C-Suite Perspectives On AI: John Loury of Cause + Effect Strategy On Where to Use AI and Where to Rely Only on Humans
An Interview With Chad Silverstein
The goal isn’t to replace people, it’s to enhance their ability to focus on higher-value work.
As artificial intelligence (AI) continues to advance and integrate into various aspects of business, decision-makers at the highest levels face the complex task of determining where AI can be most effectively utilized and where the human touch remains irreplaceable. This series seeks to explore the nuanced decisions made by C-Suite executives regarding the implementation of AI in their operations.
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 (CES), where he leads at the intersection of business intelligence and artificial intelligence. With more than fifteen years of experience, he has developed growth strategies for publicly traded companies, nonprofits, and organizations of all sizes, helping them turn complex data into actionable decisions.
His career has centered on identifying meaningful signals in complex environments, from early work as part of a research team studying deception through nonverbal cues to leading innovative data-driven marketing initiatives. Today, he oversees operations, strategy, and team leadership at CES, guiding clients through complex business challenges with a strategic, client-first approach.
Thank you so much for joining us in this interview series! I know that you are a very busy person. Our readers would love to “get to know you” a bit better. Can you tell us a bit about your ‘backstory’ and how you got started?
My path into AI has always been rooted in understanding how to 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 experience was all about identifying meaningful signals when information is incomplete.
From there, I moved into advertising and emerging technology. Those experiences shaped how I think about data and decision-making. I’ve always been focused on finding the signal that actually matters when surface-level information can be misleading.
Founding Cause + Effect was a natural extension of that. Today, we operate at the intersection of business intelligence and AI, helping organizations turn complex data into clear, actionable decisions.
At my core, I’m a data-driven leader with an entrepreneurial mindset, focused on solving complex problems, guiding teams, and delivering measurable value. AI is simply the latest evolution of that work.
It has been said that our mistakes can be our greatest teachers. Can you share a story about the funniest mistake you made when you were first starting? Can you tell us what lesson you learned from that?
One of the most important lessons I learned early on at Cause + Effect came from trying to push boundaries without fully grounding those ideas in reality. As a young company, I was constantly encouraging our team to think bigger, to imagine what was possible, and to work backward from that vision. That mindset helped us innovate, but at times we leaned too far into possibility without having the right capabilities in place to execute.
A specific example was around connecting data across systems. Back in 2015, the tools and infrastructure we take for granted today simply didn’t exist (or required highly specialized skills). I was asking, “Why can’t we connect all of this?” but the reality was that we didn’t yet have the team or technical foundation to make it happen.
The lesson for me was finding the balance between ambition and execution. You need people who are willing to push limits, but also people who can ground those ideas and bring them to life. That balance has become a core principle for how we operate today.
Are you working on any exciting new projects now? How do you think that will help people?
An exciting project we’re working on right now is the continued evolution of our proposal agent, Roz. We originally built Roz to help execute and support the proposal development process, and it’s already become a strong example of how AI can accelerate work without replacing the people behind it.
Roz helps us move faster, organize our thinking, and generate strong starting points in our own voice. It reduces the time spent on repetitive execution so we can focus on what actually matters.
We’re now using version 1.1 of Roz, and we already have a long list of enhancements planned for what comes next. Each iteration is about making it smarter, more intuitive, and more aligned with how we deliver value to our clients.
Thank you for that. Let’s now shift to the central focus of our discussion. In your experience, what have been the most challenging aspects of integrating AI into your business operations, and how have you balanced these with the need to preserve human-centric roles?
One of the biggest challenges has been the constant awareness of how quickly AI is evolving. Because I’m immersed in it every day, I see what’s coming — not five or ten years out, but within the next one to three years. That creates a sense of urgency and, at times, anxiety. It forces you to ask tough questions about whether you’re moving fast enough and whether the work you do today will still be relevant tomorrow.
At the same time, I don’t view it as a threat. I see it as progress. Every few years, we’ve had to step back and reevaluate where we create value for our clients. AI is just the latest catalyst for that recalibration.
The way I balance that is by focusing on our role as a guide. Technology will continue to evolve, but businesses will always need people to help them understand how to apply it, interpret results, and make better decisions. Our value may shift, but the need for human insight, strategy, and guidance isn’t going away.
Can you share a specific instance where AI initially seemed like the optimal solution but ultimately proved less effective than human intervention? What did this experience teach you about the limitations of AI in your field?
I tend to look at this a bit differently. In many cases, what people think of as “AI” is really just automation. There are plenty of situations where automation can handle repetitive tasks, like reviewing documents or identifying patterns, and that can be incredibly valuable.
Where things fall short is when people expect those tools to operate without oversight. This is where the concept of “human in the loop” becomes critical. No matter how advanced the technology is, you still need people to validate outputs, provide context, and make strategic decisions.
The takeaway for me is simple: trust but verify. AI can accelerate processes and surface insights, but it shouldn’t be making final decisions on its own. The goal isn’t to replace people, it’s to enhance their ability to focus on higher-value work.
How do you navigate the ethical implications of implementing AI in your company, especially concerning potential job displacement and ensuring ethical AI usage?
We approach AI as a tool to enhance human capability, not replace it. Our focus is on removing repetitive work so our team can spend more time on strategy, problem-solving, and delivering meaningful outcomes.
We recognize the pace of change and the reality that some tasks will evolve or disappear. Instead of resisting that, we invest in our people and help them build new skills and work alongside AI.
Our role is to guide this transition. Technology will evolve, but human judgment, leadership, and accountability remain essential.
Could you describe a successful instance in your company where AI and human skills were synergistically combined to achieve a result that neither could have accomplished alone?
A good example of AI and human skills working together has been the development of our proposal agent, Roz. We built Roz to help execute and support the proposal development process, and it has become a strong example of how AI can accelerate work without replacing the people behind it.
Roz can help us move faster, organize thinking, and generate strong starting points in our own voice, which is incredibly valuable. But it cannot be relied upon without the human element. What makes it effective is that AI helps us reduce time spent on repetitive tasks, while our team focuses on what really matters: understanding the client, refining the message, and making sure the final proposal clearly delivers value. The technology is powerful, but the real magic happens when it works alongside people who know how to guide it.
Based on your experience and success, what are the “5 Things To Keep in Mind When Deciding Where to Use AI and Where to Rely Only on Humans, and Why?” How have these 5 things impacted your work or your career? Please share a story or example for each.
1. Blending AI and Human Input
It’s never an all-or-nothing decision. The goal is to find the right balance between human insight and data-driven intelligence. AI is just one part of a broader ecosystem of tools that support decision-making. When first introducing our proposal agent, Roz, we very quickly found that the best results came from blending AI and human input. Roz could generate a strong starting point in minutes, but the final 20% (understanding the client’s nuance, refining messaging, and aligning business goals) require human expertise. That balance has become our model.
2. Keep a Human in the Loop
Always keep a human in the loop. That’s a best practice for a reason. People need to validate outputs, provide context, and ensure accuracy. AI can often be used in areas like financial reporting. You can run a P&L statement through it to identify patterns much faster than I could on my own. It can significantly reduce the time spent combing through data; however, those conversations that follow are what make that valuable. Human-driven interpretation and decision-making must always remain present.
3. Define a Clear Use Case
Focus on building a real use case. AI needs to solve a defined problem and deliver measurable value. That requires people to define success, identify the right data, and determine how results will be evaluated. An example of this is the excitement we are seeing to try out AI right now. Clients may approach us wanting to use AI, without having a clear problem to solve. That’s a time when you pause and work backwards to define what success looks like for them. Only after that, are you able to find the true benefits of using AI-driven solutions.
4. Prioritize Human Adoption
Prioritize adoption. Even the most advanced solution won’t create value if people don’t use it. You have to understand how your team works and what they actually need, not just what is theoretically possible. You can build sophisticated solutions for a client that they love and see the potential in, but it doesn’t mean they’ll use it. The issue isn’t always the technology; it’s usability and how it fits in their workflow. If a solution doesn’t align with how humans are working, it won’t create value, no matter how advanced it is.
5. Value Lived Experience
Recognize that AI cannot replace lived experience. It hasn’t walked in your shoes. The best ideas still come from a human perspective, with AI acting as a tool to support creativity and then accelerate and refine those ideas. We often use AI to support brainstorming, but it can’t replicate real-world context. AI works by finding patterns or ideas based on other scenarios that already exist. It won’t understand specific pressures, your own history, or your unique goals.
Looking towards the future, in which areas of your business do you foresee AI making the most significant impact, and conversely, in which areas do you believe a human touch will remain indispensable?
AI is already making a significant impact on back-office processes and highly repetitive technical tasks. Those are areas where efficiency gains can be realized immediately.
Where humans remain essential is leadership, mentorship, and culture. Building a vision, aligning a team, and earning trust are fundamentally human responsibilities. People want to work with and for other people, not machines.
I also think that those human elements will become even more important over time. As technology advances, the ability to connect, inspire, and lead will truly differentiate organizations.
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, what would that be? You never know what your idea can trigger.
I would start a movement around compassion and shared humanity. It feels like we’ve become more focused on what makes us different, instead of what we have in common.
At the end of the day, most people are trying to do the same things: Take care of their families, build a meaningful life, and find some sense of fulfillment. The details may look different, but the core goals are very similar.
If we could shift our perspective to start with commonalities rather than differences, I think we’d see a more unified and empathetic society. Even at a local level, the people in the next town are facing many of the same challenges we are. Recognizing that is a powerful starting point.
How can our readers further follow your work online?
Visit https://causeandeffectstrategy.com/ or connect with me on LinkedIn https://www.linkedin.com/in/johnloury/
This was very inspiring. Thank you so much for joining 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.
C-Suite Perspectives On AI: John Loury of Cause + Effect Strategy On Where to Use AI and Where to… was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.