AI vs. Real People—Helping Teams Avoid Information Overload | Havva Sevay

Summary

Podcast Summary: "AI vs. Real People—Helping Teams Avoid Information Overload"

Podcast: Scrum Master Toolbox Podcast: Agile storytelling from the trenches
Host: Vasco Duarte
Guest: Havva Sevay
Date: August 5, 2026

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Episode Overview

This episode centers on a timely and complex challenge: the balance between leveraging AI as a productivity tool versus recognizing the irreplaceable nuances of human judgment and coaching in Agile teams. Havva Sevay joins Vasco Duarte to discuss the risks of information overload—fueled by AI tools like ChatGPT and Copilot—and to share practical advice for Scrum Masters and Agile coaches navigating this evolving landscape. The conversation explores misconceptions about AI, the dangers of treating AI as a "magic oracle," and strategies to filter and focus team attention amidst an abundance of automated information.

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Key Discussion Points & Insights

1. AI: Tool or "Magic Oracle"?

• Havva’s View: AI is undeniably helpful but fundamentally remains a tool, not a replacement for people and their judgment.
• Common Misunderstandings: Team members sometimes treat AI as an all-knowing authority, underestimating its potential for mistakes or hallucinations.
  • "Some people...don't really realize, maybe they think real people can be easily replaced." (Havva, 02:35)
  • Respect for human unpredictability and emotional intelligence remains essential.

2. The Pitfalls of Over-Reliance on AI-Generated Content

• Risk of Unquestioning Adoption: Team members may accept AI output as factual without critical evaluation.

  • "ChatGPT doesn't realize it can be wrong...People don't really question." (Havva, 04:12)
  • Example: A colleague presenting ChatGPT's answer without verification and assuming accuracy.

• The "Magic Ring" Trap: Referring to the notion that AI is seen as "a ring to rule them all," Vasco highlights the myth of AI as the perfect solution. (Vasco, 03:27)

3. AI and Information Overload in the Workplace

• Vasco describes a common scenario: a leader uses AI to rapidly generate vast knowledge (docs, slides), then expects teams to accept and implement these outputs wholesale.
  • "Jeffrey...spreads that through the team and says, do this. This is how it should be. I’ve researched this..." (Vasco, 07:21)
• Impact: Teams face sudden surges in information and requirements outside their current work scope.

4. Team Coping Strategies for Information Overload

• Practical Filtering: Havva recommends stepping back to clarify the team's real needs rather than absorbing all AI-generated information.

  • "Product backlog can't be just fit in with everything...We have to realize, okay, what is the real need?" (Havva, 10:25)

• Structure and Prioritization: Emphasize filtering for relevance and timing—focus on what supports the team's current objectives.

  • "Don’t try to overload with information, just information which we believe is more relevant..." (Havva, 11:29)

• Manual Prioritization Before AI: Use established tools like the Eisenhower Matrix to prioritize and then use AI to assist where appropriate.

  • "Forget AI for one second...We just used the Eisenhower module, saying, what is important?" (Havva, 12:41)

5. Coaching Approaches: Moderating Expectations and Empowering Teams

• Team Exercises: Havva advocates for workshops and discussions where teams explicitly decide what information to act upon, sometimes intentionally setting AI aside to ground decisions in real-world needs.
• Role of Emotional Intelligence: Human coaches better grasp team dynamics, emotions, and unpredictability—factors AI cannot replicate.
  • “A real person can...analyze and...is better trained. And you see also the emotions, you see all the reaction. This is also what people don’t realize.” (Havva, 06:18)

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Memorable Quotes & Moments (with Timestamps)

• AI as a Tool, not a Replacement:
  "People are still unpredictable and people still reacting differently. So they underestimate real people."
  — Havva Sevay (02:35)

• Dangers of Mystifying AI:
  "There’s this trend of thinking AI...is a tool...some who think [it's] the next magic oracle...What is keeping people in that magic belief?"
  — Vasco Duarte (03:27)

• Uncritical Acceptance:
  "ChatGPT doesn’t realize it can be wrong. People don’t really question about it."
  — Havva Sevay (04:12)

• Information Overload:
  "AI can work very fast...I’m also worried about [an] overload of information. So then it’s the question: which information is important?"
  — Havva Sevay (09:26)

• Grounding Priorities:
  "Don’t try to overload with information, just information which we believe is more relevant."
  — Havva Sevay (11:29)

• Practicing Prioritization:
  "Forget AI for one second and just sort out...We just used the Eisenhower module, saying, what is important?"
  — Havva Sevay (12:41)

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Timestamps for Key Segments

• [01:49] Main Topic—AI vs. Real People: Framing the Dilemma
• [02:35] AI as Tool vs. Replacement—Present-day Misconceptions
• [04:12] Risks of Treating AI as Magic: Critical Thinking Problems
• [07:21] Leader Example—Information Push from AI to Teams
• [09:26] The Reality of Information Overload
• [10:25] Filtering for Relevance: The Product Backlog Example
• [11:29] Practical Strategies—Roadmaps and Prioritization
• [12:41] Eisenhower Matrix: Team Exercise for Reducing Overload

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Actionable Takeaways

• Practice Critical Thinking: Always verify and contextualize AI-generated outputs; encourage teams not to accept AI answers blindly.
• Manage Information Overload: Filter and prioritize information continually—apply manual tools like the Eisenhower Matrix before involving AI.
• Coach Human Skills: Leverage the unique, irreplaceable qualities of human coaches—empathy, adaptability, and emotional attunement—when guiding Agile teams.
• Integrate AI Mindfully: Use AI as a complement, not a substitute: it can assist but not replace deliberate human judgment.

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This episode provides valuable, practical insights for Scrum Masters and Agile coaches grappling with the dual forces of rapid AI adoption and the enduring power of human connection in fostering effective, resilient teams.

Transcript

[A]

Hey there, agile adventurer, just a quick question.

[B]

What if for the price of a

[A]

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[B]

Hello everybody. Welcome to our Wednesday, the biggest challenge and the biggest coaching conversation of the week. And this week we have with us Hava Sevai. Hey Hava, welcome back.

[C]

Thanks for having me back.

[B]

Absolutely. So we heard some great stories so far, but I think there's more stories to come and specifically today we want to explore a story that is is meaningful for you. A particular challenge or problem you're facing as a Scrum Master. We'll work through that problem together, trying to explore it, come up with ideas or brainstorming potential experiments. So what's the topic you have for us this week, Kava?

[C]

Well, this week's topic is about AI versus real people because I believe it's a very important topic. It's a day to day challenging, it becomes more reality. And I think all my colleagues and myself, we are also thinking about how we cope in a very fast paced environment, even in an AI world. So this was my topic. I will bring up today with you and analyzing.

[B]

All right, well let's go for it. So how would you describe the topic from today's perspective? From your perspective today, I should say how would you define what is AI versus real people? What are some of the problems you're thinking about? Maybe some stories of what that means in practice? How do you get your head around that problem today?

[C]

Haba for me is AI still a tool for some people is the all around problem solving, which I think that's the danger I still see in some people's path and they don't real, I think not really realize maybe they think real people can be easily Replaced okay, what I did, I really tried with Copilot or with Chat GPT telling me my story, telling them my patterns. I think they give us very good answer. They get really solid answer whatsoever. I believe people are still unpredictable and people still reacting differently. So they underestimate still real people. So still they need a coaching from real people versus AI. So I believe still it's the realization of the people nowadays.

[B]

So what do you think is currently the understanding that people have that kind of conflicts with that view? Right, because you're talking about is it a tool or is it perhaps something that can solve everything? Maybe you know, creating world peace, who knows? So in your mind, like what's keeping people in that mindset that AI is just some kind of magic, I don't know, maybe even a ring to rule them all, as some people wrote about at some point. So what is keeping people in that, let's call it magic belief that AI solves everything in your domain, in the people around you. What do you think is keeping people there?

[C]

I believe still they put some input on Chat GPT and some and then they believe, okay, if I put some input asking questions, they have all around the answer and maybe that's the thing. They okay, as I said, they Chat GPT has some solid answer, but what they don't realize, it is still a tool. It can make mistake, it can still hallucinate. What I mean, that has to hallucinate. It means that ChatGPT doesn't realize it can be wrong. It can, it's said, okay, this is for me the truth because it's the input. And if it's information is right or wrong, people don't really question. So even I observed how people didn't question about it. And one of my colleagues showed me an answer and said, oh, this is what Chat GPT said. And I said it's a tool and Internet is a tool. So don't rely what it say you have still twice check if this is the information is right. So even though a tool is making mistakes, Even though, okay, ChatGPT and everybody writes they need wide.

[B]

Let's explore that because I think that's a very important point. So there's this trend of thinking that AI or LLMs specifically are a tool we can use to do some work, but not perhaps all work. Then there are these people who, who think that no, no, LLMs are kind of, you know, the next magic oracle, like in the old Greek times where there's this oracle in Delphi and you go and it knows everything. Right. Can see everything. What do you think is creating is created at work from this perspective. Right. Because now we're talking about AI as a tool versus AI as some kind of magic and then how that affects us as people using the tool. Like what. What do you think this contrasting perspective tool versus magic is creating in the workforce? Like when people are using AI for work?

[C]

Oh, it's a really good question. I think with their magic, I think you put a font inside, have a specific question and as I said, already they realize that all the answers and I think if you're not really into the topic, if you don't know really the subject itself, you can't really double ch check is this information is right. So this comes out and even a person would think, oh, what ChatGPT is right. What it's saying. If you write a reaction, how could be the possible reaction of a person? How can I solve the con the problem? Maybe it is more complicated than the tool itself knows how to solve it. So I think a coach would better estimate what the problem is than a tool. A tool is good for the first couple of questions, but I think a real person versus a real person, I think a real person can more analyze and I think a real person is better trained. And you see also the emotions, you see all the reaction. This is also what people don't realize it. Yeah.

[B]

So I totally see where you're going. And I've seen the same pattern. But now I want to take outside Scrum Master and agile coach because I think it helps us also to think a little bit with distance from what is happening. Right. Like, so let's say Jeffrey, Jeffrey is a leader in an organization. And Jeffrey started using AI a while ago, let's say six months ago, just before the big jump with some of the models coming in and becoming much better than the previous models and so on. And Jeffrey just builds content and knowledge with that tool. Right. Like he goes to AI and he starts asking questions and he has some ideas and then he builds like this massive documents, 50 pages, 20 PowerPoint slides. And then he spreads that through the team. Let's say Jeffrey is kind of a team lead or a lead of several teams. And he spreads that through the teams and say, do this. This is how it should be. I've researched this, I've done my work, I've explored. I know what needs to be done. You do it this way. Let's say whatever it could be, could be an architectural decision, could be how to reorganize the teams, could be what kind of tool use for tracking bugs instead of Jira. It doesn't matter what the topic is, but Geoffrey just does all of this knowledge work? Kind of, you know, fetches lots of data from the Internet, processes that with AI creates this massive document. Here's what you need to do, here's how you describe it, here's how you migrate, and then this goes into the teams. And I'm thinking, there's nothing wrong with doing the exploration, with doing the thinking, but I'm thinking, okay, but how are the teams brought into the process of learning? So how do you see that in your work as a coach? Hava, is that happening? Do you see that happening? That because of the use of AI, coaches and scrum masters are bringing perhaps even too much knowledge, too much overload to the team?

[C]

I think yes. So even because AI can work very fast. So information, you get your information in seconds even. And I think I'm also worried about that could be an overload of information. So then it's the question of which information is important, which is less important.

[B]

So how would we like. Let's think about this in practice. Let's say we're helping a team and they are under this situation where could be a team member, could be a product owner, could be somebody else is just bringing this massive amount of information and demands to the team because they are working with AI. And of course the team is also potentially. But the team is busy with the work that they have to do all the day. Right. Like because they have the Sprint backlog and product backlog they're working on. How do we help this team to manage, to navigate this sudden onset? Could be requirements, could be, you know, changes of tools, could be changes of process, whatever that is. How do we help these teams?

[C]

I think also this needs to organize what is our current situation, what information do we need? We need to sort out. Even AI can help us and we're very fast. And give an example for product backlog. Product backlog can't be just fit in with every everything and everything posted. We have to realize, okay, what is the real need? This is what we have to teach in our teams. What is the real need? What does is the current situation? What do we really need right now and not everything. So instead of overloading this information, we need to filter which information is important now.

[A]

Yeah.

[B]

How would you do that? Because I think this is a place where the tool, AI as a tool could be incredibly useful. So right now, have you found ways of helping the teams kind of manage, perhaps cope and Perhaps even benefit from this incoming amount of information towards them.

[C]

I just first stepped back with my team and said, okay, what is the epic? What is currently the request from the stakeholder? And what information do we really need right now? What can AI help us? So I just said, okay, don't use every information. It's just like the moment you can use AI this is not a problem. I believe AI is a great tool adjust to filter this current situation. This is our information. We have a roadmap. We know what is our job. So still, we can still step back and getting the information which is needed for the moment. I said, don't think about 10 steps now. Don't try to overload with information, just information which we believe is more relevant than the other information.

[B]

And how do you do that in practice? Like, okay, so you're kind of setting the stage right? Like, understand the need. What is it really? That is important in this stage? But how do you get the team on board? And also how do you moderate the expectations of those that expect to look to use AI as some kind of magic does it all? Agent?

[C]

Yeah, what I did with my team already, we just had my little board and then we just said. We just didn't. I said, forget AI for one second and just sort out with. We just used the Eisenhower module, saying, what is important? What is not really that important? What can we leave? So I said, what is for you at the moment? So we just did manually without AI I said, what do you think? Then they did it. My team, they really used the Eisenhower method. They said, okay, this is what we need right now. This is exactly the practice you need to do every day. Even though, yes, AI use the Eisenhower model.

[B]

Yeah, absolutely. And I think that's a great way to put it, right? Like there's so many demands. And by the way, it's not a new thing that we are, in this case, that we are overwhelmed with information. We've always been overwhelmed with information. Perhaps we could argue now there's even more. But at some point too much is just too much. It doesn't matter how much it is, it just is too much. And I think this Heisenhauer matrix is a great example of how we can kind of stop and take stock, understand, hey, what are the really important steps to take and how do we orient ourselves around this? I think that's a great practical suggestion to deal with one of the potential side effects of AI, which is overwhelming amounts of information. So thank you for sharing that with us. Haba.

[C]

Thank you.

[A]

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[B]

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