An Interview With Chad Silverstein
“I realized that the biggest innovations in AI would not necessarily be the most technically sophisticated ones. They would be the ones that make complexity disappear and create experiences that feel natural for people.”
As a part of this series, we had the pleasure to interview Anshul Gandhi.
Anshul Gandhi is a seasoned AI and product leader with over a decade of experience driving innovation across enterprise platforms, emerging technologies, and go-to-market strategy. His expertise lies in establishing and scaling enterprise B2B AI products and intelligent systems, spanning AI/ML-driven decision systems, intelligent root cause analysis, resource optimization, modern data and machine learning platforms, and generative AI technologies powering next-generation business innovation and transforming how organizations operate and create value. A recognized voice in AI innovation, Anshul regularly shares insights at leading AI and technology conferences and has contributed to multiple patented AI technologies, with a strong focus on helping organizations responsibly harness AI to solve complex business challenges and create measurable impact.
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?
I did not originally set out to build a career in AI. Early in my journey, I found myself increasingly drawn to a question that stayed with me: How do we make better decisions at scale when human judgment alone becomes difficult? That question sparked a deeper curiosity around intelligent systems and how technology could be used not only to understand problems, but to proactively solve them.
Over time, I realized what truly kept me in the field. AI is finally beginning to disappear into products and experiences that naturally fit into our lives, instead of feeling like a completely separate technology people have to learn or adapt to. I became increasingly fascinated by the idea that the most meaningful innovation is not just making technology more powerful but making it more intuitive and valuable for people. That is what ultimately shaped my path and continues to motivate me today.
Can you share the most interesting story that happened to you since you started working with artificial intelligence?
I think one of the most interesting moments for me happened when I started seeing people with little or no technical background interact naturally with highly sophisticated AI systems and immediately get value from them. Earlier in the industry, there was often an assumption that people would need to understand the technology, learn new interfaces, or adapt themselves to the system. Watching that assumption quietly break was fascinating.
I remember thinking, the real breakthrough was not that AI had become smarter. It was that AI had become more human. It completely changed the way I thought about building products because it moved the conversation from efficiency alone toward empowerment, creativity, and human impact. I realized that the biggest innovations in AI would not necessarily be the most technically sophisticated ones. They would be the ones that make complexity disappear and create experiences that feel natural for people.
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?
If I had to narrow it down to three, I would say curiosity, bias for action, and empathy. Looking back, what is interesting is that I only realized how important each was after seeing them play out in real situations.
Curiosity because I have often found myself asking questions outside the immediate problem in front of me. I remember situations where a discussion began around a technical challenge, but I kept pulling on adjacent questions around user behavior, product decisions, or business outcomes because I wanted to understand the bigger picture. I learned pretty early that innovation rarely happens in neat boundaries. It often happens when different worlds unexpectedly collide.
Bias for Action because AI moves far too quickly for perfect information to ever exist. I remember situations where teams could spend weeks debating an idea, while a quick prototype or experiment could provide an answer in days. That changed my perspective because I realized that speed is not simply about moving faster. Speed is really about accelerating learning.
Empathy because I remember watching people with little or no technical background interact naturally with advanced AI systems and immediately gain value from them. What struck me was seeing people stop focusing on the technology itself and simply focus on what it enabled them to do. That was a powerful reminder that the most impactful products are often not the ones people admire for their complexity, but the ones people naturally adopt because they fit seamlessly into their lives.
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?
At a prior company, we faced a challenge where traditional security workflows generated large volumes of vulnerability alerts, but static severity scores often lacked enough context to determine what truly required immediate attention. That created significant operational overhead and made prioritization difficult across large and diverse device environments.
To address this, we used AI to combine real-time device telemetry with vulnerability intelligence and create a more context-aware view of risk. Rather than treating every vulnerability the same, the system continuously evaluated the likelihood that an issue could realistically be exploited within a specific environment and helped guide remediation actions such as update prioritization and patch sequencing.
This shifted security from a reactive process toward a more predictive one. Instead of teams spending time manually triaging large volumes of alerts, they could focus attention on the risks that mattered most, accelerate remediation decisions, and manage security more effectively at scale.
What are some of the common misconceptions you’ve encountered about using AI in business? How do you address those misconceptions?
One of the most common misconceptions I have encountered is that AI adoption starts with the model or the technology itself. In my experience, it starts with clarity around the problem being solved. People often ask, “Which model should we use?” before asking, “What decision, workflow, or customer experience are we trying to improve?” AI is not a shortcut around strategy. It is a force multiplier for people who already understand where and how value is created. I usually address this by shifting the conversation away from tools and back toward outcomes.
Another misconception is that AI creates value simply because it has been deployed. I have seen people treat AI as a feature that can be switched on rather than a capability that compounds over time. I usually address this by reframing the discussion around learning, adoption, and impact. Deploying AI is easy. Changing how people work, create, and make decisions is where the real value begins.
In your opinion, what is the most significant way AI can make a positive impact on businesses today?
By expanding human capability and improving decision-making at scale. Much of the conversation around AI has focused heavily on automation and efficiency gains. AI has the potential to go a step further by helping people process information, identify patterns, and uncover opportunities that might otherwise remain invisible.
What makes this especially powerful is that it goes beyond simply doing work faster. AI has the ability to democratize expertise by making capabilities that were once limited to specialists more broadly accessible. That changes how people work, how products are built, and ultimately how value is created. I believe the most meaningful business impact of AI will come not from replacing human judgment, but from expanding what people are capable of achieving.
Ok, let’s dive deeper. Based on your experience and research, can you please share “5 Ways AI Can Solve Complex Business Problems”?
1. Turning information into intelligence
Many businesses do not struggle with a lack of data; they struggle with disconnected data. I have seen teams spend more time searching for information than acting on it. AI can connect knowledge across systems so people spend less time looking and more time deciding. Information has always existed; the challenge has been making it usable at the moment decisions need to be made.
2. Improving decision-making at scale
AI can surface patterns and signals that may otherwise remain hidden. Think about operations teams anticipating demand changes or identifying risks before they become larger problems. The value of AI is not simply providing more answers; it is helping people ask better questions.
3. Redesigning workflows with AI agents
AI becomes more powerful when it moves beyond answering questions and begins coordinating actions. Imagine an agent reviewing a customer issue, drafting a response, updating systems, and triggering next steps automatically. The future of AI is not isolated intelligence. It is intelligence embedded into workflows.
4. Accelerating innovation cycles
AI can dramatically shorten the path between an idea and execution. Product teams can move from customer feedback to prototypes and testing much faster than before. Innovation used to be limited by execution capacity. Increasingly, it is becoming limited by imagination.
5. Scaling capability across teams
One of the most powerful impacts of AI is enabling more people to contribute in ways that previously required specialized skills. A business user can explore data, generate insights, or create solutions without always depending on experts. AI may not replace expertise, but it can dramatically expand who gets to apply it.
How can smaller businesses or startups, with limited budgets, begin to integrate AI into their operations effectively?
I would begin with the part of the business where AI can create visible leverage quickly: customer experience, internal knowledge, sales workflows, support, content operations, or decision support. I would not start by asking, “What AI tool should we buy?” I would start by asking, “Where are people repeatedly losing time or slowing decisions?”
The most effective path is usually to place intelligence directly inside existing workflows rather than creating another disconnected system. AI creates the most value when intelligence disappears into the workflow instead of becoming another destination.
For startups, the advantage is often product intimacy. They are closer to users and can turn real-world signals into product improvements much faster. The interface is no longer just where people use a product; increasingly it becomes where the product learns from people. Every interaction becomes a signal: what users ask, where they struggle, what they ignore, and what they repeat.
My advice would be simple: begin with one high-friction workflow, measure whether AI meaningfully improves it, and expand from there. The goal is not to build an AI feature. The goal is to build a product that becomes continuously better because it learns.
What advice would you give to business leaders who are hesitant to adopt AI because of fear, misconceptions, or lack of understanding?
AI conversations often become difficult because people immediately jump to “How will this change everything?” I have found that a more useful question is “What changes if intelligence becomes abundant and accessible?” That shifts the conversation from technology itself toward customers, workflows, decisions, and experiences.
Whether it is improving customer experiences, helping teams access knowledge faster, reducing repetitive work, or making better decisions, practical experience usually creates clarity much faster than abstract discussions.
Fear often grows when people interact with narratives instead of reality. Understanding usually begins when people interact with the technology itself.
I also think it is important to remember that AI does not create value simply because it exists. It creates value when intelligence becomes part of the way people already work and interact. Some of the most important technologies eventually disappear into everyday experiences and stop feeling like technology at all.
The challenge for leaders today is not keeping up with every AI announcement. It is understanding which changes genuinely alter human behavior and which simply create temporary excitement.
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?
AI will increasingly shape the business world by becoming more deeply integrated into how products, workflows, and decisions operate. The shift will likely be less about isolated AI features and more about systems that understand context, reason across information, and operate with increasing levels of autonomy over time. Businesses will gradually move from using AI to automate individual tasks toward using AI to coordinate and augment larger systems of work.
What I find particularly exciting is that we are beginning to see new AI labs and infrastructure efforts challenge some of the core assumptions behind current approaches across models, agents, and compute. We are seeing work around spatial intelligence and world models, where systems learn from interaction and environments rather than relying only on next-token prediction. We are also seeing movement toward autonomous software systems that can understand repositories, documentation, and system behavior over time rather than simply assisting with isolated tasks.
The interesting question is no longer only how capable models become. It is what fundamentally new behaviors become possible when the assumptions underneath those systems begin to change.
These shifts matter because they expand what AI systems can fundamentally do, moving beyond isolated responses toward systems capable of maintaining richer context and operating over longer horizons. The next decade may be shaped as much by compute, energy efficiency, cost, and long-running reliability as by advances in model capability itself. The future may be defined not simply by intelligence, but by the ability to make intelligence reliable, economically viable, and usable at scale.
What excites me most is that we are beginning to move toward systems that do not simply generate outputs, but increasingly understand, adapt, and operate over time.
How do you think the use of AI to solve business problems influences relationships with customers, employees, and the broader community?
I think AI changes relationships when it shifts access, agency, expertise, and trust. In many ways, the technology itself is not the deepest shift. The deeper shift is how people experience capability and who gets access to it.
For customers, I think expectations will increasingly move toward experiences that feel more contextual, responsive, and useful in the moment. People will become less interested in simply receiving information and more interested in systems that understand intent and help them accomplish goals with less friction.
For employees, I do not think the long-term impact is simply automation. I think it is the broader expansion of expertise. AI has the ability to make capabilities that were once limited to specialists more broadly accessible, changing how people learn, create, and contribute within organizations.
The broader community dimension is more complex because the same technologies that expand access can also amplify questions around fairness, transparency, and accountability. As AI becomes more integrated into everyday experiences, people will increasingly evaluate not just what systems can do, but whether they deserve reliance.
The most meaningful impact of AI may not simply be making systems more intelligent. It may be changing who gets access to intelligence, and whether people can trust the systems that deliver it.
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. 🙂
One thing that fascinates me is that many of humanity’s hardest challenges are not necessarily limited by intelligence itself. Different communities, disciplines, and institutions often understand only a small part of a much larger system.
I would want to push toward something I think of as Open Intelligence Networks, not in the sense of open data, because data ownership and privacy matter, but in the sense of creating shared models of understanding.
The idea would be to build systems that can understand environments, learn from interactions across domains, and continuously refine richer models of how complex systems behave over time. Rather than treating intelligence as isolated answers or disconnected expertise, the goal would be helping people connect perspectives, surface hidden relationships, and build a deeper understanding of problems while preserving ownership of underlying information.
The future of AI may not be defined by systems that know more facts. It may be defined by systems that build richer models of how the world actually works.
I would want AI to help make connections that people and institutions might never naturally discover on their own, because some of the world’s biggest problems may not require more intelligence alone. They may require intelligence that is better connected.
How can our readers further follow you online?
You can follow my work on LinkedIn, where I share perspectives on AI, emerging technologies, and the evolving relationship between intelligence, technology, and human potential.
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. Learn more at www.chadsilverstein.com
Anshul Gandhi 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.