Mark Tanner: Twenty years ago, we had to have computer literacy. Today, AI literacy is gonna be vitally important. They need to be able to evaluate. They need to be able to iterate on on the outputs. Otherwise, they're going to face a real competitive disadvantage when they come out in the workforce.
Narrator: This is the Transform Your Teaching Podcast. The Transform Your Teaching Podcast is a service of Center for Teaching and Learning at Cedarville University in Cedarville, Ohio.
Ryan: Hello, and welcome to this episode of Transform Your Teaching. In today's episode, Dr. Rob McDole and Dr. Jared Pyles chat with Mark Tanner, works in Amazon's logistics division. Thanks for joining us.
Rob: Jared, we're here talking about AI literacy again today, and, we have a very special guest with us. He's in the industry, and I'll let him share who he is. And, we're gonna be talking AI literacy with him today. So, sir, would you please introduce yourself?
Mark Tanner: Absolutely. So my name is Mark Tanner. I am a environmental manager for Amazon. I manage health care, pharmacy, grocery logistics, and our retail organizations, environmental, policies and compliance. Been a, leader at Amazon for, coming up on eleven years.
Jared: Sweet. Well, thanks for coming on. We appreciate hearing from people in the industry about about what's going on as far as AI literacy goes.
Mark Tanner: Yeah. I'm excited to be a part of the conversation today for sure.
Rob: So how is AI changing the way people work, like, right now for especially in your area?
Mark Tanner: Yep. So in my area, the the simple automation of routine tasks, like looking for high like, certain high volume, low judgment areas where we're typically seeing lots of people go in and and, just basically search for an answer to a question. That's a that's a that's a simple one, but we also, we also find that, in our world, it's it's all about accelerating content creation. So when we're working with stuff with, creating a flow or a process, it's all about finding an output and then using your human judgment in order to refine that output for a, for a public consumption. And then we shift our work towards high high higher value work so we're not spending a lot of time doing, simple tasks, searching dash dashboards, looking for opportunities.
You know, we we take everything down to a much smaller we make things quicker is what I would say is what the main thing the way main place I'm seeing it is we make things quicker. So we're able to do more work with less time, and we're able to be more efficient. And we're in our in our outputs or higher, you know, higher thinking, not as much of the low judgment stuff.
Jared: That's incredible. I mean, I've seen that myself with, building courses. Mhmm. Definitely an efficiency growth in using it.
Mark Tanner: We change our employees' focus. You know? Like, we are focused more on strategy, creativity. We're building relationships, and we're solving complex problems versus getting bogged down in the in the simple on the simple searching of data. I mean, that's a that's a big win for the in the AI space for us.
Jared: Yeah. For sure.
Rob: It sounds like we're we're all experiencing this. It seems like it's a a compression of the interfaces to get to value judgments or value decisions or work that is most certainly human centered. So like he was talking about changing dates on an assignment, I call that monkey work. Right? Yeah.
Like, look at date, change date, press go, and you just get almost enslaved to this push button one, push button two, push button three, now push button four. Mhmm. Repeat. Push button. Yeah.
And it sounds like you're saying this is the kind of work that you are experiencing now getting compressed out or removed out with with use of AI. Is that correct?
Mark Tanner: That's correct. So, like, I come in in the morning. I have created a full process flow where AI will look at the last fourteen hours of emails, any kind of Slack communications that come through, and provide me a starting point for my day where I don't even have to open my email to get started. It it gives me a full summary of everything that happened in the last fourteen hours, which may be a 150 emails, and it takes and says, this is the highest judgment item you need to deal with. These are your these are these are low priority, but the highest priority items that are and it sometimes hallucinates, and it thinks there's some things that are important that really aren't.
But, it saves me a lot of time of just going one by one through my emails trying to figure out where to start my day. Lots of there's lots of examples of that where it just it speeds up my day. It speeds up my my, my thought processes so I can get really started on what matters.
Jared: So you mentioned in there, early on, and this is something we want to focus on a lot in this series, the idea of, being able to evaluate what it, and you just mentioned that there with the email, like it has these hallucinations still does. And so you have to take the time to evaluate that. What do you like, what are you seeing as far as how important is it for them to evaluate these, or maybe you as a person, how important do you feel that that is?
Mark Tanner: It's vitally important that the the human aspect of this falls into comes into play. It the outputs that it creates is not the final output that we wanna that we wanna create that we wanna send. So we take the inputs, use human value in order to make the human judgment part of that, refine it with human judgment, and we turn it around, and then we and we give it a a consistent output that we wanted to do. My email, the very first flow that I set up and the very first interaction that I had in this particular space, it it came out with some some really crazy outputs as being highly important. So then I take and refine that output fifteen more minutes.
I change the the the prompts. I do a little work in the prompts. I I resort my Slacks into high priority, low priority, and then it gets smarter every day. So it the outputs this morning really were my most important items that I needed to focus on, and I would have spent twenty to thirty minutes getting down my emails to find a very high, a very, very high priority item that I needed to deal with first thing this morning. So I was able to find it quickly and efficiently without being here till 10:30.
I was able to deal with it at eight instead of a 10:30 when it would have popped
Rob: out. Wow. That's cool. So how much I mean, if you could quantify it in terms of percentage, how much time do you think it saved you?
Mark Tanner: On this particular case, it it saved me well, it saved me hours worth of it wouldn't have stopped the workflow of what I was doing. It just got me to the something that was I needed to call a regulator and and a state, and it need that needed to happen sooner than later, so I needed to notify. It was a a was a requirement to notify, so I needed to get on that one very fast. It would have delayed the process. I would have eventually gotten there, but I would say over the course of a week actually, let's break it down to the day.
Over the course of the day, I would say it adds about it saves me about five hours a day in a in a typical twelve hour workday.
Rob: Oh my word.
Jared: What are you doing with all your free time?
Rob: It's almost half. It's almost half.
Mark Tanner: Conversation I had this morning was, if I did not have AI, I'm not sure how I would be able to do all the things that were required. And prior to AI, prior to these tools being available, my days got longer. My, it extended into my weekends. It it extended through my weekends, and then I would and I was always playing catch up. And I feel like today, I have at least I can go into the weekend knowing that I've I've I'm clean going in, and then I know that I'm going to be that I that the weekend's gonna be a little easier for me, and starting out Monday will be won't be quite as daunting of a task to get started.
Rob: So it sounds like it's made his job manageable.
Mark Tanner: It makes my job more manageable.
Jared: As we train students for the workforce in higher education, what are the important aspects that they need to know or skills they need to have when it comes to AI literacy? What what would you say are some prime prime items that they need to at least have an understanding of?
Mark Tanner: For me, the student that leaves, say, Cedarville University with a degree in civil engineering. I happen to have a civil engineer that graduated last year. He needs to be able to have literacy when it comes out just like you twenty years ago, we had to have computer literacy. Today, AI literacy is gonna be vitally important. They need to be able to evaluate.
They need to be able to iterate on on the outputs. And, otherwise, they're gonna face a real competitive disadvantage when they come out in the workforce. So I was interviewing a candidate for a role this past week, and he was in a lot of his answers to me were surface level, very tactical in nature, and I was looking for a higher level leader. My question to him was, have you used machine learning or AI in order to to, you know, look at the permitting process in your in your current workplace and figure out how to save time in the in those areas? And he was he was like, not really.
And I was like, wait. Real and realistically, as I'm evaluating that candidate, I've he's way behind in the in the curve even though we're so early in AI, but we're if you're not using it or utilizing it today, you're not you're you're you're way behind the curve. And and you really need to know as a student when to use it, when not to use it, how to verify its outputs, and then and, ultimately, you need to have critical judgment on what the outputs look like coming out of coming out of college. I think of the university like a playground. If you you're you're on the playground, you are this is your opportunity to utilize the tools, to to learn to use the tools so that when you get into a different workplace, you'll be able to to put those tools into place and you and use what you've learned over the last four years of college before you got into the workplace.
Rob: You mentioned that they should know students should know when to use it and when not to use it. So it seems like you're you're, talking about ethical usage of AI. So where are those boundaries for you, and what would you what would you tell students?
Mark Tanner: So there's thing there's things that where ethical use of AI is gonna be extremely important. The legal field, the medical field, I work in a compliance related field, utilizing it to create outputs. You must the work that you create or work that you deliver is going to be it's your work. Whether you you can't go back and say, well, AI made a mistake. I'm sorry.
I, you know, I'd I'd I need I that heart surgery, I thought you needed the the, really, you don't need it. So the education level of what of the output. I I think the the legal in the legal community, understanding it it does do work in the in my compliance space, but I the outputs of that going to a regulator, going to a government agency have to be my output. So you, I'd say utilizing it to create the initial work product isn't the final work product. So this that it's that decision and judgment that goes along with all that.
Rob: So it sounds like own the outputs is what I'm hearing you say.
Mark Tanner: Absolutely. What the output is your output. Mhmm. So it's it's one of those things. The other thing is that we need to be careful on is privacy, sensitive information, proprietary data, client details, because certain tools are then once you put it out there, those those items that unless you're in a closed environment, then can become public public information where it really shouldn't be.
So understanding what you're putting into the AI tool because it will create different the outputs it creates is going to be, you don't want it to learn things that it shouldn't it shouldn't know. Should be pub should not be public information.
Jared: There's an element to this, though. And I just had a conversation the other day with somebody who ethically is not necessarily a fan of using it, period. If you had a job candidate come in and you asked them the question you did earlier and they said, no, I haven't really used it because I'm not ethically for it. How would you handle that?
Mark Tanner: I would probably have this conversation. I've had it before that in the nineties, we were talking about Internet and how an ethical use of the Internet in order to to do to do task. I would say if we are ethically 100% against the use of AI, we are miles behind the world that we live in. We can ethically use AI without without crossing over in the lines where it's where the output is not your own, where it's a where you're breaking company policies. I think of organizational policies.
Every employer has or should have acceptable use policies for AI, and it's and the professional responsibility in order to maintain those. I say as an employee coming in, especially in my space, coming in, if they are telling me that they're opposed to using AI, they are probably not, unless they have some sort of skill that is not transferable or not available in the market, they are their other candidates will will then come in and take that spot. Wow.
Rob: You heard it here,
Jared: folks. Yeah. That's
Mark Tanner: interesting. When you evaluate five candidates for a role and they and they all have very similar you know, say you take five equal candidates that are coming into a position and you and one of them says they're unwilling to use a tool that's vital in your in your community or vital in your in your organization, they have just said that they're not willing to come to work for you.
Jared: It's like, I will not use Microsoft Word, that kind of a thing. Or it's like, well Yeah.
Mark Tanner: Same thing.
Jared: Well, I guess you're not gonna hire you then. Sorry.
Rob: So if you had the opportunity to talk to faculty here at Cedarville and at other institutions where, you know, they're training these students who are gonna come probably work for you at some point and other, you know, industry, what would you wanna say to them? What would you wanna say to the instructors? What kind of encouragement would you give them?
Mark Tanner: So I would say to those instructors that students don't need to learn to become AI experts in order to to be to be employable. They need to be able to ask the right questions. So that's about prompting. That's about understanding the and getting useful output. So you give the the tool a system that gives them a good output.
They need to be able to teach, evaluate what comes back. So they need to be able to provide critical judgment. You know, as a as a college student, we need we need to be learning those critical thinking skills. So critical judgment to assess the accuracy or the, completeness and the appropriateness of the answer. And then they need to be able to teach adding the human element.
So we're gonna the creativity, the contextual understanding, the ethical reasoning, and the empathy. AI can't replicate all those human human factors today. And then then they need to teach take ownership of the final product. What are what does that final product look like? They need to be able to stand behind the work that they turn in or they or they execute because, ultimately, the they are turning in their work.
And then the educators help students develop these habits, even even small habits, are gonna are giving them a a huge, enormous, lasting professional advantage in the workplace.
Jared: So it's a competitive edge for sure. And you mentioned the internet back in the so long ago, back in the nineties.
Rob: It has been a while ago.
Jared: Has been a while. Yeah. Do you see this no longer being as much as a commodity? Do you think it's going to eventually be level ground for incoming workers? Like, yeah, AI was, was like, it's almost like I'm going go back to the Microsoft word analogy, but it's like, you know, they used to put in the, I guess they kind of still do in job applications, like must be proficient in the Microsoft office suite, stuff like that, where it seems like that's so commonplace now.
Do you think there's going to be a point where generative AI becomes commonplace where someone it's just going to be part of the expectation of anyone who's in the industry? Because it seems like it's a commodity right now.
Mark Tanner: I would say that looking not even ten years down the low you know, but five years Wow. Three years down the line, this is growing so fast that I believe it'll start becoming a a a job requirement. You have to have proficiency in AI. You will start seeing it on resumes proficient and, I'm not gonna use the tools because Bedrock or or, you know, a quick or, you know, all the different Anthropic, you know, all the different tools that are out there today. You you're gonna have you're gonna have people that are gonna come in and say, I am literate or I am an expert in the in in these areas, and it'll make them more hireable, and and especially in certain segments of the workforce.
Jared: Wow.
Rob: Yeah. The biggest concern that I see and and it'd be interesting to hear you talk about this in terms of where you're at in the industry. But if students fail to do the things that you've talked about in terms of using critical thinking, and then they just accept what it is and they don't really have those grounded knowledge sectors in their mind to be able to differentiate between good output and bad output, they're gonna just lean on what comes from, you know, the AI bot. And we both know I think all three of us know that's bad idea. And you you could cause some significant issues, but, I mean, I've seen it here in my students, and I think probably Jared has as well, is there seems to be like, that's the easier path.
I feel like water always goes to the lowest point, and and I think we as humans tend to do the same. Like, we tend to go take the easiest path.
Mark Tanner: Mhmm.
Rob: I got this. I just needed to accomplish this. I really not interested in this anyway, so I'm just gonna, you know, okay. There it is. Boom.
Handed in. Got my grade. It's good enough. Let's go. And are you seeing that in your work in your workforce at all?
Mark Tanner: So yes. So I'm gonna I'll cite a recent example. Obviously, from a from a HR perspective, I won't give any any critical detail. But recently, I assigned a manager that in a in our organization a task to complete based on a is this the task was was given that we need over the next sixty days, you need to create this output. And rather than using critical thinking skills, the prompt was put into an AI tool, and it created the output of the of my request, but actually took and got confused with the what was what was required based on my request, and it outputted a sixty day instead of a twelve month.
And it used the words from my that I had put into the to the assignment, used those words in the in the prompt, and the manager did not read the output. And so the executive summary of the document that was created for my use for the twelve month plan actually had the word for word, word for word from the from the prompt that I had had given the associate or manager to to complete the task. So it it was I actually called out an irresponsible use of AI as being because this is what I consider an irresponsible use of AI because we just took the tool. We created it. We created the output, and we didn't read the output.
We just turned it in as my work. And so when you can look at and I actually and then I took AI, evaluated the response based on the based on my input, and it gave me the outputs of why this was AI generated. And you can see formulaic. You can see the sentence structure. You can see the verbiage used.
You can you can see a human typically will utilize more words in a sentence than AI will. AI sentences are typically in a document that's created strictly by AI. AI will create similar sentence links, similar structure. It'll it'll it's it's actually very interesting to see how AI evaluates the AI output and tells you how it's an AI output.
Rob: I know that's one of the big struggles, and I know our faculty will also I mean, they've seen the same thing. You and I have seen it, and it is a concern. Right? Overreliance on a tool and not taking it seriously using, like you said, critical thinking skills. And then when they're called on it, like, how dare you?
You know? Yeah. Obviously, those things have real consequences, though. Like, if people believe and they don't use those skills like you're talking about, and this goes directly to AI literacy, I believe, is is to be AI literate is not just to know how to use the tool, but what he said earlier, know when you should use it and when you should not. And I think a lot of people
Jared: Yep.
Rob: Are are using the philosophical perspective of can means should. Can means should.
Jared: Where we are right now.
Rob: Yeah. Would you be able to speak to what your company, what Amazon is is wrestling with in terms of the ethics of things like this when you have those kinds of cases? And is there is there a corporate push to say, hey. We really need to maybe put some more boundaries around this thing or be more specific about what we intend or what's expected?
Mark Tanner: So internal to our organization, we are training we're creating we actually have lots of sessions where we get in and and do we'll call them braille lunch bag sessions where we come and we we discuss tools. We have a a reoccurring Monday meeting every other week where we actually have my entire organization gets together, and we have a contest, like, who can build the best tool? But we also evaluate the tools that have been created. And I think there's a there's a push to utilize this super fast and build quickly. Amazon is a company that, as a whole, has been has grown on the philosophy that it's day one, and it's easier to correct a mistake than it is not to try to try to fix it.
So there's a lot of work that goes on behind the scenes in order to to correct the what we're creating versus telling people not to create. And so it's a it's a it's the bias fraction mentality where we make decisions based on very limited data points in order to to to you know, because it's easier to pull back than it is to not move forward. And so I think we have a lot of discussions about using it ethically, but we don't have a lot of the push is to use it more and more. And I think that's gonna continue to be the push. But I think that we that if we like I said, I I've seen bad outputs, and and I think that's where we learn.
It's like a question in my in an interview recently was tell me about a time you made a bad decision. I think when we make a we build a tool that doesn't give the give a good output, we learn more from the the nonsuccessful push than we do for all the successes that we have. And I think that's a that's a key and I think that's what we we are learning. It's like the playground where we have I have opportunities during the week to play in a playground that is that the outputs are not gonna be used for my business work, and that and we encourage our our teams to spend hours a week. I spend three hours a week specifically working on AI tools that may or may not be directly work related, but they are, you know, learn by doing.
I'm building. I'm I'm iterating. I'm I'm and I actually built a built something last week that is that at first, I didn't realize that there was a use use case in the workforce for for my workforce, but it was a last semester, a student there was this group of students in their graduate course that completed a project in the computer in the I think it was in the cyber cyber team. My when my son graduated, I got to watch the presentations. Mhmm.
And they created a tool by using coding, and they created an app that would tell tell an organization how much money they're spending on each meeting that they have. They spent there was four students spent a semester building the tool. I built exactly the same tool in sixty seconds using AI.
Rob: Wow. How much money did you spend in building the tool?
Mark Tanner: Yeah. It it took me it really did take me the prompt took me sixty seconds to type. I spent fifteen minutes reevaluating it, asking for the cost of every meeting that I have and what's my annual cost for a specific weekly meeting and and what's my highest cost meeting of the week. And it the tool actually comes back and says, these are meetings that you should eliminate because they're redundant on your schedule. I actually it it was a it was amazing that I built I was like, I just built a tool that everybody wants to use because they want everyone wants to complain about every meeting they're in.
And I'm like, okay. Well, we got one meeting that cost $28,000 a week to run. Is it really worth $28,000 to run that meeting?
Jared: It's it's the ultimate this meeting should have been an email bot.
Mark Tanner: I actually had a a coaching session with a manager who was creating office hours, weekly office hours for a large group of invitees. And the the conversation was if do all these invitees need to be in that office hours? And and then could that office hours be shortened to forty five minutes? This is what it'll say by doing that. And then creating an agenda that is that is that is direct.
You stay on topic. You complete the meeting. You don't get you don't let it go sideways kinda like a like some conversations go, but you stay on task because if you dot, there's a cost every minute for for our team. And it's and and I spend thirty a thirty plus hours a week in meetings, and I'm like, okay. Which ones do I not need to attend?
I actually asked the tool, and it said, these these five meetings, you don't need to attend. Have your have somebody because there's redundancy in leadership in those meetings. You said one one of one out of seven of you need to be in that meeting, not all seven of you.
Jared: Yes. I need that bot. I need that. We all need that bot. Need that.
That's great. Mark, we appreciate your time. Yeah. This has been very informative.
Rob: We we won't waste any more of it. Yeah. We
Jared: I'm sure you already
Rob: uses the bot on us.
Jared: You have a counter on this.
Rob: Thank you, sir.
Mark Tanner: I really appreciate you guys today doing this. I, enjoyed it.
Ryan: Thanks for listening to this episode of Transform Your Teaching. If you have any questions or comments about our chat with Mark Tanner, feel free to reach out to us at CTLpodcast@cedarville.edu. You can also connect with us or message us on LinkedIn. Finally, don't forget to check out our blog at cedarville.edu/vocalsmall. Thanks for listening.