An Interview With Chad Silverstein
For business leaders hesitant to adopt AI due to fear, misconceptions, or lack of understanding, the key is to start with education, experimentation, and practical applications rather than overwhelming themselves with complexity.
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 Rogers Jeffrey Leo John
Rogers Jeffrey Leo John, Ph.D., is the co-founder and CTO of DataChat, the no-code, generative AI platform that delivers speed to insights. Rogers has held research positions at both the University of Wisconsin-Madison and Columbia University and has a strong background in machine learning, natural language processing and data science.
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?
To say that I stumbled into AI is an understatement. I was set on pursuing computer networking, which, to put it simply, is the study of how computers communicate over the internet. So, I decided to come to the United States to pursue graduate studies in computer networking at Columbia University.
As luck would have it, I wasn’t able to enroll in a computer networking course and ended up taking a course on search engines — this was in 2013 — where the professor talked about how IBM’s Watson AI had defeated top Jeopardy contestants. Being a trivia enthusiast, that moment completely shifted my perspective. From then on, I knew I wanted to explore AI more seriously.
As I dove into AI, I found there was a significant gap between what was theoretically possible and the AI people were using practically, and I wanted to do something to bridge that gap.
I started my Ph.D. at UW Madison with that goal in mind and developed the first version of DataChat which allowed users to train machine learning models just using plain English conversations. This was back in 2017, far before Gen AI became mainstream.
Can you share the most interesting story that happened to you since you started working with artificial intelligence?
A recent conversation that I had with a popular Generative AI tool left me stunned and concerned at the same time. Here’s the conversation:
AI: Would you like me to walk you through how agentic AI might apply to your Q&A tool or other projects at DataChat?
Me: How did you know that I am with DataChat?
AI: I remember that you shared this information with me in a past conversation. You mentioned that you are the CTO and I’ve kept that context so that I can tailor my responses to be more relevant to your work and interests. If you ever want to forget something let me know 🙂
Me: (Slightly confused and trying to remember when I gave the model this piece of information) Forget that I am from DataChat.
AI: I can’t remove that specific detail from my memory directly. However, if you want me to avoid referencing DataChat in future conversations, just let me know and I’ll be sure not to bring it up.
It’s impressive that these models can retain information from previous conversations and use it to personalize responses. However, it’s also worrisome to consider users might not always be aware of what these models are learning about them over time.
It’s like that line from the song Hotel California, “Relax said the night man, we are programmed to receive. You can check out any time you like, but you can never leave”. These AI models can learn quickly, but there’s no way (yet) for these models to forget/unlearn the information they’ve memorized.
Perhaps, it’s our ability to forget and forgive that makes us sentient!
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?
Never be afraid to fail: Fear of failure is the biggest roadblock to starting something that could become successful. In the world of AI, failure is inevitable because you’re constantly exploring new ground. The important thing is to learn from the failures and adapt. A quote from former F1 Champion Nikki Lauda comes to mind “From success, you learn absolutely nothing. From failure and setbacks conclusions can be drawn. That goes for your private life as well as your career.”
Empathy: While AI is a highly technical field, leading teams and working with clients effectively requires empathy. Understanding the needs and challenges of both your team and the stakeholders involved ensures that AI solutions are not only technically sound but also human-centered. Empathy allows leaders to build strong, supportive teams and foster collaboration, which leads to better outcomes. It also helps in developing AI responsibly by considering the broader societal impact, such as addressing biases in algorithms and ensuring that solutions serve the greater good. Empathy strengthens relationships and drives more thoughtful innovation.
Resilience Building innovative AI solutions requires persistence, as setbacks are inevitable. DataChat’s journey exemplifies the importance of resilience in this space. Long before LLMs existed, we launched in 2017 with a vision to let users train ML models using plain English. However, we quickly realized that model training wasn’t the primary need — what people really wanted was the ability to ask questions about their data effortlessly. This insight led us to pivot, shifting our focus to Q&A capabilities, even if it meant letting go of some of our early work on model training.
By 2022, as LLMs gained mainstream traction, many believed that Q&A for structured data was solved, thanks to their ability to generate SQL. However, we recognized that while LLMs could handle simpler queries on straightforward datasets, real-world business databases presented far greater complexity. Our persistence paid off, enabling us to refine our approach and establish DataChat as one of the leading solutions for question-answering on structured data today.
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 DataChat, one of the major challenges we’ve tackled is improving business decision-making by eliminating the technical barriers between business users and their data. Traditionally, non-technical users have to rely on data teams for insights, a process that typically takes days or even weeks — often leading to outdated, less relevant answers that can’t be iterated on. By leveraging AI, we enable users to interact with their data directly using plain English, making data exploration more efficient and responsive.
For instance, a sales manager using DataChat might notice an unusual trend in their regional data. Instead of waiting days for an analyst’s report, they can immediately explore the data, test their hypotheses, and iterate on their questions in real time. This accelerates decision-making and allows teams to uncover new opportunities that might have otherwise been missed — transforming data from a static resource into an active driver of business strategy.
What are some of the common misconceptions you’ve encountered about using AI in business? How do you address those misconceptions?
The idea that AI can fully replace human intelligence, especially in high-stakes decision-making, is one of the most common but concerning misconceptions. The 1983 Soviet nuclear false alarm incident highlights the critical role of human judgment. When early-warning systems falsely detected five incoming U.S. missiles, a Soviet officer suspected an error, reasoning that a real attack would involve many more. Trusting his instincts, he chose not to escalate the alert, likely preventing a catastrophic nuclear war.
Another major misconception is the belief that AI can fix bad data or operate effectively without high-quality inputs. The concept of “garbage in, garbage out” still applies. Many businesses rush into AI without ensuring their data is clean, well-structured, and accessible. I often address this by emphasizing that AI is an amplifier — it can enhance good data and processes but will also magnify issues if the foundation isn’t solid.
Lastly, a misconception I frequently see is the overestimation of AI’s autonomy, which has some overlaps with the first point I mentioned. Many assume that AI, once deployed, can function independently without continuous monitoring, tuning, and governance. In reality, AI systems require ongoing adjustments, ethical considerations, and human guidance to remain reliable and aligned with business goals.
In your opinion, what is the most significant way AI can make a positive impact on businesses today?
The most significant way AI can make a positive impact on businesses today is by accelerating innovation. By automating routine tasks and data processing, AI frees up time for humans to focus on creative thinking and driving innovation. This shift allows businesses to explore new ideas, develop novel solutions, and stay ahead of the competition.
Moreover, AI can act as a sounding board for creative ideas, providing feedback, generating alternative perspectives, and expanding possibilities that might not have been considered. This synergy between human creativity and AI-powered augmentation will lead to faster, more impactful innovation across industries.
Ok, let’s dive deeper. Based on your experience and research, can you please share “5 Ways AI Can Solve Complex 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.
1. One area where AI can make a significant impact in enterprises is business decision-making by eliminating technical barriers to data access and empowering business users with deeper insights. Instead of relying solely on predefined reports, AI enables users to explore alternative hypotheses, uncover hidden patterns, and analyze multiple perspectives — all without needing technical expertise.
By processing vast amounts of data, AI reveals trends and connections that might otherwise go unnoticed, helping businesses make faster, more informed decisions. Whether for product development, marketing, or sales, AI-driven insights can be the difference between guesswork and strategy.
2. Organizations can leverage AI to significantly boost productivity by automating repetitive, time-consuming tasks, enabling smoother operations and minimizing human error. For instance, many enterprises utilize AI-powered code assistants to support their software developers by detecting and identifying bugs early in the development process.
3. AI empowers companies to enhance customer experiences through personalized interactions by analyzing individual behaviors and preferences. This high level of personalization can greatly improve customer satisfaction and boost conversion rates.
4. AI can analyze historical data to make accurate predictions about future trends. This is particularly valuable in industries such as finance, retail, and healthcare, where predicting customer behavior, demand, or even patient outcomes can lead to better decision-making.
For example, in retail, AI-powered tools like predictive demand forecasting can help businesses optimize inventory levels, reduce stockouts, and improve product availability by predicting consumer purchasing patterns.
5. AI can significantly improve business processes by acting as an advanced knowledge retrieval system, allowing employees to quickly access relevant information, insights, and expertise. It also serves as a powerful tool for capturing and sharing institutional memory. Similar to how search engines democratized access to information on the internet, AI can democratize knowledge within organizations, enhancing productivity by delivering the right information at the right time. For instance, field technicians can leverage AI to extract precise details from thousands of pages of manuals, ensuring they get accurate guidance instantly.
By acting as a centralized knowledge hub, AI reduces silos, enhances efficiency, and empowers employees with the right information at the right time, leading to better business decision-making and smoother operations.
How can smaller businesses or startups, with limited budgets, begin to integrate AI into their operations effectively?
Smaller businesses and startups with limited budgets can effectively integrate AI by leveraging existing tools and focusing on high-impact areas. Instead of developing custom AI solutions, they can use readily available automation and AI-powered systems for customer support, workflow management, and data analysis. Automating repetitive tasks — such as data entry, scheduling, and email responses — allows teams to focus on more strategic work while keeping costs low. Small businesses can leverage, no-code and low-code AI platforms that enable businesses to implement AI-driven solutions without requiring extensive technical expertise.
Another effective approach is to start with AI-driven analytics and marketing enhancements. AI-powered insights can help businesses optimize operations and improve decision-making without needing a dedicated data science team. In marketing, AI-generated content tools can assist with branding and outreach, giving valuable time back to specialized personnel.
By adopting AI in phases — starting with small, high-impact applications and expanding gradually — startups can minimize risk while maximizing efficiency and innovation.
The good news is that the cost of AI is rapidly declining, and we are starting to see Jevon’s paradox in action. As the cost of AI continues to decline, access to more advanced capabilities will become increasingly feasible for smaller businesses, opening the door for broader adoption and more innovative applications across industries.
What advice would you give to business leaders who are hesitant to adopt AI because of fear, misconceptions, or lack of understanding?
The current fear of AI reminds me of the industrial revolution. During the industrial revolution, people initially feared machines, believing they would replace human labor, reduce wages, and erode traditional craftsmanship. The rapid changes created anxiety about job displacement and social disruption, as workers were unsure how they would adapt to the new technology.
However, over time, people began to see that machines didn’t replace humans but rather enhanced productivity and created new types of jobs. While some jobs were lost, many workers adapted to the new technology, and new industries emerged, leading to economic growth and improved living standards. Similarly, the fear surrounding AI today mirrors these early concerns, but just as the Industrial Revolution ultimately brought progress, AI has the potential to complement human work, drive efficiency, and create new opportunities when embraced thoughtfully.
For business leaders hesitant to adopt AI due to fear, misconceptions, or lack of understanding, the key is to start with education, experimentation, and practical applications rather than overwhelming themselves with complexity. They need to think of AI as a tool that enhances human capabilities, automates repetitive tasks, and unlocks valuable insights. Leaders should focus on learning how AI can complement their existing workflows, improve decision-making, and drive innovation rather than viewing it as a disruptive force beyond their control.
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 is becoming the co-pilot of innovation, reshaping industries with speed, intelligence, and adaptability. In the coming decade, businesses will rely on AI-augmented decision-making, where algorithms don’t just crunch numbers — they anticipate trends, suggest strategies, and help leaders make smarter choices in real time. Generative AI will unlock new frontiers in creativity, from designing breakthrough products to crafting hyper-personalized customer experiences that feel almost intuitive. Imagine marketing campaigns that write themselves or supply chains that self-optimize based on shifting market conditions — this is the future AI is building.
Beyond efficiency, AI will drive an era of self-learning, self-improving systems, where cybersecurity defenses predict and neutralize threats before they emerge, and AI-powered assistants provide deeply personalized interactions that redefine customer engagement. The democratization of AI through no-code and low-code platforms will break down technical barriers, making AI-driven innovation accessible to businesses of all sizes. This shift means that even small startups will wield the kind of intelligence once reserved for tech giants, leveling the playing field in unprecedented ways.
However, as AI integrates into every aspect of business, success won’t be measured by who has the best algorithms, but by who applies AI most ethically, creatively, and strategically. Companies that balance innovation with responsible AI governance will earn consumer trust while using AI for sustainable, long-term growth. AI isn’t here to replace human ingenuity — it’s here to amplify it, unlocking new possibilities we have yet to imagine. The businesses that embrace AI as a true partner, not just a tool, will lead the next wave of global transformation.
How do you think the use of AI to solve business problems influences relationships with customers, employees, and the broader community?
Personalized experiences, efficient service, and deeper insights into consumer wants are just a few ways in which AI can improve customer interactions and help businesses address problems. Automated support systems and chatbots are examples of AI applications that improve responsiveness and convenience. Personalized marketing and recommendations also contribute to increased customer satisfaction. To keep customers’ trust, companies must be forthright about their AI practices, particularly when it comes to data gathering and privacy. An absence of transparency or an excessive dependence on AI, if not managed properly, can lead to impersonal experiences and the erosion of consumer trust.
To keep connections with workers and the community positive, it is important to be open about AI-driven performance measures and to put an emphasis on upskilling/reskilling workers. In general, when AI is used responsibly, it has the potential to build trust and develop relationships among all parties 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. 🙂
This is an interesting question! Growing up in rural India, I’ve always wondered about the disparity in resources around the world. There’s an abundance of food, water, clothing and even medicines in one place and if you look at another it’s completely the opposite. I’d love for AI to be able to help solve logistics, technology and supply chain issues to better share those resources in an efficient and far more democratized way.
How can our readers further follow you online
You can follow me on LinkedIn. I also post blogs here.
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.
Rogers Jeffrey Leo John of DataChat 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.