No Brainer - An AI Podcast for Marketers Geoff Livingston and Greg Verdino

Special guest Eyelevel.ai CEO Neil Katz joins Geoff and Greg to discuss generative AI's hallucination problem. Large language models' (LLMs') propensity to hallucinate in their answers represents one of the biggest barriers to enterprise adoption. Eyelevel boasts a 95% accuracy on private instance responses by using its APIs and tools to prepare proprietary data for LLM consumption.
The trio dives into the LLM marketplace, including discussions about why brands choose to implement a private instance, how the LLM market has evolved, and what causes the hallucination problem. Then, they discuss the enterprise data problem and how retrieval augmented generation (RAG) techniques still need additional help to strengthen LLM responses.
Chapters include:
0:00 Start
4:40 Private instance versus licensing enterprise editions of LLMs
7:32 Eyelevel’s Air France implementation achieving 95% success rates
12:29: The need for enterprise data preparation
18:14 The hallucination problem with LLMs and RAG approaches
28:23 How governance can or cannot help enterprises
32:32 Why some use open source versus proprietary LLMs
39:12 The future of AI and an incredible vision

Learn more about Eyelevel at https://www.eyelevel.ai/ or contact Neil Katz via LinkedIn at https://www.linkedin.com/in/neilkatz/
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On today’s episode of No Brainer, Geoff Livingston and Greg Verdino chat with Steven Freidkin, Founder and CEO of Ntiva. Ntiva offers a full range of IT managed services to growing businesses. It’s no surprise that they’re implementing AI internally and fielding plenty of calls from clients about everything from Microsoft Copilot to more sophisticated AI business use cases driven by proprietary data.
In today’s conversation, we cover everything from why even 15 minutes of AI per day will put individual performers ahead of the pack to whether it’s worth paying attention to the hype around artificial general intelligence (AGI.) And we discuss it all through the lens of a tech pro who has rolled up his sleeves and done the work.
Chapters
00:00 Start
02:50 Introducing Steven Freidkin, CEO, Ntiva
04:10 How Ntiva uses AI internally and with clients
10:10 AI hype vs real world action
13:20 Is AI-driven productivity a double-edged sword?
15:45 Why Steven challenges everyone on his team to use ChatGPT 15 minutes every day
17:00 What about businesses that aren’t adopting AI yet?
19:25 Dispelling the myth that AI is “easy” and “cheap”
23:00 Is Microsoft Copilot an AI gateway drug?
27:00 Is AI the product or just a feature?
33:00 The importance of AI guardrails
37:00 What does the AI sector need to do better to drive real business adoption?
43:45 Is AGI real, hype, or somewhere in between?
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In this episode, Greg and Geoff answer listener questions from Lisa Nirell, Phil Gerbyshak, last week's guest Paul Chaney, and TikTok commenters in an open-mic mailbag format. They discuss the challenges of educating organizations on AI integration, emphasizing the importance of alignment, change management, and training. They explore why people prefer fake AI-generated images of themselves and the potential impact on corporate recruiting. And they delve into the topic of AI marketing ethics and the need for marketers to prioritize ethical considerations in their work.
The conversation covers the importance of making small improvements, the need for responsible AI, the bare minimum approach in marketing, connecting ethics with day-to-day work, the divide between marketers who get it and those who don't, the power of authenticity, the divide between brands who get social and those who don't, and the importance of providing value to customers.

00:00 Start
00:31 Introduction and Open Mic Mailbag
03:40 Educating Organizations on AI Integration
17:48 Fake Imagery with AI and Social Media
28:34 AI Marketing Ethics (Again!)
36:06 – Divide Between Marketers Who Get It and Those Who Don’t
53:58 -.Appreciation for the Audience and Final Word

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On this episode of No Brainer, we're joined by Paul Chaney. Paul is the editor of the AI Marketing Ethics digest -- a newsletter that explores the intersection between marketing and (you guessed it) ethical AI. Paul is a digital marketing veteran who was drawn to the subject of responsible and ethical AI when he saw how how few of the marketers who are racing to adopt generative AI tools are having serious conversations about key ethical issues like bias, fairness, human rights, and even environmental impact. He launched his newsletter to close this gap, and has been engaged the conversation ever since.

Paul, Geoff, and Greg discuss the definition of AI ethics, why they matter for marketing, what it will take to get more marketers to take ethics seriously, and how brands can determine what's "right." Along the way, they explore how the choices we make today aren't so different from the choices marketers made (or didn't make) in the early days of the internet, email, and (especially) social media. And they land on a fundamental truth: AI ethics aren't about technology but humanity.

Here's what to listen for in today's episode:
00:00 Introduction
01:30 What is AI ethics and why does it matter for marketing?
10:33 Are marketers thinking and doing enough about AI ethics?
14:36 What needs to happen to put responsible AI on the front burner?
25:49 Are generative AI companies inherently unethical?
31:00 Marketing history repeats: the internet, email, social media, and AI
38:05 How can marketers figure out what’s “right?”
46:00 The Ethical Implications of AI and Job Loss
52:15 The Future of AI Ethics in Marketing
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Yahoo! Finance found that executives are only half as likely to mention AI on their earnings calls. Research from BCG found that enterprises aren't as enthusiastic as they used to be about generative AI. Even Sequoia Capital -- a major investor in AI startups --- has argued that the majority of AI tech companies are struggling to nail their value proposition. And all of this is happening as researchers find that OpenAI's ChatGPT plagiarizes its training data 70% of the time and Google's latest releases amount to a series of self-inflicted wounds.
AI -- and generative AI in particular -- sure seems like it has teetered from the peak of hype and fallen into the trough of disillusionment. Greg and Geoff discuss why this might be the case. And why this is a good thing for any company serious about AI.

What to Watch/Listen For:

00:00 Introduction
02:44 AI Hype is Dead
10:32 Challenges of Implementing AI
15:41 Generative AI and its Limitations
31:58 The Hype Around OpenAI
33:27 OpenAI's Success and Merit
34:54 The Importance of Basic Marketing
36:18 Salesforce's Focus on Trust
37:45 The Success of Companies like Nvidia and Amazon
40:00 AI as a Baked-in Technology
42:06 Diminished Hype and the Importance of Trust
43:03 The Potential Consolidation and Failures in the AI Space
48:16 Differentiation and the Need for Viable Products
53:54 The End of Hype and the Importance of Getting Serious

As always, you can visit NoBrainerPodcast.com for complete show notes, including links to the articles referenced during this show.
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In today's podcast, the conversation centers around Google's deprecation of third-party browser cookies, its impact on marketers, and whether AI might be part of the plan for moving digital marketing forward. Greg Verdino and Geoff Livingston discuss the importance of first-party data, the challenges of unifying data, the role of AI and machine learning in data cleaning and personalization, and -- of course -- the need for a strategic approach to data, marketing, and AI.

What to Listen For

00:00 Introduction and Setting the Stage
09:00 How Google's Deprecation of Third-Party Cookies Impacts Marketers
20:00 The Importance of First-Party Data and the Challenges of Unifying Data
33:30 The Role of AI and ML in a Post-Cookie Marketing Environment
42:00 AI Depends on Strategy and Strategy Depends on Data
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