Episode Details

BONUS: Real-Time Research for Change Leaders Using AI

AI is changing how Scrum Masters, Agile coaches, and change leaders make sense of organizations. In this BONUS episode, Ari-Pekka Skarp shares how he uses AI as a research assistant, a workspace partner, and a way to bring qualitative research into day-to-day change leadership.

From Curiosity to Research Assistant

"It felt like having a research assistant."

Ari-Pekka's first serious AI use did not start in his full-time change leadership role. It started with side projects, research, psychotherapy-related work, and watching his son build websites with Lovable. That curiosity became practical when Ari-Pekka began using AI to support his PhD research. Instead of spending a full day scanning articles, he could find relevant sources, generate useful summaries, and decide where to go deeper in about half an hour. The key was not that AI replaced his judgment. Because he already knew the research area, he could evaluate the quality of what the AI returned and use it as a complement to his own expertise.

AI Expands the Research Surface

"With the help of AI, I could actually make a little more of these sidetracks."

One of the biggest changes Ari-Pekka noticed was that AI made side paths cheaper to explore. In research and change work, we often ignore interesting but uncertain threads because time and attention are limited. AI gave him a way to examine adjacent ideas without losing the main direction. At the same time, he points out a real risk: AI can guide us toward something too quickly. Sometimes a slower, more intuitive decision is more aligned with the deeper research goal. For Scrum Masters and coaches, that is an important distinction: AI can widen the field of inquiry, but it should not quietly take over the choice of where to look next.

Workspaces for Change Leadership

"I can create this kind of bird's eye perspective of the organization or the given project quite quickly."

Ari-Pekka describes a major shift when he started using Visual Studio Code and AI agents as a workspace for knowledge work, not only for software. In his workspaces, he organizes goals, themes, source material, Jira data, Confluence material, meeting notes, and other organizational signals so that AI can help him analyze and visualize the big picture. This gives him a practical way to represent relationships, dependencies, open threads, and organizational responses to interventions. For change leaders, the point is not the tool itself. The deeper idea is to structure the work so AI can help process the data while the human still owns the interpretation.

Bringing Qualitative Research Into Daily Change Work

"I try to form a hypothesis of what I find from the data, and then I see whether those hypotheses are correct or not."

Ari-Pekka uses his qualitative research background to teach AI how to process organizational data in a consistent way. He is not just asking for a summary. He is thinking like a researcher: what is the question, what data is available, what method should be used, and what output