Why We Invested in AiDash
The devastating Dixie fire, which swept through large swaths of Northern California, may have been sparked by a tree that fell onto a power line. As wildfire risk continues to increase across the country, vegetation management at utilities has become ever more critical to save property and life. And, although certainly, the most devastating wildfires are not the only risk of sub-optimal vegetation management — about ¼ of all power outages are caused by vegetation. The difficulty of managing vegetation is a driving factor behind power reliability declines across the country, as utilities struggle to adapt to changing climates.
Utilities are pouring more funding into vegetation management as an effort to increase reliability and decrease risk on the grid. However, vegetation management is very difficult to do well at scale. Consider the scope of the problem — there are more than 5 million miles of power lines in the US. The largest utilities manage over 100,000 miles of lines. The tools to successfully manage these assets at this scale are limited. Manual inspections are expensive, inefficient, and sometimes impossible. Existing imaging solutions (lidar, drones) are also too expensive to be used on more than a small subset of the network.
As a result of these challenges, most utilities do not use any imaging or inspection to prioritize their vegetation management activities. Instead, they employ “cycle trimming”. They start on one end of the network, make it to the other end after 4–7 years, then start back at the beginning. This process would work well if the network (and its vegetation) were homogenous, but this is not the case.
Satellite image analysis has always been the answer to directing these activities at scale. However, until recently, satellite data has proven very difficult to “productize”. This is a broader point than vegetation management, actually — this has been true across the satellite sector. Companies have struggled to both deliver satellite analytics in an automated manner at scale and to turn those satellite insights into products that an operations team can use. So, satellite analytics to date have been mostly the purview of teams that are experts in managing data — governments, hedge funds, and tech companies.
This is why we were thrilled to meet the AiDash team. AiDash has solved these problems. With deep AI, stitched together thoughtfully, AiDash uses satellite imagery (+ other data) to prioritize the areas of a utility network that require vegetation management. It then delivers these insights to the customer within a workflow software that allows operations teams to seamlessly create a vegetation management plan that optimizes activities around a constraint (for example, a budget). It then allows the operations team to track progress against this plan. These “data-informed” plans increase network reliability while reducing cost and risk.
AiDash’s customers are enthusiastic about the product, and the paradigm shift that it represents — a move from blind cycle-based activities to data-driven, prioritized programs that increase accountability and reliability and reduce risk. The tool is easy to adopt, easy to use, and delivers significant value. We heard, repeated over and over, the sentiment that “someone finally got this right”.
This data-driven approach is the way utilities (and other entities with distributed assets) should be managed. With satellite data, delivered at scale, operations and maintenance budgets can be turned toward the most pressing problems. AiDash has started with vegetation management, but the team is just getting started — its satellite analytics + product capabilities will continue to enable new products and markets to be launched that change the way distributed assets are managed.
If you want to join the journey, AiDash is hiring — see here!