Evolving the Enterprise

Strategic Decision-Making in the Era of Cloud Computing with Ayman Husain, Customer Engineering Leader at Google

• SnapLogic • Season 3 • Episode 11

Share episode

Join us on Evolving the Enterprise as we explore the cutting-edge of cloud strategies and generative AI with Ayman Husain, Customer Engineering Leader at Google.

In this episode, Ayman shares his unique journey from ski instructor to tech leader and dives deep into how businesses can leverage technology to drive innovation and success. Learn about the transformative impact of AI across various industries, understand the competitive dynamics among major cloud providers, and gain insights into effective data management for business continuity.

Don't miss this enlightening discussion on reshaping the future of work through strategic technology adoption.

Sponsor
The Evolving the Enterprise podcast is brought to you by SnapLogic, the world’s first generative integration platform for faster, easier digital transformation. Whether you are automating business processes, democratizing data, or delivering digital products and services, SnapLogic enables you to simplify your technology stack and take your enterprise further. Join the Generative Integration movement at snaplogic.com.

Additional Resources

Strategic Decision-Making in the Era of Cloud Computing

Welcome to Evolving the Enterprise, a podcast that brings together thought leaders from the worlds of data, automation, AI, integration, and more. Join SnapLogic’s Chief Marketing Officer, Dayle Hall, as we dive into the captivating stories of enterprise technology successes and failures through lively discussions with industry-leading executives and experts. Together, we'll explore real-world challenges and opportunities that companies face as they reshape the future of work.

Dayle Hall:
Hi, and welcome to our podcast, the next episode of Evolving the Enterprise. On this podcast, we like to dive deep with some of the industry-leading experts around strategies and technologies for driving business transformation. I'm your host, Dayle Hall, the CMO of SnapLogic.

We have a very special episode today. And as I told our guests, I've been waiting for this time to do this the right way. We were looking for that kind of leader with the gravitas that we could really pull out some industry insights. And obviously, if you've been following this podcast, you've heard me talk to our CTO, Jeremiah Stone, a few times in the last two seasons. So I thought we'd mix it up today. If you're tired of hearing my voice on this podcast, guess what, you don't have to worry about that now. Today, you're going to hear Jeremiah. Jeremiah is going to drive the discussion today. So I'm going to introduce the guests, I'm going to hand over to Jeremiah. So good, you get to listen to the podcasts and two very smart people, and the CMO gets to sit in the background. I like that.

Okay. So today, we're excited to have a special guest. It's Ayman Husain. He’s currently, new role, customer engineering leader at Google. Yeah, massive, massive role. Ayman is at the forefront of helping businesses harness the power of the cloud and AI to drive innovation and success. So in this episode, we're going to dig in a little bit more around things like generative AI and how it's shaping broader cloud strategies and the journey of cloud transformation, and also, most importantly, how customer success is evolving in the enterprise mindset. So Ayman, I'm so delighted to have you on the show. Welcome.

Ayman Husain:
It's great to be here. Thank you for having me.

Dayle Hall:
Absolutely. And of course, my esteemed colleague, who is always around for a good time, Jeremiah, thank you so much for being part of this.

Jeremiah Stone: 
Excited to be here, Dayle, and super excited to speak with Ayman, given the amazing day-to-day he must be living leading the customer engineering work at Google Cloud. So we should have a lot of fun today, and I'm certainly looking forward to it.

Dayle Hall: 
That's awesome. Okay, so Jeremiah, before we go into the list of questions, what I usually like to do is give us a couple of minutes, your career, how you ended up where you are, the twists and turns of your expertise, your education, and how you got here, so we can set that context for the guests.

Ayman Husain: 
Appreciate the time. My journey here is not the destined part or path one would take. Early after my education, I did go to university and I did complete the program and a degree for bachelor of science in computer engineering. At that point, I was not sold as I would have been in that era, which was the late ‘90s. I was like, you know what, this may not be the place I want to be. So I did take some time off and I just enjoyed living. So one of the things I tell everybody aspiring or otherwise is if you have the time and opportunity to take time off from your mainstream ideas, go do that. I actually ended up being a ski instructor up in the hills of Colorado High Country. And I did that because I had the flexibility at the time. I had very limited obligations at that point. If I look back and I think about it, I would have not done it at any other time.

But what happened was when I was coming out of it, I was thinking about finding a career in technology. The first bubble of then, the Web 1.0 or HTML world, had just happened and the job market was very struggling with innovators and the start-ups who are struggling. When I looked around, I say, you know what, I want to be a consultant. I want to be a strategist. I want to be like one of those Accenture, KPMG, Boston Consulting Group types. How do I do that?

I had a career mentor or coach at that point. And he alluded that in that time, when I was getting into the job market, if you didn't get brought on right away from school, like you didn't get recruited right out of college into those programs or those institutions, you would have a very tough time getting it because they really groom you, teach you from a very early onset. They don't like people that have been out in the job market for a while. This is challenging to discipline in that strategy management consulting mindset.

But the alternative was, hey, I can maybe join the Boston consultants or the McKinsey type, but I can try to find a way there. And I did a little bit of exploration on my own. I was like, how are decisions made in enterprises? At that time, a lot of enterprise decisions for technology was made in tea shops, and somebody was guiding them. One of the things I found out is that somebody's telling them to do something and they're going to go find vendors to go do it. So I started with the most common vendor contract works. I found myself a vendor that gave me a contract job. I started a contract job. I started doing that. I spent five years under a contract banner at a very large global centriole company. And I realized that the things I was doing were not really well designed. I sit there and think about, who came up with these ideas? These don't work. But here I am getting paid to do it. So I looked a little above it and said, who's doing that? The stuff I was doing at that time was implementing hardware. So I figured out who [inaudible] these hardware.

So I looked at the next venture of my company. At that point, I joined Insight Enterprises, which is essentially a provider of hardware solutions and services, desktop, laptop, switches, you name it. So I joined them, and I spent about five years there. And I realized that some people are, technology-wise, consulting with these vendors to figure out what switch to buy, what hardware to buy, what network to buy. And so I got a little bit of the taste of the fact that, hey, if you work for a hardware vendor, you still get to make some of the decisions and influence a customer's outcome. But it still wasn't where the decisions were being made.

So I aimed to the next level up at that point, and I found myself a strategy consulting company called Slalom, which is a small boutique organization. And I realized that they were influencing customer decision-makers on how to do some kind of transformation, either be SAP or ERP-type conversation. I started from the bottom contracting, then went to a hardware vendor, then I ended up at a strategy consulting vendor that was selling ideas and optimizing customers to do more with whatever they had. But I still realized that there was something missing there. One of the things I was missing is the dynastic companies that were influencing technology, the IBMs, the Microsofts, the Amazon, the Googles, and many more, many larger companies, the GEs, you name it, in technology, Siemens, they were all dominating a decision-making capability.

So as I swam upstream, so to speak, I found myself at Microsoft. I just joined Google about- today, it's May 2024. I just joined Google a month ago. But I was at Microsoft for six years prior to that. And I realized these companies are influencing how companies are making decisions, either be a start-up or a large dynastic organization that is trying to pivot and transform. And now with generative AI, there's a lot of change happening.

So my journey to this place, where I am today, is finding the common denominator of who makes decisions and how they're made and the influencers. I'm a part influencer. I was a contractor. I went to a vendor that provided hardware, then I went to strategy consulting. Now I'm actually working with the logo that has the solutions. And this is why I'm swimming upstream, is to get to that point where decisions are made. Companies like SnapLogic and all these are getting influencers to influence a customer to do some decisions. That's where I am today. And that's why I paint the picture of my journey, is to get where I am today requires you to understand the flowchart of how decisions are made, unless you're a software developer, and then you can take the quick path of engineering right into the heart of the things.

Dayle Hall: 
Yeah, but to go from ski instructor to where you are now at Google, that's a hell of a ride. Thanks so much for being part of the show today. Okay, Jeremiah, take it away.

Jeremiah Stone: 
Well, thank you for that background, Ayman. It definitely is interesting to focus on exactly the fundamental question, is how do decisions get made and how do we enable the most value out of the decisions that do get made? I'd love to understand a little bit more about that perspective at Google Cloud right now. What is the environment like? How are you engaging with customers? How do you find the current decision-making mixes? What's hot, what's not?

Ayman Husain:
Absolutely. When you take the wrapper off a company like Google, take everything out, even all companies like Microsoft, it is a product company. There's a bunch of products that do a lot of things, and we need to find our customers to use these products to do more of what they want to do. The decision-making factor that I've noticed in both Google and Microsoft that influences how we make products, design products, [inaudible] the product parameters and features, is based on what our customers want to use it for. And the customers, our customers use it for two different purposes. One, they want to use technology to save money, to essentially reduce costs. So how can technology help them do that? The other thing they want to use technology for is to make money. How can they use technology to make money?

Currently, I live in the state of Texas in the US and the city of Houston. If you follow the energy world news, it is a big capital for energy. And energy today is a very expensive proposition. Either you'd be in fossil fuels or new emerging green technologies. It takes money and time, and most of it is technology oriented. So these investments that people will make with companies like Google, or IBM, or the Microsofts, requires us to bring products that will make them save money or help them make money using the technology they have. So if you're a start-up in energy, you might use Google Cloud to get your start-up off the ground, maybe create the next product version of it.

If you are trying to optimize efficiencies in your traditional IT shop or traditional technologies, you can modernize using a new product from Google to do more with less. And so that's how decisions are being made, can I make money with the product you have or can I save money. And once you've taken that common denominator to the bare basics, you can position almost any product once you focus on that outcome because everybody has that same mindset of an outcome. It could be an IT manager that wants to control their budget. It could be a chief marketing officer who wants to control their marketing budget. It could be a CEO that wants to keep their stock price at a certain range by showing profitability. So all that decision comes down to those two factors, make money or save money.

Jeremiah Stone:
That makes a ton of sense. And I suppose that's eternal as it comes to thinking about technology within business. In terms of the mix that you're dealing with right now, do you find that certain industries are focused on applying technology for analytic outcomes at the moment? Or are you seeing it's kind of-

Ayman Husain: 
Absolutely. All industries, if you think of healthcare, the primary purpose of healthcare is to cure or prevent deaths. You can do one or both. You can do both, but you have to choose. So if you're in healthcare and you're not a technology shop, you're not creating technology, you want to use technology to make sure that you're preventing deaths or improving health. And that is a common decision-making of analytics data points. You’re taking all the data you got from all the systems with certain capability in healthcare and you create an analytical dashboard, and you can then add generative AI, machine learning, AI on top of it to get you results faster and quicker.

If you're a manufacturing and supply chain, the same rules apply. Think of the fact, not recently,  a cargo ship went sideways in the Suez Canal and blocked the waterway where ships were piled up behind it. Now if you were in an analytical mindset and you had the data points, you could have done a lot of scenario planning. So what if my primary supply line gets in trouble, what do I do? Do I pivot? The pandemic, those who were coming out of it had the same problem. The pandemic is the first time in human history, at least recorded history, where we did not account for the fact that you could have two things going down, the economy going down and supply chain going down, manufacturing jobs because factories were halted. No one planned for that, right? So when you think of that, if you did some what-if scenarios using analytics and data points, insights become much clearer, you make better decisions.

And so a lot of people are adopting the fact that I need to be better, quicker, faster making these decisions, and the data is what I have as an asset. And I need to make decisions on those data faster, quicker, easier. And what can I use? I can use AI, I can use ML, I can use generative AI to talk to these data points and get my results faster. And so what it's doing is converting that business analyst into a machine that can now analyze more datasets faster and quicker.

Jeremiah Stone: 
And over the last six or seven years, we've been focusing on the specific area, and there's been some big macroeconomic changes in terms of interest rate policy, etc. Have you seen that impact, which industries are surging, forward investing? What changes have you seen from a customer focus perspective given the big changes we've seen in the last-

Ayman Husain:
Absolutely. So there's three layers that I've noticed. There's that layer that is determined by human resources. There's a shortage of qualified skilled employees that employers can get in this competitive landscape. So the investments that people are making aggressively in this space are those that have to do more with less. They cannot hire fast enough. So if you think of robotics, if you think of these factories that require folks to do stuff, he cannot have enough fingers and hands. So you put things together, so you're investing in robotics, and robotics invest means you're doing data, means you're doing AI. You're creating this mesh of employee augmentation because you need to do without the human capital. So there's a lot of investment coming from industries that require human investment but cannot be as the workforce is not there.

A lot of people think we can replace jobs, but there's a truth, at least in the western half of the world, or at least in the US, jobs are not being filled because people don't want those jobs. You can look at fast food restaurants. We didn't replace the order taker with the machine and a bot because we just wanted to screw somebody out of a job. There was not people taking that role. Therefore, we had to create cognitive services to learn how you speak and order food so we can create the order properly. So the person that is most important is cooking the food, not taking the order. So if you think of that, a lot of people are making those investments in those capabilities as well.

And the other part of it is getting research and development people that are throwing money, research and development, whatever it may be. I've realized that research and development is faster when you have faster GPUs like a big context, a conversation AI, today's GPU from NVIDIA or any other chip maker. How can we take this to make better, quicker research and development. We have the capability to augment that. So there's investments happening there as well.

And then the traditional augmentation of humanity in the way of can I make accounting better, can I use forms recognizer, things like that, to make less number of people required to invest as accountants or invoice takers or order takers, so that becomes capability. So there is investment happening in that space efficiently. And that investment is also adding a tertiary investment of education and learning. These are not things you just pick up as you do job training. You have to learn. It's a lot of people that need to learn AI or Python. These are democratizing data, democratizing AI for folks that otherwise have never done it. They're doing it now because they have to just to keep the job and themselves employed.

Jeremiah Stone: 
Of those three, what do you see dominating?

Ayman Husain:
Out of these three, what I see is dominating is essentially the ability, any role function capability that needs you to do more with less, so jobs that have repeatable tasks, that are being not eliminated from the people perspective but being augmented. If I have repeatable tasks that I can augment using generative AI or AI-like tools with capabilities, even robotics, to a certain extent, I'm going to use that. I'm going to invest in that.

Now it has multiple different dynamic dimensions to it, depending on what industry you have. Some of them actually add a safety feature. So if you're in a hazardous type of role, having that automation capability, robotics gives you better safety for your employees. For example, mining, if you're in a quarry breaking rocks, you don't want to be hurt by a rock tumbling down the quarry. But if you had a robotic bulldozer, but it's operated by somebody that's several hundred feet away or yards away, they can do that efficiently and safely. So that investment actually pays dividends in the way of the fact that you're not only doing more with less, but you're also improving a safety feature that may otherwise have been overlooked in the past.

Jeremiah Stone:
I think that makes a lot of sense. It definitely is in alignment with what we're seeing. I think particularly when it comes to generative AI, the ability to work with text at large volumes, we see sort of these high-context, multi-system, repetitive tasks really seemed to be the sweet spot for that kind of technology. And it's interesting because as you pointed out, that kind of spans lots of different regions, different roles, different industries, and is not confined to one singular domain. I think it's interesting that you pointed out everything from high human interaction roles such as order taking. And order taking is not just fast food, that's also any type of commerce, ranging from high-value assets to fast-moving consumer goods, also clerical roles, etc. It does make a lot of sense. Do you see that actually impacting how your customers are looking at their overall cloud strategies?

Ayman Husain:
Absolutely. One of the things in the cloud strategy, it's fight to the lowest, cheapest option. There's many cloud providers, but there's three that rise up to the surface, AWS, Microsoft Azure, and Google Cloud. When you think of that, what our decision-makers are doing on that side is they're trying to make a decision based on a price point that will bring the best features. I use the analogy of a car, for example. You may have a preferred car at home that you love to drive and all that. But if you travel and you rent a car, you're not asking for the same car, you’re just picking up a car to get you from point A to point B from the rental agency. Technology's going that way, too. It's like, hey, I’d love the Cadillac version of it, but I could do without that if I have my outcomes figured out and I understand what I'm going to do. So from that perspective, our customers are really questioning the feature functionality of what they need, the feature functionalities of all these cloud providers, and making those decisions very fast.

And on the commercial side of it, what we are doing as providers is making it easy for this customer decision to be made. There's not a vendor lock-in. There's not a mindset of it's all us or nothing. We’re making it into our [inaudible]. We're taking vendor partner products, making them all work and live together in an ecosystem that is interchangeable. Containers is a great example of that. Kubernetes is a great example that you can have many ways of transporting these solutions across multiple cloud vendors for resiliency as well as scalability. And it's helping make those decisions based on what do I need to do, an XYZ function, within the organization for the best for that organization.

Jeremiah Stone: 
Do you see an actual capital shift on the specific areas that are being invested in? One of the things that we've read, certainly, in popular trade press, etc., that we see amounts of money that I find hard to visualize, billions of dollars being invested in generative AI, and you say you do see a shift in the overall strategies. Do you see that coming into a specific capital allocation discussion where a portion of the budget is focused on, I don't know, disaster recovery, business continuity, a portion of the conversation, and the investment strategies are based on just completing a cloud migration and a portion are actually allocated and ring-fenced for generative AI? Is that the way it's playing out?

Ayman Husain:
It is playing out like that. It depends on the part of the world you're in. But at least in the US, what we've noticed, there's been a level of maturity for customers that have had cloud of some format, the things you mentioned, backup and recovery, maybe just a cloud data center. What we're seeing is an amazing shift of that same spin towards optimizing and making a shift towards SaaS-like mindset. They want to be on a subscription model. Subscription model economics is a very different model economics if you're talking to a CFO. The people that are not really trained to understanding why per user per month per year-type conversation looks like, the annual data type conversation. Similarly, companies like Google are struggling with how do we keep our revenue forecasts. [inaudible] we are going into the subscription model as well. So there's been a significant shift for that.

And the shift has been twofold. One shift is happening because a lot of these companies, a lot of these customers, large and small, their uniqueness for  generative AI is their own data set. Right now, if you use a generative AI social, for example, from Google Gemini or from OpenAI ChatGPT, you're using a model that's trained on data that is not there. So while it's fun to write letters and emails and doing some essays or presentations, it is not tuned to your own data set. So one of the ways our customers are realizing, if I want to do what I do well and I have a plethora of data already stored, that model is not going to be useful on this. I tie those together at the same time so that digital modernization of the data points is happening rapidly and faster.

So what's happening is these data warehouse companies, Big Query, for example, from Google and others are now getting a lot of attention. How can I save a lot of data, make the data queryable, faster, quicker, efficiently so that we can do those things that data can now get transported into ChatGPT-like feature or functionality, those prompts, so that we can now query our own data and figure that out. So that shift is already also happening based on the kind of industry they are in. If you're in fintech, financials, they've been doing it for a while. Quants have been doing financial market analysis for a while, but it gets their own data. Imagine if we had data on every industry available, those quants would be significantly superior. They could use weather data, they could use supply data and war data, population change data. All of that would help them now become much more efficient in those decision-making that otherwise would have been missed.

Jeremiah Stone:  
Interesting. Where do you see companies starting in terms of their Gen AI journey you mentioned?

Ayman Husain: 
There's two parts that Gen AI journey is happening. One is the personal human productivity, essentially, can I do my job using the traditional solutions of spreadsheets, emails, whatnot, chatting? Can I do those things better? Online meetings, for example, you and I are talking on Zoom today, can I transcribe? Can I have meeting notes? Those are the kinds of things that the investment is happening for us to be better productive agents of our roles and our shepherds of our companies.

The other investment that is happening is realizing the traditional conversation and the nomenclature of IT. I have a lot of data. They don't look the same. They don't feel the same. I need to put it in a place that looks the same, feels the same so I can query it and run large language models. I can ask, what do I have to do? Well, the first thing you have to do is move to the cloud because you don't want to invest in those traditional data warehouses in-house. And so if you're moving to the cloud, you have to understand that your data has to look and feel the same to a certain extent, so it’s a normalized data.

So all the investments our customers are trying to do now is essentially that journey step up, I need to get my data in the cloud, I need to normalize my data. Consulting firms that specialize and such are getting a lot of work now visiting different datasets, creating the keys and indexes, and matching up the data so that they look coherent enough. So you can now have a good collection of data points that you can query against using generative AI tools for that purpose.

So the journey is now saying, if you were a customer of mine and you say, hey, I want to do generative AI, the first thing I'm going to ask is, where does your data live and what is the purpose? If you answered me, my data is in 20 different systems, 20 different servers, my first step for your journey would be, all right, let's get them into one place. And then once we get to that one place, now we have a better conversation with Gen AI because your Gen AI experience will not be very efficient if you're not training it against your own data because it's not going to give you those insights you need to make your business decisions.

Jeremiah Stone: 
Are you finding that to be a difficult conversation in the current financial environment?

Ayman Husain: 
It is a difficult conversation but not an impossible conversation. The difficulty of the conversation is in modern history, at least in the last 10 years, a lot of these companies have invested in different data warehouse technologies. Some of them are Gen AI ready, some of them are not. The ones that invested in those ones that are not have to take the first step of modernizing the data warehouse, consolidating or merging it into something that looks coherently easy for that purpose. The ones that started later have modern data warehouse technologies that are already Gen AI ready, they just need to bolt on and get up and running.

So the decision challenges are happening in that conversation. Like if you are a legacy company, been around for decades, you had data warehouses that are large and big and robust but not Gen AI ready, oh, well, the first thing you have to do is duplicate the data into a Gen AI solution, and that's cost that you are in planning for because you're paying twice for the same thing. But if you are not a dynastic legacy company and you start in the last 5 to 10 years ago, you probably have something that's pretty modern and can manage the modernization requirements that are very minor in the way of getting to that next level.

Jeremiah Stone: 
Do you find that enterprises are willing or ready to do that deep investment, absent clear ROI and use cases? What is the level of maturity on the-

Ayman Husain:
The maturity is low to mid, depending on the industry. The companies that are born in the cloud, started in a cloud, when I say that, it's not like yesterday. I'm talking about being born in the cloud and mobile-first worlds have been around for 10 years at least. There are a fair amount of companies that have been around. Facebook, Meta, these companies are born in the cloud. They have enough data. Their investments have already happened. That's why they're leading the charge in these conversations quite a bit. So it's very easy to have that ROI conversation. They have a level of understanding of what investments they've made that does that.

The ones that are not in that footprint, the ROI conversation is absolutely a showstopper. And you have to create a way of making this ROI. And the way you make this ROI conversation is the cost of not doing something. If you have a cost of not doing something understood, that decision-making is much easier. For example, backups in the cloud. If you didn't do backups on the cloud, what is the cost of not doing that? You could have an outage. You could have a flood. You could have a hurricane take you down, and those backup tapes that you have in your desk or basement might get all flooded. So what is the cost of not doing cloud backup? Well, the cost of not doing cloud backup is you may not be in business for too much longer if you had a significant outage. So focusing on the cost of not doing something, that's helping hand those initiative conversations.

Then in publicly traded companies, the boards, the analysts, the market, they're forcing CEOs and CIOs and CTOs to have a position about generative AI, have a position about cloud data warehouses, have a position about the redundancy [inaudible]. Cybersecurity is a big player in that space as well because we hear every day companies are getting hacked, companies getting data loss, and things are leaking in the way of data. They don't want that bad press. And so those companies are very eager to understand ROI because their ROI is bad press. ROI could be a loss on customer sentiment and faith. Companies cannot survive that sometimes.

When you look at what's happening in the aviation industry, a major aviation provider is struggling because of reputation. But they've been around for decades. They probably have a lot of data as well that could have supported some of the decision-making that they erroneously made without consideration. But they probably didn't have all those brought together in a centralized space. Now they're struggling because of that. But a new aviation company that just started in the last 10 years probably is significantly ahead because they have understood from their peers and learned from their peers as well.

Jeremiah Stone:
I like the way you frame that sometimes you have to take the sort of contra positive or negative point of view on the question and look at the cost of not doing something. Can you walk us through an example of a customer you've worked with that kind of started at that? We don't really know how to frame this problem up or quantify it, then go through that, how to manage the cost of not doing it through to invest in planning and implementation? Do you have any examples of where you worked with a client?

Ayman Husain:
Not concrete examples, these examples come from the variety of places of having my tenure in the field. If you think about remote work that was prevalent right after pandemic or during pandemic, the cost of not investing in a proper videoconferencing system with a transcribing capability, good video and analytics, slow internet, faster internet, the ability to work on mobile devices, was impact for productivity for a lot of people. So if you think about the fact that, do I need a position for remote work through Zoom or Google Meet or any of these products that are out there, you may say, hey, all my people are working, factory workers, and they just work in a factory, they don't need that.

The cost of not doing that is what if you have- let's say you were a processing facility running computers in a format like taking forms, taking orders, and that location got obstructed due to a storm or a thunderstorm. Now they have to remote work somewhere else. The cost of not doing that, you cannot do that if you did not invest it. So you have to have the mindset of what is the cost of not doing something. Now there’s the hype cycle. You want to get out of the hype cycle first. Generative AI is in the hype cycle right now where everybody thinks they need it. But ultimately, what is it that you try to do in that space?

There are people that are asking us for that ROI, determining what is the cost of not doing something. If you think about when large companies like Google sell productivity suite, for example, the Google Workspace has a lot of different technologies bundled into it, we can come up with a great ROI model on what is look like if you did not do this because their productivity will be hampered and access will be hampered, data security will be hampered, cybersecurity might be a footprint that might exploit you. There's many stories that will tell you the cost of not doing that has happened and that topology of why you should have. It can be easily understood.

Sometimes the reputation from other companies helps do that. Every time a healthcare facility gets hacked and patient data is out there stolen, hospitals just double down on money on spending to make sure that the data is locked in. And so the cost of not doing it is just that, a lot of people are suffering from the knowledge of their peers in the market and understanding what that cost was.

Jeremiah Stone: 
Do you find that that's a useful frame for our listeners to take as they look at Gen AI? I imagine I was somebody that's read a lot about this and has played around with Gemini and used it, as you pointed out, for summarizing some research or reading an email, that sort of thing. But now I want to put it together for my work. How would you guide me to frame that up?

Ayman Husain:
Absolutely. It depends on the industry. I work in an industry that the workforce is retiring and leaving. The boomer generation is not being backfilled by new recruits into the industry because of the way the industry is. The generation of people leaving have so much knowledge in their heads or in manuals or documentation that has to be available to the next person that walks in the door. And so one of the things you have to do is, do you have an onboarding, planning, training program? Do you have a training program? Do you have an apprentices program? Do you have a way to get these new people ready for the demanding role they're going to have tomorrow? And if your answer is no, we don't, or we struggle with it, or we can’t recruit or retain people, move off the process then we can retain them, well, generative AI is an investment you want to have.

And you're saying, why would I need generative AI for that? For example, all that data, all those manuals, all those reports, all the project plans, and everything that has been digitized, you and I create an interface, generative AI is an interface to talk to your data. So if you're a new employee, let's say you just joined, and I said, I don't know about your job, I have no idea what you need to do, but here's generative AI, ask it and it'll tell you. So the first thing you can do is, what is my job? I just started today. This is the group I work for, this is the department I work for, these are the people I report to. If you have that interactive conversation, you are now getting empowered with knowledge that is already in the company, and now you're fast-tracking your training and onboarding system and capability.

That mindset exists everywhere. Almost every industry is struggling with this employee experience. And then you have the millennial or the Gen Xers that are coming into the workplace. They prefer to not talk to people. They don't like sitting in classrooms. They’d rather use chat and web browsers or whatever it is to communicate and exchange data. So when you think about it, I'm creating the ability for them to learn faster. If I just say, here's all the data, just ask it and you can receive all the training you need, and then you can start iteratively going through that. There was an article that came out not too long ago that says that the newest generation of folks prefer to chat versus getting on a phone call. So if you're a call center and you have all these people sitting on a phone waiting to ring the phone to answer, there's a good chance a person will hang up before you answer because they don't like to talk to people. So if you now have a ChatGPT like generative AI chatbot that is informative, intelligent, capable of having a conversation with a person on the other side that does not like to talk to human voice, well, you just, first of all, save money, better interaction, better customer sentiment, and you're getting more done just by that fact. So there are ways to have that conversation.

Almost any industry is showing not only the investment requirement but the ROI that comes with it and the places that they need to start thinking about. Safety is a big one. A lot of the organizations bypass safety because it's cumbersome. It's a lot of reports, a lot of notetaking, maybe a lot of sensitive data, all of that needs to be researched and developed and looked at. If you compromise any of that, you could impact people's lives. And so if you think about that, that's another way to get on top of it, looking at those anomalies and what it looks like and what it doesn't look like.

Jeremiah Stone: 
So if I'm just starting my journey today and I'm thinking about my investment, how this fits in with my enterprise cloud strategy, one place I could look at is just the business continuity of my business and how I think about the transferal of knowledge and knowledge management and information management within my business today. And I think, certainly, you mentioned, you're based out of Houston, I think the natural resource industries certainly are facing what I've heard people refer to as gray 2k at the exit of the generations that are holding the information of how to manage a drillship and the goal for how to think about doing turnarounds and turnovers.

That definitely is something that is not incredibly well documented, that is passed on from shift to shift and person to person, and that kind of generational transference of information that could just as well apply to the management and running of the IT estate itself because many folks in larger, longer-lived organizations may have been implementing these systems starting in the, I don’t know, early ‘90s and could be approaching retirement age as well just by virtue of [length of system]. So that's one great cost of doing nothing is when these folks retire and walk out the door, how to run the business, how to operate the business walks with them. This could be a way to help bring their knowledge forward. So that was one good starting place I heard.

The other starting place I heard that you talked about a little bit earlier was things that are highly administrative in nature. And here, you just link that into safety also in the major asset industries having to deal with things like risk assessments and maybe work permit management, that sort of thing that you're seeing a high value there as well. Definitely, both of those are very rich with the cost of not doing something in terms of losing how to run your business and certainly high-risk industries as well. I think that really makes it much more tangible. Thank you. That's definitely very [inaudible] from that perspective.

Again, I liked the framing. From a negative point of view, have you had any experiences with companies that were not willing or able to focus on adopting these types of technologies as they come forward and how that's turned out?

Ayman Husain:
In the generative AI space, not really, because the generative AI space is so cutting edge new in the context of the value it returns is challenging to quantify. But if you step a little bit behind the generative AI conversation, if you think about chatbots on online systems, like if you are trying to change your telephone subscriber and you go to the telephonesubscriber.com website, you have a chatbot that pops up. That ability to have that rudimentary conversation does a lot of things that if you did not do, you are missing out on the capability of having that experience. So we see a lot of people that say, oh, I don't need a chatbot on my website because I'm not that engineer-tooled, and revenues do take a hit.

If you think about in the US, the popular e-commerce retailers, one is Amazon, maybe the other ones are Walmart, if you couldn't get what you're looking for within three minutes of the search, you're going to abandon it and going to move on, right? So now if you have a bot behind it, as soon as you abandon that search, I could send you an alert saying, hey, I noticed you were looking at this, oh, there's been a price change on this, do you still want to look at that in your cart, or whatever? That mindset, you cannot just ignore it, especially in the retail business because you are leaving leads behind.

The good thing about who mastered this was a company like Netflix. To keep your viewership up, you would always recommend the next best thing you would want to watch that's aligned to what you just watched. And that's machine learning, that's AI, that's extrapolating your needs and understanding based on the profile that they're creating, structuring, or just taking from the demographic information available for it. But if you just choose to ignore that, you're going to lose business. You're going to lose the leads. You're just not going to grow fast. You're not going to have the ability to keep up with the demand generation or supply generation required. And that's a telltale sign of how it is.

For example, again, using the e-commerce retailers, if something's being searched a lot and it's not available or it's not in stock, having that bot in the back should send an alert to a purchaser saying you should order more of that because everybody keeps asking it and we don't have it, we're losing all that sales somewhere. And so understanding that mindset also helps grow that footprint. So it's not generative AI right now because it's still fairly new, but if you think of the ability for us to get ahead of it by some of the low-level AI solutions that capability has been already around, it definitely gets you there.

Like the evolution of telephony, IVR. If you think about 20 years of IVR, you dial, press 1 for this, press 2 for that, why did they do that? Because they wanted to not lose you from a basis of a customer support perspective, just because the phone wasn't answered, which was 30 years ago. That's what it was, a phone rang, rang, rang, I’m busy, leave a message, and you would just hang up and not do that. Then now you have options of taking your call to a different place depending on your options. Now we're adding bots behind it so that they don't even ask you where you need to go. They just ask you to talk to talk at any time. You're saving that lead from being lost if it's especially something that's revenue generating.

Jeremiah Stone: 
It's interesting, even coming over to the front of the office as well. Ayman, coming up to our hour here, I've got three rapid-fire questions to close us out. What would you say is the single biggest challenge that prevents operating companies today from really putting generative AI to work?

Ayman Husain:
Business case, use case identification. You don't know what you don't know, and you don't have it in-house, so you either hire somebody to tell you what you don't know, and then you worry if they're trying to push you down a path. And so that has been the most challenging conversation. I can go to a fintech or a stockbroker and tell you, being a quant helps you better trade because they get it, they understand it, because they're doing that.

But in other industries, it's very challenging to have that equitable conversation because we just don't know what your business cases are and what your use cases are. Some of them are so new, they literally have to be invented by strategy and management consultancy. So that's why they are getting the upper hand today. They are the ones that are talking about the fact that they know the domain, either it be healthcare industry, manufacturing, and they are learning AI on top of it. They're partnering with companies like Google to learn more of it so that they can now present those customers that would buy strategy management consulting the ideas of where they need to invest.

Jeremiah Stone: 
Got it. Now if we turn the tables, though, for majority of companies out there that are focused on implementing an enterprise cloud strategy, what is the biggest opportunity that is sitting in people's mitts that they're just not taking advantage of, for whatever reason, and you're sort of frustrated as you're pointing at it, there's the value, there's the value, what is it?

Ayman Husain: 
I use that cliche conversation about data as your new currency. You have an amazing amount of data, you need to democratize it because you can now access it to make decisions that you already have. You shouldn't lock it out and shouldn't let people operate in silos. And so that's the value right now. And I use an example that happened during the pandemic. They were building management companies that had empty floors because people weren't coming to work. But they still had custodian services that had to go clean all the desks and chairs and all that. It's a real case that happened.

There was one building operator that says, let me take all the badging data, which is a security system, and connect it to the custodian data so that custodians, as they walk into the building, can look at badging data to see which floors were occupied or which floors are completely empty, because if they were empty, they don't need to go clean that. So they became immediately efficient and saved money, also saved from people having to go 10 floors that didn't need to be there. That was democratizing data.

You have a security system that's badging. You have a custodian system that's probably scheduling. You connect those two, democratize that, give access to the people that need to in a controlled secured fashion, and now you've maximized productivity by being more observable. So when I look at people or organizations, say you already have the data, just digitize it, put it in a place that I can look at it. You are concerned about what the data is, well, put governance, keep some security, give access control. You can get just about any way of having that data accessible to anybody. And because of that, you can actually find great nuggets of development and success that you otherwise would have missed.

Jeremiah Stone: 
Love it. Last question, if you were our listener, and then our listeners are decision-makers, are leaders, are people driving, putting data to work in the enterprise, what is the very first thing you would do?

Ayman Husain: 
The first thing, if I'm talking to somebody of authority, I ask them, do you know everything about where your data is? Forget what the data is about. Do you know where your data is? That will give you a telltale sign about how ready they are versus they are not. And so one of the things, I, as a strategy consultant, because I come from the mindset, don't jump right in and say you need Gen AR, let's go do this. I will ask you, do you know what your data does? Do you know where your data is? Do you know where your data gaps are?

A good CDO, that's a new term that's been thrown around, chief data officer, or a CTO, CIO, if they're doing their job and they have enough experience in that, they will tell you what their gaps are. That's where you start. That's what I want somebody who's listening here, is if you're in these roles, know your data, pull up a pencil and paper and write down, these are the different data systems. Everything has a database today, built in or otherwise. You cannot operate in this world and technology without a database backend. But you have to know where that data is. And you [bonus] that data repository of all your pictures. If you didn't back it up in the cloud, well, guess what, you could lose your phone and lose all the pictures with it. So if you knew your data was in the phone and you did back it up into your cloud data storage, then you can say, I know where my data is. If I lose my phone, at least I have the pictures of the kids and family. That's the mindset you have to get down to, do you know where your data is? And then take it from there.

Jeremiah Stone: 
Fantastic. Well, you heard it here, folks. The biggest challenge everybody has, business case, use case for putting Gen AI to work. So if that's your problem, you're not alone. Biggest opportunity, the data you have already, put it to work. And where you start, know where that data is so that you can actually get it to work on those use cases. Well, thank you so much for your time today. It's been an excellent conversation. And we're grateful that you joined us.

Ayman Husain: 
It was great to be here. Thank you. Appreciate it.

Jeremiah Stone: 
Fantastic. Over to you, Dayle.

Dayle Hall: 
Thanks a lot, fellas. I have to say I was sitting here on the other side of the meeting room with my popcorn just listening to you two go on. It was excellent. Ayman, I have one last question on that last point that you made, which is whether it's a CDO, or CIO, whatever, if they had to sit down and write actually just on a sheet of paper, where's all my data, how many of the C-level people in this type of role could actually define where their data is today? Is it something that they are on top of, or as it's growing exponentially, is this always just going to be a problem for them?

Ayman Husain:
It’s not always going to be a problem, but it is a problem today. I think about 20% or 30% of the people know what that is. The way we, technology companies, are solving it is we are automating data discovery. So sooner than later, in the near future, we'll have the ability to tell you where your data is. Just like banks do that with personal records, your FICO scores, and all that, you're getting hacked there or not, we will send you alerts that way. In the near future, we'll tell you where the data is.

But it is abysmally poor because, first of all, there's churn in the industry almost everywhere. So these new CEO, CDOs are showing up in organizations that do not have a good repository or documentation of it, so they really don't know where the data is. Some are emerging different business units together, business ventures, so they have different sets of data. They don't know what the data should look like or what it should represent. And so that is a challenge there. And it is being solved with technology, just using data discovery capabilities where we'll tell you where your data is, and then maybe use that to grow our business and market share.

Dayle Hall: 
It's great. Okay, so is a problem, will soon not be a problem, we hope. Fellas, I really appreciate it. Jeremiah, Ayman, thank you so much for driving this conversation today.

Ayman Husain:
Absolutely.

Dayle Hall:
And for everyone else out there, thank you for tuning in to this episode of Evolving the Enterprise, and we'll see you on the next one.

As we wrap up this episode of Evolving the Enterprise, we want to extend our gratitude for joining us on this exploration of enterprise technology. Keep the conversation going by subscribing, rating, and sharing our podcast. Together, we'll shape the future of work. Until our next episode, stay innovative and stay tuned.