Video summary

How to Use AI in Your Business in 2026

Main summary

Key takeaways

Business

Business-Focused Summary (AI Use in 2026)

Core Thesis

  • You do not need to become an “AI business” to get AI benefits—use AI as a tool like the internet (outcome-focused, not tool-focused).
  • The biggest gains come from combining:
    • Business acumen (your understanding of the real system)
    • Technical execution (AI/automation)
  • Tech-only teams tend to produce commoditized automations.
  • Don’t deploy “half-built” AI. Use apples-to-apples comparisons:
    • Match the time/human effort used to train the process.
    • Match the level of SOP coverage.

Common Pitfalls (What to Avoid)

  • Assuming you must become a tech company to use AI.
  • Assuming a “tech nerd” can implement AI correctly without deep business context.
  • Installing an incomplete AI function and evaluating it against a long-optimized human process.
  • Assuming AI replaces the sales motion—AI should speed up and improve the process, not eliminate required steps.

Frameworks / Playbooks Mentioned

Apples-to-Apples Deployment Comparison

If your human process took years of SOP + training, your AI implementation must meet the same standard.

“10-Stage Roadmap” to $100M+ (Across Functions)

A referenced claim from creator material:

  • A “10-stage roadmap from zero to 100 million plus”
  • It’s claimed that less than 1% of companies finish
  • Broken down by:
    • 8 business functions
    • What the constraint feels like at each stage
    • Symptoms during each stage
    • Steps to “graduate” stages
  • Source link mentioned: acquisition.com/roadmap (free lead capture)

Proof / Psychology Requirement (Especially for B2B)

  • AI content/avatars must be backed by real-world proof, or it’s “just words.”
  • B2C may be more forgiving due to visual trust cues; B2B is still proof-driven.

Department-by-Department Examples (How AI Is Operationalized)

Marketing

  • AI “SDR” that matches human outreach/team performance.
  • Content ideation + packaging automation:
    • Generate content ideas, headlines, thumbnails, and topics
    • Automatically run thumbnail tests and learn what performs best
    • Incorporate trend research (formats/hooks/visual hooks)
    • Use a “Venn diagram” approach:
      • cross-reference trending inputs with brand/past successful content
      • generate 10 options, then select one
  • Ads automation:
    • “Self-looking” creatives/CTAs overlaid automatically onto fresh content
    • Create static image variations from data sets
    • Example workflow:
      • community member “wins” feed → auto-generate visuals → apply templates → launch ads (example aimed at a “million-dollar-plus community”)

Operational implication: AI is used for iteration speed (creative testing, variants, daily ad launches), not just drafting.

Sales & Lead Handling

  • AI supports sales via:
    • Lead enrichment
    • Faster personalized responses (image/text/voice notes)
    • Dynamic scheduling via handoffs between agents
  • Explicit caution:
    • “AI SDR/AI seller” doesn’t remove the need for a real sales process—it accelerates it.
  • Warning scenario:
    • Running ads and having AI call every opt-in without human-equivalent qualification/execution usually fails.

Customer Support (Service Operations)

  • Example: book launch case study
    • Spun up 5 agents handling ~120,000 support tickets
    • Resolved ~90% without human intervention
  • Another example:
    • Clara reportedly replaced 700 customer service agents, saving $40M/year

Legal / Compliance Operations

  • Legal automation example:
    • A general counsel coordinating multiple AI “agents” for discrete tasks:
      • first response
      • second response
      • cease-and-desist
      • ongoing deal/litigation work
    • Goal: reduce reliance on large paralegal headcount

Risk Reduction Examples (Business Outcomes)

  • Fraud loss reduction via AI pattern recognition:
    • PayPal reduced fraud losses by $700M in a single year (and reduced fraud team size significantly).
  • Legal cost/time reduction:
    • JP Morgan’s “Coin” saved ~350,000 lawyer hours by processing/handling ~12,000 credit agreements in seconds.
  • Small-business translation:
    • Even if large examples feel irrelevant, the pattern is:
      • automate repetitive, high-volume workflows
    • Suggested approach: do one portion of your workflow end-to-end during a dedicated time block (e.g., evenings/weekends).

Actionable Recommendations (What to Do Next)

  • Start with one workflow and automate it end-to-end (beginning-to-end).
  • Use a practical loop:
    1. Find an automation method (e.g., YouTube transcript)
    2. Paste/link into your AI tool
    3. Follow the steps
    4. If stuck, screenshot the issue and ask in chat; repeat iteratively
  • Treat AI implementation like observable behavior + pattern recognition, not “magic” expertise:
    • Gather inputs (calendar, call transcripts, etc.)
    • Ask AI to surface interesting decisions/moments
    • Convert into narratives/content using real captured signals (avoid made-up claims)
  • Don’t advertise “we use AI.” Customers care about:
    • faster delivery
    • lower cost
    • better outcomes
    • reduced risk
  • For AI-driven marketing/sales content (especially B2B), ensure proof:
    • outcomes, case results, verifiable achievements—not only GPT-style assertions.

Metrics / KPIs Explicitly Cited

  • Revenue: company did $250,000+ aggregate revenue (speaker’s company, “last year”)
  • Support volume & deflection:
    • 120,000 support tickets handled by 5 agents
    • ~90% resolved without human intervention
  • Fraud reduction:
    • PayPal fraud losses reduced by $700M in one year
  • Legal time/cost reduction:
    • JP Morgan: 350,000 lawyer hours saved
    • 12,000 credit agreements processed in seconds
  • Customer service scale / savings:
    • Clara: replaced 700 customer service agents
    • Saved $40M/year
  • Opportunity timeline (market execution claim):
    • 18 months of major wealth creation opportunity from autonomous agents

Presenters / Sources Mentioned

  • Presenter/speaker: Alex (referred to as “Alex” / “Alex, can we use your AI…”)
  • Company examples cited: PayPal, JP Morgan (Coin), Clara
  • Website/source link mentioned: acquisition.com/roadmap (for the “10-stage roadmap”)

Original video