“How do you lead when AI is becoming applied within business?” That question brought Brandon Freitag back to Quantic. After completing his Executive MBA and CTO program, he enrolled in Quantic’s MS in AI Engineering to understand what AI can do, where its limits lie, and how to put it to work in an organization.
Brandon’s career has taken him from manufacturing engineering to leading technical teams at DaVita and Google Cloud. He has spent years helping organizations change how they work, and he wants his decisions about AI to draw on experience building with it. His own experiments have already given him reasons to reconsider which tools he uses. In his Quantic MSAIE coursework, he is now exploring another leadership concern: how to maintain quality when an AI agent does more of the work.
Quick Answer: How is Brandon connecting AI engineering with leadership?
“How do you go and understand what it can do, what it can’t do, where the pitfalls are, where the strengths are, and how to actually apply that in ways that create real value and make it interesting and engaging for people to be excited about what is possible?”
Brandon Freitag is pursuing Quantic MSAIE to develop the technical AI skills and business judgment he sees as essential to leadership in the next decade.
- For his machine learning and AI agent course project, he is designing a malware classifier with checks intended to keep an AI coding assistant working to an agreed standard.
- Building a team of AI agents gave him firsthand experience with how they communicate, share information, and produce advice. Their cost relative to useful results led him to evaluate other tools.
- Through his advisory work and writing, he examines why organizational change stalls and what leaders need to understand as AI changes technical roles.
Why choose Quantic’s MS in AI Engineering after an EMBA and CTO program?
“I’ve been through an Executive MBA, I’ve been through a CTO program, and now I’m doing the Master’s in AI Engineering.”
Brandon already knew Quantic’s approach to learning. This time, he wanted to build the technical depth to assess AI systems himself and use that understanding in leadership decisions.
He had seen enough AI strategy decks to know what was missing from most of them: anyone in the room who had built the thing. His concern was what happened next. Without understanding a model’s real limits, a leader could promise more than the technology could deliver or hesitate when there was useful work it could do.
Quantic MSAIE connects that technical work with the judgment needed to lead adoption in a business. For Brandon, learning about preprocessing, model selection, neural networks, and deployment belongs alongside deciding where AI fits an organization’s needs.
For Brandon, that combination is the job description for the next decade of leadership.
What connects Brandon’s career in manufacturing, IT, and cloud?
“For me, where I’m at in my career, it’s all about pattern recognition,” Brandon says.
Brandon kept encountering a problem that a new tool alone could not resolve. An organization might adopt a technology while struggling to change the way people worked with it. He became interested in finding the underlying causes of stalled initiatives and understanding what a team needed before a transformation began.
He began in manufacturing engineering before moving into IT. His work has since spanned data, programming, virtualization, architecture, and cloud, with roles at VMware, Oracle, DaVita, and Google Cloud. At DaVita, he led a global technical organization of roughly 140 people through a DevOps and cloud transformation. At Google Cloud, he established the Rocky Mountain region and later built the North America Strategic Architects practice from the ground up.
AI brings him back to that work. He wants to recognize organizational problems he has seen before while developing enough technical understanding to judge the challenges that are specific to AI.
What did Brandon learn from building his own AI team?
“So I have my own multi-agent team who each has a persona, who each does something different for me.”
That was how Brandon described an early experiment with OpenClaw. He gave his agents distinct roles, then worked through questions that were familiar from managing people. How should they communicate? What information should they share? How could he tell whether their advice was useful?
Working with the agents gave him something concrete to assess. The team was interesting to watch, but the cost of producing a useful outcome proved too high for him. He moved on to evaluate Codex, Cursor, and Claude Code against the same practical test: what did each tool deliver for the work involved?
The experiment changed his choices. He now uses agentic coding tools, including Claude Code, and selects them according to the task. That is the kind of judgment the Quantic MSAIE program is meant to sharpen: a leader’s job is to match the tool to the work, not to pick a favorite.
How is Brandon approaching quality in his Quantic MSAIE project?
“What advice am I getting back? Is it good advice? Am I having issues?”
Those were questions Brandon asked while working with his own agents. In his coursework, he is taking a related concern into the design of a system: how can he keep control of quality when an AI assistant helps build it?
For the program’s machine learning and AI agent project, Brandon is designing a static malware classifier served through a large language model, or LLM, agent. The classifier is the machine learning component; the agent provides a way to use it.
His design includes executable specifications and a gated pipeline. These give the coding assistant requirements that can be checked and conditions the work must meet before it proceeds. He wants the assistant to work against a standard it cannot quietly change.
The project is still in design. What interests Brandon is the responsibility it asks him to take: defining acceptable work and deciding how to check it as agents take on more of the implementation.
What makes Quantic’s lessons memorable for Brandon?
“The importance of story for remembering.”
That is one part of Quantic’s learning design Brandon especially values. A narrative gives him a reason to care about an idea. Returning to it, applying it, and checking his understanding help him work out whether he has learned it well enough to use it.
Continuing education also has to fit alongside professional responsibilities. Across his three Quantic programs, Brandon has looked for learning he can connect to his daily work.
“You can turn around and you can apply it day-to-day within a career.”
For someone who has kept learning through several changes in technology, that practical relevance matters. It also connects with how Brandon teaches and advises others: he wants people to remember an idea well enough to act on it.
How does Brandon connect AI with the way teams work?
“What is the culture? How do you put teams together?”
When Brandon talks about making change happen, those questions sit alongside the technical ones. His experience has led him to develop a pattern he calls the Five Breakpoints: strategic disconnection, incentive fragmentation, process friction, the technology illusion, and the momentum mirage. They describe patterns he has observed when large initiatives stall.
He has turned them into assessment tools his teams used to consider whether a customer was ready for a transformation. In his publication, Work That Holds, he writes about organizational change and agentic AI, exploring how those problems show up in practice.
For Brandon, deeper AI knowledge gives that work another point of reference. Understanding what a system can deliver helps him consider what an organization must do to make use of it. His studies connect with a question that has followed him throughout his career:
“How do you actually make change happen?”
Where Brandon wants to take his AI leadership next
“What actually would be successful for me?”
Brandon uses that question to describe the point when people move from excitement about AI to deciding what to do with it. Having worked through the growth of virtualization, cloud, and other technologies, he is interested in what remains useful once the initial attention settles.
His focus includes go-to-market and solutions engineering teams, which connect what a company builds with what customers need. His forthcoming book, Built to Be Replaced, examines how AI could change solutions engineering and similar technical roles, and what people in those roles should build next.
Quantic’s MS in AI Engineering is part of how Brandon is preparing for that work. He wants the experience of building and evaluating AI systems to inform how he leads people through change. Brandon’s bet is that the leaders who matter in the next decade will be the ones who can do both, and the program is how he is making sure he is one of them.
Explore the Quantic MSAIE curriculum to see how technical AI learning connects with leading and implementing AI adoption in business.
FAQs
Brandon wanted to understand AI’s capabilities and limits well enough to make informed leadership decisions. Having already completed Quantic’s Executive MBA and CTO program, he valued its practical approach to learning. He returned to develop technical AI skills alongside the judgment needed to guide organizational adoption.
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Building a team of agents on OpenClaw gave Brandon experience with how agents communicate, share information, and produce advice. When the cost per useful outcome proved too high for him, he evaluated other tools. That experience reinforced his focus on choosing a tool according to the work it needs to do.
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For his machine learning and AI agent course project, Brandon is designing a static malware classifier served through an LLM agent. His design includes executable specifications and a gated pipeline intended to keep an AI coding assistant working to defined requirements. It is a project in design, rather than a completed deployment.
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Quantic’s MS in AI Engineering combines technical learning with AI adoption, leadership, and management. Its rigorous curriculum is accessible to a broad range of technology-motivated professionals from business and technical backgrounds, preparing them to lead and implement AI initiatives that address real-world business needs.
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