#353 AI Leadership in the Real World: Accountability, Decisions, and Transformation

Summary

Podcast Summary: Embracing Digital Transformation

Episode #353 – AI Leadership in the Real World: Accountability, Decisions, and Transformation

Host: Dr. Darren Pulsipher
Guest: Sid Revilla (Executive Change Agent & Advisor)
Date: May 21, 2026

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Episode Overview

This episode explores the realities of leadership during digital and AI-driven transformations within complex organizations, especially in the public sector and large enterprises. Dr. Darren Pulsipher interviews Sid Revilla, an executive advisor with decades of experience in technology and telecom, about fostering accountability, making effective decisions, and leveraging human leadership in a world increasingly influenced by AI. The discussion is rich with practical advice, honest assessments of executive behaviors, and new frameworks for decision-making in the age of AI.

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Key Discussion Points & Insights

1. Sid’s Origin Story & Focus on Leadership Gaps

(01:41–03:30)

• Sid shares his 25-year background in tech and telecom, focusing on bridging the gap between high-level vision and real-world execution.
• He emphasizes that transformations rarely fail due to technology, but due to leadership that can’t scale or clarify priorities.
• Quote: "Transformations rarely fail because of technology... they fail because leadership doesn’t scale." – Sid Revilla (02:45)

2. Post-COVID Leadership & Decision-Making Shifts

(03:30–05:13)

• COVID forced faster decision-making but many leaders have reverted to older committee-driven structures.
• The proliferation of priorities and shrinking accountability are systemic challenges.
• Use the "80/20 rule"—most organizations struggle primarily due to leadership execution, not vision.

3. The Problem with Consensus and the Importance of Accountability

(05:13–07:14)

• American corporate culture often overemphasizes consensus; not always necessary for quick, effective decisions.
• "Resignation Test": If a transformation fails, who is accountable enough to resign? If the answer is unclear, there’s a problem.
• Quote: "If this transformation fails at scale, who will resign? If it’s ambiguous, then you have trouble." – Sid Revilla (07:18)

4. Balancing Input and Ownership in Transformation

(07:15–09:30)

• Leaders must seek input, but final accountability must always be clear.
• Major transformations should have clear owners at every level; push decision-making downward but define ownership.

5. Overcoming Risk Aversion and Fear in Large Organizations

(09:30–10:56)

• In large organizations, fear of being fired inhibits decision-making and accountability.
• Building trust and transparency is a gradual process; leaders must align behavior and establish outcome-based KPIs before transformation.

6. Focusing on Outcomes, Not Activity

(10:56–12:41)

• Avoid mistaking busyness for progress—measure outcomes regularly, not just at project completion.
• Leaders should learn to say "no" more often and explain why, preventing complexity from creeping in via too many initiatives.
• Quote: "Measure outcomes, not activity. You working hard doesn’t mean... you’re going to get to that outcome." – Sid Revilla (11:14)

7. Streamlining Meetings & Decision Rights

(12:41–14:19)

• Excessive meetings are a symptom of indecisive leadership; leaders should ruthlessly clarify who needs to be involved in decisions.
• The need to be visible (FOMO) leads to overloaded meetings and diffused responsibility.
• Quote: "If a leader joins a call and says, 'Everybody drop off except these three people'... That’s where people get nervous." – Sid Revilla (13:23)

8. Judgment Under Uncertainty and Data Dependency

(14:19–16:23)

• Overreliance on data for decision-making can lead to "paralysis by analysis"; sometimes leaders must exercise judgment with incomplete data.
• "Fail fast" and document assumptions—be willing to be wrong, but make decisions and learn quickly.

9. AI as Support, Not a Substitute for Leadership

(16:23–19:21)

• AI can process and analyze data faster but cannot be held accountable; leaders must still ask the right questions and interpret results.
• Dr. Darren introduces the concept of an "Integrity Packet"—a documentation practice to clarify assumptions, ownership, and accountability.
• Quote: "Organizations are rushing to automate, but in doing so, they're accidentally automating away their judgment as well." – Sid Revilla (18:54)
• Encourage curiosity—continuously challenge both AI and team assumptions.

10. The Danger of ‘Green Dashboards’ and Need for Transparency

(19:48–21:32)

• Top-level executives often see only positive reports; leaders should probe deeper into how results were achieved and whether shortcuts or changes were made.
• Leaders must create a culture where reporting bad news is safe and expected.

11. Leadership Postures in AI: Hype, Panic, Denial, and FOMO

(21:43–23:43)

• Executives respond to AI-related changes with hype, panic, denial, or fear of missing out, often driven by board-level pressure and industry comparisons.
• Advises against rushing to be first-movers; instead, pick battles wisely due to rapid technology evolution.

12. Practical Steps for Executives Leading AI Transformations

(23:43–27:44)

• Recognize the "missing 90 degrees"—AI offers speed and prediction (270°), but only humans provide critical contextual experience (90°).
• Clearly define what AI handles vs. what requires human accountability ("ownership ladder").
• Implement a "human pause check"—reviewing and auditing AI recommendations to ensure alignment with core values and strategy.
• Quote: "If a company doesn't have a way to pause and audit AI suggestions, they’ll eventually drift from their core values." – Sid Revilla (27:11)

13. The Myth of Reduced Human Need with AI

(27:44–29:50)

• Contrary to headlines, AI does not eliminate the need for humans—it increases the need for human involvement in oversight, validation, and accountability.
• Workforce roles should shift to managing and questioning AI, not be eliminated by it.

14. The Decision Quality Problem

(31:03–31:29)

• Most organizations don’t face strategy problems, but problems with decision quality.
• Focus on improving decision-making processes to resolve most transformation issues.
• Quote: "They do not have a strategy problem. They have a decision quality problem." – Sid Revilla (31:16)

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Notable Quotes & Memorable Moments

• "Transformations rarely fail because of technology... they fail because leadership doesn’t scale." – Sid Revilla (02:45)
• "If this transformation fails at scale, who will resign? If it’s ambiguous, then you have trouble." – Sid Revilla (07:18)
• "Measure outcomes, not activity. You working hard doesn’t mean... you’re going to get to that outcome." – Sid Revilla (11:14)
• "If a leader joins a call and says, 'Everybody drop off except these three people'... That’s where people get nervous." – Sid Revilla (13:23)
• "Organizations are rushing to automate, but in doing so, they're accidentally automating away their judgment as well." – Sid Revilla (18:54)
• "If a company doesn't have a way to pause and audit AI suggestions, they’ll eventually drift from their core values." – Sid Revilla (27:11)
• "They do not have a strategy problem. They have a decision quality problem." – Sid Revilla (31:16)

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Timestamps of Important Segments

• 01:41: Sid Revilla’s background and leadership focus
• 03:30: Covid’s impact on executive decision-making
• 05:13: Consensus vs. accountability in leadership
• 07:18: The “resignation test” for accountability
• 10:56: How to measure and incentivize outcomes
• 12:41: The cost of too many meetings and unclear decision rights
• 14:19: When "data-driven" goes too far – trusting judgment under uncertainty
• 16:23: The role and limits of AI in executive decisions
• 18:54: The risk of automating away human judgment
• 19:48: The myth of “green dashboards” and the need for curiosity
• 21:43: Leadership postures under AI pressure (hype, panic, denial, FOMO)
• 23:43: Frameworks for human-centered AI transformation: the missing 90°, ownership ladder, human-pass check
• 27:44: The misconception of AI replacing humans—need for new roles
• 31:03: The importance of decision quality over strategy

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Conclusion

This episode delivers a clear-eyed, practical conversation about the real work required for accountable leadership and successful digital transformations in an AI-enhanced world. Sid Revilla and Dr. Darren Pulsipher align on the necessity of clear ownership, outcome-based leadership, and the irreplaceable human role in decision-making—even as AI expands its influence. Frameworks such as the “resignation test,” “integrity packet,” “ownership ladder,” and “human pass check” provide listeners with actionable tools to strengthen accountability and ensure their organizations thrive in evolving digital landscapes.

Transcript

[A]

Yeah, no, you're right.

[B]

That seems rude. If a leader joins in a call and says, everybody drop off and here are the three people I need on the call. And that's where people get nervous. That's why I'm saying that the leadership

[A]

behavior, what decisions are they going to make? They're going to make a decision to get rid of me. I need to be visible. Welcome to Embracing Digital Transformation, where we explore how people process policy and technology drive effective changes. This is Dr. Darren, Chief Enterprise architect, educator, author, and most importantly, your host on this episode, AI Leadership in the Real Accountability Decisions and Transformation with executive change agent and advisor, Sid Revilla. Sid, welcome to the Show.

[B]

Thank you, Dr. Aaron.

[A]

Hey, I'm actually really excited to talk about this because it fits really well with a book that I have coming out in the middle of May which is called AI Teams, AI Augmented Teams. The storyline is all about getting board approval for something. This fits in really well with what we're going to talk about today, which I'm really excited about. But before we dive into that, Sid, everyone that listens to my show knows that I only have superheroes on the show. And every superhero has a background story, an origin story. So without revealing too much of your secret identity, Sid, what's your background story?

[B]

Again, Dr. Darren, thank you so much for having me on the show and I really appreciate you calling me a superhero by any stretch of imagination. I am not a superhero, but I'm glad you recognize me as a superhero so quickly. Getting into my background, I have spent over 25 years in both the tech and telecom industries. I currently live in Dallas, Texas. Professionally, I have built a career bridging the gap between a board's vision and a real world execution. That's what I have written in my books as well. Currently I am advising CEOs and founders on how transformation can be executed in a way that can actually stick in complex enterprises.

[A]

Okay, so how did you get to that point in your career? I mean, because you don't just wake up and start your career. I'm going to advise Board of directors doesn't start that way, Sid. So you had to something to get to that point.

[B]

You're absolutely right. So I have spent, as I said, most of my career in a large scale transformation roles. And one pattern kept showing up. Transformations rarely fail because of technology. Technologies come and go. We do transformations around technology, but they don't fail because of technology. I notice it fails because leadership doesn't scale. Decisions slow down, priorities multiply and complexity creeps in. So my focus now is simple. Help readers leaders reduce noise, create clarity, and make better decisions consistently. So that pattern actually drove me to pivot my career and interest to go and help and work with some of the founders and CEOs to bring that clarity and help them make better decisions consistently.

[A]

Okay, well, that helps. And how has that been? Have you seen any big changes since COVID hit in the way that executives and boards are addressing issues? Because a lot of stuff happened during the first six months of COVID where decisions were made pretty quick. Have you seen them still adopting those same muscles that they used during the beginning of COVID or are they back to the old way of doing things?

[B]

There are different ways. Covid is one big factor that actually made people think differently. Now you're working remotely, so which means they have to bring in new tools. That's technology boom again. Right? But one thing I consistently notice is people are trying to change, mostly the technology drive the change in the leadership, which is a good thing. But what's still unclear is the decision. Ownership is unclear. Committees grow, accountability shrinks. That's the key thing I am trying to bring up to the leadership level. Sometimes don't even notice this. Way too many priorities. Leaders keep saying yes, so focus disappears. So everything becomes important and nothing, nothing gets done. Again, Dr. Darren, for your listeners, I don't want them to think it's 100% of the case. It's 8020 rule. You got to apply this 8020 rule everything we discuss in general. Right. There are some companies have done fantastic and phenomenal job. Most of the companies are still struggling only because the leadership are heading in the right direction. Their vision, their priorities are correct, but execution is where they don't spend enough time.

[A]

So, all right, so that brings up an interesting point because you mentioned at the beginning that accountability and decisions have been. What's the right word? Based in committees now. So no one's taking accountability. Well, part of that's been because we were told, hey, I have to build consensus. But I think we all know some of the most successful CEOs don't build consensus. I mean, look at Elon. Elon's a great example of that. He doesn't build consensus.

[B]

Yes.

[A]

He goes in and says, this is the direction I want to go. You either tell me I'm wrong or get out of my way and do what I said. Same thing with Jeff Bezos. He created Amazon not by building consensus, but by making decisions. So what is it in American corporate America today that makes us think that we have to build all this consensus before we can make a decision. Why do you think that happened?

[B]

Yeah, well, Elon is an extreme example because he's a visionary, right? Yeah, yeah, yeah.

[A]

He's an extreme, obviously extreme example. Yeah, yeah.

[B]

I mean, they're all great visionaries. So with that visionary and the success they have, they. They build a different level of confidence where they don't need anything. You know, I call it, you know, I said so. Just go do it.

[A]

I said so. Yeah, yeah.

[B]

You know, but that doesn't work for, you know, 80% of the CEOs. You need to still get concerns, but not to the point where you slow the decisions. You make better decisions, you bring the clarity to the team. Fewer priorities, remove the complexity wherever you can. That's what makes transformation stick.

[A]

Okay, so ultimately, build consent, ask your staff for input, but the decision rests with the leader. Ultimately, they're accountable.

[B]

Absolutely.

[A]

I think that's what's missing. Would you agree?

[B]

Totally. I mean, I read somewhere and I strongly believe I was on both sides of the aisle. I call it as a resignation test. Right. You bring the clarity, you provide the vision, you come up with a transformation and then you identify one owner to execute on the transformation, and you apply this resignation test. If this transformation fails at scale, who will resign? Oh, and if the answer is clear, there is a clarity to that question, then you're golden. If it's ambiguous, then you have a trouble somewhere along the line. The decisions will not be made. Either the quality of the decision is bad or they will take forever. And that's where the committees creep in. You know, we all form, you know, kumbaya. Let's all come together and make addition.

[A]

Well, how do I balance? There's got to be a balance, right? Because I have to say, hey, you can't just be a dictator. You need to ask. And the reason you hire staff and is to give you feedback or to go execute on what your vision is. So there's some happy medium in there. But ultimately people need to know that, hey, there is accountability. If you don't get done what we decided as well, not we decided, ultimately someone's responsible. That's what I think we're getting to. On these digital transformations especially.

[B]

Absolutely. For every transformation, every, I wouldn't say activity is a low level task, but for every major implementation, there got to be a single owner. It doesn't have to be CEO for every single thing. I mean, look at the big corporations. You have layers, you have CEOs, you know, CEOs, SVPs, VPs. Directors at every level there is some level of accountability and ownership. They got to take ownership for those, you know, specific transformation activities and that has to be defined. And that way they can make the decision on their own. They don't need to wait for someone else to make addition for them.

[A]

So what I find in a lot of large organizations, they become very risk averse. So no one wants to take on that responsibility making a decision. So what can I do in a culture, in these large organizations, it's a corporate, it's an American corporate. Problem is fear, right?

[B]

It's a fear, right?

[A]

How do I overcome that in my organization? Let's say, hey, I do want, let's say I'm managing a hundred thousand person organization really big, right? Fortune 100, right. And I come in and say, look, this is the change we want to make. I want to push decision making down as far as I possibly can and give accountability down. And, and these directors are like going, I'm not going to take responsibility because I'm going to get fired if I'm responsible. That's a real fear. So what can I do? What are some tips I can do to overcome that?

[B]

Yeah. And it cannot happen overnight, right? Dr. Darren? It's going to take time. There's a trust involved, there's a transparency needed. That's a journey on its own. But the leaders can start doing few things that can establish that trust and transparency. Right. You align your leadership behavior before launching any major transformation, especially at scale.

[A]

So what sorts of, what sorts of things can I do as a leadership team before I start doing?

[B]

You can establish few KPIs or few ground rules if you will. Right. You measure outcomes, not activity. I see this trap many times. People are so busy, everyone is working hard. Don't take me wrong. They are all extremely. We have extremely hard workers in American corporate world. Right, but they're all busy. But is anybody measuring the outcome? What are they working towards? You got to explain them. Okay, here is the outcome. This is what we need to achieve. You working hard doesn't mean you being busy. Doesn't mean that you're going to get to that outcome. You measure the outcomes on a more regular basis. Don't wait till the project ends after six months or a year. So measure outcomes, not activity. And leaderships need to start saying no more often and explain why. You know, with a fast world AI especially I know you're writing a book and I just finished my AI360 book which will be published soon. AI can make decisions fast. Oh yeah. You need to know when to say no to those decisions. Right. Say no more often and explain the team why we are saying no. Stay the course. That's how you remove complexity before adding new initiatives or nuances to the existing complex transformation already in place.

[A]

All right, so say no is one of the things I heard. The other thing I heard was simplicity. Measure outcomes and measure outcomes, not activities. I love the measure outcomes because I see a lot of people in a lot of meetings.

[B]

That's a big red flag to me. Which means nobody's ready to willing to make a decision.

[A]

Nobody's doing any work. Everyone's just talking about doing work. Right.

[B]

I've seen some great leaders, Dr. Ray. It feels rude, but they join a call and they look at the people attending the call, 25 people on a call, and right off the bat they ask a question. Who is a decision maker here? Who is going to present and in some cases you're going to take a note, take the notes. So you need only three people on the call. If everybody else is supporting these three activities, you have three leaders. And if everybody is supporting these three leaders, then those three leaders are not doing their job.

[A]

Yeah, no, you're right, that seems rude.

[B]

If a leader joins on a call and says no, everybody drop off and here are the three people I need on the call. And that's where people get nervous. That's why I'm saying that the leadership,

[A]

because they think, what decisions are they going to make? They're going to make a decision to get rid of me. I need to be visible. I mean that's what it's all about. Right. And reward visibility.

[B]

If I'm not on the call. Oh my God. It's a fomo. Fear of missing out.

[A]

Yes. Yeah. And how will the boss ever know who I am if I'm never in a meeting with him?

[B]

Yep.

[A]

Right. Isn't that how it works?

[B]

That's how you know we are wired to think. And that's how it's been working because of the leaders behavior. And that's where I'm working with senior execs right now, to change and bring the clarity in that behavior and alignment across leadership. It's not an easy task. You know, it takes time, it takes a lot of trust in each other.

[A]

Right. And well, because things are changing people, when things change, people get nervous.

[B]

Right? Yes.

[A]

Right. So I love that the leadership says, hey, I only need certain people on this call. I don't need 25 people that are looking at emails or Instagram feeds while they're listening to a call.

[B]

Yeah. And this is what I. I. Coach is a big word. I don't like to use coaching CEOs, and. But this is what I kind of bring up in my conversation with the leaders, is you got to allow your leaders to make those judgment calls under uncertainty. You cannot have the whole data in the world to make a decision. Remember in 2000s, you know, we talked about this earlier. In 2000s, we all have come up with a concept called data driven decision.

[A]

Yeah, yeah, yeah.

[B]

It's a great concept. It's a powerful concept because we were winging it before. Now at least, you know, hey, look at the data. Let's analyze the data. We have, you know, systems now that can process information faster than any human can. So let's analyze the data and make a good decision. That has gone too far. Dr. Darren, now, right behind this data. Yeah, they want more data. You just go present the data and said, okay, can you get me this variation of the data? That variation of the data? No, you make the judgment call. You make the judgment under uncertainty. It's okay.

[A]

Yeah, but what if I'm wrong, Sid? What if I'm wrong?

[B]

You're fine. You can be wrong. Fail fast is another concept we've all been introduced to. Document your assumptions. Let your leadership team leader know that, hey, look, here is how I came up with this decision. Either that, or if you're not comfortable, I can wait for another three weeks to gather more data to support it or not support it.

[A]

Do you feel like AI helps with this? Because AI can bring data in and help me analyze a lot of data sources all at once? Where before it would take me weeks and weeks so I can bring more data together faster and have it analyzed. Do you feel like that helps or has it become a crutch?

[B]

No, no, absolutely. AI helps. Right? AI is giving us more answers, Dr. Darren. More data. But leadership is still about asking the right questions, reading through those data and the answers and getting to the right answer. That's what leadership is about. AI supports a decision. I mean, supports the data and the decisions, but it doesn't take the consequences. You cannot blame the tool. At the end of the day, you

[A]

can't blame the tool. Right? Because if you don't ask the right question.

[B]

Correct. If you don't ask the right questions, you cannot blame the tool. So the consequences still remain with leadership.

[A]

I actually talk about this exact same thing on my book. And I coined a phrase called an integrity packet, so which is really kind of interesting. It first started as A decision packet. And the concept was, tell me, tell me what decision is going to be made, who's responsible for it, what assumptions you made and what resources you gathered to help make, you know, the assumptions to help overcome the assumptions. And at first I called Decision Packet, and then I realized, wait, the ultimate goal is not just a decision. It could be creating something else. So I changed it to Integrity Packet because one thing we need to get across to executives and leaders is, am I making decisions based on something that's reliable and defensible without getting stuck in this paralysis analysis? Paralysis where I'm constantly asking for more and more data because I'm afraid to make an answer? If I have an integrity packet, it tells me what all the assumptions were, the risks are, who made the decisions and who's making the decision, and has the data been validated. And it's turned out to be quite a powerful concept, which. It sounds like you came up with the same thing.

[B]

Yeah, we are hitting on the same point from a different point of view. As you said. I'm seeing the dangerous gap widening between the speed of AI automation and the depth of human accountability.

[A]

Yes, I like that.

[B]

Right. The organizations are. Organizations are rushing to automate, but in doing so, they're accidentally automating away their judgment as well. Oh, let AA make the decision. And I'm just a check mark. Use three options. I'll pick option one this morning and the same question. I'll pick option two in the afternoon.

[A]

And no one's asking critical questions.

[B]

No one asking the question. Yes.

[A]

Yeah, exactly.

[B]

A is smart than us. So why you do question AI?

[A]

Well, and it does a great job at making it look pretty.

[B]

Absolutely. No doubt.

[A]

It even puts the little emoticons on there for me. Thumbs up emoticons.

[B]

Like getting the answers from your kids, Dr. Aaron. They say something, but if you ask a question slightly differently, you get a different answer. Correct. Because they process it differently, too. So AI also processes differently. So challenge it. Curiosity. Build curiosity in asking questions not only to the AI, but also to your own team members.

[A]

To your own team. Yeah, absolutely. When they present a proposal, we need to be able to ask those key questions. Right.

[B]

This I learned the hard way. I have walked into several conferences, meetings, and I see dashboards. I call them green dashboards. At the highest levels, at our level, whenever you see a dashboard, it's all green. But I know it was not green at one point of time. It was amber and red. Weeks before, months before, even days before.

[A]

Even days before. Yeah.

[B]

But when it comes to the top level meetings, the CEO meeting or SVP meetings, it's all green. Now. You can pat on your back and on your team's back and say, great job and you can walk away from the meeting or you can have the Curiosity journey asking, how did you get to green? What were some of the challenges you experienced in this area? Did you take any shortcuts? Did you shell more budget to it, throw more budget at it? Or did we change the scope? Did we extend the timeline? You will be amazed and surprised how the dashboards turn green.

[A]

I love that. I absolutely love that. Because I was an executive and, and I told my staff, don't come to me with all, all good stuff. I want to know what's really going on in the organization. But that's hard. It's hard to tell your boss. My, my team is falling behind. Right. Because it's like, what, you can't manage your own team? This goes into that accountability thing, right?

[B]

Yes.

[A]

Because we punish accountability. Right. We punish those that are held accountable when they bring bad news. So they're not going to bring bad news.

[B]

You're right. And I notice this all the time. I call the leadership posture under AI pressure, especially when we ask them to do AI transformation. Everybody has a different posture. Some. It's a hype. Oh, this is end of the world. This is going to transform everything. We need to be the first one to do this. Even though it's not perfect. Let's move fast. Right? We can figure out this later. There's a hype going on and everybody aligns to the hype, right?

[A]

Yeah, yeah.

[B]

There's a panic, you know, oh, if you don't do this now, we will be relevant. This comes from the board all the time. They sit at the top. They hear it from their peers in other industries or similar industries. It doesn't matter. They say, oh, everybody else is already doing it. We need to act before it's too late. That panic trickles down so quick, you know, your head will spin.

[A]

Yeah.

[B]

Then they're all naysayers, Right. Denial. Hey, this doesn't apply to our business. We have seen these hype cycles before. You know, our customers still value the human touch, you know, the white glow service. There's a time and a place for everything. You know, it's not everybody is wrong or right at the same time, but it all, you know, what type of business you are, where, which part of the journey you are in your business. So many factors will come in. And the last piece which I sometimes fall into is the fomo. Oh, yeah, Fear of missing out. I picked up this term from our Zenzi kids. Right. They are. They have this all the time. Fomo. Oh, I got to go to this party. Why? I don't know. Everybody else is going and I'm going to miss.

[A]

Everyone else is going, right?

[B]

Yeah, I'm going to miss something out. So our competitors are piloting this, so why not we doing this? We need to be seen as innovative. We need to be seen as the first comer. Right. I always advise against doing anything first because the technology is evolving so fast. What's great yesterday is mediocre today. It doesn't mean that, you know, I'm not saying you wait for ages, but you got to pick your battles.

[A]

No, no, I totally agree. This is awesome. Okay, let's get into some more practical things I can do with my executive team. Right. Let's say that I am a. I'm the CEO. I'm the CEO. I got my team here. I'm going to give them some accountability. I have an AI initiative because I really believe that AI can really fundamentally change the way that we do business. I don't care about my competitors as far as that they're doing it or not. Let's say I have this big, huge initiative that I want to push through. How do I instill in them that it's okay to make mistakes but still hold them accountable? This is where things get tricky.

[B]

Yep.

[A]

How do I hold them accountable but still want to make sure that they're giving me the right information? Right. Because a lot of times when we hold people accountable, they lie. Just like a gen AI will lie to you. They call it hallucinating, but they're just bold lies. If it doesn't know, it just makes stuff up. That's called a lie. And we see it in corporate America as well. If people don't know, they just make stuff up.

[B]

Right.

[A]

So how do I. What can I do now as a CEO to establish a different narrative? Because that's the narrative that exists in most organizations today. So how do I change that narrative? Any tips?

[B]

Well, a few things you need to look at. Right. Especially when you're doing the AI type transformations. Everybody needs to understand they have only 270 visibility. What I mean by that is this is what I wrote in my book AI360. I call the missing 90 degrees. So you have a 270 visibility. With the data, the speed and the predictive power of AI, you already have majority of the answers. Now, without a framework, the missing 90 degrees you need a framework that is a human experience and the context that AI doesn't get it. You need to offer that to AI. Once you give the context and you change the Context, the entire 270 degree visibility is shaken now because it changes on you quickly. So you need to have that human experience and context every step of the way. It's okay for AI to do majority of the work, but you do not lose your judgment. You need to be still involved. That's number one. The missing 9090 problem is what I call it solving the missing 90 problem. The next is the ownership ladder we talked about. You know, people confuse execution, which AI is great at, with accountability. So you need to have a clear metric saying that what AI is responsible for and what are you as a leader is responsible for and who is responsible for you cannot be there everywhere. So you need to understand what AI is responsible for beyond a point. You got to stop AI and say, you know what? Okay, I'm taking over AI, thank you so much. Okay. That's where your accountability comes into play. And then who in your organization is responsible for it? Okay, that's the ownership ladder, I call it. And that's very important. And one other thing I can think of, you probably read this many times since you're writing your book as well and you're part of your research. I kind of developed this framework. Reading, you know, getting some references is a human pass check for framework. If a company doesn't have a way to pause and audit AI suggestions, then eventually, you know, you drift away from their core values. If you just listen to everything what AI says, you got to implement and introduce that strategic friction. It's a competitive advantage. Yes. You slow down a little bit, you have that human pass check. You question AI, you know, again, it goes back to maybe the ownership ladder, you know, the accountability piece. You got to do that.

[A]

No, I like exactly what you said there because we can't. To me, an AI cannot be accountable for anything.

[B]

We hide behind it. We hide behind all the time.

[A]

Yeah, but it's not, we should not place any accountability to an AI at all.

[B]

Correct.

[A]

Right. And I think you're saying the same thing. I need that human in there.

[B]

Yep.

[A]

I need those ownership ladders. I love the concept. You know, I, I, I had something in mind called the validation ladder as well, which is that other piece that you're talking about, which is I need to validate what the AI is giving me to make sure that it's giving me what I expected and doesn't make sense. Yeah, right. So I. Dr.

[B]

I don't know if you notice this in your circles, in, in your field, I call it a fracture zone. As we, as the AI evolves, as we implement more and more AI in our transformations in our projects, people are thinking that we need fewer human beings.

[A]

I know, I know.

[B]

Right. And on back of my book, which is. It's here, I think. I don't know if you can see it. I have a paper clippings of several headliners saying that Microsoft laid off so many people because of AI. You have this tag, term, tag. Right.

[A]

But that's an excuse.

[B]

That's an excuse, absolutely. In my opinion, I feel that you need more human. I mean, not in volume wise, but involvement wise, accountability wise. You need more humans when you have AI at scale.

[A]

Yes.

[B]

That most people do not understand. They think that, oh no, no, no. We are going to let people go because of AI. I'm not saying, you know, you hire and keep everybody. There is a role for everybody. You just tweak that role, help them understand how and how to manage AI, if you will. We are so scared of AI because AI is super smart.

[A]

It's not really. It's not smart.

[B]

You know, I get 80, 20 rule. Right.

[A]

It's good. It will.

[B]

Smart people like you will think that. But a lot of people, a lot of leaders.

[A]

Yeah.

[B]

No, they don't question AI.

[A]

So there's a lot of training that has to happen. It sounds like for our leaders, this has been, this has been wonderful, Sid, because you give me a lot of. We're thinking a lot the same. Your, your book that's coming out. In my book, those two should be sold together. That's why. That's what I'm hearing.

[B]

Let's compare notes on there. For sure.

[A]

Yeah, that's absolutely right. Sid, if people want to buy your book, is it already available? It's out there.

[B]

I have published three books. It's already available in Amazon. This third book, which fourth book, which we are talking about AI360, it should be by end of this month.

[A]

Okay, end of this month.

[B]

It's called AI360.

[A]

So it'll launch beginning in May.

[B]

It will launch beginning of May. Correct.

[A]

Perfect. So it's already available. When this episode comes out, it'll be available. So go find that AI360. Yeah, go find that book out there for Sid. Sid, thanks for coming on the show. It's been wonderful. It's good to talk to someone that is thinking the same way I am right

[B]

now. This was great. Dr. Darian, I really enjoyed the show. One thing I want to remind your listeners is that they do not have a strategy problem. They have a decision quality problem.

[A]

Yeah, I totally agree.

[B]

That's what we need to focus on. And most organizations, you know, a lot of issues will go away if you focus on that decision quality.

[A]

I love it. I love it. Thanks, sid.

[B]

Thank you, Dr. Darren.

[A]

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