In this session you’ll learn how to save 95% of your time on test automation by leveraging breakthroughs in Generative AI and move 90%-100% of the work to the product management organization. We’ll cover how to improve SDLC to allow you to move up to 30% faster with up to 90% less bugs in production, and how Generative AI can be helpful to eliminate 98% of the effort on maintaining end-to-end tests.

Early career hiring is no new concept, and many of the building blocks for an early career program remain the same. However, with recent challenges in the form of the pandemic, tight budgets, and more prevalent remote work it can be hard to figure out whether and how to advocate for an early career program, how best to design it, and how you can support the engineers in the program. We will talk through onboarding early career folks remotely, structuring your teams for success, and sources for finding early career candidates.

The GenAI WAVE will dramatically alter organizational structures, your customers, the market, your team's roles and needs, and yours. Waiting to “see how it plays out” is simply not an option for leaders and companies that want to survive it. Why not lead it? Join Andrew Lau (not ChatGPT) as he discusses:

• The evolving roles of engineering as the toolsets change
• The changing demands of the engineering function as the industry leverages GenAI
• Case study on how CoPilot truly impacts an engineering team's productivity
• The shifts in leadership requirements and skill sets from hiring to the boardroom
• How to use experimentation and empirical data to learn what works for your org and what doesn’t
• Why it’s more important than ever to highlight and protect innovation

Transformative technology will continue to change the world, but supporting it with empirical data is essential. And even more importantly, this is all happening right now; if you aren’t paying attention, you must.

Leading engineering teams at GitHub in the fast-paced, ever-changing space of AI development while building GitHub Copilot, the first at-scale AI pair programmer used by millions of developers today, requires adaptability, technical expertise, and visionary guidance. In this fireside chat, Scott Densmore will share how GitHub is leading the charge on AI for developers, including how GitHub blends software engineering and data science to bring AI further into all areas of the GitHub Platform and how GitHub Copilot is empowering developers globally to build with AI. You’ll walk away with an understanding of how GitHub builds with AI, how AI can boost productivity amongst your engineering teams, and best practices for how you can introduce AI within your own organization to supercharge engineering teams.

Drawing on a long career working on B2C products and recent dive into B2B, we’ll take a look at differences in execution cadence, business constraints and the skills sets required for success in these two worlds.

From his early career at Disney Imagineering, Samir always tried to bring a 'full stack' mindset to solving problems. At Imagineering, the team thought about the ultimate 'story' they wanted to tell guests, and how to tell that story best via a combination of the senses: sight, sound, touch, and smell (usually not taste). Then, a small number of Imagineers -- often just 1 – would create an initial prototype. Thinking story-first and about the feeling you want to leave guests with has influenced the way Samir goes about solving problems, and forced him to think 'full stack'. In this talk, Samir will go over a few examples of full-stack problem-solving, and some frameworks you can apply to your work to help you break out of the engineering silo, and land the story with your customers.

With the recent advances in Generative AI technology and the new generation of LLMs, many products can now layer “AI” features very quickly into existing and new products to provide domain-specific ChatGPT-like experiences fairly quickly. There are countless new startups racing to market with products, while incumbents are adding GenAI features at a breakneck speed. But does that mean that there’s essentially no moat around these AI features?

While the no-moat was a prevailing perception in the early GenAI days, it’s becoming clear, at least to us at Lexion, that this is not entirely true, and that a lot of data, intellectual property and technology investments are still very much necessary to build compelling GenAI applications, especially for quality-sensitive domains. In this presentation, Emad shares his team’s experience in building and evolving one of the first GenAI applications and the first in the end-to-end contract lifecycle management space.