Enterprise AI Summit

Most AI success stories are based on simple greenfield projects with few dependencies and fast feedback loops. That's not the world most enterprise developers live in. Brownfield applications are complex, built piecemeal over years. Developer experiences are full of bottlenecks - slow and inaccurate feedback loops, incomplete documentation, outdated frameworks. These are problems that human developers make up for. There is now another customer of that same experience: the AI Agent. Every gap developers learned to live with becomes a hurdle the agent must overcome. To make our platforms ready for AI we have to close those gaps.

This talk draws on two years working with enterprise teams moving from tactical AI tooling adoption to rethinking their team workflows to be AI Native. It covers practical techniques for assessing your platform's AI Readiness. I'll walk through the most common issues teams find, how shifting bottlenecks change your investment priorities, and what it actually takes to build a platform experience that works for both your developers' new role and your AI agents.

Why Cost, Scale, and Architecture Matter More Than Model Brilliance

AI is revolutionizing enterprise work, yet many orgs struggle with the complex economic factors underpinning it. As workloads intensify and expand, the expense associated with leveraging advanced AI models escalates quickly. As AI becomes a staple in everyday operations, token consumption is outpacing the capabilities of traditional architectures, straining existing systems.

This session will unveil Big‑T Notation, a pragmatic framework designed to equip engineers and decision-makers with a fresh perspective on token management. By positioning tokens as the fundamental metric for both cost control and scalability, the talk draws upon established engineering principles to clarify the role tokens play within AI ecosystems. Attendees will explore how choices in model selection and prompt design directly influence financial outcomes, and discover how mechanisms like credit-based pricing can obscure genuine opportunities for system optimization.
The audience will leave with a framework for assessing their AI workloads, actionable methods to boost token efficiency, and practical advice for building resilient architectures that support sustainable growth. Ultimately, the session aims to empower organizations to make strategic, cost-effective decisions, ensuring they scale responsibly and unlock maximum value from their AI investments as the technology continues to accelerate.

Key takeaways:

• Tokens are the true currency of enterprise AI.
• Efficiency is intentional.
• Model choice is an architectural decision.
• Prompt structure impacts cost as much as model selection.
• Caching is a design principle.
• Opaque abstractions hide cost and block meaningful optimization.
• 10x organizations won’t use fewer AI tokens, they’ll use them better.

AI is quickly becoming a key pillar across many enterprises’ priorities. Here, we share how scaling inference-time compute can support product teams with the changing AI landscape, the advancements in model capabilities, and our drive for responsible innovation.

Vibe coding is unlocking something your organization hasn't seen in years: margin. Suddenly, the backlog is clearing, the debt is shrinking, and the CFO is paying attention. That's exactly when it gets dangerous.
The biggest risk of AI-driven productivity isn't that it fails — it's that it succeeds, and leadership harvests every gain straight to the bottom line. No reinvestment. No reimagination. Just a faster race to irrelevance.
In this session, Ryan Martens and Melissa Reeve lay out the fork in the road every technical leader is facing right now. Drawing on the Hyperadaptive framework and real-world case studies from companies that got this right — and one famous one that didn't — they'll show you how to turn a productivity surplus into a strategic transformation.
You'll leave with a map of the five stages to AI-native, a model for building your organization's grassroots AI movement, and one very specific thing to do on Monday morning. This isn't about doom. It's about not wasting the best moment your career has handed you.

For decades, organisations have been atomising the software engineering role into ever-narrower specialisations. Product owners took requirements, architects took design, quality engineers took testing, platform engineers took operations. What remained for many software engineers was writing code - and with generative AI, that's now the most automatable task of all.

AI adoption among engineers is high. But the expected returns aren't materialising evenly, and measuring activity isn't telling us why. My Masters research, surveying professional software engineers across 28 countries, surfaced an unexpected finding about what actually predicts how engineers experience this shift - and it isn't something that shows up on a dashboard.

This talk connects that finding with twenty years of lived experience across startups and large enterprises to make a case that the way we've defined the software engineering role may be shaping the returns we're getting from AI. The good news is that systems can be redesigned.