Changemaker Challenge 2

For this challenge I interviewed people. This is how it went.

For the first part of my network I contacted five people total. I reached out to Nand Vinchhi through LinkedIn. I also contacted Chaitali Mirani through her email at chaitali.mirani@lcginc.com. The other contacts I reached out to were Emily Harper on LinkedIn (linkedin.com/in/eharper11), Samantha on LinkedIn (linkedin.com/in/lsamantha), and Stefania Albanesi through her LinkedIn page (linkedin.com/in/stefania-albanesi-20b43637). These were my five contacts even though I only ended up interviewing two of them.

For my interview with Nand Vinchhi I asked the baseline questions including how he would define the issue of AI affecting the job market, why this is such an important topic right now, what the history behind this issue is, what he sees as the major impacts of this issue and on whom, and what the main causes are of how AI is affecting the job market. I also added my own questions like what kinds of jobs he thinks are most at risk, what new opportunities AI is creating, how fast this shift is happening, what types of skills people will need to stay relevant, what role schools and universities should play, whether society should worry about mass unemployment or if those fears are exaggerated, how he stays up to date with AI, and what advice he would give someone entering the workforce.

Talking with Nand Vinchhi was one of the most eye-opening conversations I’ve had about AI. He didn’t see it as robots taking jobs but as a major shift in how we define work. In the short term, he said AI is automating repetitive tasks like customer service, data entry, and logistics. At the same time, it’s creating new opportunities for jobs that depend on creativity, emotional intelligence, and problem-solving. What stood out to me most was when he said that we’re not losing work, we’re losing time to catch up. He believes the real challenge is the speed of change. Universities, he said, need to move faster and offer shorter, more flexible programs that teach both technical skills and adaptability. I was surprised when he said AI could actually make work more meaningful by taking over the boring parts of people’s jobs and allowing them to focus on higher-value tasks. Still, he worries that without equal access to training, people in rural or lower-income areas could be left behind. The conversation really made me think about how technology is changing not just what we do for work but how we find meaning in it.

For my interview with Chaitali Mirani, I also asked the baseline questions: how she would define the issue of how AI is affecting the job market, why she thinks this is an important issue, the history behind it, the major impacts and who they affect, and what the main causes are. I added other questions based on her job, like how AI is changing work in healthcare and research, what it has been like implementing AI tools with the NIH and NIMH, what skills are becoming more valuable, whether new responsibilities have developed because of AI, the challenges of using AI in government or research settings, whether AI always makes work more efficient, how she stays informed on AI developments in her field, how she balances automation with human empathy in mental health research, her advice for students who want to work where AI and human services overlap, and where she sees the biggest areas for job growth as AI becomes more common in healthcare and research.

My conversation with Chaitali Mirani gave me a real-world look at how artificial intelligence is reshaping jobs in health and research. She works as a project manager at LCG Inc, where her team supports data projects for the National Institutes of Health and the National Institute of Mental Health. She explained that AI tools are now being used to help organize and analyze massive data sets that deal with mental health research. This helps scientists find patterns much faster and gives them more time to focus on the meaning behind the results. Chaitali said that AI has not eliminated jobs on her team, but it has changed what those jobs look like. She has seen an increased need for people who can understand both data systems and research goals. She emphasized that communication and ethics are more important than ever, because researchers, data scientists, and health professionals all have to work together to make sure AI is used responsibly. What stood out to me most was her belief that AI should support people, not replace them. She said that empathy and human decision-making still matter the most, especially in mental health research. Her advice for students was to be flexible, keep learning new technologies, and never lose sight of the human purpose behind the work.

After completing these interviews I reflected on what I learned. I learned that AI is not just taking jobs away but changing the entire structure of work. Both people I talked to made it clear that the biggest issue is the speed of change and whether people can adapt fast enough. I learned that AI can make work more meaningful but also risks leaving some people behind if training and education do not keep up.

The most successful part of this challenge was definitely the interviews themselves. Both of the people I spoke to gave me a lot of useful insight and they had very different experiences with AI, which helped me understand the issue from more than one angle. What was least successful was probably how long it took some other contacts to answer, which made the process a bit slower.

If I did this challenge again I would start reaching out earlier and maybe use different methods of communication. I would also probably add even more personalized questions depending on the person’s work and experience.

This challenge will help me moving forward because now I have real expert knowledge I can use in future assignments. It also helped me get more comfortable reaching out to professionals and asking questions that really matter to my project.

After finishing the challenge I still have some questions. I wonder how fast schools and universities will really adapt to AI and whether governments will step in to make training more available to everyone. I’m also curious whether AI opportunities will stay equal between communities or if the gap will grow even larger.