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Hello Starting with Mahesh Mohan Thakur from GoDaddy. Let me introduce him and then we’ll bring him on stage.

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Mahesh is a technology executive, a board member, and a CEO Advisor with 20 plus years of experience building and scaling teams at Microsoft, Amazon, and even a startup Mahesh is a guest lecturer at Columbia and NYU. He has hired, coached, and transformed executives and teams at some of the world’s fastest-growing, please put your hands together. To welcome Mahesh, Oh, hey, morning. Good morning. For pleasure and an honor to be here with you. Thank you for having me. Thank you. My stages are large. I’m so excited to hear about so much of your experience. I’m excited to hear how CXOs can adopt AI and deliver impact. Go for less. Thank you. Happy to chat with you folks about it. I’m just getting my screen to be shared here in a second. Give me one moment. And you guys see my screen on the stage. Okay, awesome. So I’ll get started assuming the video audio and the slides are together. So I’m excited to speak here today on how AI-driven CEOs and board members truly deliver impact in today’s day and age. I’ll be sharing broadly three mantras for law and policy. The three mantras are as follows To simplify fast and lastly, the goodness factor. And I’m talking about you know-how in today’s day and age, there are several companies and the landscape is very broad. It is about the tape.

 

“There are traditional companies which are 100 to 200 years old. And there are these startups that are being born today which are called Digital First or in some cases digital from the broad spectrum of companies and when I get to work with these amazing board members and CXOs of these companies.”

 

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I have studied India journey so today a lot of these observations and also you know give you some key nuggets on the challenges that I’ve seen when I’ve worked including Microsoft, shared with you what has worked, what has not worked, and hopefully, we’ll have some time to answer questions. So without further ado, let’s get started. I’m going to talk first about simplifying with simplification. I would love to share something which I keep repeating and I keep coming across this more and more as I coach more senior folks. I remind them number one AI in and of itself cannot be a strategy for the business.

“Yeah is not something which you say okay, my strategy to win this market is let’s just implement AI. That’s never going to be the right approach. Nor is that going to fly with any seasoned board member.”

 

The way to approach it is my goal or my vision is to win this market share or have 50% market share for our products for our owners to lower the attrition of our customers from 30%. That’s your business strategy.

And to do that, you may now want to implement AI. So if you bring in AI to solve a business problem, and when you talk about the approach, that’s the right way to think about and that’s why I continue to articulate and remind folks that

 

 

“Just having AI is not going to help you in business or when your customers you have to have an understanding of how that technology is going to help you with their business or their business strategy.”

 

The next observational point here is not a project, but an investment for the long term. What this means is we don’t want to have projects. We’ll say let’s start this for a year and see where it goes. Or let’s monitor this every quarter or every quarter and see if we can get to a point. That’s always a bad beginning, whatever project you want to do you have to have the longer price of what AI What are all these initiatives for your company, three years, and yes, you can have measurable milestones.

 

“You can have measurable milestones even if you are having the right sort of experiments, but just do not expect something transformational to happen in three to six months from the start”

Because that will always have its own expectations and that will lead to failure at different levels. And lastly, AI knowledge is not optional. The executives and board members to understand how critical funding for a company is they also need to understand how AI works, at least the basics. We don’t want them to start coding or writing the ML models, but we at least want them to understand how AI works and how it could be applied for their business or to the customers or for customer care in whichever department and problem they solve. So understanding and having clarity is key.

“Knowledge is not optional. It’s not like I can outsource my entire tech stack or AI strategy to pursue that magic. Even that doesn’t work.”

You have to have knowledge about how things work, how things are evolving. What does that look like? And then educate yourself. So that’s what the board members of the CXOs constantly need. Not only to be aware but also to the trust and respect of the folks that they are working with. So that everybody who’s being invited to work on AI, or take the company to the next level in terms of they have the right level of prospects, knowing that the board members know that the CXOs understand what in the world of these books were talking about. And that’s a lot of respect and trust in the company.

My next section here is about digital versus traditional. And as I shared the Digital First of the most recent companies where they have an advantage when they start in some more modern tech stack. They already have a lot of data in place. That is instrumentation. But when you look at the traditional companies, they have to do that heavy lifting, they’d have to migrate to the cloud. They have to teach themselves how to build that data stack. They have to start many of these things from scratch, maybe even to a complete platform. However, when it comes to these types of companies, there are a few things that are common.

 

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Number one, the culture they have to always make sure the culture is such that you’re accepting that there is a culture of testing and learning instead of just doing whatever the report says.

If culture is a very critical aspect of making sure technology or even AI advancement happens. The next thing is more design. How do you design your organization, whether the organization has centralized or decentralized? How will the data organizations work with a platform? How will they actually function as a fashion where there is more collaboration and innovation? A lot of that becomes critical as you think about making investments in AI. These types of companies want to have to define what that looks like.

What we mean by TrueNorth here is having an idea about what it means that we become successful one year or three years out? What is that? Not for us? Usually, it’s a three to the five-year big picture.

 

And then you have goals broken down to achieve that. But more types of companies aim to define that. The next mantra is to fail and fail fast. What that means is it’s okay to fail, have that mindset, but do it fast so that you can iterate faster so that you can learn and apply that learning to the next cycle of experiments. So in this section, I’ll talk about shows and cartoons, which is like buy the cord and help you and that is a data-driven approach that you want to take versus what is the highest rate person. This is what the hotel for me to do and what my CEO wants to be done. That’s an approach at companies.

While it may seem comforting in the short term, the long term effects of these approaches backfire in everybody on the technical leads or the data scientists or the CTO LBD for so the more you have a data-centric or a data-driven approach, the better it is, for any kind of company your experimentation approaches something which is where you always start from the hypotheses of saying doing X will lead to why you’re trying to test and learn, test and learn whether it is a marketing department the product, engineering, the platform, or even the customer care, whatever department you’re in, you’re seeking to run experiments to test and learn how customers or your staff responds to changes.

When I worked at Microsoft, I ran these large-scale experiments as a part of the Bing search. Hundreds of other experiments, minor tweaks, minor changes in the product that are trying to drive a test for Bing search.

And at that point, we were trying Yahoo, these were all big competitors for Microsoft, which was the third search and declined to come up. And there I definitely had the chance to explore the platforms and go into the depth of these massive tests in a very short duration, and then rapidly iterate. And you know that all experimentation was a part of my journey when I went into it. And into it.

 

I saw Brad Smith, our CEO there was a champion of experimentation. He will talk about being able to experiment at scale, not only for us, but also for global

 

shared as examples of how to work with some of those experiments, and how they were making a meaningful impact not only for the products but also to the overall customer life cycle, which is how customers get into the product.

How do they use it? How do they, how they interact with customer care, that entire stack from their entire life journey? at GoDaddy? I saw among our CEOs come from Expedia.  He talked about the massive scale of experiments that the travel giant brand and again, coming to GoDaddy, not a lot and really push forward the culture of experimentation and rapid learning, again, not just tied to engineering or product, but across all business units across all parts of the business.

And what you’re seeing here is that more and more leaders are now starting in the scientific way of learning and evolving products versus just making a blanket investment in an area and hoping that something would come out.

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I want to observe this. Many of the companies now don’t want to get into projects which may be like wildfire, but not sure what the outcome is. So that’s where you want to question the demand and projects which are more than one year old. It takes more than one year. And then also, you will start to realize that once you run that cultural revolution, top-down, you will see that product managers, senior scientists, engineers, and VGF all come together and they all start there.

And this whole cultural revolution to show you see people across different disciplines across different business units come together, collaborate on the overall data infrastructure data, the ML models, and finally the outcome that is being delivered. When you think about AI and data committees, oftentimes known as the board will have the audit and finance committee, and these things became even stronger requirements. When a lot of stuff happened on Wall Street. Everybody on the board is particular about the flow of money.

What you see now starting as a trend is everybody’s starting to also get meticulous about the flow and the usage of data, which is good news because people understand that doing this means that they have to identify the goals that the board is not able to make great investments in terms technology in terms of resources, and architecture, the long term having at least for the CPU, and it’s super important to get a lot of respect and engagement from your senior engineering reverse engineers because they might be working on our stack which is outdated, but at least when you share what your architectural evolve and look like three years from now, they get the hope that this company is invested and serious about the technology transformation, and they feel motivated to come to work and work towards that transformation. And that inspires confidence. And longer-term. It also helps with the retention of all those folks.

And obviously, you can look back at your own experiences and you know that when the company chooses to invest in what is more than what is good for the customer. You felt good, the employees felt good, and the morale obviously, is so much better. The committee also plays a role in identifying key elements to the product life cycle. As an example when a customer’s onboard, what’s important is to know that the customers have activated the customer using a SaaS product as an example, it’s important to know how many times in a day or other customers engage with the product. If the customers are exiting or leaving the product or churning. It’s important to know what’s causing what. So those are examples of how elements are different journeys, different journey points captured, so having a clear idea about what’s critical and that also gets into how by gathering data that matters and not everything else that could be but may not be needed for the product and for the growth of the company.

You have to constantly message out the productivity and address the insecurity that employees may have about one of the FinTech companies that we were advising. We saw that the sales team was growing and increasing because of the AI investment. And what we did was we actually did an initiative for the brand and educational initiatives to share with them. Have them work towards themselves. We train them and then gamify the adoption of AI so that they can see some work with more customers and when they see that they move from being insecure to now being curious. And that’s something that we actively did in order to get people so that’s something that the board needs employees to be seeing AI as a compliment and not as competition. And lastly, I need to share the impact of all the stakeholders involved.

Remember, it’s not shareholders but stakeholders, stakeholders, your employees, your customers, your shareholders, everybody

 

And you want to make sure they all bear that witness that you’re delivering or that you plan to deliver so that they feel confident about both investment that you’re making and the resources that your mind can line up in that direction.

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Here’s an example of the goodness factor. This is the third monster. So here’s John Deere, a 200-year-old company, certified platform for the new data infrastructure together and launching the Island Sea and spray machine which precisely focuses on the beach, in the pond and gets rid of the fee. And this is such a huge productivity boost for the farmers and it goes a long way.

 

Now if you try to tell this to your stakeholders, including employees, it actually elevates their motivation and it makes them feel like they’re doing something not just for work, but for the greater good.

 

And I would say that’s a unique opportunity that CEOs have to tell everybody how there is a goodness factor in what we’re doing. For example, the IQR which is actually monitoring the medication and what it does for you and the editors if they haven’t had the time to review the reminders, they will want them to report their families or get near and dear one so that they are aware of medication. This is something that folks don’t have humans deployed, but AI can actually do it.

And that’s where the AI comes in to optimize not to compete with humans because there was no travel originally. That was there in the first place. And there’s that oneness factor. And this is a gap which I’m seeing here most recently, this week or this month. What you see as the number of jobs is almost the same as the number of people that are deployed. So clearly there are people who do not want to return to work after the pandemic.

So the question to ask is, Can AI play a role in upgrading or teaching the workforce skills so that they not only become motivated but also have an all-new way to come back to work to return to that end?

 

Again, but I hope AI comes out and solves this problem and we continue to add more goodness factors for our society for the miter startups and corporations. The board members and CEOs must gain the knowledge themselves before they go abroad. They should approach the AI and whatever they’re doing with dignity and long-term strategy, not something which, importantly, initiative, appoint the CTO, Chief Data Officer, Chief Information Officer. Whoever is responsible for this appoints the right to have a negative opinion.

 

They understand business and we are the folks who can connect the dots between the business strategy and the platform and how to get it because if they have that clarity and they will be able to attract, retain and work with those talented folks, engineers, data scientists and analysts who can make this mission happen within the company. Big Vision small bites. With this what I mean is you can definitely have a two three year vision, but figure out how you can break this down into monthly milestones and continue to measure these small bites then we need to measure the outcome. celebrate the successes and know that ultimately

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The board humans can bring in AI just how we complement each other and somebody is already in summary. Executive, somebody in cybersecurity, they complement each other to move the company forward in the right direction. Similarly

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Complementing each other and AI is a game operation. play the long game and we’ll see. To wrap it up, finally, three months was again, simplify, fail and fail fast. And there’s a good factor that the board and the CEO can absolutely define. And we’ll bring that to the company. I will stop sharing and I will pause here to take any questions that folks have. Thank you for my insightful talks. And seeing some great comments from people. I really like your three mantras as well as eco comments. You know, don’t always listen to the highest-paid board member that really so sometimes that is always a challenge. Seen and Heard. If you want to show a profit, as the bottom line for the company, you have to listen to the investors, the board members even if especially with ethics, you know because we are running an Ethics conference. 

I would love to ask your take on sometimes if you see that there is a conflict and some people want to choose profits over ethics and they are at the highest position.

 

 

How do you get the stakeholders on board to see what you are? Totally short-term profitability. So, the first one is yes, there are always going to be shareholders. Quarterly. They want to see the company’s trajectory and they actually want the C suite accountable to deliver on those results. And yes, on Wall Street.

 

You have to dilute shareholders how we did in the prior quarter, provide the forward-looking guidance. Even in the middle of it. Some companies had to do that. I had no idea. But fortunately, you know, most tech companies came out. When that happens. It’s interesting, it’s boring. But then the CEO and the chief data officer or the chief data officer, step back and say let’s step back and look at next year. This year’s second quarter, then next year, second-quarter or less we get to the results for that quarter.

 

If we have to deliver an impact, we have to start sowing the seeds. So AI is like sowing the seeds for the company and John Deere that I just shared. They had excellent equipment. They did not even have IoT sensors. They had nothing. It has been a multi-year journey.

 

So yes, there was an automation task and the stakeholders and the shareholders knew about it. But then there were conversations and there are similar, you know, dynamics of educating your shareholders to say these are the initiatives that we saw last year, which are starting to show results here. And initiatives that we are doing now with AI. They’ll start to show results either next year this time or two years or three years out.

However, the interim milestones that we will share with you. It will not directly relate itself to revenue or cost savings. But there is a path. You also have to combine that by saying if we do not do it, here’s the opportunity cost. We don’t do it. We might lose market share. If we don’t do it or possibly double. If you don’t, you will not have a new position for this product. So you got to explain that as well. That’s when folks get a good understanding.

 

The other question that you asked about being ethical about data, how sometimes that becomes important so yes, I think it’s a combination of large tech companies as well as governments who need to come together to create an ethical framework.

Some companies have already started most of the government. But when it comes to global companies, these lines sometimes get lost. And it’s something that the board and the C suite can do is you know how I talked about understanding what data is being captured in the product journey or likes or the customer life cycle and why we can ask questions, and that’s where the data and AI come in and a lot of value by asking, why do we need to store this data? Why do we need to mine this data? How exactly is this going to be used because we’ll have so much data another day? They’ll just be sitting in some location that you want to obviously spend a lot on storage costs.

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But at the same time, if you find some information which is not even safe or secure for us together, this is not the right place. Let’s take an example: a company was trying to gather Social Security information for quite honestly, it was not even a fit that had nothing to do with social. So yes, those companies get asked the questions.

 

The right questions to ask are, what are you going to do with the data or storage policy? How long do you wait in the cloud before you delete it?

 

So having some of those guardrails, but the high level I’m hoping some of the large tech giants in the US along with the government make a concrete framework that then sets the guardrails for how the corporate governance and AI companies can work and practice more than large tech corporations, be back companies and also in startups. Thank you, my gosh, I think you nailed that like two key points that I heard someone suggested one is to bring stakeholders on board. You have to show that baby steps on the pathway and even if it is two years from now, it is the same with ethics.

In the short run, it will look like the path to ethics is not helping the bottom line. But like you suggested, right? You know, our customers won’t trust us. Our customers don’t want to do business with us. That’s our business. And if we are able to translate that to communicate that to all the stakeholders and the board, then they will be on board with what we are trying to do. Right. And then the other thing that you mentioned also made a lot of sense that asking the right questions, instead of making assumptions, ask questions, have a sunset policy to the data that you’re collecting have a centralized place. I know governance and compliance are important.

A long time but in the meantime, we can each of us do our own part, as employees, as leaders, as leaders of the tech world to do the right thing as the right questions. And then hope that you know our journey to BI as well as adoption in data. And all of that good stuff is translated into real bottom line profits but in the most ethical way possible. Yes. technology but at the same time. And again, we’ll have the frameworks in place to have the guardrails in place. And you need these companies.

You know how you cannot file if you’re a public company, you can file an audit committee stance on it. So in the data and data in the AI world, you gotta have that kind of a similar process.

The warrant committee has evolved because certain instances are not so hopefully we won’t have to go through such instances all outside. We need that evolution to happen. And the more mature a company gets, the better it is for them to have that guardrail because it can also prevent their brand from being damaged because of any of this happening and that happens in the case of Fargo. Everything that we learn about what happened there. It’s a big issue as a brand and so those things start to play an important role for these large brands. But again, for smaller and more nimble companies, it’s very easy for them to adapt those early in your journey so that as they evolve, the framework of your practice has evolved.

There’s no right or wrong. Things we just like having a committee having a regulation having a framework can help bring that much perspective early on, and which can prevent you from just making decisions that might not be the best for your customers and stakeholders.

Yes. And stepping back from and all to take a look at the areas of the bigger picture of the business where we want to go. That helps a lot. Thank you, one very interesting conversation. Thank you for your time. Bye-bye!