How have you interacted with smart technologies like AI and Cloud-based Analytics platforms.
Prior to joining KeepTruckin, I worked at Uber where I served as Senior Director of Product and Data Science, managing Machine Learning and AI, Data, Marketing systems, and Operations tooling. Prior to joining Uber, I served as Senior Director of Product Management at Cloudera.
How did you start in this space? What galvanized you to start at KeepTruckin?
Without exaggeration, trucking is the life-blood of our modern economy. Trucks move over 70% of the nation’s freight by tonnage, moving nearly 11 billion tons of freight annually. This represents 200+ pounds per person per day.
KeepTruckin saw this opportunity and built a modern Electronic Log Device (ELD) to serve truckers before a mandate requiring them to deploy this device came into effect. Over the course of the last two years, it has established itself as the leading player in the space of Fleet Management. In fact, it is the most popular device amongst the small to mid-sized fleets that dominate trucking.
The end result is one of the most comprehensive and rich datasets on how trucks operate in the United States. With that footprint has comes the ability to solve critical challenges facing the industry and improving the lives of millions of people on the roads.
What is KeepTruckin and how it transforms Fleet Management using AI/Machine Learning?
KeepTruckin is the leading fleet management platform. KeepTruckin’s current hardware and software solutions include its Electronic Logging Device, Electronic Logbook App, the Smart Dashcam and App Marketplace, which offers customers a catalog of customizable integrations that can improve operational efficiencies and increase productivity to save fleets money.
KeepTruckin has built a rich understanding of how trucking fleets operate in the United States through its data collection. We are now leveraging this data to improve safety. In many ways, if you were to look at the advances happening in areas such as Machine Vision that are driving advancements in everything from home security systems to self-driving – these same technologies have dramatic applications for drivers operating giant trucks who can improve their safety by having vision systems help them improve their driving. This is just the tip of the iceberg though – we are applying AI to everything from safety to asset management (intelligent placement of assets to maximize operational efficiency) to fuel optimization and predictive maintenance systems. Our goal is to provide a next-generation data-driven Fleet Management system that improves the safety and operational efficiency of our customers dramatically
How do you distinguish between the various types of AI and Analytics?
I tend to think of leveraging data as being on a continuum. Everything starts with doing basic analysis of your data to understand the structure of your business problems and create the framework for improved decision making. Every further level of advancement is a continued optimization of these decisions through applying advanced techniques to extract the underlying structure of the data at higher fidelity as well as bringing more data to bear to understand the nuances of how things work. Current AI techniques are pretty amazing but everything starts with the basics.
Who are your competitors in this space and how do you retain your competitiveness?
One of the biggest competitors for KeepTruckin is Legacy Hardware that was installed by larger fleets ahead of the ELD mandate. By buying proprietary hardware carriers lost freedom and choice. The challenge is now to help carriers understand how high hardware costs are a significant and unnecessary headwinds to growth.
Which industries would benefit from accessing your AI-based products, services, and resources?
Supply chain, logistics, and e-commerce – in addition to any industry that depends on the movement of goods across the road. Pen and paper was the industry standard for logging these Hours of Service (HOS) for decades, but the process was inefficient, unreliable and lacked transparency into what was actually happening on the road.
Leveraging this foundation of granular industry data that KeepTruckin has collected – such as Vehicle Diagnostics, Idle Times, and Detention Times – we have the opportunity to uncover patterns and efficiencies previously unknown for fleets both large and small. Data is quickly becoming the new currency in the trucking industry. Our intent is to use Machine Learning to analyze our data library and deliver the insights and benefits we uncover to help advance our customers and the industry as a whole.
How could the automotive and Logistics businesses leverage Artificial Intelligence technology to strategically price their products? Which other technologies in these spaces integrate with AI?
According to the US Department of Energy, every hour a truck idles burns about one gallon of fuel. That one gallon quickly adds up and in the US rest-period truck idling consumes up to 1 billion gallons of fuel annually. Use of Artificial Intelligence helps companies understand the price of doing business. For example, knowing more about Vehicle Utilization can identify patterns of drivers who are idling excessively and address this behavior through coaching.
How should young technology professionals train themselves to work better with automation and AI-based tools?
Most technology professionals should not focus on advanced AI techniques. Instead, Data Literacy – understanding how to explore and interpret data, how to identify bad statistics, how to experiment effectively – married with a business understanding is the key to success in a data-driven world. We need anyone working in the business to have a foundation that helps them make better decisions.
Jai leads Product Vision and Development for KeepTruckin’s fleet management solutions. Prior to joining KeepTruckin, Jai worked at Uber where he served as Senior Director of Product and Data Science, managing Machine Learning and AI, data, marketing systems and operations tooling.
Prior to joining Uber, Jai served as Senior Director of Product Management at Cloudera. He earned his BS and BA in Computer Science and Mathematics at the University of Texas at Austin and completed his MS in Computer Science at Stanford University