An Interview With Chad Silverstein
Most people no longer doubt that AI can perform impressive tasks. The challenge is making it dependable inside real business operations. That moment reinforced that the next phase of AI adoption will be defined by execution, not just capability.
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 Frederick Lavoie.
Frederick Lavoie is President and Co-founder of Deck, a company enabling organizations to deploy AI agents across the software systems that power their businesses. He oversees the company’s product and go-to-market strategy, helping customers navigate the realities of implementing AI within existing workflows and technology environments. Frederick focuses on how organizations can turn AI from an interesting capability into a dependable tool for getting work done.
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?
It’s an interesting path because I actually came into AI somewhat indirectly. Before Deck, I spent years building technology around a problem most people never see, helping software systems communicate with each other when they were never designed to. That experience gave me a firsthand view of how much of the world’s software infrastructure is still fragmented and disconnected. Every company depends on dozens of systems, portals, and workflows that don’t naturally work together, and connecting them is often far more difficult than people expect.
After that chapter, I was convinced I never wanted to work on that problem again. Building and maintaining those connections was incredibly complex. Then AI reached a point where it changed the equation. Instead of needing to build a custom integration for every system, AI could learn how to operate software the same way a person does. Suddenly, a problem that had traditionally required years of engineering effort looked solvable in an entirely different way.
That was the realization that led to Deck. We became convinced the next challenge was not just making models smarter, but helping them become useful in the environments where businesses actually operate.
Can you share the most interesting story that happened to you since you started working with artificial intelligence?
One of the most memorable moments came during a conversation with a company that had spent months building their own agent infrastructure internally. They were convinced they could solve the problem themselves and were evaluating whether they even needed a platform like Deck. After seeing Deck operate inside their environment, the founder reached out and told us that Deck had likely saved them more than a year of development time and multiple full-time engineers.
What stood out to me most wasn’t that reaction, but it was how quickly the conversation shifted from “can AI do this?” to “how do we make this reliable enough to run a business on?”. I think that shift captures where the industry is today. Most people no longer doubt that AI can perform impressive tasks. The challenge is making it dependable inside real business operations. That moment reinforced that the next phase of AI adoption will be defined by execution, not just capability.
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?
Looking back, there’s a few themes that show up consistently throughout my career. The first is a willingness to work on problems that aren’t particularly glamorous. Connecting systems, managing access, making software work together. None of those topics generate much excitement, but they are often the things that determine whether businesses can operate efficiently. Both companies I’ve helped build were rooted in problems that many people overlooked because they were difficult and largely invisible.
The second would be persistence. A lot of the problems we work on don’t always have quick solutions. Whether it’s navigating fragmented software ecosystems or helping companies deploy AI reliably, progress often comes from steadily working through complexity rather than finding a single breakthrough. Many of the opportunities we’ve pursued only became possible because we stayed focused on them long enough to understand the underlying problem.
Finally, I would say curiosity. Some of Deck’s most valuable use cases didn’t originate with us. They came from conversations with customers. I’ve learned that if you spend enough time understanding how people actually work, you’ll often discover opportunities that no amount of brainstorming would uncover. Many of our best ideas came from asking questions, listening carefully, and following where those conversations led.
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?
A good example is our work with Rampart, a procurement platform that helps companies reduce purchasing costs by analyzing invoices and vendor data. Their challenge was that critical information was trapped behind vendor portals with logins, multi-factor authentication, and anti-bot systems. Accessing that data manually did not scale, but building hundreds of custom integrations would have required significant engineering investment.
Using Deck, Rampart was able to automate workflows across these portals and retrieve the invoices needed to power their platform without building that infrastructure themselves. In one early deployment, a developer shipped roughly 20 integrations in a single afternoon. What stands out to me is that AI’s value is not just about completing tasks faster. It is about removing technical barriers that prevent companies from building and scaling new products.
What are some of the common misconceptions you’ve encountered about using AI in business? How do you address those misconceptions?
I think one of the biggest misconceptions is that once AI models become powerful enough, the rest of the problem takes care of itself. What we’ve seen is almost the opposite. Most companies already have access to remarkably capable models. The challenge isn’t finding intelligence. It’s figuring out how to apply that intelligence inside the systems where work actually happens.
Businesses run on software that was designed for people. Applications have logins, permissions, workflows, and interfaces that assume a human is sitting behind the keyboard. That’s where many AI projects run into friction. When we talk with customers, the conversation is rarely about whether a model is smart enough. It’s usually about reliability, access, governance, security, and how to integrate AI into existing operations without creating new risks.
The companies seeing the most value from AI aren’t necessarily the ones chasing the newest model. They’re the ones figuring out how to turn AI capabilities into dependable business outcomes.
In your opinion, what is the most significant way AI can make a positive impact on businesses today?
If I had to point to one area where AI can have the greatest impact today, it would be giving people leverage.
For years, businesses have invested in software that helps employees manage information, but AI creates the possibility of software that can actually help complete work. The most valuable applications will be the ones that allow teams to spend less time managing processes and more time applying judgment, creativity, and expertise. AI’s impact will come from helping people accomplish more, not simply automating individual tasks.
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.
There are a few patterns we consistently see among companies that are successfully putting AI into practice. The biggest lessons are less about the technology itself and more about how businesses approach their problems.
1. Use AI to automate work across systems that were never designed to connect.
Companies have spent years waiting for vendors to build APIs that may never arrive. Others spend months negotiating access, paying expensive implementation fees, or maintaining custom integrations that break over time. The reality is that most business systems were built for humans, not machines.Agents that can work within existing software create a path to automation without requiring every vendor to rebuild their infrastructure.
2. Reduce the cost and maintenance burden of traditional automation.
A lot of companies think they have automated a workflow when they have actually created another system they need to maintain. If a process breaks every time a platform changes, the underlying challenge has not been solved, it has just been moved somewhere else.
We saw this with Glowtify, a platform helping e-commerce brands turn customer and product data into personalized marketing campaigns. Their team was spending significant effort connecting and maintaining data across multiple systems, each with different schemas and requirements. By creating a unified data layer through Deck, they eliminated the need for manual data pipelines, reduced engineering overhead, and allowed their team to focus on improving the product rather than maintaining integrations. The future of automation is not about building more fragile scripts. It is about creating systems that can reliably operate across the software environments businesses already depend on.
3. Scale operations without scaling headcount.
Growth often exposes the operational bottlenecks hiding inside a business. Repetitive operational work may be necessary, but these tasks should not determine how quickly a company can grow. Adding more people to support inefficient processes only scales the problem. The goal should be building operations that can handle more volume without requiring more manual effort.
4. Turn insights into action without relying on manual handoffs.
Companies have spent years investing in software that helps them understand their business. The harder challenge is getting that software to actually complete the work. A process often ends with someone logging into another platform, finding the right information, and taking action manually. That final step creates delays, adds friction, and limits how quickly companies can move. Agents have the potential to close that gap by not only understanding a task, but actually completing it inside the systems where the work happens.
5. Scale automation safely through visibility and governance.
Giving an AI agent broad permissions without an audit trail or checkpoints for important actions is not a deployment strategy. It is a liability with a nice dashboard. Imagine an agent updating records across hundreds of customer accounts. If it makes an incorrect change or acts on faulty information, the initial mistake may be small, but the bigger problem is not knowing what happened, where the error occurred, or which downstream workflows were affected.
What starts as a single issue can quickly become an operational disruption, compliance risk, or hours of manual remediation. The companies getting this right are not reducing visibility in pursuit of autonomy. They are increasing it. They know what an agent accessed, what actions it took, and where human review is required.
Autonomy becomes valuable when it is observable, controllable, and trustworthy.
How can smaller businesses or startups, with limited budgets, begin to integrate AI into their operations effectively?
For smaller companies especially, I think the biggest mistake is assuming they need a large AI strategy before they can start. Most don’t. Instead of starting with the technology, start with the friction. Look for the workflow that consistently consumes more time than it should, requires repetitive manual effort, or creates bottlenecks for your team.
The strongest early use cases are often surprisingly simple. They tend to be repetitive operational tasks that create immediate value without requiring significant investment or organizational change. The goal isn’t to automate everything at once. It’s to find one process where AI can give a small team more leverage and create capacity for higher-value work. Once the impact is clear, businesses can build from there.
What advice would you give to business leaders who are hesitant to adopt AI because of fear, misconceptions, or lack of understanding?
For leaders who are hesitant, I would encourage them to separate the reality of AI from the headlines surrounding it. The companies seeing the most value aren’t necessarily pursuing massive transformation initiatives. They’re identifying specific operational bottlenecks, repetitive workflows, or manual processes and asking whether AI can help solve them more effectively.
At the end of the day, I would evaluate AI the same way you would evaluate any other business tool, which is to focus on the outcome. If it helps your team work more efficiently, serve customers better, or remove a constraint that’s slowing growth, it’s worth exploring.
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?
Looking ahead five to ten years, I think we’ll look back on today’s businesses the same way we look at companies before cloud computing. Many organizations still rely on people to move information between systems, retrieve data from portals, update records, and complete operational tasks that exist primarily because software doesn’t work together particularly well. In many cases, the workflow itself isn’t creating value. It’s compensating for limitations in the underlying technology.
What excites me most is the idea that AI can begin to remove those constraints. Instead of software helping people coordinate work, software will increasingly help perform work. I think we’ll see AI agents become a normal part of business operations, much like cloud infrastructure becomes a normal part of building software. Companies won’t think of agents as a standalone technology category. They’ll simply be part of how work gets done. That’s where I think the biggest long-term impact will come from.
How do you think the use of AI to solve business problems influences relationships with customers, employees, and the broader community?
I think the most successful AI deployments are the ones people barely notice. Customers don’t wake up hoping to interact with AI. They want faster service, fewer delays, and fewer friction points. Employees don’t necessarily want AI for its own sake either. They want to spend less time on repetitive administrative work and more time on tasks that require judgment, creativity, and expertise.
When AI is deployed effectively, the technology itself often fades into the background. What people notice is that processes move faster, information is easier to access, and work gets done with less friction. The goal shouldn’t be replacing human relationships. It should be improving the systems that support those relationships. When AI removes operational bottlenecks, customers have better experiences and employees can focus on higher-value work.
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. 🙂
If I could start a movement around AI, it would be focused on making AI accessible to every business, not just the companies with the largest technical teams. A lot of the conversation around AI focuses on the organizations building the most advanced models, but the broader impact will come from helping millions of businesses solve everyday operational problems. Many of those organizations still rely on software and processes that were never designed for AI, which makes adoption much harder than it should be.
I think the biggest opportunity is closing that gap. Not because every company needs to become an AI company, but because every company should be able to benefit from the capabilities AI makes possible. The true measure of AI’s success won’t be how powerful the technology becomes, it will be how widely people can use it to accomplish meaningful work and create value in their businesses and communities.
How can our readers further follow you online?
I’m most active on LinkedIn and X (@fklavoie), where I share observations from building Deck and what we’re working on. If you’re interested in the deeper industry conversations, my co-founder and I also publish Founder’s Cut on Substack, where we unpack some of the operational, technical, and strategic challenges shaping the future of AI adoption.
You can also follow Deck on LinkedIn and X for company updates and what we’re learning from customers as the space continues to evolve.
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
Frederick Lavoie of Deck 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.