The Artificial Intelligence Show

Episode #194: Agentic AI Timelines, Generalists vs. Specialists, Resume Tips, AI Learning Ownership, & Handling Model Updates
Hosts: Paul Roetzer (Founder & CEO, Marketing AI Institute, SmartRx) and Kathy McPhillips (CMO, SmartRx)
Date: January 29, 2026

Episode Overview

This special "AI Answers" edition of The Artificial Intelligence Show dives into practical, real-world questions gathered from their recent Scaling AI class and Marketing Talent AI Impact webinar. Paul and Kathy tackle 15 of the most pressing questions from business leaders and practitioners, sharing actionable insights about AI education ownership, change management, AI adoption, resume strategies, and the rapid evolution of AI agents. The goal: help listeners and their organizations grow smarter by making AI education accessible, real, and human-centric.

Key Discussion Points & Insights

1. Who Owns AI Learning in Organizations?

[06:24 – 09:29]

• No universal owner: In large companies, Learning & Development (L&D) often leads, but this is inconsistent.
• Success demands clear ownership—someone must "own the goals" and not just coordinate training.
• Need to move from completion metrics to real transformation.

  “If someone doesn’t own making sure that it’s actually going to happen, then it’s going to go nowhere.” — Paul [07:57]

2. Do You Need a Change Management Consultant for AI Initiatives?

[09:50 – 11:53]

• Consider hiring or assigning a change management lead, but readiness is vital—many firms aren’t there yet.
• Sometimes, the consultant’s first task is helping you gauge readiness.

3. Role of Middle Management in AI Adoption

[11:53 – 14:21]

• Middle managers help bridge the "messy middle"—employees who want to adopt AI but need help.
• Best practice: Offer playbooks, hands-on workshops, and personalized support, not just mandates.

  “I think the job of any manager... is to help people find practical use cases that make an impact on their job every day.” — Paul [12:03]

4. Identifying Culture vs. Tech Failures in AI Pilots

[14:27 – 16:11]

• Personalize tech, then watch adoption rates; if usage is low after tailored rollouts, it’s likely a culture issue.
• Pre-pilot surveys can reveal sentiment and potential resistance early.

5. Balancing Ongoing AI Learning with Experimentation

[16:27 – 19:18]

• Personalization is key—tailor learning journeys to individual goals (executives have very different needs than aspiring “change agents”).
• Microlearning and resource variety support employees’ finite learning time.

6. Critical Skills: Moving Beyond Awareness to True AI Literacy

[20:13 – 23:20]

• Skills to prioritize: Problem-solving, critical thinking, communication, ability to use AI as a “thought partner.”
• Test candidates by giving them real problems—with and without AI—and see their approach.
• Hiring managers may not yet know how to assess these AI-era skills.

7. AI-Driven Talent Trends: Experience vs. Adaptability

[23:25 – 25:33]

• Entry-level and middle management face the most disruption.
• Domain expertise + AI skills = highest value.
• Adaptability increasingly trumps years of experience.

8. Protecting and Compensating AI "Superstars"

[25:33 – 27:54]

• Organizations struggle to fairly reward those who drive outsized value with AI.
• Options: One-time bonuses, stock options, or other variable compensation.

  “...if one employee... builds a GPT that leads to a new line of business... does that person deserve to get paid the same as the other directors? Hell no.” — Paul [26:12]

9. AI Experience for Teams with Confidential Data Constraints

[27:54 – 30:07]

• Anonymize data or use dummy datasets for proofs of concept.
• Consider safe, in-house or open-source tools, and make a business case for investment using non-confidential data.
• Explore AI use in non-data-dependent contexts (research, ideation).

10. Agentic AI Timelines: How Fast Will Agents Evolve?

[30:27 – 33:20]

• AI agents are advancing quickly, possibly reaching a “GPT-4 moment” in certain domains within a year—but it will be highly uneven.
• Adoption lags behind technology: Even if AGI arrives soon, mainstream use could take years.

11. Coping with Changing Models and Agent Quality

[33:20 – 37:22]

• No easy answer: proprietary solutions are quickly outdated, but waiting means missed opportunities.
• Pragmatic advice: Use available, flexible tools now for quick wins, but expect to pivot as the tech advances and costs (e.g., credit-based pricing) shift.

12. Generalists vs. Specialists: Which Should You Hire or Be?

[37:22 – 40:52]

• Right now, generalists who connect dots across disciplines are invaluable—specialist advantage may dwindle as AI gets better at expert tasks.
• Example: Amanda Askell (philosopher) leading AI constitution work at Anthropic.
• Liberal arts backgrounds and diverse thinking seen as strengths.

13. Resume & LinkedIn Tips: Highlighting Genuine AI Skills

[40:52 – 43:49]

• Move beyond listing courses: Show impact by building and sharing GPTs, apps, or real projects.
• Demonstrate problem-solving, creativity, and empathy through stories, side projects, volunteer work.
• Cultural fit and empathy often evaluated informally (the “car ride test”).

14. Brands, Trust, and AI-Generated Content

[43:49 – 48:24]

• Transparency and authenticity are vital—disclose AI usage and retain the human touch where it matters.
• Sometimes, the human effort is the value (“the time is the point”).
• Hybrid examples: Human-guided AI content can still be authentic if the process and purpose are clear.

  “The fact that you invested the energy to do the thing is actually what makes it worthwhile. It’s why human art differs from AI art.” — Paul [46:39]

15. Evaluating AI-Powered SDR Solutions: Buy or Build?

[48:24 – 51:05]

• Ask the same questions you’d ask of a human process: Trust, guardrails, oversight, workflow compatibility.
• Consider starting with third-party tools while building in-house expertise.
• Use AI to draft a list of vendor questions!
• In-house automation could replace most admin parts of SDRs, with humans handling the nuance.

Notable Quotes & Memorable Moments

• On Organizational Ownership:
  “If someone doesn’t own making sure that it’s actually going to happen, then it’s going to go nowhere.” — Paul [07:57]

• On Specialist Disruption:
  “Right now the most valuable user of a chatbot is someone with years of experience and domain expertise who knows how to talk to it… And that's what entry level people and often middle management lacks…” — Paul [24:02]

• On Compensation for AI Superstars:
  “Does that person deserve to get paid the same as the other directors? Hell no.” — Paul [26:12]

• On Resume Strategies:
  “The fact that you built something, especially if you didn't have to, wasn't required of you to build it, and you did it anyway. It's good stuff.” — Paul [42:21]

• On Human Effort vs. AI Automation:
  “Sometimes the time is the point. The fact that you invested the energy to do the thing is actually what makes it worthwhile. It's why human art differs from AI art.” — Paul [46:39]

Timestamps for Major Topics

• AI Learning Ownership: [06:24 – 09:29]
• Change Management Consulting: [09:50 – 11:53]
• Middle Management’s Role in AI Adoption: [11:53 – 14:21]
• Cultural Failures vs. Tech Failures: [14:27 – 16:11]
• Balancing Learning vs. Experimentation: [16:27 – 19:18]
• AI Skills & Assessment: [20:13 – 23:20]
• Talent Trends & Disruption: [23:25 – 25:33]
• Protecting AI Superstars: [25:33 – 27:54]
• AI Under Data Restrictions: [27:54 – 30:07]
• Agentic AI Timelines: [30:27 – 33:20]
• Handling Model/Agent Updates: [33:20 – 37:22]
• Generalists vs. Specialists: [37:22 – 40:52]
• AI Skills on Resumes: [40:52 – 43:49]
• Trust in AI-Generated Content: [43:49 – 48:24]
• Third-party AI SDR Tools: [48:24 – 51:05]

Overall Tone & Takeaways

Paul and Kathy maintain a conversational, honest, and pragmatic approach—emphasizing action, experimentation, and shared learning. They're candid about what they (and IT departments) don't know, stress the pace and uncertainty of AI change, and repeatedly return to the importance of human ownership, transparency, and authentic contribution in an AI-powered workplace.

Listeners come away with:

• Practical advice on AI adoption and education
• Insight into shifting talent landscapes
• Guidance for AI-centric resumes and hiring
• Strategies for building trust in AI usage

Perfect for: AI practitioners, business leaders, and professionals navigating digital transformation, organizational change, or personal upskilling in the AI era.