#251: フォワードデプロイドエンジニア、シャドーAI、オーナーのボトルネック、履歴書のAIスキル、トークン予算

So imagine that every role in the company has proactive agents that are just watching the data and then coming to you with recommendations. That is the unlock that hasn't happened in corporate yet, but I believe it's coming in the next six months. Welcome to AI Answers, a special Q&A series from the Artificial Intelligence Show. I'm Paul Reitzer, founder and CEO of SmarterX and Marketing AI Institute. Every time we host our live virtual events and online classes, we get dozens of great questions from business leaders and practitioners who are navigating this fast-moving world of AI. But we never have enough time to get to all of them. So we created the AI Answers series to address more of these questions and share real-time insights into the topics and challenges professionals like you are facing. Whether you're just starting your AI journey or already putting it to work in your organization, These are the practical insights, use cases, and strategies you need to grow smarter. Let's explore AI together.

Welcome to Episode 251 of the Artificial Intelligence Show. I'm your host, Paul Reitzer, along with my co-host today, Kathy McPhillips. Welcome, Kathy. Thank you so much. Kathy is the Chief Marketing Officer at SmarterX. She is not replacing Mike. This is an AI Answers special episode. This is our 23rd edition. So Kathy and I do these together once or twice a month. And this is our series based on questions from our monthly Intro to AI and Scaling AI classes. Those are classes we've been doing for about five years now. Intro to AI, I think, has been five years. About 65,000 people have registered for that series. And Scaling AI, we just did 13? No, 20. We just did our 20th. 20th. Wow. Okay. Yeah, it leaves a track of time. So that one we've been doing for close to two years now. and I don't know how many people have gone through that series, probably like 15,000 or something like that or 20,000. I don't know, a lot. So we appreciate everybody who's been a part of those series, and the cool thing is when we do those classes, they're free webinars that we host,

and we always leave about 15 or 20 minutes at the end for questions, and there's always more questions than we can get to. So I know on the Scaling AI we just did for November 11th, which is part of today's, there was like 15 or 20 questions left in the Q&A, And then the September 30th Intro to AI is the other one. So if you attended either of those, the September 30th Intro to AI, November 11th Scaling AI, these are the questions that were kind of left in the sandbox from those. And if you didn't join us, you're going to get a bunch of insights from the things people were thinking about as we went through those. So there's a few other listener-submitted questions in here as well. Claire on our team, who produces the podcast, does a great job of curating all of this and keeping it organized for us and preparing Kathy and I to do this AI Answers episode. Did I miss anything there, Kathy? You did not. Okay, cool. All right, and this episode is brought to us by AI Academy by SmarterX. This past Tuesday, so that would have been November 10th, we celebrated our 250th episode of the Artificial Intelligence Show podcast

with a special AI Academy offer. So if you haven't taken advantage of this yet, this offer runs through November 30th. So AI Academy helps individuals and businesses build their AI literacy and put that knowledge to work through courses, certifications, that's a hard word to say, live sessions, personalized learning journeys, and dynamic learning. We're going to be doing a lot of announcements around that coming up on November 19th. We add new content weekly covering the latest capabilities of AI apps and agents, real stories of personal and business transformation, and platform-specific courses are coming soon for ChatGPT, Claude, Copilot, and Gemini. That is one of the most requested things we hear from our business accounts in particular, and that is coming soon. So we're really excited about that. So through November 30th, you can get $250 off an annual individual AI Academy license with code POD250. So that's about 25% off of your first year of Academy membership.

And then for organizations that are driving business transformation, we have two special offers. So buy 25 or more team licenses by November 30th, and I will do a private one-hour virtual session with you and your team, or it can just be an executive briefing, whatever you want to do. And then buy 250 or more licenses, and I will actually come into a private in-person session in the first half of 2027 in the United States, is the caveat there. I can't do international travel, but if you're in the U.S. and you buy 250 or more Academy licenses for your team, by November 30th, I will come and do an in-person session with your team. So you can visit academy.smarterx.ai to learn more. And if you're going to do an individual membership, go ahead and use that pod 250 and get immediate on-demand access to the entire library of more than 400 courses and all the upcoming live stuff that's going to be happening. And then if you want to look into the business accounts, just reach out to our team. You can fill out a form on the site, and our team will be in touch with you.

We'll put links to everything in the show notes. And then if you'd like a closer look at what's going on with AI Academy, we have the November 19th AI Academy Day Building an AI Forward Workforce. That is a free webinar happening at noon Eastern time on November 19th. You can register at smarterx.ai. All right. Did I miss anything there, Kathy? I don't think so. Nice job. All right, good. All right, so let's do it. AI answers. So interestingly, when we were doing these questions, since we did the scaling AI this week, Claire had to turn around pretty quickly to get these questions done. So she uses AI to help her synthesize the questions, go through the previous questions for intro, put it into something to see, like, has this question been asked 10 times? We don't want to do an episode on the podcast with a question you've answered a million times. And then I wasn't sure what number AI answers this was. What did we do before artificial intelligence? I just went into the Google notebook and said, what AI answers number is this? And it was like, it is number 23.

So it was so helpful. So we do love being able to use some of these tools for things such as this. All right, let's jump into these questions. Number one, for organizations with strict security and compliance requirements, including SEC2 controls or HIPAA obligations, what's a practical first step towards scaling AI? I mean, the first thing is to understand the limitations you already have in your organization. So the stakeholders who, you know, kind of make sure that everything stays structured the way it's supposed to, that you understand their concerns, the risk associated, how they think about AI, and that anything that runs up against any regulations within your industry in particular, that you understand those and you're planning against those. The other thing, though, is to not let those restrictions or limitations prevent you from driving transformation in your organization or across your team. And so the way you do that is to find the use cases where you don't run up against any of that.

So the example I think we've used before is like in a health care system. You know, there are plenty, probably thousands of use cases for marketing, sales, customer success, operations, HR, finance, where you can be using AI systems, your favorite AI tool, without running into any of those regulatory risks and concerns. And so getting really good at identifying low-risk or no-risk use cases and then just stacking those is a really good way to keep moving while you're understanding the bigger picture and the risks that you need to be aware of in your organization. And that applies to hospitals, banks. We talk to companies like that all the time, and I think that messaging is relevant to them. Absolutely. Okay, number two. What would you say to an organization that plans to block access to frontier models and only allow employees to use models hosted within its own infrastructure? How should leaders weigh security costs and capability when deciding what to make available? This is definitely a problem in bigger enterprises. We do see this all the time.

Either they're blocked or they're very limited versions of access to these models. So this happens a lot with Copilot is the one I hear about a lot, where a lot of the capabilities just aren't built in. And so you have individual users who maybe are using Claude or ChatGPT on the side in their personal accounts and understand the full capabilities. They're using the reasoning models. They're using the agentic capabilities potentially. And then they go and they use the tools they're given access to in their jobs and realize they're nowhere near as capable. Yeah, I don't know. I mean, it really starts with education and literacy at the top. So if the decisions that are being made on the technology side are limiting the impact and the ROI that AI can have across the organization, might need to start with an executive briefing of what's actually possible and what the limitations are restricting in terms of our ability to make an impact on the growth of the company. and so like my assumption is usually

there is a lack of understanding if all these limitations are put into place if that's not the case and they do fully understand and they are still dramatically restricting access honestly like that is the point where you kind of start looking around and saying if you understand the full capabilities and what you could be doing in your role in that company and you realize you are not going to be able to fulfill your potential in that company and they're not going to give you access to the AI tools that you believe are necessary to unlock your potential, then you might want to look for work at a company that is going to. And this is something we do talk about. And again, I don't like look at these questions. So sometimes I may pre-answer a question Kathy has in here. One of the things to think about is when you go into the interview process, if that company you're interviewing with isn't asking you about your AI capability than your AI literacy, that's a pretty good sign that they're not doing much themselves. Whereas like if you go into a company and the first thing they're asking

is, okay, so what AI models are you used to using? Which you preferred AI system? Like that's a pretty good indicator that they're a little more AI forward than another company. And you used this stat a while ago. I forget which presentation it was in, but, you know, X number of people or their companies say you can't use AI. And then this higher percentage or people using it on their own devices because their company won't let them. So what happens when even if people know they shouldn't be doing this, they're like, we've got to get stuff done. Are they doing that and putting their companies at risk? Yeah, I mean, I do think that that's happening all the time. Obviously, it's hard to get at that data because people would have to admit that they're doing that for that to show up in the research. But, I mean, based on just, like, conversations and sort of qualitative feedback, it seems like it's pretty prevalent. I mean, I've been in a workshop where the person that's in charge of providing access to the models and is the one that's setting the guardrails was using a personal account on the side to do work.

Right. And that was a big enterprise. So it just gives you a sense, like, if the people who know the risks also understand what's possible and are going outside of their own guardrails to get stuff done, there's a pretty good chance that the people within that organization are doing something similar. Right. Okay, number three. For larger companies where governance teams aren't yet comfortable with widespread use of AI agents, what should AI teams prioritize and in what sequence? Should they focus on general AI literacy, connecting approved tools to internal data, or building reusable workflows for specific roles? I do think this one starts with that understanding of what we mean by agents. You know, I think that's a good starting point. So I've said before, but, like, agents exist on the spectrum of reliability and autonomy, are, like, the two most important variables, in my opinion, to really think about at the starting point with agents. And what I mean by that is how much human in the loop there is with the agent. And so some agents are very deterministic in nature, meaning most of what they're going to do, the rules are set by you, the user.

So it's really more just an automation than it is some probabilistic outcome being driven by an AI model that you don't even know what it's doing or how it's doing it. It just, like, creates the end product. And so we talked about things like you know Zapier and Make where let say you take a workflow as a marketer You know you going to like announce a product launch or something and you define these 25 steps of going through this process and elements of it, you just set rules. It's like, okay, I want to do this, this, and this, grab this data, do this thing. And you know exactly how the agent is going to behave. Now, again, we're using the term agent pretty loosely here because it really is just a traditional automation, but you can very confidently say, okay, these 15 steps are basically just automation agents and they're doing exactly what I tell them to do. But now these other 10 steps, I'm in, I'm using an AI agent that has some of its own ability to make decisions and determine its path forward. So it might build its own plan. It might craft its own creative. It might segment its own

lists, like it's going to do stuff where you're just now overseeing it. And so I think to set up this governance to understand how involved we need to get, we need to understand how autonomous are we trying to make these agents in our workflows across our teams. And once you do that, then you can start to have a better handle on how to set them up and how to govern them. So the better starting point for most organizations is to start very low on the autonomy scale. So more control, more clarity about exactly what it's doing, or more very low-risk outcome work. So, for example, setting up an analyst agent that every day goes and looks at marketing data and sends it to you in chat CPT each morning and says, here's what happened yesterday. If it gets something wrong, there's no harm done as long as you're not activating that data and not, like, assuming it's all 100% correct. So it's not going to anyone external. There's really no risk involved. But it's mostly just like sort of figuring out its own thing.

I just tell it, go look at the data and come back to me, and then it does its work. So, again, it does start with literacy. It starts with understanding what agents are, what the variables of using those agents are. And from there, you can start to make more educated conversations. But the more advanced they get, the more autonomous they get, the more access to data they get, the more ability they have to take actions on your behalf that maybe have more risk associated with them. you need to keep you know be very intentional with how you set those up right yeah i mean you can't go from zero to 60 and just like turn this thing on like you said like make sure that you're seeing along the way and say oh it can do that like i'm going to take it one step further one step further and keep improving on that and then there are people doing that like we there are definitely organizations that are like way out on the frontier taking enormous risk and i will share that stuff with the team sometimes be like, yeah, this is not us. Like, I'm glad someone is doing this and sharing this, but, like, we will never be doing what these people are doing. You talk about it on the podcast a little bit.

Okay. Yeah. Okay, number four. How much standardization do organizations actually need when it comes to AI platforms? How do you get employees to adopt approved tools consistently, and when does a mandate make sense? As personal AI agents like Muse become more accessible, how should leaders address shadow AI? That's kind of two questions there. Yeah. Okay. So let's start with the first one. How much standardization to organizations actually when it comes to AI platforms? So I've shared this before on the podcast and in some of our classes. You know, at SmarterX, we have access. Everybody has access to three platforms at least. Like we all have Gemini, Claude, and ChatGPT licenses. At different times, I've wanted to go all in on one of them and just like focus the team on really learning those tools. But I think the feature sets are dynamic enough and the value creation is tangible enough that to me I can justify what roughly is like $60 per user per month to invest in those platforms for each person.

That being said, we basically centralize which ones of them get access to outside data sources, like our CRM system, our financial systems, things like that. And so we're much more cautious when it comes to outside data access. And so, you know, I would say in a perfect world for me, you are centralizing around a single AI system because then you can better govern that and you can drive innovation and optimization through that one platform. but some people you may want to allow them to be continuously experimenting with the other systems running their own evals you know for high value tasks and that's basically how i use it i would say that i predominantly like right now i predominantly use chat cpt there was definitely a period this year where it was clawed like i was more so but if it's a high value task like if i'm working on a strategy or a prototype of like a tool i want to build i will i will start building

it in all three of them. And then I will see which model and which system is going to best help me achieve the outcomes I want for that high value project. And that's part of the reason why I like having all three is sometimes one of them is just better at something. And that one project I do pays for the licenses for all three of them for the year. So that's hard to do in bigger enterprise though. But I think that's what I think you need to have a group of people, a lab, a center of excellence, whatever it is, that has access to the major ones. And they're always running evals and sort of testing. And then I think the rest of the team who aren't maybe as advanced in terms of AI maturity, focus them in on a single platform and get them really good at using that platform. That's probably the best path. But, again, it's not going to be prescriptive for every organization. And there is some value in having more than one because what happens when custom GPTs go away? And you've put so much energy into that, it's good to know at least one other tool.

Yeah, they do. They make changes to their system where it's like, well, that just broke how we were using the system. Right. And then do you want to address that as personal agents like Muse become more accessible, how should leaders address shadow AI? Yeah, I mean, they're all going to have these agents that are going to be more useful on the personal side initially. Anybody who's playing around with dots, for example, or scheduled tasks in ChatGPT, certainly in Meta, Google Gemini has the ability to schedule things now. So there's some that are happening in the business world, but most of the innovation is happening on the personal AI agent side because they can, you're more likely to give them access to all the data they need to be useful. because for these agents to be extremely valuable, they have to be proactive, meaning they have to bring use cases to you rather than waiting for you to come back in and use them again. And so, like, imagine each morning you go into, like, ChatGPT or Gemini or Claude,

and it says, hey, you know, okay, I'll play this out real quick. So let's say ChatGPT has access to your CRM. So we're going to say these personal agents have now moved into these proactive agents and moved into the realm of the business world. And ChatGPT has access, let's say it's DOTS or whatever their agent is, has access to your CRM and your financial data at a limited sense. And every morning it monitors, without you telling it to, it's monitoring your CRM data based on things that it knows are important to you based on previous conversations. And it surfaces things for you and says, Hey, Paul, I noticed that webinar registrations took a spike yesterday. Great work. I wonder what's going on there. I dug in and I actually found this ad is working really well on LinkedIn. You should pass this along to your marketing team and give them a heads up to double down on that ad. That's a proactive agent. Like that's something that is watching things on my behalf and coming to me with use case ideas. And maybe it even says, here's three alternate ad copy ideas that might work really well.

Give these to your team. So imagine that every role in the company has proactive agents that are just watching the data and then coming to you with recommendations. That is the unlock that hasn't happened in corporate yet, but I believe it's coming in the next six months. So right now people are having to, like, hack this together, but I think that's going to cross over into businesses. The better agent would be for me to get that and then for you to get it saying, we already made this change. Yes, yes. Yeah, yeah. So that's, I think what will happen is like you'll, the center of excellence or labs or whatever, the centralized body within organizations with the most AI forward people will proactively go out and build personalized agents for the roles in the company. Looking forward to that. Okay, number five. How should organizations bring technical builders closer to business teams to turn AI opportunities into working solutions.

Where do forward deployed engineers fit and how should business leaders, IT, and HR share responsibility for AI enablement? This actually builds on the previous question, I guess. So the forward deployed engineer, if you're not familiar with that concept, it was like kind of made popular by Palantir and now all the AI labs are basically building consulting arms of forward deployed engineers. The basic idea here is people don't know how to use software or AI models. And so they send technical people into these big companies or their internal people that do it. And they basically look at workflows and problems within an organization and they identify how to use Claude, ChatGPT, Copilot, Gemini to solve things. And then they charge you millions of dollars to build things. That's kind of like the basic premise of a forward deployed engineer. So I think that those roles are really important right now. I actually, I'm not a believer that there is necessary a year or two from now if an organization trains their business people to understand how to use these technologies.

Because most of what needs to be built, you can vibe code with no coding background or technical knowledge. You can just go in and say, hey, I've got this idea to, like, create this app that does this thing. And 30 seconds later, ChatGPT built you a prototype you can play around with. And so if you have business leaders or even practitioners who are extremely strategic minded and really strong critical thinkers, they just need to be given access to the tools. They don't need a forward deployed engineer to come in and tell them what to do. So I do believe, though, that if you look at the historical structure of an organization, there's just going to be business-minded people who are just better at strategy and critical thinking. And I do think centralizing those people and giving them high levels of AI literacy and competency and then having them go meet with the business unit leads and the team leads and the practitioners and run workshops and boot camps and hackathons.

I think that's probably where most organizations are going to go over the next five years is centralization of that building. I just don't believe that they're going to necessarily have to be a traditional forward-deployed engineer who's a technical person. Like, I don't think they're going to need any coding background or engineering capabilities. I think they're just going to be business people who build things. Yeah, for sure. And speaking of that, number six, where do technologies like optical character recognition or robotic process automation fit today? How do you decide when to use these technologies, when to use an agent, or when to combine them? This goes back to the first conversation around agents, and it's really just, you know, a lot of traditional technology that was like early forms of artificial intelligence, like image recognition and things like that. But it's just like merging into this automation. And it just, the automation kind of got rebranded as agents, like this general term for agents. But again, they exist on this spectrum. And sometimes within that agent, there's this deterministic outcomes

where you're setting the rules. And sometimes with, you know, it's more probabilistic where you're letting the agent sort of figure stuff out. So, yeah, I mean, a lot of this is just blended in. and the technical people care what we call it and whether or not it's always been a thing for 20 years or 30 years, and the non-technical people don't. They just want it to do the thing that they're envisioning. So, yeah, I would say just increasingly it's, like, blended into the technology itself, and it's going to continue to play. But I do think that people overlook the role of traditional automation processes that like a lot of times we don need to use the most advanced model We don need Astra 6 Pro or High to do something that literally just a if statement Just use automation for that. Yep. Speaking of that, number seven, when redesigning work, does it make sense to start by mapping the tasks people do today alongside the outcomes the business actually needs?

How do you use both perspectives without simply automating an inefficient process? So we talk about two main outcomes or value creation elements of AI, which is optimization and innovation. So if you want to optimize a process, do it better, faster, cheaper than mapping tasks and workflows and what the outcomes are is a really logical place to start. And that's, again, if you take the person who runs those workflows, take a director, a manager, you know, a CELO, whatever, whoever's in charge of the workflow, and they have a high degree of AI literacy, they can look at their own processes and say, okay, I actually understand now like 30% of this workflow can be automated through different tools, and they can optimize an existing workflow. The harder part is to come in with a very high level of AI literacy and competency and say this workflow doesn't even need to exist. This role doesn't even need to exist.

And that is like a higher level that goes back to the previous conversation about this idea of like a forward deployed engineer or someone who is extremely advanced in their understanding of what's possible. That may look at like entire workflows and campaigns and say, let's just go to first principles and reinvent this entire thing. So, yeah, I think that there's this combination. But if you're not able to look at your own workflows and tasks and understand where AI fits, that would be a pretty good indicator you're at the very beginning of your own transformation process. If you look at your workflows and other people's and just like immediately want to fix the thing and like, oh, my gosh, in five minutes, I could save you like 10 hours a month. you are likely like further along in your own transformation journey and you want to like figure out how to impart that knowledge on other people. Yep. Number eight, I operate multiple businesses across transportation, tourism and hospitality and too much still depends on me personally from customer inquiries and quotes to scheduling

invoicing and coordinating staff. Where should I start to make these businesses less dependent on me? How do I still decide what to automate, what employees should handle and what needs my judgment? Wow. I guess the first step is, do you want to hire people? Like, are you, you know, if there's that many things depending on you, it seems like part of this is like you just didn't have enough staff surrounding you. But if your goal is to continue to operate multiple businesses without having to add staff, those are, I guess, two different paths I would suggest taking. So if your goal is to maintain a lower level of staff, then I would start to look at the things that are keeping you busy, that are data-driven and repetitive. And I would look at ways to automate as much of that as you could. Going through some of the things we previously discussed where you're analyzing workflows and tasks and you're just saying, okay, here's how I can plug in these different AI capabilities.

Here's the agents I can build pretty quickly without needing expertise, like technical expertise. And I would start there. Yeah, in terms of like employees, it would actually come down to an assessment of your current staff and how AI mature they are and how much they could use. But, again, I would say it probably just starts with AI literacy, understanding the current capabilities of what these models can do, and then understanding what they're going to be capable of doing over the next like six to 12 months. So you sort of take a forecasting lens to this and say, what's the impact going to be on my team and my operations over the next 12 months as the models get smarter and more capable? And then from there, you can go through and do an assessment. So we have JobsGPT as a tool we use. It's a custom GPT at the moment. We're going to be converting it over to a plug-in, it looks like, as the short-term solution. But if you go to smarterx.ai forward slash JobsGPT for at least the next 30 days until OpenAI retires GPTs.

You can use it as a GPT, and you can just put a job title in, or you can have this exact conversation, plug this question in to Jobs GPT. The other thing I would say is, like, if you're not on ChatGPT, you're on Claw Gemini Copilot, you can literally take this exact question and just put it in there and say, function as, like, a high-level business consultant and help me think this through, and then just give it more context about each of the businesses. You can just say, like, here's my five businesses, give it links to it, help me think through how to, you know, infuse AI into aspects of this, and it'll function as a pretty good advisor without having to use JobsGPT necessarily. Yeah, and if you think about an operator who, you know, customer relationships and customer increase and things like that, like that's something really that people hold near and dear to their hearts. Like that seems like something. But then I also think on the flip side, customers have an increasing appetite for just getting a quick answer and knowing that it's going to be AI. So just finding that balance of I need to let this go, and this will help save me 10 hours a month, like you said,

and our customers get the answer they want, but I'm still maintaining that relationship with them. It's a fine line and something we're trying to figure out too. Yeah. Okay, number nine. If you're a marketing consultant offering AI-enabled services like content operations and repurposing, how much client education should be part of the engagement? What do clients need to understand before buying, and where do you draw the line between explaining your approach and providing training? First, you have to check your contract if you're allowed to use AI. So, I mean, one, as a consultant, you could be a freelancer, a consultant, an agency, whatever it is. If you're working with bigger enterprises, there are most likely clauses in there that say you're not allowed to use AI or that you have to disclose the use of AI if you do and you have to get their approval. So for starters, this conversation should happen before you create any work products for them. They need to be clear on transfer of copyright and things like that, that if you're using AI to specially create content, this conversation is a very important one to have with them.

that being said you then have to get into if you are a stopgap for them until they learn how to do this themselves or if this is a sustainable business model and i say that because like i mean we'll talk about this on episode 252 but like hubspot just announced a marketing studio yesterday marketing studio is like an on-demand content creator is how they're basically building this. I don't know how they're positioning yet. I haven't dug in deeply, but the way that the, the trailer video plays it out is in essence, like just go into the studio each morning and talk about what you want to create that day and their AI creates it. So what does that mean to people whose job is to create content for people? Like, I don't know. Like this is where, again, we're all sort of like figuring this out together, but the history of agencies and freelancers, has often been, yeah, we could do the work internally.

We just don't want to. And so the question is, you know, finding the right clients who, even though they have access to automate the creation of this, they value having an outside trusted partner who is the one doing this and using the tools and deciding when it should be human in the lead, when it should be human in the loop. Those are messy discussions. but there are absolutely going to be companies that know it's possible to automate much of what they used to outsource, and they're going to get rid of their agencies and consultants. Kathy, you and I have been in the room with marketing executives who have said this exact thing. They're basically wiping out their agency portfolio, and they're just going to bring everything in-house. So it just gets harder to find the right fit for those services, and then there's going to be just lots of pressure on the pricing model around that because if they know what it costs you to create it and if your agreement with them is you're mostly using AI,

it's hard to know how to price that efficiently. So difficult. We are in the middle of an evolution of how professional services firms work with clients across every industry, not just marketing. Same thing's happening in accounting and legal and everywhere, and it's a lot of unknowns ahead. So maybe providing training is the way to go. If you can make the money doing workshops and teaching them how to do it, and if you can grow that business, it's a much probably more stable business model moving forward. And you think about that content creation through HubSpot. You're not extracting data to put into an AI to ask you what you should be writing about. It actually has access to all of that right there, and it's writing it based on what it already knows, what's performing, what's getting open, And all those things are already baked into it. Totally. Yeah, it's crazy. Number 10, an audience member working in higher ed and nonprofits shared this concern. Alumni may accept commercial brands using AI for marketing, but expect something different from their alma mater.

How should institutions respond to that concern? What does responsible AI look like when personalized outreach to alumni and donors while protecting their trust? I mean, I think authenticity matters across any business, really. So, I mean, this is certainly a question related to higher education and nonprofits, and maybe there's like a higher expectation of authenticity there. I'd have to think about that. I'm not sure if that's true. You know, what I've said about branding, I used to spend a lot more time thinking about brand and brand strategy in my early days, running my agency and stuff. and I have no idea if this is something I original thought I had or something I read somewhere but I did a lot of crisis communications work also and the thing we would talk to clients about is trust takes a lifetime to build and a moment to lose and that is relevant to like literally every brand and really every person and so if there's an expectation of authenticity from a communication channel regardless of what industry you're in you have to think through how you'll use AI

Now, I mean, I'm a donor to, you know, to universities. If the communication is personalized to me based on past donations and things, I don't know that I would look at that and think, oh, they must have used AI to, like, know how to personalize that for me. Personalization done well is extremely valuable to anybody. Like, I want you to have context and not waste my time with the same offer you're sending to everybody else. Like, I actually would rather know that you've taken the steps to, like, know more about me personally and target a communication based on my interests and things like that. So, yeah, I don't know. I think it's just, like, understanding where authenticity really matters, understanding where disclosure of use of AI matters. But I don't know. I mean, I spend a lot of time with advancements officers at high schools and colleges. And I think that there's very elegant ways to use AI to capture information. Like, here's an example.

And, again, I'm just, like, spitballing here. I have a public persona. It's not hard to, like, learn things about me. I talk on my podcast for an hour and a half every week. Like, there is nothing stopping. Man, I'm going to regret this. I'm going to start getting targeted with all this shit. But there is nothing stopping an advancement officer from building an agent that just every morning or once a week goes and figures out any interviews I've done, any transcripts I've put out there from speaking, and curates all that information. And then every day or every week sends that advancements officer ideas of personal outreach to me and, like, drafted something for you. Hey, he said this on the podcast. This might be super relevant. He mentioned the university on the podcast this week. Send him a note saying thank you. Those are the kinds of things where it's like super authentic, but you have now used AI in this extremely elegant way. But you can do the exact same thing for any sales or customer success role This doesn have to be fundraising and stuff like that You can if you have targeted like an ABM approach at an individual brand level

you can now monitor stuff in really advanced ways and then have the personalized elements like handed to you. And then you just, you can take it the final steps. And I don't, I don't know that anybody would know the difference. I think they would actually appreciate that. So I think it can be done well. Who needs Google alerts? where you could just build an AI agent to track Paul. I mean, go back to 2016 or 17 when we first started trying to identify use cases through our institute for AI. You had to go find, like, specific companies that did this stuff. Like, there was one I remember very specifically that was, like, a competitive monitoring tool, and it would alert you, like, any changes to somebody's website that day, personnel announcements, funding news, product mail, whatever. I can spin that up in 10 seconds and get it automated now. Like that was like a $1,000 a month license to a tool 10 years ago. Right. Number 11, you talk about being a change agent when AI has barely penetrated a business.

How do you communicate your AI capability, experience, and approach on a resume when the hiring manager has a low baseline of AI knowledge? Talking about outcomes without AI undersells you, but talking about agentic workflows feels like talking about Excel pivot tables to an accounting department. So they'll use paper ledgers. How do you bridge that gap? Yeah, so, I mean, any job description is going to have, like, what the expected responsibilities are and likely, you know, what goals or KPIs you work towards. And you can take any, you know, take your favorite AI system, turn it up to the highest reasoning model and say, hey, I'm talking to this company. They don't do a ton with AI, but I want to really understand their industry and the challenges they face and, you know, what maybe trying to interpret what some of their goals might be. If they're publicly traded, you can analyze their earnings statements each three months and, like, what did the CEO and the CFO talk about at their latest earning calls. Here's the job I'm applying to.

I want to go into this interview with some prototypes and some sample analyses of, like, potential business problems and demonstrate how, you know, I'm able to use AI to solve things more efficiently. And so you just walk into the interview, stuff they haven't even asked for, and you find an opportunity to say, yeah, you know, actually, I anticipate this might be one of the challenges that are faced, and I took the initiative to use Claude to do an analysis. If it's okay, like, I'm happy to send you this. I went through and verified everything myself, da, da, da, da. And you're just like, they're going to be like, wait, what? So, yeah, I mean, you can always be proactive in presenting things of value, not just, oh, yeah, I use Cloud all the time. Show me. Like, what have you built? What's, like, something you did that was specific to our company? And that goes a long way with impressing people and opening up their minds to what's possible, not only with AI, but with you as a part of their team. Right. And it also just, if they're apprehensive, if they're scared, if they just haven't, if they're just misinformed or ill-informed or whatever the case may be, coming in with a use case like that, they're like, oh, okay.

Like, that's, it just makes it so tangible and just, like, easy to understand. Yeah, and you can do the exact same thing as a change agent within an existing company, not looking, you know, to move companies. But if you're just trying to, like, make the case internally, hey, we should be doing more with AI, do the same thing. Like approach it as like, let me just make a business case for this. Build a 10 slide deck. Say, can I get 15 minutes? I want to show you like a couple of ways I think we can drive innovation if we like do more. And I want to raise my hand and like be a part of that. Yeah, for sure. Number 12, for someone making a mid-career pivot into operations or project management from a nontraditional background, what practical AI capabilities should they prioritize over the next 6 to 12 months? What's one portfolio project they could build to demonstrate measurable workflow improvement to prospective employers? This is an interesting one. So I'm just like, I'm trying to think about how project management is evolving for us. So we use Asana as our project management tool, and they have an AI capability baked right into it that you can use within Asana.

We have not, I don't think we have Asana connected to ChatGPT yet. Like, I don't know that we've gone to that extent. But, you know, I think that the agentic stuff we talked about earlier, maybe more than anything will augment what's possible with project management, you know, certainly automate elements of it. There's a lot of project management that is repetitive tasks, repetitive steps. But, you know, imagine if you, I don't know, I always have to think in like tangible terms. So imagine we're doing a product launch and within that product launch, there's elements of it for marketing, sales, customer success, operations, finance, like everybody plays a role at different stages of that. And you're trying to manage that entire process. You could go through and set up a lot of like automations, you know, of reminders and like the basic things. But you could also use AI to analyze where we're at, where are we falling short, who's specifically falling behind in what they're doing. Recommend next best steps so that it's

surfacing to you. So imagine every morning you wake up to like a control center that is suggesting to you as the project manager who to talk to, what steps to take, drafting emails for you. If there's just a barrier like, hey, I know someone's out for the week and they're not going to get this done, can you create the work product with AI? And just like, hey, I know you're really busy, Kathy. I took the initiative to just create this for you, do what you want with it. Because that goes to this whole idea of work compressing where all of a sudden the project manager who knows how to use AI can assist any of the roles. It's like, I can kind of do marketing now to a degree. So, yeah, I think it's like stepping back and just saying, how is the overall role of a project manager going to evolve when they are given AI capabilities and they know what those systems are fully capable of? And the whole idea of workflow assessment, like looking at workflows people are doing and saying, hey, I know you built out these 20 steps to the part of this

overall launch. I've taken the initiative to analyze. I think we could use AI to do like 10 of these and I might be able to save you like five hours. Great. I'll take that meeting. So yeah, I think it's like stepping back and saying, what is the future role? And actually, JobsGPT, which I mentioned earlier, there is a conversation started that says forecast new jobs is one of the things. If you click on that and say project management for this type of company, what is that role going to look like in the next one to two years? It'll actually help you brainstorm how a project management role would evolve. Nice. Okay, number 13, Paul, you've spoken about why you don't use AI for your writing. A human brings accumulated personal history, values, and blind spots to their voice. Current AI models recombine statistical patterns. Do you think AI will ever reach the point where it gains true life experience and writes as creatively as a talented human? I think that we've already kind of approached where with the right model, with the right

knowledge base, the right memory, the right access to your previous writings and what you do and your emails. Like if you've given it access to everything, it's pretty reasonable to say today's current technology is can be indistinguishable from the human writing. And I think it's more about your personal knowledge that it wasn't you and that like the writing is part of your process and it's why, you know, it's how you think. And so I actually have a reasonable degree of confidence that if I wanted to use AI to write my stuff, like my editorials and things like that, as long as it wasn't drawing on a lot of personal anecdotes and things like that, I could probably do like 95% of that writing with AI today and people probably wouldn't know the difference. So yeah, I think it's just for me, it remains a personal choice of confidence in the material of

being able to do stuff like this unscripted without ever seeing the questions before we even talk about it. Like if I was letting AI write everything and it was creating my talking points for the podcast and it was doing my editorials and it was creating my presentations and like it was doing all of the thinking, I would not be sitting here doing 45 minute unscripted answers to 14 questions I've never seen. And I think that the lesson there is in any stage of life, if you want to have confidence in a topic and develop some level of expertise and be able to be viewed as someone who understands something deeply, there is no replacement for doing the work and for me the work is writing so it doesn't matter if it can sound like me it probably can right i just don't want to yeah and like we've also talked about taking notes in meetings like we have notes you know notes apps taking notes for us in the meeting giving us a synthesis i still take notes you still take notes because that's how i process things and for me to be able to remember that and same thing with with writing yeah and even i mean even

before note takers like if you think about it if like let's say you attended a meeting kathy that i wasn't there me reading your notes is helpful but like i don't get all the context i don't like hear the tone of people's voices and the inflection and the pauses before they answer and like you don't get all that from a transcript and so yes like for me there's the part of just showing up and then the process of inputting the information. For me, I have to type it. Like, I'm not a big, like, voice-to-text guy. I find, like, the retention has to come from actually, like, fingers on keys. I don't know. It might be weird that way. But, yeah, I think that there is just an argument that for critical thinking, for retention of information, doing the work is always going to be required. Absolutely. I hope. Same. All right. Number 14, rapid-fire hot take. Do you see top performers negotiating token usage and compute stipends into their job offers?

Mostly only in AI research right now and probably software development, I could see. It would be an outlier if you had someone in marketing, sales, customer service, operations, that was negotiating token usage. That would take a pretty advanced user of AI to understand that. But I can also see within AI forward organizations like ours, we're running up against it all the time already. Like I get alerts every like three days and chat to me, like someone's requesting more credits or usage. We run into credit limits all the time at HubSpot. So it's coming. And I think even for 2027, most organizations should be thinking about what are their token budgets. And I believe we actually have a topic for episode 252 about how different companies are approaching token usage and the limitations they're putting on it. But no, it's an outlier if you have a non-technical person inquiring about compute and token usage. And that was one of our class attendees who asked that question.

So they might be thinking about it, too. Yeah. I mean, if you ask that question in most companies, you're probably going to get a blank stare. but I do think that if you're a CFO or a CIO or a head of HR, it would be good to be proactively starting to think about this kind of stuff because it is going to come up. For sure. All right, Paul, thank you as always, and thanks, everyone, for listening. We've got a busy few weeks here at SmartRex, so if you're around, visit SmartRex.ai. You can find some webinars on our homepage. You can go to SmartRex.ai backslash webinars and take advantage of that Pod 250 offer for Academy, and we will see you. You'll see Paul and Mike next week, next Tuesday. All right. Thanks, Kathy, and thanks, everyone, for joining us. Thanks for listening to AI Answers. To keep learning, visit smarterx.ai, where you'll find on-demand courses, upcoming classes, and practical resources to guide your AI journey. And if you've got a question for a future episode, we'd love to hear it.

That's it for now. continue exploring, and keep asking great questions about AI.