When Pierce Plumly helped a client sketch the architecture for an AI application, he had a course project to draw on. Through Quantic’s MS in AI Engineering, he had built an AI agent using retrieval-augmented generation, or RAG. He had also learned how to evaluate its responses. He put that understanding to work in planning a knowledge management application to support a large software implementation.
As Partner, Business Transformation at Turnberry Solutions, Pierce brings years of experience improving processes and guiding organizational change. The Quantic Executive MBA graduate returned to deepen his technical understanding and complement his business experience. His studies are already helping him contribute more fully to technical conversations and decisions. For Pierce, the connection between AI and business is taking shape in the work he does with clients.
Quick Answer: How is Pierce putting AI learning to work in consulting?
Pierce Plumly is using his Quantic MSAIE learning to contribute more deeply to technical conversations and decisions in consulting. His experience building a RAG-based AI agent has already informed client architecture planning. It connects what he learns about AI with a specific business need.
- Business experience provides context. Pierce’s background spans process improvement, automation, and organizational change. His work gives him a practical setting for applying his growing AI knowledge.
- Building makes the learning tangible. His AI agent project involved designing an application, building it with AI support, and evaluating how changing inputs affected its outputs.
- Coursework carries into client work. He helped sketch a RAG application architecture for a client’s knowledge management needs during a large software implementation.
- Flexibility makes room for continued learning. Pierce values studying on his own schedule and has found working independently on larger projects particularly productive.
What experience does Pierce bring to AI adoption?
Pierce Plumly has built his career around improving how organizations operate. He earned a B.S. in Industrial Engineering from Virginia Tech before taking on engineering and management roles at Ingersoll Rand. His responsibilities spanned manufacturing processes, supply chains, and operations. That work gave him experience with both the details of improvement and the responsibilities of managing teams.
At Arrow Electronics, his work took on an international scope. He managed operations in Nevada and the Netherlands and worked on global projects across Europe, Asia, and North America. He also taught Global Operations and Management at Sierra Nevada College alongside his industry career.
His move into consulting brought those experiences into client organizations. At Turnberry Solutions, he supported Johnson & Johnson as a senior consultant on transformations involving people, processes, and technology. Business process analysis, implementation, project management, and change management all formed part of that role.
Today, Pierce leads Turnberry’s Business Transformation practice. Its capabilities include process optimization, automation, organizational change management, and program delivery. His AI studies build on this experience. Understanding what is specifically feasible, based on the latest AI capabilities and technologies, is essential to leading business transformation during the AI era. That goal brought him back to Quantic.
Why did Pierce return to Quantic for the MS in AI Engineering?
Pierce completed his Quantic Executive MBA in 2022. He returned for Quantic’s MS in AI Engineering to deepen his technical understanding and strategies specific to AI adoption. Career advancement or a move into an AI-focused role was another motivation.
His work gives those ambitions a practical setting. Leading a business transformation practice involves people, processes, and technology. Learning how AI agents and applications are designed and built adds to the understanding he brings to that work. It also gives him opportunities to connect what he studies with the consulting responsibilities he already holds.
Quantic MSAIE’s curriculum connects technical learning with AI adoption, leadership, and implementation. This combination of skills is essential knowledge for consultants at this time. The program is designed for technology-motivated professionals across business and technical backgrounds. Through applied learning, it prepares students to lead and implement AI in real business settings. For Pierce, that connection fits his aim of expanding what he can contribute as a consultant.
His third project gave him a chance to put that ambition into practice by creating an AI agent.
What did building a RAG-based AI agent teach Pierce in Quantic MSAIE?
The AI agent assignment brought together application design, development, deployment, and evaluation. Pierce worked through the architecture of a RAG application and learned how to build it with AI support.
Retrieval-augmented generation, or RAG, supplies a generative AI model with relevant information retrieved from an external source. That information helps the model produce a response. It allows an application to draw on information beyond the model’s existing training.
For Pierce, the value lay in working through the stages together. “It was an end-to-end experience,” he says. Designing the agent and then building and deploying it gave him firsthand experience of how an agentic system comes together.
The assignment connected with his reason for returning to Quantic: understanding the specific, current capabilities of AI for workflow and process automation in organizations to complement his existing consulting knowledge and experience. He now had a project of his own to draw on when discussing this kind of application.
He also singled out the evaluation cycles as particularly beneficial. They gave him a way to examine the agent’s behavior and learn from the differences in its responses.
Why did evaluating the AI agent matter to Pierce’s learning?
Pierce wanted to understand how the AI agent’s results varied. The evaluation cycles let him examine what happened when inputs changed and measure the differences in the outputs.
He found them “particularly beneficial to help me understand how the results can vary.” They also helped him explore “how we can measure the changes in outputs based on changing inputs.”
This gave him practice in examining the behavior of an application he had built. Design, development, and evaluation became connected parts of his learning, each contributing to his understanding of the system.
For professionals considering AI in a business setting, evaluating an agent’s responses is part of assessing its usefulness and provides a continuous feedback loop during development. Pierce’s project gave him experience with both deploying an agent and examining its results. He then had an opportunity to use that knowledge in client work.
How is Pierce applying his Quantic MSAIE learning in client work?
After completing the AI agent project, Pierce helped a client sketch the architecture for a RAG application. The proposed application would support knowledge management during a large software implementation. His coursework gave him practical experience to draw on as he contributed to that planning.
The benefit also extends to how he participates in his work. “Getting the software engineering experience has already helped me immensely,” Pierce says. He can now “understand and contribute to technical conversations and decisions” that previously required him to stay at a higher level.
For someone who has spent years guiding business transformation, this expands the contribution he can make. His experience with processes, implementation, and organizational change is now augmented by a growing understanding of AI agent, application design, development, and adoption. In this client engagement, those areas of knowledge came together around a specific business need.
That connection reflects the purpose of Quantic’s MS in AI Engineering: preparing professionals to lead and implement AI adoption in business and the broader economy. Pierce is putting his learning to work while continuing his studies. The flexibility of the learning platform is something he particularly values along the way.
How does Pierce benefit from Quantic MSAIE’s flexible learning approach?
Flexibility is Pierce’s favorite part of learning with Quantic. Alongside his consulting responsibilities, he values being able to decide when to study. “I can learn on my time and my schedule,” he says.
He has also learned which approach to project work suits him. For his first assignment, Pierce joined a team because he wanted to build relationships. He found collaboration challenging because of differences in capabilities within the group, and the process took considerable time. The team completed the project, and he chose to work independently on subsequent assignments.
That change made a difference to his experience. “I felt I learned more and went faster,” he says of working on his own.
Bringing deeper understanding to business transformation
Pierce returned to Quantic with years of consulting experience and a clear reason to keep learning. His studies are helping him participate more fully in technical decisions and contribute to planning an AI application for a client.
That progress is grounded in practical work: designing an AI agent, building and deploying it with AI support, and evaluating its results. He now has that experience to draw on, needed specifically for AI-driven business transformation. His journey offers an encouraging pathway of continued learning within an established career, and particularly points to benefits for those in the consulting sector.
Quantic MSAIE brings those business and technical perspectives together. Explore Quantic’s MS in AI Engineering to learn how the program prepares professionals to lead and implement AI adoption.
FAQs
Pierce wanted to add AI depth to his business experience after completing his Quantic Executive MBA in 2022. He also saw further education as a way to advance his career or pursue an AI-focused role.
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The project taught Pierce how to architect a retrieval-augmented generation-based agent and build and deploy it with AI support. Evaluation cycles helped him understand how changing inputs affected outputs and how to measure those differences.
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Pierce helped plan the architecture of a RAG application for a client undertaking a large software implementation. The proposed use was knowledge management. He also reports that his newly acquired, AI-assisted software engineering skills help him take a fuller part in technical discussions and decisions.
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Quantic’s MS in AI Engineering bridges AI and business. Its curriculum connects technical learning with AI adoption, leadership, and management. The program serves technology-motivated professionals across business and technical backgrounds. It prepares them to lead practical AI initiatives and implement applications that address real business needs.
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