SDLCAI 2026 / From the recordings
Revisit the ideas from all ten talks. Explore timestamped key points, follow the full recordings and share a takeaway card.
Ten talks. Fifty key points.
Editorial summaries paraphrase the speakers’ arguments, with links to the passages they draw on. Each talk includes landscape and portrait graphics with speaker photos, post copy and image alt text.
Explore the programme & materialsDownload all 20 graphics & post copy (ZIP)10 talks · 50 timestamped key points
01
35:50 recording
Mo Khazali
Mo Khazali uses automotive and factory-history analogies to argue that AI adoption needs a redesigned delivery system. Shared platforms, daily learning, early quality checks and customer-outcome measures matter more than token consumption or code output alone.
Read summary02
32:09 recording
Ohans Emmanuel
Ohans Emmanuel describes a connected delivery workflow that treats agent output as something to verify. Shared intent, early plan review, paired models, visual system understanding, independent QA and production feedback form a loop from deciding what to build through learning from what ships.
Read summary03
25:30 recording
Jenni Kylmäkoski
Jenni Kylmäkoski explains why leaders should build and use AI tools themselves. Her reporting tools and an unintended Jira change reveal practical lessons about onboarding, permissions, observability, learning time and maintaining shared AI assets as products.
Read summary04
22:39 recording
Tapio Pitkäranta
Tapio Pitkäranta presents agentic probes as tangible experiments that help people clarify problems and explore solutions. Examples from a probe factory and a healthcare process exercise show how faster implementation can move the bottleneck toward ideas, orchestration and validation.
Read summary05
19:22 recording
Joongi Shin
Joongi Shin studies generative UI for people who want to complete a task, rather than design an interface. His comparison of prompts, editable subtask representations and follow-up surveys shows why helping users form and express their intentions matters alongside generating working software.
Read summary06
20:47 recording
Jussi Hacklin
Jussi Hacklin describes how Elisa supports AI adoption within regulatory and customer-contract constraints. Legal collaboration, clear leadership expectations, practical guidance, shared tooling and bounded experiments form the basis for adoption across the software lifecycle.
Read summary07
22:15 recording
Viljami Kuosmanen
Viljami Kuosmanen describes epilot's experiment giving non-engineers coding agents through Slack. Shared documentation, preview links and engineer review enabled contributions across product, design, support and QA, while a deployment near miss exposed permission gaps and review became a bottleneck.
Read summary08
28:10 recording
Sini Tistelgrén
Sini Tistelgrén describes an AI factory as a delivery system built on intent, architecture, security, independent verification, ownership and operational feedback. She explains why a quick prototype still needs a production path, and proposes bounded loops with executable controls and evidence.
Read summary09
20:55 recording
Zak Allal
Zak Allal argues that lowering the cost of repeating experiments can change scientific discovery. He describes a clinical research system built with supercomputing, Rust, Julia and C++, and presents a proposed continuous hearing atlas as an example of questioning the assumptions behind existing categories.
Read summary10
30:54 recording
Muhammad Waseem
Muhammad Waseem argues that AI makes code generation easier while understanding, integration and ownership remain difficult. He examines repository context, decision rationale and agent-facing applications, then proposes deterministic checks, human judgement and gates for safety, architectural fit and measurable value.
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