Build What’s Next: Digital Product Perspectives

AI Field Guide: The Missing Middle - How to Build an End-to-End AI

In this episode of Build What's Next, Theo Munoz, Miguel Ribeiro, and Natan Szczepaniak discuss Machine Learning Operations (MLOps) and why an estimated 80% of ML models built in notebooks never make it to production. The hosts argue that the failures stem less from technology and more from organizational issues like a lack of clear ownership, insufficient investment in data engineering, and poor data foundations. Learn how standardization, shared ownership between business and engineering, and robust model governance are crucial to scaling AI safely, especially as the industry shifts towards Gen AI. To find more episodes, visit method.com/insights/podcasts/ [http://method.com/insights/podcasts/] Episode Resources:  Method.com [http://method.com/] Theo Munoz on Linked-In: /in/theo-munoz-090a88151/ [https://www.linkedin.com/in/theo-munoz-090a88151/] Miguel Ribeiro on Linked-In: /in/miguel-ribeiro-3439328a/ [https://www.linkedin.com/in/miguel-ribeiro-3439328a/] Natan Szczepaniak on Linked-In: /in/natan-sz/ [https://www.linkedin.com/in/natan-sz/]

AI Field Guide: How AI is Reshaping the Roles of Design and Engineering

AI is reshaping the roles of design and engineering, emphasizing collaboration and how models can accelerate workflows without sacrificing quality. This week’s episode explores how designers like David Shackelford, Associate Director of Product Design for Method, use tools like Perplexity, UX Pilot, and Figma Make for rapid exploration, while Paul Rowe, Principal Software Engineer at Method, discusses the engineering reality check with tools like Claude Code and Google’s Anti-Gravity IDE. The key takeaway is a practical playbook for speed with guardrails, affirming that human judgment, taste, and accountability remain the multiplier. The Methodites cover where AI currently shines—producing accurate results for smaller, well-defined tasks—and where it struggles, often leading to code bloat and confusion with vague prompts, especially within massive enterprise codebases. Despite the excitement around "vibe coding," they stress that the core development workflow remains "build, validate, iterate," with human review being more critical than ever. Paul and David conclude that while AI is an efficiency tool that can blur traditional departmental lines and shift where time is spent, strategic roadmapping, quality assurance (QA), and deep, expert-level skill sets in both design and engineering are still indispensable. To find more episodes, visit method.com/insights/podcasts/ [http://method.com/insights/podcasts/] Episode Resources:  Method.com [http://method.com] David Shackleford on Linked-In: /in/davidzshackelford/ [https://www.linkedin.com/in/davidzshackelford/] Paul Rowe on Linked-In: /in/paulcullenrowe/ [https://www.linkedin.com/in/paulcullenrowe/]

How To Build A Scalable, Standards-Aligned Ecosystem That Teachers Actually Use

Travis Barrs of Discovery Education discusses how K–12 is shifting from tool access to learning impact, focusing on building scalable, coherent learning platforms. This involves budget realities, teacher workloads, and consolidating tool sprawl. Key points include the return of core curriculum funding, the necessity of standards alignment, and balancing Discovery's diverse brands (DreamBox Learning, Mystery Science, etc.). The underlying architecture emphasizes seamless identity/access, roster sync, LMS integrations, and cross-product analytics for targeted student support. Organizational design uses a "quartet" model—product, design, engineering, and curriculum—to embed pedagogy and rigor from the start. AI implementation follows a measured roadmap, prioritizing teacher workflows (lesson planning, assessment, recommendations) before student-facing tools with strong guardrails. Internally, AI aids in prototyping, documentation, sales, RFPs, contract review, and curriculum drafting, all under strict governance. The future is focused on hyperpersonalization, workload-reducing classroom assistants, and provable efficacy. To find more episodes, visit method.com/insights/podcasts/ [http://method.com/insights/podcasts/] Episode Resources:  Method.com [http://method.com] Travis Barrs on Linked-In: /in/travisbarrs/ [https://www.linkedin.com/in/travisbarrs/] Carol Rego on Linked-In: /in/carol-rego/ [https://www.linkedin.com/in/carol-rego/] More episodes: method.com/insights/podcasts/ [http://method.com/insights/podcasts/]

AI in Software Development: Designing & Delivering Real ROI

Forget the AI hype and focus on real ROI in the Software Development Lifecycle (SDLC). This episode features Method's Jason Rome and Raj Sethi with ISG experts Ashwin Gaidhani and Tapati Bandopadhya, who trace a clear path from AI tools to measurable outcomes. They argue that coding speed isn't the bottleneck—specs, testing, pipelines, and change management are. We break down the mechanics of ROI: how specification elaboration unlocks downstream gains, the decision between human-in-the-loop vs. agent-in-the-loop, and integrating GenAI into CI/CD. We also discuss cost, risk-adjusted ROI (F1 score plus risk), and practical wins for legacy modernization, like AI-driven requirement discovery and service-oriented modernization. The conversation also introduces 'stability lanes' and covers what leaders get wrong (tooling without process change, microservices by default), advocating instead for platform thinking and a conductor's mindset to orchestrate micro-tasks for real lift. Episode Resources: Jason Rome on LinkedIn: /jason-rom-275b2014 [https://www.linkedin.com/in/jason-rome-275b2014/] Raj Sethi on LinkedIn: in/rajsethi [https://www.linkedin.com/in/rajsethi/] Ashwin Gaidhani on LinkedIn: in/ashwin-gaidhani [https://www.linkedin.com/in/ashwin-gaidhani/?originalSubdomain=in] Tapati Bandopadhya on LinkedIn: in/tapatibandopadhyay [https://www.linkedin.com/in/tapatibandopadhyay/] Method Website: method.com [http://method.com] GlobalLogic Website: globallogic.com [https://globallogic.com/] ISG Website: isg-one.com [https://isg-one.com/]

Designing Simpler Products With Smarter AI

The most valuable features in your product might be hiding in plain sight. We sit down with design leader Andy Vitale to unpack how AI can strip away clutter, surface what matters, and move users from intent to outcome without the scavenger hunt. From dense banking apps to consumer software, we break down a pragmatic path: use agentic assistants to handle administrative tasks, boost findability with smarter search, and free up the interface to highlight real value. We dive into personalization that actually delivers. Instead of broad segments, AI can synthesize behavior, preferences, and context in real time to shape the experience—while also making existing configuration options easier to discover. Andy shares how teams can pair analytics, NPS, and session data with AI-driven synthesis to spot drop-offs faster and focus roadmaps on the true unmet needs. We also explore the trust equation: data privacy, benchmark accuracy, and the difference between AI as research moderator, synthesizer, or simulated participant. Looking ahead, we imagine agentic design systems that assemble the right UI for the moment, judgment-ready data visualizations that compress complexity, and workflow views that tell you what’s blocked, what’s yours, and what’s next. AI becomes a co-author for high performers, speeding concept validation upstream while tightening execution downstream—without losing the human taste that makes products resonate. We close with hopes and fears: faster solutions and better confidence on one side; sameness and loss of craft on the other. If you care about building simpler, smarter, and more humane products with AI, this conversation will sharpen your approach. Enjoyed the episode? Subscribe, share with a teammate who needs it, and leave a quick review to help others discover the show. Episode Resources: Michael Lewandowski on LinkedIn: in/michael-lewandowski-66769b11 [https://www.linkedin.com/in/michael-lewandowski-66769b11] Andy Vitale on LinkedIn: in/andyvitale [https://www.linkedin.com/in/andyvitale] Method Website: method.com [http://method.com] Andy Vitale Website: andyvitale.com [http://andyvitale.com]