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AI isn’t the real problem.

Marketing is.

In this episode, we talk about a growing trend:
Mass layoffs in marketing — driven by AI.

But here’s the uncomfortable truth:

AI isn’t replacing marketing teams.
It’s exposing them.

Because when marketing is seen as a workflow…
it can be automated.

We break down:

• Why marketing is often first in line for budget cuts
• Why AI is accelerating a problem that’s been there for years
• And why most teams still struggle to prove real business impact

The key question:

👉 Are you building a marketing function… or just running workflows?

Because if you can’t prove value to the business —
someone (or something) else will do it cheaper.

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Marketing isn’t underperforming.

It’s just not trusted.

In this episode, we unpack a striking insight:
90% of CEOs don’t trust marketing to report commercial impact.

Not because marketing isn’t delivering results —
but because the way we report doesn’t connect to the business.

We break down:

• Why marketing focuses on channels instead of business impact
• Why CFOs win the argument (almost every time)
• And how this has become a structural problem — not a performance problem

Plus, we share a simple 3-step approach we’re seeing work across companies:

1. Move from platform reporting → holistic reporting
2. Take true data ownership
3. Align marketing with what the CFO actually cares about

Because until marketing can prove impact in business terms…
it will never be the most trusted voice in the room.

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In this episode, we dig into one of the most honest conversations in consulting: what does it really mean to build a great team when the talent pool is small, the tools are niche, and your best people are always in demand?

We talk about:

• Why Adobe talent is so hard to find — and why the same people keep circling the same companies
• Junior vs. senior: what's the right split — and why you need to map the team before you post the job
• The stepping stone philosophy — building a company where people grow, even if they eventually leave
• Culture over control — why the 9-to-5 office empties the second the clock hits, and what to do instead
• The boomerang effect — why celebrating departures is actually good for business

Because if no one wants to hire your people, that's the real problem.

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Most companies don’t have a data problem — they have a trust problem.

In this episode, we break down a simple leadership framework we’re seeing across enterprises that are winning right now:

1. Align data to business outcomes (start with the questions, not the tools)
2. Establish data ownership (who owns definitions, standards, and quality?)
3. Build marketing data confidence (not perfect data — trusted data leaders can act on)

Because when leadership doesn’t trust marketing numbers, you get the usual loop: endless dashboards, constant explanation, and decisions made on gut feel — while AI gets trained on noise.

If you want faster decisions, better budget allocation, and fewer “prove it” conversations… this is the place to start.

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We’re back — welcome to Season 2.

In this kickoff episode, we reflect on what we learned from Season 1: why most podcasts die before episode 3 (and how we cheated the odds), what we’re changing in our recording setup, and why publishing consistently matters more than having the “perfect” production.

We also zoom out: AI is accelerating marketing and decision-making — and that only works if the data foundation is actually solid. Tools are important, but the real shift we’re seeing is organizations focusing on data, people, and outcomes (not just technology).

Plus: stress, breathing exercises, and a cliffhanger dad joke for next episode.

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Being data-driven isn’t about tools, dashboards, or technology stacks.

In this episode, we talk about data culture — and why people, leadership, and shared understanding matter more than any platform. We discuss what successful data-driven organizations have in common, how teams become motivated (not just informed) by data, and why ownership and clear outcomes are the real foundations.

Technology enables.
Data informs.
But culture is what actually makes it work.

If you’ve ever wondered why great tools still don’t lead to better decisions, this episode is for you.

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Roughly half of marketing activity can’t be tracked properly — yet we still argue about attribution models.

In this episode, we talk about Adobe Mix Modeler (AMM) and why marketing mix modeling is suddenly relevant again. Not as a replacement for attribution, but as a way to answer a much more practical question:
Where should you spend your next marketing dollar?

We discuss how AMM works, why it doesn’t care about UTMs or cookies, and how combining aggregated data with real events can surface impact you’d otherwise miss.

Attribution is still useful. But when tracking breaks down, this is often where the real conversation starts.

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This episode is a K/C-asper overload — in the best way. We’re joined by Kasper Rasmussen, co-founder of Accutics, to unpack a concept that sounds fluffy… until you realize it’s the missing ingredient behind almost every “data-driven” ambition: a shared data language.

We break down what it actually means in practice (hint: it’s not just a taxonomy or a spreadsheet). A shared data language connects everything from briefing and naming conventions inside platforms, to URL tagging, reporting structures, documentation, governance, and—most importantly—ownership.

We also tackle the uncomfortable truth behind why data trust keeps collapsing in large organizations: the numbers will never match across platforms, and that’s not a bug — it’s two different truths used for two different purposes. The real problem is when companies don’t agree on which truth belongs where, and end up arguing instead of optimizing.

If you work in a multi-market setup, with agencies, hundreds of campaigns, and “data-driven” as a board-level slogan — this is your blueprint for turning it into reality.