AI Only Helps When You Know Which Hill to Climb

AI Only Helps When You Know Which Hill to Climb

AI is everywhere, yet most teams still feel stuck between exciting demos and messy reality. Jason Rome sits down with Jon Webster, Chief Operating Officer at CPP Investments, to pressure test what has truly changed since their last conversation and what has not. We talk candidly about why generative AI adoption is starting to look like every other enterprise technology rollout: uneven, political, constrained by governance, and full of “we bought the licenses but we do not have the use cases” moments. We dig into the economics behind the hype: token pricing, subsidised plans, and why clear price signals matter if you want real ROI from enterprise AI. When AI feels cheap, sprawl is rational. When prices rise, leaders have to prioritise, measure outcomes, and decide where AI belongs in the operating model. From there we explore the human risks and skills that get exposed fast, including cognitive load, over-reliance, and the growing divide between people with strong mental models and those who skip straight to prompting. The conversation also goes deep on practical ways to work better: owning your outline before you generate, using AI as an adversary to challenge your thinking, and borrowing frameworks from great strategy writing to choose the right “hills to climb.” We close with predictions on where the next nine months may go, from more disciplined optimisation to shifts in SaaS, systems of record versus systems of action, and the leadership balance between IQ gains and EQ and empathy. If you found this useful, subscribe so you do not miss the follow-up, share it with a teammate who is wrestling with AI adoption, and leave a review with the most valuable AI habit you have learned so far. Jason Rome on LinkedIn: /jason-rom-275b2014 [https://www.linkedin.com/in/jason-rome-275b2014/] Jon Webster on LinkedIn: /in/jrwebster [https://www.linkedin.com/in/jrwebster/] Method Website: method.com [http://method.com/] CPP Investments Website: cppinvestments.com [http://cppinvestments.com]

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/]