How AI can help accelerate the circular economy

The potential and long-term uses of generative AI models are currently being hotly debated and we are watching in awe the rapid progress that Chat GPT, Bard and Co. are demonstrating. At 55, we regularly get a “jeeeez” when we discover new GenAI skills. There is finally a concrete approach that operates entirely in line with the “AI must benefit us, not harm us” approach — and this could also effectively support the current sustainability issue. 💡

Rounding out the economy

For some time now, the circular economy has been confronted with the problem of certain hurdles and residuals, as a result of linear models, to be able to overcome only when faced with difficulties — if at all. Well, that's also because we humans are very bad at agreeing on anything, but exceptionally good at pulling two ends of the same rope.

The basic idea of the circular economy is compelling. Consumer goods ultimately arise from the collection, production, consumption and disposal of materials, which poses long-term problems, in a world with limited resources, As HBR also explains. A linear model is therefore simply not sustainable, especially when consumer behavior is disproportionate to it. A problem whose consequences we are currently taking account of. 🏊

Circular economy intends to see itself as an added value to the environment, through the recycling and recovery of materials. Obstacles arise from the fact that linear economic models supposedly generate more capital for this, as sustainability always comes at a price. However, Digital, Data and Design (D^3) must contradict this. We simply need to think more effectively about the circular economy — and to do so, D^3 has identified three simple core strategies as elements of a circular model:

1. product utilization (shared economic platforms, renovations and increased durability),
2. material efficiency,
3. recycle.

As simple as it is plausible! The good news now is that current AI models could be the long-awaited breakthrough for implementing such strategies. In fact, AI should not only help with this, it must Even — but where to start?

Why everything kind of $#@%! is

Have you ever thought that the products we buy today are no longer as high-quality as they were ten years ago — or even earlier? Why is grandma's rolling pin, which she used to scare grandpa across the courtyard, still functional but broke her own when you... well... rolled dough with it? Why is this old cast iron pan, away from temporary rust removal, still one of my most functional kitchen utensils — and why is the €20 loyalty point pan from the trusted supermarket scratched again and without coating?

The answer lies in the production principle itself, which is divided into three factors: functionality (Does it work?) , appearance (Is it pretty?) and manufacturability (Is it easy to produce?). For publicly available products, the respective factors in this division should each be around 33%. The quality product used by standard Ottoman consumers therefore comprises all these factors in a balance.

Plot Twist: Unfortunately they don't — and there are certainly reasons for that, not only in production, but also in our buying behavior itself. Purchasing processes have changed fundamentally over the years. Take a suit: Did we go to the tailor who made it by hand, then instead of tailor to the department store, where the mass-produced suit was hanging on the clothes rail — at some point on to the shopping center, with boutiques of all kinds, up to today's standard called online shopping, where we don't even examine the purchased products in person until the time they are delivered, As Vox Media attests. Today's products therefore behave more like this in order to meet this mass production:

As a result, this simply means that we buy a lot. A UN report states that average buyers 60% bought more clothes between 2000 and 2014, but keep it only half as long as that. Around 40% of all British buyers They even buy new clothes every month, which is obviously a great deal and is partly due to the quality of the products purchased. Conversely, since we buy so much, we don't want to spend so much money on it.

This is also unfavorably correlated with price increases as a result of inflation, scarcity of materials and the like, as we are generally not prepared to pay more for a product than in the past. If you used to buy T-shirts for 10€ and you would now have to pay 20€ for a similarly high-quality one, you would rather buy an inferior one for 10 — which, in a somewhat stupid conclusion, probably ensures that you buy the same shirt three times in one year, even though the more expensive version would have lasted a year. Of course, this is also a question of availability. Ironically, that too is a cycle in itself. Remember the old boot parable by author Terry Pratchett:

The reason that the rich were so rich [...], was because they managed to spend less money. Take boots, for example. A really good pair of leather boots cost fifty dollars. But an affordable pair of boots, which were sort of OK for a season or two and then leaked like hell when the cardboard gave out, cost about ten dollars. [...] A man who could afford fifty dollars had a pair of boots that'd still be keeping his feet dry in ten years' time, while a poor man who could afford only cheap boots would have spent a hundred dollars on boots in the same time and would still have wet feet.

Of course, this statement should be viewed with a wink; after all, not everything that is expensive is good. 😉 Viewed positively, it gives consumers a certain amount of control and makes you more aware of your spending. Viewed negatively, however, this is also not due to our full Control — and at best, you could set an impetus in this regard.

Planned Obsolescence and Consumer Engineering

To put it bluntly: Some products are deliberately not meant to survive, but to buy new ones and are designed accordingly. Two specific strategies are typically used here, which we as consumers are certainly familiar with.

Consumer Engineering means buying new products even though old ones are still intact. Think of those who buy every new iPhone model even though the last one is 1 ½ years old and has no functional defects except for a scratch on the display. Or fashion trends: My buttoned jacket is completely fine, but there are zippers right now. So I need a new jacket.

Planned obsolescence On the other hand, deliberate wear and tear of products includes. You also think of Apple here, but the once used and almost pernicious design strategy, which caused iPhone batteries to lose performance after a certain period of use. It should be noted that this is already in the past; Apple is now dominating the market for used cell phones. As is well known, you have to learn from mistakes.

Whether FairPhones or Patagonia, brands that support the repair and replacement of wear parts break through time and again. Yet most people don't think that way yet! In both consumer engineering and planned obsolescence, the core strategic idea is fairly easy to recognize: We should buy more. We find a mixture of both in many modern products that place great value on manufacturability. If you use cheaper materials, simpler sewing patterns, etc., the product will of course wear out faster — and the products will, among other things, be due to Fast Fashion produced in this manner in conjunction with consumer engineering. As I said, this is exactly why we as consumers are not completely powerless, but the status quo is reason enough for a circular economy — and thanks to new artificial intelligences, we have a perhaps unexpected solution in this regard.

AI to the Rescue?

But it can't be as simple as “buy less, increase the lifespan of products,” right? Well — yes. Kind of. However, there is a lack of implementation and it is of course in the interest of many companies to increase their own sales growth. It is obviously completely plausible that this is made more difficult when products last longer and fewer are bought. Voting, however, doesn't really do that correctly.

HBR explainedthat a more qualitative product serves as a mechanism to reach new customers, increase customer satisfaction and introduce business models that can monetize efforts to create more consistent products. It would also be possible to establish long-lasting products as a service, where companies retain ownership of the offered item while customers pay for its use — which obviously also results in a more affordable purchase price. Something like rental apartments, but with iPads.

Advances in data and tracing technologies also allow the necessary flow of information, although, of course, AI can also assist, since diffusion models work well with given data and the resulting probabilities. In such business strategies, there would be completely new opportunities to accumulate loyal customers while using the materials used efficiently.

Material efficiency is also another aspect that AI knows how to assist, for example in the form of production optimization — initial usage methods for rapid detection of recyclable waste already exist. HBR also reports:

Our conference heard how entrepreneur Shelly Xu's SXD Zero Waste applies artificial intelligence to redesign garment mockups that generate less pre-consumer waste from offcuts during apparel production. The result is the creation of sweaters, dresses, or pants with close to zero waste in fabric and about 55% lower cost, as opposed to 10-30% waste in traditional designs.

AI could therefore be able to better monitor the necessary flow of data and information for sustainable production and thus enable more ecologically friendly products and business models. However, the benefits don't stop there. After all, many processes can fundamentally be automated with the help of AI, which means more creates time resources for other areas. In summary, it is therefore possible to three major benefits Determine:

1. Manufacture of circular products — AI can speed up the production of sustainable articles through iterative, learning model-assisted processes, which also offer opportunities for faster testing procedures.
2. Circular business models — Competitiveness can be increased through product as a service and lease, as AI can effectively manage the relevant data.
3. Optimizing circular infrastructure — Offers massive relief in sustainable logistics, inventory management and resource control.

The New York Times reports How valuable this could be for the global economy, provided that — and this aspect applies to all of the above — AI supports human work instead of replacing it. The basic idea must not be to hide your hands in a savings pocket and now declare workers obsolete, but to understand AI as a given superpower of them. This is the only way to ensure that AI acts in accordance with human values and principles and thus also supports ambitions in the circular economy.

We are happy to support projects that implement AI to make companies fit for the future — while taking sustainability into account.

What do you expect from AI in the coming years? We are looking forward to the exchange!

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