Sleeping Barber - A Marketing Podcast

AI Won’t Save You. Leadership Will.
Companies have spent the last few years buying AI tools, running pilots and training employees. Yet much of that investment still isn't producing the transformation leaders expected.
Maybe the problem isn't the technology.
In this Sharp Cut, Marc and V examine why AI transformation is ultimately a leadership and organizational design problem.
They unpack research on the divide between AI experimentation and measurable business impact, Roger Martin's idea of the organization as a “decision factory,” and why making individual marketers faster may be solving the wrong problem. The real opportunity may lie in redesigning workflows, decision rights and standards around what AI can now do.
They also examine a more uncomfortable question: as AI becomes increasingly capable at professional work, what should humans actually be getting better at?
Marc shares the results of an AI marketing workshop where three groups used very different approaches to solve the same brief. All three produced impressive-looking work. The difference wasn't production quality. It was whether the people behind the work could explain and defend the decisions AI had helped them make.
Finally, the conversation goes back to 1855 and Daniel McCallum's railroad organization. A new information technology — the telegraph — forced leaders to rethink how their organizations worked. AI may be creating the same challenge today.
The tools are increasingly available to everyone.
The advantage won't come from simply having them.
It will come from how leaders redesign the organization around them.
Chapters
00:00 AI Won't Save You. Leadership Will.
01:14 The AI Transformation We Expected Never Happened
02:23 AI Is a Mode-Seeking Machine
03:29 When AI Produces the Safe Answer
04:21 Why AI Always Says “Good Point”
05:25 The 95% AI Failure Problem
06:06 What Separates the Successful 5%?
07:45 The Organization Is the Constraint
08:00 Are We Moving Marketing Dollars Into AI?
09:08 The Modern Organization as a Decision Factory
10:46 What AI Exposes About Knowledge Workers
11:40 Does the Decision Factory Apply to Small Teams?
12:36 The Bottleneck Is Between People
13:28 The Comfortable Assumption About Human Skills
13:50 AI Is Catching Human Experts
15:36 What Should Humans Actually Be Doing?
16:03 The AI Capability Trap
17:04 Is the T-Shaped Marketer Dead?
18:24 Why AI Should Attack the Hard Problems
19:21 What Happened When Marketers Let AI Lead
20:55 Three Teams, One Marketing Brief
21:31 Polished Work That Nobody Could Defend
22:34 Evidence vs. Familiar Frameworks
24:18 Why AI Needs Human Judgment
25:17 Marketing's Stack of Bad Assumptions
26:35 The Streetlight Effect in Marketing Measurement
27:03 Are We Optimizing the 17% We Can See?
28:03 “Busy Is the New Stupid”
28:56 AI Should Multiply, Not Just Automate
30:12 It Ain't What You Do, It's the Way That You Do It
31:06 Should Leaders Lead From the Front or Behind?
32:12 What an 1855 Railroad Can Teach Us About AI
34:23 New Technology Requires New Organizations
34:51 What Leaders Should Do Monday Morning
36:21 The Difference Between the 95% and the 5%
36:34 AI Won't Save You. Leadership Will.

Marketing has more data, technology and specialist expertise than ever.
But is all that sophistication actually making marketing better?
In this edition of The Barber's Brief, Marc and Vassilis unpack five stories that all raise different versions of that question.
First, AI assistants are increasingly embedded in how people discover and evaluate products, yet they still rank near the bottom of trusted sources for shopping recommendations. Marc asks whether AI will eventually become a reliable source of marketing knowledge or simply produce increasingly confident averages of everything we've already said.
Then, V looks at PepsiCo's decision to consolidate its global media business with Publicis under a single operating model spanning strategy, planning, activation, data, identity and technology. The bigger question: has fragmentation itself become one of marketing's biggest effectiveness problems?
Marc follows with the $700 billion MarTech delusion. One former privacy executive pulled her own data-broker profile and discovered she belonged to 500 segments, simultaneously classified as male and female, low income and high income. Research discussed in the episode suggests purchased targeting data can sometimes perform little better than chance, while contextual targeting may outperform it at lower cost.
Then comes something refreshingly simple.
The UPS Store has introduced its first brand character, Blu, designed to help expand the brand's mental associations beyond shipping into printing, shredding, mailboxes and other small-business services. V argues the opportunity isn't simply creating entertaining advertising; it's building a distinctive memory structure that can work across multiple buying situations.
Finally, Marc's Ad of the Week goes to Canva's Wild Design: handcrafted stop-motion advertising in an era where almost anyone can generate something instantly with AI. The campaign combines showmanship with salesmanship while continuing to invest in a recurring character as a potential distinctive asset.
More technology doesn't automatically mean better marketing.
Sometimes the advantage may come from making the whole system work together — and remembering the fundamentals underneath it.
Chapters:
00:00 Welcome to the Barber's Brief
01:58 Why Shoppers Use AI But Don't Trust It
03:01 Who Do Consumers Trust for Recommendations?
04:13 The AI Credibility Gap
05:09 When Low-Evidence Categories Reward the First Answer
06:23 Does AI Know Marketing Effectiveness?
08:18 Is AI Learning From Its Own Slop?
09:01 PepsiCo's Massive Global Media Shift
10:29 The Problem With Marketing Specialization
12:24 Is Fragmentation Hurting Marketing Effectiveness?
15:15 The $700 Billion MarTech Delusion
16:02 When Audience Data Is Completely Wrong
17:30 Does Targeting Actually Work?
18:42 Did MarTech Solve the Wrong Problem?
19:15 What Should CMOs Do With Their MarTech Stack?
21:31 The UPS Store Introduces Its First Brand Character
23:25 Blu as a Distinctive Brand Asset
24:21 Why Brand Characters Need Consistency
25:41 Are Brand Characters Really Making a Comeback?
27:20 Marc's Marketing Effectiveness Haiku
28:05 Ad of the Week: Canva's Wild Design
29:05 Showmanship Meets Salesmanship
30:22 Building Distinctive Assets Over Time
31:51 Why Canva Chose Craft in the Age of AI
34:18 What's Coming Next
35:38 Stay Sharp
Episode Links:
Shoppers ask AI for help but don't trust its advice
Link: https://www.emarketer.com/content/shoppers-ask-ai-help-don-t-trust-its-advice
PepsiCo hands global media to Publicis amid transformation at CPG giant
Link: https://www.marketingdive.com/news/pepsico-hands-global-media-to-publicis-amid-transformation-at-cpg-giant/829556/
The $700bn Delusion
Link: https://www.mi-3.com.au/26-06-2024/data-delusion-does-using-data-target-specific-audiences-advertising-actually-make
The UPS Store enlists first brand character to support franchisees
Link: https://www.marketingdive.com/news/the-ups-store-enlists-first-brand-character-to-support-franchisees/829215/
Ad of the week - Canva - Wild Design
Link: https://www.adsoftheworld.com/campaigns/wild-design-f89d06d3-3b9d-4cd0-95de-189714c47446

Everyone wants innovation. But what exactly are we asking for?
In this Post-Pod, Marc and Vassilis unpack their conversation with Fiona Stevenson, co-author of Built for Breakthrough, and start with one of the simplest problems: organizations frequently use the word “innovation” without agreeing on what it actually means.
Breakthrough? Disruption? A game changer? Incremental growth? A new tactic?
That distinction matters because, as Fiona argued, an idea doesn't become innovation until it is implemented and creates value.
From there, the conversation turns to the organizational conditions that innovation requires. Marc and V discuss why teams rush to solutions before properly understanding the problem, why genuine innovation creates fear, and how the pursuit of certainty can push organizations toward safer incremental improvements.
They also revisit Fiona's Six I's framework:
Identify → Insights → Inspiration → Ideation → Iteration → Implementation
Rather than treating those stages as a checklist, Vassilis argues they should be viewed as multipliers. Skip one and you risk weakening the entire system.
The conversation closes with two bigger questions.
First, if innovation requires focused thinking, why do we expect it to happen between back-to-back meetings?
And second, if AI is giving us unprecedented productivity gains, will we use that extra capacity to explore new possibilities — or simply fill it with more of the same work?
A Post-Pod about innovation, uncertainty, AI, marketing and why being busy isn't the same thing as building the future.
Chapters
00:00 Welcome to the Post-Pod
00:21 Why “Innovation” Means Different Things to Everyone
01:43 What Kind of Innovation Are We Actually Asking For?
02:24 Defining Innovation Before Starting the Work
02:53 An Idea Isn't Innovation Until It Creates Value
04:04 Are We Solving Before Understanding the Problem?
04:47 The Optimization Trap
06:42 Why Fear Kills Innovation
08:39 Why Incremental Innovation Feels Safer
10:04 Fear Creates Overanalysis
10:21 “How Might We?” vs. “We Should”
11:10 Fiona's Six I's of Innovation
11:40 Innovation as a Multiplicative System
12:53 Is Intuition the Seventh I?
13:20 How Marketing and Innovation Overlap
14:28 Should Marketing Be an Innovation Function?
17:39 Innovation Doesn't Happen Between Meetings
18:32 Are We Using AI for Efficiency or Growth?
19:48 Why AI Should Create Possibility
20:08 AI Increases the Need for Discernment
20:55 Final Thoughts

Why do organizations say innovation is mission-critical, then create conditions that make innovation almost impossible?
Fiona Stevenson has spent her career on both sides of that problem.
After 12 years at Procter & Gamble, she co-founded The Idea Suite, an innovation consultancy that has worked across hundreds of innovation projects. She is also the co-author of Built for Breakthrough: Why Innovation Fails and How Smart Leaders Get It Right.
In this conversation, Fiona joins Marc and Vassilis to unpack why innovation usually fails long before the idea itself is tested.
They explore the famous Febreze story and why understanding the actual consumer problem changed the brand’s trajectory, before getting into the organizational conditions required for innovation to survive.
Fiona explains why big innovation ambitions often collide with tiny budgets, part-time teams and unrealistic timelines; why past data can become dangerous when designing for the future; and why innovators need to build evidence instead of waiting for certainty.
The conversation also covers Fiona’s six-stage innovation framework:
Identify → Insights → Inspiration → Ideation → Iteration → Implementation
And perhaps a seventh: Intuition.
Plus:
Why “we should…” is not an idea
Why “How might we?” is such a powerful innovation question
How AI is dramatically lowering the cost of prototyping
Why AI should be an input, not the final output
How implementation destroys good ideas through “death by a thousand cuts”
Why innovation needs dedicated time, not calendar scraps
How to create an innovation charter
Why the best first step is simply defining the problem properly
If your organization wants innovation but struggles to actually get ideas into market, this episode is for you.
Enjoy the show!
Chapters:
00:00 Why Innovation Dies Before the Whiteboard
01:36 Meet Fiona Stevenson
02:16 From P&G to Innovation Consulting
04:07 Moving From Client Side to Consulting
05:41 The Febreze Innovation Story
09:09 Why Consumer Understanding Comes First
10:59 What Is Innovation, Really?
13:02 The Uber Opportunity Fiona Turned Down
14:24 Different Types of Innovation
16:23 Big Innovation Ambitions vs. Organizational Reality
17:44 Why Stakeholder Alignment Matters
20:16 The Innovation Charter
21:24 Fear and the Unknown
22:00 Why Past Data Can Mislead Innovation
23:48 Why Great Marketers Can Struggle With Innovation
25:29 Getting Innovation Out the Door
27:24 The Future Has No Data
28:04 Challenging Assumptions With First Principles
30:00 AI as a Tool for Building Evidence
32:00 AI, Prototyping and Creative Potential
32:51 AI Should Be an Input, Not the Output
34:15 “We Should” Is Not an Idea
36:03 Why “How Might We?” Changes the Conversation
37:39 Fiona’s Six I’s of Innovation
39:09 Insights, Inspiration & Ideation
40:28 Why Iteration Matters
41:53 Implementation and Death by a Thousand Cuts
42:18 Protecting the Idea’s DNA
42:45 Is Intuition the Seventh I?
45:05 Why Skipping Steps Kills Innovation
47:06 The Most Undervalued Stage
48:49 How Innovation Survives Budget Pressure
50:17 Why Innovation Can’t Be a Side Job
51:39 What to Do Monday Morning
53:48 Why Innovation Needs Champions
54:26 It’s Never Too Late to Redefine the Problem
55:41 Built for Breakthrough Resources
56:11 Where to Find Fiona
Link to the book:
Built for Breakthrough - https://www.builtforbreakthrough.com/

Marketing has never been more measurable. It may also have never been more short-term.
If measurement was supposed to solve marketing's credibility problem, why hasn't five years of better data increased CEO confidence in marketing?
In this Sharp Cut, Marc and Vassilis unpack what happens when the things marketing can observe quietly become the things organizations value.
They trace the progression from visibility → accountability → optimization → observability, and ask whether attribution windows, platform dashboards and ROI have inadvertently trained marketers to optimize for short-term outcomes.
Using research from Boathouse, the Norwegian marketing industry and others, they explore why the same data can produce radically different conclusions depending on how it is framed.
They also tackle a harder question: who is responsible?
Is finance forcing marketing to think short-term? Or have marketers quietly accepted the measurement windows handed to them by platforms and brought those definitions of effectiveness into the boardroom?
The answer turns measurement into something much bigger: a leadership decision.
Because the moment you choose the window, you decide what value counts — and what value doesn't.
Chapters:
00:00 Marketing Has Never Been More Measurable
00:50 More Measurement, Same Confidence
02:14 Marketing Won the Seat but Lost the Argument
03:49 How Measurement Became Optimization
05:13 When What We Can Observe Becomes What Counts
05:30 The Fishing Net Problem
06:47 Where Leadership Enters the Measurement Debate
08:38 Are Marketers Responsible?
09:12 When Marketing Takes Credit for the Weather
11:16 The 35 Forces That Affect ROI
12:09 The Halo Effect
13:30 Same Data, Two Completely Different Answers
15:45 The Retail Number That Changes the Story
16:26 Why Every Channel Wants More Budget
17:57 Measuring Podcast Advertising With the Wrong Net
20:07 The Problem With the 95:5 Rule
21:46 Why ROI Can Mislead
24:06 How Cutting Spend Can Improve ROI
25:20 What Does “Return” Actually Mean?
27:03 When Long-Term Evidence Loses to the Quarter
28:23 Can Marketing Choose a Different Window?
31:29 Measurement Becomes a Leadership Problem
32:42 You Can Predict the Answer by Looking at the Net
34:07 Five Questions to Ask Before Measuring
35:00 What Marketing Can Learn From Finance
35:35 The Measurement Problem Is a Leadership Problem
35:59 What Comes Next: Discounted Cash Flow
Episode Sources:
Boathouse, Fifth Annual CEO Study on Marketing and the CMO, 150 US CEOs, fielded January 2026. Coverage via Marketing Dive, SmartBrief, CommPRO.
Gartner, May 2024, CMO survey on internal skepticism of marketing value.
Calgary Marketing Association and Stone-Olafson, 2024 ROI report, Alberta marketers, n=124.
Kapero, ANFO and the Norwegian Media Businesses’ Association, The Commercial Power of Brands in the Digital World, 13 Norwegian companies, 2024 data.
Podscribe, Conversion Rate by Podcast Player, more than 50 direct response brands, 30 daywindow.
Quatical, The 35 Factors That Affect Marketing ROI.
Rosenzweig, P. (2007). The Halo Effect. Free Press.
Eddington, A. S. (1939). The Philosophy of Physical Science. Cambridge University Press. The ichthyologist parable, told in summary. No direct quotation used.