The Artificial Intelligence Show — Episode #181: AI Answers

Date: November 20, 2025
Hosts: Paul Roetzer (Founder & CEO, SmartRx and Marketing AI Institute) & Katherine Phillips (Chief Marketing Officer, SmartRx)
Format: Q&A – Addressing audience-submitted questions on AI literacy, measuring skills, overcoming resistance, frameworks, adoption of agents, and more.

Episode Overview

This special "AI Answers" edition tackles pressing, practical questions from business leaders and practitioners navigating AI integration. Paul and Katherine tackle topics ranging from emerging AI literacy frameworks and assessment, overcoming resistance, aligning leadership, to preparing for upcoming advances like AI agents and search transformation. The episode is heavy on actionable insights, candid workplace anecdotes, and honest reflections on the rapid evolution and organizational impact of AI.

Key Discussion Points & Insights

1. Emerging AI Literacy & Competency Frameworks

Timestamps: 05:33 – 09:43

• No universally accepted AI literacy/competency frameworks exist yet, but 2026 is expected to bring maturation.
• Many organizations are starting to make AI literacy part of professional growth, often integrated into performance reviews (certificates, project-based milestones).
• Paul shares how their team intends to use an internal learning management system (AI Academy) to coordinate required learning journeys and track achievement—but impact is valued over box-checking:
  • Quote:
    "For us, a lot of it really comes down to the impact you're having with the tools. How are you using them to improve workflows? What innovations are you driving?" — Paul (07:41)

2. Why AI Literacy Matters for All, Not Just Early Adopters

Timestamps: 09:43 – 12:34

• AI transformation requires both top-down vision and bottom-up innovation—from executive assistants to interns.
• If segments of staff opt out, they’ll fall behind or actively hamper team productivity:
  • Quote:
    "The risk is they shouldn’t be in the company in one to two years ... If you know that someone who invests the time in getting the certificates and using the tools every day has a 10%, 20%, 30% greater impact ... how can you justify keeping the employees who refuse to do it?" — Paul (10:46)

3. Articulating AI’s Business Value to Skeptics

Timestamps: 12:34 – 14:27

• To convince reluctant stakeholders, show them tangible, personal examples—especially improvements to tasks they dislike or how AI can directly boost KPIs tied to compensation.
  • Quote:
    "You have to make AI personal to people ... Show them something that's personally relevant and help them connect the dots." — Paul (13:36)

4. Senior Leadership, Risk, and the Need for Guardrails

Timestamps: 14:27 – 19:05

• For regulated industries or critical risk, policies (not just guidelines) are essential for data handling, disclosure, and responsible AI use.
• In less critical areas, flexible guidelines may suffice, especially at a departmental level while company-wide standards evolve.
  • Quote:
    "Depending on how you're using AI ... what is allowed to be uploaded into ChatGPT, Gemini, Claude, that better be a policy." — Paul (17:30)

5. Overcoming Resistance to Training & Assessments

Timestamps: 20:00 – 23:15

• Make training benefits highly tangible: illustrate clear, direct efficiency gains, career advancement opportunities, and real use cases from within the organization.
  • Quote:
    "If you do this, you are going to be more efficient and productive ... connect the dots of why you're doing it. It is literally to help you transform your own capabilities." — Paul (20:38)
• Group onboarding, deadlines, and applying training to team projects help boost engagement.

6. ROI Demands & Building a Business Case Before Pilots

Timestamps: 23:15 – 26:28

• Use "minimum viable" examples and hypotheticals; benchmark current workflows, estimate savings with AI, and create straightforward spreadsheets to showcase potential impact.
  • Quote:
    "Show what it looks like today and how much time and money it takes, and then show what it would look like tomorrow." — Paul (24:04)
• Reference to the AI Value Calculator as a practical tool (link in show notes).

7. The Search for a Unified AI KPI

Timestamps: 26:28 – 28:19

• Revenue per employee is a common, but imperfect, unifying KPI. AI adoption should ideally increase this metric, signaling efficiency and productivity gains.

8. Data Readiness & Governance for AI

Timestamps: 28:19 – 32:29

• Don’t wait for perfect data—start with low/no-risk generative AI use cases now, while specialists work on data infrastructure.
• AI tools embedded in standard software (e.g., Gemini in Google Sheets, Copilot in Excel) increasingly help identify data gaps and optimize governance.
  • Quote:
    "There are hundreds of use cases for generative AI tools that don't need to touch any data." — Paul (28:58)
  • Senior-level employees poised to benefit most due to ability to ask strategic questions.

9. The Reality of AI Agents in the Enterprise

Timestamps: 33:01 – 38:23

• Definition of "AI agents" is still blurry; their marketed autonomy often exceeds present reality.
• Agents are currently most effective in narrowly defined, high-volume, rules-based tasks (customer support, BDR work).
  • Quote:
    "AI agents are basically just AI systems that can take actions to achieve a goal you give them. ... They are at the point where they are very helpful in some instances. ... But they are not autonomous. They cannot do entire jobs for people." — Paul (33:53)
• Fully autonomous, job-replacing agents still a few years out for most domains; highly customized agents are making faster progress.

10. State of the Art AI Models (GPT-5.1 & Gemini 3)

Timestamps: 38:23 – 42:09

• GPT-5.1 is notably more helpful and better at "thinking" (reasoning/complex problem solving) than earlier OpenAI models; not a new model, but finely tuned.
• Gemini 3 impresses with its advanced multimodal (visual+text) capabilities—e.g., processing messy whiteboard photos and synthesizing key points.
  • Quote:
    "Gemini 3 is ... the state of the art now ... a leap ahead of the other models." — Paul (41:16)
• Ongoing rapid advancements underscore the need for constant literacy updates.

11. Search Transformation: SEO to AEO (Answer Engine Optimization)

Timestamps: 42:09 – 45:39

• Marketers must diversify content (blogs, podcasts, video, earned media) to maximize visibility in AI-generated search results.
• "Old-school" content marketing—being genuinely helpful, relevant, and accessible everywhere—becomes critical.

12. Urgency, Opportunity, and Jobs

Timestamps: 45:53 – 48:21

• Expect a sharper near-term impact on jobs than many realize ("three to six months").
  • Quote:
    "I really think that there's going to be a far greater impact on jobs than people realize in the near term ... have a sense of urgency to figure this stuff out." — Paul (45:57)
• Those who are proactive with literacy and experimentation will be best positioned to thrive—as teams and individuals.

Notable Quotes & Memorable Moments

• "There's way more people resistant to AI than there are excited about."
  — Paul (00:00, reiterated at 14:22)
• "If you don’t want to use the tech, over time it just doesn’t work."
  — Paul, on tech transformations and laggards (11:38)
• "Connect the dots of why you're doing it ... it's literally to help you transform your own capabilities."
  — Paul (20:38)
• "You can build a business case pretty quickly... you could knock something out like this in 10 minutes."
  — Paul (26:17)
• "Being able to go in and say, ‘What am I missing here?’ ... That's the mindset shift. ... This is why literacy is the foundation of everything."
  — Paul (31:24)
• "Everything that's old is kind of new again."
  — Paul, on content strategies (45:26)
• "I sense a sense of urgency on both fronts ... because sitting back is just not going to help anything."
  — Paul (48:08)

Useful Timestamps & Segments

| Time | Segment | |----------|---------------------------------------------------------------------------------------------------| | 05:33 | Assessment frameworks for AI skills/literacy | | 09:43 | Why all employees need AI literacy | | 12:34 | Convincing skeptical stakeholders | | 14:27 | Leadership, risk, and responsible AI guidelines vs. policies | | 20:00 | Overcoming team resistance to training/onboarding | | 23:15 | Building a business case for AI pilots | | 26:28 | Using unified KPIs like revenue per employee | | 28:19 | Data prep and AI, governance, and generative tools | | 33:01 | Readiness and capabilities of AI agents | | 38:23 | Experience with GPT-5.1 and Gemini 3 models | | 42:09 | From SEO to AEO (Answer Engine Optimization) | | 45:53 | Job impact, sense of urgency, and positive mindsets |

Takeaways for Organizations & Leaders

• Make AI training practical, personal, and tied to role-specific or company metrics.
• Set clear, enforced policies for high-risk activities—use flexible guidelines for creativity and experimentation.
• Focus on KPIs that matter (especially revenue per employee) but don’t obsess over finding “the one metric to rule them all.”
• Don’t wait for perfect data infrastructure—there are many AI wins to be had now with safe, “standalone” use cases.
• Prepare for AI agents by watching narrow, high-volume use cases first (customer/support, finance, etc.) and understand they are not fully replacing jobs yet, but soon could impact many roles.
• Proactively upskill or risk being left behind; the job market impact is arriving quickly—adoption is not optional.
• Content strategy for the AI-driven era requires genuine helpfulness, reach, and a willingness to show up everywhere, not just on owned channels.

Resources Mentioned

• AI Value Calculator (link in show notes)
• Free monthly classes: Intro to AI & Scaling AI
• AI Academy for Marketers, SmartRx Learning Modules

This episode is a must-listen (or read!) for leaders looking to bridge the gap between AI aspiration and action, and for teams eager to upskill and stay ahead as AI’s organizational impact accelerates.