Is AI underdelivering? Or are we asking the wrong questions? This episode breaks down what actually leads to business ROI with AI (and no, it’s not more automation).

Overview

What if AI isn’t the silver bullet—yet—but the bottleneck is human, not technical?

In this episode, Brian Milner chats with Evan Leybourn and Christopher Morales of the Business Agility Institute about their latest research on how organizations are really using AI, what’s working (and what’s wildly overhyped), and why your success might hinge more on your culture than your code.

References and resources mentioned in the show:

Evan Leybourn

Christopher Morales

Business Agility Institute

From Constraints to Capabilities Report

Delphi Method

#93: The Rise of Human Skills and Agile Acumen with Evan Leybourn

#82: The Intersection of AI and Agile with Emilia Breton

#117: How AI and Automation Are Redefining Success for Developers with Lance Dacy

AI Practice Prompts For Scrum Masters

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This episode’s presenters are:

Brian Milner is SVP of coaching and training at Mountain Goat Software. He's passionate about making a difference in people's day-to-day work, influenced by his own experience of transitioning to Scrum and seeing improvements in work/life balance, honesty, respect, and the quality of work.

Evan Leybourn is the co-founder of the Business Agility Institute and author of Directing the Agile Organization and #noprojects; a culture of continuous value. Evan champions the advancement of agile, innovative, and dynamic companies poised to succeed in fluctuating markets through rigorous research and advocacy.

Christopher Morales is a seasoned digital strategist and agile leader with over 20 years of experience guiding organizations like ESPN, IBM, and the Business Agility Institute. As founder of Electrick Media, he helps U.S. and European businesses harness AI to make smarter, more sustainable decisions in a rapidly changing world.

Auto-generated Transcript:

Brian Milner (00:00 )

Welcome in Agile Mentors. We are back for another episode of the Agile Mentors podcast. We've kind been a little bit off and on recently, but I'm back, I'm here, I'm ready to go, and we've got a really good episode for you today. I've got two, two guests with me. I know that's not a normal thing that we do here, but we got two guests. First, we have Mr. Evan Layborn with us, who's back. Welcome back, Evan.

Evan Leybourn (00:23 )

Good morning from Melbourne, Australia.

Brian Milner (00:26 )

And Christopher Morales is joining us for the first time. Christopher worked with Evan on a project and we're going to talk about that in just a second, but Christopher, welcome in.

Christopher Morales (00:35 )

Yeah, good evening. Nice to be here. It's very late here in Germany. So this is an international attendance.

Brian Milner (00:42 )

Yeah, we were talking about this just as we started. I think we have pretty much all times of day represented here on this call because we've got morning here from Evan. We've got late evening here for Christopher and I'm kind of late afternoon. So we're covered. All our bases are covered here. But we wanted to have these two on. They both work for a company called the Business Agility Institute. And if you have been with us for a while, you probably remember Evan's episode that we had on last year when we kind of talked about one of the studies that they had done. Well, they put out a new one that I kind of saw Evan posting about. And I thought, wow, that sounds really, really interesting. I really want to have them on to talk about this. It's called From Constraints to Capabilities, AI as a Force Multiplier. The great thing about the Business Agility Institute is they get into the data. They do the research, they put in the hard work, and it's not just speculation. It's not just, that's one guy's bloated opinion, and do they know what they're talking about or not? So that's what I really, really appreciate about the things that come out of the Business Agility Institute is they're factual, they're data-based. So that's what I wanna start with, I guess, is... What was the genesis of this? What did you guys, how did you land on this as a topic and how did you narrow it down to this as a topic? Where did this start?

Evan Leybourn (02:07 )

Well, quite simply, it started from almost a hypothesis around so much of the conversation around AI. And let's face it, there is a lot of conversation around artificial intelligence and specifically generative, predictive and agentic AI. Focuses on the technology. And yet when we talk to organizations, a lot of them don't seem to be seeing a positive return on investment, a positive ROI. And we needed to understand why, why these benefits of like three times products or operational efficiency product throughput, three times value creation, Why weren't companies seeing this? That's really what we were trying to understand. Why?

Brian Milner (03:01 )

Yeah, that's a great basis for this because I think you're right. There's sort of this, I would imagine there's lots of people out there who are kind of going through their business lives and hearing all these incredible claims that people are making in the media about how this is gonna replace everyone. And now it's, yeah, we can, I mean, you said 3X, I've heard like, 10 or anywhere from 10 to 100X, the capabilities of teams and that they can now do all these amazing things. And if I'm just going through my business career, I'm looking at that from the outside going, is this fact or is this fantasy? this just a bluster or is this really, really happening? So I really appreciate this as a topic. A little bit of insider baseball here for everybody. You guys talk about in this report that you use a specific method here, the Delphi method. for data geeks here, or if you're just kind of curious, would you mind describing a little bit about what that means?

Evan Leybourn (04:00 )

Chris, do you want to take that one?

Christopher Morales (04:01 )

Yeah, well, so the idea behind using the Delphi method was actually inspired by my sister. She had done a periodic review that utilized this method. And essentially what it is is we utilize rounds of inquiry with an expert panel to refine the research, the feedback that we're getting. And so we collected an initial set of data. reviewed that data, tried to analyze it to come up with a consensus, and then repositioned our findings back to the experts to find out where they stood based on what they gave us. And really trying to get all of the experts to come to an agreement in specific areas. In the areas that we found gray space, for instance, or let's say, data was spread out, right? Those were really the areas where we're really trying to force these experts to get off of the fence and really make an assessment. And it was proved extremely helpful, I think, in this research because what I find in the AI space is that there is plenty of gray. And we really wanted to get to some stronger degree of black and white. I'm not going to say these findings are black and white, but I will say that in order to guide people, you need to give them degrees of confidence. And I feel like that's what we wanted to do with this.

Brian Milner (05:31 )

Well, that's the great thing about research though, Is it can give you information, but there's always the story. And it's really kind of finding that story that really is the crux of it. So we open this saying, fact or fiction. So just hit us up with a couple of the, maybe some of the surprising findings or some of the key things. For the people you talk to. Christopher Morales (05:38 ) Mm-hmm.

Brian Milner (05:53 )

Were they seeing these amazing kind of, you know, 100 X of their capabilities or what was the reality of what people reported to you?

Evan Leybourn (06:01 )

In a few cases, yes. Maybe not 100x, but 8x, 10x was definitely being shown. But the big aha, and I won'