Verizon AI Discussion with Michael Raj of Verizon

Join hosts Gary Sloper and Scott Bryan on The Macro AI Podcast for an engaging conversation with Michael Raj, Vice President of Network Enablement at Verizon. In this episode, Michael dives into how Verizon, a global telecommunications leader, is harnessing the power of artificial intelligence and advanced analytics to drive transformative change across its network organization. 

From his personal career journey to the strategic deployment of AI within Verizon, Michael offers a fascinating look at how large enterprises are identifying and implementing AI use cases to achieve operational efficiencies. Michael shares insights into Verizon’s innovative applications of AI, including the use of propensity modeling and geospatial analysis to proactively mitigate fiber network disruptions, ensuring seamless connectivity for customers. 

 Michael addresses the importance of clean, high-quality data in building robust AI models, a topic that resonates with executives grappling with leveraging historical datasets. For students and job seekers, Michael provides actionable advice on pursuing careers in AI and analytics, emphasizing the skills and mindset needed to thrive in this rapidly evolving field. Beyond technical expertise, he highlights the leadership qualities critical for successfully driving AI initiatives within a complex organization and reflects on the most impactful lessons from his career. 

This episode is packed with practical insights for professionals, aspiring technologists, and anyone curious about the intersection of AI and business. Don’t miss Michael’s inspiring perspective on the future of work in an AI-driven world and his recommendations for the next generation of innovators. 

Subscribe to The Macro AI Podcast on Buzzsprout, share this episode with others, and connect with us at www.macroaipodcast.com or on LinkedIn to submit your questions. Stay curious, keep learning, and join us for the next episode! 

Listen now to explore how Verizon is shaping the future with AI and gain career advice for the AI era!

Guest:  Michael Raj - Vice President - Artificial Intelligence & Data (CDO Organization) at Verizon

https://www.linkedin.com/in/michael83/

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Transcript

01:07
Welcome to the Macro AI Podcast where we explore the cutting edge of artificial intelligence and its impact on business and society. I'm Gary Sloper. And I'm Scott Bryan. And today we have a special guest on the show. Michael Raj is the vice president of network enablement at Verizon. And Michael is focused on leveraging AI and advanced analytics to drive business transformation within Verizon's network organization. Yeah, that's right, Scott. It's an honor to have Michael on the podcast to give our listeners some insight.

01:36
into his background and some examples of how a large complex organization is identifying specific use cases for AI to achieve efficiencies across the business. So with that, welcome Michael. Thank you for joining the show today. Gary and Scott, thank you for having me on this podcast. It's great to be here. Yeah, yeah, we're excited. ah We know you have a tight schedule. So again, we really appreciate you taking the time. And ah I know our listeners are going to be excited to hear this episode.

02:05
It'd be great if you could just give the listeners a little bit of your background, you know, based on your career and kind of how you ended up at Verizon. Absolutely. I did my undergrad in India. I grew up in Chennai. I did my undergrad in computer science engineering there. And then I came to the US for my MBA at William & Mary in Virginia. After my MBA, I focused on consulting. I joined Deloitte and did the consulting for about three years there.

02:34
While I did the consulting there, Verizon was our client.  And at that point,  100 % travel was not possible anymore. So I saw the culture at Verizon. I saw how nice it was in terms of working with the folks at Verizon. So I decided to move over to Verizon.  And then I've been working for the last 13 years  at Verizon. Initially, my focus at Deloitte was more on information management, data warehouse, and technologies that were evolving at that time.

03:04
primarily focused on tools like Informatica and Cognos.  How can we drive better intelligence in terms of the way we tell the story about the data?  As the volume of data continued to evolve, it was very clear that  the tools that we had at that point was not going to enable us to drive the scale and intensity that we want to drive in terms of the story.  Therefore, I decided to  go more into data analytics and AI. And that's how I'm in this role today.

03:33
Primarily today, I support two organizations within Verizon. One is the Network and Technology Organization, and the other one, what we call is the Verizon Global Services. So from a network and technology, you know, think about when you're driving through, when you look at the cell sites there, there's a lot of data that those cell sites generate. And the focus  on strategy to make sure that we build in the right places, we invest the right amount of capital, we have a good sense in terms of where competition is expanding.

04:02
what our customers are experiencing, what sort of network performance that we have.  My team focuses on enabling that  visual data  story in terms of gathering all of that and making sure that our network teams have the best insight to be able to build the best network in the world. On the other side, as it relates to Verizon Global Services, what we focus on is all the things related to our supply chain, our fleet. uh

04:30
the  focus that we have related to driving energy consumption down. ah All the pieces that we need to make sure that we can do better sourcing,  given  the size of Verizon, you can imagine the amount of  things that we need to go through from a sourcing perspective. How can we bring a lot more analytics, AI, and storytelling inside of that organization? So those are the two organizations that I support.

04:55
I've been in Verizon, as I said, about 13 years. I started in  an organization where we basically looked at how can we drive better capital investment strategy with data? What can we do to understand  customers' usage patterns?  How do they use our fires network? How do they use our wireless network? Those are the questions that got me started in this space.  And we've come a long way in terms of driving a lot of decisions.

05:24
in these spaces today. Yeah, that's awesome. That's a great  journey. So you've got the business background, well, first your engineering background, then the MBA, then move into consulting, and then shift over into consulting  really specifically for the client that you were working with. That's a good,  journey for you. um Now, when it comes to  what you guys are doing at Verizon in AI today, do you want to maybe talk about uh

05:53
any particular use case and maybe touch a little bit on the technical side as well as even the challenges around getting it funded and how some of the internal workings of getting something going with an AI project inside a big enterprise and how you're accomplishing that. That would be great for the listeners. Absolutely. That's very good question.

06:14
Typically, one of the challenges that lot of big companies face is  you don't want to develop a solution and then find a problem to solve. That happens in a lot of cases. ah Even if it happens, my strong suggestion is  keep it as an experimentation that you can go through quickly  versus  multiple months of engagement. So that's my first thought as it relates to some of the projects in terms of the  engagement that you want to drive inside of the company.

06:42
The one piece that I wanted to highlight,  the areas that we focus on is one of the pieces that we put immense focus is on customer experience. How do we measure customer experience? How do we make sure that our customers are getting the best experience everywhere? uh Your mobile phone is not  static in one place. Everybody's traveling, they are in different parts of the country, they are going from their home to work and  to the kids.

07:08
and back to home. So you're constantly in  traveling and you're using and you expect your phone to be working everywhere, especially with, you know, one of carriers like Verizon, as we put so much focus on network reliability, it is really important for us to measure customer experience. So one of the projects that we did  was how can we tap into an experience code for every customer of ours? So the thought process behind that is  based on

07:36
the network in terms of the factors that exist on the network. Think about the capacity that we have on the network. Think about the coverage that exists on that network  and how the customers use the phone, right? Some customers may use their phone just to call others. Some customers may be very data heavy. Some customers may be listening to your podcast and they want that audio streams to come through without any issues. So how our customers demand the network is up to the customer.

08:04
we have to make sure that we are there for our customers in terms of how they want to use us, right? So being able to look at all of this data together, bring them together in a way that  we can then  make it very personalized in terms of how the customer is using it was the critical challenge that we faced. So we brought our data engineering team together. We brought our AI modeling team together and think about, you know, the scale of this, right?

08:31
We have 100 million plus lines on our network  and all of those networks are transmitting data every single day, every single minute, every single hour, right? Constantly connected to the network.  And  as a result, when customers are mobile, their experience is also going to be different from cell site to cell site. So how do you bring this massive data  into something that you can meaningfully use?

08:54
to drive actions in the network. So that was the biggest challenge that we had as it relates to measuring customer experience. So as the teams came together, we looked at unique ways to be able to digest this data  on an everyday basis, make sure that we can look at the experience that these customers are having across every single cell site, and then bring all of that together to truly understand the experience that our customers are having.  And this has been very helpful for us.

09:22
because  we can look at it from the perspective of,  we making our investments in the right places? Do we see a step up as we activate new cell sites in certain areas? So think about the pre-experience versus the post-experience. You can do a  lot of measures to drive the intelligence here. The other project that  I wanted to highlight is  something called fiber risk scoring as well, which I thought was a very interesting project.

09:52
In that, there was not like a massive AI involved. It was a lot of analytics that drove a lot of intelligence. And that's another piece that I wanted to give to your audience as well. Today, because AI is such a buzzword, a lot of people are trying to force fit AI into every solution of theirs. My suggestion is you can think about it as, is analytics able to solve your problem? Is analytics able to give you some ROI?

10:17
Take that ROI and then stretch that into the goal of AI and how you can stretch that even more. This is one such example. uh You can imagine our fiber going all over the country and fiber is absolutely critical for us to continue to provide the best service. So when  a particular excavator, let's say they go to a particular area and do some jobs, there is a potential opportunity for them to basically  disconnect the fiber or cut the fiber.

10:47
uh as they do some work to dig. So we analyzed this data and we found that the  lack of  visibility into the impact that these excavators were causing was one of the key problems of why some of them were not paying a lot of attention. So what we did was a very simple solution, right? The thought process was we did a geospatial analysis.  We have a team that can basically look at the

11:16
topology of the land, uh the mix of the fiber, um what sort of, know, is it by a highway? Is it in a rural area?  All of that  comes into the geospatial analysis. And then we built a propensity model on that based on the historical data  of the companies as well. Is this company typically  very good at excavations? They do a good job in terms of preventing fiber cuts. What sort of history do we see? Or is that particular area

11:46
high risk, meaning a lot more cuts are prone to happen, things of that nature. So what we then did was we connected it to the 811 system, meaning 811 is basically a call before you So when a particular call comes in, yeah, absolutely. When a particular call comes in, we know the area that they are planning to dig. We know the name of the company that is planning to do this job as well. Then we push, uh we look at it and calculate the risk score, meaning

12:16
What is the strength of this fiber? How many customers is it supporting? If a cut were to happen, is that going to become a big outage? So being able to look at that and marry it with the risk that this particular excavator is bringing, then when we see that this particular dig, for example, is high risk, all we have to do is make sure we can email the customer. Or if it's really high risk, we may establish a call with that excavator to let them know.

12:43
the area that you're going to do and the job that you're going to do is super important  as it relates to the fiber that is underneath there. you can,  know, human behavior is when you get some extra intelligence or extra information before you go, you become extra cautious about this. And as a result, we saw that folks were paying more attention to it.  And, know, at least for the last several months, as we were observing,

13:09
We are seeing that there's a good relationship between the emails that were sent out  and then paying extra attention. Therefore minimizing the number of fiber cuts that can happen. And this is again, a simple example of how you bring multiple data sets together, map that with the geospatial data that you have  and connected with the actionable way to communicate to  the company so that they have better visibility. And therefore it's a win-win for all of us.  Yeah. Go ahead,  Gary.

13:39
I was just going to say, mean, this is like music to my ears. mean, 20 years in telecom, you know, that's just always been the pain point, not necessarily the carrier taking  the cut, right? It's to your point. It's the construction company. It's the road maintenance, DPW,  and there's no insight into  the prevention of that. So when you were talking about perfecting the customer experience with data and now this AI move,

14:07
I think that's huge because for every CIO, they're always asking, know, will I have a fiber cut? I'm, you know, right outside of New York City or one of the main paths going into New York City and hearing what you're doing now at Verizon, if I'm a CIO listening to this, they're probably thinking the same thing. Thank God we have the tools now and the data to hopefully prevent as many as possible, right? Nothing is 100 % guaranteed.

14:34
But that's music to my ears and that's why I'd want to partner with someone like Verizon because you're big enough and you have those data sets to go and do that. So to me as a telecom engineer, that's music to my ears. Sorry, Scott, I didn't mean to cut you off. I was like at the edge of my seat, Michael's saying that. I'm like, yes, yes, it's about time. I love it. Yeah, I have a similar background to Gary, so I was going to say something similar. But just...

14:59
Michael, thinking about the propensity modeling and geospatial analysis tools that you have now, just think about where  can it go? So are you able to, maybe the future is to add in more data sets like weather patterns, soil type,  is there more plans for it or anything you could say on that? Yeah, no, absolutely. See,  the amount of data volumes that's coming at us is increasing day by day.

15:28
One part of my team that uh focuses on some of these external data sets is the competitive intelligence team.  And their primary focus is what sort of third party data sets can we bring in to enhance the quality of data that we have uh in-house? So think about it. uh A lot of where this is moving is  first party data sets are great because that's about your customer base data sets that explain about how the customers are using the network.

15:56
you really have to marry all of this with second party and third party data sets. So along those lines, our team truly believes that we already have a lot of the access from NOAA and others in terms of the weather patterns uh and everything that's going on. Because you can think about it  as  a lot of times when we worked on a project which was focused on modeling the climate impact, what sort of impact do we expect? Especially if you think about cell sites.

16:26
you need them to function during times of disasters when a hurricane goes through some part of the country. So how do you truly look at it? And we have obviously great resources like NOAA and others whose core function  is to come up with these models. So we take full advantage of that to be able to get those kinds of data sets.

16:48
and then marry it to our internal data sets to identify  areas of impact or what is the forecast look like for the next several years  in terms of high intensity areas. A few years back, we looked at it even from the perspective of, ah you know,  there is  wildfires in certain area. What can you do from a preventive measure to avoid it? Because  I never knew that  something like a water curtain existed. Apparently, it's actually what I say. It's a curtain.

17:17
and it's filled with water and you can be proactive about it. So you need to basically combine your intelligence to identify locations that could benefit from things like that. So to your question, Scott, I feel that lot of companies and Verizon include, we have a good handle on the first party datasets  and we are starting to um expand our focus on making sure that these additional datasets that exist in different parts.

17:46
of, you know, across  government institutions and private institutions as well. How can both of them come together to truly uh energize the work that we're doing so that we can  continue to focus on what I said earlier, which is improving the customer experience  and the network performance. That's exactly it. I mean, just think about every data set that you add and every layer that you put in there. And even in the competitive intelligence side, where you're working to compete against the other carriers.

18:14
It's just driving the customer experience at an exponential rate. It's amazing what this can do and how it's going to accelerate this for the  end user. That's the goal. Yeah. Well, and what's interesting, know,  what I love about what you're doing and your team, and by the way, running two organizations, I don't know how you sleep. kudos to you on that.  know,  Scott and I have a lot of conversations independently with executives today.

18:42
and they will tell us, we have large, you know, historical data sets, but they're really unclear if it's usable to build their models. And, know, based on your experience, how would you  respond to those executives on the importance of clean data? Because you've gone through this, right? So a lot of these companies, they'll come to us and say, hey, we've got, you know, 10 years and petabytes of information, but is it clean? And that's usually what we ask and they're unsure.  maybe in your...

19:10
your opinion based on your experience, everything you've done so far with Verizon, maybe you could kind of respond to that because we do get that question often. Absolutely.  And  I would say that we are working on continuing to improve that every day as well. We have a very strong data governance team. We have a very strong data engineering team who's  absolutely partnering with us to try and make sure that

19:36
the quality of data that is ready to consume for these AI models is absolutely really good.  The piece that I wanted to highlight there is, as you can imagine, as I said, network, we are getting data from all sorts of things. Every probe on the network  is actually sending data  every minute of the day.  So we get all sorts of data, billing data, customer data. So  the talk that I would want to give in this is quality of data immensely.

20:05
super critical, cannot stress enough on it.  And my suggestion there is to start with almost a data quality scorecard that the leadership can drive. What I mean by that is, let's say you want to focus on a customer experience project. We will have to almost strategize to understand what are the existing data sets that can help us build this customer experience framework? What are some newer data sets that we know is available but is not trustworthy?

20:33
and what are some external data sets that can make this even better? The thought process is how can you put a framework  on a scorecard that significantly puts an emphasis on the quality of data,  even as simple as a red, yellow, green? Meaning, do we have a coverage? Meaning all of our customers are essentially covered. Because a lot of times you may get data and it's only 30 % of your base, the 70 % is left out, it's not meaningful. um I cannot say.

21:03
I can be good for Gary, but I don't have any information on Scott. So therefore I cannot tell if Scott has a good experience or not. You have to have like a solid coverage, know, at least 90 % plus. And do you have consistency in there? Meaning I cannot have it for one day and the next day it goes off. So I know this seems very foundational, but to the executives that are focusing on this, putting in a framework like this to be able to look at the scorecard as a red, yellow, green.

21:32
on the data sources that are really important for you to achieve that customer experience measure will make the focus go through the organization. What I mean by that is you start with the scorecard and then your  organization that's actually helping from a data engineering perspective will basically follow this as  their scorecard  and then you can have consistently  occurring uh reviews of it, right? For the last 30 days,  we have read for so many days. uh

22:01
It takes time for sure. As you think about it, it takes some investment as well to make sure that the data is of good quality. But what I will highlight is  this is something that we continue to focus on  every day as well.  It's not 100 percent,  but all of us are going towards that journey. Then  the second part that I will highlight for the executives is sometimes  as we spoke earlier, right?

22:29
In an ideal world, we may want  all the first party data combined with all the third party data in a perfect sense, but that doesn't exist in my opinion. Therefore,  we always try to focus on what is our MVP uh and then making sure that we have the data sources mapped to them. Then what is our MVP to? We have some additional pieces that can help us get there.

22:53
The reason is, and I saw one of your episodes earlier, you spoke about the ROI for some of the AI use cases with the CFO.  ROI is super, super critical. Like I said at the start, if you're building multiple solutions and you're looking for a problem, you're not going to get the business commitment or you're not going to get the support. Therefore, if you take an approach, like what I was describing earlier, we can have many wins along the way while we go for the bigger win.

23:21
What we don't want to do  is say that okay, this is going to take a year We look at another way after a year from this and come back in that model does not work for sure at Verizon We have to deliver consistent wins along the way  and along those lines is what I'm suggesting Or I'm the way we approach it is what sort of MVPs can we hit along the way? How much of a value does each of that MVP give us? What is the data sources that are needed for each of this MVP?

23:50
and continue to augment that more and more. Like I said, it is a journey. It is absolutely a journey which requires investment, which requires focus, which requires dedication from multiple teams, from your network team, from your engineering team, from your governance team,  so that we can  continue to make progress on it. Well, and I think to that point, and those are spot on because these are the exact conversations.

24:15
Scott and I independently have with executives. They want to boil the ocean, have some large project because that's what they're accustomed to.  And they don't even have the cultural buy-in from their teams, right? Because a lot of people are worried about future work and that sort of thing. But  I think that there's a lot of off the shelf things that you could satisfy as your early stage AI strategy, right? So if I'm an executive, maybe one of the easiest things for me to do is to partner with someone like Verizon.

24:45
because you've already started to take the huge investment in  trying to prevent  outages and perfecting the customer experience on my network. Whereas some of the other carriers  are playing catch up or maybe not having even started yet. So  to me as an executive, one easy step could be perhaps I look at uh working with Verizon to partner with them. And that's the first step of my AI strategy and get the team bought in like, hey,

25:13
this isn't completely scary. Verizon's helping us and they're giving us our technology and giving us the information and contacting us when a big fiber or big dig is happening that could potentially cause a fiber. That's the insight we have. We don't have to spend anything. That's just part of our partnership. So I echo exactly what you're saying because I think  it's reminiscent of

25:38
when cloud first came out, where a lot of executives felt they needed to put everything into the cloud and they didn't really have a strategy. And everybody was saying everything was cloud enabled, but it really was just a data center with private lines for a lot of services out there, not public cloud providers. And so a lot of people made a lot of mistakes, spent a lot of money and caused a lot of internal heartaches. So I love how you just phrased that. think our executives and even the individual contributors listening will really appreciate that. So thank you.

26:07
Absolutely.  the point that I wanted to add there, Gary, is  I know I'm saying consumers, but you can absolutely think about like all the small and medium businesses,  know, exactly everything that I said,  it's totally applicable to that as well. Like, you know, what you guys are doing here in terms of the podcast,  you want consistent quality network to be able to do this. And your listeners would want that quality to  actually listen in, right? So

26:34
So everything that I said is absolutely applicable to the business front. So I just wanted to make that. That's exactly what I was going to say, Michael. mean, it's the small business that has a fleet of trucks going around, you know, northern Maine, and they have they have they're leveraging internet of things for a number of their devices. So it's it is certainly applicable to the to the small businesses, medium, large businesses, global enterprises. So that's that was really good insight. And we're just one to.

27:02
want shift gears a little bit to a really popular topic. If you don't mind,  we get a lot of questions about,  Gary mentioned the future of work. And that's, that's what a lot of people are saying the future of work. And so we're just wondering, you know, what thoughts or recommendations do you have for, you know, students, people that are getting, you know, newer to the workforce, maybe they're in college now, or even in high school, we've got some high schoolers thinking about their future career paths. Is there, could you offer some, some insights, some thoughts,  recommendations?

27:32
Absolutely. Scott, the one piece I want to highlight before I go into that question, as you spoke about user of work, our consumer CEO has been very vocal on this. Where we see a lot of this that, you know, whatever I spoke earlier is helping us to, the focus for us is to be able to reduce the cognitive load of our frontline workers. And that's kind of like a big use case that we've taken on right now, meaning

27:59
You know, think about all the transactional systems that your frontline workers would have to switch back and forth. The overload of  the pieces when they have a call with the customer. So the thought process is how can a lot of the focus, initial use cases, can help them reduce the cognitive load so that they can focus on maybe the 20 % of the transactions  that really need the human intelligence to dive in.

28:26
and 80 % can actually be augmented by AI. So a real big focus from a use case perspective to try and help in the reduction of the cognitive load and also improve the efficiency of our frontline workers with all the information that we can provide them  in the place that they typically consume their information versus  if you make them go through 10 different systems, you can imagine the amount of pain that they would have to go through, right?

28:53
So just wanted to highlight that from a use case perspective as it relates to future of work. Yeah, that's great. Yeah. Just to reiterate that for the listeners, mean, shifting the cognitive load from the worker to the machine and allowing the worker to be more efficient and probably have a better quality of life on the job as well, doing something that's more impactful. Yeah, absolutely. And now shifting to the second part of the question, which was more focused on recommendations.  I believe the  landscape is changing dramatically every day.

29:22
So everything that I said is probably applicable as of June 30th. uh Maybe if you listen from two months from now, you would be like, you know, this guy is very outdated is probably the thinking that you may have because of how fast this landscape is shifting, right? uh My thought process is like, this is in my mind,  what I would recommend very heavily is like a three-way approach.  Number one is foundational knowledge. What I mean by that is,

29:49
We absolutely have to focus on the technical capabilities as you're going through STEM programs um that can help you basically have a solid understanding of dealing with data, dealing with cloud environments.  That's not replaceable. You do have a lot more options to enrich your knowledge even more. um Like for example, I personally,  I use like perplexity a lot uh to get a lot of intelligence.

30:18
in terms of  any questions that I have,  even in terms of understanding what else is going on, right? But I  go between Gemini and Perplexity a lot. So there is a lot more means for all the students to understand  and get enriched, knowledge become better every day.  That I think is foundational. uh There is no replacement for that.  Absolutely very critical. And the more time you can spend on enriching the foundational knowledge,

30:46
is really important for you as you scale through different problems or different types of problems so that you can truly connect the dots related to the problem statement and the right kind of framework that you can apply, which you probably gain from a foundational knowledge perspective. The second piece that I wanted to highlight and not a lot of students or folks that are trying to enter into this  area do is practical knowledge. There's a lot of opportunities out there, like CACL is an example.

31:16
There's lot of freelancing opportunities also where,  whether it's students or folks that are trying to make an entry into the space, so many opportunities in terms of where they can apply the foundational knowledge that they have into real world problem statements, into real world experiments, into real world solutions, right? So this can be very meaningful for them because if their resume is light  on real world experiences, they can use this as a means

31:44
to basically tell a story and strengthen their resume, whether it's Kaggle or the ability that Kaggle gives you is partner with people that you may not know. And that's basically what we may need in a real corporate setting, right? You're actually partnering with global teams. So there's a lot of opportunities. So I really want to stress on the importance of practical knowledge, taking whatever they've learned in the foundational and translating them into practical problems through

32:13
multiple means that we have. And the third part, which actually a lot of I've seen like uh not a data scientist focus on this typically  is communication effectiveness. um How they communicate. I would put storytelling and everything into this pocket.  think Gary, you spoke about your other podcast. I see a lot of the leaders that you have, you the CIOs and the CFOs on this podcast.

32:41
At the end of the day, someone has to come and convince them with a strong story.  And if you have that capability of being able to deal with data  and being able to deal with the storytelling part of it, it's a crazy combination in my mind. So you can put very heavy emphasis on your foundational knowledge, which is your technical capability. You can augment that with your practical real world knowledge. But the third piece, if you are not able to tell a strong story in my

33:10
personal opinion that may basically give you some challenges in terms of the growth opportunities that you can hit in the company.  But if you can  end focus, if you can put more emphasis on building that storytelling capability, this is in my mind, a real good combination for you to combine all three of them  because you know the data best, you have the strongest foundation. And if you are able to tell a strong story on top of it, you can really influence a lot of decisions that happen at the top.

33:40
And, and, and obviously it can accelerate your growth to the top as well. Yeah. Great points and really well said. That's awesome. Yeah. I couldn't have said it better. mean, you, we've mentioned it many times in the podcast. I even took, when I was taking a class at MIT, Thomas Marrone mentioned this as well. Just said, listen, you know, communication skills, empathy, compassion,  uh, problem solving and being able to roll all that up.

34:09
especially if machines end up doing a lot of the functional, you know, data driven analysis for you. How do you articulate that? If you're a, if you're a healthcare professional, how do you take all of that data and go to a human and explain that to them? And if you can't, because you do not have those soft skills, those communication skills,  I think it's great, great advice. You have to learn those. And that's not just for, you know, uh, budding students right now, but I think even for us in the professional world, a lot of us get used to

34:38
hiding behind email or Slack or text. And we've lost some of that. So I think with this age of AI continuing to propel, it's very important for us to maintain those soft skills and really sharpen them. Absolutely. And I think networking also is, the importance of networking is key. Like I'll give you a simple example. When I did my MBA at William & Mary, we do meet with a lot of professionals.

35:08
more like one-on-one,  getting to understand. Obviously, at the end of the day, it's more about,  I'm looking for a job. Can you help me find a job? uh But we have the coffee sessions, information sessions, you can name it, however.  And after the sessions, obviously,  we are told very much that, send a thank you card to them. Sometimes I've sent like handwritten thank you cards. It seems very outdated right now. But even a thank you email for the time that they spent with us.

35:37
And I can tell you in the past, when I did that, I used to have like multiple templates of Google Docs. Like I'd say, okay, thank you email with this because again, I don't want to type a single thing. So I'd customize it and send it, but I'll tell you now it's so much easier.  give the prompt to, you know, Gemini or perplexity or, know, whatever it is, you give a clear prompt and you say, this is what it is. The thank you email comes out of the box. So there is, there is no reason.

36:04
that a student that is looking to secure a role or a job or a person that's trying to transition to this, there are so many other means available to them today  in terms of making sure that they can go at this faster, they can go at this more holistic um by leveraging these capabilities. They just have to spend the time to understand what capabilities exist versus trying to do all by themselves, right? So truly, I think I'm super excited from that perspective, the amount of opportunities

36:34
that are available for folks trying to do this,  whether it's learning or whether it's following up with candidates or even getting information on who they are talking with,  everything has gone like a big step function. Completely agree.  It's a lot easier. And to your  point, there's no excuse. mean, if you think,  so looking back at your career and the journey you've gone through,  what would you say has been

37:00
you know, the most surprising or impactful lesson you've learned along the way that you could share. Absolutely.  Um, I believe one of the first thing that I wanted to highlight is,  um, the, the part of relationships inside the company.  What I mean by that is, uh, whether you're an individual contributor or whether you're a, you know, small, let's say people leaders or you have a huge organization.

37:27
um The power of the relationships that you form is really key. um Whether it comes to getting feedback on a proposal that you're making or it comes to getting new ideas in terms of how you can take it forward or you're trying to get some more buy-in, it's  really important  to basically make sure that you  can build a strong relationship. Because  I was once told  when we were in some training  that

37:54
Most of the important decisions about your career is probably made in a room where you are not there. Right. Yeah. know, whether it's a promotion or your ratings, it's made in a room where you will not be there. So how do you make sure that you have enough people in that room that can speak on your behalf is absolutely important, which is where the power of relationship comes in. And you're going to say something, Scott? No, I was just going to say that's right on and awesome.

38:23
Awesome information for our listeners. Yeah, absolutely. And the second piece that I wanted to highlight and I put a lot of emphasis on this is, you what I call as flagship project. What I mean by that is there could be a lot of folks and you may be working on 15 different projects. At the end of the day, if you're working on 15 different projects, you're working on 15 different projects. You're not like driving, you know, a key project or you can be spread across 15 different projects, most folks.

38:53
Something that probably can ace all 15 different projects, but most folks. So my thought process is if I ask, okay, Scott, what is your flagship project? Gary, what is your flagship project?  That thought process is mostly focused on project where you lead from the front, a project where you can really drive the impact, a project where you have highly visible for the leadership team. I strongly believe that is important. The reason is  even though you're part of 15 projects,

39:23
if you don't have one project where you can truly drive the impact and be at the front of it,  you don't get as much visibility as you would want to be.  So that's where you don't want to be a backseat  passenger all the time. In  one or two of these projects, you want to be a front seat driver as well. That way,  it comes to having a conversation with a senior leader or when it comes to your mid-year discussion,

39:49
you can truly focus on the importance of the flagship project that you did  and  the impact that that had on the business. Whether you're an individual contributor or a people manager does not matter. There can be cross-functional projects where you can take a lead on certain modules and make that your flagship project. So  I would say number two that I wanted to focus on what I call this flagship project because at the end of the year,

40:15
You don't want to be the ones that actually completed 50 different projects helping others. What are some of the key projects where you left from the front, you made an impact, and you're able to talk to it?  That is really important as well.  And the third part that I will highlight is um we got this  strong message all the time, but I cannot stress enough on it. Don't do like 1 million things. Focus on 10 things that has the biggest impact.

40:45
If there are 100 initiatives that give you, let's say, $100 million in savings, and if there are 10 initiatives that gives you $80 million in savings, guess what? You need to focus all your energy on those 10. And I know it seems very fundamental, but I think there's a lot of times we end up focusing on 100 different things. Therefore, we may lose the ball on the 10 focus things as well. You know what I mean? So what I would say is prioritization is key.

41:14
It's absolutely important for us to prioritize so that we can focus on the 10 things that gives us 80 % of the benefit versus the 100 things that basically dilutes the ROI that we could get. those are great points, Michael. think you're spot on. I'm sure Scott can attest, we used to lead large global organizations. We had folks that would just get spun up into the minutia of a million different things and really bringing that back into focus, how you can be impactful.

41:44
is not only just healthy for the organization, but it's healthy for the individual. So I think that's great advice, especially for anybody going into the workforce that doesn't have that world experience that you do. So thank you for Totally agree. Yeah, you need to move the needle. So I think we're here at the end of the show. I really want to say thank you, Michael. I'm just in awe of what you're doing and  a very inspiring leader as well. ah Thank you for the information and  the...

42:13
recommendations back into people looking again in the workforce. So hopefully you can join us again in the future for another episode. I'd love to keep tabs on  what Verizon's doing. Yeah, I said, you know, this is a great opportunity to talk about some of these exciting things and definitely, know, Verizon is  basically trying to move  very fast and have a big impact across multiple areas in this.

42:38
using AI and other  capabilities that we've continued to enable.  And I am also a big believer that a lot of folks that are trying to make the transition,  your listeners,  students that are trying to make a strong entry into this space, the amount of opportunities in my mind is going to increase  a lot, Newer capabilities that are going to come in, which was not there  five years back, 10 years back. So,

43:06
So as I said earlier, this is  a landscape that is changing so fast.  So it's very difficult to keep tabs every day, but I'm pretty sure that through all the discussions that you guys are doing, through all the opportunities that we have, all the AI tools that are available for all the students and  everyone,  they can definitely take great advantage of it and keep them updated and be very successful in this space.

43:36
Excellent. Yeah. Thank you again, Michael. Perfect conversation for our audience. Really appreciate it. And  thanks for everybody for joining us on the Macro AI podcast. So please subscribe and share with anyone who's interested in learning more about AI and business and the impact of AI going forward. So please send us your questions on LinkedIn and you can connect with any of us, including Michael Raj.  And we're at the www.macroaipodcast.com.  So until next time, keep on learning.