AI isn’t magic, and other lessons from Dr. Amita Goyal Chin
The longtime VCU professor on preparing students for jobs that don’t exist yet, the biggest mistake businesses make with AI and why adaptability matters more than any single skill.
By Megan Nash
Dr. Amita Goyal Chin has spent more than 30 years at VCU teaching information systems, a field that has changed drastically since she first stepped into the classroom. Now the associate chair of the Information Systems Department in the School of Business, she has worked closely with Executive MBA (EMBA) students on Strategic Dilemma (SD) projects—helping them break down business challenges and turn ideas into workable solutions.
She has also seen plenty of tech trends come and go. So what actually makes a difference? We sat down with Dr. Chin to talk about the skills that matter, why adaptability is everything and the biggest misconception people have about AI.
So much has changed in information systems over the past few decades.
What do today’s students need to focus on to stay ahead?
One of the defining characteristics of information systems is constant change. Over just the span of my own career, I have witnessed the rise of the internet, the mass adoption of social media and the ubiquity of mobile devices. At each stage, successful information systems professionals have not only mastered emerging technologies but also driven their adoption—transforming workplaces and society in the process.
To thrive in this ever-evolving industry, the ability to learn rapidly is essential. This begins with a strong foundation in core concepts and practical skills, which professionals continuously refine, extend and apply as new technologies emerge. Incremental learning along an established trajectory makes it easier to adapt to inevitable changes, ensuring that we can harness technological advancements to our advantage rather than struggle to keep pace.
You advise Executive MBA students on Strategic Dilemma projects—basically, real business challenges in real time. How do you keep teams from getting lost in the process?
Keeping teams aligned with a client’s challenge requires clear communication, goal alignment and structured project management. The EMBA student teams I work with are highly motivated and deeply engaged in their SD project.
Early on, we break down the SD challenge into measurable goals and collaboratively defined key performance indicators (KPIs) with the client. Regular check-ins and ongoing discussions ensured clarity on objectives, expectations and progress. Adopting an agile-inspired approach, our stand-up meetings addressed progress, obstacles and next steps. Continuous feedback loops kept us aligned, adaptable, and focused, ultimately driving the project’s success.
What separates the teams that really succeed in those projects from the ones that struggle?
It is essential to establish a deep understanding of the client’s goals, challenges and expectations from the very beginning. By asking insightful, strategic questions, my student teams ensure alignment with the client’s vision, enabling them to approach the problem with clarity and focus. Regular check-ins and open communication keep the project on track, allowing for timely adjustments based on client feedback or evolving circumstances.
As a faculty mentor, I provide guidance and support while empowering the student team to take ownership of their work. Rather than micromanaging, I cultivate an environment that fosters critical thinking and confident decision-making. This balance of mentorship and autonomy not only enhances student development but also ensures the project delivers meaningful, tailored solutions for the client.
Ultimately, this approach drives successful, actionable outcomes that benefit both the student team and the client.
We know tech moves fast. Some of the jobs students will have five years from now don’t even exist yet. How do you prepare them for that kind of uncertainty?
Preparing students for an uncertain future requires a forward-thinking approach that prioritizes adaptability, critical thinking and continuous learning. Rather than focusing solely on current trends, students should build a strong foundation in core areas such as databases, networking, cybersecurity and data processing. These fundamental concepts provide a stable base, enabling them to adapt to new tools and frameworks as technology evolves.
Beyond technical expertise, adaptability and collaboration are essential. Future roles will increasingly require interdisciplinary teamwork and the ability to translate technical solutions into business impact. This is why group projects, team-based learning and presentations are integral to the learning process, fostering both technical and interpersonal skills.
Ultimately, the goal is not just to prepare students for specific jobs but to cultivate agile, innovative professionals who can thrive in a constantly evolving world. By emphasizing critical thinking, real-world application and ethical responsibility, they are equipped with the skills to succeed—regardless of the new roles and challenges the future may bring.
AI is everywhere right now. What’s the biggest misconception students—and businesses—have about it?
The output of artificial intelligence large language models (LLMs) given a prompt is often called a hallucination—it may be a good response, it may be factual, but it can also be entirely erroneous or fabricated.
Similarly, AI-driven machine vision and machine learning systems can produce results that seem almost magical. This leads to a common misconception: that AI consistently delivers accurate, high-quality outcomes with minimal effort.
Many assume that simply integrating an AI model will lead to immediate efficiency gains or valuable insights without considering the crucial factors of data quality, business context and human oversight. Effective AI deployment in business requires significant effort in data preparation, model training, validation and continuous monitoring to ensure accuracy and reliability. AI systems are only as good as the data they are trained on—biased, incomplete or outdated data can lead to flawed decision-making.
Successful AI adoption depends on a clear understanding of its capabilities, limitations and the need for collaboration between technical teams and business stakeholders to ensure AI-driven solutions align with organizational goals.
Categories EMBA-news, News