Sessions1 List

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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 technology leaders can 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?

As mobile applications grow in complexity, performance and reliability have become critical priorities for engineering leaders. By leveraging deep, contextual performance data and AI-driven analysis, teams can detect crashes and bottlenecks early and even prevent issues such as ANRs by identifying patterns before they escalate. This round table will explore practical strategies for reducing downtime and accelerating time-to-fix across Kotlin-based apps, including those adopting modern architectures like Kotlin Multiplatform (KMP) and Composable Multiplatform (CMP). We will also discuss the emerging role of integrating AI assistance directly into development environments, enabling developers to act on actionable recommendations and resolve performance issues faster while improving overall user experience.

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?

As engineering teams face rising pressure to deliver faster with the aid of AI tools, leaders are grappling with a growing risk of burnout and cultural fragmentation. While AI accelerates output, it also intensifies the “always-on” mindset—leaving little space for reflection, rest, or sustainable deep work. At the same time, distributed and asynchronous team structures—now the norm—often erode cohesion, alignment, and psychological safety. In this roundtable, we’ll explore how technology leaders can create healthy, resilient team cultures without sacrificing speed, innovation, or trust.

How are your teams feeling right now? Are you seeing signs of burnout, disengagement, or morale dips—and how do you detect them in remote settings?

Has AI adoption raised expectations from leadership or the business side around delivery speed or productivity? How are you managing that pressure?

What rituals or practices have actually worked to maintain team cohesion and culture in a distributed/hybrid world? What hasn’t worked?

How do you balance the need for deep work vs. always being “available” in an async environment? Are there policies or norms you’ve found effective?

What does “psychological safety” mean in your engineering org—and how are you actively fostering it today?

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Many technology leaders assume their hiring function is operational, something that supports delivery, but does not define it.
This is a costly misjudgement. In the context of accelerated AI adoption, hiring decisions have a direct impact on delivery speed, team resilience, and organisational adaptability.

This session, led by Principal Recruiter Andreea Lungulescu, who designed and deployed the senior engineering hiring systems at Wayfair and Zalando, advised early-stage AI startups on end-to-end recruitment infrastructure, and leads a community of over 4,000 talent professionals across DACH, offers a direct briefing to CTOs and senior engineering leaders on where their current hiring systems are introducing risk and friction.

Key Topics:
The cost of delay introduced by unclear scorecards, vague role definitions, and untrained interview loops
How organisational blind spots (including over-indexing on external hiring and under-utilising internal capability) weaken delivery capacity
Using first-principles thinking to rebuild hiring loops aligned to business outcomes
How to evaluate hiring through the lens of AI leverage, team design, and talent market assessment


Folks will leave with a practical framework to:
Assess the current maturity of their hiring systems
Engage internal recruitment functions as strategic partners, not service operatives
Reduce attrition risk and dependency on reactive headcount growth
Prepare their engineering teams for hiring in AI-driven environments, where adaptability and delivery pace will be the dominant success metrics


This is a session for leaders who understand that flawed hiring is a barrier to scale

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?

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?

Many engineering orgs are powered by “hero culture”—where key individuals save the day through deep knowledge, quick fixes, or personal sacrifice. While this may yield short-term wins, it undermines long-term scalability, reliability, and team growth. This topic challenges leaders to shift from heroics to systems thinking: building resilient teams, automating repetitive work, distributing knowledge, and designing processes that don’t rely on luck or legends. It’s especially relevant in an era where attrition, burnout, and talent mobility make over-reliance on individuals a business risk.

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In today’s competitive and fast-moving tech landscape, continuous innovation is no longer optional—but the reality is that few engineering leaders have the luxury of unlimited resources, greenfield systems, or infinite risk tolerance. This roundtable will explore how CTOs and senior technology leaders can cultivate a culture of rapid, sustainable innovation while operating within real-world constraints: tight budgets, legacy systems, and the need for operational stability.

We’ll discuss how to embed innovation into daily engineering practice without disrupting business-critical systems or burning out your team. Help us answer questions like:

How have you successfully fostered innovation in environments constrained by legacy technology or limited resources?

What practical methods have you used to encourage experimentation and learning without jeopardizing system stability?

How do you prioritize innovation work alongside critical roadmap and operational demands?

What role does architecture play in enabling innovation around or on top of legacy systems?

How do you keep engineering teams motivated and creatively engaged when innovation must coexist with technical debt or tight delivery cycles?

How do you identify and support grassroots innovation from within your teams?

There's immense excitement around AI and ML, yet practical, real-world examples of their tangible impact on businesses and customer value are still rare. In this interactive roundtable, engineering leaders will share firsthand experiences of designing, prototyping, and scaling AI-enhanced features into production. Join us to explore: Real-world use cases where AI significantly improved customer experiences and created measurable business value. How strategic technology and architecture decisions influence the success and scalability of AI initiatives. Proven tooling, infrastructure, and processes that have stood the test of real-world implementation. Valuable insights and candid discussions about lessons learned, including challenges faced and failures overcome. Use cases discussed will include generative AI UX and predictive intelligence in telecom, hyper-personalization in food delivery, and Netflix’s approach to operationalising AI/ML with Metaflow. Bring your insights and questions to engage deeply in this exchange of experiences and practical knowledge.

Automation can save time—but only if you're automating the right things. Engineers sometimes get caught automating processes without fully understanding their real business impact. This roundtable is about exploring exactly that: how to connect engineering efforts directly to business value, using automation and no-code tools to truly benefit teams.

We'll openly share experiences, successes, and lessons learned about:

How do you identify which processes genuinely deserve automation—and which don't?

What happens when engineers automate without clear business alignment?

How can no-code tools help bridge the gap between technical capability and business goals?

How do you manage change effectively, keeping both your teams and processes aligned?

What unexpected challenges have you faced when automating business workflows?

Where do AI and no-code solutions fit into your overall automation strategy?

The traditional boundaries between CTO and CPO are blurring as AI becomes not just a tool for acceleration, but the product itself. CTOs are increasingly responsible for product strategy, user experience, and go-to-market alignment—roles historically outside their domain. This roundtable explores how technology leaders are navigating this shift, building cross-functional muscle, and redefining what it means to lead engineering in an AI-first world.
1. How has the rise of AI products changed the day-to-day responsibilities of the CTO?
2. Where do you see the clearest overlap—or conflict—between CTO and CPO responsibilities in your organization?
3. How do you stay credible as a technical leader while also being expected to shape product vision and market fit?
4. Are new org structures or team models needed to support this convergence of roles?
5. What skills or mindsets should the next generation of engineering leaders cultivate to thrive in this new paradigm?
6. How is the growing responsibility for business impact, ethics, or product outcomes reshaping the CTO role – especially when AI is the product?
7. What was one mindset shift or leadership habit that helped you adapt to this new CTO reality?

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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?

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?

Explore how engineering leaders can harness metrics to boost productivity, align teams with business goals, and improve decision-making. This roundtable will uncover practical strategies to define, track, and apply engineering metrics that inspire meaningful outcomes rather than vanity measures—while also examining common pitfalls and mistakes leaders should avoid when designing their metrics approach.
How can you leverage engineering metrics to drive productivity without creating unintended consequences?

How can leaders align engineering metrics with business outcomes while keeping them relevant to developers?

How can metrics be used to shape organizational culture and elevate the developer experience?

How do you turn engineering metrics into actionable insights that inform decisions at every level?

How should your approach to engineering metrics evolve as teams and organizations scale?

What have you found to be the most effective way to measure and improve developer onboarding time?

How do you balance tracking delivery speed (e.g., lead time, cycle time) with maintaining code quality and team health?

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