An Interview With Chad Silverstein
…I would start an educational initiative and make AI education accessible to everyone, regardless of their background or financial situation. Some people are self-starters, while others wait for a push and the rest need to be encouraged. By empowering people with the knowledge and solutions to use AI effectively, we could reduce technological anxiety, build confidence, and foster more open-minded approaches to innovation. Breaking the barriers between people and technology would unlock human potential to address some of the world’s most pressing challenges, from healthcare accessibility to climate change mitigation. This movement would focus on providing free or low-cost AI training programs, developing user-friendly open-source AI tools, organizing inclusive hackathons, and cultivating meaningful collaboration between businesses, governments, and educational institutions. As more people engage with these resources, technological skepticism would gradually transform into informed enthusiasm about AI’s possibilities, dissolving much of the fear and anxiety currently surrounding these advances…
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 Maxim Ivanov.
Maxim Ivanov is Aimprosoft’s CEO, tech lead, and company ideator. He spent over 23 years bridging the business and technology worlds and solving problems with a mix of talent, innovation, and commitment to results. As the CEO of Aimprosoft, a tech and AI consultancy company offering AI-assisted software development services, he helps clients save up to 30% of their development budgets with the help of AI.
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 always found myself caught between two very different perspectives on AI. On one hand, there are the skeptics, who see AI as full of myths and superstitions, and on the other, the people who fear AI’s power to revolutionize everything — from the way we work to how we communicate. For the longest time, I was somewhere in between.
My initial excitement came when I used ChatGPT to help with editing my writing; as non-native English speaker it was incredibly useful. Soon after, I started exploring other AI tools to help with financial planning, strategy, and even routine tasks. I remember a time when I thought AI in coding was overrated. I figured its maximum potential could only boost efficiency by about 10%. But that perspective changed once I dove deeper into the tech.
Through research and collaboration with my technical and delivery teams, I quickly realized AI’s potential was far beyond what I had imagined. We started exploring tools for code reviews and using ChatGPT to generate documentation. I began to see AI as a tool, capable of tackling complex challenges.
Before 2024 I used to think that technical excellence was enough to win in software development services, but the current market has taught me otherwise. As demand for outsourced development services decreased, we went on a quest to understand what the market really needed. And what we discovered is that the market right now craves AI consultancy.
We continue to have a great development team, but we’ve realized that what matters more than ever in 2025 is helping businesses navigate cost-cutting measures and using technology to do more at less cost.
We want to better understand what they’re trying to achieve, what gaps exist, and how we can apply new approaches to improve their business. We’re working closely with customers to dive deep into their operations, explore new possibilities, and identify areas for improvement.
Our move into consultancy isn’t because AI is replacing developers or automating what they do. While AI can augment developers by speeding up certain tasks, like writing or reviewing code, it can’t replace the critical thinking that happens when building or updating complex systems.
Currently, we are full in AI and help businesses who struggle with AI adoption to identify the best use cases and get results quickly. I’m experimenting with AI to build an agent that works with my email inbox. It’s a personal project that allows me to dig deeper into the capabilities of AI and better understand its possibilities — and its limits. The more I explore, the more I’m convinced that AI can accelerate the work of every team member, but it still relies on human empathy, critical thinking, and governance.
Also in my view, AI isn’t just meant for entry-level roles; it can benefit all levels. However, those who stand to gain the most are in senior positions, where they can guide AI to the best solutions.
Recently, I had a moment that really made me appreciate the practical limitations of AI. I decided to use ChatGPT to generate math problems for our kids — specifically, 10 examples of fraction addition and subtraction problems, where the answers equaled 1 or 2, and the denominators were between 1 and 12. I tried it with both the free versions of ChatGPT and Claude, and what seemed like a simple task turned into a challenge. Neither tool could handle it. Even the paid version struggled at first. After providing a few more detailed instructions, it finally got the job done. It was a funny moment, but it really underscored that no language model (LLM) could solve this task without significant guidance. It wasn’t just about having access to a more powerful tool — it was about knowing how to use it effectively.
These kinds of experiences are a reminder that while AI is incredibly powerful, it still has its quirks and limitations. You can’t simply point it at a problem and expect it to work flawlessly every time. That’s where human expertise and guidance are crucial.
Can you share the most interesting story that happened to you since you started working with artificial intelligence?
One of the biggest discoveries I had was that AI isn’t just a tool for code generation. It can transform the entire software development lifecycle (SDLC). That insight led us to the concept of productized SDLC powered by AI.
Most discussions around AI in software engineering focus on tools like Copilot or Cursor for code writing. But before you even get to coding, there’s an entire process — gathering business requirements, defining a project plan, breaking it into user stories, prioritizing tasks, and designing wireframes. AI can optimize all these stages. Then, once development starts, it can be used in software testing, CI/CD pipelines, and even code review.
Instead of just plugging in AI tools at random, we took a step back and looked at the entire SDLC as a structured, repeatable process — one that could be productized and consistently enhanced with AI. This approach allows us to accelerate delivery, improve quality, and standardize efficiencies across projects.
The relevance of this approach became clear when we realized that different projects, whether maintaining legacy systems, rewriting them with modern frameworks, or building entirely new products, could all benefit from an AI-powered SDLC. It’s not just about using AI; it’s about systematically integrating it into every stage of a software development lifecycle to gain real business value.
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?
1 . Product Mindset:
This mindset I had to develop through years of my career. At the early stages I spent years building projects without fully grasped what my clients did or why my work mattered. It’s not enough to just execute what you are requested for. In every project, it’s important to focus on finding out-of-the-box solutions that align with long-term growth rather than just solving today’s problem.
A great example of this was when we at Aimprosoft worked with Reekolect Corp, a startup building an AI-powered social network with features like photo restoration and colorization. They had the vision but lacked the technical experience to bring it to life. We didn’t just execute their ideas; we helped guide them through technical decisions that would be cost-effective and scalable. By negotiating better pricing for essential features like chat PubNub, we prevented a 30%-budget overrun. Instead of opting for microservices, we recommended monolithic architecture. This decision significantly reduced maintenance and development costs, while ensured 70% faster media load times.
It required deep collaboration with the client and clear communication across teams, but it ultimately enabled us to build a robust, scalable product within a tight budget and timeline.
2 . Delegation for innovation
When I first became a manager, I thought innovation was all about big, flashy tools or dramatic changes to processes. But over time, I realized that real innovation doesn’t come from buying expensive tech or making massive overhauls. It comes from making space for it, and that means stepping away from the day-to-day routine.
For a while, I was caught in the weeds — stuck putting out fires and handling routine tasks. It wasn’t until I admitted that I couldn’t do it all on my own that I started making room for true innovation. I brought on a new CRO role, which freed up time for me to focus on three key priorities: ensuring financial stability, engaging with clients and the market, and staying ahead of trends. Without that shift, innovation would have remained just a distant dream.
The same principles apply when integrating AI into an organization. If leadership doesn’t prioritize growth and innovation, it won’t happen. There are three ways I would approach this:
Dedicated innovation roles: Hire specialists or even teams whose sole focus is driving innovation. These experts are quicker to spot trends like AI and automation and can integrate them faster than most teams could on their own.
Involve external experts: Sometimes, an outside perspective can provide fresh insights from other industries, proven frameworks for success, and a wealth of experience in implementing innovative solutions. It can also be more cost-efficient.
Empower every leader: Encourage all managers — whether they’re leading innovation teams or not — to carve out time for brainstorming, testing new ideas, and leading change. Make innovation the responsibility of everyone in leadership. For example, our sales and marketing team has set a goal to integrate AI into all their activities, measuring the efficiency it brings (or doesn’t). We also hold shared sessions where we exchange knowledge with peers about new AI tools we’ve discovered.
Because here’s one truth: innovation starts from the top. If leadership doesn’t make room for it, no one else will.
3 . Hands-on AI
For non-technical leaders, there’s a fundamental truth I’ve learned: you need to understand what’s under the hood of AI. Just using AI tools without understanding them can lead to significant blind spots. I always recommend starting with the basics — understanding the difference between machine learning, deep learning, and generative AI. Platforms like Coursera and edX offer solid foundational courses that can demystify the technology.
Next, learn about the models — how do systems like GPT, Llama, and Gemini work under the surface. Getting your head around these core concepts will help you apply AI in ways that actually benefit your business.
For tech-savvy managers, I recommend getting hands-on by training your own large language model (LLM) or building an AI agent. I’m currently working on a personal project to automatically organize my email inbox. It’s a simple task, but it’s been an incredibly insightful process. I’ve learned so much about the intricacies of AI by rolling up my sleeves and diving into these projects.
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?
On one of our long-term projects, we noticed that a significant amount of time was spent on internal and support calls — 26% of the team’s working time, to be exact. This was due to lack of documentation, inconsistent development practices, misalignment among teams, and other factors. To address this, we began using GenAI to automate documentation, summarize business logic, and generate README files. For example, we tested GenAI’s ability to generate Javadoc, TypeDoc, and OpenAPI specs for our Java and Angular projects. This provided immediate clarity to developers, QAs, and business analysts without the need for lengthy calls. We also used AI to analyze service code and generate human-readable summaries of business logic, reducing confusion and saving time. The goal was to reduce the time spent on internal calls to 13%, thus not only improving efficiency but also allowing teams to focus on building rather than explaining.
What are some of the common misconceptions you’ve encountered about using AI in business? How do you address those misconceptions
1 . AI “steals” data or is uncontrollable.
The fear of AI stealing data often comes from a lack of understanding of how AI works.
The risks of data leaks are not as large or mysterious as they seem, if AI operates within compliance standards. I explain to clients that AI doesn’t steal data–just like Gmail scans emails for categorization but doesn’t expose them carelessly. AI processes information based on clear rules, and problems only arise if these rules are ignored. Responsible AI is built to protect sensitive information, and the key lies in understanding how AI works, ensuring that the data it processes is handled with care and in compliance with regulations.
Sure, the risks are real. One of the best ways to mitigate them is by maintaining full control over your data and running AI within private clouds, on-premises servers, or trusted cloud providers like AWS or Azure. It’s much like how you can choose not to have your emails analyzed — by setting up your own servers, like Gmail works. This way, you have control over what gets processed, ensuring your data remains secure.
2 . AI for the sake of AI
Many leaders push for AI adoption without creating a clear strategy or setting measurable outcomes. Such an approach often leads to projects that start with enthusiasm but ultimately fail to show real impact. I address this issue by emphasizing that AI should always be focused on driving efficiency, reducing costs, and improving decision-making. If AI doesn’t do these things, it’s just another shiny object. I suggest starting with small wins that don’t require large investments or months of effort.
I also recommend asking these key questions before implementing AI: “Where are we wasting the most time or resources? Which processes rely on manual work that AI could automate? How will we measure success — cost reduction, speed, quality, revenue? What quick wins can we achieve in weeks, not months, to prove AI’s value?”
3 . “We don’t have the budget for AI”
This belief is one of the biggest obstacles to AI adoption, particularly for mid-sized companies. In the UK, for example, only 19% of mid-sized businesses have implemented any AI initiatives.
This misconception can be addressed by demonstrating that AI doesn’t necessarily require a large upfront investment. In fact, AI can help companies optimize their operations without significant financial risk. I’ve seen firsthand how AI can be a game-changer for clients, even in financially tough times. For instance, one of our long-term clients, facing financial struggles, worked with us to optimize their development and testing processes by 30% in efficiency in just one year. This optimization was critical to their survival, and it proves that AI doesn’t need to come with huge upfront investments — it’s about leveraging the right solutions to generate clear, measurable ROI.
4 . The role of AI in job market and decision-making
There’s a misconception that AI will replace humans in decision-making roles and dramatically reshape the job market.
While AI can handle many routine tasks, complex problems still require humans. After trying to build my own AI agent for my inbox management, I realized the technology has limitations. It’s not a plug-and-play solution. The context window is limited, and you need to prepare the data properly to get accurate results. Tasks become more complicated as the level of detail increases, requiring fine-tuning, iterations, and debugging.
AI is an excellent tool for data-driven insights to assist in decision-making, but it cannot replace the human element. For example, while AI can analyze a contract and highlight potential issues, it cannot understand the deeper context behind why you might want to offer a discount to a client or how that aligns with your business strategy. AI can inform decisions, but the final decision — what’s best for your company — should always come from a human. The role of AI in decision-making is as an assistant, not a decision-maker.
In your opinion, what is the most significant way AI can make a positive impact on businesses today?
AI is driving a massive shift toward ‘doing more with less,’ and businesses that embrace AI will be the ones to lead in the years ahead. We’re seeing this firsthand as our clients navigate the challenge of cutting budgets while still delivering high-quality results. As a company, we’re asking ourselves the same question: How can we achieve the same or even better outcomes without increasing costs?
This year’s conversations with clients have highlighted how rapidly the market is evolving. Unlike the past few decades, where digital transformation was largely driven by economic growth, today the focus is on value, optimization, and stretching resources further.
As a technology partner, our role has shifted. It’s no longer just about delivering code. It’s about empowering clients to use AI, digital tools, and code to achieve more at less cost. And when new software needs to be built or integrated, we leverage AI tools to reduce both time and expenses. The AI revolution should fuel growth, but it begins by helping businesses increase value while cutting costs. Only by demonstrating this can we earn the trust to guide them through more advanced AI solutions.
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 . More accessible product prototyping
The concept of AI-powered pre-prototyping for business idea validation offers compelling advantages for organizations seeking to minimize development risks while maximizing market fit. Tools like Replit Agent are transforming how businesses validate product concepts by enabling rapid creation of functional prototypes with minimal coding requirements, reducing the barrier between idea conception and tangible demonstration. These AI prototyping platforms can produce interactive user interfaces, simulate backend functionality, and create realistic data models within hours, allowing product teams to gather user feedback much earlier.
Companies can use generative AI capabilities to quickly explore multiple design variations simultaneously, testing different feature sets and user experiences before committing significant resources to any single approach.
2 . Quality and speed of software development
AI helps accelerate specific stages of a software development lifecycle (SDLC). There are various use cases including documentation, code writing, validation, refactoring, and others. Plus, the technology can be used in practically all development scenarios that you can imagine.
Take AI-powered code review tools, for example. These tools automate reviewing pull requests, quickly spotting bugs, regressions, and edge cases that might get missed otherwise. This means fewer issues after deployment and faster development cycles, giving developers more time to focus on building new features instead of manual reviews. Generative AI also helps ensure test coverage by automatically creating unit tests and checking pull requests for missing tests, improving code reliability and meeting industry standards. AI can also assist with maintaining consistent code styles across long-term projects by enforcing unified standards, which minimizes internal debates and prevents misunderstandings that often delay project timelines.
AI also works wonders with optimizing database queries, boosting performance, and speeding up CI/CD pipelines. It can automatically improve SQL queries, reduce load times, and highlight bottlenecks in the pipeline, suggesting ways to fix them. It even finds duplicate code and suggests more efficient, modular solutions, making it easier to keep the codebase clean and organized. And beyond routine tasks, AI brings new ideas, offering alternative solutions for complex challenges — like performance tweaks in databases, especially in areas where the team might not have specific expertise.
3 . More effective cross-functional team collaboration
With AI, organizations can automate the tedious task of maintaining documentation, such as generating up-to-date OpenAPI specifications and Javadocs as codebases evolve. Such an approach significantly reduces the time different departments spend in meetings or knowledge sharing sessions, allowing them to focus on higher-value activities that drive business growth.
AI-powered collaboration tools can also translate technical concepts into business language, bridging the communication gap between technical and non-technical stakeholders. I also recommend using AI solutions like Fathom for capturing meeting notes, listing action items, and highlighting decisions to create a searchable knowledge base that reduces information silos between departments.
4 . Thorough security checkups
Instead of relying solely on manual checks, organizations can now use AI-driven tools to continuously scan for vulnerabilities, identify unsafe coding practices, and flag potential security risks before they can be exploited in production environments. These AI security systems can analyze patterns across millions of code samples to detect novel threats that traditional rule-based systems might miss, providing protection against emerging attack vectors.
For example, SonarQube uses AI to analyze code and detect security risks early. Checkmarx leverages machine learning to enhance static application security testing (SAST) and helps developers spot threats in real-time. Veracode also uses AI for dynamic application security testing (DAST), scanning live applications for vulnerabilities.
Finally, AI-powered security tools can prioritize vulnerabilities based on business impact and exploitation likelihood, allowing security or engineering teams to focus on the most critical issues first.
5 . Consistent test coverage
AI tools can generate comprehensive unit and integration tests automatically, helping companies achieve the 70% golden industry standard coverage without missing critical deadlines or overburdening development teams. By intelligently analyzing code patterns and potential edge cases, these AI testing solutions can identify testing scenarios that human developers might overlook, significantly improving product reliability and reducing post-release defects.
AI can simulate user behaviors and interactions more comprehensively than manual testing approaches, providing better coverage of real-world usage scenarios across different devices and environments. As codebases evolve, these AI systems automatically update and adapt test suites to maintain relevance, eliminating the common problem of outdated tests that plague many traditional testing frameworks.
By implementing these AI-driven testing strategies, companies can simultaneously improve product quality, accelerate development velocity, and deliver superior user experiences while achieving better return on investment from their engineering resources.
How can smaller businesses or startups, with limited budgets, begin to integrate AI into their operations effectively?
Platforms like Krista or Lovable (this one is the fastest growing startup in Europe) make AI accessible even to people with limited technical expertise. With drag-and-drop interfaces and visual process mapping, businesses can build intelligent workflows, automate decision-making, and integrate AI into their operations without needing a full development team. This is particularly valuable for ambitious businesses that lack the resources for heavy development work.
Moreover, the democratization of AI is progressing rapidly, enabling even small businesses to adopt AI with greater flexibility. As the market grows, tools are becoming more affordable and accessible. For example, changing a documentation process with AI doesn’t require huge investments, yet it can bring results in just a few months, improving efficiency and freeing up valuable time. I believe any business can start small and begin seeing ROI in months, not years.
What advice would you give to business leaders who are hesitant to adopt AI because of fear, misconceptions, or lack of understanding?
I would encourage them to start by educating themselves and their teams about AI. This shift doesn’t happen overnight, and it’s perfectly natural to be skeptical. But it’s important to see AI not as a replacement for humans but as a way to augment human skills and productivity.
I’d suggest starting with small, manageable pilot projects that have a clear, measurable outcome — like automating repetitive tasks or using AI for documentation. The results will speak for themselves, and once leaders see the value, they’ll be more willing to scale their AI efforts.
There are many resources available, from online courses to industry reports, that can help demystify AI. Begin with small, low-risk projects to see how AI can benefit your organization. By focusing on specific pain points and measuring success through metrics like cost reduction, speed, and quality, you can gradually build confidence in AI’s potential.
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?
How I see AI shaping the business world over the next 5–10 years is not just about technology — it’s about how we, as humans, adapt and collaborate with it. Here’s how I envision the future:
More time to innovate and create
Imagine a world where the mundane, repetitive tasks that drain our time and energy are handled by AI. In software development, for example, AI is already automating code reviews, generating documentation, and optimizing workflows. It’s like having a tireless assistant who takes care of the tedious stuff, allowing you to focus on solving complex problems and innovating.
But here’s another side: as these tasks get automated, we’ll need to rethink how we train the next generation of professionals. The entry-level tasks that once served as training grounds for junior employees will disappear, and businesses will need to find new ways to mentor and develop talent.
The tools of tomorrow
I’m particularly excited about tools like AI agents and workflow automation. Such tools are more independent and can handle complex workflows, from customer service to supply chain management, and they’re becoming more accessible to businesses of all sizes. For example, AI agents can analyze emails and decide what action to take — whether to process an order, contact a client, or escalate an urgent matter. It’s like having a virtual team member who never sleeps. Admittedly, they require careful preparation of data and clear instructions to function effectively, but results are worth it.
Shift of the job market
The impact of AI on the job market is often overstated. While AI can reduce costs and improve efficiency, broader economic factors — like global crises and market fluctuations — play a larger role in shaping employment trends. Many people are holding onto their money, waiting to see how the economy evolves, and that has a bigger impact on the job market than AI at the moment. AI is just one piece of the puzzle.
The irreplaceable human element
At the end of the day, AI cannot generate truly new ideas or take risks in the way humans can. It’s a tool that enhances human potential but not a replacement for the unique qualities that people bring to the table. Human creativity and innovation are irreplaceable. Their role will always be essential, even as AI becomes more advanced.
How do you think the use of AI to solve business problems influences relationships with customers, employees, and the broader community?
The biggest power of AI right now is reducing the burden of repetitive tasks, enabling the team to focus on more meaningful and creative work. For instance, in our own projects, we’ve used AI to automate documentation and code reviews, which allows developers to concentrate on solving complex problems and educating themselves on AI. This not only boosts productivity but also leads to higher job satisfaction.
For employees, there are already solutions that assess team wellbeing by analyzing the tone of communication across various channels, such as chats, to help prevent burnout. We recently spoke with a provider offering these solutions, which can have a direct and positive impact on employee engagement and overall morale.
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 start an educational initiative and make AI education accessible to everyone, regardless of their background or financial situation. Some people are self-starters, while others wait for a push and the rest need to be encouraged. By empowering people with the knowledge and solutions to use AI effectively, we could reduce technological anxiety, build confidence, and foster more open-minded approaches to innovation. Breaking the barriers between people and technology would unlock human potential to address some of the world’s most pressing challenges, from healthcare accessibility to climate change mitigation.
This movement would focus on providing free or low-cost AI training programs, developing user-friendly open-source AI tools, organizing inclusive hackathons, and cultivating meaningful collaboration between businesses, governments, and educational institutions. As more people engage with these resources, technological skepticism would gradually transform into informed enthusiasm about AI’s possibilities, dissolving much of the fear and anxiety currently surrounding these advances.
How can our readers further follow you online?
I encourage all the readers to connect me on LinkedIn, as I often post about business, technology and AI in particular:
https://www.linkedin.com/in/maximivanov/
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.
Maxim Ivanov Of Aimprosoft 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.