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Featured

AI-powered “Explain This Screen” Adds Further Personalization to Quantic’s Curriculum

In our continuous effort to enhance the student learning experience, Quantic School of Business and Technology is thrilled to unveil our latest feature that takes personalized learning to the next level: “Explain This Screen.” This innovative addition is built upon the foundation of AI Advisor, which has already made significant strides in supporting students throughout their educational journey.

Since the launch of AI Advisor last year, positive feedback and high usage rates have shown us the immense potential of integrating AI into our curriculum. Students have actively engaged with AI Advisor to clarify complex concepts and navigate their coursework. It’s been inspiring to see so many of our students leverage this tool to enrich their learning journey. And starting today, students can now use AI Advisor directly from within their lessons, getting the support they need when they need it.

What is “Explain This Screen”?

“Explain This Screen” is an AI-enabled feature designed to offer real-time, context-specific assistance within lesson screens. By clicking the “Explain This Screen” button, students can initiate an in-depth discussion about the current topic, receive guidance toward correct responses, ask questions about related concepts, explore hypothetical scenarios, and review coursework to enhance lesson understanding.

Enhanced Practice with “Review This Lesson”

As students complete their lessons, “Explain This Screen” transitions into “Review This Lesson,” enabling an interactive review session of the content they’ve just learned. This ensures that they retain what they’ve learned more effectively.

Availability

This feature is now available to all current Quantic and Valar students and alumni. However, it’s specifically designed for teaching lessons, meaning you won’t find it in exams or SMARTCASEs. Our goal is to support the student learning process without impacting assessment integrity.

Your Feedback Matters

The introduction of “Explain This Screen” signifies our commitment to leveraging technology for education, providing students with innovative tools to deepen their understanding and explore new study methods. As always, your feedback is invaluable to us. We encourage you to use the Feedback button to share your thoughts and suggestions on this new feature.

We hope “Explain This Screen” becomes an integral part of your learning toolkit, enhancing your Quantic journey by making it more interactive, supportive, and tailored to your needs.

Happy learning!

Featured

Quantic Approved for Full, Five-year Accreditation Renewal; Plans to Pursue More Degrees

February 27, 2024

Quantic School of Business & Technology (“Quantic”), first accredited as a degree-granting institution in the United States in 2020, has received approval for full renewal of its accreditation. Quantic’s accrediting body, the Distance Education Accrediting Commission (DEAC), reviewed findings from a site visit with Quantic leadership in 2023 and voted to renew Quantic for 5 years, the maximum allowed renewal term. Valar Institute, a division of Quantic, was also renewed as part of this institutional review.

Quantic’s CEO, Bill Fisher, commented:

“We appreciate the rigor with which DEAC reviewed our online MBA and Executive MBA degrees. We take this renewal as another signal that our investments into program innovation and quality are paying off and further demonstrate our commitment to delivering unmatched value and outcomes for our exceptional students.”

Quantic plans to pursue additional approvals in 2024 for selective and affordable online master’s degrees in Business Analytics (MSBA) and Software Engineering (MSSE), as well as an online bachelor’s degree program aimed to launch by 2026. Fisher continued: “With industry-leading student satisfaction and graduation rates, Quantic is well-positioned to broaden access to elite-quality higher education in business, technology, and data-oriented fields.”

About Quantic & Valar

Co-founded by former Rosetta Stone CEO and EY Entrepreneur of the Year, Tom Adams, Quantic is an edtech powerhouse reimagining elite graduate education through selective, mobile-first, and radically affordable online degrees built for the modern workforce. In 2022, Quantic and its edtech parent company, Pedago, received $15 million in VC funding from Elephant Ventures, a firm started by a former Warby Parker co-founder. In 2024 Quantic was recognized as one of Inc.’s Fastest Growing Companies (Mid-Atlantic Region). Quantic’s momentum continues to climb with over 15,000 global students and alumni and a reputation as a premier online educator challenging top graduate schools.

Valar Institute is a fully accredited division of Quantic launched in 2022 and is dedicated to empowering managers to lead in a new world of work. Valar’s online EMBA and MBA programs use the same award-winning interactive learning methodology and peer-based cohorts as Quantic but are shorter in duration and emphasize leadership and management skills.

About DEAC

The Distance Education Accrediting Commission is a private, non-profit organization founded in 1926 that operates as a federally recognized accreditor of distance education institutions. DEAC’s goal is to ensure a high standard of educational quality in the distance education institutions it accredits and a quality education for the students who annually study at its accredited institutions.

See the full press release here: https://www.prnewswire.com/news-releases/quantic-is-accredited-institution-receives-full-term-accreditation-renewal-for-mba-and-executive-mba-degrees-unveils-plans-for-msba-and-msse-302071853.html

Featured

Quantic Unveils New AI-Powered Student Advisor

It’s our mission to advance the state of the art in online instruction, and we see AI as an incredible enabler of improved outcomes and democratized access to high quality education. That’s why we’re excited to announce the beta launch of AI Advisor, an advanced chatbot powered by OpenAI, built to support our students and alumni on their lifelong learning journey.

Quantic has always believed in leveraging technology to move education forward. We’ve been a pioneer in the field of active learning and were the first to take a mobile-first approach to higher education. It’s our mission to advance the state of the art in online instruction, and we see AI as an incredible enabler of improved outcomes and democratized access to high quality education. That’s why we’re excited to announce the beta launch of AI Advisor, an advanced chatbot powered by OpenAI, built to support our students and alumni on their lifelong learning journey.

Access AI Advisor by clicking on the BETA robot icon in the dashboard.

AI Advisor serves as a personalized guide to every student at Quantic. It can act as a tutor, answering questions about curriculum and coursework, and even help students practice for exams. It can equally provide on-demand administrative support relating to requirements, policies, and the student schedule. And when the bot doesn’t have an answer, our team of faculty, academic advisors, administrators, and support staff are ready and waiting to help!

Our product and engineering teams have spent months designing, iterating, and testing the AI Advisor experience. It’s early days; we’re initially launching on desktop with mobile soon to follow. We plan to significantly extend the bot’s capabilities, and we are excited to pursue other ways of utilizing AI to enhance the student experience.

Read on to learn more about AI Advisor!

How can AI Advisor help?

Can’t remember where you learned a particular concept? Need a quick definition? AI Advisor will not only provide you with information, but it will also point you toward lessons or FAQ articles where you can learn more. You can also ask the bot to provide practice questions for a particular topic, explain a formula, check in on your progress toward graduation, or ask questions about what’s next in your curriculum.

What if AI Advisor makes a mistake?

Our team regularly monitors the performance of AI Advisor, annotating and categorizing bot output for finer-grained analysis of accuracy. We leverage techniques such as retrieval-augmented generation to ground the AI model on relevant sources of knowledge to reduce hallucinations and better ensure relevant answers to Quantic-specific queries. We’ve performed extensive internal and external testing, including formal evaluation by faculty and staff.

Still, mistakes happen! We have created tools to allow us to identify issues and improve the accuracy of AI Advisor in response to both user feedback and internal monitoring. We encourage our students to suggest new features, as well as use the thumbs up and down buttons to let us know how helpful they find AI Advisor’s answers.

How does AI fit into the vision for Quantic’s future?

In higher ed, much of the conversation about AI centers around cheating prevention or lesson plan generation. We, on the other hand, are excited to embrace this new technology as a way enrich the student experience. We founded Quantic with a vision of education that’s of the highest quality, precisely tailored to the needs of every individual, and delivered in an accessible, flexible format. With this launch, and as we continue to innovate in AI and other emerging technologies, we’re excited to be taking another step towards realizing this future.

Putting AI Into Practice: Justin Wright’s Quantic MSAIE Journey

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

Why did Justin Wright return to Quantic for the MS in AI Engineering?

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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What did Justin learn from building an agentic chat product?

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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How has Justin applied his Quantic MSAIE learning at work?

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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How does Quantic MSAIE connect technical learning with AI adoption leadership?

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.

More Student Spotlight:

More on the Quantic Blog:

Advancing AI Adoption in Healthcare: Akinsola Jeje’s Quantic MSAIE Journey

A retrieval chatbot for commercial and sales teams has become one way Akinsola “Akin” Jeje is putting his AI studies to work. Working in medical devices and the healthcare value chain, he is applying his learning to a workplace innovation involving a knowledge engine and governance layer for the tool. His experience offers a practical perspective on AI adoption in healthcare, grounded in the business operations that support the industry.

Akin chose Quantic’s MS in AI Engineering with two clear goals. He wanted to develop the skills to originate, design, build, and deploy AI systems and lead their adoption within his organization. His workplace innovation brings those ambitions together. Through Quantic MSAIE, he is developing AI expertise to put to use in a field he knows well.

Quick Answer: How is Akin Jeje approaching AI adoption in healthcare?

Akin applies Quantic MSAIE learning to a non-clinical workplace innovation involving a retrieval chatbot for commercial and sales teams. His approach connects healthcare experience, AI engineering, and governance.

Building and leading: He chose Quantic’s MS in AI Engineering to develop practical AI skills and lead organizational adoption.
Considering governance: His proposal includes a knowledge engine and governance considerations for the chatbot’s requests and responses.
Learning with support: He values the flexible format, academic and administrative staff, and extensive alumni network.

What experience does Akin bring to AI adoption in healthcare?

Akin’s interest in healthcare includes a concern for who can access it. His professional focus includes expanding healthcare provision in underserved communities, bringing a personal purpose to his work in the industry.

He studied biochemistry and basic medical sciences at Olabisi Onabanjo University. In business development at MEDICOOL LIMITED, he worked with healthcare stakeholders through collaboration, negotiation, and conflict resolution. His experience also includes a Lead role at MediCash Inc., whose work combines health and wellness information, access to services, and personalized communication.

That background gives his AI studies a practical context. He understands the healthcare environment in which he wants to introduce new capabilities, including the commercial and sales teams his workplace innovation aims to support.

Why did Akin choose Quantic’s MS in AI Engineering?

Akin wanted hands-on skills to design, build, and deploy AI systems, alongside the ability to lead adoption within his organization. His goals connect engineering with the decisions involved in putting technology to use.

The Quantic MSAIE curriculum integrates rigorous technical learning with AI adoption, leadership, and management. Applied projects connect business challenges with AI solutions, developing technical capabilities and business judgment together. The program serves technology-motivated professionals with varied business and technical experience.

For Akin, this combination is relevant to the medical devices and healthcare value chain in which he works. An innovation program at his workplace has provided an opportunity to apply that learning.

How is Akin applying his Quantic MSAIE learning to workplace innovation?

An annual innovation program at Akin’s workplace gave him an opportunity to put his learning into practice. Employees submit ideas in clinical and non-clinical categories, with shortlisted entrants presenting to a review council.

Akin’s non-clinical entry involves a knowledge engine and governance layer for a retrieval chatbot serving commercial and sales teams. Following the campaign’s conclusion, he reports receiving an excellence award and an honourable mention.

When asked what has stood out about his studies, he points to the connection with his professional work:

“I am currently deploying my knowledge from the program as I deepen my skills and domain expertise in the medical devices and healthcare supply and value chain.”

His account shows how coursework can inform an innovation proposal within an existing professional setting. For Akin, the work also raises questions about governance and the responsibilities involved in introducing AI into business operations.

Why is governance important to Akin’s approach to AI?

Akin treats governance as part of technical design. His account includes governance and regulatory tooling connected to the chatbot’s requests and responses, placing the responsibilities of adoption alongside the work of developing the tool.

For broader context, the NIST AI Risk Management Framework offers voluntary guidance on incorporating trustworthiness into AI design, development, use, and evaluation. It provides a useful reference for considering governance throughout an initiative.

Akin’s emphasis aligns with Quantic MSAIE’s focus on preparing technically proficient professionals to lead and implement AI for business needs. His proposal connects that ambition with the commercial operations of the healthcare industry.

What does Akin value about learning with Quantic?

Alongside the opportunity to apply his studies at work, Akin values the everyday experience of learning with Quantic. “Convenience, flexibility” are the first qualities he names when asked about his favorite aspects of the program. He also highlights its modern approach and extensive alumni network.

Akin also gives credit to the people supporting his education. He highlights both academic and administrative staff, noting the support available throughout the year.

His appreciation for Quantic therefore extends beyond the coursework to the people around it. He is also finding ways to participate in that wider community. Research and writing give him opportunities to contribute while developing his own AI capabilities.

How is Akin connecting with the Quantic community?

Akin is contributing to the Quantic Entrepreneur Club as a technical researcher and writer. Through that role, he has been able to “connect with current students and experts in the field.” His involvement gives him a way to contribute research and writing while building relationships beyond his coursework.

Researching and writing for the club lets him contribute to Quantic’s wider community while continuing his own education.

Leading AI adoption with healthcare experience

“I stand on the shoulders of many,” Akin says, acknowledging the people who have shaped his outlook on life.

That appreciation accompanies his ambition to keep learning. Through Quantic MSAIE, he is developing AI capabilities alongside his healthcare expertise and applying that knowledge to a workplace innovation proposal.

His journey illustrates how professionals can build on industry experience while developing the technical skills and judgment needed to lead AI adoption.

Explore Quantic’s MS in AI Eng ineering to learn how the program bridges AI and business, preparing professionals to lead and implement real-world AI initiatives.

FAQs

Why did Akin Jeje choose Quantic’s MS in AI Engineering?

Akin chose the program to build hands-on skills to design, build, and deploy AI systems. He also wants to lead AI adoption within his organization. These goals connect his healthcare experience with the engineering capabilities and implementation skills he wants to develop.

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How is Akin applying his AI learning in healthcare?

He is applying his learning to a non-clinical workplace innovation involving a retrieval chatbot for commercial and sales teams. His work includes a knowledge engine and an intelligence layer he describes as the tool’s governance layer. Akin says the innovation earned him an excellence award.

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What role does governance play in Akin’s workplace innovation?

Akin describes the intelligence layer as the chatbot’s governance layer. He also identifies governance and regulatory tooling connected to its requests and responses. His account places these considerations within the technical work of developing an AI agent or application for a business setting.

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How does Quantic MSAIE connect AI engineering with business implementation?

Quantic MSAIE integrates rigorous technical learning with AI adoption, leadership, and management. Applied projects connect AI deployment expertise with business needs, helping students develop technical capabilities and business judgment together. The program prepares professionals to lead and implement AI initiatives within organizations.

More Student Spotlight:

More on the Quantic Blog:

Leading AI Business Transformation: Jeff Dahl’s Quantic Journey

Jeff Dahl has spent more than three decades helping organizations put new technology to work, including global programs at PepsiCo and Bayer. Through Quantic’s MS in AI Engineering, he is developing hands-on AI skills while strengthening his ability to lead adoption. He brings extensive transformation experience and a willingness to learn, describing himself as a “neophyte in programming.”

As President of IntrepidaVision LLC, Jeff brings extensive transformation experience to his studies. His next step combines a professional ambition with a personal challenge: learning to build with AI while strengthening his ability to lead adoption. That learning is already informing his software development approach and client conversations.

Quick Answer: How is Jeff Dahl approaching AI business transformation?

Through Quantic MSAIE, Jeff Dahl is adding practical AI engineering skills to decades of transformation leadership. He is already using that learning in client advisory work, connecting technical understanding with organizational needs.

Applying coursework: He has adapted his software development lifecycle approach and helps clients understand AI’s benefits and requirements.
Considering safeguards: Deeper technical learning has strengthened his understanding of safety guardrails.
Continuing to learn: Flexible study and supportive peers help him develop programming skills alongside his consulting responsibilities.

What experience does Jeff bring to AI business transformation?

Jeff Dahl’s background spans military service, enterprise technology, and business transformation. He holds a B.S. in General Engineering from the United States Naval Academy. He also served in the United States Marine Corps, reaching the rank of captain.

His business career has taken him across consumer products, pharmaceuticals, and manufacturing. At PepsiCo, he spent more than a decade in enterprise solutions, enterprise architecture, and global solution design. He led the global deployment of SAP HANA across analytics platforms, secured board approval and led the $2.2 billion Global Template transformation program on SAP S/4HANA ERP.

At Bayer, Jeff helped plan the global CORE transformation program before co-leading its launch as SVP IT Lead. Leveraging SAP S/4HANA, the program aligned business processes, data, and systems across divisions, including a pilot for Bayer’s Brazil business unit.

Those roles required coordination across people, processes, and technology. Today, he advises IntrepidaVision clients on digital and AI strategy, governance, operating models, and technology modernization. He also serves as an advisor at Bain & Company on business and technology transformation.

His studies build on that experience by bringing him closer to the technical work of AI implementation.

Why did Jeff choose Quantic’s MS in AI Engineering?

Jeff chose Quantic’s MS in AI Engineering with two goals: to develop hands-on skills to design, build, and deploy AI systems, and to lead AI adoption within organizations.

He has concentrated on developing his AI programming skills, adding technical depth to the transformation experience he brings to clients.

The Quantic MSAIE curriculum combines rigorous AI engineering with adoption, leadership, and management. Designed for technology-motivated professionals across business and technical roles, it connects technical capability with organizational needs. For Jeff, that combination supports his ambition to build with AI and help lead its implementation.

How is Jeff approaching AI-assisted engineering in Quantic MSAIE?

“I’m extremely impressed by the combination of strong foundational and technical coursework,” Jeff says.

He is also “amazed at the tremendous speed and quality” of AI-based system development.

His current machine-learning project involves developing a malware classifier with assistance from ChatGPT. The work follows six steps: explain, inspect, decide, implement, verify, and document. Jeff reviews the reasoning behind decisions before moving forward.

The project has involved examining repeated records and conflicting labels in the dataset, deciding how to handle them, and checking the resulting work. Verification also caught an error in an AI-generated configuration change, which was then corrected.

These experiences bring his interest in safeguards into the practical work of building a system. AI provides technical assistance, while Jeff remains responsible for reviewing decisions and checking results. The project is still underway, connecting his learning in Quantic’s MS in AI Engineering with the judgment required to lead implementation.

How is Jeff applying his Quantic learning in client advisory work?

Jeff has used his studies to tailor what he calls a “best of” software development lifecycle approach. He combines AI learning with his transformation experience to help clients understand AI’s benefits and requirements.

At IntrepidaVision, his work includes enterprise resource planning (ERP), innovation and R&D, and data transformation initiatives. He also supports application and infrastructure modernization and cloud migration. AI therefore enters discussions about existing systems, business processes, and organizational responsibilities.

Jeff also reports a stronger understanding of safety guardrails through learning about the technology at a deeper, more fundamental level. His attention extends from how systems are developed to how people adopt them.

“AI is also a part of the cultural discipline to bring into the organization,” he shares.

Why does Jeff connect AI adoption with organizational change?

Jeff describes AI as a “step-change in technology.” Yet his central observation is that “the discipline of transformation remains the same.”

He identifies five priorities:

Leading change.
Organizational design.
Getting data right.
Streamlining business processes.
Maintaining safety and security guardrails.

In Jeff’s view, AI can enhance and accelerate transformation when organizations integrate it into these disciplines. Technical capability becomes part of a wider effort involving how people work, how processes connect, and how data is managed.

His Quantic studies are deepening the technical understanding he brings to those questions, alongside the organizational experience developed throughout his career.

How does Quantic’s flexibility support Jeff’s learning?

Jeff values being able to continue his studies wherever he is. Asked about his favorite part of Quantic, he points to

“The quality and flexibility of the learning approach and platform.”

That flexibility allows him to study and develop his skills alongside his consulting work. Quantic’s online, asynchronous coursework supports a learning approach he can carry into different locations.

For Jeff, the value of that format connects with what he is learning. Programming, development methods, and safety guardrails all have a place in his professional interests. He can continue exploring them while remaining engaged with the organizations he advises.

His appreciation for the platform includes both quality and flexibility. The combination matters to a professional pursuing substantial new skills alongside an established career.

Individual study is one part of that experience. Jeff also values the knowledge and generosity of his peers.

What does Jeff value about his Quantic peers?

Jeff describes his peers as “very collegial in asking for and offering assistance when needed.” They share relevant news, thoughts, and opinions, bringing perspectives beyond his own professional experience.

He particularly appreciates the knowledge behind those exchanges:

“Great insights with the varied depth and breadth of cohort experience.”

Supporting others’ development also has a place in Jeff’s career. He mentored transitioning veterans through American Corporate Partners from 2014 to 2018. At Quantic, he values the willingness of peers to ask questions, offer help, and share what they know while he develops his programming skills.

Building the skills to lead AI business transformation

After decades of leading transformation, Jeff is making time to learn programming, exchange ideas with peers, and explore what he can build. His experience informs his studies, while his learning is already changing the methods and understanding he brings to clients.

His journey reflects the purpose of Quantic MSAIE: helping technology-motivated professionals develop the technical proficiency and leadership capabilities to bring AI into practical business use.

Explore Quantic’s MS in AI Engineering to learn how the program prepares professionals to lead and implement real-world AI adoption.

FAQs

What does Jeff Dahl see as essential to AI business transformation?

Jeff emphasizes leading change, organizational design, getting data right, streamlining business processes, and maintaining safety and security guardrails. He believes AI can enhance and accelerate transformation when organizations integrate it into these disciplines.

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Why did Jeff enroll in Quantic MSAIE?

Jeff wanted practical skills to design, build, and deploy AI systems. He also wanted to strengthen his
ability to lead AI adoption and implementation within his organization, building on his transformation experience.

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How is Jeff using his Quantic learning at work?

Jeff has adapted his software development lifecycle approach and uses his learning to help clients understand AI’s benefits and requirements. He also reports a stronger understanding of safety guardrails through deeper technical knowledge.

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How does Quantic MSAIE prepare professionals to lead AI adoption?

Quantic MSAIE combines rigorous AI engineering with AI adoption, leadership, and management. Designed for technology-motivated professionals across business and technical backgrounds, its applied curriculum develops technical skills and business judgment. The goal is to prepare technically proficient leaders who can guide and implement AI initiatives that address organizational needs.

Explore Quantic’s MS in AI Engineering curriculum to learn more.

More Student Spotlight:

More on the Quantic Blog:

The MSAIE, Business Transformation and Consulting: Pierce Plumly’s Quantic MSAIE Journey

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

Why did Pierce Plumly return to Quantic for the MS in AI Engineering?

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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What did Pierce learn from building a RAG-based AI agent?

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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How has Pierce applied his Quantic learning in consulting?

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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How does Quantic MSAIE prepare professionals to lead and implement AI adoption?

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.

More Student Spotlight:

More on the Quantic Blog:

Quantic MSAIE: Curriculum, AI Leadership, and Real-World Application

According to McKinsey’s 2025 Global Survey on AI, 88% of respondents reported that their organizations were using AI in at least one business function, yet only 7% said AI had been fully scaled across their organizations. This gap highlights the challenge that Quantic’s MS in AI Engineering (Quantic MSAIE) aims to address: preparing professionals to lead AI implementation and transform emerging capabilities into tangible business value.

The Quantic MSAIE program is designed to cultivate the future leaders of AI adoption in both the U.S. and international economies. We are currently in the early stages of a 5–10-year period of worldwide AI integration, which is expected to be comparable to the Industrial Revolution but potentially 10x larger in impact and occurring ten times faster, according to prominent figures in AI, such as Demis Hassabis, CEO of Google DeepMind, and Jack Clark, co-founder of Anthropic.

This program bridges the gap between AI and business by combining rigorous AI agent and application development with AI adoption, leadership, management, and real-world application. It is designed to develop technically proficient AI leaders who can evaluate what is feasible, lead implementation, and help turn AI capabilities into practical business impact in their organizations and the economy.

Quick Answer: What is Quantic MSAIE designed to prepare professionals to do?

Quantic’s MS in AI Engineering is designed to develop technically proficient AI leaders who can help lead and implement real-world AI initiatives. The program bridges AI and business by combining rigorous AI engineering with AI adoption, leadership, management, and practical application in organizational settings.

Lead real-world AI implementation. The program focuses on applying AI to practical business needs, use cases, and organizational initiatives.
Build technical AI proficiency. Students develop rigorous AI engineering skills to design, build, test, and deploy AI applications and agents.
Develop AI adoption leadership. The curriculum incorporates AI leadership and management alongside technical AI learning.
Connect AI capabilities with business needs. Quantic integrates AI engineering and leadership to help professionals connect AI capabilities with real business needs.
Apply learning end to end. In the Capstone, students work within an agile team over three months to design, develop, test, and deploy a functional AI artifact.

What capabilities does Quantic MSAIE develop for AI leadership?

Quantic’s MS in AI Engineering is designed to prepare professionals to lead and implement AI in real-world business settings. The program connects AI engineering with leadership, management, and organizational needs, helping students understand both what AI can do and how to apply it effectively.

That focus goes beyond understanding AI concepts in isolation. Quantic MSAIE aims to develop technically proficient AI leaders who can evaluate opportunities, work through implementation challenges, and help organizations integrate AI into products, processes, and business transformation initiatives.

The curriculum reflects that goal by combining rigorous AI technical learning with substantial content in AI adoption, leadership, and management. Quantic integrates AI agents and application implementation, leadership, and management around the practical challenge of moving AI from possibility to implementation.

For technology-motivated professionals, that means becoming technically proficient AI leaders, with the engineering skills, judgment, and leadership capabilities to lead real-world AI implementation.

How does the Quantic MSAIE curriculum combine AI engineering, leadership, and management?

The Quantic MS in AI Engineering curriculum is built around what leading AI adoption and AI initiatives takes: the technical skill to build AI systems, plus the ability to align them with organizational priorities, evaluate opportunities, and guide implementation from idea to real-world use.

That balance is reflected in the MS in AI Engineering curriculum, which brings rigorous AI engineering together with AI adoption, leadership, and management. Quantic’s MS in AI Engineering develops these capabilities together as complementary parts of leading AI adoption.

Students build the technical proficiency to design and deploy AI systems, including the use of AI software development agents themselves, along with the leadership capabilities to apply them to business needs, organizational change, and AI-based business transformation.

This integrated approach supports the program’s broader goal: preparing technically proficient AI leaders who can help lead AI diffusion in the broader US and global economies.

How does Quantic’s MS in AI Engineering turn learning into real-world application?

Leading real-world AI adoption means identifying and connecting it to strategic goals and an organizational need, making implementation decisions, building the needed AI agent or application, and thereby carrying an initiative from concept through execution.

Quantic’s MS in AI Engineering builds that practical application into the learning experience. The curriculum is designed to connect AI engineering with adoption, leadership, and management. In the Capstone, students deliver a comprehensive AI project: over three months, they work within an agile team (or individually if preferred) to identify and conceive, design, develop, test, and deploy a functional AI agent or application, culminating in an AI system demo and GitHub repository.

The result is a learning experience centered on: understanding AI capabilities, connecting them to meaningful use cases, and developing the judgment needed to help lead AI initiatives in real-world settings.

Who is Quantic’s MS in AI Engineering designed for?

Quantic’s MS in AI Engineering is designed for a broad range of technology-motivated professionals who want to understand AI deeply enough to lead its practical use in their fields. The program combines technical rigor with accessibility for a broad range of technology-motivated professionals, including those without a software engineering or AI specialist background.

It can be especially relevant for:

Product managers who want to use AI to build products and lead innovation.
Senior AI adoption leaders (CAIO, VPs, Directors) who want to take on an organization-wide AI adoption role
Non-technical founders who need stronger AI understanding to build and compete effectively.
Supply-chain leaders applying AI to forecasting, logistics, operations, and planning.
IT professionals preparing to lead AI-driven changes across systems and infrastructure.
Data scientists expanding from model development into broader AI engineering and deployment.
ML and AI practitioners developing the engineering and leadership skills to align and lead AI system creation with real business needs.
Project managers leading AI-era projects across technical and organizational teams.
Cybersecurity professionals applying AI across offensive and defensive security capabilities.
Software engineers expanding into AI system design, strategy, and AI-assisted development.
Data professionals and business analysts moving from reporting and analysis into AI-enabled decision systems.
Technology-interested bachelor’s degree holders seeking deeper AI expertise and a Master’s qualification to complement strong analytical foundations.
Finance and investment professionals applying AI to analysis, portfolio management, and risk modeling.
Healthcare and biotech leaders working with AI across areas such as drug discovery, clinical decision-making, and operational efficiency.
Management consultants building hands-on AI expertise to guide transformation and deliver AI solutions.
Growth and marketing professionals moving beyond AI tools toward AI-powered systems and broader AI strategy.

Across these roles, Quantic MSAIE is designed to make them technically proficient AI leaders who can connect AI capabilities with real organizational needs and lead AI implementation. All such roles are well represented amongst current Quantic MSAIE cohorts. The most prevalent employers of students in the MSAIE include Google, Salesforce, Microsoft, Accenture, Wells Fargo, Oracle, Blue Cross Blue Shield, and Deloitte, amongst numerous other Fortune 500 companies.

How does Quantic MSAIE compare with other MS AI Engineering programs?

MS AI Engineering programs can differ substantially in what they prepare students to do. Some apply AI within traditional engineering disciplines or focus on physical systems and computing infrastructure. Quantic MSAIE applies rigorous AI engineering to business: building AI applications and agents and leading their adoption and implementation in organizations.

School	Program focus	Modality	Duration	Published tuition*
Quantic School of Business and Technology	AI applications and agents for organizational AI adoption	100% Online	13 months	$21,450
Carnegie Mellon University	AI applied within traditional engineering disciplines	On Campus	3 – 4 semesters	~ $60K – $90K*
Arizona State University	AI applied within traditional engineering disciplines	On Campus	1 – 2 years	~ $39K*
George Washington University	Intersection of AI and hardware, physical systems & infrastructure	On Campus	2 years	~ $66K*
Auburn University	Traditional CS, AI, Algorithms, ML, Data, Software	Online or On Campus	1.5 – 2 years	~ $28K*
Washington University in St. Louis	Traditional CS, AI, Algorithms, ML, Data, Software	On Campus	1.5 – 2 years	~ $35K*

*Tuition figures are for reference only. Please consult each school’s official website for the latest fees.

The comparison is less about identifying a universally “best” program than understanding program orientation. Carnegie Mellon combines AI engineering with traditional engineering disciplines, ASU applies AI across engineering concentrations, GWU emphasizes physical AI and infrastructure, Auburn focuses on engineering AI and machine-learning solutions, and WUSTL’s forthcoming program emphasizes algorithms and engineering AI systems.

Quantic MSAIE is differentiated by its emphasis on preparing professionals to lead AI adoption and implementation in organizational settings. The program bridges AI and business, combining rigorous AI engineering with AI adoption leadership to prepare technically proficient AI leaders who can lead and implement AI-based business transformation.

What does real-world application look like for Quantic MSAIE students?

Beyond understanding how the program approaches real-world application, prospective students may want to know what the project work actually involves. Quantic MSAIE emphasizes hands-on projects that integrate advanced AI models, AI applications, and agentic workflows, as well as AI adoption and transformation projects. Students work on translating business challenges into practical AI solutions while developing both technical AI capabilities and the judgment needed to lead an initiative.

The clearest example is the MSAIE Capstone Project, which brings together the program’s AI technical, adoption, leadership, and management dimensions through an end-to-end, real-world AI initiative.

Working as part of an agile AI engineering team (or individually if preferred), students carry out a real-world AI initiative from start to finish. Along the way, they may:

Identify a high-value AI use case that aligns with organizational business objectives.
Analyze, design, develop, and test a corresponding AI application or agent.
Apply AI engineering methodologies and the latest AI software development agents to deliver a working software product.
Carry out effective project management.
Fine-tune AI models where appropriate to the project.
Use continuous integration and continuous delivery (CI/CD) as part of the deployment process.
Work with product stakeholders where applicable to agree on requirements, then demonstrate a functioning AI application or agent that meets them.

This is where Quantic’s MS in AI Engineering connects technical capability with AI adoption leadership. Students work through the full set of decisions involved in taking an AI initiative from a business need to a functioning application or agent, including use case identification and strategic alignment, architecture, development, testing, deployment, collaboration, and stakeholder requirements.

Beyond the Capstone, students also describe applying their learning through other collaborative projects and independent initiatives. Christopher Arnold reflected on his second Quantic project:

I learned a great deal that was beyond just working off the curriculum.

He also described the confidence he gained to build independently:

I didn’t know that I could do this independently. But after the second Quantic project was completed, I knew that I could build my dream app.

Naomi Withers similarly described the value she sees in connecting learning with practice:

I value Quantic’s approach to helping professionals connect academic learning with real-world application.

While these are individual experiences, they show how Quantic MSAIE puts the program’s broader goal into practice: preparing technically proficient AI leaders who can help turn AI capabilities into real-world organizational initiatives.

Takeaway: How Quantic MSAIE prepares professionals for real-world AI leadership

Quantic MSAIE is designed for professionals who want to understand AI deeply enough to help lead its practical use in organizations and economy-wide diffusion. The program integrates rigorous AI engineering with AI adoption leadership, management, and real-world implementation.

Through hands-on projects and the Capstone, Quantic’s MS in AI Engineering gives students opportunities to work through the full arc of an AI initiative, from identifying a business need to designing, building, testing, and deploying an AI application or agent.

That combination reflects the program’s broader purpose: developing technically proficient AI leaders who can connect AI capabilities with organizational priorities and help move AI from possibility to economy-wide benefit and impact.

Explore Quantic’s MS in AI Engineering

FAQs

Is Quantic MSAIE only for engineers or software developers?

No. Quantic’s MS in AI Engineering is designed for technology-motivated professionals from both business and technical backgrounds, including managers, product professionals, consultants, data specialists, and engineers.

Applicants do not need to be current or former developers. However, they must demonstrate quantitative aptitude and meet the program’s admission requirements. Some candidates may need foundational training.

Review Quantic MSAIE admissions requirements.

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How technical is Quantic’s MS in AI Engineering?

Quantic’s MS in AI Engineering is technically rigorous while remaining accessible to professionals from varied backgrounds. The curriculum covers AI systems engineering, advanced AI agents and models, applications, and agentic workflows.

Importantly, the program integrates these technical capabilities with AI adoption, leadership, and management. Its goal is to develop technically proficient AI leaders who can guide real-world AI implementation and organizational transformation.

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How long does Quantic MSAIE take to complete?

Quantic MSAIE is a 13-month online, cohort-based program. Its format combines flexible learning with collaborative projects and opportunities to work with peers.

The program brings together AI engineering, leadership, management, and practical application to prepare professionals for leading and implementing real-world AI initiatives.

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What does the Quantic MSAIE Capstone Project involve?

The Capstone requires students to deliver a comprehensive AI project. Over three and a half months, they will work within an agile team of exceptional peers (or individually if they prefer) to conceive, identify, design, develop, test, and deploy a functional AI agent or application. This intensive project culminates in an AI system demonstration and a private (or optionally public) GitHub repository, preparing them to manage and execute complex AI projects in any professional setting.

Leading AI Adoption From the Front: Warren Rehman’s Quantic MSAIE Journey

Warren Rehman found it difficult to call his agentic AI project finished. Even after building a functional agentic system, he could see more possibilities to explore. Each adjustment to its architecture, retrieval settings, and prompts offered another opportunity to learn. For Warren, that curiosity is an important part of AI adoption leadership: understanding how systems work, not just what they promise.

As Android TV APAC Tech Lead at Google and a Quantic Executive MBA graduate, Warren brings extensive engineering and leadership experience to his studies. Through Quantic’s MS in AI Engineering, he is developing the hands-on AI capabilities to help lead real-world adoption and implementation. His journey illustrates how Quantic MSAIE brings together technical rigor, leadership, and practical application.

Quick Answer: How does Warren Rehman’s experience illustrate AI adoption leadership?

Warren Rehman’s Quantic journey shows how AI adoption leadership combines engineering expertise, strategic judgment, and hands-on implementation. An experienced engineering leader and Executive MBA graduate, he is pursuing Quantic’s MS in AI Engineering to strengthen his ability to design, evaluate, and implement AI systems. His experience developing an agentic HR system demonstrates how practical engineering work can inform leadership decisions and future organizational AI initiatives.

Leadership informed by engineering experience. Warren draws on his background in software engineering, technology strategy, and technical partnerships.
Hands-on AI development. He built and validated a functional agentic HR system using synthetic data during his MSAIE coursework.
Learning through experimentation. He explored retrieval settings, RAG chunking strategies, and prompt refinement to evaluate system performance and response quality.
Flexibility and independent exploration. Quantic’s learning format allows Warren to pursue graduate study alongside demanding professional commitments.
A focus on real-world adoption. His next goal is to explore how the architecture developed in his coursework could help address organizational challenges.

Warren’s experience reflects the broader purpose of Quantic MSAIE: developing technically proficient AI leaders who can connect AI capabilities with real-world business needs.

Why did Warren continue his Quantic education with the MS in AI Engineering?

Warren began Quantic’s MS in AI Engineering while completing his Executive MBA at Quantic. Already established in engineering and technology leadership, he saw AI as an area where practical experience would be essential to guiding future initiatives.

Two goals motivated his decision to pursue Quantic MSAIE: building hands-on skills to design, build, and deploy AI systems, and leading AI adoption and implementation within his organization.

These goals reflect how Warren approaches engineering leadership. Understanding the potential of AI is important, but so is knowing how systems behave, what makes them feasible, and what it takes to implement them successfully.

His decision to continue at Quantic was an opportunity to build on his existing expertise while developing the capabilities needed to lead as AI technologies evolve.

How does Warren’s engineering leadership experience shape his approach to AI?

Warren’s perspective comes from years of working at the intersection of engineering, product development, and technology strategy.

As Android TV APAC Tech Lead at Google, his experience spans technical partner engineering, hardware architectures, product requirements, and collaborations across the connected-TV ecosystem. He also holds a Bachelor of Science in Computer Science from the University of Alberta.

That background has given him experience navigating complex technical challenges while considering their broader business implications.

It also shapes what he wants from AI engineering education. Warren is interested in understanding AI architectures firsthand, from how their components interact to how design decisions affect performance and reliability.

For an engineering leader, that understanding can inform decisions about which AI opportunities are practical, how to approach implementation, and what teams need to deliver working solutions.

Warren’s next challenge was to put that perspective to work through an AI engineering project of his own.

What did Warren build during his Quantic MSAIE project?

During the AI Engineering Techniques and Architectures Project, Warren worked on an assignment that brought together multiple technologies to build a complex agentic AI system.

He developed and validated a functional HR system with multi-step agentic orchestration, vector database indexing, and custom Model Context Protocol (MCP) tool integration, using synthetic data. The project allowed him to explore the architectural requirements of an AI system and evaluate how its components worked together.

What made the assignment particularly relevant was its connection to his professional interests. Warren described how the project mirrored requirements and architectural challenges he had been evaluating at work.

Through practical development, he could experiment with those challenges directly rather than considering them only at the planning stage.

This type of project-based learning reflects the approach of the Quantic MSAIE curriculum, which integrates AI engineering, leadership, and real-world business application.

While built strictly as an academic proof-of-concept using synthetic data, the project gave Warren a practical blueprint for evaluating how agentic architectures can solve enterprise operational challenges.

And completing a working agentic system was not where his curiosity ended.

How did experimentation strengthen Warren’s understanding of agentic AI?

For Warren, some of the most valuable learning came from testing and evaluating the AI system and discovering how much room there was to improve it.

His experiments included tuning retrieval parameters, optimizing chunking strategies for retrieval-augmented generation (RAG), and refining system prompts.

RAG allows AI systems to draw on relevant information from external knowledge sources when generating responses. Decisions about retrieval and how information is divided into chunks can influence the quality and relevance of those responses.

Warren evaluated how different settings affected his project’s performance and response quality. Each experiment helped him better understand the relationships between the system’s components.

As he reflected:

It was honestly challenging to consider the assignment ‘done’ because there was so much room to push the solution further and keep learning more.

That determination to keep exploring offers insight into Warren’s approach to leadership and engineering. A working prototype is an important milestone, but understanding its limitations and evaluating possible improvements are also essential parts of building AI systems that are useful in real-world deployment.

For Warren, the experience reinforced the value of rigorous experimentation and engineering judgment, capabilities that also matter when leading real-world AI adoption and implementation.

How did Quantic’s flexibility and project experience support Warren’s learning?

Warren’s professional responsibilities involve frequent travel and a demanding schedule. Finding the time and flexibility to pursue graduate study was therefore important.

As he put it:

Flexibility is the key.

He values the ability to study across different times and locations, explaining that pursuing a master’s degree would have been considerably more difficult without that flexibility.

The learning experience also gave Warren opportunities to approach technical challenges in different ways.

He described his first MSAIE group project as a very positive experience. His classmates were eager to understand every aspect of the assignment, making the collaboration particularly engaging.

That shared enthusiasm influenced his approach to later projects. Thanks to the flexibility towards group work in the program, after collaborating on the initial project, Warren chose to tackle subsequent builds solo so he could write every layer of the stack, from data ingestion to orchestration, rather than dividing the architecture up. .

Quantic’s program provided the freedom to explore every technical component, work through problems at his own pace, and investigate different possibilities without slowing down his teammates.

For Warren, the combination of flexibility, collaboration, and independent exploration has made it possible to pursue the deeper technical understanding he wants while continuing his professional work.

Those experiences are also shaping how he thinks about applying AI beyond the classroom.

How does Warren hope to apply his AI learning to organizational challenges?

Warren sees potential to take what he learned from his agentic HR project further.

Having built and validated the system using synthetic data, he is now exploring how its architecture could be directly adapted to work with domain-specific organizational information and documents.

His goal is to investigate whether those capabilities could help address practical operational challenges within his department.

Moving directly from an academic project toward an organizational application introduces new considerations. A solution must meet real business requirements, work with dynamic, ever-changing enterprise data, and address the technical challenges involved in implementation.

Warren’s extrapolation to a workplace implementation remains an ongoing exploration.

Through Quantic’s MS in AI Engineering, Warren is continuing to strengthen his ability to evaluate AI architectures, understand implementation challenges, and apply engineering judgment to organizational needs.

His ambition reflects the broader goal of Quantic MSAIE: preparing professionals to lead and implement AI adoption in real-world business settings.

Leading AI adoption from the front

For Warren, effective AI leadership requires more than a clear strategy or an understanding of emerging technologies. It also requires knowing what happens when those technologies are put to work.

As he explains:

Great engineering leadership requires more than understanding what AI can do on a roadmap or a demo.

His Quantic journey reflects that belief. Building on his Executive MBA education and professional engineering experience, Warren is developing deeper AI capabilities through practical projects, experimentation, and independent exploration.

His agentic HR project illustrates both what he has accomplished and what he hopes to explore next. He has built a functioning system and is now considering how similar approaches might help address real organizational challenges.

Warren’s story demonstrates why AI adoption leadership calls for technical understanding, strategic judgment, and a willingness to engage directly with implementation.

It also reflects the broader purpose of Quantic MSAIE: developing technically proficient AI leaders who can help bring AI into practical use across businesses and the wider economy.

Explore Quantic’s MS in AI Engineering

FAQs

Why did Warren Rehman pursue Quantic’s MS in AI Engineering?

Warren began Quantic’s MS in AI Engineering while completing his Executive MBA. His two primary goals were to develop hands-on skills to design, build, and deploy AI systems and strengthen his ability to lead AI adoption and transformation within his organization.

These goals align with his established engineering leadership experience and his interest in applying AI to real-world organizational challenges.

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What agentic AI system did Warren develop during his studies?

So far, during his Quantic MSAIE coursework, Warren built and validated a functional agentic HR system using synthetic data.

He experimented with retrieval parameters, RAG chunking strategies, and system prompts while evaluating performance and response quality. The project gave him practical experience developing and assessing a complex agentic AI architecture.

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How is Warren exploring practical AI adoption in his professional work?

Warren is exploring how the architecture he developed during his academic project could be adapted to work with domain-specific organizational information and documents.

His goal is to investigate practical applications that could address operational challenges, leveraging AI transformation to enable increased automation and efficiency while ensuring human-in-the-loop direction and accountability. He has not reported deploying the academic system in his workplace.

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How does Quantic MSAIE combine AI engineering with leadership and management?

Quantic MSAIE bridges AI and business by integrating rigorous AI engineering with AI adoption, leadership, and management. Designed for a broad range of professionals from business and technical backgrounds, the program aims to develop technically proficient AI leaders.

Through applied coursework and its separate end-to-end Capstone Project, the program helps students develop the technical expertise and leadership capabilities needed to guide practical AI implementation and organizational transformation.

More Student Spotlight:

More on the Quantic Blog:

Quantic MSAIE Reviews: Hearing Directly From Students and Alumni

“My EMBA program was so good I came back to do MSAIE” was consultant Naomi Withers’s Trustpilot headline. She returned for Quantic’s MS in AI Engineering to strengthen her technical understanding for her consulting work. Her story introduces a closer look at Quantic MSAIE reviews and the student experiences behind them.

Choosing to study while working raises questions a program description alone cannot answer. How does the learning fit into a full life? Where does it show up at work? Through student feedback and public reviews, six current students share their experiences so far. Several are Quantic E/MBA alumni, returning with new questions and goals.

Quick Answer: What do Quantic MSAIE reviews reveal?

Students describe Quantic’s MS in AI Engineering as a practical way to deepen technical understanding and connect it with business needs. Their feedback highlights four themes:

A positive experience brings alumni back. Naomi Withers and Justin Wright both cite their earlier Quantic education as a reason to return.
Building makes the learning stick. Justin built an AI chat product from scratch. Warren Rehman built and tested an AI system for HR using synthetic data.
Technical understanding informs business decisions. Jeff Dahl describes using his learning to help clients understand AI’s benefits and requirements. Akinsola Jeje is applying his knowledge in medical devices and healthcare.
Flexibility makes continued study possible. Justin fits lessons around a demanding career and two young children. Warren values being able to study while traveling for work.

Inside these Quantic MSAIE reviews

Quick Answer: What do Quantic MSAIE reviews reveal?
Quantic MSAIE Reviews: What brought Naomi Withers back for Quantic MSAIE?
Quantic MSAIE Reviews: What keeps Warren Rehman experimenting?
Quantic MSAIE Reviews: How is Justin Wright putting Quantic MSAIE learning to work?
Quantic MSAIE Reviews: What is Quantic MSAIE adding to Jeff Dahl’s approach to AI adoption?
Quantic MSAIE Reviews: How is Akinsola Jeje applying Quantic MSAIE learning in healthcare?
Explore the Quantic MSAIE program
Final thoughts: Could Quantic MSAIE be your next step?
Quantic MSAIE FAQs

Quantic MSAIE Reviews: What brought Naomi Withers back for Quantic MSAIE?

Naomi Withers’s Executive MBA continued to matter after graduation. The business knowledge she gained informed her work as a consultant and advisor. Writing on Trustpilot, she described the learning as practical, engaging, and designed with working professionals in mind.

Her consulting work gave her a clear reason to keep learning:

I work with companies that are navigating AI transformation, and I wanted to complement my business and consulting background with stronger technical understanding. I value Quantic’s approach to helping professionals connect academic learning with real-world application.

She chose Quantic’s MS in AI Engineering to pursue that goal. When Naomi shared her review, she was still early in the program, balancing study with growing her consulting and coaching practice.

Her closing words capture the trust behind her return:

I am grateful to be continuing my education with a school I respect and believe in.

Quantic MSAIE Reviews: What keeps Warren Rehman experimenting?

Warren Rehman is the Android TV APAC Tech Lead at Google and a Quantic EMBA alumnus. He pursued Quantic’s MS in AI Engineering to connect executive strategy with hands-on technical fluency.

For Warren, one project kept opening up new possibilities.

It was honestly challenging to consider the assignment “done” because there was so much room to push the solution further and keep learning more.

The AI Engineering Techniques and Architectures Project mirrored challenges he was evaluating at work. He experimented with how an AI system retrieved information and responded to prompts. He then measured changes in performance and response quality. For the assignment, he built and validated a functional AI system for HR using synthetic data.

At the time of his feedback, workplace implementation remained an ongoing exploration. He wanted to adapt the architecture to use corporate documents and internal data to address challenges in his department.

Warren also travels frequently for work. Being able to study at different times and locations helps him make room for the effort a master’s program requires. That effort serves a clear purpose: developing the technical understanding he wants to bring to engineering leadership.

Great engineering leadership requires more than understanding what AI can do on a roadmap or a demo.

Quantic MSAIE Reviews: How is Justin Wright putting Quantic MSAIE learning to work?

Justin Wright also returned with a specific goal. A former professional athlete and self-taught software developer, he wanted to fill gaps as his engineering responsibilities grew. His positive experience with Quantic’s Executive MBA helped bring him back for Quantic’s MS in AI Engineering.

One project gave him the opportunity to build an AI chat product from scratch, outside the company infrastructure he knew. Working through the system helped him understand the steps and skills needed to make it useful in practice. He also credits the hands-on coursework with deepening his understanding of model training and fine-tuning.

That learning connects directly with his work deploying AI agents for enterprise 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.

Justin has also built a machine learning model for personal projects. Applying the coursework to something of his own helped him gain confidence in deploying similar solutions.

With two young children and a demanding career, finding time to study matters. Justin values being able to fit short lessons into available moments and keep pace with his coursework. Reflecting on both his Quantic programs, he says:

These programs have allowed me to expand my knowledge, level up my career, and do so in a way that is flexible enough to work with my hectic life right now.

Quantic MSAIE Reviews: What is Quantic MSAIE adding to Jeff Dahl’s approach to AI adoption?

Jeff Dahl brings more than 30 years of business and technology transformation experience to his studies. His career includes leadership roles at Bayer and PepsiCo. Through Quantic’s MS in AI Engineering, he is developing AI programming skills that complement that experience.

The coursework has made a strong impression:

I’m extremely impressed by the combination of strong foundational and technical coursework, plus, amazed at the tremendous speed and quality of AI-based system development.

Jeff is candid about being new to programming. He has focused on developing those skills and appreciates classmates who offer assistance, share news, and exchange ideas. Their varied experience adds another perspective to his learning.

He is already putting that knowledge to work. Jeff describes using what he has learned to shape his approach to the software development lifecycle. He also draws on it when helping clients understand AI’s benefits and requirements.

For Jeff, AI adoption involves familiar responsibilities: leading change, organizing teams, getting data right, and improving business processes. Learning how the technology works helps him consider the safeguards it requires:

Finally, my understanding of the safety guardrails from a deep, fundamental understanding of the technology is greatly improved.

Quantic MSAIE Reviews: How is Akinsola Jeje applying Quantic MSAIE learning in healthcare?

Akinsola Jeje, known as Akin, is bringing his learning into the medical devices and healthcare industry.

In his feedback on Quantic’s MS in AI Engineering, he explains:

I am currently deploying my knowledge from the program as I deepen my skills and domain expertise in the medical devices and healthcare supply and value chain.

He describes creating a knowledge engine and governance layer for a retrieval chatbot serving commercial and business teams. The work formed part of an innovation entry that he says earned an excellence award at his employer.

Alongside that practical work, Akin values Quantic’s flexibility, alumni network, and academic and administrative support. Through the Quantic Entrepreneur Club, he contributes as a technical researcher and writer. That involvement has connected him with current students and experts in the field.

His enthusiasm for his broader Quantic experience comes through in his Trustpilot review:

You can definitely say, I can’t seem to get enough! The programs are exceptionally curated, viable in today’s ever changing, dynamic and sometimes uncertain world we live in.

Explore the Quantic MSAIE program

These accounts show how students connect what they learn with the work they want to do. Quantic’s MS in AI Engineering brings technical AI skills and business judgment together. It prepares professionals to lead and implement AI adoption in business settings.

To explore how the program fits your goals, start here:

Program overview: Explore the program’s focus on AI adoption and its flexible, fully online format.
Curriculum and projects: Learn about the applied learning approach, AI projects, and team capstone.
Who the program is designed for: Explore the student profile and opportunities to connect within the wider Quantic community.
Admissions requirements: Review entry requirements and find out how to speak with an admissions advisor.

Final thoughts: Could Quantic MSAIE be your next step?

These students have different starting points, but each has a reason to keep learning. For some, that means serving clients with more confidence. For others, it means understanding a system well enough to build it, test it, and improve it.

Azeez Abubakar, a Quantic MBA graduate studying for Quantic’s MS in AI Engineering, offers another perspective. In his Trustpilot review, he writes:

The AI Engineering program has allowed me to build on my business background while developing more technical skills in artificial intelligence, AI systems, and emerging technologies.

What would you like to understand, build, or lead next? Explore the Quantic MSAIE program and consider how it fits the work you want to do.

Quantic MSAIE FAQs

Why do some Quantic alumni return for the MSAIE?

The alumni featured here describe both positive Quantic experiences and specific learning goals. Naomi Withers wanted stronger technical understanding for her consulting work. Justin Wright wanted to fill gaps in his engineering knowledge. Warren Rehman wanted to connect executive strategy with hands-on AI fluency.

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Can I study Quantic MSAIE while working?

Yes. Quantic’s MS in AI Engineering is a 13-month online program combining flexible, asynchronous coursework with applied projects. Justin describes fitting lessons around work and parenting, while Warren values studying around frequent work travel. Students still need to make time for coursework, projects, and collaboration.

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Do I need to be a software developer to apply?

No. Quantic MSAIE admissions criteria do not require applicants to be current or former developers. Minimum requirements include a bachelor’s degree, quantitative aptitude, and English proficiency. Some candidates may need foundational training. The program welcomes technology-motivated professionals from different backgrounds while maintaining technical rigor.

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What kinds of practical work do students describe?

Examples include Justin’s AI chat product and Warren’s HR system, which he built and tested using synthetic data. Students also describe applying their learning at work. Jeff uses it to help clients understand AI adoption, while Akin reports developing a knowledge engine and governance layer for a business chatbot.

More on the Quantic Blog:

Bridging AI and Business: Christopher Arnold’s Quantic MSAIE Journey

AI adoption is accelerating while skills remain a major implementation barrier. The World Economic Forum’s Future of Jobs Report 2025 reports that 86% of employers expect AI and information-processing technologies to transform their business by 2030, while half of executives worldwide identify a lack of skills to support adoption as the top barrier.

That is where bridging AI and business becomes essential. Christopher Arnold returned to Quantic to deepen his AI technical understanding while building on his existing business experience, a combination that sits at the heart of Quantic’s MS in AI Engineering. His journey shows how technical AI fluency and understanding AI adoption leadership can help experienced professionals move closer to practical AI implementation and contribute more meaningfully to real-world AI adoption.

Quick Answer: How does bridging AI and business strengthen AI adoption?

Bridging AI and business can help experienced professionals contribute more meaningfully to AI adoption by combining business judgment with enough technical understanding to select and lead AI initiatives that are feasible given current AI technical capabilities, not just consider strategy in the abstract. Christopher Arnold’s experience in Quantic’s MS in AI Engineering shows one path: build AI technical fluency, work through real AI agent and application projects, and apply those skills to products and organizational initiatives of your own.

Business experience remains an asset. Christopher wanted deeper technical understanding to complement his management experience.
Technical AI fluency combined with AI adoption understanding. Understanding which AI systems can be currently built can help leaders select the right AI initiatives.
Hands-on work connects concepts to real-world AI adoption. AI-based tooling assistance enabling the easier building of software systems means that the lines between traditional managers and managers of agents are becoming blurry.
Building opens new life and career possibilities. He later applied that growing technical capability to develop his own AI-enabled products on the Android and iPhone marketplaces.
AI adoption leadership depends on both sides of the bridge. Quantic’s broader program message is that technically proficient leaders can connect business needs with the delivery of AI agents and applications in real-world settings.

Christopher’s story suggests that the future business professional needs to become technically sophisticated in their understanding of AI. It illustrates how bridging AI and business can give technology-motivated professionals an opportunity to become key players and leaders in the diffusion of AI throughout the broader US and international economies.

Why did Christopher return to Quantic after his Executive MBA?

Christopher’s journey of bridging AI and business began with a practical question: how could he add deeper technical capability to the business experience he had already built?

After completing Quantic’s Executive MBA, he returned to pursue the MS in AI Engineering with two goals he stated clearly: build an AI product or launch a venture, and deepen his technical understanding to complement his business experience.

That motivation connects naturally to AI adoption. Leading or implementing AI in real-world settings requires more than strategic awareness; it also benefits from understanding how AI-enabled products and systems are actually built and specifically what AI systems are currently feasible technically and economically. Christopher’s decision to deepen his AI skills gave him a way to strengthen the link between business judgment and practical rollout.

His return to Quantic was therefore about deepening his preparation to lead in the AI economy. It was about expanding his capabilities, using AI technical learning to become better equipped for a world where AI adoption increasingly depends on professionals who can work confidently across both business and technology.

What does bridging AI and business look like in Christopher’s journey?

For Christopher, bridging AI and business meant adding AI technical and AI adoption depth to an already established business perspective.

His goal was not simply to understand AI conceptually. He wanted to become more comfortable with the practical work behind software and AI agents and applications: how products are built, how technical components connect, and how ideas move from concept to implementation. That growing AI fluency gave him a different vantage point on AI adoption.

This matters because effective AI adoption often sits between two worlds. Organizations traditionally needed people who can understand the business problem, communicate with technical teams, evaluate what is feasible, and help move useful ideas into practice. In the emerging AI economy, managers can marshall and leverage agents themselves in conjunction with human staff to, through enhanced AI technical understanding, greatly multiply what a traditional manager could do.

Christopher’s journey reflects that intersection. His business experience gave him context for why AI matters; his Quantic studies gave him more of the AI fluency to be able to thrive in the rapidly evolving AI economy. Together, those trends bring the idea of bridging AI and business into practical focus.

How did AI technical rigor become practical through a Quantic project?

Bridging AI and business became much more tangible for Christopher when technical learning moved beyond individual lessons and into a project his team had to build together.

During Quantic’s MS in AI Engineering, Christopher worked with a small team to create a complete website and database for a client. The group coordinated front-end development, back-end database functionality, and collaboration through GitHub, giving him hands-on experience with how different parts of a software project come together.

That kind of technical rigor matters for AI adoption because real implementation rarely happens in isolation. AI-enabled products and systems depend on people who can understand technical constraints, work across functions, and connect implementation choices back to business needs.

Christopher captured the value of that experience simply:

“I learned a great deal that was beyond just working off the curriculum.”

For him, bridging AI and business was no longer an abstract idea. It became something he could practice through collaboration, problem-solving, and building, all of which strengthen the technical fluency needed to contribute meaningfully to AI adoption.

How did Christopher’s peers and the Quantic community strengthen his learning?

Christopher’s experience of bridging AI and business was not only about developing AI skills. It was also about learning alongside peers who brought different perspectives, questions, and experiences to the same challenge.

He described an active Quantic community where students exchange feedback and work through ideas connected to AI and projects beyond formal coursework. That kind of peer learning matters for AI adoption, because implementing AI in real organizations is rarely the work of one discipline or one person.

Christopher highlighted the people around him as a meaningful part of the experience:

“The quality of the student body complements the incredible Quantic staff.”

Those interactions helped make AI learning more collaborative and grounded. They also reinforced an important part of bridging AI and business: becoming technically fluent enough to contribute confidently while still learning from people with different expertise.

For professionals preparing to lead AI adoption, that ability to ask questions, share perspectives, and collaborate across disciplines can be just as important as understanding the technology itself.

How did Christopher put his AI fluency into practice with Déjà Entendu?

The idea of bridging AI and business became especially meaningful when Christopher stopped thinking only about what he might build and started creating something of his own.

He developed Déjà Entendu Study, an open-source language-study app that uses large language models available on mobile devices. Christopher had already explored on-device AI through technologies such as Gemini Nano and Apple Intelligence. His Quantic experience gave him the confidence to turn that interest into a working product.

One reflection captures that change particularly well:

“I didn’t know that I could do this independently. But after the second Quantic project was completed, I knew that I could build my dream app.”

He used GitHub to manage the open-source project, Xcode for the Apple deployment, and Android Studio for Google Pixel and Samsung devices as he worked toward the Android release. He also drew on the teamwork experience he developed at Quantic, bringing designers and engineers into the project when he needed their expertise.

This is where AI adoption becomes more than an abstract idea. Christopher was learning how an AI-enabled product moves from concept to release: choosing tools, coordinating contributors, solving technical problems, and ultimately shipping something people could use.

For him, bridging AI and business meant gaining enough technical fluency to make ideas tangible. That same ability to connect needs, technology, and implementation is central to meaningful AI adoption in real-world settings.

How can AI technical fluency strengthen leadership of AI adoption?

Christopher’s experience shows why bridging AI and business can matter so much for AI adoption. Leaders do need enough technical fluency to understand what AI systems can realistically do, how they and their colleagues can leverage the incredible capabilities of agentic AI coding tools, where implementation challenges may arise, and how technical choices connect back to business needs.

That is also central to Quantic’s broader MS in AI Engineering message. Quantic designed the program to develop technically proficient AI leaders who can help lead and implement real-world AI adoption across business settings and ultimately help lead AI diffusion throughout the broader economy.

Christopher’s journey gives that idea a human example. His business domain experience helps him understand the context around a problem; his AI technical learning gives him greater confidence to engage with how solutions are actually built. That combination can make AI adoption more grounded by bringing both business priorities and AI system realities into decision-making.

In that sense, bridging AI and business is not about choosing between leadership and AI technical capability. It is about empowering future leaders with enough technical understanding to play a key role in AI adoption leadership , and ultimately to help lead AI-supported transformation of their industry sectors and the economy at large.

Building the confidence to bridge AI and business

Christopher’s Quantic journey shows how bridging AI and business can become a practical path toward more confident, informed AI adoption.

He did not leave his existing experience behind. Instead, he added AI technical depth to his existing domain expertise: learning how AI systems come together, collaborating with peers, and eventually building his own AI-enabled product. That combination of business judgment and AI fluency is central to Quantic’s approach to preparing professionals for real-world AI implementation and adoption.

For experienced professionals and managers, Christopher’s story offers an encouraging takeaway. AI adoption does not require abandoning the perspective you already have. It can mean expanding your ability to understand, evaluate, and help adopt AI in ways that serve real business needs.

That is the broader promise behind bridging AI and business: developing technically proficient leaders who can help move AI from possibility to huge real-world impact.

If Christopher’s journey resonates with you, explore Quantic’s MS in AI Engineering and how it prepares technology-motivated professionals to contribute more meaningfully to leading real-world AI adoption.

FAQs

Is Quantic’s MS in AI Engineering only for people with engineering backgrounds?

No. Bridging AI and business is central to the program’s purpose, and Quantic designed the program for a broad range of technology-motivated professionals, including executives, managers, product and project professionals, consultants, finance professionals, data and analytics professionals, and engineers.

The curriculum is technically rigorous, while still intended to help professionals from varied backgrounds build the technical fluency needed for leading and implementing AI adoption.

Do you need to be a professional developer to study the MSAIE at Quantic?

No. Quantic’s documented program audience includes professionals from both business and technical backgrounds, and applicants do not need to be current or former developers. Some applicants may choose to do some foundational training in the form of elective courses, depending on their background.

That accessibility supports bridging AI and business without impacting the program’s technical rigor. The goal is to develop the AI capabilities professionals need to engage more confidently with AI adoption and implementation.

Review the MS in AI Engineering admissions information.

Why does AI technical fluency matter for AI adoption?

Successful AI adoption requires more than knowing that AI can create value. Leaders also need enough AI technical understanding to assess what is feasible, work effectively with technical teams and AI agents, and connect implementation decisions to real business needs.

That is why bridging AI and business matters. AI technical fluency helps professionals participate more meaningfully in the journey from identifying an opportunity to implementing an AI-enabled solution – the leaders of AI adoption throughout the economy will necessarily require this deep understanding of current AI capabilities.

How does Christopher Arnold’s experience illustrate bridging AI and business?

Christopher’s story brings bridging AI and business to life. He returned to Quantic to deepen his AI technical understanding alongside his existing business and domain experience, then applied that learning through collaborative AI system development and his own AI-enabled product.

His experience does not mean every student will follow the same path. Instead, it offers one concrete example of how stronger AI fluency can help experienced professionals engage more directly with implementation and, ultimately, AI adoption and open up new life and career possibilities for them.

More on the Quantic Blog:

What is the Best Online MBA Program in 2026? Quantic Review Insights

What is the Best Online MBA Program? This is a question many aspiring professionals ask as they seek flexible, high-quality education that fits their busy schedules. In today’s fast-paced world, online education has revolutionized the way people learn, making it more accessible and convenient than ever before. According to a report by Statista, the global e-learning market is projected to reach $400 billion by 2026, highlighting the growing popularity of online programs.

But despite this massive growth, many traditional education providers and edtech “innovators” have struggled to truly innovate. For students seeking advanced degrees like an MBA, this gap often results in programs that are inflexible, impersonal, and prohibitively expensive. This is where modern, forward-thinking programs like the Quantic MBA come into play—offering a refreshing solution to the status quo.

In this article, we’ll explore what makes a great online MBA program and why Quantic stands out in key areas like flexibility, engagement, affordability, and outcomes. Let’s dive in!

What is the Best Online MBA Program in 2026: Flexibility

In a world where professionals juggle demanding jobs, personal commitments, and career aspirations, flexibility has become a cornerstone of effective online education. Traditional MBA programs often require strict schedules and in-person attendance, which can be limiting for those who need adaptable options. Online MBA programs aim to resolve this, but not all succeed in offering the level of flexibility modern learners require.

Why Flexibility Matters for Today’s Professionals

Flexibility in an online MBA program is more than just being able to study from anywhere; it’s about learning at your own pace, balancing work-life responsibilities, and accessing course materials seamlessly. However, many online programs fail to provide a truly flexible experience. Issues such as pre-scheduled live classes, rigid deadlines, or outdated platforms can hinder the learning process.

How Quantic Delivers Flexibility for Modern Learners

Quantic School of Business and Technology takes flexibility to a whole new level with its mobile-first platform. Designed for busy, on-the-go professionals, the Quantic MBA adapts to your lifestyle by offering:

Asynchronous Learning: Access course materials anytime, anywhere.
Bite-Sized Lessons: Learn in short, engaging modules that fit into your daily schedule.
Mobile Accessibility: Study entirely from your smartphone or tablet without compromising the quality of your education.

By removing unnecessary barriers, Quantic empowers students to focus on their education without sacrificing their professional or personal commitments. It’s this dedication to flexibility that sets Quantic apart, making it one of the best online MBA programs available today.

What is the Best Online MBA Program in 2026: Engagement

When it comes to online education, engagement is one of the most critical factors for success. Many online MBA programs struggle to keep students actively involved, often relying on passive learning methods like pre-recorded lectures or lengthy readings. This lack of interaction can lead to disengagement and lower retention rates. Effective engagement, however, ensures students stay motivated, absorb material effectively, and apply what they learn in real-world scenarios.

Why Engagement is Key to Online MBA Success

Engagement in online education goes beyond participation; it’s about creating a dynamic, interactive environment that fosters critical thinking and collaboration. Unfortunately, many online programs fail to replicate the classroom experience in virtual settings. Poorly designed courses, limited peer interaction, and lack of instructor feedback are common issues that negatively impact engagement.

How Quantic Drives Engagement with Active Learning

The Quantic MBA stands out for its innovative Active Learning Method, which transforms how students interact with course materials. Here’s how Quantic drives engagement:

Interactive Modules: Quantic’s mobile-first platform offers bite-sized lessons with interactive quizzes and case studies, helping students stay engaged and retain knowledge.
Collaborative Learning: Students participate in discussions, group projects, and peer-to-peer feedback, fostering a sense of community.
Real-Time Feedback: Quantic’s platform provides immediate insights, allowing students to track their progress and identify areas for improvement.

By combining technology and pedagogy, Quantic creates an engaging learning environment that motivates students to excel. This unique approach not only enhances the learning experience but also prepares students to tackle real-world business challenges.

What is the Best Online MBA Program in 2026: Personalization

In today’s digital world, personalization has become essential in online education. Modern learners expect programs that understand their unique needs, learning pace, and career goals. Unfortunately, many online MBA programs fall short, offering one-size-fits-all curriculums that fail to address individual preferences. This lack of customization can hinder students’ progress, leaving them disconnected from the learning experience.

Why Personalization Makes a Difference in Online Education

Personalization enables students to focus on their strengths while addressing specific areas for improvement. It fosters a tailored learning experience that maximizes efficiency and outcomes. However, traditional programs often lack the tools to provide this level of customization, relying instead on static content and limited interaction.

How Quantic Personalizes Learning with AI-Powered Tools

Quantic School of Business and Technology excels in personalization through its AI-powered platform and innovative learning tools. These features ensure that every student’s experience is tailored to their unique goals and progress:

AI Advisor: Acting as a personal tutor, the AI Advisor helps clarify complex concepts, provides practice questions, and even assists with administrative queries, offering unparalleled support throughout the learning journey.
Explain This Screen: This real-time feature provides context-specific assistance during lessons, allowing students to explore deeper insights, receive instant feedback, and retain material more effectively.
Mastery-Based Progression: Lessons are designed to challenge students with frequent interactions and immediate feedback, ensuring they master each concept before moving forward.

By combining artificial intelligence with active learning, Quantic offers a personalized, responsive, and effective learning experience that adapts to the needs of its students.

What is the Best Online MBA Program in 2026: Affordability

For many aspiring MBA students, affordability is a crucial factor when selecting a program. Online MBA programs, while often more affordable than traditional in-person options, can still be expensive, costing an average of $16,000-$26,110 annually. These costs can leave students burdened with significant debt, making it essential to find a program that balances quality and affordability.

Why Affordability is Critical for MBA Accessibility

Affordability ensures that high-quality education is accessible to a diverse group of professionals, regardless of their financial background. However, many institutions rely on outdated tuition models that don’t meet the needs of modern learners. For working professionals, affordability is not just about cost—it’s about value. A program must balance reasonable tuition fees with a rigorous curriculum that delivers strong career outcomes.

How Quantic Offers Affordable, High-Quality MBA Programs

The Quantic School of Business and Technology has redefined affordability in higher education with its innovative tuition model. Here’s what sets Quantic apart:

Radically Affordable Tuition: At $24,750 for the 15-month MBA or Executive MBA, Quantic’s program is a fraction of the cost of traditional programs while maintaining exceptional quality.
Employer Funding: Many students benefit from employer reimbursement programs, enabling them to complete their degrees without financial strain.
Scholarships: Quantic offers scholarships that further reduce tuition costs for eligible students.

By combining affordability with a high-caliber curriculum, Quantic ensures that students receive exceptional value for their investment. This approach makes Quantic one of the best online MBA programs for professionals seeking top-tier education without the prohibitive costs.

What is the Best Online MBA Program in 2026: Connections

In the world of online education, the power of connections cannot be overstated. Networking is a vital aspect of any MBA program, enabling students to build relationships, collaborate on projects, and gain career opportunities. Unfortunately, many online MBA programs fail to foster meaningful connections due to a lack of interactive features or networking opportunities, leaving students feeling isolated.

Why Connections are Essential for MBA Professionals

For MBA students, connections go beyond casual networking—they are gateways to mentorship, global collaboration, and career advancement. However, many programs struggle to offer the depth of connection found in traditional in-person MBAs. Limited access to alumni, lack of peer-to-peer interaction, and the absence of community-focused events are common pitfalls. Effective online programs must bridge these gaps by creating vibrant networks that mimic the value of in-person interactions.

How Quantic Builds a Global Network of Business Leaders

Quantic School of Business and Technology takes a transformative approach to networking by fostering a highly interactive and globally connected community:

Global Network of Alumni: The Quantic student and alumni network is composed of over 17,000 professionals from 155 countries, who hail from prestigious universities and work as influential leaders in the world’s top companies.
Social Learning: As a companion to its interactive online curriculum, Quantic students engage with their classmates around the world on case studies and collaborative group assignments throughout the program.
Global Events and Meet-ups: World-renowned speakers, immersive in-person conferences, global meet-ups, and student-led organizations bridge Quantic MBA and Executive MBA programs with real-life networking and professional development opportunities.

By combining technology with a human-centered approach, Quantic offers its students unparalleled opportunities to form meaningful connections, making it one of the best online MBA programs for networking and collaboration.

What is the Best Online MBA Program in 2026: Outcomes

When choosing an online MBA program, its ability to demonstrate proven efficacy and deliver tangible results is what distinguishes it as the best option. While a program may excel in flexibility, affordability, and engagement, it ultimately falls short without measurable outcomes like career growth, skill development, and real-world business acumen. Proven efficacy ensures that a program’s claims are backed by validated results, instilling confidence in ambitious professionals regarding their investment.

Why Proven Results Matter in Choosing an Online MBA

Outcomes serve as the ultimate measure of an MBA program’s credibility and value. A program with a consistent track record of success fosters career advancement, enhances skills, and opens leadership opportunities. Proven efficacy aligns the curriculum with industry demands, equipping students with essential tools to tackle real-world business challenges. Conversely, programs lacking this alignment risk leaving students unprepared to compete in today’s fast-paced job market.

By choosing a program with proven efficacy and results, students ensure they are making an investment that delivers on its promises and sets them up for sustained professional success.

How Quantic Delivers Measurable Career Outcomes

Quantic’s innovative approach to education combines proven efficacy with outstanding career outcomes, making it one of the most impactful options for professionals seeking to advance their careers. Through rigorous, data-driven results and cutting-edge learning methods, Quantic has established itself as a leader in online business education.

Quantic’s Proven Efficacy

Learning Impact: In a 2017 study, learners in the Quantic platform mastered Probability and Statistics in one-third of the time compared to edX and Khan Academy students. Quantic learners also reported higher engagement and achieved higher exam scores, with competitors being five times more likely to score below 50%.
Quantic vs Top MBA: In a study run by Stanford academics, Quantic MBA students performed as well or better than students from the top 10 global business schools, including Wharton and Harvard, in Accounting and Finance. Quantic students completed the material faster and demonstrated higher retention rates.
Global Recognition: Quantic’s reputation is widely recognized:
- The program boasts a 4.9-star rating on the App Store and Google Play, reflecting its popularity and usability among learners.
- It was named a 2024 EdTech Trendsetter Awards Finalist by EdTech Digest, further solidifying its position as a pioneer in innovative education.
- Quantic’s Net Promoter Score (NPS) rivals those of top institutions, including Wharton (51) and Harvard (41), with Quantic achieving an impressive 61 NPS, demonstrating high levels of student satisfaction and loyalty.

Quantic’s Proven Outcomes

High Promotion Rates: Quantic graduates excel in advancing their careers, with 52% of students earning a promotion within six months of graduation. Additionally, 94% of graduates state that Quantic directly helped them achieve their career goals.
Significant Salary Increases: Graduates see a median salary increase of 22% within six months of completing the program, reflecting the career-enhancing value of Quantic’s MBA and Executive MBA programs.
Global Alumni Success: Quantic’s alumni network spans 155+ countries and includes professionals working at top companies like Google, Amazon, Microsoft, and Deloitte. Quantic alumni are innovators, entrepreneurs, and executives in industries such as technology, consulting, and finance.
Employer Recognition: Quantic’s credibility is further reinforced by its partnerships with the world’s most influential organizations. Top companies not only employ Quantic graduates but also sponsor their employees to study at Quantic.

By blending proven efficacy with strong career outcomes, Quantic ensures its graduates are prepared to meet the demands of modern business. This dual focus on measurable results and innovative learning methods solidifies Quantic as one of the most impactful and reliable online MBA programs for professionals seeking to advance their careers.

To learn more about Quantic’s impact on its students and alumni, visit Trustpilot, EduOpinions and Niche, where learners share how the programs have helped them achieve their professional and personal goals.

Quantic Reviews on TrustPilot: Solomon Osadolo

Quantic Reviews on TrustPilot: Kamila Tocicka

There are also many student stories—feel free to connect with our students and alumni on LinkedIn to learn more about their experiences and insights!

Conclusion: What to Consider When Choosing an Online MBA

Choosing the right online MBA program can be a transformative decision for your career. With countless options to explore, it’s important to evaluate key aspects such as curriculum, flexibility, affordability, and networking opportunities. Below, we provide answers to frequently asked questions to help you make an informed choice.

Why is flexibility important in an online MBA?

Flexibility is essential for professionals balancing work, personal commitments, and education. The Quantic MBA provides unmatched flexibility with its mobile-first platform that allows students to learn anytime, anywhere. Its bite-sized lessons and asynchronous approach ensure students can integrate learning seamlessly into their busy schedules.

How do online MBA programs keep students engaged?

Engagement is vital to staying motivated and retaining knowledge. Quantic’s Active Learning Method uses interactive modules, real-time quizzes, and case studies to make learning dynamic and immersive. Students also collaborate on group projects and discussions, fostering a sense of community while applying what they learn to real-world business challenges.

What makes an online MBA program affordable?

Affordability is about providing value without compromising quality. Quantic’s tuition model is set at a remarkably low rate of $24,750, which is significantly less than traditional MBA programs. Furthermore, many students benefit from employer funding or scholarships, making the program accessible to a diverse range of professionals.

How do online MBA programs foster networking

Networking is a cornerstone of any MBA program, and Quantic takes it a step further. With a global alumni network spanning 150+ countries, students connect with high-caliber peers from top companies like Google, Amazon, and Microsoft. Quantic also organizes virtual and in-person events, allowing students to collaborate, exchange ideas, and build meaningful professional connections.

What career outcomes should I expect from an online MBA?

Career advancement is the ultimate goal of an MBA. Quantic boasts proven career outcome, with 94% of graduates achieving their career goals post-graduation. Students report significant salary increases and promotions, thanks to the program’s focus on practical skills, leadership, and global business acumen.

Quantic MBA vs. Executive MBA: Which Program is Right for You?

Choosing between the Quantic MBA and Quantic Executive MBA depends on your career stage, professional goals, and desired outcomes. Both programs offer a flexible, mobile-first, and AI-powered learning experience designed to help professionals excel in their careers. The Quantic MBA is tailored for early-career individuals looking to build foundational business skills, while the Executive MBA focuses on leadership and strategy for mid-to-senior-level professionals. Use the comparison table below to explore key differences and find the program that best fits your needs.

Experience Quantic Right Now

We can explain our method. And point you to our efficacy studies. And introduce you to a few of the thousands of our high-achieving students. Or you can just sample a few courses for yourself. It’s simply the best way to experience the educational power of Quantic. Sample our courses now!

Sample our courses now

Quantic School of Business and Technology is licensed by the District of Columbia Higher Education Licensure Commission in Washington, DC and accredited by the Distance Education Accrediting Commission (DEAC). The DEAC is listed by the United States Department of Education as a recognized accrediting agency and is also recognized by the Council for Higher Education Accreditation (CHEA).

More on the Quantic Blog:

Quantic Reviews 2026: Hearing Directly From Students and Alumni

Technology and AI are reshaping industries at an unprecedented pace, making the choice of an MBA or Executive MBA program more consequential than ever. Today’s professionals need innovative teaching methods, relevant curricula, genuine flexibility, and affordability in their education. Quantic School of Business and Technology, a leader in online business education, delivers on all fronts with its mobile-first, AI-powered platform. The combination of unmatched flexibility and practical, real-world curriculum helps learners achieve their career goals. But don’t just take our word for it. Here are the highly-rated Quantic reviews from students and alumni sharing their transformative experiences with Quantic’s programs.

Quantic Reviews by Onyeka Okonkwo: A Revolutionary Approach to Learning That Sticks
- An Interactive Learning Experience Like No Other
- Expanding Connections and Collaborative Growth
Quantic Reviews by Kamila Tocicka: Rediscovering Confidence and Leadership Through Lifelong Learning
- Balancing Motherhood and Education to Reclaim Myself
- Personal and Professional Growth Beyond Expectations
Quantic Reviews by Emily Stone: Empowering Working Professionals Through Flexible Learning
- Finding the Perfect MBA for a Busy Working Parent
- How Quantic Transformed My Career and Business
Quantic Reviews by Miguel Mayorga: Transformative Learning & Meaningful Connections
- Interactive Learning That Inspires Continuous Growth
- Building Networks and Embracing Diversity
Quantic Reviews by Garry Sands: From Functional Expert to Strategic Leader
- A Curriculum that Bridges Theory and Practice
Quantic Reviews by Rishabh Bitola: A Global Perspective to Drive Business Success
Discover More Inspiring Quantic Reviews
Quantic Reviews: FAQ

Quantic Reviews by Onyeka Okonkwo: A Revolutionary Approach to Learning That Sticks

An Interactive Learning Experience Like No Other

When I first discovered Quantic, I was intrigued by its mobile-first and interactive learning approach, which stood out from traditional programs. From the very beginning, the program exceeded my expectations with its bite-sized case studies and engaging, fill-in-the-answer formats that made learning enjoyable and impactful. Unlike the passive experience of video lectures, Quantic kept me actively involved, and the humor woven into the content ensured I stayed engaged throughout.

This was education like I had never experienced before—delivered in bite-sized case studies that gradually eased me into various business concepts. Learning that truly sticks! The ability to immediately apply the concepts to real-world challenges made the experience both practical and transformative.

Expanding Connections and Collaborative Growth

Quantic’s global learning community was another highlight of the program. My cohort included professionals from top universities and companies, which added immense value to group projects and discussions. The collaborative projects were particularly impactful, as they allowed me to work with diverse perspectives and apply my learning to complex business problems. Beyond academics, Quantic’s networking platform and tools, like Slack and the monthly book club, provided opportunities to connect with peers worldwide and foster valuable relationships. Overall, I had an exciting time learning at the Quantic School of Business and Technology; I totally recommend the program and look forward to putting everything I learned into practice.

Onyeka Okonkwo
Risk Manager
KPMG UK

Read the Full Review on LinkedIn | Comprehensive Review of the Quantic MBA

Quantic Reviews by Kamila Tocicka: Rediscovering Confidence and Leadership Through Lifelong Learning

Balancing Motherhood and Education to Reclaim Myself

During one of the most challenging periods of my life—balancing the joys and struggles of motherhood with a sense of personal loss—I realized how important it was to rediscover myself. While I adored my daughter, I missed the ambitious, creative woman I used to be. Pursuing an Executive MBA at Quantic School of Business and Technology became a way to regain control and reignite my passion for learning. The program taught me invaluable skills like time management and prioritization that helped me balance the demands of motherhood and my studies. For the first time in a long time, I felt as though I was in control of my life. This transformative journey not only helped me grow as a professional but also gave me the confidence to embrace my full potential.

Personal and Professional Growth Beyond Expectations

Quantic’s program gave me more than just business acumen—it reshaped how I value myself and my accomplishments. Armed with newfound confidence and skills, I successfully transitioned back to work, excelled in my role, and even negotiated a promotion shortly after graduation. The impact extended beyond my career, enriching my relationships and allowing me to reconnect with the creative, driven person I once was.

The impact of my MBA studies went far beyond just my career. I found myself feeling more fulfilled and empowered than ever before. Quantic’s approach to lifelong learning was a catalyst for change, empowering me to overcome challenges and embrace leadership both at work and in life.

Kamila Tocicka
Assistant Director
EY

Read the Full Review on LinkedIn | From Baby Blues to Leadership: How Pursuing Lifelong Learning Transformed My Life

Quantic Reviews by Emily Stone: Empowering Working Professionals Through Flexible Learning

Finding the Perfect MBA for a Busy Working Parent

I enrolled in the Quantic Executive MBA program in early 2020, seeking a flexible, affordable program to help me grow beyond my expertise in HR and Learning & Development. With over 15 years of experience designing online courses, I was drawn to Quantic’s mobile-friendly platform, which offered a highly interactive and engaging learning experience through continuous assessments and practical case studies. As a full-time working mom of three young children, the program’s flexibility was essential—I could complete coursework from my phone anytime, even while sitting on the couch or waiting in line at the grocery store! The opportunity to test-drive the platform before enrolling gave me confidence that Quantic’s approach matched my learning style and goals.

How Quantic Transformed My Career and Business

Quantic’s program exceeded my expectations, equipping me with practical business knowledge and leadership skills that I could apply immediately. The thoughtfully designed case studies and collaborative group projects challenged me to think critically and fostered valuable connections with a global community of professionals. Despite my initial hesitation, I found the group work to be a highlight of the program, as I gained insights from diverse perspectives that enriched our final projects. Since graduating in 2021, my career has flourished—I’ve doubled my consulting rate, secured high-profile clients, and expanded my expertise to become a strategic business partner. Additionally, Quantic’s comprehensive business education enabled me to grow my business, Empowered Bumps & Boobs, earning recognition in my community and beyond. Reflecting on my journey, I can confidently say,

If you’re on the fence about investing in yourself with an Executive MBA program, please use my story as one data point. For me, Quantic was the catalyst I needed to get unstuck and move toward my full potential.

Emily Stone
Founder & CEO
Empowered Bumps & Boobs

Read the Full Review on LinkedIn | My Quantic Review: Completing the Quantic School of Business and Technology Executive MBA Program as a Learning Designer

Watch the Full Review on YouTube | Quantic Executive MBA Review

Quantic Reviews by Miguel Mayorga: Transformative Learning & Meaningful Connections

Interactive Learning That Inspires Continuous Growth

Pursuing the Quantic Executive MBA has been nothing short of transformative. The program’s well-structured curriculum, featuring 60 courses and a Capstone project, is both rigorous and engaging. The interactive, mobile-first learning model kept me actively involved, with engagement every 8-10 seconds and offline access that fit seamlessly with my busy schedule. Quantic’s AI-powered advisor further enhanced my experience, providing personalized guidance throughout the program. Looking back on the interactive learning experience, this journey reignited my passion for continuous learning. A good friend once told me, “Don’t stop learning because life never stops teaching,” and this experience has truly brought that advice to life.

Building Networks and Embracing Diversity

Quantic is not just about academics; it’s also about building meaningful connections. From delivering a presentation on “How to Find the Right Contacts” to collaborating with my diverse project partners, the program offered invaluable networking opportunities. Engaging with professionals from various backgrounds broadened my perspective and improved my teamwork skills. One of the most impactful lessons was learning the importance of setting clear expectations and embracing diversity in a team setting, which I now apply in my professional role. I am grateful for the collaborative spirit of Quantic’s community, which reinforced my belief in the power of shared knowledge and innovation.

For those seeking flexible, interactive learning, Quantic is an excellent choice for professional and personal growth. The program’s affordability, continuous improvements like the AI advisor, and comprehensive curriculum make it a strong contender in today’s dynamic business environment.

Miguel Mayorga
Talent Sourcing & Intelligence
ZEISS Group

Read the Full Review on LinkedIn | My Full Take on Quantic’s EMBA: Flexible, Affordable, Transformative

Quantic Reviews by Garry Sands: From Functional Expert to Strategic Leader

A Curriculum that Bridges Theory and Practice

Joining the Quantic Executive MBA program was a pivotal step in my professional journey. The curriculum, with its focus on strategic thinking, leadership, and emerging technologies like blockchain, wasn’t just theoretical—it was immediately applicable to my work. The program’s faculty, comparable to those of top institutions like Wharton and Stanford, provided actionable insights that reshaped how I approach business challenges. Reflecting on the impact, I can confidently say, “The program is designed not just to educate but to transform how you think about business and leadership.” This transformation has allowed me to step into more strategic roles and navigate complex challenges with greater confidence.

Harnessing the Power of a Global Network

One of the most rewarding aspects of my Quantic experience was the global network I became a part of. The diversity of my cohort, representing various industries and backgrounds, allowed me to gain fresh perspectives and build meaningful connections. Whether through collaborative projects, live discussions, or shared experiences, the relationships I formed have had a lasting impact on my career. The program also challenged me to grow as a leader, helping me shift from a functional expert to a strategic thinker. As I applied these lessons to real-world scenarios, I saw tangible results in my ability to lead teams, spearhead initiatives, and drive business growth.

The Quantic Executive MBA has been an eye-opening experience in my professional journey. It has equipped me with the skills, knowledge, and confidence to take on new challenges and seize new opportunities. If you’re ready to grow, to lead, and to make a difference in your career, then I encourage you to take the leap. Quantic can take you where you want to go and beyond.

Garry Sands
Co-founder & CEO
World Of Turnips

Read the Full Review on LinkedIn | A Strategic Investment in Leadership: The Quantic Executive MBA | Quantic MBA Review

World map showing the Quantic School of Business and Technology global network, with student and alumni counts by region: North and South America 10,000+, Europe 5,100+, Asia and Australia 3,600+, and Africa and the Middle East 2,500+.

Quantic Reviews by Rishabh Bitola: A Global Perspective to Drive Business Success

Learning from a Truly Global Community

As I reflect on my journey through the Executive MBA program at Quantic, I am struck by the truly global nature of this experience. Being part of a cohort spanning over 50 countries has not only broadened my perspective but has also deepened my understanding of global business dynamics. Collaborating with professionals from diverse industries and cultural backgrounds has enriched my learning and challenged me to think beyond my assumptions.

Education knows no borders; it is the global passport to success. Grateful for the global MBA experience at Quantic School of Business and Technology, where I’ve learned to embrace diversity, expand my horizons, and thrive in the interconnected world of business. Quantic’s emphasis on diversity and collaboration has truly set this program apart for me.

Exceptional Mentorship and Unwavering Support

Quantic’s innovative and interactive platform has equipped me with leadership skills and business acumen that I can directly apply in real-world scenarios. The program’s focus on experiential learning, strategic planning, and market analysis has sharpened my critical thinking and problem-solving abilities. The exceptional faculty, with their wealth of industry expertise and academic rigor, has been instrumental in shaping my professional development. The guidance, mentorship, and unwavering support of Quantic’s faculty have played a crucial role in my journey. The global perspective they bring to each lesson has been invaluable in understanding the nuances and challenges of doing business in diverse cultural contexts. This transformative learning experience has empowered me to drive innovation, foster growth, and navigate the complexities of the global business landscape with confidence.

Rishabh Bitola
Founder & Director, Infotyke
Director, Regitree Green Foundation

Read the Full Review on LinkedIn | Expanding Horizons: Grateful for the Global MBA Experience at Quantic School of Business and Technology

Discover More Inspiring Quantic Reviews

There are many more student stories—feel free to connect with our students and alumni on LinkedIn to learn more about their experiences and insights!

Beyond personal stories, Quantic students and alumni have shared glowing reviews on Trustpilot, highlighting the program’s innovative curriculum, flexibility, and real-world impact. These highly-rated reviews provide valuable insights into what makes Quantic a leading choice for professionals worldwide. Visit Trustpilot, EduOpinions and Niche to read firsthand accounts of how Quantic has helped learners achieve their personal and professional goals: Quantic Reviews.

For an in-depth exploration of Quantic’s offerings, check trusted third-party reviews and stories that evaluate the program’s curriculum, admissions process, and outcomes:

These resources provide detailed insights into how Quantic is shaping the future of business education.

Quantic School of Business and Technology | Students Love Quantic: Infographic demonstrating overwhelming student satisfaction with Quantic. Key metrics show 97% of students would recommend the program and a Net Promoter Score of 60+, surpassing traditional business schools. This is supported by glowing ratings across multiple platforms, including Trustpilot (4.8/5), Niche (4.8/5), and Studyportals (4.8/5), confirming Quantic's excellent reputation.

Quantic Reviews: FAQ

Is Quantic a legitimate school?

Yes, Quantic School of Business and Technology is licensed by the District of Columbia Higher Education Licensure Commission in Washington, DC, and accredited by the Distance Education Accrediting Commission (DEAC). The DEAC is listed by the United States Department of Education as a recognized accrediting agency and is also recognized by the Council for Higher Education Accreditation (CHEA). This ensures Quantic meets rigorous academic and professional standards.

Is Quantic’s MBA legit?

Yes, the Quantic MBA is a legitimate program. Quantic School of Business and Technology is accredited by the Distance Education Accrediting Commission (DEAC). It is designed for high-potential, early-career learners eager to make a difference.

Is the Quantic Executive MBA accredited?

Yes, Quantic’s Executive MBA is an accredited program. Quantic School of Business and Technology is accredited by the Distance Education Accrediting Commission (DEAC). It is designed for mid-career professionals and focuses on developing leadership, global strategy, and advanced business skills.

Is the Quantic MBA recognized by employers?

Absolutely. Quantic students and alumni work at top global companies, such as Google, Amazon, Microsoft, Salesforce, Deloitte, Apple, and JPMorgan Chase & Co. Over 2,100 Quantic students and alumni are employed at companies in Quantic’s Employer Network, and many employers offer tuition reimbursement for Quantic programs, demonstrating trust in its value.

Is the Quantic MBA legit in 2025?

Yes, the Quantic MBA remains a legitimate, accredited program in 2025. It is distinguished by its mobile-first, AI-powered platform, career outcomes, and affordable tuition model, making it a popular choice among professionals seeking flexible, high-quality business education. Learn more about the Quantic Method.

What’s the deal with Quantic?

Quantic School of Business and Technology is a mobile-first, AI-powered business school. Founded on the notion that higher education lacks quality, effective, flexible, and cost-efficient solutions, it provides accredited online degree programs that rival top traditional elite business education. Quantic attracts thousands of high achievers from the world’s best companies, teaching today’s leaders future-forward skills in business and technology. Visit Quantic’s website to learn more.

Who owns Quantic School of Business and Technology?

Quantic is owned by Pedago, Inc., an education technology company committed to making elite education accessible and affordable for high-potential professionals worldwide.

Is a Quantic MBA real?

Yes, the Quantic MBA is a real, accredited degree program. Selective in admissions, we assemble global cohorts of high-potential, early-career learners, delivering an online MBA curriculum with a comprehensive student experience that includes exclusive online and in-person events, while delivering exceptional career outcomes.

Is the Quantic (formerly Smartly) MBA worth it?

Yes, Quantic’s MBA is worth it for professionals seeking a flexible, affordable, and innovative business education. Graduates report career advancement, salary increases, and expanded networks. Check out how Quantic helps leaders level up and why People love Quantic.

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