Jumpstarting Your Net-Zero Journey: Where to Begin

As technological advances continue to bring significant benefits to business processes, they also raise many questions around how leaders can leverage them for societal benefit. With emissions heading back to pre-pandemic levels following a temporary decline, technology has an important role to play in stabilizing climate systems. Artificial intelligence (AI), in particular, is uniquely well-suited to solve this problem.

According to the Boston Consulting Group, using AI for climate control could help reduce 2.6 to 5.3 gigatons of greenhouse gas emissions, as much as 10% of the total, by 2030. This could provide one to three trillion dollars in value when applied to corporate sustainability. In a sense, this urgency has inspired a healthy competition amongst organizations to achieve carbon neutrality by 2050 or sooner. Yet it’s not uncommon to come across major headlines that evoke feelings of impending doom, pondering whether we’re past the point of no return.

The truth is, it’s not too late. As we look to the future, it’s critical that business leaders keep an open mind as to how they can contribute to building a better, smarter, and more sustainable world. Artificial intelligence will play an essential role in this journey in at least six key areas. They are:

1) Measure your company’s carbon output in real time

Quantifying a company’s emissions is essential to informing sustainability initiatives in both the present and the future. Thankfully, there are numerous tools available at our disposal to estimate a company’s carbon footprint. A quick Google search reveals business emissions calculators from Carbonfund.org, the CoolClimate Network, and the United States Environmental Protection Agency. The Greenhouse Gas (GHG) Protocol, which claims to supply “the world’s most widely used greenhouse gas accounting standards,” may provide a more comprehensive and reliable emissions calculation by gathering data tied to direct emissions from owned or controlled sources (Scope 1), indirect emissions from the generation of purchased energy (Scope 2), and indirect emissions that occur in the value chain (Scope 3).

However, all these calculation tools are designed to report emissions on an annual basis, which can be inaccurate since emissions intensity can vary significantly over the course of the year. Artificial intelligence, on the other hand, can help determine more accurate real-time emissions of operations with machine learning algorithms that can analyze both historical data as well as operational data collected from various sensors. Patterns in the data can then help companies put together a more complete picture of their carbon footprint throughout the year and optimize processes accordingly.

2) Predict which processes are the highest emitters

Resource management will profoundly affect not only our economic future, but our environmental future as well. When trying to identify the processes that yield the largest emissions, operational inefficiencies are particularly low-hanging fruit, ripe for improvement. For example, a manufacturing plant may be struggling to reduce water waste, whether from constant leaks or simply using too much water when producing final products.

AI can be used to identify process inefficiencies by monitoring the health of operations via normal behavior modeling and employing anomaly detection during both production and non-production time. Machine learning algorithms can ingest historical data from a plant’s operations and use that data to build a model that acts as a profile of “normal” operations. Tying back to the manufacturing example, the model can then alert operators to anomalous behavior, such excess water consumption. This new insight will empower operators to take action before more water is wasted – all without retrofitting existing processes.

3) Provide actionable recommendations

Whether business leaders have fully assessed their company’s footprint or not, it can be difficult to determine what actions they should take to address carbon emissions. Normal behavior modeling and predictive analytics can certainly help identify inefficient processes, but that’s only part of the value that AI delivers. Business leaders can take it to the next level by not only predicting inefficiencies, but also leveraging outcome-focused recommendations that enhance decision-making at the speed needed to drive operational improvements.

Natural language processing, a branch of artificial intelligence that’s concerned with the ability of computers to understand language in a manner approximating human understanding, can often identify what may have caused a particular event and why. This happens via ingesting historical records, service manuals, documented past courses of action taken by subject matter experts, and many other forms of unstructured data that may suggest possible next steps and corrective measures. This is particularly useful when addressing equipment failures or impending failure events.

4) Optimize maintenance processes

Operational slowdowns or stoppages result in exorbitant maintenance costs and increased carbon emissions. Scheduled maintenance, otherwise known as preventative maintenance, involves checking on and repairing critical equipment at predetermined intervals. While taking action—any action—is critical to preventing downtime, scheduled maintenance is not always the ideal method. It can waste valuable production and personnel time, as well as prematurely require replacing parts or entire assets that don’t yet need a replacement, yet still fail to preclude unexpected or unusual failures.

AI-powered predictive maintenance can help systems run more efficiently and reliably. By making use of the sensor data they already have, AI-powered predictive maintenance products enable business leaders to stop wasting time and resources and start predicting and preventing failures days or weeks in advance, while leaving assets that do not need maintenance to continue running smoothly. Based on insights provided by the data, leaders can actively perform necessary maintenance to avert costly machine failures, water leaks, and potentially catastrophic damage that can prevent their companies from achieving sustainability goals.

5) Become a change enabler for your customers and partners

Decarbonization can be just as much an external exercise as an internal one. In other words, business leaders and their companies may not only be drivers of change inside their companies, but also enablers of change outside their companies as well. This requires a partnership of sorts to achieve common sustainability goals, such as supporting the goal of net-zero carbon emissions by 2050. Once a working relationship with an array of external partners, suppliers, and customers is built, both internal and global sustainability goals become easier to achieve.

Toward that end, it’s important for business leaders to determine which aspects of AI implementation their companies will own and which are best addressed through others, based on talent, budget, and other resources available. Success is also dependent on getting customers and partners involved and aligned on sustainability goals. Artificial intelligence can enable customers and partners to identify vulnerabilities in their processes, prioritize risks, provide actionable recommendations, and jumpstart their overall sustainability journeys.

6) Assess the complete competitive context

The decarbonization challenge has ushered in a new wave of competition amongst organizations who are scrambling to maintain their competitive edge. Thus, regardless of where leaders’ companies are in their decarbonization journeys, it’s important to assess how well they’re doing compared to their competitors, especially as consumers, investors, and partners demand that businesses lead by reducing emissions. As noted earlier, artificial intelligence enables business leaders to assess how well their companies are doing in the decarbonization journey by gaining real-time insights from internal data rather than relying on annual estimates. Based on AI-driven findings, they can then determine how to optimize their processes to strengthen their competitive advantage and accelerate their journey towards achieving sustainability goals.

Create your own path to success

In exploring these six different ways to leverage AI in pursuit of decarbonization, business leaders should also recognize that it is not typically necessary to begin with an all-encompassing plan that takes into account every possible future scenario. Instead, they can start the journey to improvement at any one of the six opportunities discussed above, and then proceed to any of the others (or any combination) based on their unique contexts.

In many cases, the best place to start is where there is already data available. For example, existing solutions for predictive maintenance, which already leverage data to attain high ROI, can be enhanced and scaled up with minimal changes in personnel and process as a first step in the journey. At another organization, quick results might come from enabling change in the larger ecosystem of customers and partners, such as adopting greener shipping options, after completion of a comprehensive carbon footprint assessment. No two organizations share identical contexts, infrastructure, or challenges.

The critical thing is simply to acknowledge the need for improvement, and then pursue the lowest-hanging fruit first, proceeding logically based on the potential for rapid, measurable change. Analysis of each completed stage, taking into account any new or unexpected developments that apply, will then inform the choice and execution of the next stage.

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