Hi, everyone. Today, we'll be talking about redefining leadership in the age of AI. So before we get started, imagine this.You're driving at 10 miles an hour through dense fog. Your visibility is almost zero. The road ahead is unknown. You have never been here before, and everyone else in the car is counting on you to get them through safely. That's what leading in the age of AI feels like. My name is, and I've been in the industry for over twenty plus years. I worked in different companies, different sizes, starting from MedTech, identity management, Workday, DocuSign. I also have a, podcast, which is called AI Flitter where I talk to other AI leaders.

And, so today, we'll be talking about the unspoken reality that leaders face today. And when I talk about leaders, it's also tech leads. It's, if you're responsible for for driving AI in your team, you could be an IC. What are the do's and don'ts? Building guardrails without killing innovation because we wanna balance it. How to upgrade your processes to keep pace with it, and what does early signs of success looks like, and what are some of the AI metrics we can use. So what is the unspoken reality that leaders face today? We know that AI tools are everywhere, and but not everyone knows oops. This is really messed up on the formatting. I apologize for that. But AI tools are everywhere, and, we there are no playbooks.

There are no guidelines how to use them. Everyone wants to use the latest and greatest. Execs wants the ROI, and you are stuck in the middle of all of this. So how do you lead through this fog, and what are some of the things to avoid? Because, remember, you can't see the road, but there are speed bumps on the road that you don't want to get a flat tire on. So first of all, chasing every shiny AI tool without focus. There's gonna be new things coming out every day. We don't wanna be, focusing on every shiny AI tool that comes up.

Overpromising quick wins to execs because sometimes when these new tools come up, they make it sound like this is the this is the answer to all of our problems, all of our execution, and this is going to be the magic pill that's going to solve it all. Ignoring team fears and burnout, we don't want to do that, and waiting for perfect data before starting. We don't want to be too slow either. So there's never going to be a perfect time and thinking AI will replace human judgment. If we think that, then that's hole in the conversation. But you need to have an AI in the loop. So what are some of the things to evaluate the new tools? Because there's gonna be new shining object coming up every new day. So whether you're facing a flood of AI tools or solutions, it's easy to get distracted by demos and buzzwords.

And as leaders, we need to be laser focused on what actually moves the needle. So I boil it down to three essential questions. First of all, what specific problem are we solving, and who feels that pain daily? It's not enough to say that we are buying this tool because it will improve productivity or it will help us be more innovative. That might be some of the goals, but you need to tie this to a concrete day to day pinpoint that this tool will solve. And if you can't answer that, then it's time to reevaluate. Secondly, do we have the right skills, budget, and time right now to make this work? This is a reality check. Okay? AI isn't magic dust that you sprinkle everywhere and then just walk away. It requires investment, time, as well as money.

It may require hiring. It may require adjusting workflows. So if you're just hoping that you're going to give this tool to your team and they figure it out, that's a red flag. Okay? And then the third important question is, how will you measure success in thirty, sixty, and ninety days? What tier outcomes? And the first question is going to drive that. What is the pinpoint you're trying to solve, and how will you measure the outcome of it? So, yes, execs want results, but those need to be concrete and timed. Are you saving hours? Are you reducing errors? Are you improving customer satisfaction? What is it that you're trying to improve upon? And that's what you need to measure. Otherwise, it's just all guesswork.

So by asking these questions before jumping in on the new tool, you can protect your team from burnout and your org from wasted investments in time as well as money. And then you build trust by showing you're focused on solving real problems. Because remember, you're not going to get any extra time. You still have project deadlines to meet. So if you're bringing these new tools in, you have to ask yourself, is it going to be helping? Is it going to be slowing down initially? How much? So you need to think about all of those things. So now that we know what not to do, what speed bumps to avoid, and how to evaluate the new tools, how do we start to lead through the fog? Things we'd need to start doing. First of all, you need don't need to be an AI expert. Okay?

There is gonna be new tools coming in, new LLMs, new models, new benchmarks, pretty much every day. You don't need to know the inner workings of every model. Your job is to bridge the gap between what's possible and what your team actually needs. Because if you're like me, there's gonna be my if you're subscribed to, like, newsletters and you're talking to people, there's gonna be, like, a bunch of information that's going to be coming at you. So you need to be able to translate what is it that your team needs. Is will this help you? You need to be the translator. You need to be the interpreter. Okay? And if your team comes up to you and, hey. We want to use this tool. Again, you need to be able to translate that. Secondly, you're not behind. No one is ahead, and no one has all the answers. Seriously.

Even the folks the who are selling the AI tools are still figuring it out because they're also trying to beat the competitors. So if you feel behind, you're not because there are, as I mentioned, so much information thrown at you. Okay? And the only people who'll actually fall behind are the ones pretending they know everything. Admit that you don't know everything. Stay curious. Keep asking questions. Keep turning business problems into questions that even AI can help answer. You can ask, like, hey. How does how does this tool help me with this problem that I'm solving? The best leaders right now aren't experts. Again, they're translators. They're interpreters that can translate all of this from this noise and lead from this fog. The third thing is being AI ready isn't a checklist.

It's a mindset, and you have to keep evolving. There is no playbook for this moment. If you have a playbook, it's already outdated by the time I'm talking to you because this is changing faster than anything we have seen. So any training you have so far, it's already outdated. Being AI ready means that your team knows how to adapt, you know how to adapt, how to ask better questions, and how to experiment without fear. It's not about mastering over one tool or even if you're developing AI within your product lines. It's about building the muscle to evolve over and over again, experiment, know that you don't know everything, and stay curious. So how do we build guardrails without killing infusions?

Because for anything, guardrails are important. So working with AIs right now, like parenting a teen, you want to give it freedom, but you also have to keep guardrails. So first so first thing, start with principles. What does that mean? What does it mean for you and your team and your org? So start with the principles in your team. We provide we prioritize user safety over speed. Okay? And whatever that means for you. Start with the basic principles that will help you keep grounded as you go through this, all of this ever evolving things that we are dealing with. Define red lines early. What's acceptable? What's not acceptable? Okay? What's nonnegotiable? Before anyone even touches something, what are the no go zones? What data do you never touch to experiment with? Set experimental boundaries. Team needs clear lanes to play in.

We want to experiment, but we also want to have some boundaries and guardrails. For example, when a teen is learning to drive, you have them drive with an instructor. You drive in parking lots. So in this case, set those boundaries. Only use internal data. Pile it within your own team. No external role route without review. Have the legal, people in there. And, how and make it make sure it's time boxed. How long this is, pilot is going to run, who is it going to touch, and so on. And risk based testing of AI use cases. Not everything is the same size. Right? Like, what is the low risk? Brainstorming, not taking all of these tools. Check GPT, Copilot, we're all using them. No one is well, most people are not using taking notes anymore. Like, those are low risk. Medium risk are, like, customer facing.

Things that we need to let's say, even if you are using, a tool to help draft an email, you won't review it before you send it. Don't just, rely on agents to draft an email and sell it, send it to customers. Same thing if you're developing a product. You want to make sure that you have, the milestones and the steps in between to make sure that we're having reviews at the direct, right steps. And what are the high risk? What is the sensitive data? Where is full review required? Where is automation going to get us in trouble? And this reduces bottlenecks. It, again, provides playground for people to play in, experiment with, and it also makes sure that we are not putting our customers at risk. We're not putting our data at risk, and we're still able to move fast. Your processes also need to evolve. It goes along with the last slide.

So now that when you're hiring, start screening for adaptability because no matter what the role is, their AI can just help us do things so much faster. Their thing right now you should be looking for is adaptability and flexibility in the in the candidates you're hiring for any role and how, how are they able to cap up keep up with the AI fluency. Onboarding. Add AI tools, ethical guardrails, and experimentation norms to your onboarding. Whether it's company onboarding, team onboarding, make sure you have those guardrails so that when new people come in, you're able to help guide them so they're also on the same page. Team norms, document and revisit how we use AI here, and this needs to be done frequently. So when you're in the thick of it, it's easy to feel like nothing is working, especially when progress is messy.

But there are some early signs of success that you're leading in the right direction so that you don't feel overwhelmed and nothing is working. And things to look out for is that teams are able to run small, safe experiments. They're able to, use the guidelines you have set up for them. Your AI conversations are shift shifting from tools to the outcomes. Will this tool help us get here? Will this, this new process we are doing, this new product we are developing? What is the outcome? And you're listening to conversation where instead of just sharing this saved me time, is a good sign. So it's not like, what's the point of this? This saved me time. So that is progress. It's not actually you know, you can start to see how it's adding to efficiency.

So besides these early signs of success, what are some of the actual AI metrics that should start tracking? Adoption rate. How many people on your team actively use the AI tool? Daily, weekly? And some of these tools have this built in. If no one uses it, it's a fail regardless of what you bought it for. What is the time saved on the specific task? For example, x hours saved in a report generation or regard customer response times this x percent faster. Are there error reduction or quality improvements? If you're, developing workflows, if you're coding and using Copilot, or any of the code helpers, is this cutting down on any manual mistakes? Is it improving accuracy? Is it helping you find bugs before you actually deliver? Are you able to track the error rates before and after implementation? The decision velocity.

Are decisions easier to make or faster to make without sacrificing quality? Can you measure the turnaround time on the key decisions? And what is the cost to value ratio? So how many how much you have spent on licenses, training, integration, upscaling, the time you have spent learning versus the measurable benefits monthly or quarterly. If the cost outweigh gains, then is it just cost right now, but is it over time going to help us? So you need to think short term cost value versus long term as well. Okay? And then how do we keep leading through the fog? As a leader, as a team member, and as just a person in this tech industry, you should commit to continuous learning. As I mentioned, things are changing so fast. If we don't keep up, we're gonna fall behind. So know that things are changing.

And then every week, set up again, don't get overwhelmed because there's it's not possible to learn everything. But if there's something that, you know, that your team will be tackling, then set up some time every week, to go through that. Balance speed without thoughtful with thoughtful reflection. So how do we balance speed along with reflecting on what's working and what's not working? And some of the early successes in the metrics I've mentioned, those will be helpful. And it's important to be a coach right now to your team to thrive in this constant uncertainty. There is a lot of uncertainty. There is uncertainty about things are moving too fast. There is uncertainty about jobs. There is uncertainty about hiring freeze. There is uncertainty about layoffs. There is so much uncertainty.

And then on top of that, there is less people to do the job, and there's more work. And now we're adding one more thing on it that they have to relearn and apply. So help your team also understand that, you know, it is changing. You see that, and you hear them. But how do you thrive in this constant uncertainty? Be a little bit vulnerable that no one has all of the answers, even the industry leaders right now. So all of us are in this together. So leading through the fog and leading in this AI around is not about having perfect vision. What it is, it's about having a compass. Okay? So you can see the compass and making sure we're going in the right direction.

Again, having the principles to ground you in, having the right processes, having the right mindset. And we may still be in the fog, but if we lead with clarity and agility, our teams will feel a little bit safer that you can get them to the destination. So with that, I know we have