216: How to stand out as a candidate with AI prep, portfolios and tools (The Martech job hunt survival guide, part 2)
What’s up everyone, today we continue with part 2 of a 3 part series we’re calling The Martech Job Hunt Survival Guide. Part 2 is: How to stand out as a candidate with AI prep, portfolios and tools.
Summary: Phil and Darrell spent this episode breaking down what actually moves the needle when you’re searching for a role: building the portfolio that almost no marketing ops professional bothers to save, navigating the AI experience question, knowing when to take a contract role instead of holding out, and skipping the AI job-search tools that make you look like everyone else. The honest observations from Darrell’s own recent job search make this one worth listening to, including why the colleagues most reluctant to make a lateral move are still searching months later.
In this Episode…
- (00:00) - Intro
- (01:01) - In This Episode
- (01:30) - Sponsor: Mammoth Growth
- (02:36) - Sponsor: GrowthLoop
- (05:24) - Why Hiring Managers Can't Actually Evaluate Your AI Experience
- (08:26) - How to Build a Marketing Ops Portfolio When Your Work Is Buried in Tools
- (17:56) - Why Creating LinkedIn Content Works Even When Nobody Is Watching
- (25:32) - What Hiring Managers Notice First on Your LinkedIn Profile
- (30:10) - Sponsor: Knak
- (31:13) - Sponsor: MoEngage
- (34:13) - Why Contract Work Is a Strategic Move for Marketing Ops Job Seekers Right Now
- (44:02) - Which Job Search Tools Help and Which Ones Waste Your Time
- (56:18) - How a Video Introduction or Visual Resume Gets You Into the Next Round
Why Hiring Managers Can't Actually Evaluate Your AI Experience
Every marketing ops job posting in 2026 has the same line buried somewhere in the requirements: "proven experience delivering results with AI." Walk into any interview and within the first few minutes someone will ask you to describe what you've actually done with it. That question sounds reasonable until you realize the person asking usually has no idea what a good answer looks like.
Darrell came out of a recent job search with a clear read on this. The interview questions had shifted entirely. The old MarTech interview, the 1 that asks about your tool stack and campaign history, has been replaced. AI is now the primary filter. Companies want proof of results. But AI-driven marketing ops, as an actual practice, barely existed 3 years ago. Phil put the absurdity into 4 words: "5 years of AI experience." Everyone in hiring knows it's a joke. They're writing it anyway.
The talent pool has gotten harder at the same time. Amazon's most recent layoffs displaced over 10,000 people. Layoffs at Google and across the broader tech sector added more. You're competing against that cohort now, which means the undifferentiated application is in worse shape than it's ever been. Everything has to be sharper.
But the opening Darrell is pointing at is real. The hiring managers writing "proven AI experience required" often can't define what good AI usage looks like for a marketing ops role. They're expressing a priority while lacking any rubric to test it. When they ask the interview question, they're listening for someone who sounds like they know what they're talking about. Most candidates coming through don't. You feel it during prep, that uncomfortable awareness that you don't know exactly what they want from you. The honest truth is they don't either.
That gap is yours. Research what AI actually does in marketing ops workflows: lead scoring automation, campaign orchestration, data governance, intent signal processing. Build 1 small example if you have the time. Frame your existing work in terms of where AI would fit and how you'd measure it. Darrell's framing: you can position as a credible AI enthusiast with very little preparation, because the bar inside most marketing orgs is low and most candidates aren't clearing it.
The industry required AI fluency before building any way to evaluate it. That's not a problem. For candidates willing to do the homework most skip, it's the whole advantage.
Key takeaway: Research 3 specific AI use cases in marketing ops before your next interview: lead scoring automation, campaign workflow agents, and CRM data deduplication are good starting points. Prepare 1 concrete story connecting 1 to work you've done or would do. If you haven't built anything yet, describe the workflow you'd build and how you'd measure its impact. Candidates who speak specifically and confidently about AI applications win these conversations, because they're often the only ones in the room who prepared.
How to Build a Marketing Ops Portfolio When Your Work Is Buried in Tools
Most marketing ops professionals have spent years doing meaningful, complex work. They've built lead scoring models, managed platform migrations, architected multi-channel campaign workflows. And if you asked them to show you any of it in an interview, most couldn't. The templates are gone. The diagrams were never made. The results are a rough number someone mentioned once in a meeting.
Darrell has sat on the interviewer side of enough conversations to be direct: the portfolio problem in marketing ops is almost universal. Candidates describe their work verbally, and the person asking often can't follow it. There's nothing to point to, nothing to walk through, nothing that makes the experience tangible. In a field full of technical, visual, process-driven work, almost no 1 has anything to show.
The bar to stand out is genuinely low. Darrell's starting point: if you've built a custom GPT, a Google Gem, or a basic AI agent using Zapier, that alone puts you ahead of most candidates. It takes about 10 minutes to build 1. It demonstrates something concrete about how you think and work. The same logic applies to documentation that almost no company does well: a clean diagram of your current or former tech stack, before-and-after views of a migration you led, a lead scoring template, a product requirements document for a tool evaluation. These are ordinary outputs of the job. Almost no 1 saves them.
Phil's preferred format is the case study. Take a project you led, strip the confidential details, and walk through it as if you were an outside consultant brought in to solve the problem. What was the situation before you arrived? What did you do? What did it look like after? Specific numbers and percentages help, but they're not required. A clean diagram showing a tech stack before and after a migration, or a flow chart of a campaign workflow you built, communicates competence without a single metric. For quantifying impact when the numbers are murky, Darrell's suggestion is to use AI to reverse-engineer the math. If you cut campaign launch time by 20%, work backward through campaigns per quarter, leads generated, and pipeline influenced. You can build an intelligent, defensible estimate, and most candidates don't even try.
The format doesn't need to be elaborate. A Google Slides deck linked from your resume, tracked with a Bitly vanity URL so you can see who opens it, is more than enough. The bigger benefit of building a portfolio at all is what it does to your interview prep. Reviewing your own work, articulating outcomes, distilling a project into a problem-action-result narrative means you've already done the thinking before anyone asks the question. Phil's point: the exercise of building the portfolio and the exercise of preparing for interviews are the same exercise.
Key takeaway: Start with your most recent project and build 1 case study: the problem you walked into, what you built or changed, and the measurable outcome. Add a tech stack diagram if you don't have 1. Link both as a Google Slides deck from your resume and track opens with a Bitly URL. Even a basic portfolio puts you in ...
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