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Engineering at Scale

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Conversations with engineering leaders at the world's largest and most innovative enterprises on shipping software fast, safely, and at scale in the age of AI. Brought to you GetUnleash.io

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  • 2 episodes
  • Avg 45 min
  • English

Episodes 2

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  • Thursday · 47 min

    Ship more or cut headcount: the question engineering leaders are avoiding

    Kevin Hill has spent his career inside systems that don't forgive mistakes. From running Windows Error Reporting at Microsoft, where a single conflicting data point triggered an email from the president of Windows to two engineers at once, to leading engineering at Salesforce, where global enterprise infrastructure runs 24/7. He has built a specific point of view on what scale actually demands from leaders, and it's not what most engineering orgs are currently optimizing for. In this episode with Egil, Kevin gets into why the real bottleneck in AI-assisted development isn't code generation, it's review and production safety. He also shares how he's structuring teams, building AI adoption from the peer level up, and why the identity crisis hitting engineers and middle managers right now is the conversation most leaders are avoiding. Topics discussed: Spec-driven development as a forcing function for consistent AI output quality Why middle management identity is the most underdiscussed pressure point in AI transitions The three-tier adoption model and why the middle 60-70% is where leaders should focus AI champions at team level as a structured peer-to-peer learning mechanism Why reviewing and safely shipping AI-generated code is now the core engineering bottleneck Shifting team composition from skills-based to product and value-based structures Ship more roadmap or cut headcount: the question every enterprise engineering leader is actually wrestling with How the value of "right skills" is shifting away from language expertise toward architecture and engineering judgment

  • August 13 · 42 min

    Why regulated enterprises can't skip AI governance

    Kevin Trilli has spent multiple decades in product, founded a zero-to-one company where product and technology were one job by necessity, and now holds the combined Chief Product and Technology Officer role at FIS Originations. He comes to this conversation with a specific argument: the bottleneck in AI-accelerated product development is no longer writing code. It's everything downstream of it. His framing is direct. Slapping an agent on top of a legacy platform doesn't eliminate tech debt, it just exposes it faster. Customers in B2B, especially regulated industries, can't consume features at the pace you can now generate them. And if your organization hasn't built a proprietary knowledge pipeline, your AI tooling is running on generic context and producing generic output. Kevin walks through how he's restructuring the product and engineering process at each stage, where he draws a hard line on not outsourcing strategy to AI, and what governance for agentic products actually looks like when your customers get audited. Topics discussed: Why platform limitations don't disappear when you add an agentic layer on top Using AI as a signal aggregator for strategy without letting it become a "coin-operated strategy machine" Building a proprietary knowledge pipeline as the prerequisite for any intelligent tooling Customer consumption velocity as the real constraint on B2B product development speed Governance and compliance requirements for agentic products in regulated enterprise markets Why the strongest argument for top-down AI mandates is framing it as a career investment, not a company directive Why AI fluency for senior leaders means being personally hands-on, not directing from the outside Making customer and market context queryable by anyone in the org, so engineers don't need to be in the room to know what to build

Showing 1–2 of 2 episodes