Do Good Work

How AI Will Evolve the 7 Parts of Your Business Model: 
Leverage AI to enable you to solely focus on client relationships, belief and the nuances of your work

00:00 The Bold Pricing Vow
01:42 AI Labs Enter Services
03:29 Your Offer Is Not The Moat
05:00 Seven Model Ingredients
06:47 How AI Changes Each Part
15:25 The Evolved Model Blueprint
25:26 Three Breakthrough Signals
29:49 Four Steps To Redesign
36:53 Closing Call To Evolve

Substack Version of this podcast: 
https://dogoodwork.substack.com/p/how-ai-will-evolve-the-7-parts-of

In this episode, I talk with Jeffrey Meade, founding director of entrepreneurship at Paul Quinn College, about the school’s “Every Quinnite is an Entrepreneur” program, where by 2028 every student must launch a real business that generates revenue before graduating. Jeff explains why traditional schools optimize for employment but the job market is changing, making entrepreneurial thinking—creativity, adaptability, and problem-solving—more essential. We dig into what qualifies as a “real business” (no fixed revenue threshold, but real customers, pivots, and an MVP mindset), why they focus on solving a core problem before layering in AI, and the mindset shift first-generation and Pell Grant students must make from “don’t fail” to “fail fast” in a safe learning environment. Jeff also shares how he’s open sourcing the model through case studies and a podcast, plus where to learn more.

00:45 Why Entrepreneurship Now

03:38 Mandatory Student Ventures

04:35 MVPs And First Sales

06:52 AI Tools Problem First

10:31 From Scarcity To Confidence

13:06 Safe Space To Fail

15:18 Open Sourcing The Model

17:29 Where To Learn More

Connect with Jeffrey: 

• https://paulquinn.edu/entrepreneurship
• jeff-meade.com
• [email protected]
• https://www.linkedin.com/in/jeffmeade

Connect with Raul: 

• Work with Raul: https://dogoodwork.io 

• Free Growth Resources: https://dogoodwork.io/resources

In this episode, I interviewed Patrick Rooney, a cognitive science–trained AI practitioner and founder of Leonis Strategy, about how founders mischaracterize AI by collapsing “scripted autonomy” (agents doing tasks while you step away) into personhood autonomy (will, rights, interiority). Patrick argues this isn’t just sloppy language but a leadership issue that shapes how teams relate to technology. They discuss why LLMs are plausibility engines rather than truth-seekers, how humans can pursue truth, beauty, and goodness for their own sake, and why leaders must own inputs, outputs, and responsibility instead of outsourcing judgment. We explored why LLM training is text-bound and disconnected from lived experience, the appearance-versus-reality problem behind Turing-test thinking, practical cautions around anthropomorphizing AI, and why doubling down on in-person human connection is a strategic response to AI at scale.

01:53 LLMs Are Plausibility Engines

05:10 Leadership And Culture Values

07:34 Why LLMs Aren't Intelligent

08:54 Turing Test And Training Limits

12:42 Language Detached From Reality

14:48 Personhood Rights And Ethics

19:01 Anthropomorphism Risks

19:34 Human Ownership Mindset

20:25 Outsourcing Your Thinking

22:24 IP Training Fears

24:34 Responsibility Still Human

28:41 Leading In AGI Hype

29:38 Grounding In Real Life

Connect with Patrick: 

• https://leonisstrategy.com/

• https://www.linkedin.com/in/prooney1/

Connect with Raul: 

• Work with Raul: https://dogoodwork.io/apply 

• Free Growth Resources: https://dogoodwork.io/free-growth-resources

In this episode, I’m joined by Jim Ristuccia, a former US Naval officer, founder/CEO, and Vistage Chair in San Diego who leads peer groups for CEOs of $5M+ companies and small business owners in the $1–10M range. We break down what actually happens inside a Vistage meeting—especially the “issue processing” that surfaces blind spots through uncomfortable but growth-producing questions. Jim shares how AI started showing up consistently after a 2024 speaker session, including a case where an IP law firm put its team through AI bootcamps, tripled personal productivity, and grew revenue 50–60% in a year. We discuss the adoption gap (CEOs vs. leadership teams vs. employees), why employees often feel threatened, and how leaders can create psychological safety. We also dig into practical implementation via AI champions, committees, process mapping, low-hanging-fruit use cases, and measuring ROI through time savings, productivity, and revenue.

01:56 What Vistage Really Is

02:45 Issue Processing Secret Sauce

04:46 When AI Hit the Groups

06:43 Why Some CEOs Lag on AI

08:44 Use Cases vs Business Model Shifts

09:45 AI Readiness and Industry Friction

11:20 Leading Change and Finding Use Cases

13:25 Prompts Training and Psychological Safety

15:01 AI Champion and Process Mapping

16:04 Why People Quit

16:32 Build AI Champions

17:58 Pick Use Cases Fast

18:33 Measuring AI Impact

20:00 Beyond Time Savings

21:02 Culture and Safety

23:48 Compliance and Knowledge

24:32 Jobs and Business Models

27:38 Adoption Takes Time

Connect with Jim: 

• https://jimristuccia.com/

Connect with Raul: 

• Work with Raul: https://dogoodwork.io

• Free Growth Resources: https://dogoodwork.io/resources

In this episode, I’m joined by Thaddius Ruszkowski, CTO at Fuzati, who’s built software platforms for over a decade, from 25-person internal workflow systems to consumer platforms with millions of users. Thadd isn’t just theorizing about AI agents; he’s running a fleet of them in parallel every day across multiple projects. We dig into what happens to value, intellectual property, and labor as production costs collapse, and why efficiency is only the opening round, not the real competitive advantage of AI. We also talk about managing parallel agents without losing your mind, the comeback of hardware, why on-prem/local will matter more than most people think, and why IP is actually more valuable now in a world flooded with slop. Plus, how he built an early chatbot trained on the writings of St. Thomas Aquinas.

03:12 Working With Agents

04:44 Aquinas Chat Origins

10:28 Beyond Efficiency

20:49 Net New Value

24:28 Create Net New Value

25:02 Decouple Labor From Value

27:58 Outcome Value Shift

30:26 Future Digital Assets

34:35 Three AI Futures

37:58 IP And Distribution

40:56 Local Hardware Shift

42:42 Client AI Disclosure

Connect with Thadd:

https://www.linkedin.com/in/thad-ruszkowski/

Connect with Raul: 

• Work with Raul: https://dogoodwork.io 

• Free Growth Resources: https://dogoodwork.io/resources

• Connect with Raul on LinkedIn (DMs open): https://www.linkedin.com/in/dogoodwork/ 

What AI Native Consulting Actually Looks Like: The 4 Stages

00:00 Why AI Native Matters
02:26 Four Stage Roadmap
03:10 Stage One Enhancement
06:39 Stage Two Augmentation
14:15 Stage Three Parallel Ops
18:43 Agentic Workflow Examples
24:17 Stage Four Autonomy
33:10 Autonomy In Practice
37:36 Self Diagnostic Recap
41:52 Firm Of The Future

Link to the full Substack: 
https://dogoodwork.substack.com/p/what-ai-native-consulting-actually

In this episode, I sit down with Dr. Tracy Christopherson and Michelle Troseth —co-founders of Missing Logic and authors of Polarity Intelligence—to talk about a major pivot they made from serving healthcare leaders to serving entrepreneurs, and how they had to use their own polarity framework when tension rose in their partnership. We unpack how to tell the difference between solvable problems and ongoing polarities, why “problems” that keep returning are often tensions to manage, and how to map polarities by identifying the benefits and risks of each pole, defining actions, and watching early warning signs. We also discuss identity shifts for thought leaders, the role of healthy relationships and meaningful dialogue on teams, and practical ways to measure and monitor tensions. 

01:15 Why They Pivoted

03:36 Alignment and Identity

06:39 Walking Polarity Pair

09:02 Problem vs Polarity

11:05 Mapping and Warning Signs

14:04 Tech and Dynamic Balance

17:46 Working Through Tension

22:39 Dialogue and Resistance

Connect with Dr Tracy Christopherson and Michelle Troseth.: 

• www.polarityintelligence.com 

www.missinglogic.com

https://www.linkedin.com/in/michelletroseth/

https://www.linkedin.com/in/tracy-christopherson/

https://www.instagram.com/polaritypowerpair/

https://www.facebook.com/polaritypowerpair/

Connect with Raul: 

• Work with Raul: https://dogoodwork.io

• Free Growth Resources: https://dogoodwork.io/resources

Most conversations about AI in consulting stop at productivity. Doing the same thing faster. That's the wrong frame entirely.

In this episode, I break down the three form factors for digital products: information products, software products, and what I'm calling actors. An actor is an AI agent or multi-agent orchestration that deploys your expertise on your behalf without requiring your ongoing presence at every step. The distinction that matters: an actor becomes a producer when it generates real market value, not just activity.

I walk through how I built this into my own content pipeline (four hours down to fifteen minutes), how clients are using actors to overdeliver on their initial offer at 2-3x the going rate, and why "efficiency" is the wrong frame for what's actually happening. This is the next piece in a series. The Services Stack covers the thesis. The New Value Quadrant covers pricing. The Post Scope Era covers practical sales and delivery changes. This episode covers the new form factor that makes all of it possible.

Key topics covered:

• What a "form factor" actually means and why it matters for how you price and package
• Form Factor 1: Information products (ebooks, courses, coaching, communities) and the key person problem
• Form Factor 2: Software products and why they require a team and capital
• Form Factor 3: Actors, AI agents that produce outcomes on your behalf
• The distinction between an agent that acts and a producer that creates net new market value
• Why your methodology, positioning, and expertise dictate the value an actor creates
• Real example: content scheduling from four hours to fifteen minutes
• How clients are using actors to deliver 2-3x more value at standard pricing
• Efficiency vs. production: doing the same thing faster vs. creating something new
• Why speed to value creation might be the real variable
• Human flourishing as the real goal
• Three steps to build your first actor

CHAPTERS

• Website: https://dogoodwork.io
• Newsletter: https://dogoodwork.substack.com
• https://dogoodwork.substack.com/p/how-ai-is-creating-the-3rd-form-factor 

What happens when you stop thinking about AI as a replacement tool and start thinking about it as a value creation engine? In this episode, Raul shares the paradigm shift he taught his 4-year-old son about money and value creation, rebuts Mark Cuban's argument about AI token costs, and walks through how to find your own net new value as a service founder or consultant.

Chapters

00:00 - A 4-Year-Old's Take on How Money Works (and Why He's Right)

01:30 - The Highest Calling: Why We're Meant to Create, Not Just Execute

02:30 - Mark Cuban Is Right About Token Costs. He's Wrong About the Goal.

04:00 - What Net New Value Actually Is (and Why You're Stuck Without It)

05:00 - The Key Distinction: Efficiency Is Not Creation

06:00 - How I Compressed 12 Weeks of Work Into a Month (Without Outsourcing My Thinking)

07:00 - The Test: Are You Still in Replacement Mode?

08:00 - The Firm of the Future: 70% Agentic, 30% Human, 100% Intentional

09:30 - Your Offer Dictates Everything. Start There.

11:00 - What I Learned Spending Millions in Direct Response About Upsells

12:30 - Proactive Value: Anticipate Your Client's Next Problem Before They See It

13:00 - Be a Creator and Inventor. Tinker. Play. Build for Fun.

In this episode, Raul and John discuss why hiring an agency feels risky today—cost, quality, outcomes, and commoditized “SEO/PPC shop” buying—then explore a shift toward providing open-ended marketing leadership focused on strategy, positioning, and execution using a mix of people, AI, automation, and outside resources. They argue project fees don’t fit a fast-changing landscape and consider retainers plus tool costs, while noting some work (like nailing brand positioning) can justify value-based pricing. They cover AI’s rapid acceleration, tool churn, and potential skill loss, alongside likely disruption to entry-level roles, and suggest human relationships will become more valuable in high-stakes B2B selling. John shares positioning lessons from customer interviews, a case study creating a sales/brand playbook used for onboarding, and examples of “marketing-adjacent” AI work that helps sales operations.

00:00 Do Agencies Still Matter?

01:18 What Clients Really Want

02:29 Marketing Leadership Model

04:57 Post Scope Era Pricing

06:52 AI Velocity And Tool Overload

09:00 Value Based Fees And Retainers

12:09 Skill Loss And Disruption

15:01 Meaning Work And Removing Toil

17:51 Human Connection Comes Back

22:18 Proof Points vs Positioning

22:55 Cloud Consulting Case Study

25:13 Tagline to Sales Playbook

26:52 Using AI for Messaging

28:25 Positioning Is for Buyers

29:18 When Positioning Doesn’t Land

30:49 Marketing Isn’t Just Opinion

32:39 Revenue as the Validator

33:39 AI Adjacent Opportunities

36:58 Relationships in a Tech World

39:12 Loyalty Means Real Dollars

40:59 Lifetime Value Marketing Model

Connect with John Schultz: 

https://www.netstrategies.com/

https://www.linkedin.com/in/johnschultznetstrategies/ 

Connect with Raul: 

• Work with Raul: https://dogoodwork.io/apply 

• Free Growth Resources: https://dogoodwork.io/free-growth-resources

• Connect with Raul on LinkedIn (DMs open): https://www.linkedin.com/in/dogoodwork/