MMarketing Against The Grain
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22 January 2026

Marketing is Already Dead, You Just Don't Know It

2Frameworks
8Insights

Frameworks in this episode

Productivity6 steps

AI-First Task Transformation

Turn recorded work into a scoped, costed AI-assisted workflow

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Innovation6 steps

Skill Architect Decision System

Build concise, portable AI skills with the right file architecture

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Insights & moments

The myth-busts, hot takes, explainers, and tools worth keeping.

Hot Take· 3

Hot Take16:00

Knowing the Better Workflow Does Not Change Human Habits

AI can identify what people should do differently, but it cannot guarantee that they will abandon familiar routines. The hosts identify behavioral change—not technical recommendation quality—as one of the hardest parts of organizational AI transformation.

  • People default to familiar methods
  • Knowing what to do is different from changing a habit
  • Technical plans do not automatically produce adoption
  • Transformation efforts need explicit behavioral support

“I think the big part that AI is going to struggle to help with is behavioral change.”

Kieran Flanagan · 16:00

“you kind of just default to the way you do things because that's the way you do things.”

Kieran Flanagan · 16:00
#behavior-change#habits#ai-adoption#management
Hot Take17:00

The Strongest AI Workers Combine High Agency With Low Tolerance

The hosts contrast passive acceptance of mediocre work with a more AI-native posture. Strong adopters are curious enough to build what was previously inaccessible and unwilling to tolerate weak processes when improvements can now be made in minutes or hours.

  • High-agency people experiment and build
  • Low tolerance makes inefficient work feel fixable
  • AI shortens improvements from weeks to hours
  • The combination distinguishes strong adopters

“They're super high agency, they're curious, they're out there building”

Kipp Bodnar · 17:00

“Hey, this stuff stinks and I can make it way better and I can make it way better in like minutes or hours, not days…”

Kipp Bodnar · 17:30
#agency#mindset#performance#ai-transformation
Hot Take17:00

The Strongest AI Workers Combine High Agency With Low Tolerance

The hosts contrast passive acceptance of mediocre work with a more AI-native posture. Strong adopters are curious enough to build what was previously inaccessible and unwilling to tolerate weak processes when improvements can now be made in minutes or hours.

  • High-agency people experiment and build
  • Low tolerance makes inefficient work feel fixable
  • AI shortens improvements from weeks to hours
  • The combination distinguishes strong adopters

“They're super high agency, they're curious, they're out there building”

Kipp Bodnar · 17:00

“Hey, this stuff stinks and I can make it way better and I can make it way better in like minutes or hours, not days…”

Kipp Bodnar · 17:30
#agency#mindset#performance#ai-transformation

Explainer· 1

Explainer11:00

Most Teams Cannot Clearly Explain How Their Work Happens

Complex teams often combine tools such as Canva, HubSpot, Google Drive, and Snowflake without maintaining a shared model of the process. Documenting the present workflow creates alignment before anyone proposes automation or a replacement process.

  • Tool-heavy workflows are often poorly documented
  • Team members may disagree about the actual process
  • A detailed current-state map creates shared understanding
  • Agreement on the current state enables redesign

“most teams are not clear on like how the work they do actually happens”

Kipp Bodnar · 11:00

“It's like having another work analyst.”

Kieran Flanagan · 11:30
#process-mapping#teams#operations#alignment

Q&A· 1

Q&A15:00

Your AI Adoption Goal Should Determine the Deliverable

There is no universal next step after analyzing a team's work. The useful output depends on whether the organization wants broader use of one assistant, shared skills, automated workflows, or better employee onboarding.

  • Tool adoption calls for tool-specific opportunities
  • Shared automation calls for reusable team skills
  • Onboarding calls for documentation of how the organization works
  • Transformation should begin with a clearly stated adoption objective

“It depends what you're trying to do in terms of your AI transformation.”

Kieran Flanagan · 15:00

“I think it's going to be very you wants to or different depending upon what you are trying to get your team to do.”

Kieran Flanagan · 15:30
#ai-adoption#teams#onboarding#change-management

Tool· 1

Tool02:30

The Simplest Way to Use Skill Files Without Installing Them

A skill does not need to be formally installed before someone can test it. The hosts demonstrate uploading the skill file, its references, and the work transcript directly into an ordinary AI chat, then instructing the model to run the skill against the transcript.

  • Upload the core skill and supporting files together
  • Include the work artifact or transcript in the same chat
  • Tell the model explicitly to apply the supplied skill
  • Use this approach for quick experiments before formal installation

“you just upload the files in that folder to a chat with whatever LLM you're doing, and you put the task that you want to…”

Kipp Bodnar · 03:30
#ai-skills#claude#chatgpt#workflow

Takeaway· 2

Takeaway09:30

Do Not Read Every Automation Report—Feed It Back to AI

Kieran argues that a multi-page report for one workflow can overwhelm a human reader. Kipp reframes the report as a detailed schema that can be supplied to an AI system to generate prompts, agents, workflows, and reusable skills rather than consumed line by line.

  • Detailed reports may exceed what a person wants to read
  • The report creates a structured representation of the work
  • AI can convert that representation into implementation artifacts
  • Operational detail can reassure conservative organizations

“You could literally just upload that report to Claude and have it give you the prompts of what you need to do, right? You're creating…”

Kipp Bodnar · 10:00

“I don't think I would ever actually read all these reports, but I would use them as inputs to then build the agents and build…”

Kieran Flanagan · 10:30
#reports#automation#implementation#ai-agents
Takeaway16:30

People Adopt AI Faster When It Gives Them a New Capability

Adoption appears faster when AI lets people code, analyze data, or perform other work they previously could not do. Accelerating an existing task can still be valuable, but creating a novel capability is often more exciting and motivating.

  • Novel capabilities create stronger motivation
  • Non-coders can begin coding
  • People can perform analysis they could not perform before
  • Efficiency improvements may feel less exciting than new powers

“Like applying AI to do new things that they were not able to do before.”

Kieran Flanagan · 16:30

“I think it's a little less exciting to use AI to do something you already do, even though it could help there.”

Kieran Flanagan · 16:30
#motivation#capability#coding#analysis