For more on building AI products and careers, along with early course announcement and special pricing, subscribe to the AI Career Boost mailing list at https://aicareerboost.com/interested

THE GUEST

Jennifer Deal is a powerhouse AI product leader who’s been shaping the future of e-commerce, fintech, and martech for over a decade. From building billion-dollar marketplaces at eBay to unlocking more than $42 million in revenue through bold, data-driven innovation, she’s known for blending marketing intuition with deep product strategy and technical execution. Jennifer thrives at the intersection of customer obsession and enterprise transformation—turning insights into adoption and disruption into lasting loyalty. Today, she’s driving marketplace growth and seller success at Tmoo, while helping teams launch products customers can’t stop talking about. And in this episode, we’re diving into AI enablement for sellers and marketplaces.

THE SUMMARY

What you think you know about AI is basically irrelevant: Using AI casually creates false confidence. Real leverage comes from understanding how AI systems actually behave, where they break, and how they’re built. That mindset shift alone creates a massive competitive gap.

Engineering vocabulary is a career cheat code: You don’t need to code to win in AI, but you do need to speak the language. Knowing concepts like hallucinations, constraints, trade-offs, and system limits unlocks credibility with technical teams and positions non-technical leaders as real AI partners.

AI rewards people who think smaller, not bigger: The biggest mistake businesses make is trying to “AI-transform” everything at once. Starting with narrow, practical use cases creates faster ROI and avoids the complexity that stalls most AI initiatives.

Vibe coding turns ideas into products shockingly fast: Hands-on experimentation beats theory every time. Rapidly building scrappy tools — even personal ones — rewires how people think about product, execution, and feasibility in an AI-first world.

Planning cycles are collapsing because AI moves too fast: Annual plans became quarterly. Quarterly plans are becoming monthly. Falling in love with any solution is dangerous because the tech will obsolete it faster than teams expect.

AI levels the playing field — temporarily: Everyone is early. Even engineers are still catching up. The advantage belongs to those who get hands-on now, before AI fluency becomes table stakes instead of differentiation.

AI isn’t just a tool — it changes how everything gets built: From marketplaces to filmmaking to product development, AI collapses traditional stages of work. Everything starts happening at once, forcing leaders to rethink structure, sequencing, and decision-making.

THE SHOW

Weekly conversations with the AI’s top product leaders. Join Polly Allen as she discovers the paths to success in the world of AI.

THE LINKS

Have a question you want us to answer? Send it through to support@aicareerboost.com

Jennifer Deal

LinkedIn: https://www.linkedin.com/in/jenniferdeal

My links

LinkedIn: ⁠https://www.linkedin.com/in/pollymallen/⁠

AI Career Boost: ⁠https://www.aicareerboost.com/⁠

For more on building AI products and careers, along with early course announcement and special pricing, subscribe to the AI Career Boost mailing list at https://aicareerboost.com/interested

THE GUEST

Maggie May is a sharp, high-velocity product and tech executive who has built and scaled lovable products across real estate, accessibility, and fintech. From the startup trenches to CPO seats, she’s known for pairing speed with substance—shipping fast while never losing sight of user trust and delight. At Tomo and AudioEye, Maggie led teams with a relentless focus on impact, inclusive design, and staying one step ahead of what customers and markets actually need. Now, through AskAndBeFound.com, she’s focused on helping service-based businesses get discovered on ChatGPT, bridging the gap between AI-era search and the real humans ready to serve.

THE SUMMARY

AEO/GEO is the new SEO power play: The episode dives deep into why AI-engine optimisation matters more than people realise. Traditional SEO alone won’t get you discovered in LLM search — and many businesses are flying blind thinking Reddit comments or keyword-stuffing will save them. The real levers are directories, reviews, and structured data that AI can actually understand.

The 3-part framework every business should use: Maggie breaks it down to Off-Page (directories + reviews), On-Page (classic SEO), and Technical SEO (schema files + inline markup). If any of these pieces are missing, AI simply won’t trust your content or know who you are. She makes it clear: this isn’t optional anymore — it’s the minimum viable digital presence.

Myth-busting what doesn’t work in AI search: There’s a widespread belief that you need to grind on Reddit or that AI suppresses AI-generated content. Both are wrong. The episode makes the argument that AI rewards clarity and structure, not internet folklore. If reviews and directories aren’t part of your strategy, you’re wasting your time.

Schema is the unsung hero of AI discovery: The conversation highlights how installing the right schema can transform visibility — sometimes in under an hour. Not because it's a “hack,” but because it gives AI exactly what it wants: clean, structured context. Most businesses don’t even know this step exists.

If AI doesn’t understand who you are, you don’t exist: One of the strongest opinions throughout the episode: ambiguity kills discoverability. If your business name, phone, and address aren’t consistent across platforms, AI drops you instantly. The margin for error is zero — AI doesn’t “kind of guess.”

Entrepreneurship + multi-passionate careers are a feature, not a bug: Maggie’s career arc reinforces that being multi-passionate is now an advantage — not something to hide. Each curiosity thread strengthens the others. The episode makes a strong case that exploring multiple domains is the modern form of career compounding.

Start building with AI, even if you’re intimidated: The final takeaway: the only way to learn AI is hands-on. Break things. Save versions. Ask the model questions. Whether you’re using LLMs to build products or grow your service business, the barrier to entry is the lowest it’s ever been — and the upside has never been higher.

THE SHOW

Weekly conversations with the AI’s top product leaders. Join Polly Allen as she discovers the paths to success in the world of AI.

THE LINKS

Have a question you want us to answer? Send it through to support@aicareerboost.com

Maggie Mae

LinkedIn: https://www.linkedin.com/in/maggie-mae-seattle

My links

LinkedIn: ⁠https://www.linkedin.com/in/pollymallen/⁠

AI Ca

For more on building AI products and careers, along with early course announcement and special pricing, subscribe to the AI Career Boost mailing list at https://aicareerboost.com/interested

THE GUEST

Sudha Kumar is a powerhouse product leader who thrives at the intersection of data, compliance, and user impact. With more than a decade of experience scaling B2B SaaS platforms and modernizing federal IT systems, she has delivered measurable outcomes across sectors, from accelerating healthcare audits by 30% to driving product revenue across more than 50 companies. Known for her ability to simplify complex workflows without sacrificing rigor, Sudha brings deep technical fluency and a sharp sense of user empathy to every challenge. Now focused on shaping the next generation of AI-powered enterprise tools, she is dedicated to advancing system compliance, reliability, and trust in the AI age.

THE SUMMARY

Data is the real truth-teller in product work — The conversation emphasises that users rarely behave the way product teams expect. Data becomes the only reliable signal to understand real usage patterns and close the gap between intention and reality.

Technical fluency massively elevates a PM’s impact: Being able to query raw logs, validate theories, and guide engineering with precision turns a PM into a high-leverage operator—especially in platform roles, where hidden complexity and technical debt drive long-term success or failure.

Compliance is an AI goldmine: Compliance work is repetitive, high-stakes, and deeply dependent on human interpretation. Automating the mapping of findings to regulatory standards shows immediate value, especially when errors can trigger hospital shutdown risks.

AI performance reflects the clarity of the human behind it: Iterating on prompts, supplying examples, and defining rules tightly is what improves accuracy. AI only becomes “smart” when the PM is intentional, structured and thorough with the inputs.

Data harmonisation is the hidden prerequisite for real AI adoption: At Renovo, six companies with six definitions for basic concepts made clear that most AI problems are data problems. Without consistent definitions and clean context, AI becomes a guessing machine—not a reliable system.

Mindset is the real barrier to entering AI, not skill: Sudha describes the early fear of being “left behind” and how the shift happened when she realised AI is a partner that frees PMs from tactical noise so they can do the strategic work they’re actually paid for.

Feeling behind means you’re already ahead: The episode closes on a strong, counterintuitive insight: the moment you worry you’re behind is the moment you’ve already started moving. Diving in early, experimenting, and iterating is the real differentiator.

THE SHOW

Weekly conversations with the AI’s top product leaders. Join Polly Allen as she discovers the paths to success in the world of AI.

THE LINKS

Have a question you want us to answer? Send it through to support@aicareerboost.com

Sudha Kumar

LinkedIn: https://www.linkedin.com/in/sudhadkumar

My links

LinkedIn: ⁠https://www.linkedin.com/in/pollymallen/⁠

AI Career Boost: ⁠https://www.aicareerboost.com/⁠