Good leaders today are expected to drive business agility through cooperation, collaboration, and adaptability of its team and ecosystem partners. Such leaders are also supposed to demonstrate flexibility and sensitivity? Can we harness AI beyond just automation or aid to decision making? Are there ways AI can help improve leadership quality within an organization?

Contributor

• • Jeanne Holm, Deputy CIO and Senior Technology Advisor, City of Los Angeles

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Transcript 

Sanjog Aul [00:00:22]:

Hello, and, welcome to this segment on CTN. To learn more, please visit ciotalknetwork.com. And the topic for today is AI Enabled Leadership. So good leaders today are expected to drive business agility and deliver miracles, literally, given the digital age we live in. And they’re supposed to get everyone to cooperate, collaborate, and adapt to the dynamic changes that we are seeing happening all around us. While getting the tasks done and rallying these troops, they’re also supposed to show sensitivity and also stay flexible. And on the other hand, we are seeing AI, artificial intelligence being used as a decision making tool and also for automation. So we wanted to connect the dots. And so what the question here is, is that possible for us to use AI beyond its obvious use to enable better leadership. And to discuss that, joining me is Jeanne Holm, who is the deputy CIO with City of Los Angeles. Hey, Jean. How are you?

Jeanne Holm [00:01:23]:

I’m doing great. How are you today?

Sanjog Aul [00:01:25]:

Very good. Thanks so much. And so as you saw, this is an interesting angle we’re trying to take, and I’m sure you’re brave enough to help tackle this particular topic. So let’s start. Let’s set the stage. So, you know, we always have seen leadership as an approach to gain and retain a position of authority and influence. So leadership being leadership, would you say this particular path to becoming a leader has shifted in the last decade?

Jeanne Holm [00:01:59]:

Well, I think that, you know, leadership is always about motivating and inspiring people to do great things, often sort of beyond the ability of any one individual, but to work as a team. I think the change in the last decade has really been around data driven leadership. So the idea that we are able to create better ideas, better ways of leading people, better ways of representing from the government’s perspective, better voice of representing the people we serve. This is really coming from all the data that we’re gathering from demographics, from studies and research, from the Internet of Things, and that data helps to better inform us.

Sanjog Aul [00:02:38]:

So when you look at from you looking at data as an aid and as a tool, of course, you should use whatever resources you can leverage to become a better leader. The outcome is being experienced by the citizens. So we take your organization, for example. But would they be okay for you to start looking at that data to influence them Or move the city in a direction where tax based dollars are at stake?

Jeanne Holm [00:03:09]:

Well, I think that’s there’s two ways to lead in a city. And both of them have to be done concurrently. So one is using logic, which is generally supported by data to understand the different demographics, inequities across the city, trying to help people understand location for housing. Here in Los Angeles, we are working across a huge homeless tragedy and issue. And so we’re really working at how data can better inform a lot of these kinds of policies and changes. But at the same time, you have to appeal to people’s emotional intelligence as well. So people are not moved solely by data, although they need to make sure that you have that data that’s actually taking only the emotion out of the equation and being able to inform them. So when we talk about something like 5G and broadband connectivity, actually, data becomes an important part of that emotional discussion. Los Angeles was the first city in the Western Hemisphere to go 5G. We did that by using a data driven approach to show inequities amongst our neighborhoods. Some neighborhoods on a 4G space had 98% of the homes connected, and other neighborhoods have only 50% of the homes with Internet connectivity. When we start putting data into that kind of a discussion, we can govern in a way that’s more equitable for everybody, and that’s fair, and that really helps to drive that conversation with citizens.

Sanjog Aul [00:04:38]:

So what you did was you used data in this case to aid decision making. Right? And that approach that you took is very well justified, and that’s why we use technology. We are also now lately starting to use AI. When you talk about working with people, and then interestingly, you say that you, also utilized data to impact the emotion. Is that acceptable by the recipients? I mean citizens.

Jeanne Holm [00:05:09]:

Well, I don’t think we try to manipulate emotions. I think what we’re trying to do is inform people in ways that are impactful to them. So nobody wants to see a spreadsheet of data. That doesn’t make any sense to the majority of people. But if we show maps where there’s digital inequity, and you can see by a simple color coding where the areas are, that connect often to socioeconomic areas that are underserved. Or if we talk about the specific instances where students in some of these neighborhoods can’t connect at home, and so they’re trying to go out late at night to find a McDonald’s with Internet and free Wi-Fi still available to upload their school assignments. When we combine both data and specific stories and anecdotes, then we can inform people in ways that they can decide what they want to do. So I don’t think from a government’s perspective, we’re trying to influence people so much as help them to be more educated about the entire set of issues around any kind of topic that we wanted to discuss and get their feelings on.

Sanjog Aul [00:06:23]:

So, let’s talk about the very nature of leadership or the asks from the leaders, which is today compared to, say, a decade ago. So one was that what was the path to it, but what’s the new ask of you which is creating challenges for you? What are your top leadership challenges today?

Jeanne Holm [00:06:44]:

I guess there’s two kinds of leadership challenges I see in the role I have. And I think this is probably true for many CIOs is that there’s the challenge of leading your staff and your folks to do some really interesting and inventive things. And there’s also the challenge of supporting the executives, or in my case, the elected officials, for the kinds of things that they’re trying to do and the changes they’re trying to make. And they’re slightly different. So the elected officials, they’re interested in being able to utilize new technologies and new data in ways that help to express the changes they’re trying to make. So, for example, we created a data science federation here in Los Angeles that partners 18 universities. So, we have students and professors working with elected officials in city departments to bring new technologies, new ideas, and new perspectives, and data to help inform city policies and decisions. On the other hand, you’re also trying to be a leader to your own staff. And in that case, partly it’s about data, but more it’s about inspiring people to keep up to date on new technologies, try new things, understand that measured risk and sometimes failure is part of being innovative and being able to create a culture where innovation is embraced and encouraged while still making sure that you’re running a lot of operational services.

Sanjog Aul [00:08:16]:

Now given where we are today, like the digital age that we talk about, we have uncertainties. There is shift dynamic shifts which we never expected or are able to anticipate. But then also there are opportunities. We have to deal with all of them, and that is one at the business side. But then, at the same time, we have to have the people who are working with you, because you are only as good as the people who work with you. You need to rally them in a given direction. Lead them, give them the resources, or get them trained to harness those opportunities. If you were to take a step back and say, okay. This is a tall order, Sanjog. I need a b c type of resources, which I need in a predictable fashion so I can achieve all this. What would those resources be?

Jeanne Holm [00:09:18]:

That’s a great question. If I’m not talking about sort of personnel resources, people who are inventive and have a great attitude about trying new things, so I’m talking about sort of more tangible resources. The kinds of resources that I’m looking at are access to short courses and online training, the ability to bring in experts from the community to help inform my staff about the kinds of issues going on for Angelenos or the kinds of technologies that are being tried in our city. I mean, Los Angeles is a huge city with 4,000,000 people, 500,000 businesses, and 500 square miles. So in a city as big as Los Angeles, we know that some of the very smartest people do not work for the city government, but work in all of our amazing industries. And so partly, we want to be able to create that ecosystem of inventors that work together with our city employees and help us to co learn and co create. So the kinds of resources I need are really education, access to new ideas, and then, of course, the ability to have enough infrastructure to invent, play with, test new ideas, and then operate.

Sanjog Aul [00:10:39]:

So let’s take a quick break, listeners. We’ll be right back. And then let’s talk about the dichotomy that leadership faces. One is you want the leader to be direct and be decisive. At the same time, you want that individual to have the sensitivity so that they don’t hurt anybody, with their words or with their actions. And at the same time, they want them to be flexible so that you can maneuver as the changes happen in the outside environment. So you are directly saying, okay. I want you to be direct, yet you want to be sensitive. You want to be decisive, yet flexible. These are contradictions. How does a leader deal with it and develop those unique capabilities to tackle them? And on top of it, let’s see if he can answer this question. How can AI help in this regard? Please stay tuned listeners. We’ll be right back.

Sanjog Aul [00:13:39]:

Welcome back. So decisiveness with flexibility or directness with sensitivity. These are just the two that I have brought up are seemingly contradicting requirements or rather asks from a leader. How do you think leaders today, while still staying human, can tackle it, and can they use AI to come rescue them in this regard?

Jeanne Holm [00:14:03]:

Well, I think it’s important to lead from both your head and your heart. People wanna follow a leader who’s passionate about what they’re doing, who cares about their customers or, in this case, residents and citizens, and who clearly is impacted and impactful around the kind of work that’s being done. So I think it’s important to combine the two. However, in any kind of ecosystem, whether you’re working for a software company or whether you’re working for a nonprofit or whether you’re working in the government, the ecosystem of what you’re trying to do and the environment is very complex. So having any ability to help filter the vast amount of information that’s coming in is really important. And that’s where I think artificial intelligence comes in. And often that’s combined with machine learning so that we can start to harness all the data coming in from the Internet of Things, be able to create the data fusion, and then use AI to help sort of take that signal to noise ratio down so that we’re better able to understand the important data, the important points that we need to pay attention to. And in some cases, to truly automate some more procedural kinds of things. So a good example is a lot of companies are now using AI to identify physical vulnerabilities, not like cybersecurity particularly, but just vulnerabilities in their built network and infrastructure. So it’s a pretty well understood set of criteria for age of a component of the system, manufacturers, expected lifetime, outages. There’s a few different things you can put into an algorithm that helps you take off this burden of how do we look at tens of thousands of devices in our network infrastructure and be able to identify which ones we need to prioritize for replacement and what kind of maintenance schedules. So it’s a very simplistic view of how AI can really just lift a burden of decision off of somebody while making sure that you have a more robust infrastructure. It gets more complex when you start putting real people into the equation where you’re looking at how do we better inform public policy around issues of economic growth and poverty, hunger, and longevity, and looking at these kinds of things when we’re trying to use AI for decision making.

Sanjog Aul [00:16:27]:

Now what you just mentioned is, you know, this direct decisiveness with flexibility and directness with sensitivity. What are other areas which you feel you have that predicament or rather contradiction as a leader which you need to tackle? Because I am asking you certain, specific areas, but I’m sure you are struggling with some and you’re on the air that I understand. But then if there are some things, let’s discuss. Let’s look at other areas of leadership, which challenges you. And if we could brainstorm right on this show to see if AI could help in that regard or you appeal to the people who make solutions.

Jeanne Holm [00:17:10]:

So, I think what’s an interesting challenge is this concept of sort of a change of culture. So we want our employees to be able to do great things, operationalize complex systems, and make sure that we have no outages. And yet, at the same time, we want them to be innovative. And those two things often are a dichotomy for folks because they’re paid to and expected to operationalize things. I think what’s interesting is how leadership and AI can be combined to help build a culture of innovation and appropriate risk taking inside of an organization. The idea of how do we push the human spirit to move it forward to take those new innovations in a way that helps us to achieve our overall goals. So I think that’s a particular area of leadership that isn’t really being explored by AI right now, and I am curious as to how it could be accomplished. 

Sanjog Aul [00:18:17]:

Now, when we talk about AI, this is very interesting that on one hand, we want to use it as leaders to maximize the effectiveness of us in leadership, and we also want it to be utilized to influence and manage and perhaps lead the people. But then there is also a parallel sentiment among the workforce that AI is one of the biggest threat in form of a job displacement. So when somebody is afraid of their job loss, not being able to bring food to the table, what would be the best way for you to utilize and not have that seen as a threat by your own people, who you want to support and run with you on that initiative or those set of initiatives? 

Jeanne Holm [00:19:11]:

So I think this is a great example of where you have to kind of balance the head and the heart because you can give people examples of where technologies have completely transformed industries and jobs have been secure or been reinvented. You know, we used to have people who ran printing presses in the hot lead typesetting days, and then they became word processors, and then they became digital publishers, and then they became web developers. And that was a long transition. That was one of the transitions I helped to lead when I was with NASA, with the space agency. The kind of changes though mean that people have to be open to learning and open to trying new techniques and new tasks. And so I think that, in order to make that change over where we’re really able to unleash the power of AI to transform the kind of work we do, we have to make sure that we’re educating our workforce, that we’re training them to be the co captains of that AI as opposed to sort of the recipients of the automation. And it does require people to be open to new ideas and sometimes new learning, often people who may be older in their careers. And I think that’s where leadership comes in. Being able to lead people forward in an era of trust where they know that you’re going to continue to work with them to make them employable and that they’ll just gonna go on to build bigger and better things together.

Sanjog Aul [00:20:40]:

So when you just mentioned this, with the people, you’re trying to get to do things, right, in different ways you want them to come and support you, my question still remains is that are they going to be willing to work with you just because you say it’s not a threat or just because you say that this is gonna move you forward? You’re dealing with people who have their families and they are saying my future is at stake because a lot of people make promises and don’t keep them.

Jeanne Holm [00:21:19]:

Right. And that’s where I think leadership comes to play. I mean, leadership is about inspiring people, building trust, and keeping that trust. So if you, as a leader, don’t have that trust with your employees, then it’s unlikely they’re gonna follow you into this era of change. They’re going to feel resistant. They’re going to feel afraid because you haven’t been able to instill the trust that you’re with them for the long term and that you’re part of a change together. I think the way to approach this and the way that I think we’ve seen really great examples at different places when I’ve been at the helm of organizations that have made that change, whether it was at NASA overseeing the change from traditional publishing to web development or the White House when we were working from closed data to open data with data.gov. It’s about having constant conversations with people, making sure that you take them from where they’re at to where they could be. So not necessarily making people feel bad because they don’t have current skills, but helping to get them education and co working workshops and hackathons and other ways for them to be able to get those skills in a nonjudgmental way. And then also making sure that as a leader, you work with your executives and your company to help to protect those people as they make that transition. Because the thing is we’re not gonna make this journey alone as executives. This journey is part of our entire organization. So to really be able to gather the data, use AI, program tools, be able to train and work with machine learning, you’re gonna need your staff to be there. And it has to be that staff that understands your environment and your constituents.

Sanjog Aul [00:23:10]:

So when we are going to look at everything that we all want to do at the same time, and we also want to shift, it’s like changing wheels of a moving car. Are organizations and the people and the business ready for you to adopt these AI related changes? Because this is not a trivial effort. You cannot just slap AI without having your data in order. Or are you expecting that you will just put in the AI as a tool and it’s gonna start churning out insights, which you will use to get that crystal ball or the magical answers? Because you see what I mean? So the devil is in the execution. How do you think you’re gonna bring it in?

Jeanne Holm [00:23:56]:

Yeah. And I think that for a lot of that, small steps are important. Partly building that trust with your staff and employees to make sure they know that you’re on this journey together and so that they can start to build confidence in the use of these tools and how it’s gonna start to change the nature of their work, not in a way that jeopardizes it, but that transforms it. And then, the use of AI, you know, I think there’s all this I am a huge fan of science fiction, and there’s always great stories about artificial intelligence and sort of habit avatars and human robotics. But the actuality of transformation happens in one step at a time. So here in Los Angeles, for example, we are doing things to help increase pedestrian and bicyclist and scooter safety. And so we use our old traffic cameras, and we use computer vision and machine learning, a little bit of AI to be able to count the number of people, cars, bikes, scooters in an area and adjust traffic light timing so that people can have more time to cross the street. We do that in a way that this is a small step. We’re working with staff. We’re working with our universities as part of this and the Toyota Mobility Foundation. And these small steps help to gain confidence and trust. I’m not necessarily, at this point, looking for AI to help solve our homeless situation. I’m not looking for AI to suddenly increase housing across the city, but I am looking at AI to help me understand the best placement for some of that housing that we’re looking for and the AI to help us understand the issues about affordability of housing and what kinds of policies you might be able to change that would make that better for people. So, again, I think we have to look at the promise in the long term, but take the small steps that are necessary to build our confidence and our capability to get there.

Sanjog Aul [00:25:57]:

Let’s take a quick break, listeners. We’ll be right back. And when we explore this in the next segment, the possibility of the leader replicating their own role, if you will, so that they build leaders below them. And not only they, but the direct reports are the ones below the whole chain of command. Everyone exudes leadership because this is not a privilege for people at the top alone. And all of them use AI to whatever extent they can. What would that org design look like? What would that day in the life of the top leader, the one below, and the one below look like so that they are able to deliver the business outcome. Please stay tuned listeners. We’ll be right back.

Sanjog Aul [00:28:38]:

Welcome back. So here we are talking about leadership, but one thing to clarify is that leaders exist or should exist at all levels. And each of them should have resources at their disposal because we are trying to get a whole lot here. You got a lot of information you need to interpret. You want to make them into or convert them into actionable projects. And all of that has to happen at a consistent, acceptable quality, and it should happen seamlessly. That, again, is a tall order, and, yes, things will go wrong, but not all the time. They should not go wrong all the time. How do you make all of this happen while making sure that you make available AI and harness the most value out of it?

Jeanne Holm [00:29:23]:

So let me answer that question by talking about what’s happening today in Los Angeles, like, this day. So right now, we’re working with young people in our high schools in STEM education. So we’re making sure that there are boys and girls right now getting access to some of these tools and understanding how to use them. We do that in a variety of ways, both in our school, through an after school program called Beyond the Bell and our Cycle Teachers program. And then we also make sure that the kids in our poor communities get free computers and Wi-Fi hotspot as part of the recycle early giveaway program. As those kids are going through high school, we want to make sure they keep getting education and access to those tools. And so we have a program called LA’s College Promise, and every graduate of LA Unified School District and their 650,000 kids, every graduate gets 2 to 3 years of community college. So we want to make sure that from the very earliest ages, we’re starting to grow leaders who are going to understand innately these kinds of tools and technologies. As those kids get into college, they become part of our data science federation. And we’re actually growing those new civic leaders as sophomores and juniors and seniors with a job track into the city to be able to develop artificial intelligence, machine learning, computer vision, gamification, chatbots, artificial intelligence or artificial agents to be able to build those for the city while they’re still in college. And when they graduate and come into the city, we work in a collaborative way across all 42 of our city departments through our data science group to be able to make sure that people are constantly learning together and working together. So we want to make sure that wherever you’re at in your development as a person in Los Angeles, that you are getting access to training with an actual applied work in creating civic innovations. Now when we get into the city, we want to make sure that that career growth continues. So from our youngest newest employee, our student worker all the way up through Mayor Eric Garcetti, we have a very big focus on a data driven government that uses a variety of tools and techniques to be able to create great capabilities for Los Angeles people.

Sanjog Aul [00:31:58]:

Now when we do this, and you’re trying to do what you’ve tried to do, where are the challenges shared by your people? Because you’re only as good as your people.

Jeanne Holm [00:32:16]:

So the challenges I think there’s a couple. One is that some of our older workforce is sometimes hesitant to sort of get in and start developing some of these new technologies. It can be intimidating, and I’m compassionate to that. For some people, they grew up in a certain environment, certain programming languages or a certain approach to things. We don’t do the waterfall methodology anymore. We don’t work in COBOL so much anymore or Fortran. And so it can be both intimidating, a little bit scary when you’ve been an expert in a technology that now maybe has matured or retired, and now you’re being asked to do something new, and you’re not sure if you have that same expertise in that new thing. So, again, I think a lot of it comes back to trust and support of your staff and your people in order to be able to help them learn those new things. And then to also help understand that not everybody is going to be able to jump into these new technologies. So thinking creatively about the whole aspect of using artificial intelligence, it’s not just about programming the system. Some of it is about data cleansing and data management. You mentioned that earlier. The data itself can be really difficult to either fuse or integrate, and to be able to make sure it’s clean. There’s jobs in that. And then there’s also jobs in communication. So sometimes, some of our older staff are the best at understanding the story of what we’re trying to do and be the advocate for our citizens because they’ve had the most experience representing them. And so the idea of communications and social media and information and making sense and stories out of the data is also an important part of the process. So the leader, I think it’s really important to not just look for the best Python and R programmer. You really need to have a team that can together make sense of the data and effect policy.

Sanjog Aul [00:34:22]:

Now when you have looked at the ways the different ways you have handled AI or used AI, what are some of the places where you have used it for menial work or cognitive work? What are some of the places where we are still looking for, exploring the new ways of using AI. Have you fully tested it out yet in your organization or for your context?

Jeanne Holm [00:34:57]:

Yeah. So I think we’re on that journey of doing the small steps. And I think many organizations and certainly large operational organizations like a city, you know, we don’t want to take radical experiments. We want to be able to work in ways that are going to measurably improve the environment. So some of the smaller steps are things, as I mentioned before, looking around traffic safety, making sure that we create a safer environment for particularly pedestrians and bicyclists. We have a program called Vision Zero that looks specifically around traffic safety and pedestrian safety. And so we’re using AI in some of those areas. We’re also using AI in looking at our sustainability issues. So the City of Los Angeles is one of the few cities on the planet to adopt the United Nations sustainable development goals. We have a very robust sustainability plan and a new Green New Deal here in Los Angeles. And so part of what we’re looking at is how do we start to measure and take advantage of AI in that global space of the environment and issues around poverty and hunger and health. And when we do that, we need to start taking data from many different sources. So for example, one of the projects we’re currently working on is actually with NASA, and we’re looking at air quality. So we want to take satellite data, airborne sensor data, and then ground data, fuse that together in a way that helps us understand what’s really happening across all of the different levels of the atmosphere here in Los Angeles and what kind of outcomes or interventions are having. So if we increase trees and plant more trees in the neighborhood, there’s generally a direct correlation to improvement of air quality. When we decrease the number of cars on the road, there’s an improvement to the air quality. When we decrease the number of ships coming into our port, there’s definitely a decrease in the pollutants in the particulate matter. And so using AI to be able to understand the changes before and after an intervention in a very complex model where we have model simulations, data, trying to understand what’s happening in the physical atmospheric environment is one of the areas that’s probably more aggressive where we’re using AI.

Sanjog Aul [00:37:27]:

And would you say there are some use cases which look probable, but you are still on the fence of using them? Or you have a desire that I wish AI could help you there.

Jeanne Holm [00:37:45]:

Yeah. I think the thing is that we have to understand when we have the right tool for the job. So there are very complex social issues like homelessness, affordable housing, poverty that I don’t know that AI is at the point where it’s mature enough to help us better make decisions in that area. It is mature enough to help us understand some of the aspects in that area. So when we look at things like maternal mortality and with the women dying in childbirth, and we see that there’s a huge discrepancy in the African American population, which in general across the country is about 3 times higher maternal mortality than for women who are white. We have to start to unravel that a little bit. Some of those kinds of complex challenges and huge issues in our society are not ones that I think AI is really ready to tackle. But as we start to use a scientific method to break those really complex problems down, I think we can use AI to help create the building blocks that get us to the understanding of what’s happening so we can improve outcomes for all of our citizens.

Sanjog Aul [00:39:00]:

And so one is, of course, these are very important issues that you mentioned. Are there any internal management, like government management areas where you wish in terms of you trying to lead the charge or your mayor trying to do things. It’s not about the IT leader trying to use AI, but the government leaders trying to use AI. Are there any areas you wish there were solutions to those which will help them become more effective as leaders internally. Right? Because, you know, there is a machine inside machine you’re talking about.

Jeanne Holm [00:39:41]:

Right. So, again, you know, I’m blessed with working for Mayor Eric Garcetti who’s a very tech savvy and data savvy leader. So I feel that right now, our environment at the city is one where we really do have a lot of openness in our leadership to new tools, new techniques, and a desire to have more information and data to make better decisions. I think one of the areas that we’re really interested in is this aspect around being able to predict the outcomes of policy. And, again, this is part of the work we’re trying to do with the data science federation and some specific universities like Claremont Graduate University and Occidental College. So smaller or liberal arts colleges are helping us look at the issues of how do we better understand the outcomes of policy changes. So if we, for example, are looking at solving homelessness, the obvious solution to many people is put people in a house. But in fact, it’s a much more complex situation. And when we start to create the idea of an AI tool to help us, they have to understand that the causes of homelessness are more than just not being homed. It’s because there are socioeconomic issues, there’s social issues. There’s issues around affordability of housing. There’s concerns about health and mental health. And then there’s also the aspect of understanding how we start to put the humans in that equation. So I think the challenge for us is really saying if we make a policy change to increase housing, will that actually get us the outcome we want? Or is the better idea, how do we prevent homelessness in the first place? How do we get better services out to people before they become homeless so they can get rent subsidies, better education and training, better mental health and physical health insurance coverage. If we can use AI to help predict where our services could be best rendered and predict where we can get in front of the curve, I think that would be a huge impact to government.

Sanjog Aul [00:42:03]:

Let’s take a quick break. We’ll be right back, and let’s talk about the places where we are trying to experiment. The new digital age wants us to experiment. But then you could also be having a lot of leakages in the way we try to experiment and or wastage of resources, which is very possible. While innovation is happening, there’s a cost of innovation, but where you could be overdoing it? Are there places where AI could help leaders minimize that wastage or leakage of resources as they try to experiment and navigate in the haze of today’s digital environment? Let’s explore that more when we come back. Please stay tuned.

Sanjog Aul [00:44:38]:

Welcome back. So we want to minimize leakages, and we want to reduce or eliminate wastage of resources as much as we can as we go about innovating and experimenting with things. So, Jean, in your view and in your vast experience across multiple organizations, what have you seen effective in terms of minimizing leakages with or without AI? And with the AI now available, where can it be put to use? 

Jeanne Holm [00:45:10]:

So I think that, I’ll go to the very literal aspect of what you said, leakages. So when I think about ways in which AI can really help and actually don’t jeopardize jobs but just improve our ability to provide city services, I think of our sewer system. Now stay with me. So the sewer system in Los Angeles, like in many cities, has some new pieces and some very old pieces. So we have pipes dating from the late 1800s in our system. And when we think about that using AI so first collecting data about where the different components are and then using AI to understand where we should make the fix is before we have leakages, before we have ruptures, and before flooding occurs and we waste precious water. That’s a really, really good use of AI. And we’re using in Los Angeles, we’re using artificial intelligence and machine learning to do some of that as we’re collecting our inventory of components and pieces. And we’re also using drones, some very small drones inside of areas where it’s just not safe to send a person either because the infrastructure itself might be a little bit fragile or because it’s just a dangerous condition or a very small pipe. And we’re using drones to help inspect some of that sewer system and the water system so that we’re better able to manage it. And I think that the same way in which we’re looking at predictive analytics and AI around predicting disasters and fires let me just give you two quick examples. One is we just completed 175 flights of drones used with our fire department, and those drones are helping us to look over areas for making sure we’ve cleared the brush in areas that are dangerous during a high fire season risk. And then we’re also using that during active fires to be able to go and send the areas where it’s dangerous to send firefighters, but we want to overfly and make sure we’ve actually put out all the hotspots. The second area is really looking at how we can use data and AI and predictive analytics around earthquakes. So we are in a partnership with the US Geological Survey. And this year, on New Year’s Day, we rolled out a project, a mobile app called ShakeAlert LA. And what ShakeAlert LA does is it aggregates data from sensors that the USGS has up and down the West Coast of America. And those sensors detect when vibrations occur to a certain extent where we sense that an earthquake has started. So it’s not predicting an earthquake, but it actually senses the earthquake has started. And we’re able to send an electronic signal out through the cloud to our 475,000 users of that app and be able to let them know that an earthquake has started and they’re about to feel shaking. So people get up to a minute warning that the shaking is gonna happen. And we can use AI in an interesting way there, not just looking at behavioral analytics and how we inform people to get ready because an earthquake’s happening, but also from system to system perspective where we’re working with our airport to shut off the jet fuel valve. We have public announcement systems in our city hall that are now activated by the same system and provide a public announcement. We are working with our fire department to lift the firehouse doors so that the fire trucks can quickly get out, working with our port to lift the cranes so the ships don’t get trapped. So all of these different ways of using AI, drones, robotics, data help to make us a safer, better place today, and these are just gonna increase as we get better with the technology.

Sanjog Aul [00:49:01]:

Now while we spoke great things about how AI can be leveraged, there is also a risk where we get overzealous and we try to put it any and everywhere, and there could be people who would try to abuse it or exploit it and even create security risks to the organization, to the nation. How do we prevent that?

Jeanne Holm [00:49:26]:

So security itself is sort of a multi tiered aspect. So whenever we think of cybersecurity, I think the public often thinks that somehow there’s like a big shield a cyber shield around an organization. But it’s not just that. It’s not just sort of the network defenses, but it’s also the defenses of physical security around buildings and data centers and making sure that people can’t get access physically to some of the infrastructure. And it’s most importantly, the social security because the biggest threat as any cybersecurity officer knows, the biggest threat to your organization in the cybersecurity space is not somebody hacking into your system, but the fact that somebody calls a well meaning, well intentioned employee and social engineers them into giving them information or giving them access or a phishing scam that sends an email that looks legitimate and somebody clicks on that link and then provides access and unintentionally provides access into the system. So using AI, we’re able to actually increase the ability of understanding what those potential threats are. Stop the spam and the scams before they get to our employees, educate our employees in ways that help to make them more aware, and then be able to work together throughout the community to understand those issues. Here in Los Angeles, one of the things we’ve done specifically around cybersecurity is to improve the education and awareness of folks in the community. So we have a cyber lab here in the city that works with 500 of our local businesses, and anyone can sign up, and helps people to just be educated about the current vulnerabilities, the scams that are going on, the spam issues, attacks, the ransomware issues that are happening. So all Los Angeles businesses can be safer and more secure by sharing that information back and forth and being able to help each other protect the assets. 

Sanjog Aul [00:51:35]:

On behalf of the show and our listeners, thanks so much, Jean, for sharing your thoughts on how AI can provide different resources or remove the haze or help your people at different levels become better leaders. So how AI can enable leadership, that was the discussion, and thanks so much for all your inputs. 

Jeanne Holm [00:51:58]:

Thank you so much. I really appreciate the opportunity to share, and I’m looking forward to watching on social media as I get responses back.

Sanjog Aul [00:52:06]:

Beautiful. Thanks so much again. And listeners, hope you enjoyed the nuggets shared by Jean. Please, like us on Facebook, search for CTN, and be sure to follow us on Twitter and LinkedIn. And please follow us on all different syndication platforms where you can find podcasts. Thank you again for listening to this segment on CTN. This is Sanjog Aul, your talk show host. Till next week. Take care and God bless.