The 6 step plan for sustainable technological innovation
While sustainability has emerged as a key concern for businesses throughout the world due to an increased awareness of the toll that population growth coupled with mass consumption has taken on the environment, many businesses are discovering that sustainable practices also help the bottom line - by cutting costs and increasing sales with consumers increasingly aware of environmental and societal issues.
Yet, many organizations struggle with driving technological innovation due to past initiatives which underperformed, or an unclear vision of what the ROI will look like.
We have distilled our experience to a six point checklist that can be used by any decision maker in an organization to implement a technological change program that will result in a measurable positive impact in your organization's sustainability footprint.
Step 1: Research key areas of impact
The first point is "Research key areas of impact". As we mentioned before while discussing the SDGs one of the big problems is that while there are well defined quantitative targets, they are very difficult to map into specific contributions that a particular organization or department can perform. So the very first step is to start by researching areas of impact.
Different businesses will have different areas of impact. For example, if you have a farming business then land-use and clean environment might be priorities for you. For a traditional professional services company, labor productivity and gender equality might be on top of the agenda. Going through the list of Sustainable Development Goals can provide an insight on what areas might be relevant for your company by highlighting areas in which your organization has an impact. While not directly related to the sustainable development goals, ESG task forces and standards boards are also relevant sources of information from which you can determine which metrics are most relevant to your organization.
Having determined which goals are most relevant to your organization, it might be worthwhile to determine which metrics you can have the most impact on. Factors that might be considered include: the size of your organization; your organization's impact on this metric; and how easily you can leverage existing solutions to produce meaningful improvements in this metric.
To be able to do this systematically, sketch out a proposal for the six points that we propose below and map out the metrics that you might be targeting. As well as what potential solutions could be used, scope out what is the estimated return on investment and what is the estimated impact on your organization of taking these measures.
You can then use this list to gather feedback and buy-in from different stakeholders in your organizations around which area you'll focus on first. If your organization hasn't done any sustainability initiative before, you might consider limiting the scope of your program to one single area with the most consensus, to be implemented as a trial program. After implementation, the ROI of this initiative can be used to bring your organization's members onboard for further change programs.
Step 2: Define key metrics
Once we've defined which ones are relevant and not, we can look at each goal's targets and indicators. Those are the actual quantitative measures of our progress towards achieving this goal. Now step number two is to define a key metric. Often, the SDG indicator will not be directly applicable to your company so we need to define a metric that we believe will directly and positively affect that indicator. So as an example, let's say we decide to focus on goal number three, which is to ensure healthy lives and promote well-being for all at all ages. We can go through the different targets and indicators and pick those indicators that are relevant to our company, so for example if your company has a factory in a particular community we can identify that the indicator 3.9.1 "mortality rate attributed to a household in ambient air pollution" could be impacted by our company's actions.
So a key metric we can define is what are the concrete PM2.5 or harmful gas emissions attributable to our factory? We can make a commitment to drive that number down ideally to zero and we can measure the impact of that which should directly translate to an improvement in the indicator we are interested in.
Having defined a metric, you need to decide how to measure it. Depending on the type of metric, this data might be available in the form of existing corporate data, recorded in documents or spreadsheets. It might also be measured by sensors deployed in your physical facilities, or might need to be estimated following standard accounting procedures.
It may seem that a metric for which you already have the data would be an easier target to measure. However, organizations often have trouble with information silos, and it might be inefficient to collect all the data from different scattered documents in your organization. That's where natural language processing tools could be of help.
Recently, we've seen the development of several AI-based search tools which seek to unify the information silos in a company and aggregate all the information into a data lake, or a knowledge base. Such knowledge bases contain aggregate information on the company and might make it much easier to measure this data. If you have such a digital transformation initiative in your company underway already, these metrics might be much easier to measure than otherwise. Similarly, if you already have sensors in your physical locations, data aggregation might be easier, considering your organization already has protocols for data aggregation, statistics, and summarization.
If not, the cost of deploying a network of sensors in physical locations, and the software development required to aggregate and process this data must be considered when accounting for the costs of such a sustainability project.
Some of the targets cannot be practically directly measured, and must be estimated by scientifically based accounting methods. For example, to estimate a company's carbon footprint, the GHG protocol is a good source of a consistent set of standards. As they are being calculated by indirect means, improvement in these targets will require a more detailed plan as there might be many contributing factors that indirectly affect the outcome. As such, it might not be immediately clear where action is required. Thorough evaluation of the accounting protocol to determine all the inputs that factor into it will help you map those contributions back into tangible measurable business metrics.
Step 3: Pick a solution
While in this book, we are focusing on technological solutions to achieve sustainability goals. It bears mentioning that in many cases a non-technological solution might also yield significant improvements in sustainability. For example, changes in your company culture or certain business practices might be all that is necessary to achieve a significant improvement in goals such as gender equality, better pay, or even energy efficiency. Even in such cases, quantitative measurement and tracking will help drive progress.
Assuming we've determined a technological solution is a key piece of our sustainability initiative, we need to pick the right technology to achieve our goal. To make an informed decision we need to gather more information about our problem.
In our case we are primarily concerned with an AI-first technological solution. When developing such a solution, a few of the topics we need to have a deep understanding of before starting a project are:
- Data. Do we collect enough data to measure the metric we have defined in step 2? If not, how can we measure it?
- Technology status. After consulting with experts, what does the existing technology allow us to do right now? What will it allow us to do in 5 or 10 years? What are the productivity and cost tradeoffs of the different solutions available.
- Milestones. Considering the technology available now and any technology we can develop in the future, what are concrete yearly or quarterly target milestones for our metric?
- Side effects. Are there any potential unintended consequences of implementing the technology? Have we considered all the stakeholders impacted by this change? Is this project sustainable in the long run - economically as well as politically?
A full technological solution could require a combination of physical tooling deployment, software and AI model development, or development of a completely new process to replace a legacy system. In the case of hardware development and deployment, we need to consider fabrication costs and quality, as well as in-field reliability and maintenance. In the case of software development, data intake and output, deployment requirements (for example cloud versus on-device) and integration with existing systems must be considered.
To prevent unintended side effects it is critical to not only collect information from all stakeholders but also to explain in detail what is the goal of this plan, why it is important and what steps will be taken. This enables all potentially affected parties to raise any concerns that might be overlooked by the original planners. This step also makes the transition process later on much easier, as everyone will be aware of the incoming changes and will have had a say on it. This is a key point as in many organizations the main hurdle to technological adoption and innovation is political, in the form of marginalized individuals or departments blocking a change because they were not involved in the decision process.
It is also critical to consider the post-deployment stage. Have we calculated the total cost of running the system? Is its maintenance and upkeep sustainable from the economical as well as the human resource standpoint? To keep a system running optimally it might require updating to keep up with the reality of changing environmental, political and business conditions.
Step 4: Development
Now, picking a technological solution is not the end of the story. While there is a lot of hype around AI solutions in the media, developing an AI system successfully is not an easy task. AI and machine learning model development is a very different paradigm from traditional software development, and is still an early field with not so many established practices in the industry.
The first thing to keep in mind when we are developing an AI project and specifically thinking about the whole life cycle of the project is data. This starts at the point of defining what is the metric we are measuring, which if you're following the task list that we propose you will have. Once the metric is defined we need to understand how data is going to be collected, processed and stored, not just for model training but also for inference at deployment time and performance evaluation.
The model development itself is an iterative and experimental process, which requires adequate time and computational resources to be budgeted. Consider the tradeoffs between different solutions. Is it better to adapt an established technique to our use-case? Or are we researching a novel technique? The project management that is required for those different strategies is completely different, and we will cover the techniques necessary for a successful project in a later chapter.
Step 5: Deployment
Next we need to deploy our new solution. While there are technical challenges with respect to integration with legacy systems or unforeseen bugs in production, we found that the toughest barrier to deployment is societal and political. Humans are adverse to change, and we need to make sure that the change is not only beneficial but also well understood. This is why we believe that fairness should be a priority when developing AI systems.
When it comes to this, a critical early action to take is to get the buy-in from other people in your company. Lack of enthusiasm for a project tends to be the number one stumbling block for most innovation initiatives. Present your plan early and communicate it to as many stakeholders as possible. Collect their ideas and integrate them into the plan as far as possible to increase a sense of ownership.
Listening to people's concerns and possible objections might help you formulate a plan that is less likely to step on other people's toes. Moreover, by listening to concerns in advance it will be easier to implement mitigating measures for issues that you might not have realized in advance.
Particularly when it comes to driving sustainability, we find that proposals can be presented with a sense of righteousness and that may put some people off as they feel being attacked. We propose instead focusing on the positive change that this plan can bring about, as well as a detailed ROI analysis, highlighting the value that implementing this project can deliver to the organization.
Another possible area of objection centers around the robustness of the system. Fairly so, some stakeholders might object to deploying a novel technological solution that is unproven in the market, or where there are substantial risks to a botched deployment. To mitigate this it is important to follow the practices we outline in this book for safe and robust model development, and communicate them to the team.
To summarize, to alleviate concerns around model deployment, focus on early communication with stakeholders, listening to concerns and integrating mitigations into the plan, as well as performing extensive testing before a wide rollout. In the fairness and ethics chapter we will also cover how extensive visualization of the model properties as well as taking the time to build in explainable AI might help allay concerns proactively.
Step 6: Maintenance
Finally, after deployment we cannot forget about our solution. In the case of a machine learning system, we often see the problem of data set and environment drift, which means the data that we're training our model on or the assumptions that we've made might change. The model predictions themselves might create a feedback cycle where the data that we're getting becomes different to what we saw before, so the system performance should be continually monitored and we should make provisions for a fast update cycle.
In general with increasing speed of adoption of new technologies we see that we cannot rely on a static plan for many years, as our information about the world changes as well as the available technology. Therefore a successful technological implementation requires constant monitoring and re-evaluation to make sure it remains performant and relevant for a long time after deployment.
Co-founder and CEO
Tiago holds a Master's degree in Theoretical/Mathematical Physics and a PhD in Biophysics from Ludwig-Maximilians University Munich. After graduation, he joined Google DeepMind as a research engineer. There he worked on a number cutting-edge research projects which led to publications in international machine learning conferences and scientific journals such as Nature. He then joined Cogent Labs, a multinational Tokyo based AI start-up, as a lead research scientist. In August 2020 co-founded Recursive Inc, and is currently CEO.