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

Nico Posner, VP of Product Management at Q3D Sensing and a veteran product leader whose career spans some of the world’s most innovative companies, including LinkedIn, eBay, Clover, and Xero.

Nico has spent years leading technology and AI-driven product innovation across industries, helping scale products from early-stage concepts to global adoption. At Q3D Sensing, he’s now focused on the next frontier of AI: capturing and digitizing the real world through advanced LiDAR and spatial sensing technology.

With deep experience in AI, predictive analytics, product strategy, and emerging technology, Nico brings a unique perspective on how real-world data is becoming the foundation for the next generation of AI systems, robotics, construction tech, digital twins, and spatial computing.

THE SUMMARY

What happens when AI moves beyond text and begins interacting with the physical world?

Nico Posner breaks down why the future of AI depends on accurate, real-world spatial data — and how LiDAR technology is unlocking entirely new possibilities across industries.

The conversation explores the launch of Q3D Sensing’s new OraGo 3D reality capture LiDAR sensor and why accessible, affordable spatial sensing could become a foundational layer for AI-powered workflows. Nico explains how modern LiDAR systems work, why current enterprise solutions are often too expensive or impractical, and how new tools are democratizing access to high-quality 3D data capture.

Polly and Nico also dive into:

• The growing role of real-world data in AI systems
• How LiDAR enables digital twins, robotics, construction intelligence, and spatial computing
• The difference between consumer-grade scanning and professional LiDAR systems
• Why edge processing and on-device rendering matter for enterprise workflows
• How AI is accelerating innovation, product strategy, research, and operations
• The future of AI-powered infrastructure, warehouses, and physical environments
• Lessons from building products at both startups and global technology companies
• The importance of solving real customer pain points when developing AI products

This is a fascinating look at the intersection of AI, hardware, spatial computing, and product innovation — and why the next wave of AI breakthroughs may depend less on prompts and more on understanding the physical world around us.

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

Nico Posner
LinkedIn: https://www.linkedin.com/in/nicoposner/

Q3D Sensing Website: https://www.q3dsensing.com/

Q3D Sensing LinkedIn: https://www.linkedin.com/company/q3d-sensing/

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

Bill Takacs is a veteran product executive with over two decades of experience building and scaling platforms across SaaS, security, analytics, AI, and enterprise infrastructure. He’s currently VP of Product at MTrain, where he leads product and engineering—growing the business past $10M+ ARR, cutting platform costs by 50%, and doubling team velocity. A proven zero-to-one operator and turnaround leader, Bill’s background includes roles at Salesforce, AOL Instant Messenger, O’Reilly Media, HP, and multiple venture-backed companies. He’s also a former U.S. Army Ranger and Captain, bringing a unique blend of disciplined execution and bold innovation. In this episode, we’ll dive into operationalizing AI in enterprise products, scaling efficiently, and leading high-impact product teams.

THE SUMMARY

If you’re not adopting AI, you’re already behind: This isn’t incremental tech — it’s a step-change. Companies that treat AI as a “nice-to-have” efficiency tool will lose to AI-native competitors with lower costs, faster execution, and fundamentally different products. Survival depends on embracing it now.

Most companies are using AI wrong and too safely: Simply making existing processes faster is the least imaginative use of AI. The real opportunity is redesigning workflows and products entirely — not just layering AI on top of old systems.

Start with workflows, not hype: The smartest entry point isn’t “build something cool with AI” — it’s identifying repetitive, structured tasks and automating them. Map the process step-by-step, then apply AI where it actually removes friction.

Agents are coming faster than most people think: We’re moving toward a world where teams manage AI agents that do the work — writing code, running workflows, even transacting. The shift from “tools” to “autonomous workers” is already underway.

Roles are collapsing — not just evolving: AI is blurring boundaries between product, design, and engineering. Tasks that once required multiple roles can now be done by one person with AI support. This will fundamentally reshape team structures.

No one actually knows the playbook yet: There is no “best practice” right now. Waiting for clarity is a losing strategy — the only way forward is experimentation, failure, and iteration.

AI-first companies will outpace incumbents: Startups built natively with AI will have massive advantages: smaller teams, faster builds, and radically lower costs. Incumbents must actively disrupt themselves or risk irrelevance.

The simplest advice: just start: Don’t overthink it. Pick a painful, repetitive task, try to automate it, and learn by doing. Momentum beats strategy at this stage.

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

Bill Takacs

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

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

Shampa Banerjee, PhD, is a powerhouse product leader who has scaled global digital platforms from scrappy startups into billion-dollar growth engines. As EVP and Chief Product Officer of Pluto TV at Paramount, she helped drive the platform from 20 million to over 50 million monthly active users while accelerating revenue to $1 billion ahead of schedule, expanding the service across Latin America and Europe. Prior to that, Shampa led the growth of Eros Now to 150 million users across 135 countries, delivering an extraordinary 240% year-over-year subscription growth. Over the course of her career, she’s served as both CPO and CTO across venture-backed startups and Fortune 500 companies, building world-class product, engineering, and data teams from the ground up. Trained as a physicist, Shampa brings a rare ability to recognize patterns in complex systems—pairing bold product vision with disciplined experimentation. Today, she advises growth-stage startups and global enterprises on scaling impact through AI, product strategy, and global go-to-market leadership. And in this episode, we’re diving into what it takes to scale digital platforms globally, the leadership behind building billion-dollar product ecosystems, and how AI and data are shaping the future of product strategy.

THE SUMMARY

AI adoption is failing in many companies — and nobody wants to talk about it: Many organisations are blindly adding AI because leadership mandates it, not because it solves a real problem. The smarter approach is to first ask whether AI is even the right solution. If it doesn’t improve growth, retention, or economics, it’s just expensive hype layered on top of existing systems.

Understanding how AI works matters more than just using it: Treating AI like a magical tool leads to frustration when it produces imperfect results. Modern AI systems are probabilistic by nature — meaning uncertainty is built into how they work. Leaders who understand this design guardrails, human-in-the-loop processes, and better prompts instead of dismissing the technology as “wrong.”

AI should drive growth — not just cost cutting: Too many companies frame AI purely as a way to reduce headcount or operational costs. The real opportunity is using it to expand capabilities, unlock new products, and scale output. Businesses that only focus on savings risk shrinking themselves instead of multiplying their impact.

The AI transformation requires three shifts: People, Product, and Process: The biggest challenge isn’t the technology — it’s organisational change. Teams must get comfortable with uncertainty, rethink what products they should build, and redesign processes that were inherited from the manufacturing era. Companies that only upgrade tools without updating culture and workflows will stall.

The AI revolution mirrors the early internet — but at a much faster speed: Just like the dot-com era, many experiments will fail, but the underlying ideas will eventually reshape industries. Concepts like Webvan looked wrong in the early 2000s, yet later became the foundation for companies like Instacart. Today’s AI experiments may look messy, but they are laying the groundwork for tomorrow’s dominant platforms.

Technical credibility still matters for leaders: Leaders who understand the mechanics behind technology gain trust with engineering teams and make better strategic decisions. Getting hands-on — even building a small prototype — helps leaders translate between executives and technical teams and prevents unrealistic expectations about what technology can actually do.

Hands-on experimentation is the fastest way to understand AI

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/⁠