Zach hosts tech leader, CTO, MIT professor, and author – Mark Herschberg. Mark’s resume is impressive and storied; highlights include helping HBS develop market theory training software and parachuting into Fortune 500 companies to foster start-up-like innovation. He has also worked extensively with big data and machine learning since the 90s, developing many data-driven projects, some of which use AI.

Currently, Mark is doing a lot of work with Generative AI; he holds patents that use Gen AI and is currently working with a handful of companies whose products are based on AI. Zach and Mark talk about his current projects as well as several issues Gen AI poses to tech and society at large.

They hit on hallucinations and the need for Generative AI that cites its sources. They talk about AI’s impact on the signal-to-noise ratio (misinformation) and how both machines and humans need to be better trained to fact-check information. They also discuss future engineers and the continued need to understand what’s going on under the hood of the software, even if Gen AI makes troubleshooting easier.

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• YouTube: https://youtu.be/stj4S3PiTaA
• Apple Podcasts: https://podcasts.apple.com/us/podcast/generative-ai-in-the-enterprise/id1730289289
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About The Generative AI In The Enterprise Series:

Welcome to Keyhole Software’s first-ever Podcast Series, Generative AI in the Enterprise. Chief Architect, Zach Gardner, talks with industry leaders, founders, tech evangelists, and GenAI specialists to find out how they utilize Generative AI in their businesses.

And we’re not talking about the surface-level stuff! We dive into how these bleeding-edge revolutionists use GenAI to increase revenue and decrease operational costs. You’ll learn how they have woven GenAI into the very fabric of their business to push themselves to new limits, beating out competition and exceeding expectations.

Partial Generative AI In The Enterprise Episode Transcript

Note: this transcript section was created using generative AI tools like YouTube automated transcripts and ChatGPT. There may be typos, slight content changes, or character limits for brevity!

Zach Gardner: Ladies and gentlemen, welcome to the future! As you all know by now, I’m Zach Gardner, the Chief Architect at Keyhole Software. About six or seven months ago, I realized I didn’t know enough about generative AI. I didn’t even know enough to be dangerous or even to be not dangerous. I felt that it behooved me—that’s my word for the day, “behooved”—to learn from people who have been thinking about this longer than I have. Some people have been implementing generative AI into production for about a year now, while others, like me, are still trying to find their way through Plato’s Cave, trying to discern the shadows from the real forms of things. So, I went out and talked to a number of different people. Today, with me on the program is Mark Herschberg, the author of The Career Toolkit. And very apropos product placement, by the way—it’s right over your shoulder for those on video and the audio. Thank you very much for being with me today.

Mark Herschberg: Thank you for having me on the show. It is my pleasure to be here.

Zach Gardner: Awesome. So, the disclaimer I always like to put in just in case anyone is listening and gets a wild hair: All the views and opinions expressed in this program are the views and opinions of the participants and do not reflect their employers, their trade organizations, any book clubs they are affiliated with, any fitness clubs, any yoga studios. It’s just two guys talking, that’s all. So, to get us started, can you give a little bit of

background on yourself and what brings you into the world of generative AI?

Mark Herschberg: Absolutely. My background is in computer science and electrical engineering. I’ve spent much of my career at the intersection of business and technology, working with startups and Fortune 500 companies to develop new technologies and bring them to market. Over the past few years, I’ve become increasingly interested in AI and machine learning, especially generative AI, because of its potential to revolutionize so many aspects of our lives and industries.

Zach Gardner: That’s fascinating. As someone who’s been involved in both the business and technical sides, what do you see as the biggest opportunities for generative AI in the enterprise?

Mark Herschberg: The opportunities are vast. From automating repetitive tasks to creating new content and insights, generative AI can significantly enhance productivity and creativity. In the enterprise, this means more efficient processes, more innovative products and services, and ultimately, a competitive edge in the market. We’re already seeing applications in areas like customer service, marketing, and even software development, where AI can assist in writing code or generating test cases.

Zach Gardner: That’s incredible. One of the things I’ve been curious about is the practical challenges of implementing generative AI in a business setting. What are some of the hurdles companies might face?

Mark Herschberg: There are several challenges. First and foremost is the data. Generative AI models require large amounts of high-quality data to be effective. Companies need to ensure they have the right data infrastructure and governance in place. Another challenge is the integration with existing systems and processes. It’s not just about deploying a new technology but also about changing the way people work and think. There’s also the question of ethical considerations and ensuring that the AI is used responsibly and doesn’t perpetuate biases.

Zach Gardner: Those are all excellent points. Speaking of ethical considerations, what are some of the key ethical concerns with generative AI that businesses need to be aware of?

Mark Herschberg: One of the main concerns is bias. AI models can inadvertently learn and perpetuate biases present in the training data, leading to unfair or discriminatory outcomes. It’s crucial for companies to have strategies in place to identify and mitigate these biases. Another concern is transparency. Businesses need to be able to explain how their AI systems make decisions, especially in regulated industries. There’s also the issue of data privacy and ensuring that personal information is handled responsibly.

Zach Gardner: Definitely. The need for transparency and accountability in AI decision-making cannot be overstated. Before we wrap up, do you have any advice for companies just starting their journey with generative AI?

Mark Herschberg: Start small and iterate. It’s easy to get overwhelmed by the potential of AI and try to do too much at once. Begin with a pilot project that has clear objectives and measurable outcomes. Learn from that experience and gradually scale up. Also, invest in your people. Training and upskilling your workforce to understand and work with AI is just as important as the technology itself. And finally, keep the ethical considerations front and center. Responsible AI is not just a nice-to-have; it’s essential for long-term success.

Zach Gardner: Wise words. Thank you so much for joining us today, Mark. It’s been a pleasure having you on the show and gaining insights from your experience.

Mark Herschberg: Thank you, Zach. It’s been great to be here.

Zach Gardner: And to our listeners, thank you for tuning in to another episode of “GenAI in the Enterprise.” Stay curious, keep learning, and we’ll see you next time.