I Found the Perfect First Business for Introverts

Business / Market Positioning (AI implementation for small businesses)

• Strongest opportunity now: for small operators (e.g., solo/introvert founders) because they can move faster than enterprises constrained by policies/governance.
• Target customers:
  • Startup founders
  • Teams that understand AI’s potential but haven’t implemented it well
  • (Sometimes) sales or department leaders

Core idea: speed + flexibility beats enterprise governance, so small businesses can adopt earlier and improve faster.

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Core “Playbook” / Framework

DASA Framework (used to structure AI project sales & delivery)

• D = Diagnose: identify the real business problem worth solving (“disease”), avoiding “non-existent problems.”
• A = Articulate: define what the “dream solution” looks like and tangible outcomes (e.g., time saved, money made, reduced workload).
• S = Systemize: build/identify SOPs and a working system first, then plug AI into a system that already runs.
  • Avoid automating broken workflows.
• AI implementation rule: implement after system clarity to avoid “automating chaos / scaling chaos.”

Call-to-Offer Progression (implied pipeline)

• Call 1: typically covers Diagnose + Articulate (often both in the first call).
• Calls 2–3: deeper understanding, with Articulate snowballing.
• Later calls shift toward Systemize and deal structure.
• Emphasis: don’t pitch on the first call—provide value first, then propose offers when ROI is clear.

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Go-to-Market (GTM) Strategy: “Warm Network + 100 Conversations” Test

Acquisition Method (no cold)

• Use personal network + local/online networking groups.
• Process:
  • Make a list of ~100 people in your sphere (contacts list, friends/family, teachers, church, prior co-workers).
  • Book 100 real free conversations over ~3 months (with many more reach-outs).
  • Re-engage non-responsive contacts by checking back later.

Volume / Conversion Assumptions

• Target: 100 conversations (not just reach-outs).
• Reach-outs needed: hundreds (estimate ~300 reach-outs by conservative math).
• By ~3 months:
  • Reported 100 Google Meet calls (all virtual; mostly ~1 hour).
  • Eventually estimated ~150 total reach-outs/conversations tracked loosely (lost count after 100; aiming for conversations).
• Conversation mix:
  • 100% virtual
  • Mostly Google Meet (phone/Zoom not emphasized)
• Funnel conversion timing (rough):
  • Second conversation: ~1/5 of the ~150 cohort
  • Third conversation: about half or more of the second-conversation cohort
• Closing:
  • Estimated ~12 paying customers from 150 reachouts/conversations
  • Close-rate cited: ~8–9%
  • Contrast made with “cold email,” which would likely have a much lower close-rate

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Pricing & Offers (What He Sold and How)

Early Offers (to build traction)

• No formal offer at first.
• Started with an “AI audit” concept, but evolved into education + building together.
• Low-end projects closed on later calls:
  • $500–$1,000 for small initial projects (used as a “foot in the door”)
• Early labor economics (conservative example math):
  • ~$1,500 total revenue from 2 projects at ~$750 each
  • Notes: hourly rate was “terrible” early on, but the goal was building a scalable asset (proof, testimonials, flywheel).

Current Structured Offers (education-led)

1. High-ticket private coaching (top offer)

   • Duration: 3-month deep dive sprint
   • Price: $5,000–$10,000/month
   • Capacity: 1–2 clients max
   • Time commitment: ~10 hours/week per client
   • Positioning: become an “AI officer” / operational thought leader to drive adoption and employee engagement
   • Claimed emphasis: long-term AI + tech/tool systems; not only “tools,” but adoption

2. Cohort groups (mid-ticket)

   • Duration: generally month-long sprints (references ~4 weeks / 10 weeks depending on cohort)
   • Group size: up to ~10–20 (planned/comfortable)
   • Price: $1,000 per seat
   • Example cohort: “build websites using Claude optimized for SEO + lead funnel”
   • Delivery cadence (example):
     • Monday: group lesson plan
     • Thursday/Friday: group breakdown + share progress + troubleshoot recurring issues
     • Mid-week: ~30-minute 1:1s (not every participant weekly)
   • Effort estimate: ~10 hours/week total
     • ~2 hours Monday + ~2 hours Thursday + ~5 hours between
   • Example capacity note: cohort could be scaled to ~20 people

3. Implementation builds (custom projects)

   • Price range: $1,000 up to ~$15,000–$20,000
   • Average charge (stated): ~$2,000
   • Examples:
     • AI agents integrated into business workflows (marketing/reputation content, outreach funnels, newsletters)
     • Real estate-focused cohorts and “acquisitions-agent” support (letters of intent, underwriting workflow assistance)

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Revenue & KPI-Style Metrics Mentioned

• Earlier baseline job:
  • ~$52,000/year (~$4,000/month) in clinical research project management
• Startup results:
  • ~$15,000/month profit after ~year and a half
  • Mentions projected best month around $15k
• Customer economics (stated):
  • First 12 customers generated ~$25,000 lifetime revenue
  • Average cohort/project mix cited ~$2,000 per person
• Current revenue mix (example month):
  • ~50% from “first engagement” customers (early batch)
  • Remaining from ongoing relationships/referrals from that network

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Sales / Marketing Mechanics (How Demand Is Generated)

Lead Magnet / Content Engine

• Content used mainly for credibility:
  • YouTube started ~1 year prior
  • At the time: ~100 subscribers
• Content approach:
  • Mostly screen recordings/demos
  • Low production overhead
• Lead magnets/resources:
  • “Free resources and guides” (prompt frameworks, SEO guides, how-to materials)
  • Workshops + long-form content as trust builders
• Funnel model:
  • Less “linear funnel,” more a relationship loop:
    • content → free resources → calls → advice/build → ongoing relationship → paid engagement

Outreach Message Strategy (anti-spam)

• Avoid:
  • copy/paste “pitch”
  • “audit funnel” opener
• Starts with genuine reconnection:
  • ask about life/project changes
  • reference specific items found on LinkedIn/Facebook
  • mention AI help only later if it’s relevant
  • share a booking link only if/when appropriate
• Uses demos:
  • YouTube links to show proof (even low-view videos as process evidence)

CRM Tooling Stance

• Early stage: didn’t track in CRM rigorously.
• Later: uses a CRM, but criticizes over-optimizing it (“advanced forms of procrastination”) before meaningful revenue.

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Operations & Scaling Principles

• Avoid “automating chaos”:
  • don’t deploy AI into broken/chaotic processes; systemize SOPs first
• Prefer tools/processes the client will actually use:
  • select CRMs/automation aligned with adoption realities
• Efficiency philosophy:
  • AI/automation should eliminate repetitive work (e.g., email management) so humans do higher-value tasks.

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Concrete Examples / Case-like Notes

• Enterprise constraints example:
  • Some organizations can’t deploy ChatGPT due to security concerns (example cited: a hospital system).
• Home services example:
  • first automation target often logistics/routing optimization
  • also selecting CRM/tech teams that will adopt
• Real-world business experience (claimed):
  • advised a funded? (“not funded”) tech startup for ~1 year
  • made about $15k working directly with them

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Actionable Recommendations Extracted

• Don’t start with mass outreach: use your sphere and build relationships.
• Run an experiment:
  • aim to book ~100 real conversations before expecting consistent sales.
• Provide value first:
  • give takeaways after calls
  • re-engage later with specific “tidbits” and educational content
• Structure the sales process using DASA:
  • diagnose the real problem → articulate desired outcomes → systemize SOPs → then implement AI
• Scale content and proof, not hype:
  • record demos and build trust assets that convert later conversations
• Start in-person when possible:
  • claimed in-person increases close rate and improves willingness to work together

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High-Level Market Stance (Execution-Focused)

• Belief: AI will not “kill small businesses.”
  • Instead, it increases competitive access because small operators can implement faster than enterprises with governance restrictions.
• Caution on “shiny tools”:
  • criticizes rushed adoption without understanding risk
  • example referenced: OpenAI’s “OpenClaw” (as phrased in subtitles) being risky if implemented too fast

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Presenters / Sources

• Presenter/interviewee: Ryan Schuet(t)er
  • referenced in subtitles as variations like “Ryan Shooter” / “Ryan Schue…t…e…r”
  • social handle referenced as “Ryan Shooter”
• Interviewer/host: Chris
• Podcast/episode references: “Kerner” and tko.com (referral/guest submission link)