Environmental innovation is Tech *and* Ops

A common misconception is that most innovation is invention and new technology. That’s the bias in many fields, including climate-change technology solutions. And it leads to blind spots and insufficient investment in critical resources.

The world runs on an operational stack

Entrepreneurs and their net-new tech are essential. But impactful innovation is typically not just about that: it is also - and crucially - about their embedment into the fabric of work, at scale. That requires the evolution of the "legacy stack", i.e., all layers of activities related to tech infrastructure, data, applications, processes, policies, and people (and their ways of working). In the absence of that change, the power of new technology will not translate into real-world impact.

Resources need to be invested in addressing the strictures across the stack, not just at the technology level, and in doing so at scale to shorten the typically lengthy cycle between inception and mature adoption.

Learning the lessons of digital transformation

The slower-than-expected impact of AI, modern data science, and in general "digital" on enterprise effectiveness show that excitement, money, and technical genius struggle to overcome the massive inertia of how work is currently done. In another example, the global rollout of the Hepatitis B vaccine took more than 17 years - from regulatory hurdles to, quite simply, change management through healthcare systems. McKinsey estimates that in healthcare 30-40% of the disease burden, especially in terms of reduced quality of health in the final years of life, could be eliminated by looking beyond the healthcare technology itself, and instead focusing more on other factors such as mental, spiritual, and social health as well as prevention. These levers require processes, and people, more than just technology.

While we tend to ascribe hero status to scientists and especially entrepreneurs, we often neglect the critical role of operations in the innovation cycle. 

Let’s take an extreme and well-known yet widely misunderstood case: we wish that there were more “iPhone moments" where entire categories are reimagined through a superior innovative product. Transportation, energy, food, you name it - the next Steve Jobs might emerge there. That should be certainly possible at least for a telephone, which is a product, one would think: you buy it, you update it, you trade it back or throw it away. That kind of product would be the most obvious place for new technology (or novel recombination of existing technologies, with a world-changing design) to be totally transformative.

But even the iPhone needed a lot of non-tech-product efforts: from the coordination with telecom carriers and their byzantine requirements, all the way to the game-changing AppStore which is a marketplace with all its (clever) back-office processing before it is a technology. Steve Jobs was initially lukewarm on the new device because he wanted to retain control of everything and rely on his startup methods - he wasn’t fond of engaging ecosystem players like the telecom companies, and wasn’t keen on the AppStore's operations and its community of developers. The messiness of those worlds disturbed his inspired design. In the end, the world changed because he let go of that qualm (also because of pressure from his teams) and intuited that the combination of those practices, and skills, was the future.

And - the world of climate change isn't remotely as product-centric. Rarely does the equivalent of an AppStore exist, and so does the option to plug and play your new tech effortlessly. The mosaic of economic and organizational structures is an unruly entanglement of legacy stacks, sedimented over decades.

The way to evolve them is to work on operations. One of the most successful climate stories, the emergence of electric vehicles largely at the hand of Tesla, was facilitated by a radical reimagination of sales and support operations - and not just an obviously superior product. Tesla did away with dealerships, whose incentives and competencies biased them in favor of combustion engines, and invested in a seamless experience - including an online purchase experience supported by showrooms, and delivery to the buyer's home.

Innovations for climate are large, tech-enabled operations

One can intuit the portability of this example. An analysis based on data from the seminal Drawdown.org work on climate innovation will illustrate the point further. To simplify, let's categorize the quantitative impact (tonnes of emissions abated) of each climate innovation very roughly, into three broad categories.

• Category 1 (blue): engineering jobs. Eg wind turbines, carbon sequestration, and nuclear fusion - and products derived from new product design (e.g., new lighting, electric cars)
• Category 2 (yellow): industry-based, or public-policy-based change of practices. E.g., landmass use (agriculture, forestry, etc), public transport
• Category 3 (green): social change e.g. girls' education, family planning

In all of them, the processes and the ways of working - not just the technology - will need to change. Even in category 1, where arguably a lot of the value is embedded in the technology asset, innovation requires a substantial element of process operation - from supply chain to maintenance, from financing to compliance, and from customer support to training.

• Environmental regulations' compliance is needed for value chains to change, including new areas like carbon offsets. They will look a lot like finance processes, especially finance processes of the future that use blockchain and advanced data capabilities (see Digital Gaia for example). And other critical areas, such as obtaining permissions, will look like large-infrastructure approval processes.
• Supply chain optimization for the environment, including for instance logistics and procurement, means adding explicit environmental variables (say, CO2 intensity and cap-and-trade offset management), but they still rely on supply chain optimization processes.
• Industrial asset maintenance and optimization will need to enable the reduction of use, the decommissioning or mothballing of old equipment, as well as the conversion and installation of new capabilities, and the engineering for maintainability. A lot of those processes are reasonably portable from old assets to the new (for instance, Terra.do helps oil & gas professionals retrain to cater to green economy assets).
• Most new environment-focused financial services such as banking (e.g. project finance, funds) and insurance (e.g., specialty insurance line that insures coastline properties) products will continue to look, at least to an extent, like financial products. Certainly, they will be powered by new data sources, modeled in different ways, possibly sold, underwritten, and their claims managed in different ways - but banking and insurance methods will be at their core.

At least in the early decades of the transition, the legacy of financial and industrial structures will be overlaid and complemented by environmentally-sound practices. We aren’t going to "do over" extremely large and complex structures overnight, as they have sedimented over decades and trillions of dollars have cemented them where they are.

One more thing: People's behaviors, particularly those reflected in our ways of working, are steeped in our neural endowment and are possibly the deepest legacy of the entire economic system. There needs to be an inspired design of experience for today's users - from farmers to school teachers, from underwriters to mechanics, making the new ways of working desirable will be a crucial part of adoption. That’s where better digital user interfaces, use of new visualization tools (AR, VR), and user of collaboration platforms will be crucially designed as part of modern processes - and not just plugged in.

Develop and combine capabilities, and embed them into the operational fabric of work

Another necessary yet vastly neglected condition for an invention to become innovation at scale is skills formation. The next chart illustrates the required combination of skills needed to (a) design, (b) build and, crucially (c) run the new environmental solutions. Climate innovation doesn’t fully happen until the last step is done, at scale, everywhere on the planet. What capabilities are needed, where, and how to get that done at scale?

Domain expertise is foundational - say, understanding how a wind turbine works and its energy grid implication, or an intimate comprehension of the drivers of deforestation. It is essential that technologists are able to work with domain experts to design those assets (say, materials science, or synthetic biology), and support them with digital tools (e.g., cloud architecture and product engineering) that make it easier to operate and smarter - especially when combined with machine learning that harnesses the enormous amount of data that new sensors provide (say, weather stations, or satellite imagery). And finally, it is also important that those technology solutions are designed with the help of experience designers, design thinkers, and process designers - to ensure that those solutions work around humans, for instance, to limit NIMBY behavior or enable communities to counter the encroachment of unscrupulous developers.

Climate and environment innovators will need to "get the village together" across four areas: domain, tech, data, human-centered experience, and attendant process design. Else they will become insular and ineffective, and delay the propagation of the impact.

Buttress learning operations

Many of the best and most novel solutions are typically born at the intersection of those domains. Environmental innovation will emerge from the entire system's collective intelligence, from the interplay in the networks of people that connect different areas of expertise. Those dynamics can yield novel and practical combinations that enable the adjustment and implementation of the net-new technology (more on this here).

To make that happen, people's skillsets need to increasingly “T-shape”. That is, they need to possess the narrow-and-deep stem of the T to master one area of expertise. But they also need the "range" afforded by the flat side of the T to go inch-deep into other fields in order to collaborate effectively with the respective experts. (More on reskilling for tech-led transformation here, and the implications on the climate space here).

None of this will be possible if we don’t train tens of millions of people, which is in itself massive-scale operations and won't happen fast enough if don't engineer the respective processes methodically.

Make no mistake: our educational and training systems, whether schools or other academies, will find it hard to address this challenge: they are not built for this level of granularity, interoperability, and for this speed of change. The lines between training, media, and knowledge management will need to blur if we are to shorten the cycle between invention and scaled-up innovation (more on this here and here). We saw the extent of this problem in the recent digital-tech waves.

World change = new tech + shared knowledge

None of the above is to belittle the value of radical tech shifts, and startups will do the world a great service by continuing to challenge incrementalism and foster the sense of urgency that large companies sometimes lack. However, glossing over the world's messiness might be fine only for some tech startups and their VCs when they focus on greenfield, narrow product-market-fit of ideally plug-and-play solutions, and the early part of the new innovation waves (that's where climate venture money goes). That’s when some of them may exit. But that falls short of solving messy environmental challenges, and their scale. And, like it or not, scale is often what incumbent large companies, established players - and their management, tend to do well. (More on this here).

Therein lies a conundrum: the two worlds of new-tech and scaled operations overlap somewhat, but they're not one and the same. Dangerously, more than a few people from each consider the others as “not fully fit for the job”, which is an act of self-preservation and personal positioning and significantly dilutes the impact we can create.

In conclusion, if done well, operations transformation is a core component of innovation success (more on this here). For that to happen, we need the collective intelligence of people across the operational stack. And to harness that collective intelligence, we will need infrastructures so that the right people, with the right knowledge, spurred by the right incentives, engage on the right collaboration platforms - and get the work done. That's what will drive scale and speed in climate tech adoption.

This post complements the organizational design materials at www.supermind.design and my previous blog posts (here, here, and here, for example) on designing an AI-augmented collective intelligence. I recommend reading them if you're interested in using these techniques in your own organization.