Takeaway 1:

New forms of data are giving organizations unprecedented speed and transparency

When a CEO wants an answer to a complex question, a team might be able to get it in a couple of months—but that may not be good enough in a world where competition is accelerating. One of the biggest advantages of an automated, data-driven AI system is the ability to answer strategic questions quickly. “We want to take that down to an hour or so when it’s about something going on in the physical world,” says Orbital Insight founder James Crawford.

Data and AI are not only finding answers faster but creating transparency around issues that have always been murky. Consider a multinational’s desire to ensure sustainability in its supply chain. An input like palm oil is produced on millions of farms in developing nations, and it goes through thousands of refineries and mills before it reaches one of that multinational’s factories. That’s a difficult supply chain to trace. But Orbital Insight has been able to use geolocation data and satellite imagery to track the physical supply chain—not based on paperwork that may not be accurate but based on real-time snapshots of where trucks are driving and where deforestation is occurring.

Unstructured data, especially in the form of images and video, remain challenging for organizations to utilize due to the complexity of building and maintaining cutting-edge algorithms. CrowdAI is unlocking the ability to extract insights from images and video. Users begin by labeling objects or pixels in raw imagery—perhaps the most time-consuming step in creating a computer vision model. “Our platform speeds up the labeling process by incorporating user-generated labels to automate further labeling, constantly iterating on that human feedback,” says CrowdAI founder and CEO Devaki Raj. In this way, firefighters can use apps on their phones to track the behavior of wildfires in real time, and vaccine manufacturers can use computer vision on their production lines to spot tiny defects in vials that human eyes might miss.

Another start-up, Measurable AI, has found a way to take some of the guesswork out of corporate financial performance. CEO Heatherm Huang explained that his company uses natural language processing and machine learning to aggregate email receipts on its own mail app, with user permission, for statistical modeling. This kind of analysis can predict reported earnings better than traditional stock analysts can. When Zoom adoption spiked in 2020, for example, Measurable AI’s algorithm was able to estimate quarterly earnings within 1 percent of reported earnings, compared to an industry consensus that was off by more than 10 percent.