Responsible Use of AI in SAFe Organizations

0:00So welcome to skill number 10. This is the last skill before we get to our final exam.

0:07I'm really glad you've continued to train with me. This one is all about the responsible use of AI

0:13in safe organizations. Now AI has rapidly become part of modern product development. Teams can use

0:21it to generate ideas, summarize decisions, write code, analyze data. The possibilities are endless

0:29and used correctly it can dramatically accelerate learning and delivery. It can dramatically

0:35accelerate value delivery. It's amazing, but used incorrectly it undermines the very principles that

0:43makes agile and safe effective. For example, teams can lose ownership, conversations can disappear,

0:52decisions become centralized again. In safe, the safe framework, AI should enhance collaboration

0:59and learning, not replace them. The goal is not to avoid AI because it is an amazing tool and I

1:06love the opportunities it has given me. The goal is to use it responsibly so it strengthens agility

1:14rather than weakening it. And here's a simple rule. If AI replaces conversation, ownership,

1:20or learning, you're using it wrong. So get ready for a fun skill. Let's do this.

0:00So welcome to section number one. AI is simply an assistant. It cannot be a decision maker. AI

0:08systems are extremely good at generating possibilities. They can analyze a ton of data,

0:15suggest options, and accelerate research. However, AI lacks the context, the intent,

0:22and more importantly, the accountability required to make real decisions. So agile

0:29teams must continue to rely on discussions, a shared understanding, and responsibility for

0:36the outcomes. In a safe environment, AI can support thinking, but it can never replace it.

0:43Teams remain responsible for interpreting the information and deciding what actions to take

0:50based on the needs of the client, the PI objectives, and that entire list we've talked

0:55about over the last nine skills. AI can assist the team, but the decisions must always remain

1:01human. And in this section, as we get into it, I want to point out that yes, I am acknowledging I

1:08am using this particular image to represent AI. I know it's a little corny, but I like it anyway.

1:16So let's jump into it. Oh, I also have some AI spotlight moments along the way as well

1:23to show you that while AI is a useful tool, it does in fact make some pretty serious mistakes

1:31along the way. So let's take a look at the first one right now. It's already been proven over the

1:36last couple of years that AI produce incorrect probability calculations, financial summaries,

1:43story point, and capacity estimates. So not only is it giving you wrong answers, but it's doing it

1:50with high confidence. That's like your friend saying, yeah, rattlesnakes don't bite anyone.

1:55Go ahead and poke it with that stick, because that's exactly what you're doing here.

2:00If you're relying on data with an overly confident projection without verifying it,

2:08you're already out of the gate wrong. And in SAFE, AI might suggest story points, but the work

2:15shortest job first scores or capacity plans must be mathematically sound. If they aren't,

2:23you're poking that rattlesnake I was just talking about. So let's look at some of these points here

2:27and hop into this. I love AI. It's great at generating ideas and ideas I didn't even think of.

2:34I might come up with one or two and ask it for five more. There never seems to be an end to how

2:39many ideas it can actually generate or summarize and the options. I do a ton of predictive project

2:46management work, and you want to talk about summarizing data very, very quickly. GPT is

2:53phenomenal, but it can't understand the project context. It can't understand the human need of

2:58your clients. It cannot understand the strategic intent of your company. It cannot understand your

3:05team dynamics, how long you've been working together, who understands what, what your skills

3:10are, your strengths, who's introverted, who's extroverted, where there's conflict. It doesn't

3:15understand any of that. It only understands what you input. And if you can't successfully put that

3:22all in, basically it's just going to parrot on a neutral playing field. And again, I love AI,

3:30but it's just a tool. You must interpret the suggestions from AI before you act on them,

3:37because we've already seen in this example, it will give you outputs with a high degree of

3:44confidence and they can be completely wrong. And trust me, I've seen this firsthand. I ran an

3:51experiment I saw online and it said, ask chat GPT, how many R's are in the word strawberry?

3:57And so I asked it and it said, well, three. And I said, are you sure it's not two? And it

4:03immediately says something like my mistake. You're right. There are only two R's in the word

4:09strawberry. You can make it change its mind. It's the most helpful hype crew I've ever seen.

4:15It tells me how great and awesome I am, regardless of how great or not so great I may actually be.

4:22So you have to understand it before you act on anything. Decisions plus human judgment,

4:29equal accountability. AI will never be held accountable. So understand that whatever

4:37decisions you make from that AI tool, that data compilation analysis suggestions, that's on you,

4:46not chat GPT. You're never going to see chat GPT behind bars, but it could be you. Well,

4:52probably not that serious, but maybe out of a job. Conversation ensures a shared understanding.

4:58There's how I interpret it, my peers interpret it, my leaders interpret it, and how we collectively

5:04interpret it. If you're not having conversations, you're going down the wrong path. It is great at

5:10accelerating potential solution exploration and then implementation. It is amazing. And I've seen

5:18scenarios where I will ask AI a question and it will give me a really sound answer right away.

5:24But in more complex conversations or inputs, I've seen it take 10 minutes to wait and think about it

5:32and then give me a mathematically defensible solution. So it really does depend what kind

5:39of solution you're trying to find. You must collaborate. You must discuss whatever the

5:44insights are. They must be validated. Well, chat GPT says if I use this code right here, then I can

5:50interface with this hardware over there. Okay, let's run an experiment and let's find out. Let's

5:56not just push it into the widget without validating, without verifying, and then push the widget over

6:03to leadership. That is a recipe for disaster. You cannot outsource thinking. You cannot outsource

6:12decision-making. Remember, if accountability lies with you,

6:17then the thinking and decision-making must also be with you.

0:00So welcome to section number two, preserving team ownership. Now, one of the core principles of

0:06SAFE is that the teams own the work they deliver. They estimate the work, they commit to the

0:12objectives, they take responsibility for delivery. That's how it has to work. And when the tools

0:18begin doing this work for them automatically, ownership shifts away from the people who are

0:23actually doing the work. Now, notice I said ownership of the work shifts away. I didn't say

0:30accountability did. AI is great at assisting you with planning and estimation, but it can't replace

0:36the conversations the team must have to create a shared understanding. When teams participate

0:43directly in planning and commitments, predictability is going to improve. Ownership creates accountability

0:49and accountability drives reliable delivery. So take a look at the screen for just a moment.

0:56It looks like we're estimating here. Well, I see a 13 for a very large estimate and then

1:02some smaller estimates over here, excuse me. But then I'm looking at 34 and I'm wondering,

1:08what? What are you basing that on? And if you've ever estimated story point size,

1:14you know what I'm talking about. Anytime I start getting past eight, I start to freak out a little

1:20bit. 34? 34 would make me flee the room in terror. Well, maybe not in terror, but I would leave in

1:27disgust at a minimum. So let's clear the board and let's wander over here and look at how great

1:33AI has been. Here we go. So in this particular reality check, one of my favorites, AI hallucinated

1:41legal citations in federal court. Several attorneys were sanctioned after they submitted

1:49AI generated case citations, which were completely made up. Can you imagine walking into court as a

1:56lawyer with a responsibility to your client and to the system and having not checked the citations

2:03you were about to present? It blows my mind. But what's the parallel here? Well, AI would be great

2:11at creating backlog items or compliance references, or even documents that may not work.

2:18They may be out of compliance. They may in fact be illegal, whatever it is. So if AI can hallucinate

2:26legal citations, what do you think it can do for your product? I'm just going to let that one stand

2:32there. The teams must estimate their own work. Anything short of that is a mistake. Now, can you

2:38use AI to help you? Of course you can. But there's a danger here. The data that you input,

2:46AI is a system, and trash in will give you trash out. That's piece number one. Piece number two is,

2:53are you protecting the data that you're actually using as an input? If you're using some sort of

2:59open AI source, whatever data you're plugging into it is not secure. It can be used somewhere else.

3:07And I don't fully understand how all of that works, but I do know this.

3:11If you put client data into an open AI source, you've just violated some rules. So at a minimum,

3:19you need to be using something homegrown, something built within your company that's

3:23going to protect whatever the data is that you're putting into it. And that includes your estimations.

3:30Once you get those estimates, you need to have conversations. We need to talk about it.

3:35I may think it's a fantastic estimate. They may think it's terrible. I need to understand why they

3:41believe that. They need to understand why I believe that. And we need to move from both of

3:46these opposites back towards the middle. And the middle is called an agreement. And suggestions by

3:53AI must be validated by the team. I think one solution would be to do X, Y, and Z. Okay,

4:01can we do X, Y, and Z, team? Well, actually, no, because here they're talking about AC,

4:06and over here we're talking about DC, and there's no integration point. So obviously,

4:11we can't use this solution, but we wouldn't know it if we didn't talk about it. Teams, not tools,

4:18commit to delivery. AI can never take responsibility for the value you're trying

4:25to create and deliver. You have to do that. And as soon as you push it off, I promise you,

4:32you're the one hallucinating at this point if you think you are always going to have a

4:36positive outcome. Ownership drives accountability. Ownership drives buy-in as well. If I'm constantly

4:44letting the tool do the work for me, I don't have any real buy-in. I'm just inputting data,

4:50looking at the output, and then trying to implement. Because at some point, it's going to

4:55fail, and then I have no choice but to tell them what I did and then blame the tool for what I

5:01should have been doing. This is my fault. You're never going to see Grok or Claude or Claw,

5:10that's another one, or GPT getting fired because they didn't own it. It's not going to happen.

5:17Participation improves predictability. It can't just be me and the tool. It can't just be one

5:22developer over here and the tool. We have to do it collectively. And if we do it collectively,

5:28we're going to get better at inputting these requests, generating these ideas, these hypotheses,

5:36and then collaboratively looking at the outputs. But if one person is always doing it and they

5:42have a flawed input model, we go back to trash in, trash out. Now, AI insights are great, and you

5:49can use them to make estimation decisions through discussion. But it should never be taken as 100%

5:56correct. For example, you may ask AI how long it takes you to mow a yard. You may tell it how many

6:03steps a minute you take. You may tell it the kind of lawnmower, the horsepower, whether it's gas,

6:08diesel, or electric, the length of the grass, the size of the yard, the humidity, the temperature,

6:14whatever it is. And what it's going to do is give you a neutral answer minus any unknowns,

6:22any uneven surfaces in the yard, any storms that might pop up, any falling sticks or trees or

6:30animals running through your yard, or gopher burrows, whatever it is, gopher holes, excuse me.

6:35I don't know what a burrow is, but actually I think a burrow is a donkey, but I'm off topic.

6:40It's just an insight. It is not the automatic answer. And finally, if the team wants autonomy,

6:49they have to take responsibility for the estimates that they are putting out for these story points.

6:55They have to take responsibility for what they're committing to, because anything short of that

7:01means they're going to lose autonomy and their ability to make local decisions.

0:00All right, so welcome to section number three, where we are going to protect built-in quality.

0:07Now, AI can produce a ton of outputs very quickly, talking about code, documentation,

0:13and analysis. You can do all of this in seconds. And while it's great for accelerating development,

0:20it also introduces new risks. AI-generated outputs can appear very convincing while

0:27containing hidden errors, while containing assumptions. Without proper validation,

0:33defects can scale rapidly. Safe emphasizes built-in quality, which means verifying the

0:40correctness continuously throughout development. And AI may accelerate creation, but quality still

0:47requires verification. Teams have to review and test and validate everything before it becomes

0:56part of the solution. And I love this graphic. Can you imagine being the team that used this code

1:04from Mr. AI bot over there, and it resulted in a failure? Can you imagine how much rework there

1:11might be because of that? Can you imagine how much time they may have lost because of that?

1:16And again, this is a hyperbolic example, but it happens. It has happened. And used incorrectly,

1:24it can happen again. So let's jump over and take a look at our AI in the spotlight moment,

1:31our reality check. This one is a good one too. A biased healthcare risk algorithm. It basically

1:40said that Black patients required less healthcare based on spending. Black patients were spending

1:47less money and or they were seeing fewer Black people, which must mean they require less medical

1:54care. Ergo, Black people are super healthy, probably healthier than anyone else. You can infer

2:01all kinds of outcomes from this. The assumptions are endless. Why does it matter here? Because AI

2:08trained on historical system data can underprioritize certain features, users, or

2:16risk categories. Imagine what happens if you use some sort of AI input to tell you who your

2:24client market is going to be for whatever app you're about to put out. And if a bias is injected

2:30into that learning model, what do you think is going to happen with the output? It's one issue

2:36to have a code defect. It's a whole other animal to inadvertently show a bias towards an entire

2:44demographic, whoever it is. It doesn't matter. Black people, white people, men, women, it does

2:50not matter. So you have to be very careful. So let's jump into these. So here we go. AI output

2:57has to be reviewed always. This isn't a one-sigma, three-sigma, six-sigma moment. It is 100% necessary

3:08to review the output. Can you imagine what happens if you set a three-sigma standard and you have,

3:13I think it's 2,300 opportunities for problems to get past you? That's bonkers. 100% is the only

3:22standard you can play with here when you look at the AI output. And it's fast. But the faster it is

3:29usually means the greater the exposure. Easy example. I love chat GPT and I've asked some

3:36very complex questions. Sometimes it spits them out so fast, I look at it and immediately know

3:43there's no way it could have done this this fast. Plus, I'm kind of a skeptic when it comes to this

3:48particular tool, but I love using it. But in other situations, other versions of AI, it will say

3:55thinking. And you can see tiny print, compiling data, analyzing data, assessing data. Which

4:02recommendations make the most sense? And I can watch it thinking in real time in as much as it

4:08actually thinks before it gives me the answer. And when it gives me one answer, I'm always suspicious.

4:16But when it gives me three or four, I feel better about it. Plus, it generates more ideas to ask

4:22further clarifying questions. And that's really the name of the game here. You have to set it up

4:28to ask clarifying questions and ensure you're giving it everything it actually needs to make

4:33an informed, unbiased recommendation. Teams are responsible for validating correctness. Imagine

4:41if you got output from one AI model and then took it and input it to another AI model and asked for

4:48it to verify correctness. That's bonkers. I don't even understand the risk implications there, but

4:54it feels pretty bad. If accountability lies with the people, then the peoples must validate

5:01correctness. And if you have weak acceptance criteria, you're going to have poor quality

5:06verification. You must have very clear, measurable acceptance criteria for any definition of done,

5:14for any increment to be considered properly working, for incremental delivery, anything

5:22short of that, and quality is going to be skewed. And AI can, of course, help you understand what

5:28the quality standard might be or is. It can also help you understand what experiments you should

5:34run to verify it, but you can't let AI do it for you. This one's easy. Teams confirm the solutions

5:42meet the requirements. That's like when you ask AI a question and it gives you a completely

5:47ridiculous answer. I call them one-offs. The answer has nothing to do with what you asked it.

5:55So you may see a great solution, but it may have nothing to do with your requirements. So you have

6:00to be careful there. AI can write some amazing code, but you must review it. AI makes mistakes.

6:08We already saw it hallucinated legal citations. Do you not think it might hallucinate some code

6:15in whatever code language you're using? And the answer is bet on it. And you have to be transparent

6:22about how you're using it. What were the inputs? What were the outputs? What were the suggestions?

6:29What is your justification? If you can't explain some of that, you will find hidden issues.

6:36Transparency, inspection, and adaption. Those are the pillars that agile, scrum, and safe is built

6:45upon. You take away any one of those pillars and the other two fall down. And lastly, built-in

6:52quality requires people. Human judgment. You cannot give it up to AI. Go to any automated

6:58assembly line for auto manufacturers. You have tons of robot arms moving things around, and

7:04they're welding, and they're moving doors, and they're dropping in bolts, and they're doing some

7:07amazing work. But all the way down that line, you have people inspecting the outputs. Nothing has

7:15changed there, and nothing is going to change here. The people are responsible for built-in quality.

0:00Welcome to Section 4, where we talk about transparency and how to maintain it.

0:05Transparency is fundamental and safe. The teams, the stakeholders, must clearly understand the

0:11work that's being done, why, and how. Now, AI is great at generating artifacts very quickly,

0:18documentation, if you will. But those artifacts need to be discussed. They need to be understood.

0:24And if you're shortcutting that, you no longer have transparency. Work might appear complete,

0:30but if it lacks a shared understanding across the team, how can it be considered complete?

0:36To maintain alignment, AI-generated artifacts must be visible. They must be reviewed. They

0:42must be understood by all of the people responsible for delivery. Transparency

0:47ensures that everyone within the ART, the Agile release train, stays aligned.

0:54And I've spent a lot of time in the last nine skills talking about how alignment delivers

1:00autonomy, and trust, and decision-making, and shared value delivery. Yada, yada, yada.

1:08You know what I'm talking about. So let's move forward. I'm off that soapbox. This is a very

1:14interesting reality check as well. Flattened probability interpretations. When I saw this one,

1:21my eyes bugged out of my head just a little bit, and I'll tell you why. AI has shown problems

1:26telling the difference between probabilistic statements and deterministic recommendations.

1:33Now, probabilities are percentages. 80% probability, 90, 15, whatever it is. Deterministic,

1:41that's usually one number, 10, 5, 4, whatever it is. Imagine what happens if you're using a tool

1:48that can't tell the difference. Then the probability of getting anything done according to AI can

1:54always be 100%. And in SAFe, it can distort the confidence votes, the portfolio forecasting,

2:03or risk-adjusted estimates, which is unforgivable because it's going to impact capacity. It's going

2:11to impact velocity, and it will almost certainly impact delivery, the last issue that you want to

2:18have. So here we go. Buckle up. It's about to get bumpy. All of the work must be visible on the board,

2:26wherever it is, digital or a real board, a chalkboard, piles of paper, all of the above,

2:34none of the above, whatever it is. If I don't clearly understand what we asked AI,

2:42what recommendations it gave us, our decision logic to use one of those recommendations,

2:49the outcome and or the output, whether or not the test failed, and that list goes on and on,

2:56then we don't have visibility. Go back to what I said about the three pillars of Scrum,

3:01transparency, inspection, and adaption. If we don't have transparency, how can we clearly

3:08inspect it and then adapt to whatever the outcome is? We can't. It's bananas, which means we have

3:16to look at every generated artifact. And by artifact, it could be a burndown chart, burn-up

3:23chart, a risk-adjusted estimate like I had earlier. Go to predictive. It could be the risk plan.

3:31It could be the work breakdown structure. It could be the estimates for any particular task

3:37in the schedule. It could be the cost estimates. Can you imagine the impact of generating

3:43an AI-driven risk register with recommended actions and cost and assigned risk owners

3:50and equipment needed? It sounds great, theoretically, but the reality is you can't do it.

3:57Hidden work reduces art alignment. I talked about that. If we don't have transparency,

4:03at some point along the way, we are going to break the train. That cannot happen. If there's

4:09anything in the backlog, everyone needs to understand what it means. Now, sure, there are

4:15some story points that may have large estimates because we clearly don't understand the work yet,

4:20but we're going to break them down further and figure it out. But if there's an artifact there

4:24or a code fix there, tech debt or some other kind of enabler or enhancement, and we don't

4:30understand it, why is it there in the first place? It simply shouldn't be. Eventually,

4:37we will produce something, and stakeholders will ask us how we went from point A to point Z.

4:43Our decision logic needs to be sound. It should start with, well, we input this data into the AI

4:51tool. It gave us these recommendations. We tried these experiments, and we confirmed that only one

4:56of them would do X, Y, and Z. Plus, it meets the acceptance criteria in the contract. However,

5:02you made your decision, if that was it, should sound reasonable. What it shouldn't sound like

5:09is, well, we relied on AI, and this was the cheapest path, so that's what we did. Yeah,

5:15I know it didn't work out, but it was the cheapest path. I was trying to save you money.

5:19It doesn't matter if you're trying to save people money if you're not delivering value. Delivered

5:24value and increasingly higher value through incremental delivery is the name of the game here.

5:32Documentation. We must have it. Everyone must understand it. It must be visible. I'm going to

5:38leave that one to stand on its own. And never mind the fact that transparency prevents rework.

5:44If we don't understand what led us down this path, we can't prevent rework from happening in

5:50the future. Go back to code generation. Unless we review 100% of the code being generated by the AI

5:58tool, we have no idea how many mistakes may actually be there. If we have some crazy experiment

6:05where we say, just go ahead and check every other line. Okay, that's 50%. Is that enough to avoid

6:13rework? I think we know the answer to that one. And finally, the art, the agile release train,

6:21depends on visible work. If it's invisible or tightly held or questionable or even sketchy,

6:29how are we going to maintain trust? And the answer is, we can't.

0:00Okay, as we start section five, I'm going to stay off screen for just a moment because I don't want

0:05my big fat mug over the word responsible. This is all about using AI in discovery and the planning

0:13portions of your project regardless of the project type. So here we go. Now AI is extremely valuable

0:21during discovery and research and planning because it can help teams analyze a ton of market

0:27information or exploring options and identifying potential risk. It's really amazing. During early

0:34exploration, AI can accelerate the learning process, but discovery activities generate

0:41hypotheses, not final answers. So don't think just because you're asking AI what you should do that

0:47it gives you a recommendation that you should do it. You should not be doing that. You should be

0:52using it to help you figure out what your opportunities might be. That's very different. AI

0:59can assist the product team in generating ideas, analyzing data, but the prioritization and

1:05commitment remain human responsibilities, and I've talked about this in previous nuggets. Look, teams

1:12must validate the ideas through experimentation and feedback. That is a rule that cannot be broken.

1:19So let's move forward here. Let's take a look at our AI reality check. This one is pretty bad too.

1:27Let me grab Mr. Penn. All right, I've got Mr. Penn. We have misaligned fraud detection. Now back in 2023,

1:36they discovered that some AI fraud detection algorithms for some platforms had a racial bias

1:43because they were canceling orders for people within a certain demographic or a certain location

1:51or within a certain annual income. So AI can penalize new customers or transactions in new

1:58markets because they're just not part of any predefined patterns, and if you can't make the

2:04leap to this one, here's an easy one. How many times have you been on travel, maybe for the first

2:09time in a couple of years, and you use your credit card or your business card and suddenly you get an

2:15alert on your phone that says suspicious activity for this card, this location. Press Y to accept.

2:22Press N to be connected to a customer service rep because I've seen it a bunch, and I've also had

2:28people call me. Hey, this is Capital One Bank. We noticed card activity in Eugene, Oregon. Is that you?

2:36Oh yes, it is me. I'm out there for the next couple of days. Okay, then we'll take the flag

2:41off and we'll let you go about your life. It happens all the time. So let's jump into this one

2:46and see what we've got. Obviously, it's going to accelerate research and analysis. I cannot

2:53understate how fast AI can sort through piles and piles of data to pull out trends, to analyze,

3:03to make recommendations, to show you patterns. It is amazing. In another example, I set up a way to

3:11take a list of commands and directives and drop it into ChatGPT and then drop someone's LinkedIn

3:19profile link in there, and it would spit out a two-page report on any particular person.

3:24And I did this back when I was in sales, two and a half years ago. What they liked, what they

3:29disliked, any patterns, places they traveled, conversation points, and stuff like that. It only

3:36took about five minutes per person, and it was wildly fast. But let's keep going. Obviously, it's

3:44not a one and done. AI and ChatGPT and large learning models can do a whole bunch of stuff

3:51all at once, simultaneously. So instead of exploring a single option, or perhaps one at a time,

3:57you can set the parameters and let it explore multiple options. And by multiple, I mean as many

4:03as you can think of, it can probably figure it out. I've casually asked it for 30 examples for

4:09various things in project management, and it takes about 0.3 seconds to start spitting it out.

4:15Wildly fast, but I'm not trying to sell you on it. You already know it. Those insights are

4:20going to support product decisions. Maybe it's a make-or-buy decision. Maybe you're trying to

4:25decide if this particular piece of unique code needs to be made in-house, or maybe you can buy it

4:31off the shelf and drop it in and deal with it as tech debt later on. Whatever it is, this can help

4:38you figure it out. It can also help you figure out availability. You may have something unique to do,

4:44and it's wildly available in the marketplace, or it's not. So then you have no choice but to build

4:50it. Remember, you are going to develop hypotheses. You're going to experiment and confirm or refute.

4:59It is the only way to validate whether your hypothesis is correct or incorrect. This is science.

5:06Nothing's changed. Planning activities must be collaborative. It can't be just you or just them.

5:13It is not the me anymore. It is the art, the agile release train. It is the we. We are all doing it.

5:22If there's a trade-off, we have to talk about it, and that might go back to the previous example of

5:28build it in-house or buy it off the shelf. That may go to the heart of testing something in-house

5:35or pushing it out to a vendor, whatever it is, and that list is endless as well. Data is going to help

5:41you justify your decisions, and one of those decisions is going to be what the priorities are.

5:47If you're looking at a backlog and you're trying to figure out which one of them is a priority

5:52as it relates to fastest deliverable value, maybe AI can help you with market research,

5:59which demographic and which location is using which brand of soda or buying what kind of car

6:05or using what kind of phone app, whatever it is. There's a ton of data out there.

6:10If you can pull valuable information from it, and finally, continuous learning is the name of the game.

6:16You may get wonky results with AI at first until you learn the input patterns and what kind of

6:23language you should be using to teach it to help you get the appropriate results. You may not get

6:29it right the first time, but I guarantee you that over time, through open collaboration and

6:35conversation, you'll dial it in a lot faster. I know I have.

0:00Okay, in section six, we're going to talk about leadership responsibilities with AI.

0:06And let's face it, change and control starts at the top. So if we don't have clear guidelines

0:13or guardrails or a vision, it's going to be crazy time lemonade. Leadership plays an important role

0:19because they're the ones introducing and or allowing AI within the organization. And without

0:25clear guidance, AI tools can unintentionally recreate traditional command and control

0:31structures. And I'll explain what I mean in just a moment. Leaders have to ensure that AI adoption

0:37strengthens, not weakens, team autonomy. Responsible leadership encourages experimentation

0:45while maintaining very clear guardrails. The goal is to empower teams with new capabilities

0:51while preserving the principles of agility. So let's jump into this one. I am stoked. We're

0:58almost to the end. Here we go. This is a really neat reality check too. This is all about what

1:04the courts have ruled. And the courts have ruled that professionals are accountable for AI generated

1:12content submitted under their authority. So even if it's fake, even if it's wrong, even if it's

1:18plagiarism, even if it's copywritten or whatever it is, it's on them. You're never going to see

1:25ChatGPT or Grok or Claude behind bars, but it could be you. And as it relates to SAFe, SAFe leaders

1:33are responsible for the outcomes of AI-assisted summaries, art level updates, regulatory content,

1:41portfolio management, portfolio projections, ROI projections, whatever it is. AI is a fantastic tool,

1:50but it should never be mistaken for a one-stop shop that's infallible. That is crazy talk. It

1:58cannot happen. So let's get into this. If we don't have guidelines, we're just going to make them up

2:04as we go. It's really about authority. And there's only two ways to figure out what your limits of

2:09authority are. Ask or exceed them. One of them is proactive and one of them is reactive. You're almost

2:18never going to be fired for asking what the guidelines are, but you might be fired for

2:23violating the guidelines you may or may not know even exist. So my suggestion here as it relates

2:30to AI, be more proactive. Guardrails ensure that AI supports agile principles, transparency,

2:39inspection, adaption, decentralized decision making, and that list simply goes on and on.

2:47Now earlier I said AI can actually introduce traditional command and control infrastructure,

2:54and now we're going to talk about it. Rather structure, not infrastructure. It's not making

2:58any buildings, but it could be making decisions, and this is what I'm talking about. Let's say we

3:03have a product management VP or maybe a C-suite leader who has some sort of AI-driven portfolio

3:11manager, whatever it is, and he or she looks at it and goes, wow, this is suggesting that if we just

3:17do two more increments here, we can increase ROI by this much. Or this one is suggesting that this

3:23should be a priority over that without any backup. Or perhaps it's saying that this team's capacity

3:30has fallen below that team, and I need to do something about it. When you have control over

3:35these kinds of tools, it's easy to use them as a sounding board instead of people, and when you rely

3:44on AI, it's going to give it to you in a sterile, neutral way. But it shouldn't be used to make

3:50hard-line decisions like that. It's just giving you insights. It's giving you options. It shouldn't

3:57be giving you definitive directions to move in. Teams have to retain their autonomy. If we trust

4:03them to do what's right, then we can trust them to do what's right with AI. If we give them guardrails

4:09and we trust them, let them experiment, let them fail, let them learn from it. They need to maintain

4:14autonomy because as soon as you move back to a command and control structure, you're not an

4:20agile anymore. You're not building high-performing teams. You are putting your thumb clearly on their

4:26head, possibly at the behest of AI. We need to be safe to explore the tools, and we have talked about

4:33this. Psychological safety, the ability to ask questions, try new things, to fail, to push back respectfully

4:42against ideas, whatever it is. But we still need to explore different tools. To do that, we need safety.

4:51It's not rocket science. This isn't the only place we need safety, but it's definitely one of them.

4:56Anytime you bring a new tool into the system, the art, in this case, people are going to be

5:03suspicious. You have to let them figure it out, let them make a few mistakes, let them get better.

5:09And as long as you give them the autonomy to do that, they will. I feel like I just said this. I'm

5:15going to let that one stand on its own. AI has to be modeled responsibly at all levels, and here's

5:23what I mean. If you give them guidelines where they can do X, Y, and Z, and then explain their logic,

5:29and someone walks into your office and asks you a question, and then you use AI to answer that

5:34decision without explaining your logic or even understanding it, that's irresponsible use. Or if

5:41they perhaps walk in there and see your screen open, and you're using AI to produce

5:48some not-so-family-friendly images, that's not responsible use. If you're going to tell people

5:56what to do, you have to do that as well. I believe it's called walking the walk and talking the talk.

6:04You have to do them both, or you really can't do neither, not as a leader. And adoption should

6:10strengthen the teams, not weaken them. It should strengthen their agility, their ability to adapt,

6:18their ability to be transparent, to inspect, to experiment, to learn, without fear of any kind of

6:25retaliation, as long as they're staying within the guidelines set by leadership.

0:00Okay. In this final section, we have ethical and customer impact considerations. Look at that

0:07bottom statement. Assess AI practices as part of routine inspections. We already know about

0:13the feedback loop. We should be using them here, and if we are using an AI tool, we have to adopt

0:20that into our practices as well. For example, check out this routine art inspection checklist

0:27here. We're going to review the stories. We're going to measure outcomes. We're going to assess

0:31the usage, evaluate the risks, and see what improvements might exist. Here's the interesting

0:37part. If you use AI, you should be able to clearly measure whether you are getting better or worse,

0:44and if you're getting worse and you can pinpoint it to AI use, then you need to stop. If you're

0:49getting better and you can pin it to AI use, then you need to clearly understand exactly what part

0:55of the art that is benefiting from AI use. If you don't clearly understand what's making things

1:02better or worse, then you shouldn't be doing it. There's a huge impact to your company. Maybe some

1:09sort of legal violation. There's a huge impact to the customer. Maybe some sort of proprietary

1:16information leak or personal information. This list is out of control. This stuff can get out

1:22of control very quickly, and there's a reason they have cybersecurity insurance for hacking,

1:29penetrations, exposures, and stuff like that. AI is a great way to fall into that trap,

1:36and I think I'm remembering that the rapper Eminem is currently suing someone for $140 million

1:45because they used his entire library to help train one of their models, and they didn't ask him. I

1:52think that applies here as unethical use. So let's jump into this one with our final AI reality check.

1:59There was a tool called Compass within the U.S. criminal justice system, and it had a bias. It

2:06overpredicted the likelihood of recidivism, meaning the potential to offend again to commit crime

2:14for black defendants far more than white defendants. The impact is fewer black defendants

2:22were getting out earlier. They weren't getting parole. They were being penalized by a biased AI

2:29model. It's like whatever you think is a bias, and if you build a tool without establishing a neutral

2:37platform or a neutral standpoint, you are going to inject your own bias into it, and the reason

2:45it matters here is because a risk algorithm might overscore or underscore a group of people. Maybe

2:52you think that women of a certain age bracket will be early adopters of this new feature,

2:58and you're wrong, or maybe you think that this particular demographic won't like this feature,

3:04but then they actually request it. Those are biases, and it happens all the time with people.

3:10We have internal unknown biases. We just have to deal with it, and so if we're not using the

3:18appropriate mindset and having conversations with others so we can balance these biases out,

3:26you're going to end up with these kinds of problems. So I think I've said enough about that.

3:30Let's just rock on. You have to consider the customer impact before you use AI to make decisions,

3:36before you use AI to prioritize what you think the customer is going to find more valuable.

3:42You may expose some sort of proprietary customer information or capability within open AI,

3:50and now it's just out there, and if someone finds it on the back end, and I don't know how all that

3:56works. There's a bunch of brainiacs out there doing magical stuff with AI, but if they find it

4:01and then try to benefit from it, who do you think is on the hook for it? That would be you,

4:07because you're the one that caused it, and like I said, the system may introduce bias if not

4:13monitored. I've seen this firsthand. I've asked chat GPT about which states are the best,

4:20which religions are the best, which age brackets are the best from a generational standpoint,

4:26Millennials, Gen X, Gen Z, Boomers, and stuff like that, and when you read some of the answers,

4:33you can clearly see the bias. Now you might say maybe you just asked the question wrong or the

4:40data input was wrong, and that's possible, but the bias isn't hard to see, at least not for me.

4:46I talked about this. I'm going to let it stand. You cannot use an open source system for your

4:53project, for your product, for your customers, for your clients. You simply can't do it unless

5:00you get express written and signed permission to do so, where they very clearly understand

5:06that their information is going to be put out in an open AI landscape on the interweb,

5:14and I'm willing to bet if you explain that to them, they're going to say nope, not having it.

5:18Don't assume. Assumptions are beliefs you hold to be true or false in the absence of proof, and if

5:24they're wrong, if you can't validate them, you cannot implement them, so it's either a yes, this assumption

5:33is valid or not, and if it's valid, maybe then we can implement it, but if you can't validate it or refute

5:39it, it's kind of hanging out in the middle, then you absolutely can't do whatever you're considering,

5:45and if the art is going to maintain trust, we must maintain transparency, and that includes with our

5:51customers. They need to know what kind of AI tools we are using, how we are using them, and what we're

5:58doing with the output. If you can't explain that, you're probably going to get into trouble in some

6:04manner. Ethical considerations guide responsible delivery, and here's what I mean. Honesty is

6:11something you have when everyone can see you. Ethics are what you have when no one can see you,

6:17so just because no one's around doesn't mean you have free reign to start inputting data or using

6:23AI in a manner that you shouldn't be doing, because the organization retains accountability.

6:29They can be sued. They can be dragged into federal court. They can be dragged before Congress.

6:35So could you. It could cost them millions and or billions, whatever it is. This is a brave new

6:42world as it relates to AI, and responsible innovation enhances long-term value. You can't

6:48roll this out in various localities and not do it at the art level. You can't have some sort of

6:55haphazard AI system at play within the art. You need to clearly understand the impact within the

7:01company and outside of the company to your clients, customers, and end users,

7:06and if you don't understand that, then you're not responsible enough.