An Interview With Chad Silverstein

Automating the mundane — AI is great at repetitive, predictable tasks. At scale, it can do this work much more efficiently than people can, and those people can be left to focus on more valuable and higher-impact responsibilities. If we go back to the auto-grader example, faculty and TA time can be repurposed for student-facing activities like office hours and support. Those are the things that we are here for — engaging students and helping them learn. AI in this context will allow us to provide a better student experience at scale through the use of higher-order assessments that have our students creating and synthesizing without sacrificing TA and faculty time for grading that could otherwise be used to support meaningful and personal student engagement.

In today’s tech-driven world, artificial intelligence has become a key enabler of business success. But the question remains — how can businesses effectively harness AI to address their unique challenges while staying true to ethical principles? To explore this topic further, we are interviewing Jason Nichols.

Jason Nichols is a clinical professor and the undergraduate assistant chair of the Information Systems Department at the W.P. Carey School of Business at Arizona State University. Nichols also teaches courses for the Computer Information Systems degree program through ASU Online. His department is nationally recognized for its contributions to AI research, and they have recently launched degree programs for AI in Business at both the undergraduate and graduate levels, along with executive education and certificate programs.

Thank you so much for joining us in this interview series. Before we dive into our discussion, our readers would love to “get to know you” a bit better. Can you share with us the backstory about what brought you to your specific career path in AI?

I have an undergraduate background in computer science, and AI has been around for quite some time, so I can remember my first exposure to AI from a technical perspective back in, I believe, 1999 or thereabouts, through an elective course on AI as part of my computer science degree program. I loved working through that degree because, to me, algorithms and data structures were puzzles to be solved, and that appealed to me from a purely inquisitive standpoint. I don’t know how else to say it — it was just fun to solve puzzles.

Fast forward a bit, and my interests shifted out of the purely technical and into a different type of puzzle — how does a business meaningfully leverage a technology to create real value for the organization? What I liked about this puzzle was that it was non-deterministic: there are best practices, strategies, and approaches to apply within a given context, but ultimately, there is no one correct answer. The puzzle is more complex in that respect — there are many more pieces to consider, and solutions in this space span well beyond the technical. This was my gateway into the field of Information Systems. Bigger puzzles? Wider-ranging solutions? A broader, more diverse skillset to apply? Sign me up.

With regards to leveraging AI for meaningful business value, the technological landscape is evolving very quickly right now, resulting in a bit of a moving target when it comes to some of the puzzle pieces themselves. Still, the puzzle, in general, remains the same. What works for one business may or may not work for another, and the success of any given AI initiative is dependent upon a broad range of technical, organizational, and environmental factors. Broadly, these are the types of puzzles that we work to solve in the field of information systems, and we have been working in this space for quite some time now.

From a career perspective, my ambitions align with training people in this skill set and watching them find success in that skill set’s application within their own organizations. I’m a teacher. I work hard to hone the craft of teaching others how to solve these types of puzzles on their own. That is, I have come to discover, its own sort of puzzle in of itself. I find this puzzle rewarding, and I work on this puzzle every day.

Can you share the most interesting story that happened to you since you started working with artificial intelligence?

This is a simple story, but it’s one that I’m proud of and one that is in line with my focus and aspirations to help support others through training and education to achieve career success through the application of AI and other technologies within a business context. My department has been thinking about and active in this space for quite some time now. We are top 10 in the nation for our research and are proud to have some of the brightest minds in this space with us on our roster, pushing the forefront of the role and impact of AI in transforming organizations and how work gets done.

For a number of years, I taught a master-level course titled “AI in Business.” In the context of our master’s programs, the goal is to prepare students to lead and manage technology within their organizations. As an AI course within these programs, the goal is to prepare students to understand when and how to apply AI within business processes and workflows in order to create meaningful business value.

As part of their final exam, I had students in the course create a playbook for how to evaluate proposals on AI implementations and come up with a supportable go/no-go decision. Building this playbook required them to draw in and integrate frameworks, taxonomies, concepts, and perspectives that we covered throughout the term together. They would then validate their playbook against a few AI proposals/scenarios to evaluate its effectiveness.

This is a very long setup just to get to the punchline that some of my students in that course used to reach back out to me after they graduated to let me know that sharing their playbook with leadership in their organization led them to promotions into new AI-centric roles. As someone who works to prepare people for roles in this space, I feel that this sort of outcome is the highest compliment I can receive, and it always makes me proud of my students, of my work in the classroom, and of the programs that we offer here at W.P. Carey.

You are a successful leader in the AI space. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?

I am a successful educator in this space. My department as a group is a successful leader in AI. If I had to pinpoint three traits that we embody in order to maintain and enhance this position (and that I think are also relevant to other businesses and organizations as they navigate this space as well), I would suggest that they are:

1. Recognizing and leveraging each other’s strengths: We are a group comprised of world-class researchers, folks with top-level executive leadership experience in industry, and folks who make teaching and pedagogy their passion and their mission. Our Venn diagram has considerable overlap across these categories, but it is part of our culture — to always be learning from one another, and in that fashion, we let our research drive our understanding of the evolving forefront of AI, our industry experience drive our understanding of how businesses are and will be adopting these technologies for transformative value and our pedagogical expertise drive how we can best train students to have successful careers in this space. All three of these perspectives are critical to our success.
2. Approaching AI holistically: There is a lot that goes into a successful AI implementation beyond just the tech, and we work to deliver courses and programs that teach our students to approach AI from a mindful perspective. A Mindful AI perspective considers and plans for impacts at the individual, organizational, and societal levels. A Mindful AI perspective considers the ethics, privacy, and security of any AI solution. A Mindful AI perspective understands that governance, metrics, and change management are just as critical to the success of any technology implementation as the technology itself. This is so important to us that we have an entire center of excellence dedicated to this notion, the Center for AI and Data Analytics for Business and Society (AIDA).
3. Staying agile: We prepare our students to successfully navigate and thrive at the intersection of business and technology. That landscape is evolving very quickly right now, so our curriculum evolves where it needs to at a rapid pace as well. This is a commitment that we make as a group, and it means that things have gotten really busy around here lately, but we do not view there to be any other viable approach that would serve our shared mission appropriately. We are mindful of the fact that we are training students for their futures — we take this responsibility to heart — and we work to ensure that we are equipping them with the right tools and the right skillsets to head out into industry and help shape that landscape on their own and within their organizations.

Let’s jump to the primary focus of our interview. Can you share a specific example of how you or your organization used AI to solve a major business challenge? What was the problem, and how did AI help address it?

How about a story about how our students are solving problems with AI instead? At both the undergraduate and the graduate level, students in their capstone courses work to build out systems to solve business problems, and those problems and systems can be very wide-ranging.

This year, our undergrads, for instance, are busy solving problems in the higher education space. In partnership with ASU’s Enterprise Technology group, they are working on an AI-driven auto-grader following a faculty-designed rubric to evaluate open-ended assignments like essays, reports, and presentation transcripts at scale. This has been a great problem space for the students to work in because it gives them hands-on experience designing and building out AI systems infrastructure on the AWS cloud platform. But beyond this, it also has them thinking through and preparing for how a system like this would impact the university and its relevant stakeholders — faculty, students, and TAs, for instance. They’re not only getting practice building out the system itself, but they’re also getting practice planning and preparing for the various trainings, support, and transitions that would be needed in order to roll a system like this out successfully. This year is resulting in a functional prototype, and next year’s cohort is hoping to integrate this technology within the university’s learning management system.

What are some of the common misconceptions you’ve encountered about using AI in business? How do you address those misconceptions?

I’ll share a couple that we hear a lot from our students and that I think are representative of some perspectives in the workforce currently as well: that AI is going to take our jobs and that the goal of AI is to automate everything.

To the former, I lean on a simple explanation from one of my colleagues that seems to be spreading rapidly: you shouldn’t be worried about being replaced by AI, but you should be worried about being replaced by somebody who knows how to work with and use AI effectively. So, be that second person, and you will be in good shape.

To the latter, we reinforce that the most effective AI systems are hybrid, where the AI is doing the heavy lifting and the humans are providing judgment, oversight, orchestration, and awareness of context. Full automation is generally more successful in narrow and highly predictable contexts. It certainly has its place and its value, but businesses broadly are more complex than that.

In your opinion, what is the most significant way AI can make a positive impact on businesses today?

At its heart, AI is a tool for businesses to automate predictions and augment decisions. For all involved, this type of support allows us to focus on more interesting things, like oversight and judgment.

I’m optimistic that, as AI continues to roll out across the organization, individuals will find themselves empowered to focus less on routine tasks and more on the kinds of tasks that provide a greater sense of fulfillment — orchestrating the work, handling exceptions and complexities, and meaningfully assimilating the outcomes. These are richer areas for us to focus our attention on, and they require skill sets that we all are built for, but admittedly, we may all need to practice or sharpen a bit in this new context. Things like critical thinking, problem solving, maintaining a broader systems perspective and key elements of management as well.

AI can facilitate a transformational shift away from the mundane, but this shift must be supported through appropriate training, mature governance, and a thoughtful approach towards reallocating people into these new roles as they emerge and evolve.

Based on your experience and research, can you please share “5 Ways AI Can Solve Complex Business Problems”? These can be strategies, insights, or tools that companies can use to make the most of AI in addressing their challenges.

1. Automating the mundane — AI is great at repetitive, predictable tasks. At scale, it can do this work much more efficiently than people can, and those people can be left to focus on more valuable and higher-impact responsibilities. If we go back to the auto-grader example, faculty and TA time can be repurposed for student-facing activities like office hours and support. Those are the things that we are here for — engaging students and helping them learn. AI in this context will allow us to provide a better student experience at scale through the use of higher-order assessments that have our students creating and synthesizing without sacrificing TA and faculty time for grading that could otherwise be used to support meaningful and personal student engagement.
2. Decision support — Tie your AI systems to organizational data, and you have an always-on knowledge expert ready to answer any question for the domain it is trained on. How much time do your best and most experienced employees spend answering emails and Slack messages to help their colleagues out? How much more valuable could they be to your organization if they had that time back to get their work done instead?
3. Risk management — AI is a powerful tool for detecting security breaches, compliance issues, and fraud. This sort of capability helps businesses to be proactive and address risks before they turn into real problems for the organization.
4. Brainstorming, running scenarios, and asking what if: We use AI for idea generation for things like assessments and student activities. It is a great tool for whiteboarding on any creative task. It can also be used to play out possible scenarios and consider the likely outcomes, to get a glimpse forward during a planning exercise, and to prepare for alternatives as needed.
5. Rapid prototyping: With proper guidance, AI is great at building things. It’s possible to spin up solid working prototypes very quickly now, and these prototypes facilitate good design when they are placed in the hands of the proper stakeholders for feedback and further ideation. This capability is an accelerator for high-quality analysis and design exercises.

How can smaller businesses or startups, with limited budgets, begin to integrate AI into their operations effectively?

Small organizations can take the same approach that many larger organizations take as well: starting small. There is a common pattern that folks in this space refer to often: crawl, walk, run. Running is typically a big investment, but anybody can crawl. If done right, crawling allows the organization to begin to experiment with AI while minimizing the cost and the risk and creating opportunities to safely make mistakes and learn from them along the way as well.

Most of the major vendors offer a team or business product that will cost anywhere from $20 to $50 a month per seat. These plans, in general, do not use your data for model training, and also protect your data in transit and at rest. Pick a champion in your organization, set them up with an account, and give them time and access to training so they can use the tool responsibly. Then, let them experiment. Keep them focused on their workflows and thinking about ways they can either do something significantly better with the tool or significantly more efficiently. This is a good start. The goal is to slowly build a culture around it through small successes, and as that culture expands, a couple of things start to happen: other folks will want to find ways to create value with the tool as well, and the boundaries of off-the-shelf AI tools will start to be discovered.

Once you understand those boundaries within your organization and confidence and competency for AI use within your workforce grow, you can start to look into customizations and system integrations, along with other types of AI, to unlock deeper value. Get a few of those under your belt, and now you’re walking.

Find ways to measure the value that’s created along the way. Use the tools in ways that either make or save money for your business. Once AI becomes the first consideration for how something new or novel should be done, then, my friend, you’re ready to run.

Like I said, though, running is where things can get expensive. You’re in the big leagues at this point, so you will need maturity and expertise in data infrastructure, governance, and risk management to do this right.

What advice would you give to business leaders who are hesitant to adopt AI because of fear, misconceptions, or lack of understanding?

Invest in yourself, learn more about AI, and then re-evaluate as appropriate. I’m not going to tell you that you should be adopting AI. I don’t know your use cases, and I don’t know your business — you do, though. So look into some executive education programs, look into ours if you like what you’re reading (because we are proud of them, and we do good work in this space), and then make your own decision. But make it from an informed position, just like you do with every other aspect of your business.

In your opinion, how will AI continue to shape the business world over the next 5–10 years? Are there any trends or emerging innovations you’re particularly excited about?

Everyone is talking about agentic AI right now, so I will, too. Agentic AI is an exciting evolution of the chatbots we are all becoming so familiar with lately. It empowers the AI to transition from assistance to action. From a human perspective, this allows us to continue to shift into oversight roles rather than micro-managing AI at the task level. We will see agents taking on roles within the organization itself and humans collaborating with these agents as a normal part of their everyday workflow. These agents can enhance our ability to monitor operations across diverse systems and platforms and alert their human counterparts to anomalies or situations that need attention. They can help the business to become more proactive and accelerate execution once decisions have been made. We are not too far off from a world of AI colleagues, but again, this future is predicated on significant steps forward in data infrastructure, governance, security, observability, change management, and reskilling for the human workforce as well.

How do you think the use of AI to solve business problems influences relationships with customers, employees, and the broader community?

The relationship aspect of this question is the interesting part to me, and this is one area where a mindful approach to AI becomes so important.

Relationships are built on trust, and trust is earned over time, so our deliberate efforts to ensure that AI behaves transparently and ethically in its interactions with all of these stakeholders are absolutely critical. Any one of these stakeholders will lose interest and appetite for the responsiveness and personalized attention and focus that AI can unlock if they just fundamentally don’t trust the system. We need to keep a sharp focus on standing up AI systems that behave responsibly within our communities and maintain strong oversight to correct any behaviors or interactions that deviate from this mandate.

Ultimately, trust is the precursor to fostering these relationships successfully. These AIs represent your business just as much as your employees do. Ensuring that they behave transparently and ethically becomes the responsibility of the business, and nothing less than your business’s reputation is on the line.

You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people through AI, what would that be? You never know what your idea can trigger. 🙂

I have to speak to what I know, and what I know is training. Kids should be educated very early on how to use AI responsibly and how AI can be used as a tool to help them grow and develop. We talked earlier about the fundamental responsibility of ensuring that AI behaves ethically and responsibly as an emerging component in society, and I would argue that the opposite is true as well. As a society, we have a responsibility to ensure that we are engaging AI from an ethical and responsible perspective. This starts in childhood, and this is enabled through proper education and training.

How can our readers further follow you online?

You really can’t. I’m not active on social media. What you can do if you’re interested, though:

• Follow W.P. Carey; our school is always up to exciting things: https://www.linkedin.com/school/wpcareyschool/
• Check out my department to see all of the great programs that we offer (in-person and online), and contact us there if you would like to learn more or just have a chat: https://wpcarey.asu.edu/information-systems-degrees
• Follow AIDA to keep up with advancements on the research side of things: https://www.linkedin.com/showcase/center-for-ai-and-data-analytics-for-business-and-society/

This was great. Thank you so much for the time you spent sharing with us.

About the Interviewer: Chad Silverstein is a seasoned entrepreneur with 25+ years of experience as a Founder and CEO. While attending Ohio State University, he launched his first company, Choice Recovery, Inc., a nationally recognized healthcare collection agency — twice ranked the #1 workplace in Ohio. In 2013, he founded [re]start, helping thousands of people find meaningful career opportunities. After selling both companies, Chad shifted his focus to his true passion — leadership. Today, he coaches founders and CEOs at Built to Lead, advises Authority Magazine’s Thought Leader Incubator.

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Jason Nichols Of ASU Online On How Artificial Intelligence Can Solve Business Problems was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.