An Interview With Chad Silverstein

The “intelligence” of your AI is only as good as the data you are willing to fight for. Whether it’s 1996 or 2026, if you don’t have a clean, solid data foundation then your model is just a very expensive guessing machine.

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?

As a part of our series about how artificial intelligence can solve business problems, we had the pleasure of interviewing Danilo Kirschner.

Danilo Kirschner is the Managing Director of Zoi North America, where he partners with industry leaders to address their most complex challenges through innovative, AI-driven cloud solutions. He excels at transforming vague or conflicting business demands into clear, actionable requirements. By bringing structure to complexity and applying the right technologies, Kirschner ensures state-of-the-art implementations are aligned with strategic goals. In leading Zoi’s North American expansion, he draws on the company’s proven global success to drive sustainable growth and to redefine what’s possible with cloud-based business solutions.

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 journey with AI didn’t start with the current wave; it began in 1996 while I was studying Economics and Computer Science at the University of Leipzig, Germany. For my diploma thesis, I chose a topic that felt like science fiction at the time: “Stock price prognosis with Artificial Neural Networks.”

Back then, you couldn’t just download a pre-trained model. I had to learn the inner workings of AI by architecting the neural networks from scratch: designing the training patterns, managing the cycles and rigorously testing data to see what actually moved the needle. That experience instilled a lifelong interest in me: I love to work on the intersections of business and technology, always putting the business function first.

“Form follows function,” if you will. This led me to spend the late 2000s as a consultant specializing in Strategic IT Portfolio Management and Digital Transformation. Until a couple of years ago, AI clearly wasn’t ready for enterprise class applications. With the newest developments in Agentic AI, in particular, we live now in a new era with capabilities which were not even imaginable in 1996.

In my role as Managing Director for Zoi North America, I help our customers move past “AI tourism,” those isolated pilots that never reach production. We help our customers to achieve practical application. And we focus on realizing tangible ROI by embedding AI into everyday use cases, ensuring their AI strategy is perfectly aligned with their business vision rather than being just a technical experiment.

Can you share the most interesting story that happened to you since you started working with artificial intelligence?

While I can’t share specific, confidential customer stories due to the nature of our strategic work at Zoi, I can tell you about the most foundational challenge I ever faced. It’s a story that perfectly illustrates that while technology changes, the core logic of AI — and the hustle required to make it work — remains the same.

To appreciate this, you have to imagine being in 1996. The internet was in its infant stage. There were no APIs, no Kaggle datasets and certainly no way to just “download” the historical financial data I needed as training material for my thesis. So, I did the only logical thing a student could do: I grabbed a box of floppy disks and walked into a local community bank.

I will never forget the face of the clerk when I made my request. They looked at me as if I had just asked for the keys to the vault. After a significant amount of explaining and convincing — explaining what a “Neural Network” even was in a pre-Google world — he finally guided me to the branch manager. The manager looked at me with the same disbelief when I told him, “I want to train a computer program to predict stock prices and for that I need historical time charts of these indicators” and I handed him a printed list.

Perhaps he found it interesting or perhaps he just found it funny, but he gave me the contact for the regional manager. At our meeting, the regional manager took my floppy disks, looked at me skeptically and told me to come back in a week.

True to his word, a week later, he handed back the disks. He had provided hundreds of CSV files: exchange rates, index values, single major stocks, unemployment rates — ten years of high-quality economic history.

The result? Even with the limited processing power of 1996, the effort paid off. My models consistently outperformed standard statistical methods by an average of 10 percentage points. In the best-case scenarios, the prediction accuracy topped 80%!

Not bad for a kid with a floppy disk and a dream of automated intelligence. It taught me a lesson I still share with our clients at Zoi today: the “intelligence” of your AI is only as good as the data you are willing to fight for. Whether it’s 1996 or 2026, if you don’t have a clean, solid data foundation then your model is just a very expensive guessing machine.

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. Structural “Out-of-the-Box” Thinking: I’ve always believed that growth happens at the edges of established rules. By habitually questioning “the way things have always been done,” I’m able to look past rigid patterns and identify how emerging technology can be applied in creatively transformative ways. While I truly enjoy the “cool factor” of new innovations, I’m also aware that in a business context, novelty is never a substitute for utility. For me, it is a professional imperative to think strategically and ensure that every “out-of-the-box” idea is grounded in a long-term decision that drives sustainable value rather than just temporary excitement.
2. Pragmatic Persistence: Much like my experience hunting down data with a floppy disk in 1996, I rarely lose patience when working toward a high-stakes goal. We currently operate in an economy driven by the pressure of quarterly results, short-term gigs, and ever-faster turnaround times. In this environment, I believe steadfast persistence has become a rare competitive advantage. It allows me to filter out the noise and remain focused on what truly matters: the long-term vision. With that, I help our customers to realize their strategic vision by implementing the right solutions to support it — form follows function.
3. Informed Intuition: I am a believer in the power of “following your gut.” While my thinking is rooted in logic, data and objectivity, I learned early in my career that acting against my instinct often leads to suboptimal outcomes. This is confirmed by science. Intuition isn’t mystical; it is the synthesis of decades of experience and pattern recognition. Symbolically speaking on major decisions, my logic takes me 80% of the way and that final 20% requires the visceral judgment call that only my instinct can provide.

What does that have to do with AI? Nothing, and everything. With the great amount of possibilities, and also complexity, that AI brings, I believe these traits help to navigate opportunities and risks successfully for me personally and also for our customers.

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?

We work with industry leaders across many sectors. I could give you dozens of examples of AI solving mission critical challenges. But let me highlight our work with Kärcher, as it perfectly illustrates what happens when you truly democratize technology. (For reference, Kärcher is a family-owned global leader in cleaning technology. If you work in a large office building anywhere in the world, you have probably seen their equipment.)

A standout result of this environment was a Business Process Model and Notation (BPMN) agent developed by a production team to solve a persistent bottleneck in manufacturing. In a high-compliance environment, keeping process models updated in SAP Signavio is a massive manual task that often lags behind the reality of the shop floor. The team solved this by creating an agent that allows employees to simply film their work steps with a smartphone. The AI analyzes the footage, recognizes the logical sequences, and automatically transfers that implicit knowledge into standardized, auditable models.

This story is so powerful because it proves that the most innovative ideas don’t come from a central IT department; they come from the people who live with the problem every day. We provided the technical enablement, but the creative execution came entirely from the workforce. And while this is a great example, we are now seeing many similar initiatives emerging from across their organization.

What are some of the common misconceptions you’ve encountered about using AI in business? How do you address those misconceptions?

One frequent misconception is that AI adoption is primarily a technical challenge. While many hurdles have initially been purely technical, in my experience, the most significant challenges are of an organizational and cultural nature. Many companies attempt to control risk by limiting access to a small group of experts which creates bottlenecks and prevents the organization from discovering high-value use cases.

On the other hand, there is this expectation of a magical problem solver. This expectation that AI can solve problems just by subscribing to a service. Decision makers need to be clear that there are a number of prerequisites that need to be fulfilled, most importantly making high-quality and complete data for training and operations available. We provide AI enablement sessions for C-level leaders, as well as AI-readiness assessments, demand analytics, and target planning methods that are proven to help our clients understand capabilities and prioritize use cases that are feasible and create business value.

Another common issue is the obsession with calculating a precise ROI before scaling. At an early stage, such precision is nearly impossible. AI should be viewed as foundational infrastructure — similar to the cloud — rather than a single business case to be justified in isolation. We address these points by shifting the focus toward enabling the entire organization to learn and improve continuously with the technology.

In your opinion, what is the most significant way AI can make a positive impact on businesses today?

The most significant impact is closing the gap between technology adoption and actual value creation. For a long time, companies have paid a “tech debt tax” for software that is outdated, prone to security risks, and often never fully utilized. AI allows us to redesign these systems and business processes by removing digital friction and making expert knowledge available to everyone. Shifting to an integrated “agentic enterprise” creates a resilient organization that can adapt to market dynamics in real-time.

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. Prioritize Broad Impact over Elite Access

Limiting AI licenses to a select group of “digital natives” is a severe strategic error that stifles innovation. AI must be treated as a foundational utility, provided to the entire workforce just like email or the internet. By removing these initial filters, a company unlocks the potential of every employee to identify department-specific value. We see this at Kärcher, for example, where the most creative solutions emerge directly from the people who experience operational friction every day.

2. Mandate Top-Down C-Level Accountability

The AI transformation is a leadership responsibility that cannot be delegated to the IT department alone. Executives must actively exemplify the change and participate in dedicated workshops to understand the genuine capabilities and limitations of the technology. This hands-on engagement at the top is essential for realistic expectation management and industrial-scale implementation.

3. Implement Targeted Empowerment through “Snackable” Content

Standard, lecture-style training sessions are ineffective for sustainable workforce development. Instead, organizations should provide short, engaging learning modules — what we call “snackable content” — combined with the identification of internal multipliers within organizational departments. This approach builds specific communities where expertise is shared organically. It ensures that even less digitally native departments can apply AI to their specific workflows in a tangible way.

4. Adopt a Pragmatic Infrastructure Mindset for ROI

Attempting to calculate a precise return on investment before a rollout is nearly impossible and often stalls progress. Companies should avoid the trap of searching for the “perfect” business case and instead view AI as a necessary infrastructure investment. This pragmatism allows an organization to eliminate fundamental inefficiencies across its enterprise. The goal is to build organizational muscle through use, rather than observing the technology from a distance.

5. Build Maturity for the “Agentic Enterprise”

The true value of AI in the near future will be driven by autonomous systems that execute end-to-end business logic. We are moving toward the “agentic enterprise,” where specialized agents handle complex processes independently. Companies must develop their organizational maturity today through daily interaction and prompting. Those who establish these fundamentals now will be the ones capable of successfully adapting to the upcoming leap in autonomous automation.

How can smaller businesses or startups, with limited budgets, begin to integrate AI into their operations effectively?

Smaller businesses actually hold a massive strategic advantage right now: they aren’t weighed down by decades of legacy structures. They can build their “IT house” correctly from the ground up.

In the past, size was a proxy for power. To achieve global reach or high-volume output you needed thousands of employees. AI has fundamentally broken that equation. We are seeing small, lean teams accomplish what used to require established corporations with massive overhead. In fact, there is a widely discussed prediction in Silicon Valley that we will soon see the first one-person billion-dollar company. AI is the ultimate force multiplier.

What advice would you give to business leaders who are hesitant to adopt AI because of fear, misconceptions, or lack of understanding?

My advice is to look at history: we’ve been here before. Whether it was the introduction of the steam locomotive in the 19th century or the internet in the 90s, every era-defining technology was initially met with fear, skepticism or dismissal as a short-lived trend. People thought the locomotive was a dangerous anomaly; they thought the internet was a hobby for nerds. History has a habit of rewarding the curious and leaving the hesitant behind.

The biggest mistake a leader can make right now is waiting for “perfect clarity” or a stable set of best practices. In the context of AI, that clarity only emerges through active engagement. This is why it is essential that CEOs take the lead. You cannot delegate your core business strategy to an IT department and hope for the best. The CEO must understand the capabilities of AI deeply enough to integrate them into the high-level business vision.

Consequently, the role of the CIO must also evolve. The old school CIO who simply manages IT is a relic of the past. Today’s CIO needs to develop a much stronger understanding of the business itself, working in tandem with the CEO to provide the specific infrastructure and capabilities required to support the AI-driven strategy.

AI is a capability that must be developed internally. If you wait to see how others do it, you aren’t just losing time, you are losing the ability to compete.

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?

The next decade will be defined by the transition to the “agentic enterprise.” We are moving past AI as a simple assistant and toward autonomous systems that can execute complex business logic independently.

I am particularly excited about four specific shifts:

1. We will see the rise of “agent swarms”: coordinated groups of AI agents working together to solve multi-layered problems. The orchestration of such swarms presents a fascinating new challenge for management and HR. Leaders will no longer just manage humans; they will lead mixed teams of humans and AI agents. HR will need to redefine what “headcount” and “performance reviews” even mean in an era where an agent is a permanent, high-output member of the team.
2. We are entering the era of physical AI in the industrial sector. Digital intelligence is converging with physical automation where robots learn and adapt in real-time. In this new landscape, employees shift from being “doers” to becoming supervisors of autonomous systems. It is a fundamental redesign of the workforce and those who start building that “AI readiness” today are the ones who will lead tomorrow.
3. A company’s market valuation will soon be tied directly to its “AI readiness.” What was true for my diploma thesis in 1996 remains the absolute truth today: your data foundation is your most valuable asset. Ironically, AI will be the tool that helps us achieve this readiness. We are already using AI to drastically reduce technical debt, for example, by re-writing and re-factoring decades-old COBOL solutions into modern, cloud-native architectures. AI is effectively “cleaning the house” so it can eventually live in it.
4. The Sovereign Cloud AI “Bubble”: As regulators define more frameworks for data privacy and AI ethics, we will see a surge in Sovereign AI. Large enterprises will demand models that run entirely within their own private cloud “bubbles.” This ensures total data sovereignty and compliance while allowing them to leverage the full power of the technology without the data ever leaving their controlled environment.

How do you think the use of AI to solve business problems influences relationships with customers, employees, and the broader community?

When applied correctly, AI acts as a “complexity solvent”. It dissolves the internal frictions that historically led to fragmented customer experiences. We are rapidly approaching a point, particularly for standard tasks, where customers will not be able to identify whether they are interacting with a human or an AI. Initially, this seamlessness creates a level of responsiveness that was previously impossible, setting a new baseline for what we expect from brands.

By stripping away the repetitive, cognitive “grunt work,” AI allows employees to finally focus on work that demands judgment, creativity and deep domain expertise. However, this isn’t a passive transition. Employers have a massive responsibility here. We need rigorous change management and training to facilitate the mindshift required to work alongside AI agents. Employees are being promoted from “doers” to orchestrators and reviewers of agentic workflows. Leading this transition with transparency — clearly defining where decisions are automated and where human oversight is non-negotiable — is the only way to maintain internal and external trust.

Regarding the broader community, we are seeing a double-edged sword. On one hand, the potential for “good” is staggering; look at the current breakthroughs in medical research and drug discovery powered by AI. On the other hand, the physical reality of AI is hitting home. The ongoing discussions around data center locations and the resulting pressure on electricity prices demonstrate a growing hesitation. Companies cannot ignore this. We have an obligation to work on compromises and aggressively reduce our energy footprint. To lose the community’s trust over energy consumption would be a strategic failure that no amount of “cool tech” could fix.

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 a movement called “The Human Compass.” We often talk about AI in terms of efficiency and profit, but a great potential lies in helping us navigate the most difficult human terrains: breaking bad habits and restoring physical and mental vitality.

The movement would focus on three specific pillars:

1. Digital Decoupling for the Next Generation: It sounds paradoxical to use AI to get kids off screens, but we can design AI-driven “practical play” systems that encourage children to put the screen away and engage in real-world, hands-on hobbies by gamifying the physical world rather than the digital one.
2. The Recovery Assistant: Using AI’s ability to recognize patterns and provide constant, non-judgmental support to help people struggling with addiction. Whether it’s navigating a moment of crisis or reinforcing positive behavior, AI can provide a level of ubiquitous support that human systems currently struggle to scale.
3. Dignity in Aging: For the elderly, I see a future where AI keeps them mentally and physically active through tailored cognitive challenges and companionship. Furthermore, through physical AI, we can finally address the devastating shortage of care personnel in hospitals and retirement homes. Robots should never replace the “heart” of care, but they can handle the heavy lifting and routine tasks, allowing human caregivers to focus entirely on empathy and connection.

My hope is that the organizations realizing massive gains from AI will see it as a strategic imperative to reinvest a portion of those profits into these initiatives. If form follows function, then the ultimate “function” of intelligence, whether biological or artificial, should be to improve the quality of life for the most vulnerable among us.

How can our readers further follow you online?

You can find me on LinkedIn at https://www.linkedin.com/in/danilokirschner/. I am always happy to connect with readers and fellow innovators to continue the conversation on how we can bridge the gap between AI potential and real-world business value.

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.

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Danilo Kirschner of Zoi 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.