The Human Side of Artificial Intelligence | Marta Haynes, IBM's Data Leader

Most people in data and AI did not start their career on a bond trading floor. Marta Haynes did. She spent 12 years at IBM getting enterprises ready for AI. She trained over 20,000 salespeople globally. She has watched companies discover the gap between the data they thought they had and the data they actually have.

My guest today is Marta Haynes. Welcome to woman leads AI podcast. So Mart thank you so much for um coming in here and uh making the journey from us. The first uh thing that I would love to know is you started actually your career as as a bond trader and then you transitioned to tech. Can you tell me more about how um you know starting in kind of financial sector and then moving over to the tech sector how it shaped uh how you approach your your current role?

>> Yes, absolutely. So, thank you for having me, Victoria. It's just a treat and a delight to be here with you in person in London. Um and I love that you're asking me about my bond trader day. So when I was in my 20s, I worked on the short-term fixed income uh trading desk for US Bank and I managed money market funds and uh short-term fixed income product.

So it was a particularly interesting time because I started in like 2002 and when I exited the industry it was like 2008. So, it was um I will never forget like August 10th, 2007 was the day that it was a Friday and we saw some very large swings in the money market uh cash flow. So, it's like a billion dollars coming in. We couldn't get a bid on treasuries. Where did all our like Euro dollar time deposits go?

It was just very goofy stuff. And I just remember looking at my coworker leaving that day like what is going to happen on Monday? Well, on Monday is when the Countrywide stuff and um I think it was Northern Rock like actually started to um break the buck and so it's when like the credit crisis actually started. So in you know we think about the Lemans and the Bears of the world like in 2008 and everything and like when City Creek went below a buck but basically it started kind of a year before the softness and the weakness. So money markets are not supposed to be this exciting, right?

And so, um, but they were because they're trying to maximize money. They were slicing and dicing and structuring deals, you know, things that should have never happened, a money market would never break the buck, etc., etc. So, um, it was really scary. I mean, I thought that we, even though we didn't break the buck and we didn't have like these kind of investments in our fund, um, we thought we were going to bring down the entire financial system just with some of these trades.

So what that really informed me on was um financial markets, financial risk, right? So I always have my own like risk tolerance. And then this was back in the day before a lot of the compliance was automated. So even though that we were starting to get our compliance uh platform up that was supposed to allow us to trade and automatically notify us through automation if we were doing a trade that was out of compliance or out of our risk uh range. I still kept my little spreadsheet because I just didn't trust the technology.

So, um, that was very interesting. So, data driven decisions, but you're not using the data to make the decision for you, right? So, you're reading the data. You're looking at the story that the data is telling and then you're still using your gut like as a gut check like yes, I want to do this trade. No, I do not want to do this trade.

This is about my risk tolerance, etc. So, um, kind of transitioning into tech. Well, I didn't directly transition into tech because I actually, um, decided that I was going to leave financial services because I wanted to go help people. So, what was really interesting to me as people's lives were falling apart. Um, friends at Bear Sterns, friends at Lehman Brothers that had worked there for decades, um, were losing their homes.

There was no new job for them. You know, it was just jobs were just flattened. I just kind of felt like I wanted to go and work with people a little bit more and like this making money and working on Wall Street kind of stuff just wasn't as exciting to me. >> So then I took two years and I started my own yoga company and I thought I was going to go back and be a psychologist >> which sounds kind of cool. I don't know like I got to chase down a dream and I started my own little company and I had a book of 100 clients.

Um but what was not cool so I think I was about 29 at the time. Um, I was having a hard time paying my bills and it was just like I felt like I was a smart person and I shouldn't be struggling from a financial perspective, you know, cuz yoga teachers don't make that much and all I had ever known was kind of a a good income. So, I think um I had to take a third job at Nordstrom selling swimsuits in the middle of the winter >> to Minnesota. It was very hard and it wasn't because I don't love sell like I I love selling so I'm in sales right now. I love fashion like it's super fun.

It was more people treating me poorly and looking down on me because I was working at Nordstrom as a 30-year-old. So anyway, I'm like, "Oh, darn it. I have to go back and get my NBA. This is going to be super boring because I've already done so much with business." But I was like, "Well, I also didn't want to pay for my NBA.

" So, I applied um for a scholarship at the University of San Diego. They had an international NBA program and I got almost a full ride to go do my international NBA there. So, moved from Minnesota to Southern California. Um, cut my teeth in tech there. So, one of my jobs was a due diligence analyst um for Tech Coast Angels, which at the time was the largest angel investing network in the uh US.

So, there was 110 angels and I think I had to help them make a 50 they had to do two $50,000 investments a year >> to be in this club. So I got to see so much cool tech and like on the back side of my MBA program when I was done it was kind of like do I go work for one of these startups and see in Southern California IBM was also recruiting me out of my MBA program like oh I guess I'll just go work for boring old IBM like I'll just move back home to Minnesota you know and I still wasn't married at the time and I think I told all my Southern California girlfriends like I made a joke I'm like these guys are flakes out here like I'm just going to go back home and take my boring old job at IBM and marry like some boring old guy from northern Minnesota that would never cheat on you and wants to be a family person and all this kind of stuff. So I moved home and I met my husband 6 weeks later literally six and he is from northern Minnesota but he is the most amazing like person in the world and then it's been IBM you know for pretty much almost 13 years. I did do a little boomerang stint where I went to Oracle and came back. It was like at I was at IBM for about three or four years.

Um, and I didn't feel like my career was advancing. I wasn't getting like the pay bumps that I wanted in sales. So, I went to Oracle and came back like in like nine or ten months and I've been at IBM ever since. Um, but to your original question, you know, how does my time in financial services inform, you know, the work that I do with my clients today? Well, I have a very specific view on risk and compliance and the importance of that.

Um, to this day, I still read all of the contracts that my customers and I are looking at, whether it's like signing a cloud contract for the first time. I feel very comfortable um, negotiating redline terms with lawyers because when I was a trader, I would go read the bond indenture statements myself and some of them were like municipalities where it was like back in the day where they were like printed bond invenentures and I'd go through no AI tool, right? And so and I think there's still just a little bit of that bias like I still love my spreadsheets and I know that's not very cool but not very AI toolish but again the data informs us the data tells a story it does not make the decision for us you know but automation can help us you know so >> the uh you know it's making me very nervous what you said about your yoga studio >> because uh I have recently quit my job actually >> so >> congratulations. So it does feel like this big step and you know I hope that after two years you know the women lead the air project is really gonna um bloom but um you know it can it can go both ways and I think uh what you know what I take away from from your story is it it doesn't actually matter you know whether that business works out or it doesn't like there are so many opportunities down the road um and you know IBM was only one of the things that you uh like you had as an option. >> Um so that makes me feel >> oh my gosh.

So Victoria like I love that you are doing this and I love that I did that at that time of life. >> Um but I do kind of I think sometimes you know we all want to be like social media influencers and you know when you're in your 20s and 30s you know maybe life doesn't seem as serious or you look at things differently and that's a really great time. I mean, you can always take a risk. It's just the investment horizon is a little bit shorter the older that you get. But the truth is is like you're learning from this and you're going to keep learning from this and more opportunities are going to open up.

So, who knows, you know, where this will go. Um, I think in life, you just got to show up and you got to do the work. >> Uh, fingers crossed for that. >> I'll keep my fingers crossed too, but I don't think you need it because this is so amazing. When I think about data, the first thing that comes to my mind is spreadsheets and Excel, right?

And I think a lot of people when they think about the data, that's exactly like what they have in mind. But how do you think about the data and also what role do you think data plays as we adopt um you know AI tools in the future? What does it mean for the companies? >> Yeah. So when I think of data now in context of the work that I do at IBM, it's structured, unstructured, semistructured.

So I think about the type of data that way and much of our world is unstructured data. Okay. >> Can you explain what's the difference between the two? >> I'll give like an example. So structured data would be like data that lives in a DB2 database.

Unstructured data is like readings off of a sensor. >> Okay. Yeah. >> You know, so it's like we live much of our world in the unstructured data. >> Now at IBM, um, our point of view, and there's some in the industry as well, is that we are actually on the verge of our third AI winter.

So, I don't know if you've heard of this, but like AI winters, you know, AI comes in and comes out and and whatnot. Um, the jacket that you wear in winter is what keeps you warm. So the customers that can know how to incorporate their unstructured data and manage the explosive growth of unstructured data are going to be the customers that are the most successful in surviving the winter that's coming. So I also think about data in terms of where does it reside. >> Is it in the cloud?

Is it on premise? Is it on you know some random server underneath somebody's desk? All of that data is super important as we know because like the stronger the data that's getting fed into the model, your model is only as good as your data. So what I'm seeing from customers is it's not so much that they have like a model problem, they have a data problem. And it's a problem that I've been talking about for my entire career.

you know, broken data pipelines, data silos, um not having access to the data, and then more recently with all the different hybrid cloud approaches like IBM's approach, but it's like well, how do you do you have to move the data? And then you have like this explosive storage cost of where you're storing the data. IBM would say like no, you do not have to move your data. We have like a common data platform which is called Watsonx.data data um where your pl your data can live where it resides and you pull all of it into it like a single pane of glass and manage it from there.

So that's how I think of data. I think also you know just taking a step back data tells a story. >> It is a s it's like a record of something. So the more data that you have the richer your story is going to be and how do you use that data? The governance piece is also very challenging.

Okay, >> because you know you need to have a context for the data. You need to have access control who has the data. And so I think somebody asked me the other day, well, when you are selling to a customer, who do you talk to? It's like, well, how many people at the customer account are you talking to? I'm talking like 10 to 15 different functions because if the infrastructure team is not in line with the security team is not in line with the analytics team is not in line with the business or the end users you know we've got some major issues >> and control issues.

So, >> so let's say um uh let's say I'm the CEO of a Fortune 500 company >> and you know I feel like the data you know the data that I have in the company some of it is structured in Salesforce and CRM some of it is unstructured what would be you know the the product or the outcome from you know when when the customer you on board the customer and then as they go through that journey of of working with you and with IBM. >> So I think the most important thing and it's not just if you're working with IBM is if you're working with Salesforce. Salesforce is doing some really um exciting stuff right now with their agent force and the headless um kind of announcement that they made a couple weeks ago. But where I see my customers struggle like they want to boil the ocean. Okay.

So taking a step back we all know that AI is important. Many have like an AI budget but they don't know what to do with it. And they have all this pressure. Everybody needs to have an AI strategy. You have this huge budget.

What are you doing with it? But that budget in this like business environment is coming from someplace else. So what are you doing? Well, what everybody should be thinking about is what is a quick win? What is the minimal viable product?

You know the MVP. what is a super tight use case that will have the greatest impact. So I think of like a Salesforce, you know, um like a service cloud for example, where they can use agent force to help modernize their customer call center. Well, we know that like every time a customer calls, it's 25 bucks per customer call. Well, if you can use automation and then um like agent force can do uh deterministic and uh probabilistic kind of like logic and reasoning.

So you're getting the agents to make the decisions, cut the human out, you can save up to like 30 to 40% in workforce costs and retrain those employees. So, I'm not I'm definitely not somebody I'm sensitive to the fact that there is a lot of um concern in the industry about job loss and AI replacing humans and all this kind of stuff. Um my take on that and I'm not saying that's right, you know, it's a human compassionate take. It's coming. It's here.

It's here. So, you know, we kind of all need to like get on board. It's not something that could maybe happen. It's been here for a really long time actually. And so I think people should feel emboldened to embrace the technology and bring that into their life in in their workplace because those that do that are going to survive and then the companies that don't are going to lose their competitive edge.

So to the CEO, you know, I would just say, well, what's your number one business problem that you're looking to solve and how can we create like a very tight use case and a timeline and just get really specific about that. And then I think too um sometimes companies they the software is so expensive or that project is so expensive that they don't want to add in the services component or get the consultant in to come help them be successful because they can just teach themselves. I think AI tools will solve some of that because you can just take your question and put it into cloud or something like that and say like but do you have level of confidence in it >> the the solution or the script that they're writing for you or whatever. I think in the US we just had something interesting happen. I didn't read the article.

I did, my husband did tell me about it where somebody went up to like McDonald's, like a McDonald's drive-thru, and McDonald's has just been in the news like with their AI and the person asked a Python scripting question to the little place where you're supposed to put in your order and it gave him an accurate answer. >> Right. >> So, that just goes to show, you know, again, governance, guardrails, how is this being used? >> Yeah, it's being introduced everywhere. Um when it comes to data why you know continue with that example as as a leader uh I imagine as you said that let's say when we when we have that uh when we have the data then when we introduce AI tools it means that uh we can use all of the resources that we had as a company and make sure that those AI tools are um you know are answering the the kind of customers questions or employees questions in the best way possible.

Is that is that correct in terms of what you know what if my data is unstructured it you know I don't know where you know things are uh and I just want to introduce you know AI tools into my business like what is the risk of um of doing that without without having a lot of kind of structured data and using that data as you implement AI tools. So from my perspective there is the risk in introducing AI tools into the business is when there is not some guard rails around which ones you're using and how you use it. >> Okay. >> So at IBM you know we have we have our own internal AI tools to help us with sales messaging or product positioning. Um just for an example we don't want like I would get in big big trouble if I was putting our you know price differentiation into ChatGPT or Gemini you know now so there's a risk there right in terms of like how your employees are using the AI tools um but I think also what I like to do when I'm prepping for client calls because I know my clients are doing it so I do go out to ChatGPT and what do you think about IBM data stage what do you think about it versus Informatica?

How does Muleoft come into play? You know, what what is the latest trend in data bricks right now? You know, and so I kind of I'm not putting competitive information into the model and then training by our competitors on it, but I am getting a take because you have to put yourself into your customer's shoes. So like what are they reading? What are they and then that helps me it informs me um for when I do my additional research and positioning.

>> Yeah. And in terms of the tools that you use um every day, like how do you see them changing as AI is being introduced into the into the processes? So I think um we what I've kind of seen in the last 12 months from my view of the world is like we have like the little darlings of the day, you know. So it was like at first it was chat JPT and that was more like adopted and then now Gemini seems to be evolving quickly and then for me like a claude feels a little bit more intense. So that's actually a tool that I would like to get to know a little bit better because I have a personal goal of increasing my um social media presence and I hate doing it and I just feel like so it's like I know once I come up with the rhythm like I can get the post but I'm sitting there thinking oh this may be a good use case for Clyde to help me kind of come up with the right posts or whatever um pictures or images or whatever.

So um and who knows I mean in this space like another another model could just crop up out of nowhere that we don't even know you know so I think um again we don't wait for the new model right let's use I would encourage anybody who's just feeling a little shy about this whole AI tool and how do I use it just play around with it it's not like it's going to tell you you're stupid in fact I love talking to the AI tools because they always make me feel like really loved they're very compassionate that you know >> uh you know I was I was listening to a podcast and they had this ad for uh HubSpot and they said 80% of the data in uh I think it was small business companies or um in general in businesses is unstructured data um and it goes and it basically goes unused. Do you see that as well? And uh do you um have you been able to use that unstructured data and change it into a structured data that you can then use in uh whether that's introducing AI tools or improving processes. >> Yep. So there is a way to ingest what I call it.

So ingestion is bringing the data into a data pipeline basically and there is a way to do that with structured data and unstructured data. So where um the conversation gets interesting and exciting for me as like it gets ingested into the data lakehouse and so in that you know the data lakehouse how do we apply the analytics layer on top of that to pull out the insights but unstructured data is not going anywhere you know to my like earlier point you know it is part of this like third AI winter that's coming so there's um yeah there's just it's just that's what we you know images >> voice to text you know all that kind of stuff is just it's not going anywhere it's just going to grow exponentially so >> yeah I you know what uh something that my partner showed me is how in Hopspot you can you know you can set it up in this way when you send an email you know whether the other person opened that email or not uh and you know previously working in like really big organizations in sales roles. I was like, you know, oh wow, this is groundbreaking, right? Because if I was a leader of a large company and I had a sales team of, you know, 100 200 people, knowing whether the customer opened that email, even you know, from the position of a salesperson, uh knowing whether they've opened it and looked for it, you know, how long they >> the number of seconds or minutes. Yeah.

uh like all of that is such a valuable information and it's not just from the perspective of a salesperson but also the managers you know you can find out so much but I I haven't seen it you know being introduced and it's not like it's not an AI tool it's just a tool that exists so how much do you see those kind of like little misses that the companies like don't utilize >> so I I think again a broad brushstroke kind of statement. I think there's many features within software that companies are probably not taking full advantage of because you know they maybe purchase a software for an initial use case but they don't have time to do the all the other special bells and whistles you know that come along with it. I know at IBM we have a similar um thing about the the customers opening but it has to be sent like through like a specific process and then you know if the customer opened it up or not and then like some of our customers maybe I shouldn't be saying like spammed us like all of our stuff ended up in the spam folder because too many IBM sellers and I don't know I mean that's just like a whole another can of worms because of the constant bombardment of LinkedIn messages and emails and texts and all that kind of stuff. But I think um you know companies really need to understand what they're buying and um then the downside of not communicating with your salesperson um is that you don't know about some of the new features >> and functionality, you know. So I sell DB2 which is a database that has been around forever and I love DB2.

I've sold it my whole career. um we have a modernization um that basically brings a lot of automation into DB2 and saves a lot of manual work. So I'm just kind of trying to talk to all my DB2 customers. is like, "Well, it's been quite a while since we've had something like this in DB2." And they're very, very excited, but they wouldn't necessarily think to take a meeting with IBM, you know.

So, I would say like continual education, you know, about about these features. And then I think, you know, this is true in life, too. Sometimes you just get so busy with the day-to-day that you don't take the time to be a learner. You don't take the time to be curious. you don't, you know, you're just trying to tread water and keep your head above water like just to get through the day with all the work and everything and so you don't have that think time built in >> to think about how can I use this better.

Um I know it's kind of funny, one of my passion projects is I work part-time on the weekends for my church and um I manage a team of 160 volunteers um as part of guest services. >> It may surprise. So I'm like the director of guest experience. So um we love welcoming new people to our church and we want to make sure that they are feeling welcomed and loved by Jesus. So we have these teams of volunteers that are part of the greeting team on.

>> So one of the biggest kind of um problems that we have is the substitution request process. So we have an app and you're supposed to do a certain process. Well, I was on the phone with the software company because I'm like well I want to put a request for enhancement. this is a known issue. How do we get this fixed?

You know, let's talk through what the automation is and the the way we can tweak it. But um that I think would be an example also of what I'm seeing at many of my customers like the end users don't always get to be part of the purchase decision. >> They don't always get to be um in the room when you're talking, you know, somebody's maybe selling to a leader. Oh, this automation really is cool. And I see this in my customers all the time.

It's like well this director of data just purchased this tool and I didn't like it or I have to learn some new skills or whatever you know I didn't want that I wanted to keep doing this so um that software purchase decision you know there's many aspects to it but I think an ongoing feed and caring for the environment and um modernization so I think also AI tools could help with that >> right so you don't need to like figure out how to write your own data stage job you can I mean you can >> and if you upgrade you know we have a data engineering agent that can help you know amplify that work but there's just with the AI tools it's just do you trust it? >> Yeah. >> And you know what you said about um like software companies that don't actually you know talk to their customers and continue to improving product. It rings so true because h I heard someone saying they were in a room with a bunch of uh leaders within bank space like executives that are responsible for uh for their respective uh banks and they were like oh um uh who out of you have used your own uh bank app in the last month and you know there's no no shows of hands and the guy leading the uh workshop. He's like, "I know that uh you know, none of you because I've used it and it's absolutely terrible, right?

" And it's, you know, I feel like that about so many tools where oh, you know, as a user, you're like, you're stuck there and you're thinking, oh my god, if they just, you know, had a button where I can say how like I would love to use that tool, but this is such a big block. Um but anyway this is >> it's like so how do you so how do you have that continuous feedback loop you know and how can you build that into >> your AI project >> truly you know because again the space is changing and evolving so rapidly that the way even if you had like the tightest use case in the world that had you know proved out its ROI in less than a month you know chances are okay so if you if you proved out your concept then obviously you scale it, right? But then in 3 to 6 months, the landscape could change again, the tool could change again, there could be a better tool. So, you just kind of have to be in that >> um continuous feedback loop kind of environment, I think, mindset. >> Yeah.

I and I think you know what you said about uh learning from the sales rep. This is something that a lot of people they like they they just don't want to interact with a salesperson because they're believers. They're just here to sell me this and you know I I don't want to even speak to them. And I think that is such a mistake because I was in a row of >> ar and you know what happens is you can actually learn so much from a saleserson at at the end of the day if you're not interested in in the solution like that's fine you know no one is going to force a a product down or a service down your throat but I think it's such a great opportunity to to learn and to educate Okay. And I think a lot of people miss that and they don't uh do it.

Would do you see that as well? >> I do. But I also can see on the flip side like sales people need to adjust their approach. >> So, you know, the industry is moving towards SAS. This is a consumptionbased model.

And so if you sell a one-year SAS contract for this product and your customer is not using it, there is absolutely nothing that is going to keep them contractually obligated to you for more than a year and then you're just toast. So you have to be in there um helping the customer find the priority of the use case, helping the customer, you know, consume that SAS product so they can get the value out of it that you pitched in the first place for them to buy it and hopefully it would expand from there. um I do think people just generally are overwhelmed. So I do think that AI tools can help and um with that to help reduce some of the manual work or help them just like think better or whatever. Um I'm not I love sales and I think I'll always sell something as long as I have breath in my lungs because I love I love it.

I think it's fun. Um, but I can see a future where Yeah. like sales people are replaced by agents because if you don't if you have an agent that can go in and just answer your question very easily, then you don't need to pick up the phone. I mean, their their reasoning and their logic is faster than a human being. So, >> and the thing is you can actually pick up the phone because you know I tested 11 Labs which is such a um great um it's an it's a company that allows a lot of like voice generation tools.

>> Okay. And it's incredible because the way that it sounds like it is human, right? So I think uh you know I wonder whether actually a saleserson on the other end in the five years might be you know Amy but Amy is not real right. >> I wonder if it's now to be honest. I mean I had um what was it that I scheduled?

Oh, my husband and I were doing some spring cleaning and there's this company in the US called junk be gone and you can like so basically it comes it well the people come like a drag and they they give you a bid or a quote for how much stuff you want removed. Well, I booked online and I got a call back from I think the person's name was Amy and I didn't know until like four exchanges into it that I was actually talking to an agent. So then I said, "Is this a human?" and she's like no I am you know an agent chatbot blah blah blah you know and really I think that chatbot and agent gets incorrectly kind of like mixed right so like an agent is an autonomous complex system you know that can plan perceive reason and act right so chat bots are just kind of like where the industry was like two or three years ago you know before like this evolution in the agentic AI but anyway yeah I can totally see future where sales is different. So, how are we all modernizing?

How are we embracing what that future looks like? I still would like to think at the end of the day that a human like would want to talk to a human before they sign a large multi-million dollar multi-year contract. They want to know that a human is involved somewhere. But it it might you know it might change in such a way that the the grunt work is done by an AI and then for example when you're in a position where uh the contract is about to be signed uh and then you bring like the ex like sales executive on the line. Um and I can definitely you know see that happening.

I would like it if they then I don't have to talk to procurement. >> Like if you don't want to talk to sales people then I don't want to negotiate with procurement. >> Yeah, it's difficult. I uh I emphasize one thing I would love to know um you have trained over 20,000 IBM employees uh on data and AI. >> Yeah.

>> First of all, how did you do it? >> It was crazy. Okay. So, >> and what did you learn from it? >> Oh my gosh.

And this one of the women that I just love the most, Mary Tafuri, she was like our executive director in charge of all of this. So, basically, and my years all blend together, the COVID years. So, we had like the first round of COVID in the United States. We were all in Las Vegas at our annual training. I think it was February.

And we like all got home and then everything shut down. Everybody got sick with COVID. So then things opened up a little bit in the summer and then that following January we thought we were going to be able to all be together again in Vegas. Six weeks before we were all planning on doing Inerson presentations we had to pivot to online content. >> Yeah.

>> This was five or six years ago, you know. Yeah, it's probably five years ago. So none of us were like set up in our home offices for videos. Like none of us were set up for being yeah like filmed and all this kind of stuff. So we basically just did it.

And unfortunately we even had to do like a pivot I think with the software platform that we're using it. They didn't stress test it >> with the appropriate number of people. So it's like well we I'm just making up numbers. Let's say that we had 10,000 people logging on it once and they only stress tested it to like 500. I mean, it was really bad.

So, we had like basically everything in that pivot. We had six weeks to create content. So, what did I have to do? We had to get all of the sales leaders recording videos. We had to create um you know assets for this enablement.

We had to come up with like basically a run of show. We had to have like a like an in-house production >> group. We had to figure out how to make it entertaining. I actually taught like a yoga class like my husband took his phone. We went to a park like a state park and we did a mindfulness walk through the park and I was like doing some breathing exercises.

So we were trying to do these fun because Mary was super awesome and drove like mindfulness. So, we're trying to do mindfulness breaks because it's one thing if you're in Vegas and you're like taking breaks and walking in between sessions and everything, but if you have six hours of enablement, where do you take the break? You can't expect somebody to sit in front of their computer for 6 hours. >> So, um we incorporated mindfulness and tried to make it fun. We tried to use gamification.

But how did we get to 20,000? Well, we had three geography geographies. um you know basically the Americas, AMIA and then APEC New Zealand and Australia. So we came up with a global kind of program that we then repeated for three three rounds. So that's how we got to the 20,000 >> and >> um and I was like and we're all doing it from home.

So like I was on the European time zone and I was on the Apac time zone like teaching and doing some of the stuff live because we also haded people covering live in case the virtual technology went down. I mean it was just it was insane. So I guess I'm probably not giving you a really great answer like how did we do that? I have no idea. A lot of coffee, grit, determination.

Um what did I learn from it? The impact an amazing leader can have. because Mary was an amazing leader in rallying the troops and getting behind you and how amazing it is because there's times in life when you have a really great boss that you can work hard for them and you'll like do anything for them. But that team was so motivated and everybody like pitched in and we did what we needed to do. But yeah, it was virtual and it was and then how do you even Yeah, I mean content that resonates, you know, how do you find customer win stories from all these different it just >> I now that I think about it I don't really know how I did it.

you said you know the importance of the of like a great leader right and what do you think is the role of leadership now uh and from the perspective of you know when it comes to AI tools there is a new AI tool coming every day but also when I think about organizations um more broadly you know I feel like the AI tools are changing all the time so in means that the employee education has to be continuous. But how what do leaders do to you know to ensure that they um their employees get that education? >> Well, I can think of a very practical um example in my husband's firm that he's in cyber security and in tech. He works for a small consulting firm and the founder on I think Monday and Wednesday afternoons for one hour everybody uses that for AI tool exploration. >> So I think that's a very practical step that leaders can take book time on your team's calendar and just say we're going to explore this together.

>> You know give them time back to do that. I think um there's also ways that leaders can encourage employees to get curious. And I think sometimes with this AI stuff, people feel like they're not smart enough or they think they don't know or it's too hard or some it's like it doesn't feel approachable or I'm not technical enough. Anybody can use this stuff. It's natural language.

You can speak to it as if you're speaking to a human being. But I think sometimes that fear, that apprehension kind of keeps us back. So to the extent that I I always think that the best leaders model kind of the behavior they want to see out of their employees. So even if you're not like booking time on their calendar to work with AI tools, maybe you can just say, "Hey, I was I ran this through Claude, you know, if that's an approved model for your company or oh, I was just talking to Chad GPT about this and just kind of weave that in and lead by example." >> Yeah, I think that's um it's such a great answer.

I was at the event and there was someone from 11 Labs and she said, you know, we use uh Claude for everything. If I want a new dashboard, I just, you know, use cloth and I create it. And we even, she said, we even have a slack channel where people post, you know, the skills that they created for CLA and, you know, exchange those um uh those skills with each other. And I think it really has to be the the culture of the company that there is, you know, they embrace that technology instead of, oh well, uh, you know, this is the license um, you know, use it however you want, right? You really have to bring it use cases and how you actually, um, get people using these tools.

Yeah, >> I love that collaborative kind of example that you gave because I think that's just like true of like the open source community in general, you know, like, hey, I have this idea and you could use it this way and then, oh, did you think about tweaking it that way or whatever. I mean, I think I maybe I said this to you when we chatted a couple weeks ago or maybe I was talking to somebody else, but it's like I think we all need to embrace being a prompt engineer in our life, >> you know? So, prompt engineer like ask a question. Okay, I need an iteration >> and you know if someone is in a role in HR finances somewhere else what and they feel apprehensive and they feel like I'm not that technical you know you you said start but how do you actually start using you know these tools? Well, I mean, you could just ask.

You literally could pull up Google and go AI mode and ask anything. I'm anxious and nervous about using this. I feel like I'm too dumb. How do I get started educating on AI tools or how do I get started with Gemini and see what it says and then ask it another question. Um I think there's also a lot of great learnings like Corsera is a great um resource in terms of just little courses that you can do that are free um and or you can get a certification.

You know last year I did one generative AI for everyone by um Andrew Ang. And so that was like a really great 10-hour course that I did to supplement what I'm learning at IBM because I think sometimes when you work for a tech company and like a tech vendor, you can just get so your view of the world becomes so myopic, you know, like for me at IBM it's like bluewashed, you know, but I want to know what is actually going on outside of IBM, like in tech more broadly. So sometimes like a Corsera or a master class there's just tons there's tons of of kind of self-education out there too. >> Yeah. I found that you know honestly YouTube is >> is the the best education tool I think you know it's better than university.

I honestly if I wanted to, you know, get a skill in, let's say, Adobe Photoshop or any of the other like 3D modeling, YouTube has just so much and you can learn like so much. Um, it's Yeah, it's incredible. Um but something that you know you mentioned when um I was speaking to you before um the recording um and I would love if if you were able to and you feel comfortable sharing that story um which was about you know one of the hardest decisions that you had to make uh and how you know AI how you how you used AI to to make that decision. >> Oh yes I'm more than happy to share. I may shed a few tears.

I don't know. Um, so taking a step back, so one passion project and hobby that my husband and I do together is we adopt rescue horses. So we have um right now we currently have Grace and she is sassy. She is our third um Arabian uh rescue mayor. So um Seriana was our first and uh Dancer was our second.

So in the horse world, we have these things called like heart horses. And um for anybody who's a horse person listening to this, you know what I mean, but basically this animal is just like they're your heart and you're their heart. Um it is some of the best money and the best work I have ever done in my life is rescuing these horses. They are very expensive. They are high risk and not expensive because you have to pay a lot for them, but just like the vet bills and everything.

But like the mayors that we have adopted have been uh broken bones. They've put pins in people. They have been returned multiple times for having behavior issues. Um Gracie was like feral when we got her. But Dancer was a special case because when we had So I went to the horse rescue to volunteer and that's how we met Grace.

And then Dancer was there as well. dancer was very sick and she had a long-term illness called heaves which um is basically like horsey COPD like she couldn't breathe. She always kind of had had asthma and horses are flight animals. So can you imagine how you would feel if you're constantly like assessing your environment for like if you have to be ready to run from a predator and you couldn't breathe, you know, and your movement was like inhibited by that. but she wasn't bad enough to be like put down, you know, to end her life.

So anyway, we I totally wanted to bring Dancer and Grace home at the same time and my husband was like, "No, absolutely not." Like you don't have time. Like um it's too much money, just everything. He's like, "Martin, no." So like I after we adopted Grace and brought her, I would still go and volunteer and I would always tell Dancer like I am coming back for you the minute I can.

And I had to wait for like 6 months. And 3 months before we actually adopted her, I would say, "Okay, Scott, my husband, like, have you changed your mind about dancer yet?" He said, "No, absolutely not." And I go on a business trip and I just say, "I expect a different decision when I get home." And I come home, "Have you changed your mind yet?

" "No, Marta, blah blah blah." And he's like, "We can't." You know, he kept coming back, you know, that's too expensive. Well, I figured out I was so stubborn about this and I had made a promise to her like, "I am not leaving you here. like I you are going to be mine.

So I figured out that I could trade in my car and get and like downgrade my car and it'll be the exact same amount as her medication and her board. So finally I like ran the numbers. There was a data driven person and I said, "Okay, it's not too expensive cuz I'm trading my car for this Tesla." And he's like, "Oh, fine. I'm never going to be able to stop you.

" So, um, we had her for like 10 months and then her condition worsened in Minnesota. We have been having really poor air quality the last couple of seasons because of the wildfires coming down from Canada and she just was very sick and she wasn't getting better. So, what was happening is she would have an episode, an acute episode and she get really sick and I'd have to call the vet out and they'd have to shoot her up with more medication. It was always more and more medication and her immune system was just not responding. So every time like she got really sick, I'd just be really low and I'd be really sad and then she'd get better and I get really super hopeful.

So I was just kind of doing this >> and I just felt like am I just leaving this horse alive for myself? So I started using Chad GPT to just talk about it and then I asked them to make or asked them I asked Chad GBT to make me a quality of life kind of table and scoring system >> and um then the really powerful thing. So I still was like, "Oh." And then I looked like more bad days than good days, not responding to the medication, like not eating. And then in the evolution and prompting of the model that I was using, um, it came back with, "Have you thought about her dignity of life?

" >> And that was when I knew it was time that I had to put her out of her suffering. So um, basically I said, "Honey, you know, cuz Scott had fallen in love with her, too. I mean, she's just the most amazing, fierce horse. Um, she had been through so much. Um, anyway, so I showed him the table and like the metrics and the scoring.

He's like, "Okay, we got to we got to put her down. You know, we got to maintain her dignity of life." So, I was able to do that thankfully while she still could like walk and um make it to the spot where we had to put her down. And then I would say afterwards too like I still in my grief process I could go back and I and because I had all the history in that model I could talk through I could talk through the experience. So then it became like my um little therapy tool.

But it I think there is a realworld impact here to using this technology in in a human way because when you're in the emotions like it's hard not to like it's hard to think about the data. It was also useful because this condition like took many twists and turns. So, I knew the instantly how to talk to my vet. And I use it like with my parents when they go to the doctors or something like that. It's like I know I could easily access the language and the terms that I needed to be able to communicate clearly and understand versus all of that stuff that I didn't know.

>> Yeah. I'm I'm so sorry that this you know this happened but I think I would love to know you know what what was different about using the like AI tool uh and why did you feel comfortable kind of doing that in that situation rather than you know someone who's going to say oh you can talk to you know a friend and uh they they can talk through this with you. What was different about it? Well, there is a privacy piece where I felt like I could just be well, I was it was just me and my computer, so I didn't have to worry. I mean, I'm sure there was like bias in the model or whatever, but it was still more scientific feeling than talking to somebody else who would just be giving me their opinion.

>> Yeah. the instant gratification from feeling like my an my questions were answered immediately. My concerns were addressed immediately in the tone and it was almost freakish like in the tone that resonated with me like the message delivered exactly how I needed to hear it to be able to act on it. all that was freakishly like on the mark. >> Whereas, you know, I don't know, in these sensitive situations, you know, maybe somebody would say, "Oh, you put enough like she lived a good life.

Like, you rescued her like it's her time or you put enough money into that horse or something like that." It's like I would put thousands and thousands of dollars more into her, you know, like don't talk to don't talk about her that way, you know? She's not like a a piece of property. She's my friend, you know? So >> I think that the privacy piece um also acknowledging the emotion while still having the data >> and then um just kind of judgment free zone >> and you know I I can really relate to that story because as I mentioned my grandma passed away last year.

>> I'm so sorry. Um, and you know, being able to like to kind of put information about her condition and the lapse that she got and being able to, you know, talk with with LGBT about, you know, what does that mean, what, you know, her next like few days will look like depending on these and these factors. Um, and yeah, I really relate to the fact that, you know, it's judgment free zone. And also uh you're not afraid to ask as many questions as you want >> and questions that you know sometimes they're about how you're feeling um about about that situation and it like it is just so helpful right and I think a lot of people a lot of people use these tools in that way uh but I think a lot of people are thinking oh no these are only for like work use case, which I think it's not, you know, I think there's um there's like such a big potential in in personal use, you know. >> Oh, yeah.

I was just using it um actually last night at the airport before I left because I am putting on um a special thing for Mother's Day at my church and we were doing these really yummy strawberry coconut cream drinks, but there's like a a certain kind of cup that you need. So, I just put into Gemini, what is the best, you know, 10 oz uh cup, sippable cup with no straw or something >> on Amazon, and it just gave me a link to three products. So, rather than even using like Amazon search engine, I use that model to like go out and find it. So, there's so many practical >> Yeah. >> practical things.

>> Yeah. And maybe that's the best thing for you know you asked about like the HR person that is scared to use area feels intimidated to use AI. Well just >> start small start small with something that is completely low risk. >> Another thing I would love to know is you also mentioned when we spoke that you used a AI tool for a um to prep for a conference. >> Um can you tell me about that as well if if you're uh if you're okay with sharing that?

>> Yep. So, um, IBM, the IBM team was invited to go be on a panel of speakers at an AI conference that was local. Um, and the audience was a mix of corporate and educators. And I was part of that group and I felt super excited because this was a growth opportunity and I was going to be up on stage with some distinguished engineers and um, very knowledgeable people in the AI space. And then I met we did our first internal meeting and the marketing lady was all very happy to have me on and I some of the teammates weren't as confident in my ability to be up on stage with them and some of them were you know but I could just get this vibe and you know this is women lead AI so this is very like women specific kind of experience that I'm having that I've had frequently in my career but I was the only woman that was going to be up on stage And I was feeling um hurt.

I was feeling kind of devalued, like was I not smart enough to be on stage? And I was then I got really stubborn. >> So I'm like, you know what? And so I just said to the whole group, I'm like, well, that's great, but um you know, 50% of the people in the audience are going to be women, and you better think about how IBM is going to show up. if it's just men on the stage, how are you going to resonate with 50% of your audience?

And the marketing lady like agreed with me and nothing more was said. Well, I just got this little fire in my belly and I basically just self-educated on generative AI as much as I could cram into the panel. Um, I even went so far as to help craft the questions that we were asked by the panelist uh the leaders of the panel to make sure that I could answer them. and I used AI. I prepped and I prepped and I prepped and that's when I took that generative AI for everyone Corsera course to quickly ramp um on just the industry lingo.

And I I had so much fun. I was up on stage um I had people coming up to me afterwards that said that my messaging was really resonating etc. So I'd say what now am I as intellectually capable as a distinguished engineer? Absolutely. But like am I as knowledgeable as a distinguished engineer on AI that's been doing this for 30 years?

No. But AI helped me get up that learning curve very quickly and it gave me some confidence um to get up there on stage and I'm hoping that it embolden some other women in the audience to think about similar opportunities and saying yes to similar opportunities because that's what we need when you know women lead women lead AI. We need more women using their voice. I think sometimes we feel as women that we don't have a seat at the table or we got to earn that seat at the table or our opinion doesn't matter or whatever. But what we need is to just show up and use the voice that we have.

Our thoughts matter, our our feedback and our inputs matter, our contributions matter. Yeah, >> we just, >> you know, this is why I created this this community and and the podcast because I felt like AI is a technology that's it's not going to go away. is going to be implemented into every product, every service that uh is going to come. And if women are not you know interacting with these tools and they're not using these tools then uh you know where does the improvement come from? It comes from getting the data from the customers and then feeding those tools uh with with more data, right?

But if women are not using these tools as much then uh those things will not be optimized for for women and for me that's like it's unacceptable right. Uh it's the same way that a lot of other things uh were you know for example the um uh in the cars the um the uh sorry the the belt the the strap belt >> seat belt yes the seat belt uh you know for so many years it was like it was not tested on on women uh and and this is uh this is the example of you know with with that technology the like there are going to be consequences if women are like not using these tools. >> Do you think that I I was kind of thinking about this adoption gap and do you think women aren't using them or do you think there's like a small population of women that maybe are using them but they're like nervous or shy to admit that they're using them because they don't think they're good enough at it yet? >> That's what I would love to know from you. But my um I was I was trying to research that um and you know so Harvard Business Review they they basically uh looked at different studies and they found that women are adopting these AI tools 25% lower rate than men.

Um and interestingly they said because I was thinking oh um maybe it's like about uh access let's say but then you know every woman who has um like internet uh she can use Gemini chat GBT any model and they have actually tested that as well so they created uh like a study where they would give the the same access to the tools to uh men and women, but women were still using uh these tools at 13% lower rate. >> Really? >> Were they giving them access to perform a specific task? >> I would have to check that. But >> so cuz it's just like I just there's this thought that I have and I don't know if it's true, but like is it kind of like cheating to use AI tools, you know?

So, I think we've talked about this like one thing that I use AI for frequently is for communication because sometimes my communication style is very direct and that doesn't work for all audiences obviously. So, if it's kind of a topic that I know is a little bit of a sensitive topic or whatever, I will run it through a couple of iterations of the model. But then it's like, is that cheating? Because I'm smart enough to sit here and grind my gears on the perfect way to put it. But I kind of want the input of that.

So then I'm thinking like is it kind of cheating to use like these AI tools for certain things? Is that part of >> Yeah. And I think I think that is a part of it. Another one I feel like is >> even you know myself when I think uh when I think applying for a job, right, I think I need to meet all of the specifications of the job, right? Uh, but men, they will look at the description and the bullet points and they will be like, I need half of them apply.

Good enough. Like 60%, 50 I'm in the strike zone. Yes. >> Oh my gosh. So now I'm going to have to give you a challenge or like anybody listening to this, go out and find a job that you are only as a woman like Yeah.

Just just do it. just use the model to rewrite your resume and the cover letter and just do it. >> Um, but that's, you know, that's the thing. So, I wonder whether it is a similar case with AI tools where they feel like I don't know how to use it, you know, 100%. So, you know, I need to get better learn about it and then, you know, try that tool.

Um and I think yeah there might be an aspect to that as well you know. Well, and I think even I was thinking in our conversation even some of these this content feels a little foreign on my tongue, you know? So, it's like I speak English, but this feels like if I was trying to speak French again and I haven't spoken French, it's like awkward, right, to find the right words and kind of like I feel like I'm even fumbling a little bit. But with reps, with more repetitions, then it'll become more natural. Um, but yeah, I just I just really want to encourage all women to just go out there and try it and don't, you know, don't feel like you have to apologize if you don't know all the answers.

Maybe that's something because it's new. There's an insecurity before you use it that you feel like you need to know all the answers and use it perfectly the first time and all that kind of stuff. I think that that's not just for women, but I think just in life you can get really stuck >> if you have that mindset. >> Yeah, 100%. And you know, you studied um economics, stats, and women's studies.

>> I was so excited for this question. I haven't thought about it in years. >> How uh economics and stats obviously come together, but how do women's studies come into the mix? >> So, basically, so I am a type A. Duh.

Um and I kind of am an overachiever, right? So doing just one major for my liberal arts degree wasn't enough and I actually was like two classes shy of a triple major which would have thrown sociology into >> the mix. Um women's studies for me was a way to bundle kind of my general education requirements and look at the world through the lens of the feminist or the female lens. Um, so much of our history is written by men. And the thing that I love about liberal arts and I hope that we do not lose the passion for liberal arts is that that is what is so cool about liberal arts.

You learn how to look at an idea through a lens. So whether you're looking at it from the philosophy lens, the theology lens, sociology, women's studies, um, anthropology, economics, math, whatever, you know, science, like, but you're looking at it through that lens, and that creates a framework and a discipline that's unique if you're comparing it to another lens. Um, women's studies was one of the most fun things I've ever done. Um, I was, you know, young, obviously. I'm 19, 18, 19.

And um it also gave me room for some creativity that economics didn't. So kind of my whole thing in life is like okay some things in life are just a grind and you don't want to do them. They're super boring. They're super hard. They're not fun but and but it's very good to do hard things.

Very good. So economics wasn't that it was super hard. It just wasn't super fun. Right. So like learning the models and learning the framework for microeconomics or macro what was fun was applying those models into like real life situations.

So women's studies was kind of like my fun degree where okay maybe I had to be in statistics economics world for a whole bunch. Then now I'm going to take a JT term class like the month of January and just read um American female authors from the south or like my my favorite class I took actually was the art and philosophy of Georgia O'Keeffe. So I don't know if you're familiar with her but she was basically um prolific in the 1920s to the 1940s in terms of her art form. She would create these really huge, beautiful, oversized flowers. And then later on in life, she would do these kind of cool bone things like New Mexico kind of um landscapes.

She helped shape modern art in America. Um her work was misunderstood. So because some of her flowers were a little bit more like I don't know maybe we'll call them they were over her work was over sexualized basically. So for as a young woman I thought this was very interesting. She said her whole goal in life was to fill space in a beautiful way.

And the most beautiful way that she could fill a canvas is putting a big flower on it. But then all the other again mostly men like in the art world like criticizing and commenting our work like they they try to over sexualize it and make it all the stuff that it wasn't. um which is so anyway I was just very inspired by that class because how do I want to live my life? Well, that's a pretty cool big kind of order but like a simple one like I want to fill the space that I inhabit like in a beautiful way. Um so I got to do all sorts of cool stuff with my women's studies major and it all kind of basically came to a climax um in my senior year.

I wrote a thesis looking for racial bias in the WICK program. So the WICK program is in the US it's a special food stamp program for women, infants, and children. And there was a qualitative study that said that this program was biased against African-American women because of the food bundles. Um, apparently there's a lot of research that African-Americans are lactose intolerant and they're gluten sensitive. So these were the products that you would buy with this food stamp.

So there's this qualitative thing, but nobody bothered to do the research on getting the data. So basically, one of my professors went and worked for the US government out in Washington DC. And I'll never forget, she sent me the census data, came on a CDROM, multiple >> and it was all dummy variables that I had to convert using the statistics program called MiniAB. And I had to build my regression model and tweak it and look for this to see if there was really the racial bias against African-American. And I I couldn't prove it, but it was a phenomenal exercise in learning how to manipulate data.

And I think in even at IBM, we say, well, the data will confess anything if you torture it enough. Right? So, and I will never forget as I was trying to complete my project because my um program adviser like really wanted us to get published because if I would have been able to prove this, it would have been kind of groundbreaking >> in the field. So, 40 hours in one week. This was just 104 classes by the way >> where I remember like um seeing all of college where I went was very beautiful cobblestone kind of buildings and streets and just like in small town Northfield and um in the computer lab this is so many years ago but like the mini tab computer lab statistics computer lab was in the basement of like this castle tower so I had to run up like I'd run down to the lab put in these inputs and try to tweak it.

Then I'd run up four flights of stairs, no elevator to her office to try a taller and she'd be like, "No, Marie, you need to go try this. There's no laptops right into you laugh, but I was like I didn't even have email until my second year of school, you know." So anyway, so yeah, I didn't ever find the racial bias, but I spent like 40 hours in one week trying to like tweak the model. Um so for me that that women's studies was a special time of life but I bring it forward you know I've always been interested in the voice of the woman and I think there was a lot of credibility you know to it as well so how can we use data I think sometimes when we talk about feminism or women's topics it's easy to feel well I should speak for myself it's easy for me to feel angry sometimes if I think about it too much because of the disrespect that I see sometimes or just kind of the inequality that I've seen and experienced in my lifetime. But the more that we can bring a rigor and a science and like bring the data to this conversation is strengthens our voice.

>> One thing that I would love to know is how I think a lot of there are a lot of women who work in the fields that are very male-dominated. I imagine uh even potentially in your team there's probably more men than women. >> Yes. >> Uh and you know I worked as a as a sales specialist I will work with a lot of surgeons and even though the world is changing there are more women becoming surgeons about 80% of my clients uh were men. >> Yes.

How do you bring bring out your best uh when you are working in a male dominated field? >> Well, I'll say one thing like I'm not always at my best. But when I am at my best is when I am well um a couple of things. So I I don't want to get too far down the road before saying I have worked with so many great men as well as great women and I never want to forget that I have so many people supporting me men supporting me and I think that is true in any organization. So what is a saying like it only takes one bad apple to spoil you know the bushel or whatever I don't know that a grandma would say or something but um yeah like in those moments there is something I would say after many years in the industry where there are um competing standards and competing um norms um like behavioral norms that seem to be acceptable and I'll give you an example.

And I don't think that I'm unique in this. Like a woman will say her idea or I will say my idea. Nobody says anything in the meeting. And then a guy will basically say exactly what I just said and everybody's just like, "Yay, oh that's like amazing." You know, or you know, I think the more trendy term is like mansplaining.

It's like, "Oh my gosh, I have to listen to this." Like uh I can't like I already know all of this. Like I'm listening listening listening, you know? So I think um well one I think is really super important to have like your girlfriends that you can just go laugh with about this appropriately and blow a scene. You know it's not like you're injured forever you know or I hope not like sometimes traumatic things happen in the workplace unfortunately still even in this postme too world that we live in and I would just encourage anybody who is experiencing that to report it.

Um, but don't I guess my piece of advice would be don't stop being your authentic self. Even if you wonder if it's being received well or not, like you have a voice, you have an intellect, you have a brain, you have contributions that you can make. And I think just monitoring situations over time. Do I feel a value part of this team? Um, if the answer is no over time, then it's time to find a different team because you have to value yourself before other people will.

And sometimes there's just an disagreement about the value you bring to a team or an organization. You know, sometimes depending on a situation, if it's kind of one person that's like a single bad actor that's kind of impacting team dynamics, you could always just speak to that person individually and you know, you could use a use chat GBT and talk about like how to like approach conflict or the best way to word it, but also don't sugarcoat it. when you do this, I feel that my goal is for us to work together as um a an amazing team and I think we both have something to bring to the table, but I need to talk I need to let you know the impact that your behavior has. >> I think that's really good advice. >> I mean, just the other day, one of my really good friends like called me up after a call and he said, "What in the world was that?

" He said, "Marta, you're like my little sister." Like, and he didn't mean it in a condescending way. He said, "That is just unacceptable. I cannot believe how you're being talked to." And so, it's like that makes me feel kind of good because I was kind of like feeling a little pouty, but um and sad, but you know, it's just also as just individuals and I think all of us cuz I think on the other end like women aren't always friends either, right?

So like as we have this women lead AI conversation, I would just like coach all women like if you're in a place to celebrate another woman's success. Like this is not like a zero sum game. Like there is abundance. We do not live in a scarce world. So please celebrate another woman's success because sometimes we as women are our own wor I've seen it like we're our own worst enemies as well.

So >> um >> 100%. >> Yeah. you know, if if you could send like one message to um every leader in a corporate uh role who is wondering how do I prepare uh you know my company um my employees and also my data to be ready to adopt these AI tools and be ready kind of for uh for that third winter. Uh, what would you say to them? >> Start with a really super tightly scoped MVP use case and commit the resources to seeing that project through.

And then um don't be afraid to scale if you're successful. that is uh I think the that you know when I think about like what you said about the MVP and the use case I think that's so important because if people start thinking like oh this is you know this is how I can use it and it actually helps them with that one thing then it gets them so much more excited for the next one and the next one. Um but let's do a fast run. Okay, >> that's okay. >> Yeah.

>> So, um what are you what have you been uh reading, listening, uh watching to uh keep up with AI and make sure that uh you know you feel uh you feel you are understanding these uh these tools and developments better. >> Okay. So, I think you mentioned YouTube. I'm huge into like YouTube education. Um I am using the models.

I am experimenting in the different models and I think that the next round will be like I want to go a little bit deeper on Claude for some very specific use cases. Um I am a huge fan of podcasts. So like one podcast that maybe is not talked about in this space is like the Sean Ryan podcast. So he is a military veteran. He's a former Navy Seal.

Like he does a lot of military stuff but he does a lot of tech stuff and he does a lot of like God stuff. So I think that sometimes when um and not like stuff I should say content. Um sometimes we can get so like focused only on tech industry and of course source isn't tech tech >> but there's just um an amazing kind of adoption of AI in other industries. You know look at like the fitness industry for example um that you can go out and learn about some real applications outside of just like the tech company's view of the world. >> I'll check out that podcast that's going to be on my to list.

>> Um so finish this sentence. One thing that AI will never replace about a great leader is >> the nonverbal face to face being in the same room compassion or conversation. We still need humans. >> Yeah. And uh best piece of advice that you've been given uh either from a person or uh from an AI model.

I have been given so much good advice and and it's hard to pick but I did have a conversation with my friend about leadership like a couple of weeks ago and she said Marta you've heard this clarity is kindness and um I think clarity in life pres pro provides you with focus clarity um is kindness to others as you kind of let them know about boundaries or goals or ways of interacting but clarity is kindness that was kind of the thing husband. >> That's Yeah, >> bubbling up with me. >> I'll have a a think about it. >> Cuz it's hard for women, too. Sometimes I think it's hard for us to be clear.

>> Yes, it's okay for you to joke with me about my blonde hair. No, it's not okay for you to joke about Playboy. I'm going to be real clear about my boundaries. >> You know, like if you're talking, you know, about these things like sometimes we apologize for our boundaries. Sometimes we don't value our own values so we're not clear.

So, >> so uh if uh you know someone listening to this podcast would love um to get in touch with you uh whether they're interested in kind of uh IBM solutions um and you know would love to know uh more about how they can you know adopt these solutions and in their own company um what what would be the best way to to get in touch with you? >> Oh, just find me on LinkedIn. >> Okay. Yeah. Well, thank you so much for uh for coming on here and for this podcast.

It's been such a great conversation and you know, I learned uh yeah, I learned so much. So, thank you so much for the invite. This has been so much fun and hopefully I'm like overcaffeinated and jade and all this stuff. Hopefully, we had some good content out of this. But I just this was a blast and it was just really fun to be here supporting you and your project and hopefully we can keep the conversation going in another date.

And this is a wrap on today's episode with the incredible Marta Haynes from IBM. If you enjoyed this conversation, please share this episode with your friend and leave us a review. It generally helps more people discover this show. I'll see you in the next episode.