A (3:24)

Wave.

C (3:26)

So the current organization that I'm working with, AI, is doing wonders. So there are few teams, as I said, are delivering way faster than how they were delivering before. But there are other teams that are struggling. They're kind of losing the project. And our company had to downsize a few teams, right? So these are the challenges that we are facing. But you know, we as Scrum masters, I would say we are already dealing or used to deal with complexity. So stakeholders, organizations, they're constantly changing and we often, most of the times we don't have a roadmap. But what gets us through is curiosity or small experiment, inspection and adaption. I realized why not apply the same mindset to AI? So instead of seeing AI as this huge wave or an intimidating beast, we see it as a small tool and we start experimenting with it. We start asking questions like what is one thing that my team is struggling with and how AI can help resolve this issue. I did try out a few experiments, so let me just talk about it. In one of the teams that I was working with, the product owner had this habit of jotting down one liner user stories in Excel. So whenever he used to have a conversation with the stakeholders and they used to give him the requirements, he just used to go ahead and write this in Excel. And then he was taking a lot of time to convert these one liners into actual user stories in jira. And this was a repetitive task and this was taking a lot of time out of his bucket. So here I leveraged Miro AI's automation. So Mira AI has this plugin wherein we can create, automate and create user stories in JIRA directly. And this little automation saved him a lot of time, every sprint. And more importantly, it kind of shifted the team's perception of me. So I wasn't just the more process police here. I was someone who was making their lives easier. And this also helped to build the trust within the team, right? And it also created a buzz and other teams started noticing and asking, how did you do that? And that kind of created a ripple effect within my organization.

B (5:53)

So what I hear you describe is this perspective change, right? Like from seeing it as a wave, see it as a tool, then look for opportunities to apply it, then run an experiment. Like, hey, you know, the PO is doing this. This is taking a lot of time. Why don't I help the PO sorting this out? Like, in this particular case, it makes perfect sense. But one of the things that I want to explore is this, okay, this difference between feeling that a change is an obstacle to transforming that into, wait a minute, what if I look at it as an opportunity? What could I build? A different relationship, A different process, A different tool or automation? Like, in this case, that would become an opportunity, something that would be helpful for the team. And that, of course, as you said, then people started looking at you differently, of course, because of what you did, specifically that particular tool. But I think it's also important to highlight, not only because of that, but because you were taking advantage of something that some of them were already considering. More of a threat than a help, Right, Exactly.

C (7:04)

And one thing what I know is that nobody rejects change. So if something makes their lives easier, they embrace the change. So this is one of the learnings that I got through that experiment.

B (7:17)

Actually, that's a very good point. And that's a very useful question to ask ourselves. If there's something that is changing, how can I take this and help somebody's life be easier? Right? Like, if we can do that, then we have the opportunity to make that life, that person's life easier, but also the opportunity to explore the value of that particular idea. And I'm sure you have discussed with teams that started asking you, hey, what could we do with this? Can you tell us what you did? And so on, and then they come up with different uses that you didn't think about, right? And this, it's not only that a good idea expands and spreads, but also that a good idea is built upon, and somebody else comes up with even something we didn't even think about.

C (8:06)

Just to create that conversation and keep that conversation going. So that was the whole idea.

B (8:13)

And when we think about Wednesday used to be the change question here on the podcast. Well, when you think about change, this makes perfect sense, right? Because what we are trying to do is not to push change, but rather recognizing that something has changed. Like in this case, the opportunity of using AI, Then what can we do to take advantage of that or at least make somebody's life easier? And this is how change really spreads, right? And when we think about coaching, we can help our teams and the team members to think about things that way, right? Like when we ask questions, I wonder what we could build that would help us with that tool that just came in, like in this case AI or Miro or whatever that would be. The questions that we ask also help people realize what are the possibilities?

C (9:06)

Exactly. I would suggest look for friction within your own team. Notice when people are doing repetitive or low value tasks like copying data, say rewriting notes or just documenting meetings. These are like gold mines for AI experiment. Start small and then create something big.

B (9:25)

Yeah, start small and then create something big. Very well said. Thank you for that inspiring story, Sean.

C (9:32)

I'm glad I could share the story.

B (9:36)

Great stuff and great story.

C (9:41)

I would say that there are a couple of more tools that come in very handy, something that I have tried. One of them is Perplexity AI. Perplexity AI have this feature called Labs. One use case that I would like to mention here is I'm part of a central Agile team here and we had recently rolled out some standard matrices across the organization. Committed versus completed metrics, scope change metrics and things like that. We rolled out with a great intent that it creates more transparency. But then what happened was we had the data there, but we didn't have the insights. The teams were not sure how to interpret this data. We just have around four to five coaches within the organization and we can't go to. We had around 200 plus teams. We can't go to each of these teams and explain. Perplexity Lab helped me to develop a system wherein teams could just take a screenshot of their dashboard and add it there.

B (10:42)

Right?

C (10:42)

And AI would give them the insight about how their graph should look like, what are the industry standards according to our organization and give them valuable insights so they can use this data point as conversation starters in their retrospectives or in their meetings. This is one other experiment that I had tried and I think other Scrum masters can also give this a try.

B (11:06)

Thank you for sharing that one more story.

A (11:11)

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B (11:16)

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B (13:00)

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A (13:18)

Carry.