Podcast Summary: The Artificial Intelligence Show – Episode #185: AI Answers – Getting Started with AI, Core AI Concepts, In-Demand AI Jobs, Data Cleanliness & AI Fact-Checking

Date: December 11, 2025
Hosts: Paul Roetzer (Founder & CEO, SmartRx & Marketing AI Institute) and Kathy McPhillips (Chief Marketing Officer, SmartRx)
Format: Q&A, focusing on practical listener questions from AI literacy events
Episode Theme:
This episode of "AI Answers" tackles real-world questions from business leaders and professionals eager to advance their journey with artificial intelligence (AI). Paul and Kathy dive deep into getting started with AI, essential concepts for professionals, new job opportunities, data hygiene, and the challenges of trusting and governing AI systems.

Main Themes & Purpose

• Accelerating AI Literacy: Making AI approachable and actionable for everyone, irrespective of their technical background.
• Practical Guidance: Offering insights into integrating AI into existing workflows, identifying new job roles, overcoming common fears, and responsibly leveraging AI outputs.
• Real-World Examples: Drawing from hands-on experience and current industry trends to illustrate both the opportunities and real challenges professionals face as AI adoption accelerates.

Key Discussion Points & Insights

1. In-Demand AI Roles for Non-Coders

[03:54–08:47]

• Emergence of AI-Infused Roles:
  AI is increasingly integrated into traditional jobs—customer success, sales, research—rather than entirely new AI-specific roles.

• Example: AI Operations (AIOps):
  Roles are developing that focus on orchestrating multiple AI tools and agents across business functions to drive efficiency.

• Advice for Professionals:
  Focus on layering AI skills on top of existing expertise and demonstrate value to employers through forecasting and workflow improvement proposals.

  "More and more it's probably going to be just taking existing roles and infusing AI requirements into them versus changing titles to have AI in them." — Paul [06:41]

  "Be proactive in connecting the dots for your leaders of what the value of those [AI] are. And if they don't appreciate it, there's going to be a market for that talent." — Paul [08:22]

2. Core AI Concepts for Communicators and Marketers

[09:01–10:55]

• Fundamentals First:
  Understanding AI foundations and piloting basics is crucial, regardless of your industry or role.

• Competitive Advantage:
  Professionals with even basic AI literacy quickly leap ahead in their fields.

  "If you just have a really, really strong foundational base of understanding what AI is, what it's capable of... that is going to put you in the top 1% basically in your field at this point." — Paul [10:14]

3. Applying AI Beyond Traditional Knowledge Work

[10:56–13:21]

• Definition of Knowledge Work:
  If you use a computer and apply reasoning/creativity, you are a knowledge worker.
• Broader Applications:
  Even those in trades or field work can leverage AI for administrative, planning, and personal tasks.
• Encouragement for Continuous Learning:
  Exploring AI in personal life may lead to future career opportunities in knowledge work.

4. Breaking into the AI Industry

[13:21–16:15]

• Identify Market Needs:
  Whether considering starting a company or switching roles, focus on how your skills solve real problems.

• Advice:
  Build a pitch deck focused on problem-solution fit, market potential, and competition.

  "What is the problem I'm solving by having these capabilities? Is there that need for it?" — Paul [14:09]

5. Data Cleanliness: Generative vs. Predictive Use Cases

[16:15–19:09]

• Start with Generative AI:
  For companies with messy data, pilot generative AI for quick wins (emails, content) before tackling complex predictive projects.

• Predictive/Advanced Use:
  More advanced applications need significant data preparation and likely expert involvement.

  "Generative AI use cases is the most obvious thing. So if you're not doing that yet, that is, you can be starting this afternoon…" — Paul [16:39]

6. AI for Data Organization: Expertise Still Matters

[19:09–22:27]

• AI Can Assist in Cleaning Data:
  Tools can help organize and prepare data, but validating their output requires domain expertise.

• Parallel with Legal and Finance:
  Use AI for "legwork," but always review with qualified professionals before making critical decisions.

  "As with any advanced use case of AI, you have to have expertise in the domain with which you're working with it." — Paul [19:09]

  "We do now have these capabilities to do things we couldn't have done before, that we aren't experts in. But we also have to accept the fact we're not going to know if the output isn't right." — Paul [20:37]

7. Sourcing and Operationalizing AI Use Cases

[24:13–28:08]

• Problem-Based vs. Use Case Models:
  Use frameworks to identify key issues and break jobs into tasks to prioritize impactful AI applications.

• Workshop Approach:
  Host collaborative, stigma-reducing workshops to brainstorm and share AI adoption ideas.

• Culture of Openness:
  Encouraging open discussion about AI use reduces fear and accelerates organizational learning.

  "By doing it in this embracing workshop model, it's like, 'hey, we want you all to be using it… We actually need you all to be using it.'" — Paul [27:35]

8. Introducing AI to Technical Workforces Without Replacement Fear

[28:43–30:47]

• Start with Pain Points:
  Show technical teams how AI can solve their least favorite tasks or bottlenecks.

• Focus on Augmentation:
  Frame AI as an enhancer, not a replacer, of expertise.

  "Find the thing in their job they hate doing and build a GPT for them, build a gem for them. Show them a workflow where AI helps them do the thing they don't enjoy." — Paul [28:50]

9. Trusting Generative AI Beyond its Mechanics

[31:12–34:16]

• Skepticism is Healthy:
  Understanding the probabilistic nature of large language models helps set realistic expectations.

• Analogies to Physics:
  Even without fully understanding why AI works, use cautious trust mirroring other scientific laws.

  "It’s hard to get over that. But it's also like, I don't understand why the speed of light is a thing... I trust gravity every day. And so I think with AI models we kind of have to get to that point…" — Paul [32:54]

  "All the scaling laws tell us that they’re just gonna keep getting smarter, the hallucinations will keep going down, and we will have this alien technology everywhere." — Paul [34:01]

10. Are AI-Driven Search Tools Disrupting Google?

[34:20–36:43]

• No Major Impact…Yet:
  Despite recent fears, Google remains dominant and is methodically integrating AI into its platform.

• Financial Advantage:
  Google’s ad business funds ongoing innovation, unlike startups that face sustainability challenges.

  "I think over time, Google search just becomes AI mode... But as of right now, Google's business is humming along." — Paul [34:53]

11. The Next Shift: Local AI & Advanced Agents

[36:58–41:04]

• Better Reasoning:
  AI’s ability to pause and “think” is rapidly improving, increasing reliability and sophistication.

• On-Device Models:
  Apple and others are working towards running advanced AI local to the device, reducing reliance on cloud compute, boosting privacy, and changing competitive dynamics.

  "The bet they're making is that if you take... like a Gemini 3 or even a Gemini 4, they will be able to compress that model and serve it up to you on device probably within one to two years." — Paul [40:20]

12. Do Companies Understand AI Before Reducing Workforce?

[41:04–45:41]

• Reality Check:
  Most organizations do not fully grasp AI’s implications and may cut staff prematurely, missing growth opportunities.

• AI as Partial Task Worker:
  AI can’t do entire jobs yet—just assists with specific tasks, making genuine workforce reductions more nuanced.

  "AI is incapable of doing anyone's job today... it is increasingly good at doing tasks within that job." — Paul [41:46]

  "If we don't create the need for more work and more jobs, we will see an onslaught of workforce reduction in the next 18 months." — Paul [43:48]

13. What Could Slow Down AI Advancements?

[45:51–49:02]

• Potential Factors:

  • Supply chain issues (e.g., chip manufacturing)
  • Poor enterprise value realization
  • Intellectual property lawsuits or restrictive regulations
  • Physical model limitations (“scaling laws stopping”)
  • Voluntary or involuntary model halts due to safety concerns
  • Global competition, especially from China

  "One of the other ones I mentioned is this idea of a voluntary or involuntary halt to model advancements... If the US slows down, China won't. It'll be their chance to catch up." — Paul [48:27]

14. Guardrails for Recursive Self-Improvement and Fact-Checking

[49:07–54:20]

• Current Approach:
  Labs self-regulate the guardrails for AI training data and improvement, but regulations are lacking and vary by country or corporate ethos.

• Challenge of Biased Data:
  Models could learn from incomplete or distorted sources, raising societal risks.

• Need for International Collaboration:
  Solutions depend on cross-lab cooperation; trusting individual organizations isn’t enough for global safety.

  "You basically are in a position where you’re saying, I kind of trust Google would, would maybe like have some guardrails in place, but would Xai put the same guardrails in place? ...Is China... going to stop it? Probably not." — Paul [50:52]

Optimistic Closing Thoughts

• Outlook for AI’s Impact:
  Despite real challenges, the hosts foresee a “golden age” of innovation, creativity, and entrepreneurship ahead—provided society faces obstacles openly and proactively.

  "We get to live through maybe the most innovative phase in human history." — Paul [53:31] "With enough conversation, enough focus on a positive outcome for this, the net in the end is going to be a really good thing for society." — Paul [53:56]

Notable Quotes

• [06:41] "It's probably going to be just taking existing roles and infusing AI requirements into them versus changing titles to have AI in them." — Paul
• [10:14] "If you just have a really, really strong foundational base of understanding what AI is, what it's capable of... that is going to put you in the top 1% basically in your field at this point." — Paul
• [16:39] "Generative AI use cases is the most obvious thing. So if you're not doing that yet... you can be starting this afternoon." — Paul
• [19:09] "As with any advanced use case of AI, you have to have expertise in the domain with which you're working with it." — Paul
• [28:50] "Find the thing in their job they hate doing and build a GPT for them, build a gem for them. Show them a workflow where AI helps them do the thing they don't enjoy." — Paul
• [32:54] "I trust gravity every day. And so I think with AI models we kind of have to get to that point where it’s okay to have skepticism..." — Paul
• [43:48] "If we don't create the need for more work and more jobs, we will see an onslaught of workforce reduction in the next 18 months." — Paul
• [53:31] "We get to live through maybe the most innovative phase in human history." — Paul

Timestamps for Major Topics

• In-demand AI Roles for Non-Coders: [03:54–08:47]
• Core AI Concepts for Communicators and Marketers: [09:01–10:55]
• AI Beyond Knowledge Work: [10:56–13:21]
• Breaking into the AI Industry: [13:21–16:15]
• Data Cleanliness and Use Cases: [16:15–19:09]
• AI for Data Organization: [19:09–22:27]
• Sourcing & Operationalizing Use Cases: [24:13–28:08]
• Introducing AI Without Fear: [28:43–30:47]
• Trusting Generative AI: [31:12–34:16]
• AI-Driven Search vs. Google: [34:20–36:43]
• Next-Generation AI (Reasoning & On-device): [36:58–41:04]
• Workforce Impact: [41:04–45:41]
• Barriers to AI Progress: [45:51–49:02]
• Guardrails & Fact-Checking for Recursive AI: [49:07–54:20]
• Optimistic Closing: [53:18–54:20]

Memorable Moments

• Kathy recounts her relief at a story where AI streamlined sales roles but allowed employees to focus on more valuable work, reflecting a positive vision for AI in the workplace. [08:34]
• Paul describes using AI as a springboard for legal and financial processes, but emphasizes always “bringing in the experts” for high-stakes decisions. [21:39]
• Paul likens trusting AI models to trusting laws of nature like gravity, underscoring an attitude of cautious optimism and acceptance. [32:54]
• The hosts discuss the need for open conversations to remove stigma around AI: “We actually need you all to be using it... Let’s learn from each other.” [27:35]

Takeaway for Listeners

This episode offers both practical and philosophical guidance on navigating AI’s rapid evolution at work and in society. From job roles to data, trust concerns to governance, Paul and Kathy urge listeners to embrace continuous learning, share knowledge, remain vigilant of risks, and focus on opportunities to innovate and grow—together.

For more resources, classes, and to ask your own questions, visit: [SmartRx AI / Marketing AI Institute].