, not the other way around. Streets are more pedestrian-friendly, public transport is efficient, and green spaces are abundant — all tailored to the unique rhythms and needs of its inhabitants. This is the potency of interdisciplinary collaboration. When diverse perspectives come together, solutions aren’t just effective — they’re resonant, sustainable, and holistic.
Healthcare: A New Dawn in Patient Care
The intersection of healthcare and AI has reshaped the future of diagnostics and patient care. AI-powered imaging tools, for instance, can scrutinize scans with precision, sometimes catching nuances that the human eye might miss. These advanced diagnostics not only promise timely interventions but also hint at a future where predictive healthcare might become a norm, offering treatments even before symptoms manifest.
But technology, no matter how advanced, must resonate with the human experience. Enter design. It focuses on streamlining patient interactions, ensuring that the journey — from diagnosis to recovery — is patient-centric.
Beyond the individual, the healthcare ecosystem’s holistic health is paramount. Systems thinking ensures that while AI facilitates individualized care, the broader community benefits. This could mean equitable access to AI-powered tools, considering environmental impacts of healthcare infrastructures, or even understanding the socioeconomic implications of medical innovations.
Education: Tailoring Tomorrow’s Learning
The classrooms of today are a far cry from what we once knew (remember rolling in the TV cart, anyone?). AI sits at the heart of this transformation. Adaptive learning platforms personalize education, understanding each student’s pace and style, offering resources tailored to individual needs.
But in this transformation, design’s role in curating experiences becomes pivotal. Platforms must cater to diverse age groups, cultural backgrounds, and learning styles. This means interfaces that are intuitive and resonate across a spectrum of learners.
Also, the interconnected web of educators, parents, and global education standards should shape the direction of AI in education. Systems thinking ensures that while individual learning experiences are enriched, the overarching goals of education — be it fostering global citizens, promoting critical thinking, or ensuring equitable access to quality education — are not forgotten.
Navigating the horizons opened by AI (now and in the future), it’s evident that boundaries between disciplines can limit our potential. The unique interplay between AI, design, and systems thinking epitomizes interdisciplinary collaboration. AI, with its predictive abilities and beyond, reshapes industries ranging from healthcare to urban planning, becoming a catalyst for innovation. Yet, devoid of context, its sheer power can sometimes lack direction.
Design serves as the bridge. Rooted in human-centered principles, design infuses empathy into the process, ensuring technological advancements truly serve their human beneficiaries.
Yet, to truly thrive, we need the holistic perspective of systems thinking. In our interconnected reality, small changes can have vast repercussions. Systems thinking helps us appreciate and navigate these networks, ensuring solutions not only tackle immediate issues but also align with a bigger narrative.
The path forward is clear: it’s not about individual mastery, but embracing the potential of interdisciplinary collaboration so that we don’t create AI solutions that merely “throw technology at a problem,” expecting all complexities to be resolved.
For those at the forefront of change and innovation: Let’s broaden our aperture. In the blend of AI’s capabilities, design’s human-centeredness, and systems thinking’s encompassing view, we can create our roadmap for a better tomorrow.
