Tripp Cox Of EagleView: How We Leveraged AI To Take Our Company To The Next Level

An Interview With Cynthia Corsetti

Our learning and development team is using AI tools to create voice-over audio for modules in our learning management system. This has saved hours of work on each new module or module update.

In the ever-evolving and never-ending landscape of business, staying ahead of the curve is a prerequisite for success. Artificial Intelligence (AI) has gone from being a futuristic concept to a daily business tool that executives can’t ignore. In this interview series, we would like to talk with business leaders who’ve successfully integrated A.I. into their operations, transforming their companies in the process. I had the pleasure of interviewing Tripp Cox.

As the Chief Technology Officer at EagleView, Tripp Cox brings more than 20 years of experience at technology and professional service organizations, focused on business outcomes through increased product usability, data attribute development, and scaling engineering to meet product growth strategies. Prior to joining EagleView, Tripp served as EVP of Research & Development at Calero. He also worked as the top technology executive in multiple high-tech ventures, including Damballa, MindSpring, EarthLink, and Illuminate360.

Thank you so much for doing this with us! To set the stage, tell us briefly about your childhood and background.

I’m about as close as you can get to being an Atlanta native, having moved to the Southern suburbs prior to starting elementary school. I’ve always been really curious about technology and took a strong interest in electronics and computers during my school years. After embarking on an initial career path in culinary arts as a professional chef, I jumped at the chance to shift into technology when the Internet took off in the mid-90’s and created unprecedented career opportunities. My first tech job was at MindSpring, which was a rocket ship of growth and provided me a rich environment to further develop my technical and leadership skills. Almost 30 years later, I have been blessed with a wealth of opportunities to continue learning and growing.

What were the early challenges you faced in your career, and how did they shape your approach to leadership?

I have always been fortunate to work with incredibly smart and talented people. I entered the tech industry lacking certain academic foundations, but I overcame that with voracious self-study, enrolling in university courses, and pursuing many other professional development opportunities. My experience led me to value intellectual curiosity and aptitude in people over their credentials and proven qualifications. As a leader, I am more than willing to invest in developing high potential people, even if it means we work through some learnings along the way.

We often learn the most from our mistakes. Can you share one mistake that turned out to be one of the most valuable lessons you’ve learned?

I was working in technical support at one point and dealing with a particularly difficult customer. I put him on hold and complained to a co-worker about how difficult this guy was being. At least, I thought I had put him on hold. He overheard me make a disparaging comment about him. Unfortunately, he was good friends with the CEO of the company and told me he would be giving him a call to let him know about his experience. I immediately marched over to the CEO’s office, confessed my sins, apologized sincerely, and told him I understood whatever action he decided was appropriate. He was incredibly gracious, telling me he would smooth things over with his friend and to just not let it happen again. I learned two things that day: (1) Always double-check your work; and (2) Never take a second chance for granted. Being allowed to stay in that job made the rest of my career possible.

A.I. is a big leap for many businesses. When and what first sparked your interest in incorporating it into your operations?

I consider AI a term of art that applies broadly to a wide range of statistical methods. Others may disagree, but I see it as an umbrella term that is inclusive of machine learning use in software systems. My very first exposure to it was when we implemented an online supervised Bayesian model to classify email as spam and not-spam as part of subscriber-facing email services at MindSpring. That was about 25 years ago. Although it wasn’t perfect, it was certainly very useful and formed a jumping off point for me on how statistical algorithms could be built into software systems to make them more intelligent and powerful. Fast forward many years, and we are living in an age where software systems can train themselves using generative algorithms. It’s an incredibly exciting time to be in tech.

AI can be a game-changer for individuals and their responsibilities. Can you share how you personally use AI and what are your go-to resources or tools?

Lately, Bard by Google is one of my favorite go-to tools. I use it all the time to improve the effectiveness of my written communications and summarize highly complex topics. Of course, I proof-read and edit everything it generates, conduct additional research to verify facts it asserts, and refer to additional sources of information to better understand topics in depth. But it’s surprisingly good and pretty reliable in most of my experience with it. I also recently had Dall-E take a high resolution EagleView image of my home and design a swimming pool and hardscape for my backyard. It wasn’t bad!

On the flip side, what challenges or setbacks have you encountered while implementing A.I. into your company?

As with most aspects of technology, the people are sometimes the hardest part. Anyone who has real experience building AI-powered solutions knows that perfection is unattainable, but that just isn’t so obvious to your average business person. Properly presenting the capabilities and limitations of AI to stakeholders across the business is something I’m still working on being better at doing. It’s tempting for people to ask for an AI solution to XYZ, but clearly defining a problem statement is really where the conversation needs to start. Sometimes AI can be a helpful part of the solution, but many times a simple workflow or rule-based solution is a better fit. Every time we’ve run off to build a model without getting strong agreement on the problem to be solved, we’ve eventually had to go back to the drawing board.

Of course, if you ask a data scientist about their biggest challenges, it’s usually about the data. The data isn’t diverse enough, not specific enough, lacking quality or lineage, being to large. Usually there are multiple data sourcing, management, and cleansing challenges to be overcome before you can really even get started on the journey to create some value.

Let’s dig into this further. Can you share the top 5 A.I. tools or different ways you’re integrating AI into your business? What specific functions do they serve and what kind of result have you seen so far?

1 . Using coding assistants to help our developers be more productive. Many of our developers are now using these assistants daily and finding productivity gains. It’s still early days, but we’re finding that our devs accept AI suggestions about 30% of the time. They tell us they feel like they are getting more done with AI, but we haven’t seen any breakthrough productivity gains just yet.

2. Generative chat bots are helping our marketing team draft ideas for marketing copy. We’ve been able to reduce our usage of creative agency services and delay some additional hiring based on the results so far.

3. Our learning and development team is using AI tools to create voice-over audio for modules in our learning management system. This has saved hours of work on each new module or module update.

4 . EagleView has long used AI methods to help extract information from our high-resolution aerial imagery. We continue to increase the level of automation in many aspects of our services by incorporating AI into everything from image processing to 3D modeling.

5 . Most recently, EagleView introduced compelling a new service called SolarReady. It’s an API-accessible data service that helps Solar marketers quickly ascertain the suitability of residential properties for solar system installation based on the roof’s geometry and sun exposure. SolarReady is powered by multiple generative AI models that do everything from interpreting 3D scenes to simulating seasonal vegetation.

There’s concern about A.I. taking over jobs. How do you balance A.I. tools with your human workforce and have you already replaced any positions using technology?

I don’t know about other companies, but we always have more we’d like to do than what we can afford to do. AI is starting to make it possible in small ways for our employees to be more productive and get around to doing more things on our backlogs as a result. We hope to see this accelerate as AI capabilities improve, so that we can take on even more innovation opportunities.

Looking ahead, what’s on the horizon in the world of AI that people should know about? What do you see happening in the next 3–5 years? I would love to hear your best prediction.

In the very near term, I think we’re going to see an explosion of additional foundation models being created and offered through a hodgepodge of purpose-built platforms and services. Eventually, I think a few leaders will emerge at putting some common infrastructure and services around multiple foundation models, becoming a sort of foundation model marketplace. Enterprises with a need to keep data private and manage risk will probably be able to safely integrate with a larger number of foundation models in platforms like these in 3–5 years’ time.

Companies with unique data will have tremendous opportunities to create enterprise value by more directly productizing their data through novel foundation models or novel extensions to others’ foundation models. This will require an additional dimension of product strategy thinking and planning for many companies.

If you had to pick just one AI tool that you feel is essential, one that you haven’t mentioned yet, which would it be and why?

I don’t think any single AI tool is essential, really. For companies who use Microsoft’s collaboration suite, I think Microsoft Copilot is likely to quickly become integral to day-to-day productivity and in some ways will become essential to how Microsoft-platformed companies operate.

For the uninitiated, what advice would you give someone looking to integrate AI into their business and doesn’t know where to start?

Just get started. Open up ChatGPT, Bard, or any of the other public tools and start asking it questions about the hard problems you’re trying to solve or things you’re trying to figure out. Be careful not to share any confidential information unless you fully understand the terms of the license and are comfortable with the risks. But get familiar with what it’s good at and what it’s not so good at. Take a tutorial on prompt engineering. See how setting context and assigning roles to the Ais helps improve your results. Then, start to think about how it can improve the way your customers experience your products and services. Customer service is a great first use case to consider. Software development with Github Copilot, Amazon CodeWhisperer and the like is another great starting point for quick productivity boosts.

Where can our readers follow you to learn more about leveraging A.I. in the business world?

@trippcox (X)

This was great. Thanks for taking time for us to learn more about you and your business. We wish you continued success!

About the Interviewer: Cynthia Corsetti is an esteemed executive coach with over two decades in corporate leadership and 11 years in executive coaching. Author of the upcoming book, “Dark Drivers,” she guides high-performing professionals and Fortune 500 firms to recognize and manage underlying influences affecting their leadership. Beyond individual coaching, Cynthia offers a 6-month executive transition program and partners with organizations to nurture the next wave of leadership excellence.