How AI is Transforming Industries and Reshaping the Workplace

Welcome to the Hitchhiker’s Guide to it, brought to you by Device42. On this show, we explore the ins and outs of modern IT management and the infinite expanse of its universe. So buckle up and get ready to explore the ever changing landscape of modern IT management.

Michelle Dawn Mooney

Hello and welcome to The Hitchhiker’s Guide to It, where we explore the latest innovations and best practices in the world of technology. I’m your host, Michelle Dawn Mooney, and today we are diving into the exciting world of artificial intelligence. We will be discussing how AI is transforming industries and reshaping the workplace, with a special focus on its practical applications in it. I have a great guest for this conversation today. Adnan Massoud is chief AI architect at UST. Adnan, thank you so much for being with me today.

Adnan Masood

Thank you for inviting me, Michel.

Michelle Dawn Mooney

Looking forward to the conversation. Let’s start off here before we kind of dive deep into the Q&A, can you share your journey into AI and how your experiences have shaped your approach to advancing AI technologies?

Adnan Masood

Thanks for the opportunity and it’s great to be in AI at this time. Ai is definitely reshaping industries at an unprecedented pace. It’s turning data into actionable insights. It’s automating the mundane, driving the strategic decisions. I think the future belongs to those who are integrating AI responsibly. And if you can take one thing from this whole discussion, is that to be a leader in that space and not a laggard. Um, my name is Adnan Masud. I run the AI and machine learning group at UST. I’m a machine learning PhD, and I’m also a visiting scholar at Stanford University. I worked under Doctor Chris Manning, one of the leading researchers in NLP space. My job is at at an intersection of academia and industry. So I bring in the cutting edge research from Stanford AI lab and MIT Csail, and try to bring it for our customers and provide them the best in class algorithms and techniques and tools. And we’ve been working with these projects for a long time. Like the things which come out of labs and then they make it to industry. I’m also a Microsoft Regional director. It’s an honorary title given to people who work very closely with Microsoft technologies.

Adnan Masood

So I guess I have a little Microsoft bias, but I also work with other hyperscalers, including AWS and GCP. So that’s a quick introduction for things I have done and my work with academia and industry. Like I said, my job is at the intersection of academia and industry to bring in the cutting edge research and then implement it and apply it in retail finance and healthcare. And these are the three major sectors where UST operates. For our for our top fortune 100 clients, we work very closely with board members, with Executive suite with developers, with engineers and data scientists, and try to bring in that thought leadership. And my because of my work in financial services as well as working with academia, I can bring in that thought leadership, both from implementation and applied side to build large, scalable systems, but also can provide insights into what and how these solutions can be applied over in real world in a safe, secure, ethical and responsible manner. So that’s a quick introduction to what I’ve been doing lately, and thank you for the opportunity.

Michelle Dawn Mooney

So how are AI trends currently influencing workplace dynamics, including those shifts in roles, responsibilities and then workflows?

Adnan Masood

So it’s a packed question. The shifts which are happening in AI integration for the workplace. We are seeing that. And you are noticing that probably as well, whether it’s marketing, whether it’s HR or even like content writing. Any of those spaces within an enterprise are getting impacted by AI, and it’s shifting in the workplace dynamics very quickly, and it’s building an environment of automation and augmented intelligence. So you will hear me say this term augmented intelligence a lot, because we are seeing a significant shift as AI is automating these repetitive tasks. So we have these repetitive tasks where we are, um, whether it’s uh, employees are engaging in things which are low impact work. So something which can easily be automated, whether it’s like filling out a time sheet or doing going through a workflow in an HR pipeline or writing just general text, doing triage, for example, on, um, it that’s the the things which do not require creativity and critical thinking, right? That’s the things which we are. I’m seeing more and more being migrated over to augmented intelligence. You have probably seen already the AI copilot. You Copilots. You have seen the generative models being used in the tools, which can be applied, and we have built several tools like this. One of them is UST Chord Crafter, which enhances productivity by optimizing the workflows. So application modernization workforce workplace evolution to build this cognitive ecosystem where you can see the AI driven decision making enhances the operational transparency and agility. So it’s impacting the entire spectrum of workplace. But if I have to sum this up, I think the most profound shift shift in the workplace impact is the development of the new roles and the new jobs and new areas, which is centered around AI governance and oversight, because that’s a really, really important area.

Michelle Dawn Mooney

And now you’ve seen so much, you know, over the years and especially I feel like the last few years, it’s just everything is skyrocketing exponentially and very quickly, too. So in your opinion, what are the most important practical applications of AI in IT today, and how can businesses best leverage them?

Adnan Masood

I think this is a question that I get asked the most. What are the key practical applications? What are the key use cases? So if you have to sum it up in, let’s say, three major categories predictive analytics, cybersecurity, IT, automation, I think these are the three major buckets that will make AI driven cybersecurity. For example, we have a company a USD has a sister company called Cybersecurity Cyber Proof, where we built AI into our products for detecting anomalies, preventing breaches in real time. How you can safeguard critical infrastructure when it comes to predictive analytics, that is, to allow IT departments to foresee and stop the system failures. That’s something our clients are very interested in. Those are really critical to involve AI to ensure continuous operation. So if you want your system to have predictive capabilities to understand when their things are going to go down, or what kind of failures you should anticipate. Ai works really, really well, right? And the third part of the operational excellence is that like businesses focusing on. And you’ll see more and more use of that in operational excellence. So intelligence not just a cool technology but rather integrating in the infrastructure for process optimization. So leveraging like I said earlier, augmented intelligence or AI powered automation to tools like chatbots. Right. That’s one of the biggest use case to be able to communicate explain things. How can you do automated code reviews? Um, you can see nowadays, uh, like a lot of code which is in GitHub or other platforms are is AI generated. So how can we use AI to generate code which can then be used as part of the products? How can we do automated code reviews? How we can do network management which will enable cost savings. So I is a horizontal theme across multiple different verticals where we can apply to predictive analytics, cybersecurity, AI, IT, automation, application modernization, building these, these platforms on top of it. It really, really increases efficiency. Cost savings, improves the uptime, and really improves the user experience.

Michelle Dawn Mooney

Absolutely. And I can attest just in the last few weeks. I mean, I think we’ve all used the chat bots, right? We’ve all been on those shopping sites or different sites where somebody or something pops on and asks you. And I was surprised. There was one time in particular that I literally thought I was talking to a human being, only to realize that it was a chat bot. It was so sophisticated with the verbiage and the way the language was presented. It’s amazing. So you are involved with Stanford AI lab. How does cutting edge AI research that you see there influence real world business strategies?

Adnan Masood

Usd has been involved with Stanford AI lab since 2016, in a variety of different scenarios. So we have taken our clients over to Stanford and did coordination. When we will be that, we will be the execution partner and Stanford will provide the thought leadership, the research, the implementation. Stanford being a research university, they don’t necessarily implement the solutions directly with the industry, but they enable and accelerate the research and development part of it. So what I have been doing, working with the Stanford is to bring that bridge between academia and industry. It’s really important to have that execution mindset and having that strategic innovation. So it’s not just completely research oriented and neither it’s completely very applied. So it’s that balance we have to form. So I’m that that doing that balancing act with the Stanford and the clients we work with, the companies, the major fortune 100 companies, they are all looking for the boundaries of what AI can achieve, which is just adds to their intellectual property. So whether it’s in natural language processing or whether it’s reinforcement learning, um, scalable innovation idea, for instance, you have a, um, the idea of how you can take large language models and apply that in a specific areas in claims processing within healthcare. Or how can you do better fraud detection within financial services using reinforcement learning? The Stanford has the world class professors.

Adnan Masood

We work very closely with the Stanford AI lab, which is called Sail. And those researchers and professors work at really at the the foundational the pioneering step of where AI is. So majority of the things you see in media nowadays um actually comes research comes out of Stanford and MIT. Um, and one of the key things I’ll just talk about briefly is the ethical AI, which we work very closely in the Stanford, because Stanford provides this this framework for holistic evaluation of large language model. So you don’t really have to just say like, oh, our LLM or our model or IAM model is great. How is it great. Like what’s specific benchmarks and quantitative data is about which makes it great. So that and ethical AI and the transparency of the AI models to make sure that they don’t have bias. How do you measure those things? So all the kind of research and credentialing comes from Stanford AI lab, which UST uses, and AI enable to be at the forefront of the AI research. So these are like one of our core principles for AI. First, strategies that we make, not only make the companies we work with our clients technologically advance, but we also align them with the AI ethical standards. So there’s an institutional legitimacy around that.

Michelle Dawn Mooney

We’ve heard a few different businesses kind of thrown in the mix here. You mentioned, uh, health care finance. I talked about retail. It really is across the board where AI is being used right now. But what industries are you seeing the most significant AI driven transformations, and can you share some examples of how AI is reshaping them? Absolutely.

Adnan Masood

We work very closely with healthcare, finance and retail as a three major verticals. You can probably add manufacturing to it as well, but healthcare, finance and retail are what comprises of majority of our customers, the largest of the world. Customers who are part of our portfolio. And you can see that they’re all going, and I’m going to be cliche here, is that a seismic, AI driven transformation? You will hear AI from all the board members. You will hear AI from all the different conversations you have in there now, especially in AI. So for example, in healthcare we work with the largest payers. Payers is the insurance providers. And it’s how can you create personalized plans for those, uh, the people who you are working with your your customers. How can you create information? How can you look into large slots of information, PDF documents, and all kinds of different plan information to find out the relevant information for your patient? So this is a really great use case when a customer service person gets called. He has to. He or she has to look through a bunch of different documents and be able to figure out what does that plan cover or what are the different information. Uh, what is a copay for that? A specific plan for this specific person. And that can take a lot of time. And the accuracy is really, really important. Now with grounded AI models, what we can do is we can look into that information and then give that citation means a reference to the customer service person.

Adnan Masood

So he doesn’t have to actually go and do all that lookup. It’s already being done for them. So it’s kind of like really expedites the process flow, gives the efficiency, gives the confidence that they can cite directly from those tons of documents and give you the right answers as needed. Um, personalization of the treatment plans, predictive analytics. We have seen that happening in finance. We work with a lot with fraud detection algorithms to improve the customer service through algorithmic decision making, as we call it. So there are tons of decisions you make from loan process all the way to credit risk assessment. We work with large credit bureaus as well. So how do you make those algorithmic decisions transparent explainable. Explainability is a big area. So finance is struggling with that. With regulations coming down the pike, how can you make sure that the algorithms are not black boxes? They can actually explain themselves. And USDA has done a lot of work in that space where we have taken algorithms from open source and then built our IP on top of it where we have, um, we have techniques like Shapley values and counterfactuals to actually look through these algorithms and figure out what why our decision is being made. Is it biased? Is it is it okay? So those are the capabilities in finance, especially if you apply in retail.

Adnan Masood

Um, we talked about retail. Inventory management is a very low hanging fruit. People talk about leveraging AI for inventory management. But what I found to be like a killer use case is possibly a personalized customer experiences. So how can you create a competitive moat using your data? So retail is a very, you know, tight margin space where you have to have the superior data driven insights. Because if you don’t have that then you end up not having the using the information. So our AI solutions in healthcare really streamline this whole process of decision making. It improves the efficiency and outcomes. And I think in uh, mostly in retail and also in finance, supply chain and operation optimization and AI driven forecasting is are the really killer use cases, which I’m seeing more and more getting picked up. Other things include AI I Copilots you’re seeing that more and more. You’re probably using one right now. Um, a lot of people are using that as an augmentation tool, but they are not the end all be all like they are very specific generalized tools. Specific, generalized, uh, they are writing your emails or more of a low hanging fruit. But when it comes to really AI driven insights, you have to have custom applications for those tools to be implemented based on your domain and your data.

Michelle Dawn Mooney

You touched on ethics. I want to dive a little deeper because a key key area in all of this. How do you balance the promise of AI innovation, which is fantastic with concerns around ethics, privacy and workforce impact?

Adnan Masood

So ethical AI is a great area and I have written a code. I’ve written a book with my coauthor, Heather Dar, recently called responsible AI in the enterprise. And in the book we discuss exactly the things you have just referred to in your question around ethics, privacy, and workforce impact. It’s a whole area I think we can have, uh, several days session on exactly that, just on the definitions of what is fairness, what is ethics, what is privacy, and how does it impact, uh, people? But having said that, I think it’s a really important point you brought up is the balancing innovation with ethics. Right. That’s how do you do that? And you probably are seeing new and new regulations coming down the pike, including European Union, came up with their own AI regulations where they have categorized applications in multiple different categories. So you have high risk applications for AI, then you have medium risk, low risk. And they are forbidden applications which you can’t use AI for. So from a business perspective, whenever I’m talking to CXOs and I’m talking to the board members, I talk about balancing innovation with purpose driven strategy. So what is the mission and purpose for your organization? And make sure that AI has to be developed and deployed with transparency, accountability and privacy in mind.

Adnan Masood

So most people try to inject these capabilities at the end saying, oh, we will have security and transparency towards the end. That’s not how it works. At USD, we really emphasize on responsible AI. We have a framework called responsible Rails where we focus on bias mitigation. We focus on ethical governance. We focus on building these checklists where you can actually look at what are the capabilities which are getting impacted. But when it comes to the workforce impact, I think that’s a huge topic, right. Um, I we approach it and most of the major companies that I know of, especially in R&D spaces, we approach as augmentation rather than replacement. So we are going to use not. We won’t say that AI is going to replace you, but we advocate for reskilling. Now, are there professions which will have severe impact due to AI. There’s definitely going to be impact in that space. But reskilling initiative or continuous learning is the only way we will overcome all those hurdles. Like you got to prepare our employees for AI enhanced workplace, which we are doing. We are training 25,000 of USD employees to become AI focused and learn about AI. It doesn’t mean they’re all going to be gen AI developers, but they are in multiple different areas.

Adnan Masood

And how can they use AI in their space? That’s one of the things we are doing internally, but also we are doing the same thing for our customers where we are upskilling them using GitHub Copilot, whether they are using AWS tools or any of the hyperscalers, how can you use AI within their environment? Also understand what kind of regulations are coming down the pike. So especially talking to the board members and the people, what are the privacy concerns? Are you you have GDPR compliant systems? What how do they work in the AI world? So we have to ensure that the businesses can innovate responsibly without compromising the compromising the user trust or data security because that becomes a fiduciary responsibility. Now, that’s not only a brand issue, but also it becomes an issue with how you portray your business in the future, because you don’t want to have any of the horror stories. And there’s just tons of them. You probably have heard of them repeatedly about people, companies getting sued because they have declined somebody’s, um, claim because AI algorithm said so, right. That’s not acceptable and that’s not legal. And we, uh, we work to help companies mitigate that.

Michelle Dawn Mooney

Let’s talk about that, because this is not a bed of roses, right? Ai is fantastic on so many levels. But there are some concerns noticeably that, uh, just based on the last story that you shared, people would have. Let’s talk about some misconceptions for a moment. What are some of the most common ones about AI in the workplace, and how can businesses address them as they want to adopt AI technologies.

Adnan Masood

So one of the most common misconception I think about AI is that widespread job losses, when in reality we can see that AI creates opportunities for strategic reskilling, right? It is augmenting the human capabilities, making humans work on a higher cognitive levels. It allows your employees to focus on more creative problem solving and high level decision making. So we have gone through these kind of revolutions in our time. We know the teller machines. When the ATMs come in, the tellers would have no job. And what’s going to happen? And we see that some of those fears are not really based in reality. Like another major misconception in AI is off the shelf solution. Like a successful AI, deployments requires agile governance, continuous learning, and really deep understanding of the domain. So you can’t think about this off the shelf solution of like taking ChatGPT or one of one of the the tools and see if that can integrate really and understand your business because that’s not how it works. I requires a contextual understanding of your data and your business in order to be able to give you insights. And that process requires time and investment, and that’s your technological moat. So businesses can address this misconception by having a culture of like cognitive diversity. And for people to be able to learn from what’s happening in that space with that, with a psychological safety.

Adnan Masood

Right. So employees need to be able to encourage to work alongside AI so they leverage their capabilities in a psychologically safe environment with maintaining human oversight. So one thing I think I would like to point out as human in the loop, this is space. So human in the loop is critical for all high functioning, high risk applications, whether it is loan giving, loan to people, whether it’s employee selection, whether it’s any of the any things which actually impact human lives. And there are a lot of things in there which impact human lives. Now, if I get a wrong recommendation for a Netflix movie, that’s okay. I think that that’s not the end of the world. But if there’s a loan application which is submitted by two people, and just because they have different gender, one gets a higher credit limit, another one gets a lower credit limit. That’s a very risky implementation. And there’s something wrong there. So figuring out that those misconceptions, including job losses, including biases around data sets, including thinking that you can remove all the biases just by doing data processing, those are some of the things which we need to address.

Michelle Dawn Mooney

And I think the main point there is education, right? This conversation being one of the pieces of hopefully educating people more about what those misconceptions are and what the reality is. And of course, it’s ever changing as well as as this is so new day to day for organizations that are ready to adopt AI technologies. Adnan. What advice would you have? What initial steps would you recommend IT leaders take to facilitate a successful and then maybe even more importantly, a sustainable AI integration process?

Adnan Masood

That’s a question I typically get asked by board members and C-level executives and even, you know, middle management, where they want to understand how to get started. What are the things we should do to embrace? And I always tell them that you need to begin with a clear understanding of core competencies. So don’t change your business for AI because you have been successful at that business. See what I can do. Help and accelerate your core competency and core capabilities. What are the areas where I can add immediate value? What are the things where you can build to actually give your core competencies the next edge, where you can really provide value on top of that. There are some execution things. So for example you have to have a data governance framework which because data governance framework is critical for AI success, right. Garbage in, garbage out. You don’t want to put bad data in there to get the wrong results, right? So foster a culture within your organization. We touched upon that earlier, is that continuous learning and experimentation is really, really critical for you, right? So leaders who are trying to adopt AI must also have an AI ethical, ethical AI strategy within their organization. So they have to make sure their models are transparent.

Adnan Masood

They are fair. They are auditable. They are safe. And just getting started. Start with pilot projects but they should not be trivial. Non trivial impact based pilot project for an AI initiative and then have ROI associated with that. So figure out what are the ROI associated with that. This exercise I’ve done with my team and USC does it on a regular basis. Where USD goes to the companies, and then we talk to them about what are the top use cases they have and how you can prioritize them based on your need, the urgency of that, the ROI based on it, and experimentation, the culture of experimentation to be built within an organization where people are allowed to fail fast and refine ideas and build upon top of it. So, for instance, you have to build a cross-functional synergy between different departments to be working to work together for bringing this AI driven culture within an organization. So it’s a it’s a steps of digital thinking and digital transformation, which comes as part of your, your enterprise. One thing I will conclude with that is that, uh, one of my Harvard professor doctor has written a book about it. It has a called Digital Mindset.

Adnan Masood

And she talks about the 30% rule. So a lot of people who are struggling with learning about AI and AI, and they are hard to keep up with this knowledge base. They always ask like, where should we start? So in in language, when you’re trying to learn a new language, there’s we’ve seen that the people who learn language as a second, as a second language or third language, they don’t have the entire vocabulary of of a native speaker. They typically have like a 30% vocabulary, and that 30% actually gets you functional. You can communicate, you can converse, you can understand. It won’t make you be able to write like Shakespeare, but it will still be able to have you communicate on a basic level and be able to understand that. And that level of literacy should be built across the organization. If you learn 30% of what I does, that includes understanding basic understanding of algorithms. What is unsupervised learning, supervised learning, how deep learning works, what’s I mean, you’re not going to be coding an AI algorithm anytime soon, but it will give you that 30% will give you that literacy, the AI literacy, which will help you guide your organization through this transformational AI change.

Michelle Dawn Mooney

People are listening. They have questions. Maybe they want to learn more about what you’re talking about. Are there any resources or places you can send them?

Adnan Masood

Yeah, absolutely. So USC has a whole generative AI page. You can go there. You can see myself and my colleagues where we are. We have talked about generative AI in detail. We’ve talked about that implementation in business. Um, we have written a book called responsible AI in the enterprise. You can see this book. It’s available. And also you can find out several white papers and case studies we have published on Ucsd’s website. We have published our point of view on application modernization. Also how you can use responsible AI and responsible rails. So that’s another one of the, I guess, areas you can explore from our website. And I’m on Twitter and LinkedIn and you can email me my first name, my last name at gmail.com. I always love to have conversations around gen AI and uh, in AI in general, especially in the in the context of business and how businesses are using it. And I would love to share some thoughts around that. So but thank you for your time. It was a great conversation.

Michelle Dawn Mooney

I appreciate you exploring the future of AI and workplace transformation. So a big thank you to you and uh, appreciate your time today. And I’m sure a lot of people are going to hopefully want to follow up on this. So thank you for being here today.

Adnan Masood

Absolutely. I would love to follow up. And you have my information. Uh, you can see me on LinkedIn, Twitter. Ust has a really great web page about generative AI. And we have all the information related to gen AI and the emerging trends over there, so we keep it up. You will also find out my book on responsible AI. My colleague Heather, she’s a chief data scientist at uh, she’s based in UK, and we coauthored this book about responsible AI implementation in the enterprise. So USDA has been working in this space for application modernization using AI as well as responsible AI. So, uh, please check it out. One last thing I want to leave you with is that AI is definitely reshaping industries at an unprecedented, Unprecedented pace. So don’t be a laggard in that space. Try to become a leader because that will actually increase your intellectual property, your presence in the industry. Because this is like turning the data into actionable insights. Automating the mundane processes and driving the strategic decisions using AI is not a nice to have. It is quickly becoming a must have for the enterprises. The future belongs to the enterprises which integrate AI responsibility responsibly and balance the innovation with ethics, privacy and workforce. And a lot of things we have talked about here is that I think the model is that you, as businesses must act now, build the AI fluency and prioritize your AI driven value creation. So AI driven value creation is your race for AI dominance. And the by leading that, you will redefine the markets. You will unlock new business models and you will build the operational excellence which is essential to your enterprise.

Michelle Dawn Mooney

Great conversation anon. Really appreciate you being here and explaining the future of AI and workplace transformation. I think we could be here for a couple of days talking about everything, just barely scratch the surface. But a big thank you to anon for sharing his insights into how artificial intelligence is reshaping industries and driving innovation. Anon, thank you so much for being here today. Appreciate your time.

Adnan Masood

Thank you very much.

Michelle Dawn Mooney

And if you would like to learn more, be sure to check out our resources and connect with Adnan for further insights into AI trends and applications. We will have some details in the show notes as well. Thank you again for tuning in to The Hitchhiker’s Guide to it. Brought to you by Device42. I’m your host, Michelle Dawn Mooney, and I look forward to bringing you more expert conversations at the forefront of technology and business innovation. We hope to connect with you on another podcast soon.