These notes are based on the YouTube video by Liam Ottley

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Key Takeaways

• Sell outcomes, not AI features – Clients care about booked appointments, lead‑to‑sale speed, and risk reduction, not the underlying technology.
• Focus on a single, repeatable outcome first (e.g., “lead → booked appointment”) and expand later; trying to own the whole funnel from day one is a recipe for burnout.
• The ACDC framework (Attract → Convert → Deliver → Collect) is presented in the video as a practical way to discover and own high‑impact problems across any vertical.
• Standardize the “upper‑half” of the business process (lead capture, qualification, booking, follow‑up). These steps are largely industry‑agnostic and lend themselves to scalable, templated solutions.
• Keep the tech stack simple and stable – use established platforms (e.g., GoHighLevel, Make, ClickUp, HubSpot) and avoid chasing the newest, unproven tools. Voice‑AI tools such as Vaffy or alternatives like 11 Labs have been used in some implementations, but they are not required. For a deeper dive on scaling a voice‑AI agency, see the guide on building a $1M Voice AI business.

🔗 See Also: If I Wanted to Build a $1M Voice AI Business in 2026, I’d Do This

• Hybrid pricing – a setup fee + monthly retainer, with optional hand‑over packages and feature‑add‑on updates, balances cash flow and client autonomy.
• Pre‑sale education beats endless discovery calls – a short pre‑call video that explains scope, pricing, and deliverables filters out unqualified prospects and saves time. Learn more about building AI systems that actually run your business in our related post.

💡 Related: How to Build AI Systems That Actually Run Your Business (Not Just Chat)

• Document before you automate – map the process, test it manually, then codify it in Make/GoHighLevel; this reduces breakage when tools change.
• Hiring strategy – prioritize “T‑shaped” talent (solid AI knowledge + business sense) and start with low‑commitment roles (virtual assistants, junior devs) before adding senior developers.
• Future outlook – AI will become native to major SaaS platforms; agencies should position themselves as integration partners rather than proprietary tool builders. This mirrors the AI‑native startup model discussed elsewhere.

🔗 See Also: The AI‑native startup: 5 products, 7‑figure revenue, 100% AI‑written code

Core Concepts & Detailed Explanations

1. Outcome‑Based Selling

• What it means: Pitch the result (e.g., “30 % more booked appointments”) rather than the method (“we’ll use GPT‑4 to draft emails”).
• Why it works: Business owners are increasingly AI‑savvy and skeptical; they want certainty, time savings, and risk mitigation.
• Implementation:
  • Define a clear KPI for each offering.
  • Build a service‑level agreement (SLA) around that KPI.
  • Use case studies and before/after metrics in sales decks.

2. The ACDC Model

Stage	Goal	Typical Activities
Attract	Generate interest	Paid ads, podcast appearances, SEO content
Convert	Capture leads	Landing pages, chat widgets, forms
Deliver	Provide the promised outcome	AI‑driven response, qualification, booking
Collect	Secure payment & feedback	Invoicing, post‑call summaries, upsell opportunities

• How it was applied: In the video the presenter first explored all four stages with prospects, then narrowed focus to the Convert stage (speed‑to‑lead & database activation).

3. Niching by Outcome vs. Industry

• Outcome‑first: Choose a problem that exists in many verticals (e.g., “lead → appointment”) and craft a universal solution.
• Industry‑later: Once the solution is proven, identify the most profitable verticals and tailor messaging.
• Benefit: Avoids the “early‑adopter ceiling” that occurs when you target a single niche too early.

4. Tech Stack & Integration Layer

Layer	Primary Tool	Role
Data Store	HubSpot, Notion, Airtable, Google Sheets	Central repository for contacts, outcomes, metrics
Orchestration	Make (formerly Integromat)	Connects AI modules, CRMs, VOIP, calendars
CRM / Sales Automation	GoHighLevel (GHL)	Leads, pipelines, email/SMS outreach, booking
Project Management	ClickUp	Task assignment, SOP tracking, client onboarding
Voice AI (optional)	Vaffy / 11 Labs	Automated call handling, rescheduling, confirmations

• Integration philosophy: Build all workflows inside GHL; use Make to sync with any external CRM that has an open API. If a client refuses to switch CRMs, a Make bridge keeps the data flowing.
• Avoiding “tech‑stack fatigue”: Stick to 2–3 core platforms; add new tools only when they solve a proven gap.

5. Service & Pricing Models

Model	Description	Typical Client Type
Setup + Retainer	Fixed onboarding fee, monthly maintenance & support	Clients who want a “hands‑off” solution
Hand‑Over + Ongoing Updates	One‑time implementation, then a subscription for new feature releases & training videos	Teams with internal tech talent
Pay‑Per‑Lead / Pay‑Per‑Result	Fees tied to each qualified appointment	High‑risk for agency; only viable with airtight attribution
Hybrid	Mix of fixed retainer + usage‑based add‑ons (e.g., extra AI calls)	Most profitable for the model described in the video

• Why hybrid works: Guarantees baseline cash flow while still monetizing incremental value (e.g., a new “no‑show recovery” module).

6. Process Documentation → Automation Pipeline

1. Document every step in a visual flow (Figma, Lucidchart).
2. Validate the manual process with a pilot client.
3. Blueprint the flow in Make (scenarios) and GHL (snapshots).
4. Automate once the process is stable; keep a “manual override” for edge cases.
5. Iterate only after a full testing cycle (internal → client → live).

Key lesson: “Don’t automate first; automate last.”

7. Team Structure & Hiring Tips

• Early hires:
  • Salesperson who understands AI limits (prevents over‑promising).
  • Developer who can work within the chosen stack (Make + GHL).
• Later hires:
  • Virtual assistants for content posting, routine follow‑ups.
  • Senior dev once the product suite stabilizes.
• Upwork recruiting hack: Post an invite‑only job, embed a unique “code word” deep in the description to filter out bots and low‑quality applicants.

8. Client Education & Pre‑Call Video

• Components of the video:
  • Service scope (what’s in/out).
  • Pricing tiers and “no‑cheap‑AI” disclaimer.
  • Sample results & KPI expectations.
  • Call‑to‑action to confirm attendance or cancel.
• Impact: Cuts unqualified discovery calls by ~10 % and sets realistic expectations, reducing later scope creep.

9. Scaling & Future Trends

• AI as a commodity: Large SaaS players (Google, Microsoft) are embedding generative AI directly into their platforms, making custom wrappers less distinctive.
• Agency role shift: From building proprietary AI products to integrating and optimizing native AI within existing business workflows.
• Modular operating system: Build reusable “blocks” (lead capture, voice routing, follow‑up) that can be dropped into any client’s stack, regardless of the underlying CRM.

Summary

The case study highlighted in the video turned a modest‑scale agency into a repeatable, outcome‑driven business by:

1. Zeroing in on a single, high‑impact KPI (lead → booked appointment) and building a templated solution that works across industries.
2. Using the ACDC framework (as described in the video) to surface problems, then narrowing to the most universal part of the sales funnel.
3. Standardizing on a minimal, reliable tech stack (GoHighLevel, Make, ClickUp) and integrating with client CRMs only when necessary.
4. Packaging services with a hybrid pricing model that secures cash flow while allowing clients to stay up‑to‑date via subscription‑based feature releases.
5. Investing heavily in documentation and pre‑sale education, which streamlines onboarding, reduces unqualified leads, and builds trust.
6. Hiring strategically—first a sales‑tech hybrid, then a solid developer, and finally support staff—while leveraging Upwork’s invite‑only workflow.

The overarching lesson is that the boring, well‑documented processes are the real engine of growth. AI remains a powerful enabler, but the sustainable advantage comes from repeatable outcomes, stable tech foundations, and clear client communication. As AI becomes native to mainstream platforms, agencies that position themselves as integration specialists will thrive in 2026 and beyond.