CLASS 17July 21 · AI, Design & Innovation: Systems Thinking

Class 17: Systems Thinking. "AI is never incremental. It is always exponential."

Taught byKatrin ZimmermannPartner, CreativeAI Academy
Transformation Partner, Credera (Omnicom)

The class in brief

Katrin placed the same object, a taxi, on three altitudes of design, named four mechanisms for how systems actually change. This pushed the room to chase an idea's effects past the obvious first move.

After this page you can place any brief on the classic-design-to-computational-design ladder. You can also name which of the four systems-thinking levers you are actually pulling. And you can map an idea's effects out to the third order before you trust the story its first order tells you.

Her slides carry a confidentiality footer, so this page teaches her frameworks in the corpus's own words, full credit to her, with a curated set of her slides below.

Never incremental

Why this matters · 6:44 PM

Where you think you will end up is a straight line. Where you actually end up is a curve that breaks upward without warning.

Katrin opened on the gap between predictable, linear progress and the exponential curve AI actually produces. This is the "vision gap," between comfortable incremental thinking and a result that outperforms it once you cross into growth.

Her framing for the moment we're in: "We are probably at the moment where the Model T was rolling off, and we all were able to drive something that yet had to find stoplights and seat belts and all of the things."

In other words, the tools already work and are already useful, but the safety rules and norms around them have not caught up yet.

The Leverage Points ladder, the class's central diagram: intervention strength rising from surface events to education and mindsets.

"We do not have yet the systemic understanding of how AI is going to change how we work, how the organizations work that we work in, how the systems, the government, the structures that we live in will be evolving."

Katrin, on why doom dominates the conversation

types of business innovation she named: product, process, business model, service, marketing, organizational, technological.

2 yrs

the shift she named out loud: two years ago nobody introduced themselves afraid of losing their job to AI. "There is a right to the fear."

The framework · 6:47 PM

One taxi shown at three levels of innovation

Classic designfeature and quality focus

A taxi is easy to spot from a block away because it is yellow, and classic design stops at the object itself.

Design thinkinghuman needs and experience focus

Not the cab, the inconvenience of hailing one. This is Uber's whole redesign, and it never touches the vehicle.

System design, computational designsystems and data focus

What should a city center look like if you balance livability, economic growth, transportation, and climate all at once, for the next 25 years. More variables than one human brain can hold at the same time, and a different class of design problem entirely.

Worked live against a taxi roof sign, the room called the taxi roof sign every type at once, product and marketing, and Katrin agreed it "straddles over different concepts."

Her cleanest example at the top rung was Tesla, not because the car is clever, but because Tesla runs four businesses solving each other's problems at once.

The EV.
The charging network.
A separate profitable battery-lifecycle business.
The driving data harvested from the fleet toward autonomy.

The craft · 6:57 PM

The four levers of systems thinking

Feedback loopsaction, effect, fed back into the next action

Modern songs run shorter intros than older ones because music is now designed around listening-behavior data. "We are optimizing for listening behavior even in the design of music today," at the cost of creative diversity.

Stocks, flows, and balancing loopswhat comes in and what happens to what goes out

Her example: the EU AI Act, throttling the inflow of what AI is allowed to do to individuals and institutions once a feedback loop's negative side starts winning.

Leverage pointsnot every lever moves the system the same amount

Donella Meadows's ranking of intervention points. The counterintuitive finding: patterns and trends are a weak lever. The strong levers are mindset, culture, and above all trust. Her proof case: Airbnb cut discriminatory outcomes by redesigning the booking flow that shapes trust, not by only reweighting the recommendation algorithm.

Causal loops, the flywheela virtuous cycle, once it is spinning

Bezos's napkin sketch: lower prices drive traffic, more sellers, a lower cost structure, more selection, lower prices again, with Prime bundled on top. Every student in the room was a Prime member; nobody could name what any single piece of it actually costs.

The three-horizons matrix, mapping market and technology knowledge to separate near-term bets from exploratory innovation.

The exercise · 7:17 PM

Out-think your own tool

The challenge

Pick a self-optimizing system you already use daily, then find what the AI's answer left out.

Ask a tool what happens in the long run as your chosen system (Spotify, TikTok, Instagram, Maps) keeps optimizing itself. Then do the harder part alone or with a partner: find the loop the AI's answer left out.

Like Bezos's napkin sketch, the first pass usually gives you only the virtuous flywheel. What is the balancing loop it skipped? Consider these examples.

Burnout.
Regulation.
Filter bubbles.
Saturation.

The closing question Katrin put to the room: the AI is itself a self-optimizing system. Would it flag its own limits?

One classmate tested it on a major AI lab itself

Asked an AI about Anthropic to check for bias, and got an answer that was "surprisingly not biased at all, very much complimentary." What it never raised on its own: where humans fit in a future of very capable AI.

Katrin's read: "it's not flagging its own limits. It's going on everything can be better and ignoring the reality of the human."

One classmate turned autocorrect into a framework

Ran smartphone autocorrect through the exercise and came back with four evaluation dimensions: accuracy, intent, identity, agency. The systems insight underneath: accepting a wrong autocorrection because you're in a hurry reads to the system as endorsement, so the system reinforces the mistake.

One classmate found the flattering-objective trap

Asked what happens if Spotify keeps optimizing itself, got an answer built around a better listening experience. Re-asked with the real goal stated instead ("maximize profit for Spotify") and got a completely different answer. Models default to the flattering objective unless you name the real one.

The judgment · 7:49 PM

The first-order effect is the obvious one. It is rarely the one that costs the most.

Multi-order effects are the chain reactions that follow an initial change, including second-, third-, and later-order effects that often outweigh the first. Four examples worked live in class.

The cell phone, built for easier communication, produced isolation as conversation moved to text.
Airbnb's growth in short-term rentals depleted long-term housing.
The car connected distant places and widened commutes, while driving climate impact and reshaping cities around parking.

Asked for a positive chain instead, the room mapped GLP-1 drugs as the fourth example.

A diabetes treatment.
An accidental weight-loss effect.
A population-level drop in heart disease.
A stock downturn for soda and fast food.
Snack companies reformulating toward a healthier market.

5th

order effects are the homework's target depth. The instruction: iterate, do not accept the first pass.

change per map. The exercise's constraint: pick one real shift and trace what actually ripples from it.

Methods and prompts

Five methods to take with you

METHOD 01 · THE EXERCISE part of: keep the judgment human

Predict the balancing loop before you ask

Guess what a self-optimizing system's own AI-generated growth story leaves out before you check. The gap between your guess and the real answer is the actual lesson.

Where it came fromKatrin Zimmermann, the guest from CreativeAI Academy and Credera, built this into the class exercise where students picked a self-optimizing system they use daily and predicted what its AI-generated growth story would leave out.Use it whenUse this before asking a model what happens as a self-optimizing system keeps optimizing, so you can check your own instinct against its answer.

Same idea elsewhereJudge AI output with your own trained eye (class 1) · The sea-of-sameness check (class 2) · Pick the relationship on purpose (class 2)

Working prompt

Before I ask you anything, my prediction for what a self-optimizing system like [system] would leave out of its own growth story: [my answer]. I answer first, then you check me. Now tell me what happens long term as [system] keeps optimizing itself, and tell me if I called the missing loop correctly.

You will know it worked whenit tells you what actually happens long term as the system keeps optimizing, and confirms whether the loop you predicted was missing was the real one.

METHOD 02 · GOAL RESTATEMENT part of: structure your ask

State the real goal rather than the flattering one

Models default to the objective that sounds good. Restate the actual goal and watch the answer change.

Where it came fromThis technique came from a classmate's live exercise in class 17, who asked a model about Spotify's optimization goal, got a flattering answer, then restated the real goal as maximizing profit and got a different result.Use it whenUse this when a model's answer about a system or decision sounds too generous, and you suspect it assumed the wrong objective.

Same idea elsewhereGive it two keyframes instead of one description (class 2) · Speak the language of the internet (class 3) · Write it as a GOLD block (class 4)

Working prompt

I asked you about [system/decision] and got an answer built around [the flattering goal]. The real goal is [the actual goal, stated plainly]. Answer again with that as the stated objective, and tell me what changes.

You will know it worked whenthe second answer visibly changes once the real goal replaces the flattering one, not just a restatement with the same conclusion.

METHOD 03 · THE HOMEWORK part of: go wide then narrow

Map the multi-order effects

One real change, mapped out to the third through fifth order, refusing the first pass every time.

Where it came fromKatrin taught this as the homework method for class 17, instructing students to map a real change out to its third, fourth, and fifth order effects rather than stopping at the obvious first result.Use it whenUse this when you want to trace the long-term consequences of one real change before treating its first, obvious effect as the whole story.

Same idea elsewhereThe lateral reframe (class 2) · Start a fresh chat instead of one long thread (class 3) · Force the creative partner deeper (class 4)

Working prompt

Act as a multi-order thinker. Here is a real change: [the first-order innovation]. Map its 2nd and 3rd order effects, both good and bad. Then push further: what does each of those produce at the 4th and 5th order? Do not stop at the first pass, keep going until the chain gets genuinely surprising.

You will know it worked whenthe chain actually reaches a fourth and fifth order effect that feels genuinely surprising, not a list that stops at the obvious second order.

METHOD 04 · THE CRITICAL LAYER part of: keep the judgment human

Mark what the AI missed

Predict the human-layer gap before you compare notes. The gap itself is the design opportunity, not a flaw to smooth over.

Where it came fromKatrin taught this as the critical third phase of the homework, telling students to predict what a designer, community member, or ethicist would add to an AI-generated map, since that gap is itself the design opportunity.Use it whenUse this after a model maps out effects for you, to catch the human-layer considerations it is likely to have missed.

Same idea elsewhereJudge AI output with your own trained eye (class 1) · The sea-of-sameness check (class 2) · Pick the relationship on purpose (class 2)

Working prompt

Here is the multi-order map you gave me: [paste]. Before you respond, my guess at what a designer, a community member, or an ethicist would add that you missed: [my answer]. I answer first, then you check me. Now tell me what you actually left out, and mark every addition as a human one.

You will know it worked whenit marks each addition it names as a specifically human one, and tells you plainly whether your own predicted gap was the real one.

METHOD 05 · THE LADDER part of: structure your ask

Place it on the innovation ladder

Classify a brief across all three altitudes before committing to one. The computational-design tier is usually the one nobody tries first.

Where it came fromKatrin taught this three-tier ladder in class, classic design, design thinking, and computational design, using a taxi as the example that runs through all three levels at once.Use it whenUse this when you want to check whether a brief is being solved at the feature level, the human-experience level, or the full systems level.

Same idea elsewhereGive it two keyframes instead of one description (class 2) · Speak the language of the internet (class 3) · Write it as a GOLD block (class 4)

Working prompt

Here is a brief: [paste]. Answer it three ways: as classic design (the feature or quality fix), as design thinking (the human need or experience fix), and as computational design (the systems-level fix that only makes sense once you factor in data and constraints a person can't hold in their head at once). Show me all three.

You will know it worked whenthe three answers are genuinely different in kind, not the same fix restated as a feature, an experience, and a system.

The homework, mapped

The close · 8:34 PM

The three horizons of innovation and the fourth Katrin adds

Horizon 1 is improvements to what already exists: variants, extensions, and cost reduction, on technology and markets already served.
Horizon 2 is adjacent growth: a market not yet served, or technology not yet deployed, her example a gas vehicle moving to electric.
Horizon 3 is genuinely new markets, products, and technology, her example a connected EV heading toward autonomous driving.
Horizon 4 is Katrin's own addition: AI aimed at whole systemic problems at population scale, climate, cancer, the kind of question no single business unit owns.

The homework runs the same discipline on a real problem.

Pick an industry or process actually going through AI-driven disruption.
State its first-order innovation plainly.
Use AI to map the effects out to the third, fourth, and fifth order, iterating past the first pass every time.
By hand, mark what a designer, a community member, or an ethicist would add that the map missed, since that gap is the design opportunity.

Due the following Tuesday or Wednesday, solo or in pairs, on the highest-power model available.

Try this prompt

Quiz me on the four systems-thinking mechanisms and the innovation ladder's three tiers. Then give me a real industry change and make me map its multi-order effects, my prediction of what's missing first, before you check me.

You will know it worked whenit quizzes you on the four systems-thinking mechanisms and the ladder's three tiers first, then makes you map a real change's multi-order effects, your prediction first.

The shelf

Tools and references

Tools that night

ChatGPT or Claude as a systems-thinking and multi-order-thinking partner
Miro, where the multi-order innovation map homework is collected
A high-power model recommended specifically for the homework

Named in the room

Donella Meadows, "Leverage Points: Places to Intervene in a System," the canonical short version
The EU AI Act, her balancing-loop example
The Cannes Lions "Sato" case study (Japan) · the three-horizons framework, Baghai, Coley et al, 2000

Every frame from this class

23 captures, in order. Click any one to see it full size.

Never incremental

A hand-drawn line chart showing a dashed flat line and a red squiggly line curving up sharply, with two stick figures captioned My life is so predictable and I did not expect this. — Guest lecturer Katrin Zimmermann, a transformation partner at Credera and partner at CreativeAI Academy, opened with this hand-drawn chart contrasting the flat progress people expect with the exponential curve AI actually produces, captioned I did not expect this.

A line chart with a straight gray Incremental Thinking line and a pink Exponential Thinking curve crossing it around the Grow phase, credited to Causeit. — This chart from Katrin's talk plots incremental thinking against exponential thinking across four business phases, naming the gap between them the vision gap, the discomfort of moving toward an undefined destination.

The innovation ladder

A coral slide with three white text questions about innovation, AI, and the role of AI design. — Katrin framed the lecture with three questions: how innovation is evolving in the age of AI, how AI will evolve innovation, and what role AI design plays in that shift.

A wave diagram connecting seven labeled boxes: Product, Process, Business Model, Service, Marketing, Organizational, and Technological Innovation. — This infographic lists the seven established types of business innovation, product, process, business model, service, marketing, organizational, and technological, the taxonomy Katrin used before pushing the class toward a new one.

Three illustrated columns labeled Classic design, Design thinking, and System design or Computational Design, each with an icon and a quoted example question about a taxi. — Katrin used a taxi roof sign to show three tiers of innovation thinking, classic design, design thinking such as Uber, and system or computational design, the tier the rest of her lecture builds toward.

The four levers of systems thinking

A white slide with black text defining systems thinking as understanding interconnected relationships rather than isolated components. — This slide gives Katrin's working definition of systems thinking, understanding the interconnected relationships between elements in a larger system rather than viewing components in isolation, in order to spot patterns, feedback loops, and interdependencies.

A hand-drawn diagram with ACTION and EFFECT boxes connected by arrows forming a loop, labeled FEEDBACK in red. — This hand-drawn diagram illustrates a feedback loop, action leads to effect, which feeds back into the next action, the first of the four systems-thinking concepts Katrin walked through.

An illustrated bathtub with arrows labeled Inflow at the faucet, Stock at the water level, and Outflow at the drain. — This bathtub diagram illustrates stocks and flows, an inflow fills a stock and an outflow drains it, the basic structure Katrin used to explain balancing loops such as regulation.

A seesaw infographic ranking Surface Events as the weakest lever up through Patterns and Trends, Structures, Foresight, to Education and Mindsets as the strongest lever. — Based on Donella Meadows's leverage points framework, this infographic ranks system interventions from surface events, the weakest lever, up through patterns, structures, and foresight to education and mindsets, the strongest lever, the human layer of trust and shared values.

This is the Amazon flywheel, a causal loop diagram showing how lower prices drive traffic and more sellers, which lowers cost structure and expands selection, feeding back into still lower prices.

Multi-order effects