An Interview With Chad Silverstein

Building Trust Through Responsible AI: As AI becomes more integrated into business, addressing concerns around privacy, bias and security is non-negotiable. Leading organizations are establishing robust governance and ethics frameworks to ensure fairness and transparency. Building trust with customers and employees is essential for realizing AI’s long-term value.

In today’s tech-driven world, artificial intelligence has become a key enabler of business success. But the question remains — how can businesses effectively harness AI to address their unique challenges while staying true to ethical principles? To explore this topic further, we are interviewing Rajeshwari Ganesan.

Rajeshwari Ganesan is a global leader in Artificial Intelligence and Machine Learning, recognized for her pioneering contributions to the field. She holds the distinction of being the first Distinguished Technologist at Infosys, a leading global IT company with over 330,000 employees and more than four decades of experience. In her role, she spearheads AI innovation in enterprise applications, advising global CXOs on digital transformation strategies. Her groundbreaking work in predictive analytics and ML is evidenced by 14 U.S. patents and over 30 publications in top-tier journals. Rajeshwari’s vision extends beyond the corporate realm; she actively shapes AI education through her role on the US Infosys Foundation, embodying her belief in AI as a catalyst for positive societal change.

Thank you so much for joining us in this interview series. Before we dive into our discussion, our readers would love to “get to know you” a bit better. Can you share with us the backstory about what brought you to your specific career path in AI?

My journey into AI wasn’t purely academic; it started by focusing on a human problem. During a hackathon, my team and I developed an assistive app for autistic children called “Occam’s Razor.” At the time, smartwatches weren’t mainstream, so we designed a simple wearable device to detect subtle changes in heart rate and skin response that could indicate rising stress. The device would then send a discreet alert to a caregiver’s phone, notifying them that their child might need support.

The project was designed to augment a caregiver’s intuition, not replace it, providing peace of mind while allowing children more independence. The project was recognized with a social impact award, but more importantly, it taught me a foundational lesson: the most powerful applications of technology begin with a deep understanding of human needs. That principle has guided my career ever since.

Can you share the most interesting story that happened to you since you started working with artificial intelligence?

Early in my career, I was focused on a technical question: how can we use AI to predict and prevent failures in large-scale cloud systems? I developed a framework for this with a university, and after a three-year peer review, it was published in the IEEE Transactions on Reliability. I was proud of the work but assumed its application would remain in the academic and cloud computing spheres.

Years later, I was surprised to learn that my research had been adapted in fields I never anticipated. A team of researchers used my methods to improve the safety protocols for surgical robots. Another group applied the core concepts to manage risk in other cyber-physical systems, such as autonomous vehicles and smart city grids.

It was a powerful reminder that foundational work, even in a niche area, can provide a launchpad for innovation in completely different domains. Seeing a concept for cloud reliability contribute to safety in medical technology was a truly formative experience.

You are a successful leader in the AI space. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?

1. First-Principles Thinking: I approach AI by asking fundamental questions about how complex systems work, both in nature and in technology. For instance, observing decentralized systems like ant colonies, which achieve sophisticated coordination without a central commander, challenged my thinking about AI. Instead of focusing solely on building a single, monolithic “superintelligence,” this inspired me to develop algorithms modeled on collective sense-making. This approach has been key to my work in creating AI systems that align better with how human teams actually collaborate and solve problems.
2. Human-Centric Empathy: My work always begins with the question, “What does the user actually need to accomplish?” Effective AI must understand the context and assumptions behind a user’s request. In my work on AI agents, I advocate for building them with internal models that reflect a user’s goals. This ensures technology serves as a true partner for humans. For example, when integrating AI into a workforce, this means we focus on upskilling employees to collaborate with AI agents on complex tasks, rather than having them compete on routine ones. This approach ensures AI solves real pain points and builds trust.
3. Systems-Level Problem Solving: Early in my career, I learned that what seem like isolated software glitches are often symptoms of deeper, systemic issues. By asking how components and processes are interconnected, I found that small, targeted interventions could create exponential improvements in reliability. This shifted my focus from single applications to entire ecosystems of people, processes, and technology working in harmony. My goal is to build platforms where technology empowers communities and removes systemic barriers, inspired by leaders like Nandan Nilekani who have successfully executed such large-scale, system-driven solutions.

Let’s jump to the primary focus of our interview. Can you share a specific example of how you or your organization used AI to solve a major business challenge? What was the problem, and how did AI help address it?

A primary challenge for any large digital business is service reliability. Even a minor glitch in a service with millions of users can cascade into a major outage, impacting customer trust and revenue.

Instead of reacting to problems, our approach uses AI to proactively identify potential failures. Our machine learning models continuously monitor a client’s software environment, detecting subtle anomalies and performance deviations that are precursors to system failure. This acts as an early warning system, allowing teams to address issues before they affect a single user. This has drastically minimized downtime for our clients.

At Infosys, we are scaling this AI-first mindset. We have invested in training over 250,000 employees in AI skills and use our platform, Infosys Topaz, to help clients accelerate their own AI adoption. For example, we recently helped a major telecom client move an AI concept from idea to full deployment in just four weeks — a process that previously took many months.

What are some of the common misconceptions you’ve encountered about using AI in business? How do you address those misconceptions?

One common misconception is that AI is a “magic box” that can be switched on to run a business on autopilot. The reality is that today’s AI is more like “amplified intelligence.” It can process information and execute tasks with incredible speed, but it still relies on human expertise for setup, context and quality control. Most of the value comes from how well you frame the initial problem and how carefully you interpret the output.

Another misconception is that AI is an immediate threat to all jobs. More accurately, it is a tool that augments human capabilities. It allows people to offload routine tasks so they can focus on more strategic work, learn new skills or contribute to areas they previously couldn’t. Critical decisions, quality assurance and creative problem-solving still require human judgment. The goal is not replacement, but collaboration, where humans and machines each play to their strengths.

In your opinion, what is the most significant way AI can make a positive impact on businesses today?

In a world with vast amounts of data, AI’s most significant impact is improving the quality of our decision-making. It’s true value lies not just in automating tasks, but in its ability to analyze complex information and reveal patterns, risks and opportunities that humans might otherwise miss. Whether predicting customer behavior, optimizing a supply chain or identifying a new market, AI provides the clarity needed to move forward faster and with greater confidence.

Ok, let’s dive deeper. Based on your experience and research, can you please share “5 Ways AI Can Solve Business Problems”? These can be strategies, insights, or tools that companies can use to make the most of AI in addressing their challenges. If possible, please share examples or stories for each.

• Amplifying Human Intelligence, Not Replacing It: The most effective AI strategies use technology as a force multiplier. AI handles routine or data-intensive tasks, freeing up employees to focus on creativity, strategy and complex problem-solving. This shifts the conversation from job loss to human-AI collaboration, where technology enhances human talent.
• Focusing on High-Impact Problems: Instead of applying AI randomly, leading organizations map their entire value chain to identify where AI can deliver the greatest return. By strategically matching the level of AI autonomy to the task-more human oversight for high-risk functions, more automation for low-risk ones-businesses can maximize both efficiency and safety.
• Accelerating Innovation and R&D: AI can dramatically speed up decision-making by processing massive datasets in a fraction of the time it would take a human team. In research-heavy industries, this translates directly to faster drug discovery, more efficient product development, and more resilient supply chains.
• Investing in an AI-Fluent Workforce: Technology is only as good as the people who use it. Successful companies build a tiered talent strategy: fostering basic AI literacy across the organization, training practitioners who can deploy AI tools and cultivating experts who can build new AI solutions. This creates a workforce that can confidently adapt to change.
• Building Trust Through Responsible AI: As AI becomes more integrated into business, addressing concerns around privacy, bias and security is non-negotiable. Leading organizations are establishing robust governance and ethics frameworks to ensure fairness and transparency. Building trust with customers and employees is essential for realizing AI’s long-term value.

How can smaller businesses or startups, with limited budgets, begin to integrate AI into their operations effectively?

Smaller businesses have a key advantage over large corporations: agility. With powerful AI tools now available at low cost, they can experiment and pivot quickly. The best approach is to pick one specific, well-understood pain point — like customer support inquiries or inventory management — and test an off-the-shelf AI solution for a short period, such as 30 days.

They should measure the impact rigorously. If the tool doesn’t save significant time or reduce errors, they can drop it and try another without a lengthy procurement process. Their ability to use direct team feedback to guide decisions is a competitive edge. Every major AI transformation starts by solving one small problem well. Startups should focus on that first success and build from there.

What advice would you give to business leaders who are hesitant to adopt AI because of fear, misconceptions, or lack of understanding?

It’s natural to be cautious, especially given the pace of change. However, today’s AI is largely made up of specialized tools with clear capabilities and limitations. Leaders retain significant control over how these tools are implemented.

The most effective way to start is with a contained pilot project that addresses a specific, measurable business problem. This serves as both a hands-on learning experience and a proof of concept. Remember that AI acts as “amplified intelligence” — it makes teams more productive, but it requires human oversight to set goals and verify outcomes.

Crucially, investing in your workforce is as important as investing in the technology. Leaders should prioritize continuous learning to close the AI skills gap. This includes fostering collaboration models where humans and AI strategically divide tasks, ensuring all employees have the skills to work alongside these new tools. Building a culture of learning is the surest way to adopt AI thoughtfully and unlock its competitive advantage.

In your opinion, how will AI continue to shape the business world over the next 5–10 years? Are there any trends or emerging innovations you’re particularly excited about?

Over the next decade, I expect AI will evolve from a specialized tool into a network of autonomous agents integrated into core business processes. This will automate a significant portion of routine knowledge work, which presents both a challenge and an opportunity. Businesses that fail to adapt their models and workforce strategies will risk obsolescence.

This shift isn’t just technological; it’s also a major social and organizational change. As AI handles more analytical and repetitive work, uniquely human skills like strategic thinking, creativity, empathy and complex problem-solving will become even more valuable. The key trend I’m watching is how companies manage this transition. The most successful organizations will be those that not only adopt AI but also fundamentally redesign workflows and roles to foster human-AI collaboration. They will need to be proactive in reskilling their workforce to prepare for a future where value is created through partnership with intelligent systems.

How do you think the use of AI to solve business problems influences relationships with customers, employees, and the broader community?

When implemented thoughtfully, AI can deepen relationships across the board. For customers, it can enable highly personalized experiences and instant support. For employees, it can eliminate tedious tasks, freeing them to focus on more meaningful and creative work that requires a human touch.

However, this comes with a significant responsibility. To build and maintain trust, businesses must use AI in a way that is fair, transparent and accountable. Communicating clearly how and why AI is being used is critical. Ultimately, AI’s positive influence depends on a human-centered approach that creates lasting value for everyone involved.

You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people through AI, what would that be? You never know what your idea can trigger.

I would champion a global “AI Sandbox Movement.” This would be an open, accessible ecosystem designed to democratize AI and its benefits. The core idea is to provide smaller organizations — like local governments, non-profits and startups — with access to powerful AI tools and simulation environments that are typically only available to large tech corporations.

Imagine a platform where a town council could simulate the economic impact of an AI-driven factory on local jobs before it’s built, or where educators could collaboratively design and test new learning models for an AI-powered classroom. By making the tools to understand and model the future accessible, we would empower communities to make more informed decisions. This initiative would help ensure that AI’s development is guided by collective wisdom, shaping a future built by the many, not the few.

How can our readers further follow you online?

https://www.linkedin.com/in/grajeshwari/

To stay updated on our latest work in AI, data and digital transformation, I recommend following Infosys on LinkedIn and visiting our website at www.infosys.com. I also regularly share highlights and perspectives on my personal LinkedIn profile.

This was great. Thank you so much for the time you spent sharing with us.

About the Interviewer: Chad Silverstein is a seasoned entrepreneur with 25+ years of experience as a Founder and CEO. While attending Ohio State University, he launched his first company, Choice Recovery, Inc., a nationally recognized healthcare collection agency — twice ranked the #1 workplace in Ohio. In 2013, he founded [re]start, helping thousands of people find meaningful career opportunities. After selling both companies, Chad shifted his focus to his true passion — leadership. Today, he coaches founders and CEOs at Built to Lead, advises Authority Magazine’s Thought Leader Incubator.

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Rajeshwari Ganesan of Infosys On How Artificial Intelligence Can Solve Business Problems was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.