Building an agentic chat product from scratch gave Justin Wright a new perspective on his work with enterprise AI. Working outside his company’s familiar infrastructure, he could trace how the system’s components fit together. Putting AI into practice in a new setting helped him understand what it takes to build for real business needs.
Justin leads solutions engineering at Maven AGI, where customer needs shape the technical work behind enterprise AI deployments. He returned to Quantic after his Executive MBA to pursue Quantic’s MS in AI Engineering. His studies are helping him connect deeper systems knowledge with the judgment needed to advise customers and guide implementation.
Quick Answer: How does Justin Wright put AI into practice through Quantic?
Justin Wright is applying his Quantic learning to understand AI systems more deeply, advise enterprise customers, and scope deployment requirements. Through Quantic MSAIE, he is strengthening the connection between engineering decisions and business needs. His experience reflects the program’s purpose: preparing professionals to lead and implement real-world AI adoption.
- Building deepens systems understanding. Creating an agentic chat product outside his familiar workplace infrastructure helped Justin understand how its components work together.
- Technical learning informs customer conversations. Justin reports that coursework has helped him understand customer needs and define technical requirements for deployments.
- Personal projects reinforce practical skills. Building his own machine-learning model helped him apply coursework and gain confidence in developing similar solutions.
- Flexibility supports continued learning. Bite-sized lessons help him fit graduate study around a demanding career and life with two young children.
How did Justin’s career evolve from professional athletics to enterprise AI?
Justin’s path into enterprise AI involved a career change after professional athletics. When he retired, he began considering his next direction. He found Quantic during that transition and credits his Executive MBA experience with helping open a new career opportunity.
Software development was already an interest he had pursued on his own. In his personal reflection on that career path, he describes teaching himself to code while still at school. His years of coaching also taught him to make complex ideas understandable, a skill he brings to enterprise AI.
Today, his work at Maven AGI spans custom AI agent integrations, technical evaluations, and implementation planning. He leads the solutions engineering team serving midmarket and enterprise customers.
The role calls on his ability to understand a customer’s goals and work through the technology needed to support them. As his engineering responsibilities grew, Justin wanted to strengthen the knowledge behind those decisions.
Why did Justin return to Quantic for the MS in AI Engineering?
Justin returned to Quantic MSAIE to develop practical AI engineering skills and support his continued career growth. He wanted structured learning that would build on his experience as a self-taught developer.
“I wanted to find a program to help fill my knowledge gaps and professionalize my engineering education.”
He identified two priorities: developing skills to design, build, and deploy AI systems, and advancing his career in AI. His positive Executive MBA experience made Quantic a natural choice for that next stage of learning.
The Quantic MSAIE curriculum connects practical engineering with AI adoption, leadership, and business application. For someone whose work involves advising customers and helping deliver AI systems, those capabilities belong in the same education.
Justin found an opportunity to bring them together in a project closely related to his professional work.
What did building an agentic chat product teach Justin?
Building an end-to-end agentic chat product helped Justin understand more fully how the systems he works with come together. The project involved the type of technology he deploys professionally, but outside the company infrastructure he already knew.
That gave him an opportunity to work through the components and the steps required to build a functioning product.
“This really helped to solidify the steps and skills required to get this type of product working for real-world use cases.”
The value extended beyond that assignment. Practical work accompanying individual lessons helped concepts stick and deepened his understanding of how AI systems operate.
The learning related closely to questions Justin encountered at work. What does a customer need, and what will it take to deliver? That fuller understanding has carried into how he approaches customer needs and deployment requirements.
How has Justin applied his learning to enterprise customer needs?
Justin reports that his Quantic MSAIE coursework strengthened his understanding of how the different components of Maven AGI’s product work. He connects that knowledge directly to his work with customers:
“This has helped me better understand our customers’ needs, act as a more trusted advisor, and better scope the technical requirements of our deployments.”
These are practical responsibilities in AI adoption. Customer conversations help establish the problem to address. Technical scoping helps define the work required to put a suitable system into use.
Justin’s account shows how deeper engineering knowledge can inform both. It gives him a stronger foundation for advising customers and planning implementation around their business needs.
That connection is central to Quantic’s MS in AI Engineering. Fellow Quantic graduate Christopher Arnold found a similar connection, gaining the confidence through his studies to bring his idea for an AI-enabled language study app to life.
Justin has also taken his learning beyond his professional responsibilities, exploring ideas through personal projects.
How has Justin extended his learning through personal projects?
Justin has built his own machine-learning model for passion projects, drawing on knowledge from Quantic MSAIE throughout that work. It gave him another opportunity to apply coursework to something he wanted to build.
His studies also deepened his understanding of model training and fine-tuning. That gave him confidence to build and tune local models. Working on his own projects helped make the learning more concrete:
“This helped make the lessons I learned in the coursework more concrete and gave me more confidence in deploying similar solutions in the future.”
Continuing that learning alongside work and family has made Quantic’s flexible format especially valuable to Justin.
How does Justin balance graduate study with work and family?
With two young children and a demanding career, Justin values being able to study during brief openings in his schedule. Quantic’s short lessons help him pace his coursework and keep up with his studies alongside those responsibilities.
“My favorite part about the Quantic program has been the bite-sized and flexible style of learning.”
He also finds the lessons fun, which helps the concepts stay with him. Projects give him a chance to put those ideas to work on problems with practical relevance.
That relevance matters to Justin. He describes assignments that broaden his skills and build confidence in addressing challenges in his career. The time he invests in study connects with the work he wants to do.
The people learning alongside him have also become a valued part of the experience.
What has Justin valued about the Quantic community?
Justin began connecting with his Quantic MSAIE classmates before formal classes started, meeting with several peers early in the process. What stood out was the breadth of experience they brought to the program and their shared curiosity about AI.
That mix of perspectives is part of the appeal of studying alongside technology-motivated professionals from different backgrounds. Justin’s connections also extend across his two Quantic programs. He still speaks regularly with the classmates from his Quantic Executive MBA capstone team.
His positive experience has prompted him to recommend Quantic to people in his professional network seeking further education. He also encouraged a good friend to sign up for a future MSAIE cohort.
“I’m happy I found Quantic, and it has been a challenging but rewarding journey for me.”
Building the confidence to lead AI adoption
Justin’s path from professional athletics to enterprise AI has involved learning throughout his career. His Quantic Executive MBA and self-taught development experience are foundations he continues to build on through Quantic MSAIE.
The progress shows up in his work. He understands AI systems more clearly and feels better equipped to advise customers and scope implementations. Personal projects give him further opportunities to turn learning into something he can build.
His experience offers an encouraging perspective for professionals considering their own next step. Existing business and industry experience can become a foundation for developing the capabilities to lead AI adoption – even those with significant technical knowledge benefit from formalizing their AI knowledge and expertise.
Quantic’s MS in AI Engineering is designed for a broad range of technology-motivated professionals across business and technical roles. Its rigorous engineering, leadership, and practical application prepare students to help lead and implement AI adoption in real business settings.
Explore Quantic’s MS in AI Engineering
FAQs
Justin wanted structured learning to build on his self-taught software development experience. His priorities were gaining practical skills to design, build, and deploy AI systems and supporting his career growth. His positive experience earning a Quantic Executive MBA also influenced his decision to return.
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Building the product outside his familiar company infrastructure helped Justin understand how its components fit together. The assignment reinforced the steps and skills involved in developing an AI system for real-world use cases.
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Justin reports a deeper understanding of Maven AGI’s product and its components. He says this has helped him understand customer needs and act as a trusted advisor. It also supports technical scoping for deployments.
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Quantic MSAIE brings together rigorous AI engineering, leadership, management, and practical business application. Applied learning helps students connect AI capabilities with organizational needs. The program’s purpose is to develop technically proficient leaders who can guide and implement real-world AI adoption.
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