Start AI Governance With the Outcome, or Waste the Spend
Episode Summary
Yogita Parulekar founded Invi Grid because she was tired of the conversation where a security team tells a CIO what is already broken. Her argument here is that AI governance is not a new discipline invented for AI. It is corporate governance principles applied to AI, and she walks them in order: objectives and goals first, then structure and leadership and culture, then the risks to those objectives, then strategy, evaluation, and only then the technical and operational controls. Security sits inside that, not beside it, because security and privacy are risks to the objectives. Her sharpest point is what happens when the first step is skipped: if you do not know what outcomes you want, the time, effort and money are just waste. She says that is the single most common mistake she sees, and she names explainability as the word she would put at the center of the culture.
Key takeaways
She founded the company to get out of a conversation she had stopped believing in. She was tired of going to CIOs and CTOs and telling them all the misconfigurations and vulnerabilities they needed to fix, she says, because it is never a happy conversation to have and it was not moving the needle on cybersecurity. Her answer was first principles and design thinking: build a product that makes secure by design, and governance by design, easy to do
The layering she insists on is governance over security, not governance beside it. Asked about securing AI she splits the answer in two, the overarching governance layer and the security layer within it, and hands the choice of which to take first to Christina, who picks governance. She returns to the ordering four minutes later to say why she likes it: security and privacy are the risks you deal with in order to make sure your objectives and goals are met
Her reason for defining the term at all is that she thinks it has been ruined. AI governance is a very misused and abused term and very less understood, she says, and it relates beautifully to corporate objectives and goals on one hand and security goals and objectives on the other. It is, in her phrase, what ties everything together
The definition she gives is deliberately unoriginal, and that is the point. AI governance is about applying governance principles to AI, she says, and corporate governance is already pretty well defined: objectives and goals clearly laid out, a structure with leadership and culture articulated so those goals can be met, managing the risks to them, a strategy for achieving them, continuous performance evaluation, and the technical and operational controls for the same
Applied to AI, the first principle is the one she says gets skipped. What are we trying to achieve with AI, and what outcomes do we expect, whether the solution is internal or external facing. If we do not know what the outcomes are, she says, all the time, effort and money we spend on it are just a waste, and we will get nowhere
The second principle is structure, and for AI it starts with knowing which AI you mean. Traditional AI, the generative AI people talk about today, or agentic AI, and what you want to achieve with it. Starting with an understanding of what AI can and cannot do is critical, she says, including which AI and machine learning solutions are deterministic and which are not
She names three kinds of risk and then adds two more. Algorithmic risk is which models you choose. Data risk is which data sources you use, and whether you are ready for the data. Output risk is whether the outputs can hallucinate. To those she adds scheming and adversarial attacks. Understanding those risks to achieving your objectives is, she says, the third layer
Her account of how clients actually arrive is that the governance work is usually already behind them. It is a mix of both, she says, but often they come to Invi Grid after the decisions are made, asking how to implement what they have faster, better, cheaper and secure because they have compliance requirements to meet. Sometimes her team has to take a step back and make them reconsider decisions they should have thought about earlier, which she describes as going a little beyond scope
The single most common mistake she sees is not a technical one. It is not thinking through what outputs and outcomes they exactly need out of that AI, why they are building it, and why they chose that specific approach. She calls that the number one single common mistake, and one that keeps occurring again and again
The second most common mistake follows from getting the first one right. Having chosen the right things to transform and augment with AI, the next thing she sees is not having thought through very clearly on the security and governance side
Her account of why adoption stalls starts with what employees are feeling, not with the technology. Everybody in the company is asking how AI is going to help them, she says, and on the other hand there is a lot of anxiety and a fear that partaking in it will take their job away. Add hallucinations and the mix gets difficult
She gives two failure modes that drop engagement fast, and one of them is not accuracy. If a user sees wrong output, and especially on a customer-facing system, that is a bigger problem, and if the output is wrong all the time the engagement drops very quickly. The second is latency: she compares it to the early days of telephone support, where the line would not understand you and the customer got irritated enough to ask for a human
Her answer to both is the governance layer again, addressed upfront rather than after the complaint. Make it very clear what you are trying to achieve with AI, to the internal audience as much as the external one, and build a culture of transparency by design, documenting everything, and trust by design
The word she singles out is explainability, and she wants it visible rather than documented. Explainability of your AI is, in her words, the more important word she likes: what is it doing, why is it doing it, how is it doing it. She suggests showing the model working through its response on screen as it goes, and says baking that into the culture of the AI team is absolutely critical for overcoming resistance, anxiety and fear
For an executive early in the journey she describes the squeeze rather than the answer first. Boards are themselves being asked whether the company is doing AI, she says, and are pushing it down to CEOs and CIOs, who are hearing a lot about when the benefits show up in cost reduction, process improvement, customer-facing work or investment. If you have not started already, she says, you are late
Her caution is that answering that pressure badly is worse than being late. If you have jumped in by just asking how high, because you were told to, without thinking it through, you may be burning a lot of time, money and effort without any real results to show
Her actual starting instruction is homework, and it is about telling the kinds of AI apart. Start by learning more about AI, GenAI and machine learning, she says, and work out whether traditional AI is enough or you need generative AI and when you would use it. Then look for four signals that it might help: a need for creativity, a need for personalization, a need for a lot of data crunching, and a need to remove the mundane where the mundane requires expertise and is therefore error-prone
Asked for her leadership edge, she answers with a tension rather than a strength. Understanding what she needs to do to stay competitive in this mad race for AI and AI everywhere, and balancing that against what will actually make money
About Yogita Parulekar
Yogita Parulekar is founder and CEO of Invi Grid Inc., a secure-by-design cloud and AI infrastructure company whose goal, as she puts it, is day zero security and compliance for its customers. She brings more than twenty years of cybersecurity, cloud architecture and digital transformation experience, and advises Fortune 500 leaders on adopting AI with governance and compliance built in from the start. She founded the company out of frustration with reactive security: she was tired of telling CIOs and CTOs what was already broken, and wanted to build something that made secure by design, and governance by design, easy to do. This is her first appearance on AI Realized. She returns on episode 44, where the subject is what an agent is permitted to do rather than how governance is structured, and the two conversations are worth reading in order.
In this episode
| 00:42 | Welcome |
| 01:16 | What Invi Grid does, and her role |
| 01:25 | Secure by design, and day zero security and compliance |
| 01:45 | Why she founded it: the conversation she was tired of having |
| 02:30 | Securing AI in the cloud |
| 02:45 | Two layers: governance overarching, security within it |
| 03:10 | A misused and abused term, and why she starts there |
| 03:31 | AI governance is applying governance principles to AI |
| 04:17 | Risks, strategy, evaluation, and the controls last |
| 04:45 | Principle one: objectives and goals for the AI |
| 05:09 | Principle two: structure, leadership and culture |
| 05:54 | Principle three: algorithmic, data and output risk |
| 06:45 | Where security and operational risk enter |
| 07:19 | Do clients build these layers before you arrive |
| 07:34 | They usually arrive after the decisions are made |
| 08:20 | The common mistakes leadership teams make |
| 08:31 | Mistake one: not knowing the outputs and outcomes |
| 08:50 | Mistake two: security and governance not thought through |
| 09:15 | What the roundtable heard about outputs and agents |
| 10:08 | Where AI can actually transform and augment a process |
| 10:17 | Anxiety, and the fear of losing a job to it |
| 10:43 | Wrong output, and how fast engagement drops |
| 11:04 | Slow response, and the early days of telephone support |
| 11:28 | Coming back to governance, and addressing it upfront |
| 12:29 | Explainability, and showing the reasoning on screen |
| 13:20 | Guidance for executives early in the journey |
| 13:43 | Boards being asked whether the company is doing AI |
| 14:12 | Late is bad, but jumping without thinking is worse |
| 14:40 | Learning the kinds of AI, and four signals for where it helps |
| 15:45 | Resources she offers to send afterwards |
| 16:20 | Staying competitive, against what actually makes money |
| 16:40 | Wrap-up |
In Yogita’s words
“AI governance is about applying governance principles to AI”
— Yogita Parulekar (03:31)
“If we don’t know what the outcomes are, all the time, effort, money that we will spend on it are just a waste. We will get nowhere, right? So starting with the objectives is always very crucial.”
— Yogita Parulekar (04:45)
“The most common mistake is not thinking through what exactly, what are the outputs and outcomes they exactly need out of that AI, and why are they building it, and why that specific approach they have used.”
— Yogita Parulekar (08:31)
“Explainability, I think the more important word that I like is explainability of your AI. What is it doing? Why is it doing? How is it doing?”
— Yogita Parulekar (12:29)
“That is why I like to first talk about governance and then move into security and, because this is how it’s tied together.”
— Yogita Parulekar (06:45)
“I was tired of going to the CIOs and the CTOs and telling them all the misconfigurations and vulnerabilities that they need to fix. It’s never a happy conversation to have, and I just wanted to change the game.”
— Yogita Parulekar (01:45)
“If you haven’t started already, you’re, you’re late. But on the other hand, if you have jumped in without, by just asking how high, because you’ve been asked to do it wi- without thinking through it, you may be burning a lot of time and money and effort without really, wi- without any real results to show.”
— Yogita Parulekar (14:12)
“Understanding what I need to do to stay competitive in this mad race for AI and AI everywhere, and balancing that out with what will actually make money.”
— Yogita Parulekar (16:20)
Resources
Yogita Parulekar
Yogita Parulekar on LinkedIn: Her profile, where she writes about secure-by-design cloud and AI deployment
Invi Grid: The secure-by-design cloud and AI infrastructure company she founded and runs. Its goal, as she describes it, is day zero security and compliance for its customers
Ideas and terms discussed
AI governance as applied corporate governance: Her definition, and the spine of the episode. AI governance is applying governance principles to AI, and those principles are the ones corporate governance already has: objectives and goals clearly laid out, a structure with leadership and culture articulated so the goals can be met, management of the risks to them, a strategy, continuous performance evaluation, and the technical and operational controls for the same. She gives them in that order deliberately, with the controls last
Governance over security, not beside it: Her structural claim, and the reason she takes the two questions in the order she does. The governance layer is overarching and the security layer sits within it, because security and privacy are risks to achieving the objectives rather than a separate program of their own. It is also why she says the term ties everything together: corporate objectives on one side, security objectives on the other
The risks she names: How she breaks the risk principle down for AI. Algorithmic risk is which models you choose. Data risk is which data sources you use and whether you are ready for the data. Output risk is whether the outputs can hallucinate. To those she adds scheming and adversarial attacks. Each is framed as a risk to achieving the objectives rather than as a risk in the abstract
Explainability, shown rather than filed: The word she singles out. Not just what the AI is doing but why and how, and visible while it happens: she suggests showing the model working through its response on screen as it goes. She puts it alongside transparency by design and trust by design as the three things to bake into the culture of the AI team
Secure by design, and day zero: The idea her company is built on, and the alternative to the conversation she left behind. Rather than auditing a deployment and reporting misconfigurations afterwards, security and compliance start at day zero, which she extends to governance by design
Four signals that AI might help: Her homework for an executive who has not started. A need for creativity, a need for personalization, a need for a lot of data crunching, or a need to remove the mundane where the mundane requires expertise and is therefore error-prone. She pairs them with the question of whether traditional AI is enough or generative AI is actually required
Named on air
The AI Realized executive roundtables: Christina brings one of them into the conversation, a roundtable on the economics of AI where the same point came up, that you need to know what outputs you are looking for in order to measure them. Christina adds the agent version of it, that beginning with the output before building the agent helps, and that the outcome has to be motivating enough for people to go through the change, such as relieving toil or connecting systems that are not connected today
Since this conversation
Invi Grid completes Google’s ISV Startup Springboard program: Company news dated 3 November 2025, two and a half months after this recording. The program is Google Cloud’s accelerator for independent software vendors, and the announcement is framed around the same secure-by-design deployment she describes here
Frequently Asked Questions
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AI governance is the application of ordinary corporate governance principles to artificial intelligence. Yogita Parulekar, founder and CEO of Invi Grid, defines it that way deliberately, because she thinks the term has become misused and abused and very little understood. The principles are the ones corporate governance already uses: objectives and goals clearly laid out, a structure with leadership and culture articulated so those goals can be met, management of the risks to them, a strategy, continuous performance evaluation, and the technical and operational controls for the same. What makes it AI governance is applying that list to a specific AI system and asking what outcomes are expected from it.
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AI governance starts with the objectives and goals for the AI itself: what you are trying to achieve and what outcomes you expect, whether the system is internal or external-facing. Yogita Parulekar puts that first of six principles and is blunt about the cost of skipping it: if you do not know what the outcomes are, the time, effort and money spent on it are just a waste. She also names it as the single most common mistake she sees in enterprises, ahead of anything technical, and says it keeps occurring again and again. The technical and operational controls come last in her ordering, not first.
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Security belongs inside AI governance rather than before it or beside it. Yogita Parulekar splits the question into an overarching governance layer and a security layer within it and offers the host the choice of which to take first. Her reason for liking governance first is that security and privacy are risks to achieving the objectives, which means you cannot judge them until the objectives exist. She sets that against the reactive pattern she founded her company to escape, where a security team arrives after a deployment to report the misconfigurations and vulnerabilities that need fixing.
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The risks in an enterprise AI deployment fall into three named kinds plus two more, and each is framed as a risk to achieving the objectives rather than a risk in the abstract. Yogita Parulekar names three: algorithmic risk, meaning which models you choose; data risk, meaning which data sources you use and whether you are ready for the data; and output risk, meaning whether the outputs can hallucinate. To those she adds scheming and adversarial attacks. Understanding those, she says, is the third layer of governance, after the objectives and the structure. She adds a practical one that sits outside the list: a customer-facing system that is wrong or slow loses engagement very quickly.
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Employees come to trust an AI system when the anxiety is addressed upfront and the system explains itself while it works. Yogita Parulekar starts from what employees are actually feeling, which is both a hope that AI will help them and a fear that taking part will cost them their jobs. Her answer is a culture built on transparency by design, documenting everything, and trust by design, with explainability as the word she singles out: what the AI is doing, why it is doing it, and how, shown on screen as it thinks through a response rather than filed away. Baking that into the culture of the AI team is, she says, absolutely critical.
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An executive who has not started should do the homework on which kind of AI the problem actually needs before committing to anything. Yogita Parulekar is direct that if you have not started you are late, and equally direct that jumping in because a board asked, without thinking it through, burns time, money and effort with no real results to show. The homework is learning the difference between traditional AI, generative AI and machine learning, and then looking for four signals that AI might help: a need for creativity, a need for personalization, a need for a lot of data crunching, or a need to remove the mundane where the mundane requires expertise and is therefore error-prone.
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[00:42] Christina Ellwood: Welcome to AI Realized, the podcast for enterprise executives leading AI deployments. From tackling security data and operational challenges to navigating organizational transformation, AI deployment offers a unique opportunity to redesign organizations from the inside out. I'm Christina Ellwood, your host for today's episode, and we're talking today with Yogita Parulekar, founder and CEO of Invi Grid. Welcome, Yogita.
[01:12] Yogita Parulekar: Thank you, Christina. Thank... I'm glad to be here.
[01:16] Christina Ellwood: It's such a pleasure to have you. Give us a really brief description of the work that Invi Grid does, and your role there as CEO and founder.
[01:25] Yogita Parulekar: Invi Grid is about secure by design cloud and AI and cloud infrastructure deployment. So starting with day zero security and compliance for our customers is our goal.
[01:41] Christina Ellwood: Fantastic. And you started the company. What was your inspiration?
[01:45] Yogita Parulekar: I was tired of going... You know how security is reactionary and after the fact. I was tired of going to the CIOs and the CTOs and telling them all the misconfigurations and vulnerabilities that they need to fix. It's never a happy conversation to have, and I just wanted to change the game. This is not helping anybody. It's not moving the needle on cybersecurity, so how do we change it? First principles, design thinking, and here we are. That's what we do. Let's do secure by design, and let's build a product that actually makes it easy to do this secure by design, governance by design.
[02:26] Christina Ellwood: That's a great inspiration, and I think a common one among entrepreneurs. The pain that they have leads to their designing and building a solution to that pain. So it's obviously very important when deploying AI to the cloud to have it be secure. Talk to me a bit about securing AI.
[02:45] Yogita Parulekar: Yeah, absolutely. So there are two things, actually broad things, and I can cover both of them. One is the overarching governance layer, and then the security layer within it. So which one would you like me to tackle first?
[03:03] Christina Ellwood: The overarching- I think the overarching sounds like a great place to start, and then we can go into the underlying
[03:10] Yogita Parulekar: All right. Awesome. And the reason I like to start with the overarching layer is I think it's, A, it's, it's a very misused and abused term and, uh, very less understood, and it relates and very beautifully to corporate objectives, corporate goals on one hand, and security goals and security objectives on the other hand. So it, it's what ties everything together. So let's see, what does it mean? So AI governance is about applying governance principles to AI as a... So what does that mean? Uh, what does go- what are these governance principles? So if you look at historically, corporate governance is pretty well defined for us right now, and those principles are essentially are about, A, having your objectives and goals very clearly laid out for the company B, having a structure, leadership, culture, all of that very clearly articulated so that you can carry out and meet your objectives and goals. Managing risks, risks to achieving those objectives and goals. Creating a strategy to achieving those objectives and goals. Continuous performance evaluation to make sure that those objectives and goals are met, and laying down the technical and operational controls for the same. So now if I apply these same principles, governance principles to AI, let's see what they mean, right? First, objectives and goals. What are we trying to achieve with AI? What are the outcomes we expect with this? Whether it's internal or an external-facing AI solution. Are we... So that's, that's where we have to start, because if we don't know what the outcomes are, all the time, effort, money that we will spend on it are just a waste. We will get nowhere, right? So starting with the objectives is always very crucial. The next thing is to laying down the structure or the leadership and the culture. So are we gonna... AI has-- is very unique and specifically gen AI. Again, this is where starting with understanding what AI can and cannot do is critical. And again, are we talking about AI in its traditional sense, the gen AI that we talk about today, agentic AI? What are we talking about, and what do we want to achieve with it? Starting with that and then understanding that, hey, maybe it can be deterministic, it can be non-deterministic. Which AI and ML solutions are deterministic, which are not. And then where it can lead to hallucinations and all the risks and challenges, so understanding the risks becomes critical. The risk can be algorithmic risks, what models you're choosing. It can be data risks, like what data sources you're using. Are you ready for the data? It can be output risk. Can the outputs hallucinate? The scheming, what about adversarial attacks? So understanding all of these risks to your achieving these objectives is the third layer, right? So we started with objectives for AI, laying down a structure for stewardship, for culture, for understanding risks, and laying down the strategy and the performance oversight is all very critical, and that is what is- A part of and required before you start off anything. That's your governance layer. And as you saw, as I mentioned risk, this is where also, and I talked about algorithmic risk or data risk or output risk, this is also where you start talking about security and operational risks. And that is why I like to first talk about governance and then move into security and, because this is how it's tied together. Because if your security and privacy are the risks that you need to deal with in order to make sure that your objectives and goals are met.
[07:19] Christina Ellwood: So when you're working with a client, do you work with them on establishing all of those layers, or is that work they do before your team gets involved in the implementation of AI in the cloud?
[07:34] Yogita Parulekar: It's a mix of both. Yeah. Uh, unfortunately, often they come to us when they have made some of these decisions, and they, "Just im- this is what we have. Just tell us how to implement this faster, better, cheaper, and secure because we have to meet these compliance requirements."
[07:56] Christina Ellwood: I see. So it's, so that means you're, you're having to help them through those items.
[08:02] Yogita Parulekar: Those items, but sometimes we have to take a step back and make them think through some of the decisions they should have otherwise thought about from a governance perspective. And that's where we go a little bit beyond our scope sometimes, but we got to do because that's for your customer success.
[08:19] Christina Ellwood: I see. What are the common mistakes that people make? Enterprises specifically, I guess these would be leadership teams, not single individuals, but what are the common mistakes?
[08:31] Yogita Parulekar: I think the most common mistake is not thinking through what exactly, what are the outputs and outcomes they exactly need out of that AI, and why are they building it, and why that specific approach they have used. That's the number one single common mistake that we see keep occurring again and again. And the second one is assuming they have gotten that, chosen the right things to transform and augment with AI, the next common thing we see is not having thought through very clearly on the security and governance, uh, side of things.
[09:15] Christina Ellwood: We actually hear about the first one, this not thinking through what your outputs are, and from a number of different angles. At one of the round tables we were talking about the economics of AI, and this is one of the things that was talked about, is you need to know what outputs you're looking for to be able to measure them. And the same with agents, that if you begin with the output that you're looking for before you even start to build the agent, that that helps. And picking an outcome that is motivating to the people who are involved. So in the case of agents, picking toil or drudgery, or picking something where they're trying to use the agents to connect disparate systems that aren't connected today. But those were pain points that were so high, people were willing to go through the effort to make the change, but also to help to define the output and evaluate the output. So is that, does that square with your own experience as well?
[10:08] Yogita Parulekar: Oh, absolutely. Understanding, you are absolutely right. Where can AI actually transform and augment the processes at the enterprise? How can it help me? Finally, everybody in the company is looking at, how is AI going to help me? Because on the other hand, they're also very anxious. There is a lot of anxiety. There is, and the, and fear of, is this going to take my job away if I partake in this? And then obviously there are all these other risks of, uh, hallucinations and stuff. So the moment they see, a user sees wrong output given by your AI, and if it's a customer-facing one specifically, that is a bigger problem. Internal, maybe there is a little bit more tolerance and going through the pain. But if there is, if the output is wrong all the time, the engagement drops very quickly. If the, if the, if the response takes too long, if you remember the, when we used to init- in the very initial days of support, telephonic support, and we used to call the number line, and it, it would just not understand what you're saying, and the customer would get very irritated. It's, "Okay, just, can I just talk to a human, please?" It's going to take that long and it's going to irritate and annoy. That's another thing where engagement quickly starts dropping. Again, internal versus external. Internal may have more tolerance to these things, but not really. So between their own anxieties and between all of these hallucinations and getting things wrong and taking time, it can be very challenging to implement and make sure that you're getting the outcomes It is very, this is where coming back to governance, laying down the leadership structure, laying down the culture comes into play, where you essentially address it upfront. Address it by making it very clear what are we trying to achieve with AI, whether it's your internal audience, even to your internal audience, in fact, more on that. And if, and especially external audience, building the culture of transparency by design, documenting everything. Trust by design. Explainability, I think the more important word that I like is explainability of your AI. What is it doing? Why is it doing? How is it doing? Even as AI is working through the responses to the user, probably showing it even on screen as to how it is thinking through, showing. Building all of that and baking it in the culture of the AI team is very critical. It's absolutely critical, whether it's an internal face- facing or an external facing AI, to bake this culture in so that you can overcome any kind of resistance, any kind of anxiety, any kind of fears about the AI solution that you are trying to implement. Does that answer your question?
[13:15] Christina Ellwood: It does very well, actually, and you've given lots of guidance for executives. But let me ask this question about executives who are early in their journey. So what's your guidance for executives in their early stages of their AI journey? Because if we get off on the right foot, oftentimes it's easier to stay on the right foot. So maybe you would give some advice to them about that.
[13:43] Yogita Parulekar: Yes. So this is where I think I then, uh, on one hand, I'm sure that your boards are being asked this question, "Are we doing AI or not?" So they're pushing it down to the executives, the CEOs, the CIOs are hearing a lot, "Do we have AI? What are we doing at the, when are we going to see the benefits of AI in terms of cost reduction and process improvement and, or customer facing or maybe investment from a VC?" So you're probably getting all of these pressures and so if, if you haven't started already, you're, you're late. But on the other hand, if you have jumped in without, by just asking how high, because you've been asked to do it wi- without thinking through it, you may be burning a lot of time and money and effort without really, wi- without any real results to show. So I would start by learning more on AI, on the different kinds of AI. AI, GenAI, ML. Like is traditional AI enough? The... Or do we need GenAI? When do we use GenAI? Or maybe when there is create-- so identifying the use cases when there is need for creativity, when there is need for personalization, when there is need for a lot of data crunching, when there is need for removing the mundane, where mundane requires expertise and hence it is error-prone. So these are, so tho- understanding these are probably areas where AI could help. So understanding where AI can actually transform and augment your enterprise, and potentially earn you revenues with if it is external facing, and if you have any-
[15:33] Christina Ellwood: Okay, great ...
[15:34] Yogita Parulekar: for your customers. So understanding that will require doing some homework, and I would start there.
[15:41] Christina Ellwood: Are there resources that you would recommend to them?
[15:45] Yogita Parulekar: Oh, goodness. I can pass on some URLs and links post this for that. Yeah.
[15:52] Christina Ellwood: Great. We'll put those in the show notes for everyone. So Yogita, you're a leader of a company. You work with executives who are facing these challenges. In this AI revolution, what's your defining edge as a leader? d- as a leader
[16:12] Yogita Parulekar: defining
[16:15] Christina Ellwood: Think of it as your AI, as your leadership superpower
[16:20] Yogita Parulekar: Oh, AI leadership superpower? Understanding what I need to do to stay competitive in this mad race for AI and AI everywhere, and balancing that out with what will actually make money.
[16:40] Christina Ellwood: Okay. Thank you, Yogita Parulekar, CEO of Invi Grid. Thank you so much for joining me today on AI Realized.
[16:51] Yogita Parulekar: Thank you.