This blog is part of our focus on Cisco employees who are “Striving for Sustainability” by finding opportunities to integrate sustainability in their day-to-day work.

We’re seeing sustainability evolve into a space where science, practical business decisions, and environmental impact intersect. That’s where Mike King, who leads Carbon Accounting and Analytics from Cisco’s Chief Sustainability Office, comes in.

Mike believes sustainability is an incredibly interesting engineering problem, and in his view, there is no bigger issue to tackle. Learn more about his approach:

How did your engineering background shape the way you approach your work in sustainability?

Mike: Having that technical foundation, along with time spent in the supply chain as a mechanical engineer at Cisco, taught me to see sustainability as a systems problem. Understanding how Cisco designs, builds, and ships products gives me a solid grasp of the whole process, which is especially valuable when we’re trying to quantify our sustainability impacts. I feel confident in knowing what the process looks like and what we’re dealing with at each stage.

From an engineering perspective, I tend to break things down into steps. I naturally move through the process step by step but always keep the big picture in mind. The engineering design process is inherently iterative. For example, I teach an engineering class at Arizona State University, and we go through cycles of coming up with an idea, building it, testing it, seeing what worked and what didn’t, and then refining it. I bring that same approach to our sustainability projects, such as our Product Carbon Footprint process. It does not have to be perfect on the first try; it is about building, finding issues, and improving each iteration.

Why is high-quality data so critical to making sustainability efforts credible and actionable?

Mike: Data is what grounds sustainability in reality. When you can clearly see where impacts are concentrated, whether in components, manufacturing, or energy use, it helps teams focus on actions that can drive meaningful change.

High-quality data enables everyone to make more informed and confident decisions. For example, an engineer can connect environmental impact to product cost, while a customer can look for ways to lower the electricity costs of operating our products.

With our Sustainability Data Foundation (SDF), Cisco aims to provide a single source of high-quality data for both internal teams and customers. Given the growing complexity and demand for environmental sustainability data, it is critical that we consolidate datasets and make the data easy to access and understand. The SDF brings together a range of information, from product information and supply chain details to customer usage and third-party emission factors, so that everyone has the data they need to support credible and actionable sustainability efforts.

How can large companies balance the growing demand for artificial intelligence (AI) and digitalization with the need to improve energy efficiency?

Mike: AI and digitalization offer opportunities for efficiency, but they also bring increasing energy demands. The key is to be intentional about how we design and deploy technology. Some companies, like Cisco, are prioritizing energy efficiency right from the start, making it a core part of how new solutions and tools are developed. It is also important to consider the broader system’s impacts so that innovation and sustainability can move forward together.

Machine learning (ML), a core subset of AI, is just one area where we have already seen real, positive impacts. For example, ML algorithms can optimize buildings and facilities by using predictive analytics to manage energy use more efficiently. These kinds of applications have been around for decades and are proven to help companies reduce their environmental footprint.

Based on your experience building Cisco’s Life Cycle Assessment (LCA) program, how can LCAs drive innovation and help businesses make better sustainability decisions?

Mike: LCAs help make environmental impacts visible across the full life cycle of a product, which often leads to better design conversations. For example, if you are designing a product, LCAs can show whether switching to recycled materials or reducing the energy intensity of a supplier’s location will make a bigger difference.

At Cisco, LCAs have helped us pinpoint environmental “hotspots” in different life cycle phases, like manufacturing, transportation, or use. Without LCAs, it would be hard to know where to focus. They give engineers the insights needed to optimize components and give customers the data to choose more sustainable products. LCAs also go beyond just carbon, highlighting other impacts like water use and material choices. This helps teams understand trade-offs and make informed design changes without unintentionally increasing other impacts.

Looking ahead, where do you see sustainability creating the greatest business value for large organizations?

Mike: The greatest value comes when sustainability is embedded into everyday business and technology decisions. Companies that use sustainability data to guide design, sourcing, and innovation tend to be more resilient, more efficient, and better prepared for the future.

On the upstream side, this means finding ways to reduce costs through smarter material choices, increasing product longevity, or boosting reusability. These strategies can help a company save money while reducing environmental impact. Downstream, it’s about delivering value to customers and partners. That might mean helping customers achieve energy savings or address emissions reduction targets.

At Cisco, I see my team sitting right in the middle of that equation, helping to drive benefits for both the business and our customers by making sustainability a core part of our decision-making process.

What do you wish more people understood about sustainability?

Mike: It’s not about finding a perfect answer. Real progress comes from making better decisions over time, learning from trade-offs, and focusing on what matters most. It is about using data to guide improvements, rather than waiting for perfect solutions.