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The Ultimate AI Strategy for Enterprise Companies
Incorporating Artificial Intelligence (AI) into any business model is a demanding task, especially for large enterprises with multiple employees. The first step into this transformation is to gather a dedicated team armed with an intrinsic passion for learning and a comprehensive understanding of the business strategy. These teams could be formed centrally or within a specific business vertical such as Human Resources, Sales, or Customer Support.
Four Pillars of AI Implementation
There are four mainstays when considering AI addition:
- Insight into the business strategy paired with the recognition of AI-solvable challenges.
- Insights from an architectural perspective and infrastructure preparedness.
- An emphasis on education and adoption, highlighting how AI affects the workforce's roles and prospects.
- Lastly, ensuring the considerate use of AI technology.
Low-risk areas where AI can be straightforwardly implemented, like Customer Support or Employee Support, are excellent places to begin the transformation.
AI Adoption: A Time-critical Strategy
Unsurprisingly, many businesses, even in tech-forward countries like the US and Japan, straddle the fence when it comes to AI integration. Nevertheless, hesitation could mean missing out on significant innovations, analogous to early computer and internet adoption. Companies reluctant to embrace the influence of AI could find themselves at a disadvantage.
Addressing AI Education and Training Challenges
With technology evolving at an unprecedented pace, keeping up can be challenging. Tailoring learning paths to fit the unique disciplines within an organization can be a fruitful approach.
Companies finding themselves constrained by AI inertia are advised to start small, experimenting in safer realms before dealing with bigger challenges. big or small, every stride counts.
Inviting AI into Everyday Life
AI implementation at the enterprise level is undoubtedly a challenge. Specific obstacles include, but are not limited to, understanding cost implications and estimating ROI. However, meeting these challenges head-on can set the foundation for a transformative future.
In conclusion, the journey of integrating AI into enterprise isn't easy. However, with the right team, strategy, and an open learning environment, this leap can catalyze significant innovation and growth.
Topics Covered in This Episode
1. Steps to Implementing AI in Companies
2. Four Pillars of Implementing AI
3. Impact and General Perception of AI
4. Challenges of AI Adoption
Podcast Transcript
Jordan Wilson [00:00:17]:
It's no secret that AI implementation at the enterprise level is pretty hard. Right? Especially if you work at a company with 1,000 or tens of thousands of employees, and maybe you're seeing these smaller companies get all of these great gains from implementing generative AI. And you're wondering, how can I escape this AI inertia? How can our big enterprise company with so many moving parts pick up some steam in this AI world to keep up? Well, luckily for you, we have some answers for you today as we have a leader at enterprise AI, at Microsoft joining us the show, joining the show today. So I'm extremely excited, for today's show. So before we get started, we're gonna start as we do going over the AI news for the day. So if you're brand new here joining on the podcast or the livestream, thank you. Everyday AI, this is for you. We're a daily livestream podcast and free daily newsletter helping us all learn generative AI so we can leverage it to grow our companies and careers.
Jordan Wilson [00:01:18]:
So go to your everydayai.com and sign up for the free daily newsletter. But let's dive in to the AI news for today. A lot going on as always. But first, Meta is withholding new AI models from the European Union amid regulatory uncertainty. So Meta will not release its next multimodel AI model in the European Union citing on citing unclear regulatory guidelines according to Axios. So this decision could escalate tensions between the US tech giants and EU regulators, highlighting a trend where American companies are withholding products from European markets. So Meta's multimodel llama model, which integrates video, audio, images, and text, will be available in other regions such as the US, but not in the EU due to that regulatory unpredictability. So a text only version of Meta's llama 3 model will be available in the EU, indicating Meta's selective approach to product releases in the region.
Jordan Wilson [00:02:17]:
So Meta had planned to use publicly available posts from Facebook and Instagram users to train its models and had informed EU regulators well in advance but received minimal feedback. Alright. Our next piece of AI news for today, a quarter of Japanese firms have adopted AI while many remain hesitant. So, a new study shows that nearly 25% of Japanese companies have integrated artificial intelligence into their operations. So that's according to a Reuters survey conducted by Nikky Research. Despite this, over 40% of firms have no plans to adopt AI, highlighting a divide in the technological adoption across corporate Japan. Yeah. That's right.
Jordan Wilson [00:03:01]:
I didn't stutter there. 40% of firms, according to this small scale study, have no plans to adopt AI. Wow. Right? So the study included responses from about 250 out of the 506 companies, and it was conducted earlier this July. So key motivations for AI adoption included addressing worker shortages, reducing labor costs, and accelerating research and development. Alright. Our last piece of AI news for the day, at least for the podcast, some new, health care and AI news out of Stanford. So a recent workshop by Stanford h I, sorry, Stanford Health Artificial Intelligence or Stanford HAI, as it's called, has highlighted significant gaps in health care AI regulation, pointing out minor tweaks won't suffice.
Jordan Wilson [00:03:50]:
So, some findings from this survey. It found that only 12% of thought leaders believe health care AI should always have a human in the loop. Wow. 12% only bullet believe should have a human in the loop. That's kinda that's a shocking number there. And also a strong 58% say human oversight is unnecessary with proper safeguards, while 31% support human supervision most of the time. Alright. So there's a lot more, not just AI news, but just everything that's happening in the world of AI, new fresh finds from across the Internet, tools, software, all that, as well as in the newsletter today.
Jordan Wilson [00:04:27]:
Go find out, why OpenAI researchers have developed a model where it competes against itself. Alright. So enough about that. You didn't tune in to go over the AI news. You are here to learn about how enterprise companies can escape the AI inertia. Right? I I can't imagine. Right? I run a small business. I can only imagine when there's tens of thousands of employees, how AI adoption actually works.
Jordan Wilson [00:04:53]:
So we're gonna have some answers today. So I'm, extremely excited to have our guest for today. So please help me welcome Rajamma Krishnamurthy, the senior director of leader of leader enterprise AI at Microsoft. Rajamma, thank you so much for joining the Everyday AI Show.
Rajamma Krishnamurthy [00:05:10]:
Good morning, and thank you for having me, Jordan.
Jordan Wilson [00:05:13]:
Alright. I'm excited for this one. And, hey, shout out to Rajamma. She's one of only, like, a dozen of people who have joined us early west coast time. So thank you for waking up early with the rest of our audience. And, hey, to our livestream audience, thank you for joining us, Tara and doctor doctor Scott Ernesto, Danny, everyone, Danny. Sorry. If you have questions, please get them in.
Jordan Wilson [00:05:32]:
But, Rajamma, let's just start. Tell us a little bit about what you do in your role in leader enterprise AI at Microsoft.
Rajamma Krishnamurthy [00:05:40]:
Alright. I have been in enterprise technology for, most of my career. I was in HR technology, in some companies on the East Coast before I moved to Microsoft 10 years ago. And, over the last 2 years, I have moved into the working in AI, and I started with working in AI, leading an AI center of excellence, which which is just something to kind of pull together, people to move us out of the inertia, and I'll talk about it a little bit more later. And then, I also now, after doing some of that work, I have moved into a role where I am actually a product leader in a product that is going to be in your desktop, in your desktop somewhere near you very soon. So that's what I do. And when I'm not doing that, I'm also a professor adjunct professor at I NYU, teaching technology and AI.
Jordan Wilson [00:06:34]:
I I love this. So not only someone helping enterprise companies use AI at one of the largest companies in the world in Microsoft, but also someone teaching it. So I think we're gonna have a lot of insights today. But, maybe, Rajammal, let's just start at the end. Let's just start and answer everyone's question right now. What are some of the biggest reasons that enterprise companies right? Because first of all, number 1, AI is not new. AI itself. Right? It's been around for many decades.
Jordan Wilson [00:06:59]:
Generative AI, you know, you could say it's kind of new ish. Right? Couple years. But what is one of the biggest reasons that enterprise companies still today in 2024 haven't fully implemented generative AI into their companies?
Rajamma Krishnamurthy [00:07:13]:
I think cost is a big factor. And, the and the understanding of what the ROI is against the cost is also a big factor. Mhmm. So nobody has said, oh, there's a lot of studies or it's gonna reduce 40% of, you know, they it's gonna increase your efficiencies by 40%, productivity by another 30%, and so on. But it's nobody has the evidence in front of them. It it's they can't touch and feel it. So people, are finding it difficult to put the money, where, it is still studies and, it nobody has pulled it out, finally. The other problem is the education.
Rajamma Krishnamurthy [00:07:52]:
You know, to be able to having having people know what AI is about, where they need to implement it, how they will get what they need to get out of AI, is something, that's another thing that stops them from thinking about this space.
Jordan Wilson [00:08:07]:
Yeah. And it's, you know, just in about a 45 second answer there. I mean, what you just said right there, that's the the the the $1,000,000, the $1,000,000,000, or the $1,000,000,000,000, answer that people are are searching for. Right? Like, trying to understand the return on investment and and and the cost and education. I mean, those those pieces are so important. You know? But, I'm curious, even for you. Right? And I've had a handful of other people from Microsoft, and I love asking them the question. Right? Because people who are building, you know, Copilot and all these great AI features that we use, I just had an episode this week talking about the Microsoft Edge browser and how much I love it.
Jordan Wilson [00:08:46]:
But, you know, what have you even personally seen yourself or maybe in your department, in in your experience actually implementing generative generative AI from one of the largest companies in the world. How have you started to see, you know, some of those things, productivity and efficiency, even if you don't have a specific stat? Maybe can you talk about what your experience has been, not just building the products that we're all using, but actually using them and benefiting from them as well?
Rajamma Krishnamurthy [00:09:11]:
Sure. Let me talk about something that we all use daily, and so that it can you can relate to it. You know, we all use our emails daily. I know you talked about Edge browser, and you're also, I use my Teams daily. This is how I do, do my work. The other day, I had to go find, some card document that one of my colleagues had handed over to me, maybe 3 months ago or something like that. All I had to go in, was going to Copilot and ask, hey. Can you give me a list of documents that Jordan handed over to me in the last 3 months? It gave me all the list, and it took me less than 10 seconds 20 seconds to go find what I needed to instead of combing through my emails, combing through all the documents, which would have taken me at least another 10 minutes.
Rajamma Krishnamurthy [00:10:01]:
So just imagine, just that little act of doing that has been so valuable to me. I use generative AI or Copilot daily in my work. Whatever I do, I, and it gives me the summary that I need to what I need to focus on today. So whole lot it's it's the personal assistant that I don't have to pay for, and it helps me again and get through my day in a in a much more easier way. So, if you talk about Hub productivity, I'm it gives me the time, to kind of go do my real work, which is I need to think about strategies. I need to think about the products that I'm building. I need to think about where it where I need to take that and instead instead of, like, worrying about, you know, which document that I need to go find in in the next 30 minutes.
Jordan Wilson [00:10:46]:
Yeah. And, you know, I'm sure that you you know, you and and your team there spent a lot of time working with these large enterprise companies, you know, asking some of these same questions and trying to find some of these same solutions. You you know, let's just say assuming companies have Microsoft 365 Copilot. Right? Like, where do you start with enterprise companies? You know, obviously, you have to have conversations around governance and in data security. But let's just say after companies have that piece figured out and they're like, okay. Where do we start? Where do we start, you know, seeing some of this ROI? Where do we start seeing some of this increased productivity and efficiency? Where can you point enterprise leaders on, hey. This is a great place to start, to to see some of that return.
Rajamma Krishnamurthy [00:11:30]:
First of all, I don't think the AI is the destination. AI is your enabler. It's your accelerator. So we need to make sure that, you know, you're not you're not looking at which how do I implement AI? Instead, you're actually focusing on how do I, what is my business strategy? What are the business problems I need to solve? Where do I need to be in the next 2 years, 3 years, 5 years? And how where does where can AI help? For example, if you want, your your customers are not happy with your customer support or, if you are, having, problems with, let us say, even internal employee, shared services. Where where do your problems lie, and how do you actually kinda go after it? And I think those are some of the things I would, I would focus on the business strategy. So if if you say if somebody, explains that, you know, oh, you know what? If I can get the customer support thing solved, I can actually start thinking about focusing on my marketing problems, focusing on, you know, great creating new products and moving my company forward, then go after it. Look at these other for to me, having, things like customer support or employee support and those things are low hanging fruits in your company. And these are the easiest things to fix because AI can read your content that, an individual was doing, can summarize answers, can personalize solutions for whoever is asking those questions.
Rajamma Krishnamurthy [00:12:59]:
This this could be where I would start. These are not sensitive use cases even via EUAI act reading. So this would be some things that I would absolutely, encourage customers, to think about.
Jordan Wilson [00:13:13]:
You know, one thing that I'm always thinking about is where companies are at now. Right? Presumably, most enterprise companies are have either implemented generative AI across their processes or are somewhere in that process. Right? Whether that's a, you know, 3 month, 6 month, year long process, I'd say most enterprise companies are somewhere there. What's your kind of message or what's your thoughts on companies that are maybe still even on the fence, especially, you know, larger companies when we're saying, you know, maybe, I don't know, Fortune 500 or Inc 5000 companies that are still on the fence. You're right. And I kind of read that news story there about, you know, these 40% of Japan companies that are saying, no, we're not gonna use it. For those big companies that are still on the fence, is that a smart place to be? And I know it's kind of crazy to be asking that question here in in 2024, but what are your thoughts on on those companies that are still, like, scratching their heads and like, ah, let's let's be slow. Let's see what everyone else does.
Rajamma Krishnamurthy [00:14:14]:
I I think that, it is not just true about Japan. There's a whole lot of companies in the United States as well that are still on the fence and that's still thinking about maybe we will let the others go, in front to make the mistakes that they need to make, and then I will follow and be on the right path. I think, first of all, AI is here to stay. It is part of how we will do work in the, in now and it'll in the near future and and in the far future as well. So to to get off that, and you're already a year or 2 late in the space of generative AI. And if you haven't done used AI as, you know, machine language or anything else that AI was already, in the enterprises for, then you're already too late in time kind of using those tools to improve and accelerate and enrich your your experiences, your customer experiences, or just kinda getting through a day, for your marketing professional or your legal professional or your HR professional in your company. So, to, to kind of answer your question, remember those days when, we we were all implementing ERPs and, you know, they said, well, it's gonna take 6 years before you realize what, what the ERP will do? There is no magic with AI either. When you start implementing these, it will take up, you know, a few months, a few years for you to see.
Rajamma Krishnamurthy [00:15:34]:
In some cases, you'll see, results very quickly. In some cases, it's gonna be a little bit of a slow medicine that's going to cure a lot of things in your company. So what, there needs to be patients in the in the in the long haul ones, and there needs to be excitement about the short haul ones and kind of moving forward, which means the excitement comes with a little bit of risk taking and understanding the risks while you take them and so on. So my advice to the most of these companies are get off, the inertia and kinda move forward as quickly as possible because, you're already if you haven't started, you're already behind, and you don't want to be behind your colleagues in the, or your competitors in your in your space because they will certainly have an advantage over you in in all all kinds and all forms. Whether it is retaining the right talent because they have better experiences or retaining the right customers because they have better, support and care than you because of AI.
Jordan Wilson [00:16:30]:
Yeah. And I think those are just some great pieces of advice there. You know, one, analogy that I always use is probably a little too much as I say. Like, this is like, you know, companies maybe in the early 2000 that were like, we're not going to use computers or the Internet. Right. We're just going to keep doing things the way it was in the eighties. Right. Nothing was wrong with that.
Jordan Wilson [00:16:51]:
So let's just say, though, that there was someone on the fence, someone listening maybe on the podcast right now, a decision maker at a big company, and they're like, alright, We're gonna we're gonna do this. We're gonna go all in. Right? We we need to compete. We should have done this a year ago, but let's let's go. What are those first steps that people need to take, especially in big companies? Because, I I I personally think sometimes it's the smaller and the medium sized companies that have a huge advantage here. In enterprise, it's it's it's difficult. Where do those companies start when they finally get off the fence?
Rajamma Krishnamurthy [00:17:21]:
I would say, first of all, start putting the a team of people together, a like minded set of people together that are passionate. And, and I wouldn't say experience because all of us are getting experience as we speak. But I would call it people who have an innate passion for learning this space and wanting to do some work, have a good understanding of your business strategy, and have been and of your business, pull these people together. It does not need to it would be lovely if you can have it at the central level with your CIO and your business leaders kind of leading that, empowering that team. Or even if you want to start small, go into one of your verticals. I would call it a vertical in an in an enterprise would be an HR or, customer support or, you know, sales or whatever else. I would say that take us put a small, band of people. Start looking at, this in, in 2 or 3 pillars.
Rajamma Krishnamurthy [00:18:19]:
The first pillar being the business strategy. So like I said, AI is not the destination. It's it's your accelerator. It is the rocket on which you are gonna go sit and solve your, you know, zoom into the solving problem space. So so bring put your business strategy. Understand what are the business problems, that could be solved by AI. To solve those business problems, look at from an architecture perspective. What do you have the data to solve the problems? Do you know what the models that you will use? Do you have the infrastructure to kind of, kinda go after it? The 3rd pillar is obviously education.
Rajamma Krishnamurthy [00:18:54]:
Education is not just about doing and executing and getting results. It's also about adoption. So ensure that you're educating your various levels of, organization that needs to be educated about how AI is going to help their, work, how they need to future proof your their own careers because AI is here to stay. So if there is reluctance on their side, it is odd you know, we can sell that by making sure that they know that this is not just about the company. It is also about them because the next job they wanna find is gonna ask for AI skills. Similar to how today peep you know, even about 5 years ago, people were asking for Excel skills and, you know, computer skills and so on. And for engineers, it was about cloud skills. Now it is all about AI skills.
Rajamma Krishnamurthy [00:19:43]:
So, excuse me. So that is that is that. So the 3rd pillar I would call is, educational culture, the adoption and and so on. And then the last, is more of an horizontal pillar. If pillar could be horizontal, is basically, the conversation about responsibility. Everybody needs to be educated about how to use AI responsibly. How do you think about it? How do you go about it? You know, from the very inception of what projects that you need to choose to how do you test for it, how do you put it into production, how do you make it explainable, and, transfer into whoever is using it, and then how do you monitor that for any kind of ethical collaboration? So I would say that all of these four things, get that started. Within the within the strategy, start thinking about the low hanging fruits that you can go after.
Rajamma Krishnamurthy [00:20:38]:
You have the data ready. You can easily get a model. You can easily ground it to whatever data that you have, and you can start the ball rolling on those. Like, I told you about anything that can read your content and answer questions is a is a most easy space. So think of, you know, areas where you can do it very easily, whether it's customer support or employee support or anywhere else where you want to summarize or create content. Go go in and go get that done as quickly as possible.
Jordan Wilson [00:21:08]:
Y'all, my my fingers hurt from typing so many notes, from from Raja here. She's dropping so much great advice. So so one thing that I I I picked up in there is talking about even your next job. Right? AI skills are going to be a requirement. And, you know, you talked a little bit about, you know, being also a professor at NYU, of AI. You know, with that in mind, what would you say, you know, especially for students? Because I don't know. And let me know if I'm wrong here. But I think that, you know, maybe some of the the the skills that we've built over the course of decades.
Jordan Wilson [00:21:45]:
Right? People who are in the middle of their career, they've really been rewarded for their their institutional knowledge. Right? Oh, they they know something that very few people in the company know, so they get rewarded for that. But now when we have very capable models, right, such as, you know, GPT 4 o that you see in in Microsoft's, you know, copilot, now sometimes that that knowledge isn't as exclusive to a certain few. Our students or our people who are working AI first, working AI native, are they at an advantage for their careers that we maybe haven't seen or understood yet?
Rajamma Krishnamurthy [00:22:23]:
No. I I don't think so. And the reason is you have the institutional knowledge you had or you have. You have been using that to think through. I mean, you you're not going to talk about institutional knowledge. You're using it as the foundation to, make decisions. So now somebody else is going to provide you with all that knowledge and all you if you have to make the decisions. So the the thinking part of what you are, the the intuition part of what you were, the instincts and the, insights that you were able to generate with that, that still works.
Rajamma Krishnamurthy [00:22:56]:
So one of the things that I tell my students is that, you know, we have been across any of the professions that we are working with, we've got to start thinking about how do you bring that in, which is how do you read data and make decisions. And that is what so that kind of help, you know, yes, I I have used this, analogy quite a few times. But, basically, think of the AI right now as your golf carry. It's going to bring you all of that, you know, on I have I don't play golf, so in any case, carries your bags. It'll tell you what, you know, iron to use. You've got to pick that the right iron to use because you know the this is what it's gonna take to kind of go move forward with the next shot. So that is what you need to know. You need to be able to look at that bag full of even if a carry is suggesting to make your decision, that decision making, that thinking power will always remain.
Rajamma Krishnamurthy [00:23:53]:
And so we need to teach our students how to read data, how to take, other people's suggestions, how to and how to bring in your own thinking part of it, how to be intuitive about your decision making from all of that information that will be laid in front of you, basically. But the best part is you don't have to have the groundwork to go find the information. It's gonna be made available to you. I love that part. I I'm really jealous of the next generation of workers because this is going to be you will be doing the fun part of your work mostly, and, you know, the grand part of the work will be done for you. That's the that's that's gonna be very exciting.
Jordan Wilson [00:24:29]:
Yeah. That's that's a good point is, you know, the future of, you know, knowledge work, which I know someone, you know, Bob here is talking about and, you know, who knows what that future will be. But I think you kind of just answered it right there where, you know, hopefully spending more time. I like to say more time on the meaningful and less time on the mundane. Right? Those mundane knowledge tasks. And I love that example that you gave there of AI as your golf caddy. Right? I'm trying to learn golf. I'm terrible at it.
Jordan Wilson [00:24:54]:
But I think that's good. That's a really good example. But but getting back to to enterprise and and getting back to implementation and this this whole concept of, you you know, getting over this inertia, so to speak. Is this one of the first times ever? I think well, and but I want your your your thoughts on this. I think, historically, it's always been the enterprise companies that, oh, only they could afford a certain, you know, software solution. Only they could implement and have the best technology. But now it it seems like for the first time, anyone with, you know, $20 or $30 a month can go get Copilot Pro Pro as an example and and use a similar technology that the biggest companies in the world are using. Is is that true? Right? Is this maybe do you think it's one of the first time that the playing field is actually kind of level? And then if so, maybe on the flip side, what's some advice for some of those smaller companies to chase after those enterprises?
Rajamma Krishnamurthy [00:25:50]:
I, I think I I certainly think technology per se actually democratizes a lot of these things. I mean, whether it was the cloud, whether it was the Internet, or now AI more so than ever. So, and, for the for the, smaller companies that can move fast and that have the advantage, move fast, get the advantage. You know, you you can compete with the big ones there because you have, you know, made most of the groundwork go away in your area. And, and given your team, your people more time to think, more time to create, and more time to work in spaces that they didn't have before. And, that will certainly put you in a great advantage, more so than ever. And, I can I am already seeing that in smaller companies? I I sit on a few advisory boards on, you know, smaller start up companies or even on a, you know, VC firm. And I see that there is just that excitement, and I see, how quickly they are adopting to it.
Rajamma Krishnamurthy [00:26:53]:
And not only, you know, AI companies, but also companies that are not AI, kind of bringing AI into their space. I was, some of those examples are really, like, beyond me. Like, for example, there is a company that, that just, gives cancer care, to patients and how AI can kind of help, nudge on the right times, be there be a buddy, and so on. So things like that that were never thought so before can now be brought into your, areas that you had now never thought AI or even technology can help. You always thought just human beings will be there to help. But these buddies are helping, you know, people who are vulnerable, who are suffering through certain things to be having some companion that's just kind of, hey. Did you take your medicine today? Or, you know, did you go visit your doctor today? Do you need any help? Do you need somebody to come and help you kind of take you to the next appointment or things like that? You know, people are not, you know, don't have to just rely on somebody, some other human being, but they also have they are obviously, in any such cases, you want, humans in the loop. You want human care, but you also need the structure that comes with some, you know, in cares like this, will can be provided by AI, basically.
Rajamma Krishnamurthy [00:28:12]:
Yeah.
Jordan Wilson [00:28:12]:
And and and, Rachama, getting back to something that we talked at the very top of this show, right, when we said, hey. Let's let's kind of fast forward to the end and talk about what are some of the reasons why there is this AI inertia and why it takes so long for enterprise companies to gain momentum. And, you know, some of the things you said is cost and understanding the ROI. But real quickly, I wanted to talk about the educational side, because I think that's ongoing. Right? And at least from my perspective, it can be hard for companies to to become educated when the pace of development seemingly is impossible to keep up with. Right? I follow AI every day. I have a daily AI livestream podcast. It's hard for me.
Jordan Wilson [00:28:50]:
So I can't imagine for people that aren't spending hours a day. But then, you know, related here, Tara's question, is what are some, examples of training in organizations that you've seen successful? So, yeah, how can companies both educate and train their employees to keep up?
Rajamma Krishnamurthy [00:29:07]:
I would say the the you don't know, but many of the rebels in your in your organization are already using Chargeptivity. They are just using it on their side. They are coming back and, you know, you are doing that. So that's a great thing. I would encourage it. So don't be don't discourage that in the first place. But, you know, how many of us really prompt? Do we actually use prompt? No. We mostly, we use search.
Rajamma Krishnamurthy [00:29:32]:
You know, we were using search with Google and Bing. Now we are using search with ChargeGPT and Gemini and, you know, Publicity or whatever you the choice of, models that you're using. So what I suggest is that start there. Start having some basic courses about prompting. How do you use prompt? How do you talk to AI? How do you, what are the options that you have in, you know, talking to AI? So that's one. Another thing is across the board, each of the discipline do not need the same kind of learning. For example, an engineer would learn different needs to learn different, things about AI. Our product leader wouldn't learn something else.
Rajamma Krishnamurthy [00:30:10]:
An office administrator would need to learn something else and so on. So there's a there are different, learning paths for different people. So figure out what those learning paths need to be. There is enough online courses across, the LinkedIn and other places which are just free. And then there are, you know, obviously, paid courses available for a more in-depth learning about data structures and, you know, deep learning and other things that you want to have many of your engineers learn. So I would say, first of all, have everybody learn prompt engineering. Have everybody learn what ethical use of AI is. It doesn't it's it's very important because then both the users and the creators need to know ethics about AI And then, create these multiple learning paths based on the discipline.
Rajamma Krishnamurthy [00:30:59]:
One size doesn't fit all, basically.
Jordan Wilson [00:31:02]:
So so much good advice here. This is this is gonna be hard. You know, I every single day, I I go back and, you you know, write this newsletter to to recap. This is gonna be a hard one, Rashma. You've given us a lot to think about. But, as we wrap up the show here, you know, we've gone through a lot. We've talked about, you know, even your simple daily efficiencies, you know, what companies who are still on the AI fence should do kind of these these three pillars to, AI success at the enterprise level. But but as we wrap up here, maybe what is your one most important takeaway, for those companies that are still, you you know, kind of, trying to get over this AI inertia and to pick up momentum at the enterprise level.
Jordan Wilson [00:31:42]:
What's your one most important takeaway?
Rajamma Krishnamurthy [00:31:45]:
I like I said earlier, AI is here to stay. You can't avoid it. If you avoid it, you will be the you'll be behind your competitors. Start thinking about spaces where you will feel the most comfortable, and just kinda move on, move out of your inertia. I would say, don't worry too much about the cost and the ROI just yet. Start experimenting because with learning will come comfort, and with comfort will come, the next steps in moving into bigger spaces. There are some moonshot spaces in your area that you will get to, but you can't get to it unless you know what you're doing in the, the more, smaller and more comfortable spaces, because you've got to learn this space, and then you can go solve the big problems in your enterprise with AI.
Jordan Wilson [00:32:35]:
Amazing. Amazing. I mean, thank you so much, Rashima, for joining the Everyday AI Show. Hopefully, our audience just gained a lot of insights from a leader, literally a leader in the world helping us all, you know, use AI there at Microsoft and also an educator. This was a great show. So thank you so much for your time. We appreciate it.
Rajamma Krishnamurthy [00:32:57]:
Thank you very much, Jordan, for having me. Have a wonderful day.
Jordan Wilson [00:33:00]:
Alright. As a reminder thank you. As a reminder, everyone, please, if you haven't already, go to your everydayai.com. Sign up for that free daily newsletter. This is gonna be a tough one to write. It's gonna be a whole lot of information we're gonna be sharing as a recap from today's show, as well as some other resources. If this was helpful, please share it with a friend. Subscribe on Spotify or Apple Podcasts, wherever you're listening.
Jordan Wilson [00:33:21]:
And no matter what, please join us tomorrow and every day for more everyday AI. Thanks y'all.