Podcast Summary: AI-Driven Marketer — “Experimentation With AI Is Over. Here's What Wins in 2026”

Host: Dan Sanchez
Guest: Courtney Baker, CMO of KnownWell
Release Date: January 28, 2026

Episode Overview

In this episode, Dan Sanchez and Courtney Baker tackle the challenge facing all marketing teams in 2026: AI has moved out of the experimental phase, and now real, lasting success depends on wisely integrating AI into everyday marketing operations. They discuss the pitfalls teams often fall into during AI adoption, offer practical advice for marketers and leaders, and lay out what “winning with AI” looks like for the years ahead. Their candid, practitioner-minded conversation is rich in concrete examples, real-world lessons, and “in-the-trenches” insights about moving from hype to lasting value.

Key Discussion Points & Insights

1. The End of the AI “Experimentation” Era

(00:10–04:53)

• Transition in the Industry: Dan and Courtney agree that nearly every marketing team is engaged with AI to some degree. The era where “just experimenting” was enough is over.
• Quote:
  “I think there was a season where that was awesome...But I think we've moved past that phase where your team is going to get tired of just experimenting. What we need to focus on now is taking problems, documenting what those problems are, and then saying, can we use AI to solve this problem?” — Courtney Baker (01:36)

2. How AI Adoption Goes Wrong

(01:36–04:53)

• Mistake #1: Experimenting for experimentation’s sake—teams dabble with tools but don’t align them with concrete business problems.
• Mistake #2: AI sits outside existing workflows—if AI tools aren’t embedded in daily processes, adoption fizzles.
• Dan compares this phase to the early, awkward days of social media marketing.
• Quote:
  “Experimentation's not enough...if you missed that period, you now have to catch up because you are now behind about a year, maybe two years.” — Dan Sanchez (03:54)

3. Moving from Experimentation to Operationalized AI

(04:53–06:55)

• Success Signs: Teams are proactively coming to leadership with AI-driven solutions—AI becomes routine, not leader-mandated.
• Embedding AI in Routine: AI should inform regular activities—like pipeline reviews and team meetings—driving process rather than sitting on the periphery.
• Quote:
  “When your team starts to come to you with ideas or even, hey, here's this AI platform that I want to use, here's the use case, here's the outcomes...I think that's when you know, hey, this has shifted.” — Courtney Baker (05:54)

4. Practical Wins: Custom GPTs and Embedded Tools

(06:55–10:14)

• Many teams underutilize powerful, straightforward tools like custom GPTs, which can save time and enable bespoke workflows.
• Overcomplicating automation (with tools like Make/N8N) slows teams down—start with simpler, high-value AI helpers instead.
• Quote:
  “Until you've built like a dozen custom GPTs, don't worry about Make, don't worry about N8N, don't worry about vibe coding anything...” — Dan Sanchez (09:48)

5. Setting Expectations for AI-Driven Marketers

(13:01–17:11)

• The main thing leaders want: outcomes, not AI for its own sake. AI is one lever among many, not the end goal.
• Creating a zero-fear environment is key—innovation needs room for risk and failures without judgment.
• Quote:
  “If marketing teams, I believe, are in a state of fear, you are not going to produce great marketing. That’s my personal belief.” — Courtney Baker (15:20)
• Focus on using AI to solve real, existing problems or bottlenecks.

6. Pushback & Managing AI Quality Concerns

(18:44–24:51)

• The most common resistance is a misunderstanding of AI’s probabilistic outputs vs. deterministic, “perfect” data.
• Some expect 100% accuracy from AI-driven signals, not realizing the creative and inferential trade-offs.
• Quote:
  “People want AI intelligence or signaling to be 100% accurate...but the reality is they don’t really understand what AI is at its core...” — Courtney Baker (19:01)
• Marketers must balance discernment, experience, and data-driven action—AI needs human oversight for effective deployment.
• Quote:
  “We became so data driven that we couldn’t actually, like, have this thing called discernment anymore...” — Dan Sanchez (24:51)

7. Guardrails and Governance in AI Implementation

(27:42–32:28)

• Don't let bureaucracy or over-engineered guardrails stifle productive experimentation, especially in smaller or startup environments.
• Tight rules (like those from big enterprises) don’t always fit; right-size governance to your organization’s needs and regulatory context.
• Use enterprise tools (like Google Workspace with Gemini) for efficient, secure, and low-friction AI adoption.
• Quote:
  “I would recommend keeping the guardrails as low as you can, especially to produce, let’s say, a V1 of something.” — Courtney Baker (28:06)

Notable Quotes & Moments

• On Early Experimentation: “People are now doing this. Most companies are now in experimentation phase and we're all looking at it...like, yeah, year behind on this one.” (03:31 — Dan Sanchez)
• On the Value of Custom GPTs: “Some of my favorite things about AI are these custom GPTs. I mean, literally save me so much time...” (08:53 — Courtney Baker)
• On Lowering Fear in Teams: “Set an environment that allows experimentation...marketing teams, I believe, are in a state of fear, you are not going to produce great marketing.” (15:20 — Courtney Baker)
• On the Data–Discernment Balance: “Isn’t that why you hired someone who's a senior level? Because they didn’t come with the data, they came with the discernment. And that’s still what drives things with AI.” (24:51 — Dan Sanchez)
• On Organizational Guardrails: “Never apply big company rules to small companies.” (30:21 — Dan Sanchez) “Put appropriate size guardrails for the appropriate size company and regulations that you’re working within. And I would default to the lowest guardrails that you can.” (30:38 — Courtney Baker)

Important Segment Timestamps

• Common AI Adoption Traps: 01:36 – 04:53
• The Shift to AI-Driven Workflows: 05:54 – 06:55
• Custom GPTs and Automation Adoption: 06:55 – 10:14
• Team / Leadership Expectations with AI: 13:01 – 17:11
• Handling Pushback, Accuracy & Discernment: 18:44 – 26:34
• Governance and Guardrails Discussion: 27:42 – 32:28
• KnownWell Product & B2B Service Insight: 34:04 – 36:50

Closing Thoughts

This episode emphasizes that for marketers in 2026, AI’s value comes not from novelty, but from integration—built directly into the processes and culture of marketing teams. True AI adoption is about outcomes, purposeful problem-solving, and empowering teams to experiment bravely, learn from failures, and move quickly—but always with an eye on delivering genuine customer value. The days of “dabbling” are over; the next phase is deliberate, outcome-focused, and driven by practical human discernment paired with the right AI tools.