An Interview With Chad Silverstein
At CES, we view AI as a way to enhance what people do, not replace it.
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 John Loury.
As a part of this series, we had the pleasure to interview John Loury.
John Loury is a data-driven executive and President of Cause + Effect Strategy, where he leads at the intersection of business intelligence and artificial intelligence. His work focuses on helping organizations apply AI in practical, measurable ways to solve real business challenges, from improving decision-making to uncovering new growth opportunities. With more than fifteen years of experience, John has developed growth strategies for publicly traded companies, nonprofits, and organizations of all sizes. His approach centers on turning complex data into clear, actionable insights that drive performance. His career has consistently focused on identifying meaningful signals in complex environments. That foundation began with being part of a research team studying deception through nonverbal cues and has evolved into leading AI-informed strategy and data-driven marketing initiatives. Today, he oversees operations, strategy, and team leadership at CES, guiding clients in integrating AI responsibly while maintaining a strong human-centered approach to decision-making.
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 path into AI has always been centered on the question: how do you make better decisions in complex environments? As a research assistant in grad school, I worked with government teams to detect deception through micro-expressions and nonverbal cues. That work focused on identifying meaningful signals when information is incomplete or intentionally misleading.
That foundation carried into advertising and emerging technology, where the challenge became translating large volumes of data into decisions that actually drive business outcomes. Over time, I developed a consistent approach centered on cutting through noise and focusing on the signals that matter.
Founding Cause + Effect Strategy was a natural extension of that work. Today, we operate at the intersection of business intelligence and artificial intelligence, helping organizations apply them in practical, measurable, and aligned ways with real business goals.
To my core, I’m focused on solving complex problems, guiding teams, and delivering measurable value. AI is a powerful tool in that process, but the priority remains the same: making smarter, more informed decisions that move organizations forward.
Can you share the most interesting story that happened to you since you started working with artificial intelligence?
In Cause + Effect’s early days, the focus was exploring what was possible with data. I was constantly pushing our team to think bigger. I was asking questions like, “Why can’t we connect all of this data across systems?” and encouraging everyone to imagine what’s possible if we could unify it and use it to drive better decisions. That mindset helped spark innovation, but it also exposed a gap between vision and reality.
Back in 2015, the tools and infrastructure that make this kind of work more accessible today either didn’t exist or required highly specialized skills. We were trying to solve problems that neither the technology nor our team were fully equipped to handle yet. It was a moment where ambition was ahead of execution.
What made that experience so impactful was the lesson it taught. I realized that innovation isn’t just about pushing boundaries; it’s about grounding those ideas in what’s actually achievable and building the right team and capabilities to get there. That balance between imagination and execution has shaped how I approach AI ever since.
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?
Dream big. I’ve always tried to start with what’s possible, not what’s practical. When we founded Cause + Effect, the idea that companies should be driven by data to make better decisions wasn’t widely accepted. In 2015, that wasn’t a mainstream mindset. We were often told no. We were hung up on. But starting with that bigger vision allowed us to keep pushing forward, even when others haven’t seen it yet.
Remain dedicated. It’s easy to get discouraged when you hear, “no,” repeatedly or when something doesn’t go the way you expected. We’ve had plenty of those moments. What made the difference was staying committed to the core idea, even as we learned and adjusted how we delivered it. We’ve evolved a lot over time, but our core belief of helping businesses use data to drive better decisions has never changed.
Have the blend of what I describe as confidence and naïveté. When you start a business, you must believe you can do something better or different from what already exists. On paper, that sounds almost irrational, but that mindset is necessary to get started. I was just naïve enough to believe we could build something meaningful. Over time, that evolved into an informed confidence that allowed the business to grow. There was even a point early on where I truly questioned whether I was the person to lead this company into the next phase. With the support of a strong team and a willingness to grow, we’ve continued to scale, including 68% year-over-year growth going into our eleventh year.
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?
One of the most impactful ways we’ve used AI is through a tool we built called Roz, our internal proposal agent. The problem Roz helps solve is the time and effort required to develop high-quality, customized proposals while still maintaining speed and consistency across the team.
Roz helps execute and support the proposal development process by generating strong, structured drafts based on our knowledge base and prior work. It allows us to move much faster and ensures that what we produce reflects our voice and approach. The result is a much more efficient process in which our team can spend less time on execution and more time on insight, positioning, and impact.
We’re currently using version 1.1 of Roz and already have a long list of enhancements planned. It continues to improve, but the real strength comes from how it works alongside our team, not in place of it.
What are some of the common misconceptions you’ve encountered about using AI in business? How do you address those misconceptions?
A lot of businesses think that AI is something that doesn’t affect them now, or that it won’t happen for another few years. But AI is here now, and there is also a lot of fear around it.
One of the most common misconceptions I see is the idea that AI is here to replace people. People hear about automation or read headlines and assume that roles will disappear entirely.
That’s not how we approach it, and it’s not where we see the most value. At CES, we view AI as a way to enhance what people do, not replace it. The real opportunity is finding the right balance. We address misconceptions by encouraging people to use AI, especially for mundane tasks that slow people down. The best outcomes come when both humans and AI work together, not when one tries to replace the other.
In your opinion, what is the most significant way AI can make a positive impact on businesses today?
In my opinion, the most significant way AI can make a positive impact on businesses today is by taking on the mundane, repetitive work that slows people down and pulling that effort forward into something more valuable.
What that does is free people up to focus on what actually drives impact. Interpreting information, making decisions, solving problems, and thinking strategically about how to move the business forward. That shift is where the real value is created.
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. Software delivery and engineering productivity
AI can reduce time spent writing boilerplate code, generating tests, documenting functions, and assisting with refactoring. That matters when a business problem is not “write one app,” but coordinating thousands of developers across large codebases. Accenture’s GitHub Copilot rollout is a strong enterprise example, because it was deployed at a very large scale and framed as a productivity lever for complex delivery organizations.
2. Customer service automation
AI helps when companies face high-volume, repetitive, multilingual support demand. Klarna’s AI assistant is a good example because the company published concrete operating figures rather than vague claims. Klarna said the assistant resolved common issues such as refunds, returns, and payment questions at scale.
3. Supply chain and merchandising decisions
AI can help companies manage the complexity of product catalogs, supplier coordination, demand shifts, and internal workflows. Walmart uses generative AI internally for merchant and operational use cases, demonstrating how AI supports decisions across a complex retail system rather than only front-end chat experiences.
4. Fraud detection
Fraud is a classic complex business problem because attackers adapt quickly, and the cost of false positives is also high. Mastercard’s launch of AI-enhanced technology is notable because it combines modern AI techniques with real-time transaction scoring in a global network, and the company published a specific performance claim to support its effectiveness.
5. Drug discovery and R&D
Some business problems are complex because the search space is enormous. In pharmaceuticals, AI can help prioritize candidate molecules, predict interactions, and narrow experiments. Isomorphic Labs’ partnerships with Novartis and Eli Lilly are real, high-profile examples of AI being used to tackle costly and uncertain discovery pipelines.
How can smaller businesses or startups, with limited budgets, begin to integrate AI into their operations effectively?
The best place to start is testing in a safe, and controlled way. There are many free tools available, and while they can be helpful for personal use, I would encourage businesses to consider entry-level paid solutions that provide proper data privacy and guardrails.
You don’t need an expensive enterprise system to get started, but you do need to make sure your data is protected. Once that foundation is in place, the goal is to begin experimenting and establish your comfort level with technology.
I’ve seen leaders start by simply using AI to generate ideas, analyze information, or support business development efforts. That alone can open people’s eyes to what’s possible. From there, the focus becomes thoughtfully integrating those tools into decision-making, always with a human reviewing and guiding the output.
What advice would you give to business leaders who are hesitant to adopt AI because of fear, misconceptions, or lack of understanding?
First, they need to understand that AI isn’t going anywhere. It’s already here, and its capabilities will only continue to grow. Ignoring it or hoping it fades away isn’t a strategy.
I would encourage leaders to start by having conversations with peers and trusted advisors. A lot of what’s out there online is focused on big, attention-grabbing ideas rather than practical applications. That can actually increase anxiety instead of helping.
The real value comes from understanding how AI applies to your specific business. That’s where peer conversations, local networks, and trusted partners can make a big difference. Focus on building your AI fluency, not just consuming headlines.
At the end of the day, the goal isn’t to replace what you do. It’s to figure out how these tools can support you, improve efficiency, and help you create more value.
In your opinion, how will AI continue to shape the business world over the next 3–5 years? Are there any trends or emerging innovations you’re particularly excited about?
Artificial intelligence is already delivering meaningful impact across back-office operations and repetitive technical tasks. These are areas where businesses can drive immediate efficiency, reduce manual effort, and improve accuracy through automation.
The more strategic question is: where does human judgment remain essential? Leadership, mentorship, and culture cannot be automated. Setting a vision, aligning teams, and building trust are still human responsibilities that directly influence how effectively AI is implemented within an organization.
In many ways, AI increases the importance of these human elements. As more processes become automated, differentiation comes from how well organizations lead, communicate, and make decisions. The companies that succeed will be the ones that combine AI-driven efficiency with strong, human-centered leadership.
How do you think the use of AI to solve business problems influences relationships with customers, employees, and the broader community?
AI has the potential to strengthen relationships if it’s used thoughtfully and with the right balance. From a customer perspective, AI allows businesses to be more responsive, more personalized, and more efficient. It can surface insights faster and help companies better understand what their customers actually need.
For employees, the biggest impact is in how work changes. When used well, AI removes a lot of the repetitive, low-value tasks that can slow people down. That gives teams more time to focus on strategic thinking, creativity, and problem-solving. At the same time, it’s important to be transparent and intentional. People need to understand how these tools are being used and how they support their role, not threaten it.
At a broader level, AI also pushes organizations to think more carefully about responsibility. How data is used, how decisions are made, and how outcomes impact different groups all become more visible. That creates an opportunity for businesses to build stronger, more accountable relationships with their communities.
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 focus on building a movement centered on compassion and shared humanity. Too often, attention is placed on what separates people rather than what connects them. Most people are working toward similar goals. They want to support their families, create a meaningful life, and feel a sense of purpose. The paths may differ, but the underlying motivations are remarkably consistent.
Shifting the starting point to those shared experiences can change how people relate to one another. It creates space for more understanding, even in everyday interactions. The challenges faced by someone in a neighboring community are often closer to our own than we realize. Recognizing that common ground is a powerful place to begin.
How can our readers further follow you online?
Visit https://causeandeffectstrategy.com/ or connect with me on LinkedIn https://www.linkedin.com/in/johnloury/
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.
John Loury of Cause + Effect Strategy 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.