Sessions List

AI coding assistants have been hyped as game-changers for software engineering, but how effective are they in practice? This session provides a reality check on current AI coding tools, analyzing what works, what doesn’t, and how these tools impact software development workflows. You'll get a chance to discuss best practices, challenges, and the future trajectory of GenAI in software development with your peers and examine: > What is the current state of AI coding assistants? >How are AI coding assistants improving developer productivity? > What AI tools are effective for software development today, which ones are disappointing? > What are the future trends and implications of AI-driven software development? > What are the implications for cross-platform development?

High-performing software engineering organizations don't happen by accident—they are the result of deliberate choices in leadership, culture, process, and talent strategy. In this discussion we will examine how top technology leaders build and sustain engineering teams that consistently deliver impact at scale. >What foundational principles have been most effective in building and scaling a high-performing engineering organization? >How do you balance technical excellence with delivery speed, especially under pressure from product or business stakeholders? >What practices have you found most effective for maintaining alignment, morale, and performance across distributed or hybrid teams? >How do you invest in developing engineering leaders who can scale with the organization?

Open-source technologies are no longer just cost-saving alternatives—they're strategic assets shaping modern enterprise tech stacks. This panel explores how CTOs and engineering leaders are leveraging open-source to drive innovation, improve agility, and gain a competitive edge. >How do you evaluate the maturity and reliability of open-source projects before integrating them into your enterprise architecture? >In what ways does open-source contribute to your organization's innovation and time-to-market goals? >What are the key risks—legal, security, or operational—of adopting open-source at scale, and how do you mitigate them? >How do you balance community contribution with internal priorities when your teams depend heavily on open-source projects?

The rise of agentic AI presents both opportunities and challenges for multiplatform software development, potentially automating aspects of code generation, testing, and deployment while raising critical questions about integration, security, and the evolving role of human developers. This discussion explores how intelligent agents could reshape the development and maintenance of applications across diverse operating systems and devices. >How might agentic AI tools transform traditional workflows in multiplatform development, and which tasks are most likely to be automated first? >What are the key technical and organizational hurdles in integrating agentic AI into existing multiplatform environments and toolchains? >As AI agents take on more development tasks, how will the role and skillsets of human multiplatform developers need to evolve? >What security, privacy, and ethical risks arise from relying on agentic AI across platforms, and how can they be mitigated? >How might agentic AI enhance code reuse and consistency across platforms, and what limitations or trade-offs should developers be aware of?

Agentic AI—autonomous systems capable of initiating and executing tasks—are poised to fundamentally reshape software engineering workflows. This panel will explore how these systems are redefining developer productivity, altering team dynamics, and challenging traditional software delivery models. >How are agentic AI systems currently being integrated into engineering workflows, and what real impact are you seeing? >What types of engineering tasks are best suited for agentic AI, and which still demand human oversight? >How do agentic AI tools affect team structure, developer roles, and the skills you prioritize when hiring? >What governance, trust, or safety frameworks do you think are essential when deploying autonomous AI agents in production environments?

In an era of rapid technological change, sustaining innovation requires more than just great ideas—it demands intentional culture, systems, and leadership. This discussion explores how engineering leaders can create environments that encourage experimentation, reward learning, and adapt to evolving business and technical landscapes. >What practical strategies have you used to embed innovation into the daily routines of your engineering teams? >How do you balance the need for innovation with the pressure to deliver predictable outcomes and maintain stability? >What role does leadership play in modeling and enabling a culture of risk-taking and experimentation? >How do you identify and nurture internal champions or grassroots innovation within larger organizations?

As AI rapidly reshapes the technology landscape, engineering leaders must make bold, strategic decisions to stay ahead. Join this discussion as we explore how CTOs are navigating the AI disruption—rethinking product strategy, talent, infrastructure, and organizational design in an AI-first world. >What does it mean to adopt an “AI-first” strategy, and how are you aligning your organization around that vision? >How are you evaluating which parts of your tech stack or workflows to reinvent with AI—and which to leave untouched? >What new capabilities or roles are becoming essential on engineering teams as AI becomes more central to product development? >How do you manage the ethical, security, and governance challenges that come with embedding AI deeply into your systems?

This discussion explores how engineering organizations are evolving in response to the dual forces of AI and post-COVID workplace shifts. From hiring practices to team composition, we’ll examine the growing tension between specialization and generalization, the changing value of traditional roles, and what it takes to build high-performing teams in this new era. Let's discuss: >How have AI advancements changed the way engineering teams are structured and scaled? >Are generalists becoming more valuable than specialists in today’s AI-augmented teams? >What skills are engineering leaders prioritizing now that were less emphasized pre-COVID or pre-AI? >Has the shift to remote or hybrid work flattened team hierarchies or changed the role of architectural leadership?

As mobile applications become critical to enterprise success, CTOs must find scalable ways to ensure app reliability, performance, and agility. In this discussion, we'll explore how AI-powered mobile observability platforms can transform Android app maintenance—enabling proactive issue detection, faster resolution, and improved user satisfaction without ballooning operational overhead. >How can AI-driven observability reduce the operational burden traditionally associated with Android app maintenance? >What are the key metrics CTOs should monitor to evaluate the effectiveness of mobile observability tools? >How does early detection of performance and stability issues through AI impact business outcomes like retention and revenue? >What challenges do enterprise teams face when integrating observability platforms, and how can they be overcome at scale?

In a recent roundtable of engineering leaders, when asked how GenAI will ultimately shape the future of their work, most simply looked around the room, waiting for others to answer. As one CTO put it, “If a room full of engineering leaders isn't proactively figuring this out and shaping where we're headed, we're totally doomed.” This discussion will explore how tech leaders can move beyond waiting and seeing to proactively shaping their organizations alongside the GenAI revolution. >At a time of such profound possibility and so many uncertain outcomes - have we stopped to think about what outcomes we even want as an industry? >How can CTOs influence the cultural, ethical, and operational frameworks around GenAI adoption in a way that reflects their values and long-term vision? >What does a “proactive” response to GenAI look like in practice—inside your teams, your product strategy, and the wider industry? >Are current industry forums and standards bodies doing enough to help shape GenAI’s development, or do we need new mechanisms for engineering leaders to collaborate and lead?

CTOs are under intense pressure to define and deliver an AI product strategy that satisfies boards, investors, and customers—without derailing existing roadmaps or overcommitting to unproven tech. From hiring the right people to retraining existing teams, from communicating LLM limitations to dealing with relentless change, this panel will dive into the real-world challenges CTOs face when trying to build sustainable, value-driven AI strategies. •What are the first concrete steps a CTO should take when asked to "define our AI strategy"? •With the velocity of change in AI, how do you make roadmap decisions that won’t be obsolete in six months? •How do you explain the current capabilities and limitations of technologies like LLMs to non-technical stakeholders—without overselling or underselling? •How do you ensure high quality standards in a product that has LLMs integrated into it? •How do you distinguish between true AI product value versus features that are just hype or investor bait? •What qualities do you look for when hiring AI talent, and how do you compete in such a crowded market?