In an era where technology is reshaping every aspect of life, artificial intelligence (AI) is emerging as a transformative force in education. With the potential to expand access to higher education through individualized learning platforms, intelligent tutoring systems, and AI-driven administrative tools, it also carries risks. It could help underprivileged students close longstanding educational gaps, but it might also deepen existing disparities. In this article, I explore how current AI advancements serve as both a solution and a challenge in the pursuit of equitable access to higher education.

Bridging the Education Gap—Where Does AI Come in?

While one-on-one tutoring helps students learn concepts, complete projects, or develop new skills, providing on-demand aid to all students is challenging due to a low human instructor-to-student ratio, time constraints, and limited resources to address the diverse needs of students. This is where AI-ed comes into play. 

Educational applications powered by artificial intelligence can tailor content to meet each student’s individual needs and learning pace (Luckin et al., 2016). AI-powered tutoring tools, such as Carnegie Learning and Squirrel AI, provide 24/7 assistance to students, reducing the workload for human instructors. 

Intelligent Tutoring Systems (ITS) utilize AI techniques to learn about a student’s cognitive needs and provide the most suitable learning activities, often without the need for a human instructor to be present (Luckin et al., 2016). These tools can aid students with various learning styles, disabilities, and language barriers, making higher education more inclusive and accessible (Holmes et al., 2019). 

According to Jyoti D. Rozario, a graduate and incoming master’s student at the University of Maryland, these tools can be incredibly empowering, sharing, “Tools like ChatGPT or AI-powered study apps have helped me quickly grasp difficult concepts, draft outlines, and manage time more effectively. For someone juggling multiple classes, a full-time job, and other responsibilities, that kind of support feels like a lifeline.” By offering personalized, on-demand support, AI-driven tutoring systems not only supplement traditional teaching but also empower students to take greater ownership of their learning journeys.

In a survey we conducted of 40-50 anonymous participants, including students, working professionals, and professors, 87.5% of participants identified personalized learning and tutoring as the area most transformed by AI in education, 55% cited student engagement tools, and 57.5% pointed to administrative efficiency, like grading and admissions.

Artificial intelligence has advanced a lot in the past few years. Nowadays, almost all technological solutions are built around sophisticated machine learning models, thus improving the convenience factor for users. AI streamlines a vast number of administrative processes for higher education, such as application screening, grading, and student support services, additionally ensuring a fair evaluation process by reducing human bias (Heffernan & Heffernan, 2014).

Professor Faisal Quader of the Department of Information Systems at the University of Maryland remarked, “AI is a powerful tool that can accelerate learning. But if you jump into using it without understanding the basics, you risk misusing that power.” His comment highlights the importance of pairing AI-driven support with a strong grasp of foundational concepts, ensuring that technological assistance enhances rather than replaces deep learning.

Building on that perspective, others in the field emphasize that the responsibility doesn’t lie with the technology alone but also with the learners who use it. 

“As an AI security researcher, our job is to build safe and responsible models to be useful in different areas, in this case, learning. However, the learners themselves must first learn how to be responsible in using the models for them to be truly helpful, instead of harmful,” said Nicole Meng, a PhD candidate specializing in AI. “Using an LLM to enhance learning is like exploring an ancient library filled with old, sometimes unreliable books. In both cases, the information comes from previous records: for LLMs, that’s internet data; for libraries, it’s written works. You still need to verify whether the information holds today, and neither can replace independent, critical thinking in the learning process.”

Will AI Widen the Educational Divide? What Are the Risks?

By enhancing results, providing real-time feedback, and personalizing learning, AI has the potential to revolutionize education (Holmes, Bialik, & Fadel, 2019). However, if we’re not careful, AI could end up widening the gap between students who have access to resources and those who don’t. For example, AI tools rely on resources such as high-speed internet, modern devices, and well-trained teachers to function effectively (Luckin et al., 2016). Students in underfunded schools or low-income communities often lack these basics, meaning they could miss out on the benefits of AI entirely. 

This digital divide is noticeable even among peers. “Not every student has the same access to devices or stable internet,” Rozario explained, “And sometimes it feels like there’s a growing gap between those who can leverage AI tools and those who can’t.” 

Additionally, the quality of AI systems depends on the quality of the data on which they are trained. While recent advances have improved inclusivity, studies continue to show that AI tools can underperform for certain groups, such as non-native English speakers or students with learning differences. Without intentional design and equitable access, AI risks reinforcing the very inequalities it aims to solve, leaving the most vulnerable students further behind.

AI is often framed as either a solution or a threat to educational equity. When asked how AI is affecting educational inequality, 72.5% of participants in our survey believed that the impact varies depending on how AI is implemented. In comparison, 32.5% felt that AI is increasing inequality due to unequal access to technology. These results underline a critical point: without targeted interventions, AI could become another amplifier of privilege.

The key to avoiding this divide is making sure AI is implemented equitably. This involves investing in technology and infrastructure for underserved schools, ensuring that AI systems are trained on diverse, inclusive datasets, maintaining transparency and privacy standards to protect students, and providing teachers with the necessary training to utilize these tools responsibly and effectively. (Holmes et al., 2019). 

The role of teachers is likely to undergo substantial change, too. 82.5% of survey respondents agreed that teachers will increasingly rely on AI to manage administrative tasks, while nearly 50% said teachers will become more like facilitators or coaches. This shift could depersonalize the student-teacher relationship and reduce opportunities for critical thinking and creativity.

As artificial intelligence becomes increasingly integrated into educational settings, educators, students, and policymakers grapple with harnessing its benefits without undermining core learning outcomes. 

For example, students may face new forms of academic dishonesty. AI-generated content and automated tutoring platforms make it easier to cheat or plagiarize, posing a challenge to educators in finding effective assessment methods that preserve academic integrity. 

Reflecting on these ethical challenges, Professor Quader cautioned, “Yes, cheating or copying is a concern. Students must ensure they understand the concept without relying on AI to do everything for them.” His warning aligns with broader academic fears about overreliance on AI and the erosion of academic integrity, reinforcing the core message that AI must be used intentionally and ethically to truly benefit education. 

In our survey, 87.5% of respondents marked overdependence on technology as a major ethical concern in AI education tools. 82.5% were worried about data privacy and student surveillance, while 42.5% highlighted a lack of transparency in AI decision-making. These concerns underscore how unchecked technological advancements can introduce new vulnerabilities into education systems. 

AI in Education: Take it from Students

While data and expert opinions are critical, student experiences provide unique insight into how AI is shaping the learning journey. For some, AI tools serve as essential aids, supporting time management and comprehension. 

Others also caution against overdependence. As Rozario shared, “Without proper guidance, it’s easy to let AI do the thinking for you, and that defeats the purpose of learning. Over-relying on AI for every aspect of our learning may diminish the ability of critical thinking. AI tools should be used to augment our thinking and help automate daily tasks.”

This captures the tension between AI’s educational potential and the risk of disengagement. Striking the right balance between using AI as a tool and maintaining academic integrity is essential to ensuring that learning remains meaningful and student-driven. 

When asked about the impact of AI tools on student motivation and critical thinking, 52.5% of survey respondents felt that AI weakens both. An additional 30% believed that while AI improves motivation, it weakens critical thinking, and 7.5% felt the reverse, highlighting a fragmented perception of AI’s educational value. Only 15% believed that AI strongly improves both motivation and critical thinking, and 5% thought it slightly improved both.

What Does This Mean for the Future?

Artificial intelligence is expected to play a central role in shaping personalized, efficient, and inclusive learning experiences. As schools and institutions increasingly adopt AI-driven tools, it is essential to ensure that this integration supports equity, fosters creativity, and upholds ethical standards. 

In terms of future risks, the most commonly cited concern was the loss of teacher-student connection, selected by 65% of respondents. This was followed closely by the dehumanization of learning with 55% and the widening of the digital divide with 37.5%. Dependence on commercial AI platforms and the potential for cheating were also concerns, at 37.5% and 20%, respectively, as was algorithmic bias in student evaluations at 22.5%. 

Notably, none of the respondents felt that there were more benefits than risks, underscoring the importance of carefully structured and ethically-guided integration. These insights suggest that while AI holds promise, it must be implemented with a strong emphasis on equity, transparency, and the preservation of human-centered learning practices.

When asked about priorities for AI tool development in education, the top response was personalized learning pathways with 70%, followed closely by enhancing student engagement and supporting students with disabilities, both with 62.5%. These findings reflect a strong demand for AI systems that cater to diverse learning needs and make education more accessible. Additionally, 50% of respondents emphasized the importance of reducing teacher workload, while 42.5% prioritized improving access in underserved regions, reinforcing the dual focus on efficiency and equity.

In terms of curriculum design, 90% of respondents believe schools should focus more on critical thinking and creativity to prepare students for an AI-driven future. 65% support teaching AI literacy and ethics, while 45% advocate for integrating AI tools into everyday learning. These insights suggest that, alongside technical skills, ethical and creative capacities must be central to the evolving curriculum. 

Responsible Implementation of AI: It’s All About Balance

AI holds significant potential to transform higher education by improving accessibility, personalizing learning, and streamlining traditional administrative processes. However, its implementation must be equitable to prevent deepening existing educational disparities. 

Investing in machine learning infrastructure, diverse data collection, and instructor training is crucial for creating inclusive AI systems. When used thoughtfully and responsibly, AI can help bridge the education gap and provide opportunities for students from diverse backgrounds to succeed in the modern academic landscape. While AI is not a silver bullet, ethical and equitable use can make it a powerful tool.

The post Is AI Bridging the Gap or Widening the Divide in Education? appeared first on The Urban Watch Magazine.

When Anishek Kamal moved from a small town in India to the United States, equipped with a computer science degree and a job offer in tech, he thought he was about to live the American dream, until reality set in. Without the kind of financial safety net that some immigrants from more privileged backgrounds have, he quickly found himself struggling to afford life in America. He still had to pay off his student loans, cover rent and bills, and manage a car loan, all while sending money home to support his family. Soon, he maxed out his credit cards, and by 2022, he had racked up over $100,000 in debt. 

Despite the shame that often comes with debt, and money being a taboo topic, Kamal is open about his past struggles. Through extensive research and data analysis, he found a way to become debt-free.

“Debt is not just a money problem—it’s also a behavioral problem,” Kamal says.

It’s his vulnerability in speaking from personal experience, combined with technical knowledge, that makes his story unique.

That technical knowledge came from years in the data and cloud space, where Kamal built his career at companies like Lululemon, Press Ganey, and Neal Analytics before eventually landing at Microsoft. There, he has led AI and big data initiatives, helping Fortune 500 clients optimize systems and migrate to the cloud. But behind the scenes, he was still grappling with personal debt and beginning to ask deeper questions about why smart, capable people like himself were falling through the cracks of traditional financial services.

He tried everything—Excel spreadsheets, financial advisors, loan and refinancing advice, and countless apps that offered generic tips. It was overwhelming, and he found no clear, data-driven way to understand his options and make better decisions.

That’s what led to DebtZero, the fintech platform Kamal founded to help people get out of debt faster and with less mental strain. By analyzing spending patterns, income fluctuations, and behavioral habits, the platform builds personalized repayment plans and allows users to automate payments in one place. No more juggling multiple apps, due dates, or interest calculations. Kamal interviewed over 350 people and found that 73 percent had experienced the same kind of cognitive overload he had. He recognized there was a clear need for what he was building. DebtZero promises not only to help users save money but also to offer the peace of mind that comes from finally feeling in control.

Outside of his full-time job at Microsoft and building his app, Kamal also makes it a priority to give back. He has mentored over 400 aspiring data and cloud professionals, taking countless calls for free to help people break into the field. Many of those he’s advised are now working at top-tier tech companies.

The Urban Watch sat down with Kamal for an exclusive interview about how his personal experience with debt led him to found DebtZero, how AI can be built with empathy, and what it takes to design a system that truly works for people, not against them. 

Tell me a little bit about your background and where you grew up. How has that shaped who you are today, personally and professionally?

I grew up in a small town, Darbhanga, in the state of Bihar, India, where resources were limited, but the people worked hard to create a better life. My father worked incredibly hard to provide for our family, and that work ethic definitely rubbed off on me. We didn’t have much exposure to technology growing up. I remember the first time I saw a computer; I was in elementary school, which was pretty late compared to kids in more developed areas. My uncle had a PC, and the entire family was using it. I was so fascinated by computers that by the time I was in ninth grade, I was assembling computers for friends or acquaintances for a couple of hundred Indian rupees each. 

This background shaped me in a few important ways. First, I developed a deep appreciation for education as a path to opportunity. Second, I learned to be resourceful and solve problems with whatever tools were available. And third, I gained this perspective that technology isn’t just cool – it’s transformative. When I finally got my hands on a computer and started coding, it felt like discovering a superpower.

Professionally, this journey has made me particularly drawn to projects that democratize access to resources and knowledge. I think that’s part of why I’m so passionate about fintech now – it’s about using technology to solve real problems and create more equitable access to financial tools that were previously available only to the privileged few.

How did you become interested in working in tech?

My journey into tech started with pure fascination. I remember writing my first lines of code and being amazed that I could create something functional from nothing but logical instructions. It felt like a perfect blend of creativity and problem-solving.

I started as a big data developer working with Hadoop, which exposed me to the power of handling massive datasets. What kept me in tech was seeing how these skills could be applied to solve increasingly complex problems. As I moved into cloud technologies and solution architecture, I loved the challenge of designing systems that could handle enormous scale while remaining resilient and efficient.

The evolution from writing code to architecting solutions for Fortune 500 companies was natural because I’m fundamentally driven by impact – how can we use these powerful tools to solve meaningful problems? That’s ultimately what drew me to AI as well. It’s not just about the technology itself, but about the problems we can solve with it that weren’t solvable before.

What has been the most significant personal challenge you’ve ever faced? 

Moving to the United States was probably my biggest personal challenge. I came here with essentially maxed-out credit cards, student debt from my bachelor’s degree, tremendous uncertainty, and the feeling of having to start completely from scratch. There’s something humbling about leaving behind your support network and familiar surroundings.

The financial pressure was intense – I remember calculating exactly how many months of my new salary would go toward paying off those cards. That experience of carrying debt while trying to build a new life taught me a great deal about financial stress and how it affects every aspect of life and decision-making.

Looking back, this challenge provided the perspective that eventually led to DebtZero. I experienced firsthand how even someone with a good education and technical skills can struggle with debt and financial navigation. It made me realize that financial systems aren’t designed with accessibility or empathy in mind, and that’s something technology could help address.

What do you consider your biggest professional accomplishment so far?

While there are several technical achievements I’m proud of, I believe my most significant professional accomplishment has been successfully transitioning from a solutions architect at a major tech company to a founder building something from scratch.

The shift required completely different skills–moving from implementing technical solutions within established frameworks to identifying market opportunities, validating ideas, building from zero, and creating something that genuinely helps people.

What makes this particularly meaningful is that I’m not just applying technical skills, but actually addressing a problem I experienced personally. Creating a product that can help millions of people achieve financial freedom is infinitely more satisfying than any technical problem I’ve solved previously.

How does DebtZero compare to other debt management companies? What makes it stand out? 

Unlike traditional debt management companies that focus primarily on debt settlement or consolidation, Debtzero takes a more holistic, technology-first approach to debt freedom. Other companies typically negotiate with creditors after accounts have gone delinquent; we’re focused on optimization strategies that work with current, good-standing accounts.

There are three key differentiators that make us stand out:

First, our approach is personalized based on behavioral patterns, not just numbers. Traditional services apply the same mathematical strategies to everyone, but financial success is behavioral. We analyze spending patterns, income volatility, and psychological factors to create strategies that users will actually stick with.

Second, we’re focused on education and empowerment rather than dependency. Many debt management companies create ongoing reliance on their services, while we’re building tools that increase financial literacy and independence over time.

Third, we use technology to optimize the entire financial picture, not just debt. Our system analyzes cash flow patterns, payment timing effects, and opportunities across all financial accounts to identify savings that traditional approaches may overlook.

 What inspired you to launch DebtZero? 

Launching DebtZero came from a deeply personal place. When I moved to the U.S., I was carrying student loans and quickly found myself in credit card debt. The financial system here was completely different from India’s more cash-based approach—everything revolved around credit. Despite having a tech background and a decent job, I struggled to make sense of it all. I was making minimum payments, juggling multiple loans, even sending money back home to support my family. By 2022, I was over $100,000 in debt.

I tried everything—Excel spreadsheets, financial advisors who weren’t interested unless you had assets, random advice on 401(k) loans and refinancing—it was overwhelming. What I really needed was a clear, data-driven way to understand my options and make better decisions. There was an app in India that helped track and pay credit card balances in one place, and I couldn’t find anything like that here. That’s when I got the idea for DebtZero. 

The final catalyst came during my time working on a credit union project, where I analyzed millions of financial transactions. I observed patterns of people making costly mistakes not because they lacked discipline, but because they lacked visibility and optimization tools.

I recognized an opportunity to apply my technical expertise in data and AI to help people overcome the same challenges I had faced. The more research I did, the more I realized there wasn’t a solution that truly leveraged technology to optimize debt management in a personalized way. That gap in the market, combined with the potential for meaningful impact, made launching DebtZero feel like the most important work I could be doing.

What role does AI play in Debt Zero’s platform?

AI is core to Debt Zero’s platform, but in ways that might be different from what people typically think. We’re not using AI to make decisions for users–we’re using it to uncover insights and opportunities that would be impossible to find manually.

First, our AI analyzes transaction data to understand unique spending patterns and financial behaviors. It identifies patterns invisible to the human eye, like how spending in certain categories triggers cascading effects across someone’s finances.

Second, we use machine learning models to predict cash flow volatility and potential financial stress points before they happen. This allows us to proactively recommend adjustments rather than reacting to problems after they occur.

Third, we apply optimization algorithms that consider hundreds of variables simultaneously–payment dates, interest calculation methodologies, cash flow patterns, and psychological factors–to create debt payoff strategies that are both mathematically optimal and behaviorally sustainable.

Fourth, we use natural language processing to translate complex financial concepts into accessible guidance tailored to each user’s financial literacy level.

The AI doesn’t replace human judgment–it enhances it by revealing options and insights that wouldn’t be apparent otherwise, then empowers users to make better-informed decisions about their financial future.

What kinds of data are you analyzing to offer clients the most effective repayment strategies?

We analyze multiple categories of data to create truly effective repayment strategies, all while adhering to strict privacy protections and obtaining user consent.

Transaction data is our foundation—we look at spending patterns, income timing, volatility, and cash flow dynamics. This helps us understand not just how much money someone has, but when it’s available and how predictable their financial situation is.

We also assess debt-specific details, such as interest rates, payment due dates, minimum payment requirements, and how interest is calculated (e.g., daily average balance vs. previous statement balance). These overlooked nuances are critical for optimization.

Behavioral data plays a key role as well—how consistently users stick to plans, where they’ve struggled or succeeded, and the kinds of financial decisions that tend to trip them up.

What makes our approach unique is that we don’t just look at each of these data types in isolation. We examine the relationships between them. For example, how income timing interacts with payment due dates, or how certain spending patterns affect debt payoff consistency.

Importantly, we maintain bank-level security and transparency. Users always know what data we use and why, ensuring trust is built into every step.

Who is your ideal user, and what does a typical customer journey look like on the platform?

You don’t have to be in debt to use DebtZero; it can help you optimize your payments and stay debt-free. However, our current target user is someone who has more than $10,000 in debt and is actively seeking to eliminate it, but feels overwhelmed by the complexity of the process. They usually have a mixture of credit cards, student loans, car loans, and possibly a mortgage. They’re making payments but not seeing the progress they want, and they’re seeking a more effective approach rather than simply budgeting harder.

That said, debt is not just a money problem; it’s also a behavioral problem. That’s why we provide users with educational content specifically tailored to their situation, helping them build financial skills and confidence alongside their progress toward debt freedom.

The typical customer journey begins with what we call a ‘Financial X-Ray’–connecting their financial accounts to provide us with a comprehensive view of their financial picture. This is a low-friction process that takes about 5 minutes but provides immediate value.

Next, we generate their personalized Debt Freedom Plan, showing them exactly how much faster they could be debt-free using our optimization strategies. This is a key moment because most users discover they can cut years off their debt payoff timeline without increasing their monthly payments.

Once they have their plan, we help implement it through a combination of guided actions and automation. This includes optimizing payment dates, restructuring how payments are applied to different debts, and setting up the right automatic transfers.

As they progress, we provide ongoing monitoring and adjustments. If we detect changes in their financial situation, the system recommends modifications to keep them on the fastest path to debt freedom.

Debt is deeply personal and often stigmatized. How do you design AI systems that are both effective and empathetic?

I’m passionate about designing AI systems that balance effectiveness with empathy. In the debt management space, shame and vulnerability are common experiences.

We start by acknowledging that debt isn’t just a financial issue–it’s an emotional one. Our design philosophy centers on judgment-free interaction. The system never uses language that implies moral failing or personal shortcoming when discussing financial situations.

We’ve also built what we call ‘context awareness‘ into our AI. It recognizes emotional cues in how users interact with the platform and adjusts its tone and recommendations accordingly. For example, if someone is showing signs of financial anxiety, the system focuses more on small, achievable wins rather than long-term projections.

Another key aspect is appropriate personalization. The system remembers individual circumstances and preferences, avoiding the need for users to repeatedly explain their situation, which can reinforce feelings of shame.

We’re careful about timing and delivery of information. Financial insights are delivered when users are most receptive, not as interruptions or judgments. And we frame everything in terms of opportunities rather than mistakes.

Perhaps most importantly, we maintain a clear line between what AI should handle and what requires human connection. For particularly sensitive situations, we make it easy to connect with a human financial coach who has the context from the AI system but brings the empathy and nuance that technology alone can’t provide.

The goal isn’t to make our AI seem human, but rather to ensure it respects and accommodates human emotions while providing effective financial guidance.

What steps are you taking to ensure transparency and fairness in automated decision-making, especially for users from financially vulnerable backgrounds?

Transparency and fairness are foundational to DebtZero’s approach, particularly because we work with many financially vulnerable users.

First, we practice radical transparency in how our system works. Users can see exactly what factors influence their debt freedom plan, what data points we’re using, and the reasoning behind recommendations. We avoid black-box solutions where users have to simply trust the AI without understanding it.

We’ve implemented fairness audits throughout our development process. This means regularly testing our recommendations across different demographic groups to ensure our system doesn’t perpetuate biases or deliver better results to certain populations. If we detect disparities, we address them at the algorithm level.

We’re also careful about assumption-free design. Traditional financial systems often make assumptions about income stability or access to banking services that don’t hold for many vulnerable populations.

Our systems are designed to work effectively for gig workers with irregular income, users with thin credit files, or those who’ve had negative banking experiences in the past.

We provide options, not mandates. Our AI presents multiple paths forward with clear explanations of the tradeoffs, empowering users to make informed choices rather than forcing them into a particular approach.

And finally, we maintain human oversight of our systems, particularly for edge cases. When our AI encounters situations outside its confident decision-making parameters, especially for vulnerable users, those cases are reviewed by humans to ensure appropriate recommendations.

The ultimate measure is outcomes–we track whether our recommendations are actually helping users across all demographic groups achieve financial progress, and continuously refine our approach based on those results.

Can people use both a traditional debt settlement company and your app?

Yes, they can. In our first version, users will be able to see all their debts in one place and make payments directly through the app. The focus is on visibility and control.

In future versions, we plan to partner with credit unions to help users access lower-interest loans, compare rates, and understand where they qualify. The goal is to offer smarter refinancing options that work in the user’s favor.

Traditional debt settlement companies focus heavily on negotiation, and while that can be helpful, it can also be inefficient and confusing. We aim to bring that same value, but in a more transparent, automated, and user-friendly way.

Ultimately, our app is designed to help people, not institutions. But to offer the best tools and rates, strategic partnerships will be necessary.

To end on a broader note, as someone who works with AI every day, what do you see as the most pressing challenge or ethical dilemma facing the field today—technically, socially, or economically?

I think the most pressing challenge facing AI today is the growing disconnect between who builds these systems, who benefits from them, and who bears the risks.

Technically, we’re creating increasingly powerful tools without sufficient guardrails or oversight. Many AI systems are deployed with inadequate testing for biases, edge cases, or potential harms. The pace of development has far outstripped our governance mechanisms.

Socially, we’re facing a potential deepening of existing inequalities. AI could either bridge or widen gaps in access to healthcare, education, financial services, and job opportunities. Without deliberate attention to inclusion and equity, these systems will naturally tend to benefit those who already have advantages.

Economically, the concentration of AI capabilities in a small number of large companies raises serious questions about power distribution.

When a handful of organizations control the most powerful AI models, they effectively become gatekeepers for innovation across many sectors.

The ethical dilemma that keeps me up at night is the risk of ‘automation bias’–our tendency to defer to algorithmic recommendations even when we should be skeptical. As AI becomes more embedded in consequential decisions, from medical diagnoses to lending approvals, this bias could lead to a progressive abdication of human judgment and responsibility.

Addressing these challenges requires technical solutions, like better testing methodologies and more transparent systems. But equally important are social and governance innovations–developing new frameworks for accountability, ensuring diverse perspectives in AI development, and creating mechanisms that distribute the benefits of AI more equitably.

Learn more:

Check out DebtZero

Connect with Anishek Kamal on LinkedIn Book a call with Anishek

Book a call with Anishek

The post This Immigrant Founder Is Changing How We Talk About Debt appeared first on The Urban Watch Magazine.

In a field often dominated by men, Somya Singh is forging her own path—combining a passion for problem-solving with cutting-edge work in AI, cybersecurity, and UX design.

Born and raised in New Delhi, India, Singh moved to the United States at the age of nine, where her early exposure to coding inspired a lifelong interest in technology. She earned her bachelor’s degree in Information Systems from the University of Maryland, Baltimore County (UMBC), and quickly launched her professional career with roles at Optum and Sparksoft Corp, rising from a computer systems analyst to a lead position overseeing cross-functional tech teams.

Currently pursuing her master’s degree in Information Systems at UMBC with a specialization in AI, data science, and UX/UI, Singh continues to push boundaries outside the classroom. She is actively researching deepfake detection in cybersecurity with the goal of helping protect against one of today’s fastest-growing digital threats. She has also judged prestigious hackathons like HackDuke at Duke University, bringing her real-world expertise and an eye for innovation to the next generation of tech creators.

The Urban Watch sat down for an exclusive interview with Singh to discuss her journey, her proudest accomplishments, and her advice for women looking to break into—and lead—the future of tech.

Could you tell me a little bit about your background and where you grew up?

I was born and raised in New Delhi, India, and moved to the United States when I was nine. My father has a degree in computer science and came here on a work visa, so I’ve been around coding for as long as I can remember. That definitely sparked my interest in tech early on.

In high school, I was part of the career academy program and originally leaned toward computer science. However, I eventually switched to information systems, which combines business management and coding. I liked that it provided a broader perspective—it felt more connected to how technology actually impacts organizations and people.

After graduating from high school, I earned my Bachelor’s degree in Information Systems from the University of Maryland, Baltimore County. I started out as a computer systems analyst at Optum and then moved into a lead role at Sparksoft Corp, where I worked for three years. Now I’m back in school, pursuing a master’s in Information Systems with a focus on AI, data science, and UX/UI. I’m really interested in how advanced technologies can improve user experiences and push innovation forward.

With your interest in UX/UI, what trends do you think will dominate this field in the next five years? How are you preparing to meet those changes?

I think we’re going to see a lot of changes in UX over the next few years. I’m currently specializing in UX design as part of my master’s program, and one thing we focus on is evaluating websites to see how they could be improved.

Take sites like Best Buy or Amazon, for example. Sometimes the usability isn’t great — there’s too much clicking or too many steps to get to what you need. In the future, I think there will be a much bigger push to minimize friction and make digital experiences more intuitive and accessible for everyone.

I also think companies will become even more intentional with their designs, thinking carefully about what actions they want users to take and how to guide them toward it without overwhelming them. It’ll be about designing from the user’s perspective first, not just the company’s goals.

To prepare, I’m studying not just design principles but also user behavior, accessibility standards, and how AI and personalization can be integrated into better UX. I want to make sure I’m creating experiences that are easier, smarter, and truly centered on the user.

You’ve had a progressive career in Information Systems, from a Computer Systems Analyst at Optum to a lead role at Sparksoft Corp. Can you share more about your journey and what influenced your career choices?

My journey in Information Systems has been driven by a strong desire to solve complex problems and make an impact. While I was at Optum, I constantly challenged myself to take on complex, time-sensitive projects and find efficient, effective solutions. Over time, this mindset became a habit—and I found myself genuinely enjoying both the challenges and the problem-solving process.

As I gained experience, I developed a deeper understanding of complex systems, strengthened my technical skills, and built the confidence to step into leadership roles. This growth led me to a senior role at Sparksoft Corp, where I took on more responsibility, led cross-functional teams, and worked on innovative solutions aligned with the company’s objectives. I also was never afraid to tackle challenging problems or take initiative by stepping outside my comfort zone.

Each role has been a stepping stone, helping me build a well-rounded skill set and a strategic approach to tackling problems. It’s been a journey of continuous learning and growth, and every step has made me more confident and better equipped to navigate the challenges of the tech industry.

What was the biggest thing you learned over the course of your three years at Sparksoft?

One of the biggest things I learned was time management. It’s very different from college, where you mostly manage your own schedule. In a full-time role, especially in tech, you have constant deadlines, meetings, and deliverables, so I had to really learn how to prioritize and stay organized.

I also developed a lot of people skills. At Sparksoft, I worked directly with clients like the Centers for Medicare & Medicaid Services, gave reports, and presented updates regularly. At first, it was intimidating, but over time, I became much more confident communicating clearly and professionally with both clients and my team.

On the technical side, I learned a lot about testing. Our team was responsible for manual and automated testing, finding bugs during new releases each week, and reporting discrepancies. It taught me to be detail-oriented and proactive, skills that are crucial in any tech role.

What do you consider your biggest professional accomplishment so far?

One of my biggest professional accomplishments has been judging multiple hackathons, including HackDuke, which is hosted at the prestigious Duke University. It was an amazing experience to be part of such a renowned event, surrounded by talented students who are really pushing the boundaries of innovation.

At HackDuke, I judged projects across different categories like sustainability and AI. I was paired with another judge, and together we evaluated four teams. They gave us a judging rubric—things like reviewing their code and making sure they weren’t relying on tools like ChatGPT—but I also brought my own perspective. I always look for creativity: is the project something original, or is it just a variation of something that already exists? Seeing that originality is really important to me.

I’m also really proud of earning a senior role in the tech industry through hard work and perseverance, especially as an immigrant woman. Both experiences have taught me the value of dedication, resilience, and continuing to challenge myself to grow.

Which projects are you the most proud of and why?

One of the projects I’m most proud of is the Stock Trading Behavior Analysis with Machine Learning. It started as a personal project that I worked on with a friend during my spare time earlier this year. We wanted to analyze the behavior of stocks and use machine learning to predict better buying decisions, especially for people who like to trade actively.

The project involved studying trading patterns, identifying mistakes, suggesting smarter strategies, and even simulating alternative outcomes to show traders what could have happened if they made different choices. Even though the stock market has been down recently, it’s been exciting to see how the model could evolve—and I’m already thinking about making some changes to improve it.

I’m especially proud of this project because it combined my passion for data science, AI, and real-world applications. It really reflects my drive to always keep learning, building, and finding ways to create tools that can make a real difference.

What are you currently researching? 

Right now, I’m working with my professor on a research project focused on deepfake threat detection within the field of cybersecurity. Our goal is to contribute to the growing efforts to identify and mitigate the risks that deepfake technology poses. We’re aiming to have our work published in an academic journal.

Deepfakes have become a major cybersecurity threat. While the most visible examples are fake celebrity videos, the bigger concern is how deepfake technology is being used for phishing attacks, scam messages, and other types of fraud. For instance, there have been cases where people received text messages that appeared to be from coworkers or trusted sources—and one of my own coworkers fell for it, accidentally giving away personal information like addresses.

That’s why part of my advice is simple: if you ever get a suspicious link from an unknown number, don’t click it. And if an email looks sketchy, it’s better to let it go to the spam folder.

Our research right now is focused on tracking deepfake incidents—where and when they happen—and analyzing patterns. Once we have enough data, we’ll develop tools to help detect and prevent these types of attacks more effectively.

What is your ultimate career goal, and how do you envision reaching it?

My ultimate career goal is to create a groundbreaking solution through research that makes a real impact on technology. I want to develop something that not only solves today’s problems but also helps shape the future.

To get there, I plan to keep expanding my skills in AI, data science, and UX/UI while staying connected to the latest tech trends. I believe collaboration is essential, so I’m excited to work with others who are just as passionate about innovation. By staying curious, open-minded, and dedicated, I hope to create solutions that change the way we experience and interact with technology.

With rapid advancements in AI and Data Science, where do you see the biggest opportunities for innovation in your field?

I see incredible opportunities for innovation, especially in areas like personalized experiences, predictive analytics, and agentic AIs. I’m particularly excited about how AI can transform UX/UI by creating digital experiences that adapt to individual users in real time, making technology feel more intuitive, responsive, and engaging.

The rise of agentic AI systems—AIs that can autonomously make decisions and take actions—also holds huge potential. These systems could revolutionize industries like healthcare and finance by improving efficiency and enabling more intelligent, human-like interactions. The ability of agentic AI to process complex data and make better decisions without constant human oversight is a game-changer, opening up new possibilities for innovation and smarter, more dynamic solutions.

What has been the most significant personal challenge you’ve ever faced? 

One of the most significant personal challenges I’ve faced is navigating life as an immigrant and building a career as a woman in the tech industry. Moving to the United States at a young age meant adapting to a new culture, overcoming language barriers, and finding a sense of belonging—all while trying to stay true to myself.

As I moved into the tech world, I faced the additional challenge of working in a male-dominated field. It’s not just about proving your skills; sometimes it’s having to raise your voice—literally—to be heard in meetings where you might get interrupted or spoken over. There is also the reality that it can be harder for women to secure the same opportunities or leadership roles.

Despite all of that, I’ve stayed determined to break those barriers. These experiences have built my resilience, strengthened my confidence, and fueled my passion for pushing for greater diversity and inclusion in tech.

What advice would you give to other women entering this field?

Don’t let a lack of experience hold you back—just start from wherever you are. What matters most is your passion and your willingness to learn. Focus on the career you truly want, not just what’s available or easy. Don’t settle.

Be genuinely interested in the work you’re doing, because that passion will carry you through the challenges. And most importantly, use your voice. Speak up in meetings, advocate for yourself, and don’t be afraid to take up the space you need. You belong here.

**

You can follow Somya on LinkedIn and GitHub. 

The post Women in Tech: Somya Singh on Leading with Curiosity, Courage, and Code appeared first on The Urban Watch Magazine.

Dina Khalil is not your typical tech enthusiast. With a background in journalism, her fervent love for storytelling drives her to create immersive VR/AR experiences that shed light on social issues. Dina thrives on pushing the boundaries of technology and creativity, using immersive technologies to craft experiences that inspire, educate, and connect people on a profound level. 

Dina started her journey by studying Journalism at the American University of Kuwait. However, she soon discovered that traditional journalism alone didn’t fulfill her passions, as she also harbored a strong interest in design, coding, and filmmaking. This realization led her to pursue a Master’s degree in the Interactive Telecommunications Program (ITP) at New York University. She attended a UX/UI boot camp at Columbia University to refine her skills further. There, she learned the significance of user-centric design and the development of interactive interfaces that deeply engage and resonate with users.

She later joined Meta, where she contributed to groundbreaking projects harnessing the potential of virtual and augmented reality to enthrall audiences. Working alongside cross-functional teams, Dina utilized cutting-edge tools such as Unity and Meta Spark Studio to craft immersive experiences seamlessly blending technology with storytelling.

Currently, Dina works as a contractor at Google while simultaneously pursuing independent projects highlighting critical issues like freedom of the press.

In an exclusive interview, The Urban Watch sat down with Dina Khalil to delve into her unique upbringing in Kuwait and her journey from journalism to technology. 

Please share your background with us. Where did you grow up, and what led you to New York? 

Although I’m an Egyptian citizen, I was born and raised in Kuwait and only visited Egypt in the summers. I always describe Kuwait as less traditional than Saudi Arabia but more conservative than Dubai. 

I went to the American University of Kuwait where I majored in journalism, got a minor in business administration, and took graphic design as an elective course. When I graduated, I decided to focus on video journalism and start an indie studio with my friends, but unfortunately, that plan didn’t work out. I felt really lost and down on myself for not sticking to one thing. Having studied many different things harmed my job hunt, too. Every rejection I got made me feel like a failure. 

I secured some short-term contracts and gained experience. I had a four-month contract as a social media graphic designer at a pharmaceutical company and also worked in customer service for some time at the British Council.

To find my passion and direction, I began volunteering. One notable organization I volunteered for was Nuqat, a non-profit in Kuwait dedicated to cultural development in the region. They host conferences that are similar to TED talks, centered around creativity. I remember attending a talk about coding, but the speaker wasn’t very encouraging. He repeatedly emphasized that coding was very challenging and not suitable for everyone.

Coincidentally, I stumbled upon a free workshop called “Intro to Coding,” hosted by two organizations, Women Techmakers and Google Developer Group (GDG). Intrigued, I signed up. I initially anticipated it would be challenging, but to my surprise, I was captivated by the subject. Suddenly, things started to make more sense, and I fell in love with coding.

Although I finally knew what I liked, I still wasn’t entirely sure about my next steps. After several talks with family and friends, I decided to pursue my master’s degree. I turned to Google and searched for programs combining coding, design, and filmmaking. The first result was NYU’s Interactive Telecommunications Program (ITP). New York was appealing, though I doubted that NYU would accept me. 

Despite my reservations, I went ahead and applied, reasoning that I had nothing to lose. When I received my acceptance letter, tears of joy streamed down my face. I moved to New York and have been enamored with the city ever since.

What’s the biggest challenge you’ve experienced? 

I think living as an expat my entire life made me feel like I didn’t belong anywhere. Not in Kuwait because I’m not a citizen, and not in Egypt because it’s culturally different from Kuwait. This definitely created a big challenge for me and made me doubt myself. It was very hard to get out of my comfort zone. Sometimes, it still is, but pursuing my passions makes it easier. 

In Kuwait, the difference in the treatment of foreigners was noticeable, especially in professional settings. While I didn’t really notice this as a child, it became glaringly evident in the workplace. After graduating from undergrad, I landed a job at a startup founded by an alum at a marketing agency with a reputation as an excellent workplace. However, my experience there was far from ideal, so I quit after just one week. I’m glad I had the choice to leave because of my privileged background; that was not a choice for many others. My workload was imbalanced, and my salary was significantly lower than that of my colleagues. I felt undervalued and unappreciated and immediately knew it wasn’t the right fit for me. 

Let’s delve into the social issues close to your heart and how they shape your work. 

Equality is something I’m deeply passionate about. I was drawn to journalism during my studies but encountered obstacles even in my thesis year. I wanted to investigate the mental strain experienced by domestic workers, mainly focusing on instances where they tragically took their own lives or harmed the families they worked for. Often, these workers were from India or the Philippines, and the mistreatment they endured was appalling. While conditions are slowly improving with the millennial generation, the early 2000s and 2010s were especially troubling, with reports of suicides among domestic workers all too common. My thesis topic was rejected, and I felt this would happen in the workplace if I stayed in journalism.

Consequently, I shifted my focus toward social media and design, recognizing the potential of technology for digital activism. I could enact more meaningful change through tech projects than through writing alone.

The issue of press freedom hits close to home, particularly considering the violations witnessed during the 2011 Egyptian revolution, where journalists were targeted and killed. Many sought asylum in countries worldwide, including the U.S. This sparked my desire to foster empathy for journalists, who are often misunderstood and unfairly vilified. While some may perceive journalists as mere paparazzi, their role in reporting on politics and decision-making processes is invaluable, providing essential information for society.

One project I am working on that holds a special place in my heart and encapsulates my passion is an AR app called “Freedoom of the Press.” Originally conceived as part of my thesis, it has since evolved into a standalone augmented reality app. 

The concept behind “Freedoom of the Press” is to present augmented silhouette figures, symbolizing the spirits of female journalists who lost their lives while working. These figures share their stories with users, engaging them through interactive elements to evoke empathy and understanding. It’s my way of paying tribute to these courageous journalists we’ve lost. Recently, I incorporated a geolocation feature into the app. Users who visit specific areas receive notifications indicating where a journalist was killed, allowing them to listen to their story through audio recordings. I’m excited about this addition and hope to finalize the app’s development for publication on the app store soon.

Initially, I aimed to highlight the disparity in press freedom between the Western world and the Arab world. However, events like the arrest of journalists covering Black Lives Matter (BLM) protests during the Trump administration challenged this perspective. It became apparent that freedom of the press is under threat globally. Journalists who are simply doing their jobs should not face persecution or violence.

The peak of the pandemic in 2020 saw a concerning rise in attacks on female journalists. Maria Ressa, a Filipino-American journalist, was notably targeted for her reporting on the president of the Philippines. Discussions on platforms like Reddit highlighted the gender bias in how she was treated, underscoring the misogyny prevalent in the field. Female journalists often face more significant risks than their male counterparts, a reality reflected in my choice to depict female silhouettes in my work.

Another app that I am proud of building is called “Conciously Controlled.” The app showcases real-time articles from The New York Times op-ed section in large speech bubbles. Smaller bubbles represent publications from various countries that may not have as significant a voice as those in the U.S. The redacted parts symbolize censorship, indicating limitations on freedom of the press in certain regions. 

How did your background in journalism contribute to your approach to VR/AR development, especially in terms of storytelling?

Journalism shaped my storytelling voice and taught me to prioritize and balance the details throughout the experience/project. It also trained me to think of the narrative and organize it to be as inclusive as possible. It taught me to ‘KISS’ – Keep it Short and Simple.

Delivering journalism through VR/AR offers a more engaging experience, allowing people to immerse themselves in a headset and encounter unique narrative experiences. 

Even without the headset, visually-driven and data-heavy stories like those on COVID-19 by the New York Times are more engaging. This approach to storytelling has an accessibility angle because individuals with dyslexia or those who do not speak the language can rely more on visuals to understand the content.

What do you think is your biggest accomplishment?

It’s challenging to single out just one. Everything I’ve experienced in the past has prepared me for where I am now, and everything happening now is shaping me for the future. However, if I had to choose, I would say that obtaining my CPACC (Certified Professional in Accessibility Core Competencies) certification last year would have stood out. Passing and receiving this certificate was a tremendous honor for me. It signifies that I am proficient in designing for accessibility. I dedicated considerable effort to studying for it, with the aim of integrating accessibility-friendly designs and interactions into my work and projects.

What are your aspirations for the future in terms of pushing the boundaries of VR/AR technology, and how do you envision contributing to transformative experiences that inspire, inform, and connect people?

As I mentioned earlier, obtaining my CPACC certification was my most significant achievement, and it aligns closely with my aspirations for the future of VR/AR. I love this tech so much that I want everyone to be able to enjoy it. However, I recognize that current VR/AR projects often lack accessibility. I have also been guilty of creating inaccessible projects in the past due to the challenges of designing for accessibility without proper knowledge and research.

I am committed to sharing my knowledge and finding solutions to make VR/AR technology more accessible. I’ve begun taking initial steps in this direction by advocating for accessibility considerations in the early stages of projects with the art directors I collaborate with. It’s essential to consider how differently-abled individuals can enjoy these projects, and I’m dedicated to ensuring inclusivity in every aspect of my work.

As someone deeply involved in AR/VR development, how do you perceive the current state of the industry, and what trends or advancements do you find particularly exciting or impactful for the future of virtual and augmented reality?

The industry is rapidly evolving, and the pace of innovation is exhilarating. It will continue to progress and reach even greater heights in the future. I’m fascinated by integrating artificial intelligence (AI) into extended reality (XR) projects. This integration allows lifelike interactions with non-player characters (NPCs), significantly enhancing the immersive experience. It represents a powerful fusion of both industries, promising to deliver impactful messages and artistic experiences to the public.

I envision a future where 360-degree videos captured on devices like GoPro can be experienced within VR headsets, providing users with the sensation of being present in the scene. Late-night shows and news programs could potentially leverage 360-degree video technology to enhance viewer engagement and immersion, offering audiences a more interactive and immersive experience.

While this trend has enormous potential, widespread adoption may be delayed due to accessibility issues surrounding VR headsets. However, with companies like Apple entering the market and marketing headsets as personal computing devices, I anticipate that VR will become increasingly prevalent over time, although the high cost may initially limit its accessibility.

Regarding media consumption, social media, and video journalism are reshaping how information is disseminated. Citizen reporting through videos has become common, although there is a risk of misinformation due to the potential for manipulated content. Even written content, such as blogs, can be manipulated using tools like chat GPT, further complicating the landscape of news consumption. This phenomenon reflects a growing skepticism towards traditional news organizations perceived as biased, leading many individuals to rely on alternative sources for information. 

What advice can you offer for aspiring AR/VR professionals?

Never depend on one gaming engine or software; always learn different ones. This not only enhances your resume but also expands your debugging skills. Follow your true passions in design and development, regardless of trends. Trust your skills and ability. Make friends, not connections. Take deep breaths and, most importantly, enjoy the journey. 

***

If you’re eager to delve into immersive storytelling, Dina suggests exploring the following selections from The New York Times:

“Your Guide to Augmented Reality in The Times” 

“How We’ll Bring the News Into Your Home”

“Explore NASA’s InSight Mission on Mars”

Dina particularly enjoys articles by Yulia Parshina-Kottas, who graduated from the same program at NYU as her. Dina had the privilege of receiving this journalist’s feedback on her thesis project. 

***

You can follow Dina’s work on her website. 

The post Dina Khalil On Redefining Storytelling in the Digital Age appeared first on The Urban Watch Magazine.

In today’s bustling tech landscape, Aveneel Waadhwa, a Product Manager at Microsoft on the Azure Optimization team, stands out with his future-forward, innovative, entrepreneurial spirit and global perspective.

Born and raised in New Delhi, India, Waadhwa embarked on a journey that spanned continents and diverse educational and professional terrains. His path, shaped by a deep-rooted passion for technology and a commitment to fostering community, epitomizes the modern product manager: adaptive, forward-thinking, and driven by a vision to impact the world through innovation.

Waadhwa earned his bachelor’s in Data Science & Economics from UC Berkeley and gained experience at multiple startups before joining Microsoft. At Microsoft, his impact has been significant, helping the company save millions of dollars to accelerate innovation and launching a custom GPT and plugin for Microsoft Copilot from the ground up.

Beyond his corporate role, Waadhwa is deeply committed to making a difference. He won the “Hack for Good” category at the 2021 Microsoft Hackathon, benefiting Amrita-Seattle, a charity supporting at-risk children worldwide. His team built a robust website that expanded the charity’s reach and positively impacted children’s lives. Additionally, he co-founded Aspiring Product Manager, a non-profit organization dedicated to helping aspiring product managers break into the tech industry through mentorship, guidance, and job application feedback.

In an exclusive interview, The Urban Watch sat down with Aveneel Waadhwa to delve into his career journey, the transformative impact of AI in product management, and his vision for the future of tech entrepreneurship.

Tell me a little bit about your background and where you grew up.

I grew up in New Delhi, India, and moved to the US at 18 to attend the University of California, Berkeley. I double majored in Data Science and Economics, graduating in 2021. I’ve worked at Microsoft for three years and interned as a product manager at three different startups during college. 

Were you always drawn to numbers and interested in working in this field?

My interest in numbers and data science developed over time. Initially, my interests were all over the place—I enjoyed art and history, studied Spanish for a year in college, and even took some film classes. However, my data science class became my favorite, and I also enjoyed the psychology aspect of economics—understanding how people think and make decisions.

Tell me about your life in New Delhi. Were your parents entrepreneurs?

Growing up in New Delhi was a unique experience. I was diagnosed with leukemia at age 2 and battled it until I was 7. Fortunately, my parents and grandparents, who are all doctors, identified it early. My older brother also pursued an engineering degree, which had a significant influence on me. While my parents weren’t entrepreneurs, their dedication and work ethic inspired me to start something of my own one day.

What has been the most significant personal challenge you’ve ever faced? 

Navigating the United States with uncertainty and anxiety as an immigrant has been my biggest personal challenge. Moving to a new country as a teenager and being an international student meant starting my network from scratch. Entering the job market for the first time as an immigrant was also a significant hurdle.

You’ve lived in the U.S., UK, India, Portugal. How has this multicultural experience influenced who you are today and how you approach professionalism?

Living in diverse cultures has made me open-minded, inclusive, and understanding of different work styles. It has helped me embrace various professional approaches and ways of thinking from people around the world. Empathy and good communication are essential skills for product managers, and my multicultural experiences have enabled me to develop these skills.

I interned in the UK one summer, where I reconnected with family and worked for a startup. I also completed a rigorous startup training program at the European Innovation Academy in Lisbon, Portugal, where I met professionals from various fields, including marketing and design. In this program, we formed teams of five and started a company in three weeks. It was an exciting experience as I had always wanted to create my own company and build something from the ground up.

At the Academy, we worked from 8 a.m. until late at night, culminating in pitching our projects to investors. My team created a travel app that generates real-time itineraries based on user preferences, such as soccer, jazz, or Spanish food. The app optimizes these preferences to provide sample itineraries.

This experience improved my communication skills and made me a better product manager by combining people skills with technical expertise. It also sparked my love for travel; I’ve now visited 43 countries.

Based on this experience, I was hired while still in college to work on another travel app called Welcome, which was acquired last year.

How did your career path lead you to become a product manager at Microsoft?

I have always been passionate about innovation and entrepreneurship. I asked my professors how I could become an entrepreneur and founder. I learned that being a product manager was the perfect role for this. As a product manager, you create a roadmap, work with engineers, design products, and oversee their launch. You also collaborate with marketing and advertising teams. The opportunity to wear many hats excited me, and I knew it would prepare me for success as a founder.

During my senior year of college in 2020, I applied to and interviewed with many big tech companies. The job market was very challenging due to COVID-19, but I networked extensively to get referrals, participated in mock interviews, and prepared thoroughly for my applications. In Fall 2020, one semester before graduation, I was able to land a job offer from Microsoft.

Could you elaborate on the importance of launching a custom GPT and plugin for Microsoft Copilot?

I’m most proud of developing the custom GPT and FinOps (financial operations) plugin that I launched for Copilot, Microsoft’s AI companion. I led this product from ideation to execution, guiding it from concept to launch. Integrating with Azure enabled users to efficiently access and edit financial data directly within Microsoft Teams, eliminating the learning curve associated with navigating new financial portals and saving considerable time for internal users.

What are the advantages of Copilot? How does it differ from ChatGPT?

Copilot serves as an AI assistant embedded in various Microsoft products such as Windows, Office 365, Teams, and Excel. Unlike ChatGPT, which primarily responds to queries, Copilot acts as a proactive assistant. It can analyze documents, draft emails, and even create PowerPoint presentations. This integration into daily workflow tools enhances productivity by automating routine tasks and providing contextual assistance beyond simple question-answer interactions.

What was the process like for launching a custom plugin?

The project began during the hackathon week in September 2023 and took approximately eight months to complete from ideation to general availability launch. Microsoft’s annual hackathon provides employees with the opportunity to work on innovative projects of their choice for a week. Inspired by the success of large language models like ChatGPT, I proposed the idea during the hackathon, received approval, and successfully implemented it by the end of the semester.

What are some of the biggest challenges you face as a product manager?

As a product manager, one of the biggest challenges I face is navigating ambiguity and uncertainty while planning for the future and developing products. Often, there’s a significant information gap when entering new markets or exploring unfamiliar territories. Effectively estimating impact and resource allocation is crucial in this role, alongside the need to quickly adapt to shifting priorities and market dynamics.

Tell me about some of your most significant accomplishments.

Besides launching the Copilot plugin, my most significant accomplishment is helping my organization at Microsoft save millions of dollars. My focus has been on optimizing Azure resources through app development and product launches that facilitate cost savings for our customers. Identifying and implementing these cost-saving initiatives across various areas within a large company can have a profound impact. It not only justifies our efforts but also frees up resources that can fund innovative projects, particularly in AI, which may need more initial funding at the start of the fiscal year. This funding accelerates innovation and product development.

Another accomplishment I’m really proud of is winning the Microsoft Global Hackathon in the “Hack for Good” category back in 2021. Our project supported Amrita-Seattle, a charity that serves at-risk children and youth globally, providing nutrition, education, healthcare, and clean water solutions in the U.S., India, and Nepal. Within a week, we developed a comprehensive website for the charity to facilitate donations. I maintained ongoing communication with the charity and later presented our work to Microsoft’s leadership team, showcasing the impact of our efforts.

You’re also a co-founder of Aspiring Product Manager, helping aspiring product managers break into tech. Tell me more about the organization, your role there, and some key advice for aspiring product managers.

I co-founded Aspiring Product Manager with two friends who are PMs at Coinbase, aiming to support aspiring product managers, especially college students, in breaking into the tech industry. Securing a product management role straight out of college can be challenging, mainly since, historically, many startups only hired PMs once they reached a team size of 35 or 40 employees. However, smaller companies are now beginning to hire PMs earlier in their growth stages as well.

Our organization serves as a valuable resource, focusing on helping aspiring PMs understand the product management interview process. The most common question we receive is what students should study in college to become a product manager. We recommend focusing on computer science and data science, with additional coursework in entrepreneurship and psychology to understand user behavior and enhance technical skills.

As a non-profit volunteer initiative, we started on LinkedIn and have since expanded to include a newsletter. We primarily cater to college students, offering resources and community support. While we have previously held in-person meetings, our resources are now primarily accessible online. Our initiative welcomes anyone interested in product management, and membership is free of charge as we focus on giving back to the community and supporting aspiring product managers in their career journeys.

What trends do you think will most impact the technology industry in the next five years?

For any new tech company, investing in technologies such as AI is essential. Most companies want to work on anything new as fast as possible. It’s a race for innovation. Soon, we will see AI everywhere in our daily lives. For the next few years, it will only help innovation. The government is also investing in AI, underscoring its growing importance. For product managers and young ambitious people in tech, we always want to be working with cutting-edge tech. The goal is to make our lives better and more efficient. Every product manager wants to make their products better. AI tools are making our lives easier, helping us focus on big decision-making rather than menial tasks. 

How is AI changing the field for product managers?

As outlined in my op-ed, AI is revolutionizing the day-to-day tasks of product managers, making their jobs easier and allowing them to focus on critical priorities. This new wave of AI will make product managers more crucial than ever. PMs are accountable for making responsible decisions regardless of the product they own, but they also hold the power to shape the future of AI products and, in turn, the future of our world. With great power comes great responsibility, which should make Product Managers feel critical, influential, and responsible for the ethical use of AI in their work. As PMs don’t spend hours writing product requirement documents (PRDs) and can automatically generate analytics dashboards with suggested metrics, they can devote more time to understanding customer needs, developing product vision and strategy, building customer empathy, and leading cross-functional teams.

What’s your ultimate career goal? 

My ultimate career goal is to start my own company and lead a team, drawing from my experiences at Microsoft and startups. I aim to continue working in AI, focusing on cutting-edge technologies. I have a strong interest in the travel space and the coffee industry and a passion for healthcare. These areas align with my ambitions to innovate and make a meaningful impact in diverse sectors.

What do you like to do for fun in your spare time?

I enjoy traveling whenever possible, trekking, and playing and watching soccer. I’m also a massive cinema buff. On Saturdays, I frequently host coffee events in New York City called “Customs,” where I bring friends and network with others to build community. I hope to expand these events into public gatherings in the future.

You can follow Aveneel Waadhwa on LinkedIn. 

Sign up for his Aspiring PM Newsletter. 

Read his op-ed on how AI will reshape product management. 

The post Meet the Product Management Rock Star: Aveneel Waadhwa appeared first on The Urban Watch Magazine.

Artificial Intelligence (AI) has already significantly impacted various aspects of our lives, sparking discussions about its benefits and the need for regulation. Whether it’s fashion or any other industry, the influence of AI is a topic of ongoing debate. AI is not yet as smart as the human brain but it sure does mirror us, albeit without emotions.

We interviewed distinguished AI researcher and senior data scientist Sumedha Rai, based in New York, to delve into her journey in AI and discuss the latest developments, regulations, and future trajectory of the field.

With extensive experience spanning multiple sectors including technology, finance, medicine, and SaaS, Rai’s primary focus lies in Natural Language Processing (NLP), a field of AI that has gained prominence with advancements such as ChatGPT. NLP enables human-machine interaction through language, allowing machines to comprehend nuances in communication, including sentiments, overarching themes, variations in word usage, and context-dependent meanings.

Rai’s work revolves around deciphering user sentiments and categorizations, generating compelling conversation topics, soliciting opinions on contemporary issues, and translating manual notes into actionable insights for software. She thinks the world of NLP and AI offers immense possibilities and finds great joy in exploring its potential. 

Tell me a little bit about your background. Where did you grow up, where did you go to college, and what did you study? What motivated you to pursue a career in AI/ NLP, and how did you get started? 

I grew up and studied across India, the UK, and the US. I was a big computer science enthusiast in high school and was almost greedy to learn anything related to technology. During my Bachelor’s, however, I pivoted to majoring in Economics and subsequently studied Finance during my first Master’s. Coding took a back seat, but I was learning statistics and math, and anything analytical captured and held my interest. I spent a few years in investment banking and private equity, and it was then that I realized that I was constantly trying to connect and evaluate tech companies for investments because I was so enthralled about learning the tech behind the product.

During evaluation calls, I asked more technical questions rather than focusing on business-related metrics. Two years later, I quit and pivoted towards data science in the policy sector. I aimed to ride the rising tide of machine learning (ML) while still retaining the comfort of being enveloped in economics and finance.

After spending some time in the UK, I worked at the Central Bank of India, where I gained valuable experience. During my time there, I explored how learning ML and AI could integrate into a variety of use cases with the right data and mathematical models. Following my stint in India, I began my graduate studies at New York University, which opened up a whole new world of AI for me!

During my time at NYU, I had the opportunity to learn about deep learning from Dr. Yann LeCun, the founding father of Convolutional Neural Nets. It was a surreal experience, and I discovered something fundamental and beautiful: anything can be considered data if you know how to use it! This realization was transformative and expanded my understanding of the possibilities within the field of AI.

Natural Language Processing is one such field where, for the longest time, we haven’t fully harnessed the potential of textual data or grasped the enormous volume of data available. I appreciate the fact that our curriculum included courses on the ethics of machine learning. These courses were a constant reminder to exercise caution and mitigate the potential harm biased data can cause.

The first research paper I read on a pre-trained transformer like BERT– a machine learning framework that Google developed in 2018 to improve NLP– brought me immense joy; it was so eloquently crafted. From that moment on, I actively pursued projects with text data. When I began furthering my career in AI, I strongly advocated for ingesting diverse types of text data to develop actionable insights for the business.

Sometimes, a straightforward model revealing customers’ overall sentiment can significantly influence the future strategy for a newly released feature. This insight can save a considerable amount of time and money. While I am engaged in various machine learning projects, my involvement in Natural Language Processing (NLP) projects has steadily increased and proven successful in deployment. I am also actively engaged in academic research in the field of NLP.

Tell me about some of your biggest accomplishments/awards/projects you’re most proud of.

My passion for coding goes back to high school. I graduated from high school in India, where I formally learned coding for four years in two different languages as part of my curriculum. My dedication paid off when I was ranked number one nationwide among approximately 100,000 students who took the final exams each year. Achieving the “All India Topper” title was a personal triumph I hold dear and take great pride in.

Upon entering my first master’s program, I was awarded a full scholarship, a recognition granted to only two individuals in the university. While academic achievements are significant and reflect years of hard work, professional success also requires consistent effort and dedication.

One of my proudest projects was building a model for a bank that predicted the probability of default – the likelihood that a borrower will fail to meet their debt obligations. The dataset for this endeavor comprised a mix of structured and unstructured data, including numeric and text values. Some of the data was proprietary, and some of it was publicly web-scraped. The sheer volume of data I processed for this prediction was staggering, and it became one of my longest-running projects. The bank where I implemented this model successfully deployed it for two years before we updated it to incorporate newer techniques.

One of my other proudest achievements is in the field of fraud modeling, a field I actively create models in and also speak about. Fraud prevention is an integral component of every financial institution’s operations. Leveraging machine learning, we can proactively mitigate fraudulent activities by forecasting the likelihood of fraudulent events and taking preemptive measures. This initiative comprises a suite of specialized models tailored to combat various forms of fraud, which we identify as pivotal to safeguarding our organization. My overarching predictive modeling endeavors generally encompass a robust array of advanced fraud prevention techniques, fortifying defenses against threats such as account takeovers, transaction fraud, payment fraud, and similar malicious activities.

What are some real-world applications of NLP that have had a significant impact on various industries? 

Large language models are currently at the forefront of discussions, and ChatGPT, rooted in Natural Language Processing (NLP), is creating significant waves. It is utilized daily by millions of people across various industries, highlighting the impact of leveraging vast amounts of data with the right model architecture. This technology demonstrates an incredible ability to understand context like a human and deliver real-time responses that are grammatically precise and highly relevant. Remarkably, all of this is achieved by a trained machine.

As an NLP researcher, I possess an understanding of the underlying building blocks of this model architecture. Yet, despite my expertise, I am continually amazed by its capabilities. The fact that a machine can exhibit such sophisticated language skills never fails to impress me.

I’ve gained substantial experience in the fintech and healthcare domains. The automation of tasks, such as analyzing financial and legal documents and categorizing them into relevant sections, has proven to be a remarkable time-saver. Many banks have streamlined these processes, leading to significant efficiency improvements in upstream and downstream operations.

Furthermore, sentiment analysis based on comments, surveys, and social media data following crucial market announcements allows for a near-real-time understanding of public reception. These insights are invaluable for financial firms as they enable swift assessment of public sentiment and facilitate data-driven decisions. 

NLP has found extensive application in medical document processing and analysis in healthcare. I do research with OLAB, a healthcare lab at NYU that uses ML-guided investigations to augment clinical work in treating neurological disorders and cancer. One notable project, NYUTron, developed by the research team at OLAB, has created a health system that utilizes unstructured clinical notes from physicians. This system predicts crucial metrics like patient readmission rates, length of stay, insurance denial, and even in-hospital mortality. These predictions are pivotal in healthcare, where accuracy is a matter of life and death, potentially saving lives through timely interventions.

What are NLP’s current challenges and limitations, and how is the field addressing them?

Since NLP is a language-specific technology, its challenges are similar to those encountered when teaching a new language to a beginner. Grasping the alphabet and mastering grammar represents just one aspect. Additionally, we use the same words to convey different meanings based on their context of usage. This nuanced understanding, referred to as ‘context,’ has seen significant progress in training our machines by exposing them to numerous language examples.

However, difficulties occur when attempting to convey sentiment through the ‘tone’ of our voice. Sarcasm and a straightforward sentence might employ the same words, but their delivery distinguishes their intent. What may seem positive to a machine could carry a negative connotation. Teaching a machine something we often take for granted—the concept of ‘common sense’—also poses challenges. Moreover, the ever-changing landscape of slang requires constant model updates to keep pace with this informal aspect of language.

Another challenge I have encountered relates to industry-specific language, often called industry jargon. An NLP model well-suited for healthcare may not yield accurate results when applied to financial or legal documents. Researchers are continuously involved in training industry-specific models to enhance the accuracy and usefulness of this technology across various fields. However, challenges persist in highly specialized fields or areas lacking sufficient data to train models for optimal performance.

The lack of digital data for some low-resource languages poses a significant hurdle. Due to limitations in data resources, training NLP models for these languages proves difficult.

Are there ethical concerns or biases associated with NLP, and how can they be mitigated?

This question is one of the most important questions discussed today. It is also a question that every researcher should consider when comprehending data for their models. In simple terms, a biased dataset used to train a model will inevitably lead to biased results in the model.

Harvard Business Review highlighted the detrimental effects of biases in NLP, as they could potentially hinder individuals’ opportunities and economic participation. As an example of gender bias in models, Amazon’s previous resume-filtering algorithm displayed a notable preference for words like “executed” or “captured,” which were more commonly used by male applicants.

However, when I asked ChatGPT a gender-biased question such as “What professions do men generally have?” it responded with, “Professions are not inherently gender-specific, and one’s gender should not limit the choice of profession. In many parts of the world, there has been a move towards breaking down traditional gender roles, and individuals of all genders pursue a wide range of careers.” This demonstrates that the model can adjust and reduce biases if the underlying data is updated to reflect a more contemporary understanding of our society.

I cannot stress enough how careful we must be about biases when we train our machine-learning models. Biased or unbalanced data ingestion can lead to results that may discriminate based on race, religion, or demographics. 

For example, imagine you want to build a model to predict the most common professions of people from Columbia University. However, if you only collect data from Columbia alumni who became journalists because you know a lot of journalists from Columbia, your model might incorrectly conclude that most Columbia graduates pursue journalism careers. This is a classic case of selection bias because your sample, in this case, only includes a specific group of people (journalists), and it doesn’t accurately represent the diverse range of professions that Columbia graduates actually pursue. As a result, your model’s predictions about the most common professions would be biased and inaccurate due to the limited and unrepresentative sample. 

Now imagine if these biases were related to sensitive matters such as crime, social issues, and other public concerns– they could have significant and undesirable repercussions. To mitigate these concerns, rigorous anti-bias checks should be integral to data preparation for NLP models. Regular updates and incorporating new and balanced data from credible sources can enhance the robustness of the models and minimize the risk of discriminatory predictions. Additionally, I believe that for critical issues, the utilization of NLP models should be coupled with impartial human review of the results to guarantee fairness and accuracy.

Some famous authors have sued Open AI for copyright infringement, alleging illegal use of copyrighted material to train AI models. If the plaintiffs win this case, how could this case reshape AI?

I wouldn’t say this is surprising. Training a large language model requires a huge amount of data, and some authors or content creators may be uncomfortable that this training was conducted without their explicit consent or involved data repositories containing copyrighted information. I’m uncertain about the feasibility of obtaining approvals for such an extensive volume of data used in model training. 

To my understanding, organizations such as OpenAI and other prominent developers are allocating legal funds and resources to tackle such situations. This approach offers mutual benefits and serves as a means of collaboration with writers.

Such cases may become more common as large language models continue to be developed for various applications. While data available in the public domain can be incorporated without legal issues, we may witness lawsuits arising from other concerns. However, I don’t think this will significantly hinder AI’s almost unfettered pace of experimentation and innovation. Alternative methods and agreements will probably surface to make this process advantageous for both parties involved.

Some people are concerned that AI is going to replace jobs. Others say that employees who know how to utilize AI will stand out in their fields. What do you think?

Personally, I lean towards the second viewpoint with a slight modification. I believe companies and individuals who have embraced AI and kept pace with the rapidly evolving and potent technology are more likely to thrive in the future.

There was a time when computers were a novel innovation, and some individuals hesitated to adopt them due to trust issues surrounding their use to store and process sensitive information. Today, it’s almost impossible for a business to remain entirely offline. 

We are moving in the same direction regarding AI. Early adoption and education will be crucial for success in certain industries. Furthermore, individuals with expertise in using AI will possess a valuable skill set that will become increasingly important in the hiring process. In the realm of economics, the traditional labor-versus-capital paradigm is evolving. While some manual roles may become automated, this will also enable the workforce to focus on more innovation-driven positions that drive technological progress.

When it comes to innovation, nothing can truly compare to the human mind. So, while there might be a reconfiguration of roles, I am not in favor of being overly apprehensive about such a powerful technology. History has shown that similar narratives played out when computers were introduced, yet they ultimately brought significant positive changes.

What are your thoughts on fashion AI?

When people think about AI, the terms that immediately come to mind might not include ‘fashion.’ I’ve noticed discussions about AI in medicine, marketing, and algorithms, but there is less emphasis on fashion and AI, despite my belief that this industry was an early adopter of AI technology.

We’ve seen virtual ‘try-ons’ for clothing and accessories, visual searches for products, and lookalikes based on a user’s picture. Recently, AI has been used for sustainable fashion, inventory management, and supply chain optimization. An interesting take on fashion and AI that I read recently was trying to predict the different fabrics used within a clothing line and automate the labeling process of garments. Like medicine and finance, I think AI will change how we embrace “fashion” and adopt it in the future.

In the field of NLP, I’m excited about the prospect of fashion utilizing text prediction analysis to assess recent trends and gauge user sentiments towards them. This technology could extend its reach to a broader audience through multilingual support within brands, particularly in content creation, customer support, and feedback collection. Additionally, this data could enhance personalized user recommendations and overall user experience.

Do you think AI can replace fashion models or actors in the future?

This question is exciting because, from my perspective, the fashion industry has been at the forefront of embracing AI. An excellent example is the Metaverse Fashion Week held in March 2023, which was already the second time this event was hosted. It was fascinating to see that some of the biggest names in the fashion industry participated, and the event was truly remarkable. It seamlessly blended the digital and real worlds, creating a lifelike experience. The avatars, runway shows, networking spaces, and, oh, the dresses themselves were all so impressive. It’s a testament to the fashion industry’s innovative approach to integrating AI and technology to create captivating and immersive experiences. 

Despite the integration of AI, I remain unconvinced that it can entirely replace fashion models or actors. In these industries, audience relatability is paramount. The genuine glow of a skincare product on a human model, the confidence exuded by a woman strutting down the runway in a flattering dress, or the emotion conveyed by an actor to their audience is challenging to replicate digitally. These experiences are relatable because they allow you to envision yourself in their shoes. To put it somewhat poetically, I firmly believe that the irreplaceable and intangible human essence that connects the fashion and entertainment industries with their consumers cannot be mimicked. While the allure of integrating this technology is undeniable, and these industries are known for their adaptability to new trends, I foresee a journey of integration and enhancement rather than outright replacement.

While integration is positive,  I believe it must be undertaken with an ethical foundation, keeping in mind the well-being of employees. Implementing changes in any industry should be done in a regulated way and at a measured pace.

What laws and regulations need to be in place as AI further develops? 

It’s interesting that you’ve raised this question because I recently wrote an article discussing the need for AI regulations in various countries. One of the biggest concerns has been data privacy, given some AI models’ extensive use of personal data. Individuals should have the right to determine precisely how much data they are comfortable sharing. If that choice makes certain services unavailable to them, that’s fine, too. However, the essential element is the availability of that choice.

In many instances, I’ve encountered people willing to disclose information if it leads to enhanced convenience, while others are more cautious. This ‘right to choose to disclose’ is, in my opinion, very important. It is problematic that many companies present lengthy and convoluted agreements about data disclosures, often leaving consumers unsure about what they consent to disclose. This practice troubles me, even as someone who recognizes the importance of data for building effective AI models.

The European Union (EU) has been notably proactive in safeguarding the data rights of its citizens. The General Data Protection Regulation (GDPR) is one of the most rigorous data privacy laws in existence. It not only mandates compliance for organizations within the EU but also applies to any company, regardless of location, if it handles data related to EU citizens. Several other countries are developing and implementing data privacy regulations, while some have minimal regulations.

With the White House’s announcement of the Executive Order on AI, we are much closer to having comprehensive federal data and AI laws in the United States. This development is significant, as the absence of federal frameworks leads to individual states enacting their own privacy laws. Such a scenario could complicate the operations of AI businesses working across state lines in the future, as compliance requirements would vary from one state to another.

What advice do you have for individuals who want to pursue a career in NLP/AI or enhance their skills? Are there specific resources, courses, or tools you recommend for learning NLP/AI?

Drawing from my personal experience, adopting a structured approach greatly helped my understanding of the fundamentals of AI. Before pursuing my Master’s in Data Science at NYU, I had some experience in data science. However, during my Master’s, I dug deep into foundational subjects like linear algebra, probability and statistics, data structures, and algorithms. I also learned how to write cleaner, more efficient code, and aligned with the best practices of coding standards. These skills serve as the cornerstone for machine learning and AI, and I firmly believe that taking shortcuts in learning AI would prove detrimental over time.

While using pre-built libraries can expedite the coding process, it’s very important to understand the underlying principles. You must be able to diagnose technical issues at the very source. If a model yields unsatisfactory results, it’s essential to question its fundamental assumptions. Similarly, to convert a problem statement to a predictive model, you must be able to conceptualize how to quantify it and prepare it for machine processing. Therefore, if you haven’t yet familiarized yourself with the basics of mathematics, statistics, and computer science required for ML, I recommend seeking structured online courses to acquire these skills. YouTube channels like “3brown1blue” offer good visualizations of linear algebra concepts, and it helps to learn from visual representations of complex ideas.

Additionally, acquire real-world datasets for practice in model development. Platforms such as Kaggle provide problem statements and datasets, and numerous other websites offer free datasets for experimentation.

For those interested in NLP, I remember reading the research paper that initiated the wave of transformer models, titled “Attention Is All You Need” by Vaswani et al. I strongly recommend this paper for those starting in the field. Jay Alammar’s blog is another resource known for its intuitive explanations and helpful illustrations. You can also benefit from video lectures and courses by renowned experts in the AI field. 

Dr. Andrew Ng offers courses on Coursera and other online learning websites (even YouTube – DeepLearningAI). Professor Canziani at NYU strongly advocates free education and has shared YouTube videos and lecture notes on Deep Learning on his website. Many universities also release lecture notes and videos for official courses taught online. 

If you can’t find code notebooks for practice, I recommend seeking out coding examples or notebooks that contain actual code for NLP or AI problems, understanding them, and then attempting to replicate the process with a different dataset or another use case. Learning from your coding mistakes is key to understanding and applying these concepts to real-world applications.

***

If you’re interested in further reading on AI, here are some published articles by Sumedha Rai:

Sumedha Rai’s Op-Ed on “the Race for AI” in The Business World. 

Comparing Large Language Models

Fraud Detection

Bias Detection in AI

You can follow Sumedha on LinkedIn

Or visit her website 

The post Senior Data Scientist Sumedha Rai on the Future of AI appeared first on The Urban Watch Magazine.

Aveneel Waadhwa, Product Manager at Microsoft working on the Azure Optimization team, shares his thoughts on product management in the age of AI.

Imagine a world where product managers possess the superpower to predict customer needs with pinpoint accuracy, streamline workflows effortlessly, and make data-driven decisions in real time— all through the transformative power of artificial intelligence. 

In this article, I dive into how AI revolutionizes product management, enhances efficiency, and creates new opportunities while exploring the ethical responsibilities accompanying these advancements.

Can AI Replace Product Managers?

Let’s address the elephant in the room: Can AI replace product managers (PMs)? For now, the answer is no. With their unique blend of hard and soft skills, product managers bring an irreplaceable human touch to technology. They wield the power to make decisions that could enhance or disrupt systems, a responsibility AI cannot replicate. This reassurance should instill confidence in product managers about the value of their skills in the age of AI.

While AI can aid in various aspects of product management, such as data analysis and automation, it falls short in areas requiring human creativity, emotional intelligence, and “soft skills”— indispensable for effective product management. For example, while tools like Gamma AI for presentations or Otter.ai for meeting notes streamline our tasks, product managers uniquely provide the nuanced understanding of customer needs and the empathy necessary to build strong teams.

That said, I’d argue that this new wave of AI will make product managers more critical than ever. PMs are accountable for making responsible decisions across their product portfolios while also holding the power to shape the future of AI products and, in turn, the future of our world. With great power comes great responsibility, which should make product managers feel critical, influential, and responsible for the ethical use of AI in their work.

Leveraging AI at Work

In my role at Microsoft, I regularly utilize AI to enhance decision-making and streamline processes. For instance, Azure Machine Learning analyzes vast volumes of data to uncover patterns and insights that inform our product strategies.

Recently, my team launched a Copilot plugin for internal customers, reducing access time to view and edit Azure budgets, forecasts, and projections by approximately 50 percent. This plugin also makes it easier to surface cost-saving recommendations to customers and help answer their FAQs and product queries.

During a project to optimize our organization’s Azure services, I had one of the most impactful experiences with AI. By integrating Azure Machine Learning, we identified subtle customer preferences that we would have otherwise missed. This led to a more personalized product experience and higher customer satisfaction. Using Power BI for real-time data visualization allows us to make informed decisions swiftly, adapting our strategies to market changes almost instantaneously. Copilot has saved me countless hours drafting initial document versions, allowing me to focus on strategic planning and team coordination.

Here are some AI tools that I frequently use at work:

• Azure Machine Learning: Data analysis and trend identification. 
• Copilot in Microsoft 365: Assisting in drafting documents like Product Requirement Documents (PRDs). 
• Power BI: Creating interactive dashboards for real-time insights.
• Dynamics 365 AI: Understanding customer feedback and predicting market trends.
• Copilot plugins: Managing internal tasks and tooling, including dogfooding the plugin I launched. 

Integrating Microsoft’s AI tools into my workflow enhances productivity and frees up time for innovation and creative problem-solving, resulting in improved product outcomes.

Equipping PMs for the AI Revolution

Proficiency in AI fundamentals and key machine learning concepts is now more crucial than ever. According to the World Economic Forum, 23 percent of global jobs will change in the next five years, driven by advancements in artificial intelligence and other text, image, and voice processing technologies.

Reflecting on my decision to major in Data Science, I value the hands-on experience gained from running machine learning models and exploring AI libraries and frameworks. This knowledge has been invaluable in understanding the capabilities and limitations of AI tools.

Future computer science and data science curricula will likely incorporate responsible AI requirements. Programs like Stanford’s Symbolic Systems or my alma mater, UC Berkeley’s Cognitive Science, which blends technical skills with humanities, will become more relevant. These programs prepare PMs to navigate the ethical considerations of AI, ensuring privacy, data protection, and bias mitigation are prioritized in product development. 

Prioritization is a significant responsibility for any product manager, especially those involved in AI products. Beyond speed, considerations of privacy, data protection, ethics, and biases must inform the crafting of product roadmaps. While this shift in mindset may take time for some PMs, it’s a necessary change. The responsible use of AI is not a passing trend; it’s a prerequisite for the future of product management. Emphasizing responsibility should empower product managers and underscore their accountability for the ethical use of AI in their work.

Ethical Use of AI

The ethical use of AI is paramount in today’s technological landscape. Product managers must ensure that AI is used responsibly, minimizing risks and maximizing benefits for society. Here are some concrete examples of how PMs can use AI ethically in their work:

• Transparency and Explainability: Ensure that AI models are transparent and their decisions can be explained. Using AI tools like Explainable AI (XAI) can help product managers understand and communicate how AI makes decisions, which is crucial for maintaining user trust. Lack of transparency can lead to mistrust among users and stakeholders, potentially resulting in the rejection of AI solutions. For example, a lack of transparency in AI algorithms used in hiring practices can lead to legal challenges and reputational damage for companies.
• Bias Mitigation: Actively work to identify and mitigate biases in AI models. This includes using diverse datasets and regularly auditing AI systems for discriminatory patterns. Tools like IBM’s AI Fairness 360 can help in this process. Failure to address biases can result in discriminatory practices, leading to unfair treatment of certain groups. This can cause significant harm to affected individuals and expose the company to legal and regulatory repercussions. For instance, biased AI in credit scoring systems has led to unfair loan denials and increased regulatory scrutiny.
• Data Privacy: Prioritize data privacy and protection. Ensure that AI systems comply with regulations like GDPR and CCPA and implement robust data anonymization techniques to protect user information. Neglecting data privacy can lead to severe data breaches, resulting in substantial financial penalties and loss of customer trust. For example, the GDPR has imposed hefty fines on companies for data privacy violations, highlighting the importance of compliance.
• Ethical AI Usage Policies: Develop and enforce ethical AI usage policies within the organization. This includes setting clear guidelines on the acceptable use of AI and training employees on ethical AI practices. Without clear policies, companies risk unethical use of AI that can lead to public backlash and damage the company’s reputation. Ethical lapses in AI deployment can also result in long-term negative impacts on brand perception and customer loyalty.
• Stakeholder Involvement: Involve diverse stakeholders in developing and deploying AI systems. This ensures that different perspectives are considered and the AI system aligns with the values and needs of various user groups. Ignoring stakeholder input can result in AI solutions that do not meet user needs or ethical standards, leading to poor adoption and potential failures. Stakeholder engagement is crucial for developing effective and ethically sound AI.

The Future of AI in Product Management

As AI evolves, its impact on product management and the broader technology landscape will be profound. Increased regulation of AI technologies is expected to ensure ethical and responsible use. For instance, the European Union’s AI Act aims to establish a legal framework for AI, focusing on transparency, accountability, and ethical use. This act will categorize AI systems based on risk levels, ensuring higher-risk applications undergo rigorous scrutiny. Similarly, the United States is considering the Algorithmic Accountability Act, which would require companies to assess the impact of their AI systems and mitigate any potential biases or harms. Detecting deepfakes and AI-generated content is also a challenge, with companies like Reality Defender and GPTZero working with governments and universities to solve it.

Product managers must stay well-versed in these regulations to ensure compliance and ethical product development. This requires continuously updating their knowledge to align with new legal requirements and societal expectations. As AI tools become more sophisticated, PMs will play a crucial role in interpreting AI-generated insights and ensuring these technologies benefit users and society.

While AI tools might suggest a reduced demand for product managers due to increased efficiency, the reality is more nuanced. AI will streamline many tasks, but the role of PMs will also expand to include new responsibilities, such as ensuring AI alignment with regulatory requirements and interpreting AI-generated insights. This will likely increase the need for skilled PMs who can navigate this complex landscape.

AI Tools for Product Managers

Here are some AI tools that product managers can use to streamline their daily tasks:

• Gamma AI: For AI-powered presentations.
• Copilot on Word or Notion AI: For drafting documents.
• ChatPRD or WriteMyPRD: For crafting 1-pagers and PRDs
• ClickUp: For automating workflows.
• Mixpanel: For AI-driven product analytics.
• Productroadmap.ai: For creating product roadmaps efficiently.
• Copilot on Teams or Otter.ai: For effortlessly taking meeting notes.
• Enterpret: For analyzing customer feedback across channels.

These tools significantly save time and offer a glimpse into an exciting future where PMs can focus more on strategic decision-making and less on mundane tasks.

AI and Techno-Optimism

Techno-optimism is the belief that technology, especially new technologies such as AI, will ultimately improve our lives and solve many of humanity’s problems. I am optimistic about AI’s potential to balance technical and soft skills in the tech industry. Developing empathy and collaboration skills will be crucial in the age of AI. Tech companies, often criticized for lacking humanity, can benefit from this shift as AI encourages more human-centric decision-making.

A great example of this is Blank Street Coffee. The company uses automation to handle routine tasks, freeing up their service workers to focus on engaging with customers and improving their experience. Instead of just making coffee, employees can spend more time building relationships with customers, enhancing overall satisfaction. This model shows how AI can foster better human interactions by allowing workers to focus on what they do best—providing excellent customer service.

As we stand on the brink of the AI revolution, it’s clear that product managers are uniquely positioned to harness this technology for profound impact. AI will transform product management, technology, and the world. By embracing AI responsibly and leading with empathy and innovation, product managers can shape a future where technology serves humanity in meaningful and ethical ways. Let’s champion solutions to significant problems and ensure that as AI grows more intelligent, it enhances our human experience and drives us toward a better and brighter future.

This article was originally published on MSN.com

Aveneel Waadhwa is a Product Manager at Microsoft, based in New York City, working on the Azure Optimization team. He is also the co-founder of Aspiring Product Manager, a non-profit organization dedicated to helping aspiring product managers break into the tech industry through mentorship, guidance, and job application feedback. With a background in Data Science from UC Berkeley, Aveneel is highly knowledgeable about AI and its applications in product management. Outside work, Aveneel enjoys traveling to new countries, hiking, hosting coffee events, playing soccer, and watching movies.

The post How Will AI Reshape Product Management? appeared first on The Urban Watch Magazine.