Skip to content
Shows/Human + Machine
Human + Machine·Episode 4·Series

How We Use Claude in Our Work

A candid look at our real AI workflow

Apr 23, 2026·31:40·Daniel Rodriguez, Paul Maxwell

Video coming soon

No YouTube video has been added yet.

Your browser does not support the audio element.

About this episode

No theory this time. Paul and Daniel open up their actual day-to-day: where Claude saves them hours, where they deliberately keep it out, and the habits that keep the work high quality.

Key ideas

  • 01Claude accelerates a specific band: drafting, exploring, summarizing
  • 02Keep judgment-heavy, high-cost-of-error work fully human
  • 03Context-first sessions produce consistent results
  • 04Assume the model can be confidently wrong and read everything

Show notes

What we covered

Everyone talks about AI in the abstract. This episode is concrete: Paul and Daniel walk through their real workflow, the specific tasks they delegate to Claude, the ones they never would, and the guardrails that keep the output trustworthy.

In this episode

  • The everyday tasks Claude genuinely accelerates
  • Work they deliberately keep fully human
  • How they structure a session for consistent results
  • Catching and correcting the model's confident mistakes
  • The habits that keep AI-assisted work high quality

Links & resources

  • Paul and Daniel's session structure template
  • Notes on human-only work categories
  • Daniel's correction and review habits

Timestamps

Chapters

  1. —What we actually delegate
  2. 5:20The human-only category
  3. 13:00Structuring a session
  4. 20:40Catching confident mistakes
  5. 27:00Habits that keep quality high

Quotable

“Cold prompts get cold results, every time.”

— Paul Maxwell

Full transcript

Transcript~1 min read

Show transcriptHide transcript

Paul: People assume we use Claude for everything. We don't. We use it for a specific band of work: drafting, exploring, restructuring, summarizing. The stuff where a strong first pass compounds.

Daniel: And there's a whole category we keep fully human. Anything where being confidently wrong is expensive, or where the judgment is the actual product.

Paul: The consistency comes from how we start a session. Context first, clear task, then iterate. Cold prompts get cold results, every time.

Daniel: And we assume the model can be confidently wrong. So we read everything. The speed is real, but it never removes the responsibility to check.

Mentioned

  • Claude
  • Anthropic

Related links

  • Our AI session structure

Hosts

  • DR
    Daniel Rodriguez

    Host

    RevOps Director, RevOps HQ

  • PM
    Paul Maxwell

    Host

    CEO, RevOps HQ

Topics

  • AI Tooling
  • Artificial Intelligence

Subscribe to Human + Machine

  • YouTube
  • Spotify
  • Apple Podcasts

Related episodes

Human + MachineGiving AI the Context It NeedsThe gap between mediocre and remarkable AI output is almost always context. Paul and Daniel dig into context engineering: what to include, what to leave out, and how to build reusable context that pays off every session.
Human + MachineThe Limits of Claude's HubSpot ConnectorConnectors make AI-to-CRM feel seamless, but every integration has edges. Paul and Daniel map what Claude's HubSpot connector handles gracefully and where you still need custom tooling.
Human + MachineUsing Claude with HubSpotPaul and Daniel walk through concrete ways they use Claude alongside HubSpot, from drafting to analysis to cleanup, and the context pack that makes the results reliable instead of hit or miss.
Human + MachineAI Slop Is Your FaultPaul and Daniel make an uncomfortable case: most AI slop comes from lazy prompting and missing context, not the model. They show how to take responsibility for quality and get dramatically better results.