SAVVI AI's Maya Mikhailov joined Andreas Welsch for his popular podcast "What's the BUZZ - The Business of AI"
Watch this episode on YouTube: https://youtu.be/_9XrCpC1zFE
In this episode, Maya Mikhailov (Founder & AI Leader) and Andreas Welsch discuss how AI leaders can set expectations with their leadership.
Maya shares examples of stakeholder management and provides valuable advice for listeners looking to balance excitement for AI with the due diligence needed for successful AI projects.
- Manage your senior leadership’s expectations
- Make AI tangible for their leaders
- Focus on generative AI
Andreas Welsch: 0:21Today we'll talk about managing your leader's expectations when running AI projects. And I figured who better to talk to about it than someone who's got a ton of experience in startups and corporates doing just that. Maya Mikhailov. Hey Maya.
Maya Mikhailov: 0:35
Thank you for having me.
Andreas Welsch: 0:37
Awesome. Hey why don't you tell our guests a little bit about who you are, what you do and how you got here.
Maya Mikhailov: 0:46
Absolutely. My name is Maya, I'm Co-Founder and CEO of Savvy.Ai, where we help bring machine learning tools to every team. Before that, my previous company, GPShopper, was bought by Synchrony Financial, where I went on to start a new division at Synchrony and be the general manager of a leading in FinTech product innovation using AI and machine learning technologies. So we built a lot of products using AI and machine learning for the bank. And here I am.
Andreas Welsch: 1:16
Fantastic. Hey again, thanks for joining. Really sounds like you've seen quite a lot especially both in startup in and corporate. Maya, should we play a little game to kick things off? What do you say?
Maya Mikhailov: 1:28
I guess we are.
Andreas Welsch: 1:31
Awesome. Hey, this one is called In Your Own Words. When I hit the when I hit the buzzer, the wheels will start spinning and when they stop, you'll see a sentence. And I'd like you to answer with the first thing that comes to mind and why. In your own words. And to make it a little more interesting, you'll only have 60 seconds for your answer.
Maya Mikhailov: 1:52
All I'm ready. I've been practicing my game show.
Andreas Welsch: 1:56
Perfect. And so folks, for those of you watching this live, also drop your answer in the chat and why. Alright. So now Maya, are you ready for What's the BUZZ?
Maya Mikhailov: 2:07
I am ready. No whammies.
Andreas Welsch: 2:10
Fantastic. Then let's roll. So if AI were a bird, what would it be? 60 seconds.
Maya Mikhailov: 2:23
I think recently AI would be a phoenix. It has risen from the ashes of an AI winter where everyone said AI was overhyped. It was too much. It's not gonna be useful. Companies can't execute against it. And when ChatGPT dropped on an unsuspecting world at the end of October, all of a sudden we saw interest in the AI just rise from the ashes. And all of a sudden everyone wants to get in and know how it can transform their companies, transform their teams and the way they work.
Andreas Welsch: 2:53
Awesome. Hey within time and an awesome answer. Thank you so much. I agree, right? At the end of last year, we saw a lot of the media and experts talking up the next AI winter. And then boom.
Maya Mikhailov: 3:06
Boom. I think there was something very visceral about ChatGPT. It's not so much yes transformer technology, large language models. Very interesting. But I think what made it more interesting is the visceral connection that people had. They got it. Before AI was always behind the scenes. Our planes would arrive at the gate on time. Our packages would come in. Our Netflix suggestions were personalized. They didn't see the AI and now they could see it get their hands on it. All of a sudden, in every boardroom, leaders are like, what are we doing? What's our AI strategy? How are we gonna make this work? Because they themselves can see how.
Andreas Welsch: 3:45
Excellent. Yes. I think that's exactly the point. And that's why I'm so glad we're having this conversation today about expectations. Because I also see them rising right in the conversations that I have. To your point, it's always so what are we doing with AI and, to your point right now, you can actually feel it. So look if you look at any leadership survey, pretty much AI is somewhere at the top and top of mind for business leaders. But I also feel when you actually do talk to leaders, there's still many that are struggling to make sense of this whole AI thing and I get it, right? It's not necessarily their core competency. And most of the time, it doesn't even have to be. But I also feel that this limited literacy on AI creates some challenges and sometimes a mismatch of expectations. What do you typically see?
Maya Mikhailov: 4:33
First of all, I think that I would challenge how much literacy they actually need, because they do need a bit of AI and data literacy. But look, leaders don't necessarily know how to program yet. They use computers every single day. What most business leaders wanna know is, how will this be a workforce multiplier? How will this drive outcomes? What they do need to know is a realistic version of what AI can do for their particular company, given their particular data and their particular circumstances. They need to know the risks associated with AI, whether that's reputational risk, ethical risks, et cetera. And they need to know what they can do often with their current team. Many leaders are still hesitant in this economy to necessarily make a huge investment or a huge pivot into a new technology. They need to know what they can do today to achieve results in the short and medium term so that they can invest in the long term. So I think that's the premise of what leaders need. To be honest with you, I've been talking to a lot of CEOs and CTOs recently, and in a weird way, they feel overwhelmed. It's becoming everything everywhere, all at once. AI is everywhere. It's affecting everything. It's transforming every single thing you do, every job, every this, every that. And yet it needs to be boiled down to effectively communicate with them in a practic timeline, in a practical way. They need to know, Hey, do you know this problem we've been having with how to efficiently roll trucks to get product from A to B? This is actually a machine learning problem. You can solve this with AI instead of trying to solve this with spreadsheets and with guesses. So they need to know how it will practically affect their business and generate.
Andreas Welsch: 6:18
Excellent. Yeah. I think that practicality is really key. And making sure it's connected to the business and actually starting with a business problem and not just the usual that we've seen all too often: A technology looking for a problem.
Maya Mikhailov: 6:32
Yeah. And, I think to a certain amount, they need to know how they could do it with the tools they have. Because if you start talking to your leadership about AI and machine learning, and you start with a conversation that says: first, we need to hire the following 10 people that everybody else is trying to hire in this economy. We need to invest X, Y, Z money that we might not even have because we're doing some bell tightening. We need to undergo a 24 month data re architecture project. And maybe, after all of those things are done, we can start on our AI journey. And that's just, that's not what they wanna hear. And that's not the reality of the situation.
Andreas Welsch: 7:15
Now, I'm wondering, you said you founded two startups, you've sold one of them to a large financial institution, and you've joined them as an AI leader and especially in FinTech being such a hot space and a leading space. I was wondering how have you actually managed those expectations towards your leaders in a corporate environment? What's helped you and how have you gone about it?
Maya Mikhailov: 7:35
I was really lucky at Synchrony Financial. I had an incredibly supportive leadership team. I had an incredibly supportive bo board, and they saw the potential of what we were doing. They saw AI, they saw machine learning, and they saw that it had potential not just in these big use cases that every bank is going after, like anti-money laundering and fraud. But it had that potential as that workforce multiplier, as that intelligence level up of their software and their products. So in that sense, I feel like I was very fortunate. But the reality is also a lot of the support that we are able to get for our division was because we were able to tell a story and we were able to tell a story around what we are building around how the goals of what we are building. Aligned with what the company's goals were. It seems pretty simplistic, but you'd be surprised at how many data leaders, AI leaders get stuck in the mechanics of what they're doing and they forget. They get lost in the forest and they forget to bring it back to the overall corporate goals and objectives. We came armed with numbers. Here's how it's gonna help. Here are the results, what we're expecting. And we didn't necessarily discuss with the board and leadership things like root means, square error or heteroscedasticity. Very few boards wanna discuss heteroscedasticity. Maybe at OpenAI they do. But more boards are like, what are the outcomes? What are the risks? What can we accomplish by next quarter, by two quarters from now, et cetera. So when we framed this as a story and a narrative that they can digest and understand and pull back to their own corporate goals and objectives, we were much more successful.
Andreas Welsch: 9:12
I think that's a really good recommendation. Frame and phrasing it in simple terms, in terms that relate to these goals and to these objectives. Look I'm wondering also now, because AI is so much more accessible and it's accessible to anyone. And I feel if you've been following the news even at the beginning of the year with.
Maya Mikhailov: 9:35
I've been following the news so much that I'm inundated with news, like there's like a new AI announcement while we're talking like. Four new AI startups have launched while we're having this conversation.
Andreas Welsch: 9:46
Fully agree, right? It's so hard to stay on top of the news and some days it feels like your head is spinning just trying to keep up. Now, I feel with World Economic Forum Davos at the beginning of the year, that's when I feel it was really propelled as a topic again. And, it was so much top of mind now with AI and generative AI being so accessible to anyone including business leaders where it's no longer just technology conversation. How do you see these or management of these expectations? Because, hey, look, ChatGPT is super easy. It's super simple to use. I can just pop in a question and I get a response. Why can't we do that? How do you manage that gap between what you mentioned, right? There are some foundational things that you need to have in place. Some are there, some are on the way of getting there, before you can use generative AI. Does it always have to be built from scratch, for example, or most of all, how do you manage that expectation?
Maya Mikhailov: 10:45
I think the first thing is and I really have a lot of sympathy right now for AI leaders because they have to walk such a fine line. They have to walk a fine line between being hype master, which, and the, hyper of AI is eventually gonna lead into problems when it doesn't do all the magical things that they read about in the media. And they have to walk a fine line between being like a wet blanket. Sandbagging all the results so that the overall company gets impatient or the business gets impatient with waiting for these deliverables to happen. So they're right now juggling a lot of plates. But the reality is, that first of all, a business with generative AI, they need to establish a framework. They need to establish a framework of behavior of what's okay. They need to establish a data security framework. The folks at Samsung, those developers may not have known that they can't put proprietary super secret chip data into ChatGPT, because it helps them document or QA a process that they were doing. We're human beings. Human beings naturally gravitate to convenience. So the first thing the company has to do is figure out a framework of what makes them comfortable with these generative AI tools, and then establish a knowledge base help. Let that empower the SMEs to establish a knowledge base, because they know how these tools can help their businesses and help their businesses succeed. And so they can establish these are the top prompts I use. But the second thing is and, this is very, important, AI is not just ChatGPT. And even though ChatGPT is a thing we can wrap our brains around, there isn't one model to rule them all. This isn't Westworld quite yet. So there isn't this one magic model that's gonna solve your problems. ChatGPT will not tell you which truck to roll in your warehouse. ChatGPT will not make a continuous decision for you about what piece of content to put in front of which users at any given time on your website. It won't do certain things. So the first thing you have to do is reframe your management into, there are certain things that ChatGPT is good at. There are certain things that AI vis-a-vis machine learning or optical recognition is good at. And then look at your problem sets. So the first thing you have to do is, go look at the problems across your business and establish a roadmap of what can be reasonably accomplished. What are these small and mid-size wins that can be accomplished with small to mid-size risks so that you can start building credibility for larger AI programs? If your business is a little bit hesitant about it, if your business is just saying, get on the gas, there certainly is more opportunity there, but on the other hand, you also have to think again about data privacy, about data security, and about the ethics of what you're doing and where it can be used and where it cannot be used. Not just because you're in a regulated industry, but because you're not gonna fire hose all your company's private data into somebody else's open model.
Andreas Welsch: 13:47
There's a lot of a lot of insight and a lot of truth that, that you're sharing. Because things that you've built before the advent of generative AI, they're still valid.
Maya Mikhailov: 13:58
People ask me what I do and I was like, I'm just in boring machine learning. You don't wanna talk about that. But the reality is, it's that boring machine learning that's actually gonna be that hidden workforce multiplier, while everyone else is too busy putting into ChatGPT, how do I write a memo to management about the wonderful things we're doing with AI?
Andreas Welsch: 14:20
I'm taking a look at the chat and I see Michael has the insightful question here, and he says, Will domain specific tools like BloombergGPT for finance or Harvey AI for legal research become the new normal in the next 24 months? What do you see? What do you believe?
Maya Mikhailov: 14:35
When you're talking about large language models and some of these generative AI models. I really think that you're just gonna see some specialization as companies discover that they don't need the whole history of language. They need language that's specific to their business. They need knowledge that's specific to their business. So do I see a rise in purpose-built tools that are specific to certain industries like healthcare or like BloombergGPT? Absolutely. But I still see a wide range of problems. That are very common across many businesses that AI and machine learning can address. But yeah, I definitely see with large language models there's gonna be a lot of kind of hyper tuning, if you will, against certain industries and certain data sets as companies will also demand, like their own LLM. Because they don't wanna necessarily feed back that data into a generalized model, especially if it's company if it's chip design data, that's the most proprietary data they own.
Andreas Welsch: 15:34
That makes sense. I think it'll be really interesting seeing where this goes and how many of these models or what types of models will be available publicly or will be available commercially to the point of BloombergGPT and similar ones. I think definitely an exciting space we get to be a part of and get to shape over the next couple of years.
Maya Mikhailov: 15:56
Yeah. It's a very exciting space and I think it's evolving. Not just because there's new news coming out about transformer models and LLMs every single day. I think it's an exciting space, because companies are still trying to figure out their way through it and figure out we've been told for the last decade that data is oil. That our data is the most precious resource we have as a business. We have to protect it at all costs. And now all of a sudden people are saying now just put all of that in my black box model. So I think a lot of companies are trying to figure out, is that the right strategy for us? What is the right strategy for us? What types of AI or machine learning work for us versus what types don't really generate a huge benefit to the business? And again, what can we execute today in the sh short to medium term? To start getting some of these results under our belts and some of this learning and experie.
Andreas Welsch: 16:50
I do have a follow up question to that one. And what we've also talked about not throwing the old AI stuff out the window and that is in all of this excitement, hype, but also complexity, technical complexity new security, ethical questions coming up, what role do you see AI leaders play? What role can they play in their organizations right now? What do they need to do to help their organization be successful and grow and thrive in the generative AI era?
Maya Mikhailov: 17:22
I think they play a very important role right now, because right now they're the ones educating the organization. They're the ones creating this framework, and that's what's really important to create a framework where your company can succeed to create a security framework, a data usage framework a knowledge base. These data leaders, these AI leaders are at the forefront of helping their company design how this AI transformation will affect the business in the next year, 5 years, 10 years. They have a very important role to play and they have an important role to play into educating teams into what AI can do for them and can't do for them. Because again, the more there is a sensationalism about what AI may become and that it may take over humanity and welcome our new AI overlords, they have a role to play in saying, look, this is what this means for you today. This is what we can accomplish with our team today. So I, think that they are both the educator and they are the realizer of some of those corporate goals and objectives.
Andreas Welsch: 18:25
I think that's a fantastic message for those of you in the audience, if you're in an AI leadership role or aspiring to become an AI leader. There's a real opportunity and especially also a real need here to help your company, to help your peers, to help your leadership better understand what all of this AI stuff is about and what's tangible and what you can do with it and what you can't or shouldn't be doing with it.
Maya Mikhailov: 18:49
Absolutely. And to lay the foundation right now of a successful AI transformation, because that is what's next. We've we've spent the money creating data streams, data rivers, data lake houses. I don't know what else we're building on that data, but we data whitewater rafting, if you're in California this spring we've spent the resources to do that, and now it's time to put that data to action. And that's what the promise of AI is. That automation that productivity and efficiency gain of putting that data to work for us rather than just staring at it on a dashboard.
Andreas Welsch: 19:28
Fantastic. Hey, maybe can you summarize the three key takeaways for our audience today, because we're getting close to the end of the show.
Maya Mikhailov: 19:35
Absolutely. I think first of all, when talking to your leadership about data and AI projects, specifically around AI projects, you have to ground them in what can be accomplished. You have to ground them in what can be accomplished today with the resources you have and what your roadmap is in the future. I think you also have to help educate them as to the different types of AI that it's not. ChatGPT is the one model to rule them all, that there's still machine learning out there. There's still other types of AI out there that can help their business more practically in the short term and accomplish the results they need. And finally, I think you have to remember that this is a longer term effort. All of this transformation that we talk about, all of these AI gains that we're talking about, they don't necessarily happen tomorrow. So I think there is a line to walk between getting your leadership excited, bringing it back to their goals and objectives, and realizing that you're at the beginning of a journey. Show some wins during the journey. Tell the story. Tell how it relates back to your business objectives. But don't forget it is in fact a journey.
Andreas Welsch: 20:39
That's awesome. I think that's a very practical and a very realistic assessment as, as well that it is a journey and then you're, in it for, the long run and, not just not just for a sprint. Thank you so much for joining us, Maya, and for sharing your expertise with us. It was great having you on the show.
Maya Mikhailov: 20:56
Thank you so much for having me, and thank you everybody who joined us.
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