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

Amri Abuseman is a powerhouse engineering leader known for building quality-first cultures across high-stakes industries—from healthcare and FinTech to telecom and HR tech. As Director of Engineering at Flatiron Health, she leads teams operating at the critical intersection of software delivery, system reliability, and regulatory rigor—where the margin for error is small and the impact of great engineering is enormous. With deep roots in quality engineering, Amri brings a powerful blend of technical depth and strategic leadership. Her toolkit spans everything from AI-driven test automation to enterprise-scale release management, helping organizations ship software that is not only fast, but reliable and resilient. But what truly sets her apart is her ability to align product and engineering through clear strategy, strong technical fluency, and genuine cross-functional empathy. Amri is the kind of leader who doesn’t just ship software—she builds the systems, cultures, and practices that make high-quality delivery sustainable at scale. She’s passionate about creating engineering environments where quality is embedded from the very beginning, not treated as an afterthought. And in this episode, we’re diving into what it really takes to build quality-first engineering organizations, how AI is reshaping test automation and software reliability, and why aligning product and engineering is the key to delivering software that truly matters.

THE SUMMARY

The fastest way to learn AI is to build, not study: Spending months watching tutorials or completing courses rarely leads to real capability. The only way to understand AI tools properly is to experiment, build rough prototypes, and learn through failure.

AI is removing the biggest barrier to building software: Modern AI coding tools are enabling people with little or no programming experience to create real products. This shift means the next wave of builders may come from non-traditional technical backgrounds.

AI tools only succeed if they actually fit developer workflows: Engineers quickly abandon tools that slow them down or misunderstand their code context. Real productivity gains only happen when AI tools integrate seamlessly with existing development habits.

Buying AI tools doesn’t guarantee productivity gains: Many organisations assume that simply adopting AI tools will instantly improve output. In reality, poor integration, unclear use cases, and workflow friction often lead teams to stop using them entirely.

Regulated industries are still pushing forward with AI innovation: Sectors like healthcare and finance face strict restrictions on using public AI systems. Instead of avoiding AI altogether, companies are building internal AI environments to stay competitive while maintaining compliance.

Product managers can’t afford to stay non-technical anymore: Leaders who avoid experimenting with AI tools risk losing visibility into what their teams can actually build. Getting hands-on with AI tools dramatically improves product intuition and decision-making.

The real AI skill isn’t prompting—it’s persistence: Success with AI often comes down to patience and experimentation. The builders who succeed are the ones willing to debug, refine prompts, and iterate repeatedly until something useful emerges.

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

Amri Abuseman

Linke

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

Brian Diller is an AI product leader focused on turning complexity into clarity in higher education. At Watermark, he’s leading the design and launch of student success and course evaluation products, thoughtfully integrating AI into workflows that help institutions better support learners and make more informed decisions. With a rare blend of systems thinking and creative empathy, Brian brings a unique perspective to product leadership. Before stepping into the world of AI and product development, he spent years as a music professor—an experience that continues to shape how he approaches collaboration, problem-solving, and leadership today. Known for translating complex ideas into practical solutions, he focuses on building tools that are not only powerful, but genuinely useful for the people who rely on them. Brian is especially passionate about the responsible application of AI in real-world decision making—ensuring that emerging technologies support human judgment rather than replace it. And in this episode, we’re diving into how AI can meaningfully improve student success, the challenges of designing for higher education, and what it really takes to bring responsible AI into everyday institutional workflows.

THE SUMMARY

AI product leadership often starts with curiosity, not expertise: Getting involved in AI initiatives doesn’t require deep technical skills upfront. Asking to participate in projects, raising your hand early, and being willing to learn in public can quickly position you as the internal expert in emerging AI workflows.

AI works best as a thinking partner for product managers: Tools like Gemini and ChatGPT are incredibly effective for brainstorming product features, exploring competitive strategies, and refining ideas. Instead of replacing PM judgment, AI amplifies creative problem-solving and structured thinking during product discovery.

One of AI’s strongest use cases is synthesizing overwhelming data: Large lecture classes can generate hundreds of course evaluations, making manual analysis nearly impossible. AI can summarise patterns, detect recurring themes, and highlight actionable feedback, allowing educators to quickly understand what students are actually saying.

AI can transform fragmented student data into meaningful stories: Academic advisors often manage hundreds of students with scattered records across multiple systems. AI can aggregate these signals—grades, advising notes, life challenges, and historical context—to produce a coherent narrative that helps advisors respond with empathy and better guidance.

Giving product managers control over prompts is powerful: When PMs own the prompting strategy instead of engineers, they gain direct influence over how AI interprets data and solves user problems. This shifts AI development closer to product thinking—where the focus is storytelling, user pain, and the outcomes the system should prioritise.

Prototyping AI products with synthetic data accelerates innovation: Using generated datasets allows teams to experiment safely, test hypotheses, and validate whether AI can detect meaningful signals. It also enables colleagues to explore prompts, break the system, and collaboratively refine how AI behaves.

AI adoption inside organisations often starts with one brave experiment: Many teams are still figuring out how to work with AI. Jumping into a messy, ambiguous project—despite uncertainty—can rapidly build credibility and create momentum for wider AI adoption across the company.

THE SHOW

Weekly conversations with the AI’s top product leaders. Join Polly Allen as she discovers th

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

Phil Fairbrother is a product leader working at the intersection of AI, experimentation, and human-centered design. With experience spanning insurance, e-commerce, and creative technology, Phil has built a reputation for turning ambiguity into measurable growth. From launching high-performing chat-based sales funnels at SelectQuote to developing custom CMS platforms that empower independent creators, he thrives on solving complex problems with clarity and momentum. Known for his cross-functional leadership style, Phil blends agile execution with discovery-driven strategy—aligning teams around insight, experimentation, and real user needs. He’s deeply passionate about accessibility, ethical design, and harnessing AI not just to optimize products, but to elevate how teams collaborate and how users experience technology. And in this episode, we’re diving into how AI-powered experimentation, thoughtful design, and product leadership can drive meaningful growth in an increasingly complex digital world.

THE SUMMARY

AI Turns Product Managers Into Founders: You don’t need a technical co-founder anymore to build. With vibe coding and tools like Claude Code, you can go from idea to working product in hours. The barrier isn’t skill — it’s starting.

Agentic AI Is the Real 10x Multiplier: Senior developers using AI as a co-worker can massively increase output — not by blindly accepting code, but by reviewing and directing it. The future isn’t AI replacing devs. It’s AI amplifying the best ones.

Specialised AI Agents > One Big Copilot: Instead of one generic assistant, imagine a team: business analyst agent, brainstorming agent, PM agent writing PRDs. Product workflows can now be systematised and accelerated — especially for greenfield projects.

AI in High-Trust Industries Requires Restraint: In regulated spaces like insurance, hallucinations are unacceptable. The smart play isn’t flashy AI — it’s practical use cases like fallback IVRs during peak season or AI sales training.

Vibe Coding Isn’t Enough — You Need Taste: AI tools default to generic design. If you want standout products, you must be explicit about aesthetic, brand and feel. Prompting isn’t technical — it’s creative direction.

“Time to Wow” Is Everything: Modern users expect instant magic. If your AI tool doesn’t prove value in 30–60 seconds, they’ll assume they can do it in ChatGPT themselves.

Build First. Validate Fast. Don’t Overbuild: Just because you can build features instantly doesn’t mean you should. Tokens and time still matter. Bounce ideas off real users before you go deep.

AI Makes Niche SaaS Possible Again: A CMS tailored for indie authors and alternative music publications? That’s viable now. AI reduces build cost so niche markets become profitable opportunities.

Imposter Syndrome Doesn’t Go Away: Even experienced leaders feel like frauds in AI because it’s so accessible. Accessibility doesn’t invalidate expertise. If you’re building and leading — you’re legit.

The Only Advice That Matters: Start Now - Courses, podcasts, experiments — it doesn’t matter how. Waiting is the only guaranteed way to fall behind.

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

Phil Fairbrother

LinkedIn: https://www.linkedin.com/in/phillip-fairbrother

My links

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

John Boothroyd is a trailblazing product executive with over two decades at the forefront of digital transformation, AI, and sustainable infrastructure. From scaling hundred-million-dollar businesses at Optus to leading Honeywell’s leap into AI-powered building optimization, John has built a career turning complex systems into scalable, high-impact solutions. Today, he’s driving innovation across renewable energy and aging care—advising solar battery and IoT ventures tackling climate resilience and care challenges. With patented work in intelligent energy systems and a relentless net-zero focus, John blends commercial execution with planetary-scale vision. And in this episode, we’re diving into how AI, energy, and care innovation are shaping a more sustainable future.

THE SUMMARY

AI is most powerful when you stop treating it like a search engine: The real productivity leap happens when AI becomes a thinking partner — arguing with itself, pressure-testing options, and surfacing trade-offs. One-shot prompts cap value early; iterative challenge unlocks depth.

If AI keeps getting stuck at ~60%, you’ve hit a “thinking budget” wall: Free or low-tier tools often fail on complex, multi-step tasks because they simply don’t have enough reasoning capacity. Upgrading resources or switching platforms can turn a frustrating loop into a clean, end-to-end output.

You’re already building software — you just don’t realise it: Multi-tab spreadsheets with cross-referenced formulas, ROI models, and scenario analysis are tools, not “admin work.” AI now gets you ~90% there instantly, flipping effort from creation to refinement.

Agentic AI isn’t about workflows — it’s about autonomy: True agent-like behaviour shows up when tools can find relevant resources, select inputs, and produce usable deliverables with minimal instruction. Specifying what you want instead of how is the tipping point.

Connecting AI to your own data is the real force multiplier: When tools can reason across internal documents, policies, and systems, outputs shift from generic advice to context-aware execution. That’s when AI starts surprising you in useful ways.

The fastest way to learn AI is to build, not read: Trying multiple tools, experimenting with higher-capability tiers, and creating real artefacts beats passive learning every time. Mastery comes from friction, not tutorials.

AI isn’t just a productivity play — it’s an impact lever: Applied well, AI can drive measurable outcomes in energy efficiency, climate transition, healthcare, and aged care — shifting systems from reactive processing to personalised, human-centred design.

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

John Boothroyd

LinkedIn: https://au.linkedin.com/in/johnboothroyd

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

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/⁠

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

Taylor Algren is a product leader, founder, and strategist with over a decade of experience tackling real-world challenges at the intersection of tech, health, and global operations. He’s launched scalable platforms, advised early-stage startups, and brings a unique mix of systems thinking and human-centered design to every project. Now building a stealth-mode AI health-tech startup focused on chronic disease care, Taylor is known for his execution, mentorship, and commitment to meaningful, equitable innovation.

THE SUMMARY

Impact starts with lived experience — and this founder proves it. The episode dives into how years navigating the healthcare system as a cardiomyopathy patient revealed just how broken, bureaucratic, and non-human-centric chronic disease care really is. The constant cycle of refills, prior authorisations, pharmacy stock outages, and insurer rules creates a system that pushes patients to give up — at the cost of their long-term health.

AI isn’t a buzzword here — it’s a survival tool: The conversation highlights how agentic AI can step into the administrative chaos patients face: tracking expiring prescriptions, pre-auth deadlines, pharmacy stock issues, and even escalating tasks clinicians don’t have time for. The strong opinion: AI isn’t replacing clinicians — it’s replacing the waste that’s burning them out and harming patients.

Easy Medicine" tackles the most painful, immediate problem: cost: Instead of starting with the flashy stuff, the MVP focuses on something every chronic patient feels — medication prices. By pulling manufacturer coupons, GoodRx prices, and Cost Plus Drugs data, the tool delivers real savings instantly. It’s practical, high-impact, and built entirely using modern AI coding tools, no traditional coding required.

Rapid AI-powered building is the new superpower: The episode pushes a strong stance: don’t start with a course — start by building. A one-hour prototype is now more valuable than weeks of prep. The biggest skill in AI product building isn’t knowing how to code; it’s knowing what’s worth building, validating fast, and iterating with real users. Tools like Lovable, Bubble, Replit, and Figma have lowered the barrier to near-zero.

Purpose drives better products: Taylor’s journey from Botswana to Google to chronic illness to founder underscores a theme: when you’ve lived the problem, you build differently. His story is a reminder that the most meaningful AI solutions don’t start with technology — they start with empathy and frustration with the status quo.

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

Taylor Ahlgren

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

My links

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

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