An Interview With Chad Silverstein
If applied properly, AI does not just save time. It changes how you approach complex work altogether.
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 Kurt Diederich.
As a part of this series, we had the pleasure to interview Kurt Diederich.
Kurt is the Chief Executive Officer of Finys, a leading provider of insurance software solutions for property and casualty insurers. He began his career building custom systems for insurance carriers — including policy, billing, and claims platforms — along with web-based front ends. Over time, he deepened his industry expertise and led the design and development of the Finys Suite. Kurt brings extensive knowledge of the entire product development and delivery lifecycle and focuses on transforming and modernizing carrier core systems.
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’ve been drawn to technology since I was about 13, when I got my first computer and started writing simple games. From that point on, the direction was pretty clear. I studied computer science, started a company right out of college, and have spent my career building software, primarily for the insurance industry.
What has stayed consistent the whole time is that I’ve always been focused on building. Not just using technology, but understanding how it works and how to apply it in practical, business-relevant ways. In insurance, that matters because technology only creates value when it improves real operations, whether that is underwriting, claims, policy administration, or distribution.
AI was not an immediate fit for me. When large language models first emerged, I found them clunky and inconsistent, so I largely ignored them. What changed was revisiting them in a low-stakes way and seeing one handle a practical task better than I expected. That was the moment it clicked. Not because of that one task, but because I realized the underlying capability was much broader.
From there, I started applying AI inside the business, first in small ways, then across more workflows. The impact became obvious very quickly. In insurance, where so much work is knowledge-based, document-heavy, and process-driven, the productivity gains are real. If used properly, they compound fast. I believe AI will fundamentally change how work gets done across carriers and MGAs.
Can you share the most interesting story that happened to you since you started working with artificial intelligence?
One experience that really changed my perspective on AI came from legal discovery work. I had to review hundreds of emails spanning a long period, which is normally a manual, time-intensive process where accuracy matters, as you cannot afford to miss key facts or connections.
Instead of reviewing everything conventionally, I used AI to process the full set. It summarized threads, identified recurring themes, surfaced relevant relationships between conversations, and helped organize the material in a much more usable way.
What stood out was not just the speed, but the clarity. It made the information easier to understand than if I had gone through it manually. And it did it in minutes rather than days. I still validated the output, but the effort it saved was significant.
That experience was especially meaningful because it closely aligns with how insurers operate. Carriers and MGAs handle large volumes of unstructured information every day, including submissions, claims files, correspondence, endorsements, audits, and legal records. That was the moment it really clicked for me that, if applied properly, AI does not just save time. It changes how you approach complex work altogether.
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?
Three traits have made the biggest difference for me: skepticism, practicality, and relentless iteration.
First, skepticism. When AI tools first began to gain attention, I was not convinced. I had tried early versions and saw inconsistent results. My instinct was to dismiss them. But instead of writing them off entirely, I stayed open enough to revisit them later. That second look made all the difference. In insurance, where precision and trust matter, healthy skepticism is a strength as long as it does not turn into paralysis.
Second, practicality. I am not interested in technology for its own sake. I focus on tools that produce measurable business value. The moment AI became meaningful to me was when I applied it to real work, not theory. In our world, that means faster implementation, cleaner data handling, better decision support, and reduced manual effort for carrier and MGA teams.
Third, relentless iteration. Getting real value from AI is rarely a one-shot effort. Early attempts often miss the mark. You have to test, refine, adjust prompts, reframe workflows, and sometimes abandon an approach completely. That persistence is what turns AI from an interesting demo into something operational and repeatable.
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 biggest challenges we help solve is enabling insurers to migrate off legacy systems. Many carriers and MGAs are still running platforms that are 10 to 15 years old, with thousands or even tens of thousands of lines of embedded business logic tied to rating, underwriting rules, policy workflows, forms, and product configuration.
That is not a simple lift-and-shift problem. Traditionally, teams have to manually analyze the legacy system, interpret the business rules, and then rebuild them in a modern platform. It is slow, expensive, and prone to error.
We used AI to read and interpret the legacy logic, then translate it into the structure required by our modern platform. Instead of starting from a blank page, we were able to quickly generate a strong working baseline. That did not eliminate the need for expert review, but it moved the team much further, much faster.
The result was a major improvement in productivity. Work that would normally take weeks or months was compressed significantly, and our teams could focus on validating, refining, and improving the outcome rather than reconstructing everything manually. For carriers and MGAs trying to modernize without stalling the business, that is a meaningful shift.
What are some of the common misconceptions you’ve encountered about using AI in business? How do you address those misconceptions?
Two of the biggest misconceptions I see are that AI will replace people, and that adoption is mainly a technology problem.
The first misconception is that AI will eliminate large parts of the workforce. In practice, we see that AI augments people in insurance. It helps underwriters, claims professionals, analysts, developers, and operations teams move faster, reduce errors, and produce better work. The opportunity is not simply to reduce headcount. It is to increase the effectiveness of the skilled people you already have.
The second misconception is that adoption is mostly about selecting the right tool. It is not. This is as much a cultural shift as a technology shift. You can put powerful tools in front of your team, but if people do not understand how to use them, do not trust them, or do not see practical value, nothing really changes.
What actually works is creating space for experimentation, sharing real use cases, and building confidence through tangible wins. That is especially important in insurance, where adoption must occur within established workflows and regulated environments.
In your opinion, what is the most significant way AI can make a positive impact on businesses today?
The most significant impact AI is having right now is giving knowledge workers real leverage. It enables people to produce more output, and often better output, without a linear increase in effort.
In insurance, that can show up in many places: reviewing submissions, summarizing claim files, drafting requirements, generating configuration logic, supporting service teams, accelerating testing, or helping developers move faster. Across those activities, AI is reducing friction and increasing throughput.
The result is not just speed. It is higher capacity and better quality. Teams can clear backlogs faster, respond more effectively, and deliver more polished results without having to expand staff at the same pace.
For carriers and MGAs facing margin pressure, talent constraints, and rising customer expectations, that kind of leverage is extremely valuable.
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. Clarifying poorly defined problems
AI can help refine requirements, identify ambiguity, and expose gaps early. In insurance, teams often start with incomplete specifications around product rules, workflow requirements, or legacy logic. AI helps strengthen that starting point, so projects begin with better clarity.
2. Turning unstructured information into usable insight
Carriers and MGAs sit on large volumes of unstructured information, including emails, documents, forms, claim notes, underwriting guidelines, and business rules. AI can organize that information into usable context, making downstream work faster and better informed.
3. Accelerating solution development
Once the problem is clearer, AI can generate a strong initial solution. In software, that may mean requirements, code, test cases, or configuration logic. In insurance operations, it could mean summaries, decision support, or draft communications. The point is to quickly create a high-quality starting point.
4. Improving quality through large-scale testing
AI makes it easier to test at a scale that was previously impractical. Instead of validating only a limited number of scenarios, teams can evaluate thousands or even hundreds of thousands of combinations. For insurers, that is especially valuable when testing rating logic, policy rules, and claims workflows.
5. Coordinating multi-step workflows
This is where some of the biggest long-term value will come from. AI is beginning to connect multiple tasks into broader workflows instead of supporting isolated activities. We are still early, but over time, this will reshape how insurance works, moving from intake to decision to execution.
How can smaller businesses or startups, with limited budgets, begin to integrate AI into their operations effectively?
Smaller businesses and startups often have an advantage because they can move faster. They typically do not have the same layers of governance, process, and organizational inertia that slow down larger enterprises.
Cost is also less of a barrier than many people assume. Tools like ChatGPT and Copilot are inexpensive relative to the productivity they can create. Even free or entry-level tools can be useful, as long as organizations are thoughtful about privacy, security, and what information they share.
The key is to start small and stay practical. Pick a real, repeatable problem, something like processing submissions, drafting communications, summarizing documents, or accelerating development work, and apply AI to that specific use case. Do not try to transform the whole business at once.
If one tool or workflow does not work, move on. When you find one that does, standardize it, reuse it, and build from there. For smaller MGAs and insurtechs, especially, that approach can create a meaningful advantage very quickly.
What advice would you give to business leaders who are hesitant to adopt AI because of fear, misconceptions, or lack of understanding?
A lot of hesitation comes from the belief that AI is overhyped. My view is that the value is real, but you only see it when you apply it to actual work. Like most tools, you get out of it what you put into it.
The bigger issue, in many cases, is resistance to change. Leaders who have been successful operating a certain way can understandably feel skeptical about adopting something new, especially when it moves as quickly as AI does. But the real risk is waiting too long. This shift is already underway, and organizations that delay too much will fall behind competitors that are learning faster.
The practical path is to start small and build momentum. Look for people inside the organization who are already using AI effectively. We often think of them as champions. They are valuable because they bring real examples, not theory. Once others can see a peer saving time, improving quality, or solving a problem faster, adoption becomes much more natural.
For carriers and MGAs, that kind of internal proof matters more than broad industry headlines.
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?
Predicting that far out is difficult because the pace of change is so fast. That said, a few trends already seem clear.
First, AI will continue increasing the output of knowledge workers. In software, that is already visible in coding, testing, documentation, and requirements. In insurance, we will see similar gains in underwriting support, claims analysis, operations, product configuration, service, and compliance-related work.
Second, we will likely see AI expand further into physical work through robotics, especially in repetitive or risky tasks. That may be more visible in other industries at first, but it will still affect insurers through the risks they cover, the businesses they insure, and the claims environments they support.
Third, one of the biggest shifts will come from connecting AI across workflows instead of using it for isolated tasks. Today, much of AI remains point-solution-oriented. Over time, it will become more embedded in end-to-end processes, which is where the operational impact will be much larger.
I remain more skeptical about true artificial general intelligence. Current systems are very strong at pattern recognition and synthesis, but that is different from original thought. Even so, they are already powerful enough to materially change business operations.
How do you think the use of AI to solve business problems influences relationships with customers, employees, and the broader community?
With customers, AI increases value. Businesses can deliver better products faster, improve responsiveness, and often reduce costs. In insurance, that could mean quicker implementations, better service, more accurate workflows, or smoother policy and claims experiences. As a result, customer expectations will continue to rise.
With employees, the impact is more uneven. People who adopt AI effectively can increase their output and become more valuable to the organization. Those who resist it may find themselves at a disadvantage as AI becomes part of everyday work. That creates an important responsibility for leadership to help people adapt rather than letting a capability gap widen across the workforce.
At the broader community level, AI has the potential to increase productivity at scale. In some ways, it does for knowledge work what industrialization did for physical production. Over time, that should reduce costs, expand access to services, and increase the amount of value individuals and businesses can create. If managed well, that can raise the overall standard of living.
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 responsible adoption without overregulation.
There is a real risk that we slow AI down too much by reacting primarily to worst-case scenarios. Overregulation raises costs, limits access, and tends to favor the largest organizations that can absorb complexity. That creates a barrier for smaller businesses, startups, and smaller insurers or MGAs that could otherwise benefit meaningfully from these tools.
That does not mean there should be no guardrails. There absolutely should be protections around privacy, security, and appropriate use. But access needs to remain open enough for individuals and businesses to experiment, learn, and improve.
To me, the bigger risk is not that AI moves too fast. It is that we slow it down so much that we delay the very real benefits it can deliver across industries, including insurance.
How can our readers further follow you online?
You can follow me on LinkedIn at https://www.linkedin.com/in/kurt-diederich/
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.
Kurt Diederich Of Finys 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.