S4:E8 Leadership & Success Podcast — LIVE with Coach BZ: Leading in the AI-Enabled Future

Hi, I'm John Maxwell. When the top companies in the world are looking for trainers, speakers, and coaches, there's one name that stands above the rest, and that's Maxwell Leadership. Our certified team members have gotten proven results for companies all over the globe, and our reputation, I'm proud to say, is second to none. Some of our members have risen to the top of their profession, including the one you're going to meet today. I proudly introduce you to my friend Bob Fabien Zinga.

Welcome to the Leadership and Success Podcast with your host, Coach BZ. If you need to develop into a better leader, this podcast is for you. If you want to achieve a greater level of success, this podcast is for you. His mentor, Dr. John C. Maxwell, said it best: everything rises and falls on leadership. We hope to inspire you today and provide you with an insight that has the potential to positively impact the trajectory of your life. Welcome to the Leadership and Success Podcast.

All right everyone, uh, Time is a charm, they say. We're having some technical difficulties this morning, so with no further ado, uh, I'm going to let Joe and Mary AI take over. Today we're going to be talking about Leading in the AI-Enabled Future: US Navy Cybersecurity Commander's Journey. That's an article I wrote last month, and we are going to go through this devotion today. Again, let me know where you are coming from, uh, which city, state, country, or region, area, in the comment. And also, if you have a question for me, go ahead and type it in the comment section. So I'll be back in about 30 minutes. Joe and Mary AI, it's your turn. You're next.

Welcome to the deep dive. This is where we take a whole stack of information, maybe your articles, your research notes you've gathered, and we really unpack it. We try to find those essential nuggets of knowledge, those surprising insights.

Exactly.

And our mission today, well, it's a really timely and honestly critical topic, leading effectively in the age of artificial intelligence. We're talking about navigating this landscape, it's evolving so fast and not just with tech skills, but with real leadership, the kind that understands AI's power and knows how to harness it for good.

Indeed. And for this deep dive, we're drawing quite a bit from a really compelling piece by a US Navy cybersecurity commander, Bob Fabien Zinga.

BZ Zinga, right.

Yes, titled "LEADY: Leading in the AI-Enabled Future." 'A U.S. Navy Cybersecurity Commander's Journey.' Now, what's really powerful here, I think, is his perspective. It's not just military-focused.

No, that's key. Right.

It's about universal leadership principles. They apply to anyone facing AI's transformative power, whether you're, you know, in the boardroom, managing a team, or maybe out there on the digital frontier somewhere.

And his background really speaks to that, doesn't it? Over 2 decades leading teams, both military and Silicon Valley.

Exactly. That combination gives him this unique lens. He bridges these highly technical domains with really practical ethical leadership. It's quite a blend.

OK, so what's our core focus here then? What are we trying to get at in this deep dive?

Well, I think it's about understanding the specific challenges AI throws at us, but also the incredible opportunities it presents for leaders.

Right, the practical side too.

Absolutely. And maybe even more importantly, the— practical, actionable steps we can all start thinking about, maybe even taking to prepare our teams and ourselves for this future.

So we're kind of following Commander Zinga's journey, seeing how navigating this rapid tech change maybe lit a path for the rest of us.

That's a good way to put it. It's like a roadmap, really, for how to not just survive, but actually thrive in this AI-enabled world.

What's really striking to me is how he takes this complex strategic thinking and just boils it down.

Yeah, into practical habits, actionable habits.

And he grounds it all in his military experience, but also brings in these broader ethical thoughts. It's not just about the tech adoption.

Yeah, it's holistic. It goes way beyond just, okay, here's the new AI tool.

Yeah.

And I think that approach, well, it promises to offer you, the listener, some significant aha moments.

Yeah, like how you can adapt your own leadership.

Exactly. Adapt your style, your strategy, to meet what this new era demands. Real practical takeaways you can hopefully apply pretty quickly in your own work.

Okay, let's jump right in then. Commander Zinga kicks off his article with this really powerful story, an anecdote that just immediately sets the stage. It conveys the urgency here.

Mm-hmm.

He talks about being on the bridge of a Navy aircraft carrier, the USS Essex.

Right, during this big multinational exercise.

Yeah.

US, Australia, Japan.

Yeah, and it was a pivotal moment for him. He describes seeing, really seeing, how satellites and cyber sensors were fundamentally changing the battlefield.

It wasn't just about ships and planes anymore.

Not at all. It was this invisible digital domain suddenly influencing every single tactical decision, reshaping reality, you know, in real time.

And the core realization he had then— wow, it hits home today, doesn't it?

It really does.

He immediately understood that, and I'm quoting here, "New technologies were arriving faster than our traditional training cycles." That's the crux of it. And that's not just a Navy observation, right? That's everywhere.

Absolutely universal. Finance, healthcare, manufacturing, you name it. Even creative fields. The sheer speed, the velocity of AI development and rollout.

It just outpaces our ability to keep up. Our training programs, our workflows.

Exactly. It creates this constant kind of widening gap between what the tech can do and whether we're actually ready to use it effectively.

So, The big implication for you listening: what Zinga saw back then, that's precisely what we're all grappling with now, globally, with AI.

This speed of technological change, it demands we evolve too. Not just our individual skills, but critically, how we lead our organizations.

We can't just stand still.

No way. We have to proactively embrace this mindset of, like, continuous adaptation, continuous learning. Otherwise, we get left behind.

And not necessarily by competitors, but maybe by the very systems that were supposed to help us.

Yeah, it's about building that deep organizational agility, isn't it?

It really is. And connecting this back to Zinga, that initial insight, that powerful moment on the bridge, it became the catalyst for his commitment, his unwavering commitment to continuous learning throughout his career.

So it wasn't just about taking courses.

Oh, no, much broader. It went from mastering really complex communications at sea, which is all about intricate tech and human coordination, to him actively, like, experimenting with the latest data analytics tools in his own time. And that personal journey, it directly leads to his central point, his thesis: our skills must evolve with the mission.

That really lands. The mission itself keeps changing because of the tech. Exactly. Whether it's military, business, whatever. Yeah. The goalposts are constantly moving because of technology. Mm-hmm. So our skills, our team's abilities, they have to flex and grow right along with it. Otherwise.

Irrelevance.

Yeah. And the analysis here points to this critical imbalance, something we don't always talk about enough. Tech velocity just outpacing human training cycles.

Creating that constant skill gap.

Which keeps widening. It's not just what AI does. Automation Insights. It's how fast it completely reshapes how we operate.

New models, new apps coming out all the time.

Right. Especially with things like GenAI. By the time you roll out a traditional training program, the tech might have iterated 2 or 3 times already.

Making the training partly obsolete right away.

Precisely. And Zinga states it plainly, it's worth repeating: Today's leaders, military or tech, face a similar challenge as artificial intelligence reshapes every domain of operations.

So it's not just a military problem, it's everywhere.

It's a defining feature of our modern information economy, affects product development, strategic planning, Everything. And if you can't bridge that gap—

Big disadvantages. Frustration. Maybe even failures.

Exactly.

This leads us to something really interesting. This idea Commander Zinga brings up. The era of AI everywhere.

Ah, yes. He borrows that from IDC analysts.

Cites a Sprinklr-sponsored IDC whitepaper, "How AI is Changing the Game." And that paper argues that these next-gen techs, especially generative AI, Maybe explain that quickly.

Sure. GenAI is basically AI that can create new stuff—text, images, code—rather than just analyzing existing data. Got it.

So GenAI and other advanced AI will fundamentally change not just what we do, but how we use data and critically how we deliver value. That's the core idea.

We're moving really fast beyond AI as just some niche tool. It's becoming omnipresent, embedded in almost everything we do at work.

So in this new era, what's the key takeaway, the critical insight for listeners trying to get an edge?

It's that data and agility become our force multipliers. That's the phrase they use.

Data and agility. Meaning, just having tons of data isn't enough. Nope.

It's your ability to use that data quickly and effectively, combined with how flexible your organization is, how agile in adapting to new info and changing conditions. That combination determines success.

Like an equation: data plus agility equals exponential impact. That's a great way to think about it.

Without both, AI's potential just sits there, largely untapped. Okay, so let's get practical.

What does this mean for a leader on the ground?

What do you actually do? Well, first, leaders have to proactively foster workforce agility. That means continuous upskilling, helping your people leverage these AI tools.

Not just a one-off training session. Definitely not.

It's an ongoing investment, making sure they can interact with AI, use it, but also understand its potential and its limits. Maybe internal hackathons, sponsoring certs, building AI training into daily work. Okay, that's one. What else? Second, and this is absolutely critical, leaders need to build robust data governance and ethical guardrails.

Ah, governance and ethics. Crucial as AI becomes more widespread.

Paramount. Because the quality, the security, the ethical use of the data feeding these systems, it all matters immensely. Without strong governance, AI can, you know, accidentally amplify biases. Compromise sensitive info. Lead to really bad unintended consequences.. And that erodes trust, trust in the systems, trust in the organization using them.

Which brings up a really important question then, as we're navigating this era of AI everywhere, what's the biggest risk? It's interesting.

The IDC paper explicitly points to workforce challenges and employees' skill sets as topping the list of risk factors for future strategies.

So it's not the tech itself being risky, it's our capacity to use it.

Exactly. We tend to fixate on the tech advancements, the latest models, the algorithms, But the real bottleneck, the biggest vulnerability for organizations, it isn't the code, it's the people.

Are people prepared, skilled, empowered to use these tools well and ethically? Right.

If your team isn't equipped with the knowledge and that adaptable mindset, the most advanced AI system is just, well, an expensive, underutilized asset.

But if you do focus on ensuring that workforce agility, you mentioned upskilling, hiring. Right.

Through upskilling your current team or targeted hiring, or maybe a mix. If leaders do that proactively, it directly drives growth and it significantly improves the employee experience too. How so? Well, think about it. When your people feel equipped, confident, capable with new tech, it boosts more than just productivity. It lifts morale, engagement, creates that psychological safety around trying new things, which in turn improves mission outcomes, directly improves mission outcomes, whether that's profit, market share, efficiency, or, you know, national security objectives. It creates this powerful positive loop.

Investing in people transforms that potential risk, the skill gap, into a real engine for success. Precisely.

It becomes a competitive advantage.

Okay, so we've established the need for agility, for upskilling. But what's the ultimate point? What's the goal of all this effort? Commander Zinga hammers home this idea of focusing relentlessly on outcomes and experience.

Yes, almost singularly focused on that. It's not about collecting the latest AI gadget just because it's new. No shiny objects. Right. It's about what those tools achieve, the tangible impact on the mission, the customer, the user experience. The IDC paper sees the same thing, noting that leading organizations have shifted from a technology focus to an outcome-focused mindset.

So they're not asking, what tech can we buy?

No, they're asking, what problem are we trying to solve? What experience are we trying to create? And how can AI help us get that specific result? That makes sense.

Like, you don't buy a fancy sports car just to let it sit there. Exactly.

The look might be nice, but you buy it because it gets you somewhere faster, maybe safer, more comfortably, whatever the goal is.

The car is the tool. The journey, the arrival, the benefit— those are the outcomes.

Perfectly put. With AI, the algorithm, the model, the data— they're just tools. The improved decisions, the better customer satisfaction, the efficient processes, the critical insights— those are the real outcomes we need to focus on. Keep your eye on the destination, not just the vehicle.

And in the military context, this outcome-driven approach means a really big shift, right?

A crucial shift. Moving away from just getting advanced tools to intensely focusing on achieving specific, measurable mission outcomes. So it's not about having the coolest AI system. No, it's about whether that system directly, demonstrably, efficiently helps you succeed in the operation. It demands a really deep understanding of the objectives and applying the tech purposefully.

Zinga gives some good examples, doesn't he? Like with intelligence analysis.

Excellent tangible examples. He says deploying machine learning for intel analysis only makes sense if it delivers faster, more accurate insights that improve decision-making in real time.

If it doesn't speed things up, reduce ambiguity, improve the actual intelligence.

Then its value is questionable. It's a solution looking for a problem. Same with, say, modernizing a command and control system with AI dashboards.

You don't measure it by features or how slick it looks?

No, you measure it by how much it improves operational tempo and reduces errors. The real value is in those tangible improvements—efficiency, accuracy, mission effectiveness—not just having the tech.

This idea of value exchange sounds really important for leaders. Absolutely crucial to internalize.

When you tie technology directly to clear, measurable results, you know, mission accomplished or cyber threat thwarted, it creates this crystal clear understanding of purpose and impact for everyone.

It aligns the whole team from the top down.

Exactly. Everyone knows why you're doing this AI initiative, how their work contributes to a real outcome, and what success actually looks like. It builds ownership. Accountability. That makes a lot of sense.

The IDC paper supports this too, mentioning outcome-based approaches.

Yeah, even in things like pricing and metrics for business. And you can translate that directly to a military unit or a business division. Tie performance to measurable mission outcomes, not just activity. Prove the value. Show it's a strategic investment with clear returns.

Okay, this is where the rubber meets the road. If we want to build this outcome-driven culture focusing on what AI achieves, We need practical habits, leadership habits. Right.

And Zinga outlines 5 key areas, 5 foundational pillars that leaders can focus on right now to get their teams ready.

And these aren't just abstract ideas.

No, they're actionable steps you can start implementing today in your own area. Let's dive into them. All right.

Pillar 1: Cultivate continuous learning. Now, this sounds obvious, but Zinga takes it further, doesn't he?

Much further than just mandating some online courses. His core advice is for leaders to actively sponsor training, facilitate war games.

War games being like simulated exercises?

Exactly, testing strategies and skills in a safe environment. And supporting certifications in AI, cyber, data science—it shows real commitment from the top.

But it's more than just formal stuff.

Oh yeah. He encourages engineers, analysts to experiment with AI tools during downtime, foster curiosity, Reward hands-on exploration. Make learning part of the job, not an add-on. And sharing what you learn. Critically important. Share lessons learned from pilots, exercises, openly. Make it a collective, transparent learning experience.

So what does this mean for me day-to-day as a leader? It's about creating an environment—

where learning and experimenting are just part of the daily rhythm, part of the culture. Maybe Dedicating innovation hours or learning Fridays.

Giving people space and resources, software, cloud credits, time to just play with tech safely, even fail safely.

Precisely. It encourages being proactive, not reactive, about tech change. Mm-hmm. Your team builds intuition and comfort with AI before it becomes absolutely mission critical. It builds resilience, adaptability.

Okay. Pillar 2 is promote cross-functional teams. Silos are a huge problem in so many places.

A persistent, frustrating challenge. Zinga says leaders need to actively break them down, get operators, coders, analysts working together collaboratively.

So intentionally structure teams and projects so people have to mix expertise.

Yes. Tackle problems holistically. The benefit is huge. It expands everyone's perspective.

The operator gets the real-world context. The coder knows the tech possibilities. The analyst sees the data side.

And when you combine those, you get a much more agile, intelligent, resilient, Digital workforce, right? A team that uses AI effectively because they understand the whole picture from data input to real-world impact.

And IDC sees this too. Connecting insights across functions leads to better decisions.

They call it a unified customer insights fabric in business terms. But the principle is the same. Better decisions come from connected insights.

And why does this matter so much for AI?

Because AI thrives on diverse data and perspectives. If you train and deploy AI from just one siloed group with their narrow view or dataset, it misses things, introduces bias. Absolutely. Yeah, it can lead to flawed outputs, inequitable results. Think biased hiring algorithms or ineffective intel assessments. Cross-functional teams ensure a more holistic, robust, and ethical approach. They make sure the diverse human element guides the tech.

That's powerful. Okay, pillar 3: Implement strong data governance. This feels foundational, but maybe easy to skip in the rush. Tragically overlooked.

Often. Zinga's advice: establish clear policies on data quality, access, security before you deploy AI. It's like building a solid foundation before putting up the walls.

Because the AI is only as good as the data it gets.

Exactly. Garbage in, garbage out, but amplified by AI.

And the IDC paper warns about this too, saying we need governance tailored for AI.

Yes, that adopting AI at scale highlights the need for data governance that is tailored for more pervasive AI and GenAI capabilities. Traditional governance might not be enough. Why not?

What's different about AI?

AI often uses huge amounts of diverse, sometimes sensitive data from all over. You need governance that addresses specific AI risks, managing intellectual property, protecting personal data, ensuring data provenance, tracking data lineage for training. It's a higher level of rigor.

So practical steps mean clean, structured data, everything from sensor logs to personnel records. Unwavering commitment to that.

Yeah, right. And ensuring all teams follow consistent Consistent tagging, categorization, documentation. Imagine training AI on a database where info is recorded 10 different ways or fields are missing. The AI would struggle. Bad outputs. Or even dangerous ones. Good governance makes data consistent, accurate, accessible, secure. It turns data from a liability into a massive asset for AI.

What's really striking is Zinga's emphasis on doing this before deployment.

It really underscores a fundamental truth. You can have the best AI algorithms, the most computing power, the smartest data scientists, but if you feed the AI garbage data— inaccurate, inconsistent, biased, incomplete—

you get garbage insights, flawed decisions.

Every time. Data quality isn't just an IT detail. It's a strategic imperative. It directly impacts mission success and trust. So the question for you, the listener, is: are your basic data practices really ready for AI? Have you invested the time, resources, cultural effort to clean, structure, secure your data before going big on AI?

Without that foundation, AI's potential is wasted or worse.

Exactly. It could lead to really detrimental outcomes.

Okay, pillar 4, and this connects back to some deeper themes. Focus on ethics and trust. As AI gets more involved in decisions, ethics becomes non-negotiable. Absolutely paramount.

Zinga's advice is for leaders to actively lead discussions on Ethical AI use. This isn't just compliance. It's fostering a culture where ethical thinking is baked into every stage of AI.

Developing clear guidelines, guardrails.

Yes, for all AI projects, specifically designed to minimize bias, ensure transparency, explainability, accountability.

And leadership is crucial here. The IDC paper says leaders should set up processes to evolve these guidelines.

Critically important point. Ethical considerations aren't static. They evolve as tech matures, as societal impact becomes clearer, as new uses emerge. Leaders must be proactive, constantly refining and enforcing these frameworks. It takes foresight, principle over expediency.

In the military context, what does that look like?

Auditing AI for bias? Rigorous auditing, yes. Especially in sensitive areas. Meticulously protecting civilian data under privacy rules. Holding teams accountable for AI-driven decisions. Because even if the AI recommends something, the human leaders overseeing it bear the ultimate moral and strategic responsibility. So, for you, listener, whether military or civilian, it's about proactive ethical leadership. Not just the code, but the human impact. Building unwavering trust in these systems. Making sure AI serves us responsibly.

And the final pillar, Pillar 5: Measure Impact, Not Outputs. This ties right back to being outcome-driven.

Simple idea, but often missed, Zinga says. Tie new AI initiatives to hard, measurable metrics.

Not just activity metrics like we deployed X models.

No, metrics showing real-world improvements, reduced decision times, better resource use, higher mission success rates, measurable bumps in customer satisfaction. Quantify the value AI brings.

And he suggests that crawl, walk, run adoption strategy.

Very practical. Start with small, high-value pilots. Measure the outcomes carefully, iterate quickly. If it works, demonstrably delivers impact, then scale up to walk. Eventually, maybe run for broader deployment. Reduces risk, avoids wasted effort. Builds momentum and buy-in too. And the key question he says to ask at each phase is really insightful: Did this tool improve an end-to-end process or eliminate a bottleneck?

That's simple but powerful. Shifts focus from just deploying tech to does it actually solve a real problem? Does it improve things systemically? Exactly.

It pushes leaders to think about holistic, measurable improvements, not just isolated AI features. Define success by problem solved, not just what the AI does.

So connecting this to the bigger picture, keeping those measurable goals in sight ensures tech serves the mission, not the other way around.

Prevents falling into the trap of AI for AI's sake, investing huge amounts in things that look cool but don't deliver real value. This pillar is about validating every AI investment, through clear, tangible improvements in outcomes.

Now, Commander Zinga does something really interesting. He takes this even deeper by weaving in insights from timeless wisdom, specifically biblical texts.

Yeah, that section, Think on These Things, it adds this compelling layer of depth about what it means to lead ethically, effectively in this AI future.

It's about grounding all this rapid tech progress in deeper principles, ensuring our moral compass stays true.

Right. Bringing that human element, the character aspect of leadership, right into the center of the discussion, which often gets lost in purely technical talks.

It does raise a fascinating question, though. How can ancient texts really speak to something as modern and complex as AI?

It's a great question. And what's fascinating is how they provide this moral strategic compass that goes beyond just the technical side. They encourage a more reflective, purposeful, principled approach.

An anchor, maybe? So we're not just swept away by the tech changes.

Exactly. Prompting leaders to think about long-term implications, the ethical dimensions, inviting you, the listener, to look beyond the algorithm's output to the broader impact on people, teams, society. Okay, let's explore some of these.

The first one is from Colossians 3:23: Whatever you do, work heartily as for the Lord and not for men. How does that apply?

Well, in this age of AI, where performance is so often measured by metrics, dashboards, machine efficiency, Zinga suggests leaders need to anchor their drive not just in getting public praise or hitting numbers, but in a deeper sense of purpose. Working heartily means bringing intentionality, excellence, moral accountability to every AI initiative, whether you're coding, commanding, or setting strategy. True impact comes from that wholehearted, purpose-driven service, not just superficial output. It really asks you, are your daily efforts driven by metrics or by meaning and mission? What if your main audience was a higher purpose? Not just your boss or the quarterly report.

Hmm. Interesting. Next is Daniel 12:4, "And knowledge shall increase." That sounds eerily relevant.

Astonishingly so, right? Foreshadowing today's digital data explosion, the infodemic. The leadership insight here is crucial. Leaders must learn to tell the difference between just having data and applying wisdom.

With information overload potentially paralyzing us.

The best leaders cultivate clarity, sharp discernment, and strong ethical filters. True leadership transforms raw knowledge into actionable insight, and insight into responsible action. So it makes you reflect. With knowledge exploding around you, what filters are you actively using to separate noise from wisdom? How intentionally do you manage your information intake?

Good question. Then there's Proverbs 4:7. The beginning of wisdom is this: Get wisdom, and whatever you get, get insight. So relevant today.

Tech gives us speed, AI gives us scale. But Zinga's point is neither gives us wisdom.

So wise leaders pursue insight just as intensely as they pursue innovation.

Exactly. Prioritize deep understanding over just piling up more information, because only genuine insight, that deeper understanding, can guide us toward ethical, strategic, meaningful outcomes. It asks you, think about a recent decision where you had all the data but still lacked clarity. Where do you turn when you need insight, not just more data?

Okay, how about Ecclesiastes 7:1, Kuv? Wisdom is a shelter, as money is a shelter, but the advantage of knowledge is this: wisdom preserves those who have it.

The insight here is subtle but profound. Data can offer immediate protection, right? Mitigate risks, identify threats. But wisdom offers something more lasting—preservation.

So, in roles like cyber, intel, governance, leaders shouldn't just use information as a blunt tool.

Right. Wisdom tempers knowledge. It ensures our strategies don't just shield systems but actually preserve people, trust, and our core purpose in the digital age. It prompts you to ask, is your current strategy truly preserving what matters most to people—trust, integrity—or is it more focused on just controlling assets?

This connects to the next one, James 1:5: If any of you lacks wisdom, let him ask God.

This seems to emphasize humility.

Which can feel counterintuitive in high-tech fields, right? Mm-hmm. But tech can only make predictions based on existing data patterns. Zinga suggests that true discernment, judging well, seeing nuances, making sound decisions in ambiguity comes from wisdom, potentially from beyond just human intelligence.

So great leaders, especially navigating tricky AI and cyber terrain, need that humility. Need to embrace it.

Seeking guidance beyond human smarts or tech capabilities can lead to discernment, which he calls the foundation of principled leadership. It asks, what challenge are you facing now that needs more than just human insight or an algorithm? Have you sought deeper wisdom or just consulted your team and tools? And the last one mentioned is 1 Chronicles 12:32. Of Issachar, men who had understanding of the times to know what Israel ought to do.

This feels like a direct call to action for leaders today. We need to be like the sons of Issachar, deeply aware of the rapidly changing times, fluent in the language and implications of AI, and crucially grounded in moral clarity and purpose. It's not enough just to understand the tools; we have to understand their impact.

So leadership now demands situational awareness plus the courage to act decisively for people and principles.

Absolutely. It challenges you. Do you really understand the forces shaping your team's future? What principles anchor your decisions amid all this change? Are you just reacting to AI trends, or are you leading with foresight, understanding, and purpose?

Wow. These insights really do bridge that gap between cutting-edge tech and, well, timeless human values. Yeah. It really hammers home that AI leadership is fundamentally about character, foresight, not just technical skill.

It's about who you are as a leader, how wisely you consider the implications of these powerful tools. Uh-huh.

Leading with integrity and wisdom. Exactly. It's a powerful call to move beyond just being technically competent to really understanding impact and responsibility.

And Commander Zinga's background really embodies that, doesn't it? Yeah. Military, Silicon Valley, that philosophy of integrity, service, excellence, respect. It all underpins this holistic view.

Yeah. His work across cybersecurity, AI, emerging tech, leadership, governance, national security. Yeah. It shows how you have to integrate all these fields now for truly strategic leadership.

To really succeed and have a lasting positive impact in the 21st century.

Okay. So let's try to wrap this up. What does this all mean for you, our listener? This deep dive into Commander Zinga's thinking, it shows leading effectively with AI isn't just about adopting tools.

No, it's about fundamentally reshaping our whole approach to learning, collaboration, data, ethics, how we measure impact.

It's about being deeply agile, relentlessly outcome-focused, and crucially, wise in every decision.

And the core message from Zinga feels clear and pretty urgent. The choices we make today about integrating AI will define how effectively and how ethically our teams use this tech tomorrow.

It's an ongoing journey, then continuous adaptation, guided by that focus on people, purpose, and principled decision-making. So maybe the final provocative thought for everyone listening is this: in a world where AI offers incredible speed and scale, are you prioritizing cultivating wisdom and ethical insight just as much as you're prioritizing innovation and efficiency?

Yeah. Are you building leaders of character alongside leaders of capability?

How will you prepare your team and how will you prepare yourself for this AI-enabled future? How do you ensure that as knowledge keeps increasing, so does our wisdom in applying it for the good of our organizations and maybe society too? That's the challenge. Thank you for joining us for this deep dive. Until next time, keep exploring, keep learning, and keep leading with purpose.

Thank you so much, Mary and Joe AI. I appreciate that. So that was it, folks. So we talked about the article I wrote about living in the AI-enabled future, and that future is right here, right now. But again, like we say in the Navy, mission first, but people, people always. And I really truly believe that with the technology expanding and automation, artificial intelligence, and quantum computing and all of that, as a leader, you still need to prioritize people, right? It's people, processes, and technology. So we don't just use technology because it can do something, but because it's trying to really solve a problem. And I think at the center of it, is really prioritizing our folks. All right, that's all I have for today. Let me check and see if there is any question, uh, in the comment section. Okay, I don't see any. And for those of you watching even the replay, please let me know where you are watching from, like city, state, region, area, country. I do appreciate that. And I'm gonna meet with you again tomorrow. And, uh, until next time, this is Coach BZ. Have a wonderful day.

Thank you for listening to the Leadership and Success Podcast with your host, Coach BZ. If you like what you heard, please subscribe to our channels and come back for more wisdom nuggets on how you may develop into a better leader and achieve greater levels of success. Leadership is the most critical skill. The world will always need leaders to lead others, deploy the next disruptive technology, or execute a business strategy. You may as well decide on counting yourself among the 21st century leaders. See you right here next time on the Leadership and Success Podcast with Coach