What if the only thing standing between your team and breakthrough performance is how fast you can learn? In a world where AI can surface insights in seconds, why are so many teams still waiting days or weeks to know what's really going on?
I'm Lance Dacy, otherwise known as Big Agile, and over the last seven weeks we've built a foundation together. We've explored re-skilling in the AI-driven world, agile strategy, and enterprise agility. We've talked about economic volatility and adaptive supply chains. For those of you who might be curious about that topic, it's one of my favorites. Psychological safety was just a couple of weeks ago, and then one of my absolute favorite subjects came up last week: leadership agility, based on Bill Joiner's book of the same title.
What I want to do today is bring all of that together. None of the capabilities we've discussed over these weeks matter if we can't learn fast enough to act on the reality we're facing. This week, we're talking about accelerating feedback loops with AI and automation. Or put more simply: how can modern teams combine Scrum's empirical approach with AI-enhanced data feedback to learn faster and make better decisions?
As always, I want to keep this practical, not just theoretical.
The Empirical Foundation
We know in Scrum that we continually use feedback loops to help us make decisions with known data. We call that computational irreducibility. We likely don't know that we've solved the problem until we've actually solved the problem.
The empirical approach breaks the problem down into small, iterative, and incremental delivery so we can inspect as we go forward. But I find the new competitive moat is learning speed. Scrum is already built for that: learning something and building something.
Let's start with a simple truth in today's market. If you can learn faster than your competitors, you win. It's as simple as that. Not because you're smarter, not because you have better tools, but because your decisions are grounded in reality sooner, empirically.
The Urgency Deficit
In the book Leading Change by John Kotter, which we've referenced quite a bit in organizational transformation activities, he calls this deficit the urgency deficit. Most organizations don't move slower because they want to. They move slower because they don't see the truth fast enough to respond to it. Markets are basically moving faster than leadership teams can make decisions. Customer expectations are shifting faster than steering committees can actually respond.
Have you ever seen that before in roadmap planning?
In software development, where we spend a lot of our time here at Big Agile, building software is not predictable manufacturing. We cover that over and over again. It's messy, it's creative, it's adaptive, and it's new product development. There's no playbook where I can sit down and implement a feature by looking at code I'm going to write line by line, repeatedly.
What that really means is only empiricism works. I'm hesitant to use the words "always" and "never," but in this scenario, I really believe that. Only fast feedback can help solve that problem. And that's where modern AI enters the story.
AI as Friction Remover, Not Human Replacement
You can talk about the job markets and how people are nervous about AI replacing humans. Rightfully so; there's a lot of buzz around that. But I've written blogs and gone through masterclasses on AI and data transformation, and I just don't find that's really going to be the case. In fact, if you really knew how AI was working, you'd be a lot more hesitant to say those things. But it doesn't mean we can't apply it in the right places.
I like to use Scrum, or any agile approach, as the engine. What I find AI can do is help remove the friction.
Scrum already gave us a powerful learning system with daily inspection, sprint cycles, regular review of outcomes in the sprint review, and continuous improvement reflection. These are all mantras of the agile mindset.
The challenge isn't the framework itself. I believe it's the friction within that's slowing down those feedback loops. This friction has existed for many years. It's just that there's new technology these days that can help us break the cycle.
We tend to wait for reporting. We wait for analysts. We do manual checks. You have to read and interpret the data. You have to have meetings, or as I prefer to call them, working sessions and collaborative sessions. Then you've got to dissect and converge on opinions. None of us are smarter than all of us together. I call that the collective intelligence, the wisdom of the crowd.
All of those things delay and dilute learning, rightfully so, for good reason. But how could we speed that up?
AI can actually change that. AI doesn't change Scrum. AI can accelerate Scrum's purpose. Scrum's short cycles plus AI's short learning cycles equals a team that sees reality faster than the organization around it, or better yet, faster than your competitors.
The Modern Feedback Stack
I want to introduce a simple model that I use with organizations and refine constantly. It's not perfect, but it's basically a loop.
We start with humans. Let's not get rid of the humans. Humans process data, read over data. We use AI to help with that, to refine decisions, to experiment. But guess where it all comes back to? The human. We leave the human in the loop but use AI to augment the process.
This is what I call the modern loop.
Here's where I think most organizations break and get stuck: waiting for analysis, debating opinions, interpreting noise. The variable of treating noise as signal can be detrimental to us. We want to use the process behavior charts we've been discussing and make sure the noise we're looking at is something we need to respond to, or not.
What AI does is help reduce analysis time, clarify signal, and remove wasted debate in our collaborative sessions.
I don't find that AI is going to replace the judgment of humans. AI should accelerate judgment. One day maybe it will replace judgment, but right now it's almost like hiring a child on a team. They have to be fairly well supervised. But AI does some things really well. Let's not shy away from it, but let's also not get rid of the humans at the same time.
Three High-Value Use Cases for AI-Accelerated Learning
There are three places where we can accelerate learning today with practical applications.
Customer Insights. Teams often wait for customer sentiment, NPS trends, or support themes. AI gives you that sentiment fairly quickly through clustering from chat logs and usage patterns, detection from telemetry, and topic extraction from calls and tickets. AI is wonderful at that. It takes forever for humans to process all of that. You learn what customers actually experienced today, not what they said last quarter.
Flow and Delivery. This is one I'm really passionate about. Scrum masters and engineering leads often spend hours combining Jira and Azure DevOps tickets for trends. AI can instantly spot anomalies in cycle times, in-progress heat maps or hotspots, stalled work within the workflow. If you're doing a Kanban board, AI can look for those things and also identify dependency risks. The result is that flow issues surface in real time, not at the end of the sprint. We have plenty of blogs and resources on how to do that as well.
Quality and Risk. Testing and QA produce massive data streams that teams rarely have time to analyze. You can use AI to generate code coverage heat maps, predict high-risk components, identify defect clusters by looking at automated test cases, and recommend test paths based on where things are breaking in the system. The result is that quality decisions become proactive rather than reactive. That's an insightful way to help a team achieve flow.
A Simple Model to Operationalize AI-Enhanced Feedback
I've created an "onion" model that gives you the various levels: collect, interpret, act, verify, and finally, adjust.
First, collect the right operational and customer data. Then interpret that data with AI assistance to enhance clarity. Act based on insight, not politics. You know what I'm talking about with politics. You get people in the room who throw opinions around like it's real data. We want to use real data to make decisions and make sure politics have very little influence on the actual outcomes.
Verify these outcomes with short experiments. Adjust continuously, not just annually like a lot of teams do.
Just like agile preaches: keep cycles short, keep experiments cheap, and keep learning as a continuous process.
A Word of Empathy
Change is hard. We often hear that people don't like change. I don't really think that's the case. People don't like change they didn't initiate. If they know why they're changing, it's a lot easier to digest.
I want to slow down for a moment and acknowledge something you might already know. If this were easy, everybody would already be doing it.
Many organizations are just overwhelmed. Data is noisy. AI feels intimidating. Teams are stretched thin. Leaders don't want to break what's working. They're thinking, "I sleep good at night. I don't want to change anything that could break." People fear the social risks of exposing problems and learning something uncomfortable.
That's okay. That's why psychological safety is so important in an organization.
Transformation doesn't happen because we push harder. It happens because we reduce the friction to learning.
As a leader, lead with empathy. Create psychological safety. Help people succeed rather than shame them for not moving fast enough. Every step you take towards faster learning is a step towards a healthier culture, good or bad, just getting used to that change.
A Closing Challenge
I have one question for you to think about: Where is your team waiting too long for clarity?
Is it customer insight? Is it the flow of the team? Is it quality? Is it decision-making? Is it leadership alignment with too many politics?
Once you figure that out, ask: What could AI tell us within minutes that we currently wait days or weeks to understand?
Just start there. One loop. Shrink it by half, then do it again. Shrink it again. Because when your learning loop accelerates, your team accelerates, for good or bad. Sometimes they'll accelerate in the wrong direction, and we have to course correct. But your delivery can accelerate, your customer impact can accelerate, and your strategy can accelerate based on this.
The organizations that outlearn their market will outperform their market.
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I'm Lance Dacy with Big Agile. Just remember: the future doesn't belong to the teams with the most data. It belongs to the teams who learn from the data the fastest.
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