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- [00:01]
Michael Stelzner
Looking to transform your marketing with AI? Social Media Marketing World 2025 has all the AI training you'll need. Join me and top AI experts in San Diego this March. Get your tickets now@socialmediamarketingworld.info and save big. Welcome to the AI Explored podcast, helping.
- [00:27]
Lauren Schiavone
You put AI to work.
- [00:29]
Michael Stelzner
And now, here's your Michael Stelzner. Hello, hello, hello. Thank you so much for joining me for the AI Explored podcast brought to you by Social Media Examiner. I'm your host, Michael Stelzer, and this is the podcast for marketers, creators and business owners who want to know how to use AI. Today, I'm very excited to be joined by Lauren Schiavone. And we're going to explore how to move from simply running AI experiments inside your company to to coming up with a system, a process for integrating AI across the entire company. If you work for a company that has more than a handful of employees, I think you're going to find today's interview absolutely fascinating because Lauren comes from a background working for a really, really big company and she is going to break down how to get AI from kind of the early stages all the way through to scaling across the entire company. And I think you're going to find it absolutely fascinating. If you're new to this podcast, follow this show in whatever podcast app you're listening to because we've got some great content coming your way. Let's transition over to this week's interview with Lauren Schiavone, helping you simplify your AI journey.
- [01:47]
Lauren Schiavone
Here is this week's expert guide.
- [01:51]
Michael Stelzner
Today, I'm very excited to be joined by Lauren Schiavone. If you don't know who Lauren is, she's the founder of Wonder Consulting, a business designed to help non technical leaders thrive in an AI driven future. She previously led product marketing and e commerce initiatives for Procter and Gamble. Lauren, welcome to the show. How you doing today?
- [02:11]
Lauren Schiavone
I'm great. How are you?
- [02:13]
Michael Stelzner
I am very good and I'm excited to have you here. Today, Lauren and I are going to explore how to move from simply experimenting with AI, which happens so much inside of businesses today, to actually integrating AI it into your processes across the entire business. Now, before we get into this, I would love to hear your story, like, how in the world did you get into AI? Start wherever you want to start.
- [02:36]
Lauren Schiavone
Awesome. Yeah. And Mike, thank you so much for having me. I'm excited to be here. So, until this past year and a half, I actually spent my entire professional career at Procter and Gamble. I did that working on some of the largest, biggest brands and some of the largest retailers. And I had the opportunity to spend an incredible 16 years there. I started at P and G and Consumer Insights, then I moved into brand management. And finally my interest in E commerce is what took me to sales and my final chapter there at P and G, I spent a lot of time working on product innovation, retail innovation and driving transformation and change across the enterprise. And in doing so, that's really where I developed my passion for leveraging innovation to drive business growth. And last year I made a very personal decision to leave P and G. I have two young children and frankly I just wanted to do something more flexible so I could spend a little bit more time with them. And in this time away from P and G, I finally had a moment to lift my head up from the corporate world. And when I did, it became very, very clear to me AI isn't just poised to revolutionize marketing and sales. It's going to disrupt everything right though every industry, the way we live our lives and certainly the way we work. So I was super inspired and energized by the possibilities of AI. So I really decided to dive in and immerse myself in understanding AI and how it can be a growth driver. And as I upscaled it was interesting because I reflected on my time at P and G and I just had this real aha moment. You know, if I had these tools back then I could have streamlined low value tasks, spent more energy on strategic work that really excited me and ultimately have more time for the work and people that inspire and energize me. So after understanding the possibilities, I decided I really wanted to empower other non technical leaders to harness this power of AI in practical, actionable ways. So this past summer I decided to combine my 16 years of experience, experience of leading business growth and innovation at P and G, together with my industry leading AI education, which of course is ongoing every day to form Wonder Consulting. And with Wonder Consulting, my goal is to empower leaders to thrive in the AI driven future and leverage the power of AI to accelerate business growth. And I'm really committed to demystifying AI to make it practical and actionable for non technical leaders to harness it for growth.
- [04:53]
Michael Stelzner
Awesome. So taking me back to P and G, some of the brands that you worked with, would people know, can you name some of the products and then also share what it meant to be like responsible for innovation inside of a company like that?
- [05:05]
Lauren Schiavone
Yeah. So I spent a lot of time in my early career on Olay and Venus, which is the sister brand to Gillette. And I spent a lot of time on Swiffer and Mr. Clean. That's where I did a lot of my time on innovation. And innovation really is the lifeblood of P and G and what really drives and grows a brand over time. And it's a really important role because you are really trying to deeply understand the consumers, the unmet needs and pair that with an unmet need, sort of in the category to accelerate not just brand growth, but also category growth.
- [05:37]
Michael Stelzner
So does that mean that you're essentially coming up with ideas for new products? I mean, is that kind of like an R&D department, for lack of better words?
- [05:45]
Lauren Schiavone
Yeah, so certainly there's an R and D component, but there's a very commercial component. Because R and D is sort of responsible for the technology component of new product, but the commercial leaders, brand insights, sales, etc. Sort of responsible for commercializing that idea to make sure it's an idea sort of that's right for the consumers, that has the right communication, the right selling story, the right copy and media plan, all of those things.
- [06:10]
Michael Stelzner
And was AI at all part of your experience there? I mean, just because I, I know that sometimes big businesses have access to a lot of technology that the smaller businesses don't have yet, you know.
- [06:21]
Lauren Schiavone
Yeah, when I left P and G of August of 23, and at that time not really, you know, still, arguably those are still kind of some of the early days of AI, particularly gen AI. So no, not, not at that time or not that I was aware of. But now obviously I know things have sort of progressed and certainly they are using it across some use cases.
- [06:44]
Michael Stelzner
So today what you're doing is you're taking your background and experience with process, for lack of better words. Right. And innovation and you're helping businesses essentially like expand a little bit more on kind of the practical tactical of what you're doing for your clients today.
- [06:59]
Lauren Schiavone
A key part of what I do is really helping organizations understand how to drive AI transformation. So I sort of leverage this five step approach of assess, assessor, current state, establish, establish your goals, your AI councils, etc. And experiment. You get to experiment with all your different pilots in a really thoughtful way. Then scale, so scale what's working. Then finally adopt. And adopt is all about how do you future proof both your organization and your business. And so that's a sort of the core of the work that I do. And in addition to that, I spend a lot of time upskilling leaders in the areas that I spent A lot of time in my career. So, for example, AI for marketing or AI for Consumer insights or AI for sales.
- [07:42]
Michael Stelzner
Very cool. Okay, so all that background and folks, there's a lot of wisdom that's about to come our way here in just a few seconds. But the question that I have for you now is why should anyone listening today, the businesses that are listening today, why should they integrate AI into their existing systems and processes? Because I'm sure a lot of them are not even seeing an application there.
- [08:05]
Lauren Schiavone
Yeah, it's a great question. I think oftentimes AI has a reputation of being a productivity tool and certainly it can be that. But you are, if you are truly leveraging and harnessing the power of AI, AI can be a growth accelerator. And we all want to grow and accelerate our business. So if you reframe it like that, if you want to grow and accelerate your business, AI is a great way to do that. And the companies that do that, the best way, I truly believe will leapfrog the ones that do not.
- [08:34]
Michael Stelzner
And how does that connect to systems and processes when it comes to AI? Just connect those dots for me.
- [08:38]
Lauren Schiavone
Yeah, definitely. Because to get to AI really being a growth accelerator, really to get to that AI transformation, you've got to move from what I call random acts of AI into true integration into business processes. Because that is when and where the transformation occurs.
- [08:55]
Michael Stelzner
And what does it make possible?
- [08:56]
Lauren Schiavone
It makes possible truly one people using AI day to day and leveraging AI to really accelerate business critical work versus sort of maybe just nice to have work. And with that it allows you to begin to think about how you can have AI be a growth accelerator. So maybe for you that's getting to innovation faster, maybe for you that's getting to bigger and better creative ideas because you're not spending time on tedious tasks. So it's going to look different for everyone. But essentially, if you can integrate AI into your business processes, that's sort of the foundation in which you can start to accelerate AI.
- [09:35]
Michael Stelzner
And I fundamentally 100% am on board with this. I have recently, within the last about month or so, trained all of my employees on how to leverage AI, set a new company wide expectation that they use AI to improve their work consistently. Even have expected my leadership team to report to me on how they're using AI, what they're learning from it, and also all the people that report to them, reporting up to them, you know, because I believe that AI allows average workers to become excellent workers, and I believe AI allows excellent workers to become super workers. Right And I believe that when all that happens and I can kind of see the path, right, I kind of see all the possibilities now with the team you have, you can accomplish so much more, right? Where in the past you had to go outside and hire more people, now you can actually take the people that you have and you can radically do a lot more. I mean, have you found this to be true as well?
- [10:31]
Lauren Schiavone
Definitely. Like, that's the amazing part about it in some ways is all of a sudden, you know, you don't need to necessarily hire a multifunctional team. You can leverage AI to do a lot of that. And I think it's incredible that you've sort of started that culture within your organization because that is one of the critical, important steps for making this work.
- [10:51]
Michael Stelzner
Excellent. Okay, so let's start with the basics. Like, what is it that we need to be thinking about when it comes to employing AI into our company wide systems and processes? Where do we start?
- [11:06]
Lauren Schiavone
So like you said, you know, many companies have taken their first steps on their AI journey. So maybe that's doing some upskilling or giving employees access to ChatGPT. And certainly they've started to experiment. And look, that's really great progress, right? You've got to get started somewhere. And the companies who have started are far ahead of the ones who haven't. And many companies have started to do a lot of experimentation. And again, experimentation is great. It's a great way to learn, become comfortable with AI. But here's the problem, okay? Oftentimes I see companies stuck in what I call experimentation land. They are doing a lot of experiments, but they are struggling to scale AI and drive real adoption, which significantly limits the potential of AI to drive transformation. So how do we solve this? Right, for AI transformation to truly occur, occur, as we said, we need to move from random backside AI to true integration into business processes. And we know that when AI is fully integrated into workflows, it shifts from being sort of another tool or an interesting experiment into a core enabler of business growth. And so, you know, why is this so important? Well, I learned this firsthand at P and G, where I spent a lot of time driving organizational change. And the number one lesson I learned is if you want people to do something, you've got to integrate it into the core business processes. So you can think of it this way. It's not about convincing your team to try AI, it's about redesigning workflows. So AI just naturally enhances what they already do. Because when AI is built into standard Processes, that's when you will see real adoption and transformation. And the organizations that are successfully scaling AI understand this. They're redesigning their processes with AI at the core and some from the ground up. The goal, right, isn't to have people using AI, it's to make AI simply part of the way your organization works.
- [12:54]
Michael Stelzner
Yeah. When we were prepping for this, one of the things you talked about was to identify a lot of the repeatable processes. Can you talk to us a little bit about that?
- [13:03]
Lauren Schiavone
Yeah, definitely. And, but before we get to that, I'm going to just take us sort of one step back because I think one other thing has to be in place before sort of you identify the right use cases. So first we need to check the health of your AI Council. Without a healthy, well functioning AI council is going to be very difficult to scale AI.
- [13:21]
Michael Stelzner
Most people don't even have that. So why don't you just describe that real quick?
- [13:24]
Lauren Schiavone
Oh, definitely. So an AI Council is an organization of folks who are essentially responsible for driving the AI transformation across their company. The core set of people that try to set up what are the goals of AI integration, maybe what the pilots are, what the roadmap looks like, what the policies are so that that core group of people. And it's interesting you said that Mike. I think in my experience I found sort of like people are establishing AI consoles, sort of like checking the box. Yeah, right. And it's become a bit of a check the box exercise unfortunately, which means the work of the AI Council is really becoming quite stalled at many organizations are really struggling to gain momentum. So if this is sort of sounds like you or it's happening to you, or if you don't even have an AI counsel just, just yet, I actually created a, what I'm calling an AI health checklist to diagnose and address these challenges, to make sure you've addressed these pitfalls. Because like I said, the AI Council really is the foundation here. On an AI Council you've got to get the right people in the room. If your AI Council is filled with only senior leaders, you can expect lots of strategic dialogue but not much action. You've got to balance the team with action oriented contributors. Maybe that's mid level managers or practitioners who can actually own experiments, drive progress and really get shit done. Then of course have one to two senior leaders that can serve as sponsors, executive sponsors that can, can provide guidance, clear breakdown barriers and ensure of course there's alignment to business goals. The next one, next couple might seem obvious, but they're just not happening. You've got to make it a priority. If you've got folks on these councils and it's treated like an extracurricular activity, you can expect minimal progress. It needs to be a key part of each member's work plan, needs to tie to their performance goals. It needs to be supported by their managers. Leaders who are going to prioritize this work are going to see a lot more success. You also have to provide adequate resources to this group. Right. Too often AI councils are expected to deliver results without having the necessary tools, budgets or support. And finally, and this one does sound super obvious and simple, but it's not always happening. There needs to be a regular cadence of meetings. So, you know, you'll see meetings sort of pushed off because someone's traveling or they're scheduling conflicts or other distractions. And before you know it, six months has passed with, you know, no forward movement. We know that's not okay because at the pace of AI, six months is simply too long.
- [15:55]
Michael Stelzner
That's like two years and.
- [15:57]
Lauren Schiavone
Exactly. You've got to be connecting and making sure you know that the work is, is moving forward. So you know, if you haven't started one yet or if you're feeling like, hey, my counsel council sort of stalled out. There's two pieces of advice I like to give and Mike would welcome sort of your advice as well. First, narrow the scope so enterprise wide councils, right, they can get bogged down in politics, red tape, et cetera. So the advice I like to give is, and I give this to every marketing leader, is simply create your own AI council for either your team or your function specific. You can focus on what you can control, you can run your own experiments. You don't have to wait for the entire company to align or catch up. Just take the lead, run, create momentum and you know, let the others follow and show them lead by showing them what's possible. And then the second is consider getting some outside help. So certainly this might sound like self promotion, but I just have seen when an external voice can kind of come in. As an unbiased person who's not afraid to speak transparently to a group of leaders, it can just really drive a sense of urgency, hold folks accountable and again make an AI complex that sort of stuck, move them into making a lot of progress.
- [17:08]
Michael Stelzner
Yeah. And for the smaller businesses that are listening, in our case it was just me and two other employees in the company and I believe everyone was on my leadership team of eight. But some of the leaders on there were like in the Trench kind of practical tactical product division heads. And we moved pretty quick because I as the CEO was very motivated. Right. And that's where what Lauren's talking about, if you can get someone at the top that's very motivated, then you can move mountains, because we did move mountains. And sometimes that can be a real headache if you're dealing with upward resistance.
- [17:40]
Lauren Schiavone
Yeah.
- [17:40]
Michael Stelzner
So assuming that the people that are listening right now have the ear of whoever's important inside the business, or are that person like me or you, where they head up their own business, what's next?
- [17:51]
Lauren Schiavone
Yep. So then we want to get to what are the right use cases or what are the right work processes that we want to prioritize. The first thing that I always like to do is just sort of spend some time brainstorming what are your key work processes? Right. What are the things that you do to deliver your business or deliver the things that you, your clients need, your customers or your consumer needs. And after you've done that, you're going to want to ask two important questions. First, is the work process repeatable and completed regularly? So you want to focus tasks that occur frequently versus sort of one off tasks. Those are not ideal starting points. And then the second question you want to ask yourself is, is the work process business critical? So the process should directly impact the organization's ability to deliver results and drive growth. AI needs to be seen as an enabler of your top priorities and not a distraction from them. So experimenting in areas that aren't business critical often lead to stalled efforts because they don't resonate with your leadership or drive meaningful outcomes. Once you've gone through those two questions, then I and you sort of have a more narrow list of use cases or work processes. Then I like to prioritize them on what I call a prioritization matrix. And it has two axes. So on the Y axis think about how much dislike doing the task. And these can be things that are often repetitive or time consuming, that drain your energy and maybe team morale. And then on the X axis, think about what is the potential impact. So evaluate the tasks potential to save time, reduce costs or drive business outcomes. If AI could help you do that.
- [19:26]
Michael Stelzner
Task, the XY axis. Right. Dislike doing the task, but. And the impact. Right. So can you give an example, even if it's generic, of something you've done for your business or done for another business, just so people can in their mind understand what kind of task might be a good candidate for AI?
- [19:46]
Lauren Schiavone
Yeah, I think a great one is writing business proposals. So when you know a client comes to you and sort of wants to partner with you and sort of gives you a bit of a brief turning that into proposal back to the client for them to agree to. So that's a process that hopefully is happening pretty often. It's a process that is time consuming and you know, really nobody likes to do. And if you could improve it with AI, not only would you save time, but hopefully you could get more yeses in that process. So proposal writing is certainly one of them. I can also share a couple other. I think another great one is creating brand content. Now you might sort of vary based on which type of brand content it is or how much you dislike it, but brand content is something that is, you know, being drafted all the time and oftentimes it takes a lot of time and energy to make sure that content is really in the brand's tone of voice. So one of the things that I like to do is a brand tone of voice analysis and take that analysis, put it into custom GPT and then that custom GPT can help me very quickly get to content that's written in the brand voice. And then I think I shared this with you Mike, previously, but one I like to use in my own business is helps me with contract reviewing. So when I left png, I had to sort of learn all these new skills I didn't have before. And one of those skills was reviewing contracts. And so I leveraged ChatGPT. Now I've created a custom GBT to help me review my contracts, to help me understand what are areas where I should be concerned, helps explain to me and you know, high school language why I should be concerned and then it offers up language that I should suggest it be instead. And this of course ChatGPT is not a lawyer. You want to consult your lawyer, but it does save me a lot of time and energy when I do enroll my lawyer.
- [21:34]
Michael Stelzner
You probably already know this, but marketing is changing faster than ever and AI it's leading the charge. At Social Media Marketing World 2025, we're bringing together the brightest minds in AI marketing to help you stay ahead. We're talking real strategies, proven workflows and hands on training you won't find anywhere else. Our AI track is designed specifically for marketers just like you ready to transform your marketing. Join us in San Diego this March. Head to social mediamarketingworld.info and secure your spot today. Okay, so here's what we've talked about so far. First of all, we've talked about how, hey if you don't have an AI council or committee, put one together and really a cross functional, ideally kind of role where it's a bunch of people kind of running point on a project like this and then ultimately begin identifying mission critical parts of the business that are repeatable. For example, if you run an agency or consultancy and you're regularly writing proposals, that's a great example of it, right? And it's maybe something you're not really loving, but you know that it's going to have a big impact, right? So once we've identified these repeatable mission important projects, slash processes, what's the next thing we need, we need to do?
- [22:58]
Lauren Schiavone
So two additional filters you're going to want to sort of take to help you prioritize is first understand where can AI actually help. So it's possible in some of these use cases, maybe a tool like ChatGPT isn't going to necessarily be able to help you a lot. And if you're not sure if AI can help you or not, it's a great exercise to sort of put that process into ChatGPT and simply just ask it, right? How can you ChatGPT help improve this, this process? And then the last thing you want to think about is consider the human element. You want people to lead these experiments who are passionate about driving change. So you might have two processes that fall in the top right quadrant and one falls in a department that's resistant to change and another is in a team that's eager to innovate. So I would start with the team that's eager to innovate so they, they can build momentum across the organization.
- [23:49]
Michael Stelzner
And really I think what we're doing here is we're looking for a case study, almost right, that we're going to ultimately use across the entire company, right? So once we've identified that team, person, people and process, what comes next.
- [24:03]
Lauren Schiavone
So the next step is to move from ideation to experimentation. And this means running focus experiments that prove AI's potential impact and to your point, can deliver those quick wins to build momentum. And it's really important to have the right mindset when it comes to experimentation because these aren't casual experiments, right? They're strategic pilots to make sure that we are delivering value. And I see that many organizations are not always approaching experimentation in the right way. Of course. Would love your thoughts there, Mike, but here's a couple of tips for designing good pilots. So first, set clear success criteria for your pilot. Maybe that's time saved or accuracy improvements or increased closure rates. Having these measurable outcomes ensures that you're objectively evaluating the experiment success. You want that criteria to be specific and measurable, and you really want to make sure that you're understanding the baseline for the work process that you're trying to measure. So ultimately, you can understand roi, because that's going to be a big important part. As you work to do broader sell in, which we'll talk about next, you'll want to assign a dedicated owner for the pilot, right? Someone who's passionate about the project and has the ability to drive it forward. And you're going to want to make sure that you are giving this person what they need, whether that's a team, whether that's tools, whether that's training, funding, and of course, the support that they need to prioritize it within the broader work they might be doing. You're also going to want an executive leader, someone in the C suite, perhaps, who can serve as that pilot sponsor. Their job, right, is to advocate for the pilot, remove any roadblocks, and really make sure that they're helping to communicate the results to the shareholders or stakeholders. And ideally, this person can make the go, no go decision on expanding the pilot. And then I like to make sure that a pilot is up and running within 30 days. And that's because, you know, too many organizations get stuck in planning mode trying to just create the perfect pilot. And it's just important to remember progress over perfection, right? Move fast and learn. You can pivot as you learn more and more. And to that point, you want to monitor pilot pilots closely. Do not let them run on autopilot. You're going to want to gather feedback, what's working, what's not working, what are the benefits, challenges. And you're going to want to use those insights to iterate your approach. Don't wait to the end of the experiment, you know, to start thinking about those things and doing those things and let your pilot run for 90 days. That should be long enough to demonstrate results. But throughout those 90 days, make sure you're optimizing as you go.
- [26:33]
Michael Stelzner
Okay? I love how operationally minded you are. I know that I've got a lot of creatives and marketers that listen to this podcast, so they're potentially going to struggle with how to come up with the key performance indicators, you know, and how to actually track it, right? Because it seems very ethereal when you're using AI to create something, right? So talk to me a little bit about, give some wisdom on how to come up with what clear success looks like because it can be so nebulous and abstract, but it needs to be clear if it's going to be communicated to someone higher up inside the organization. Right?
- [27:09]
Lauren Schiavone
Yeah. I think the best place to start, like even if you're creative or non operational person, like you still at the end of the day have, you know, expected deliverables in your role, whether that's creative quality or you know, delivering things on time. So first and foremost, just think about what are the measures that day to day you hold yourself responsible for? Because in theory those are the, you know, critical business measures. And could AI help with one of those? Could it help you, you know, get the work done faster? Could it help you close more sales? Right. Whatever the measures that day to day, you're accountable for. I also, like, not everyone is good at this. Like find someone, you know, like with a different skill set. Maybe it's a finance leader or more of a data person. Right. That can help dialogue more with you.
- [27:55]
Michael Stelzner
Can't AI also help you figure this stuff out too?
- [27:57]
Lauren Schiavone
That's. Now is the third thing I was going to say is like, and if that fails, you know, or like in Parallel, go to ChatGPT, say, you know, I'm working on this experiment, act as my advisor. Right. Or coach and help me identify like clear, measurable outcomes of whether or not, you know, we can define that this experiment is successful. So yes, absolutely.
- [28:20]
Michael Stelzner
Okay, so let's go back to the examples that you talked about and let's identify what, just so people can wrap their head around it, what the measurable outcome would be. Let's start with the proposal writing like what would be the thing you would measure?
- [28:32]
Lauren Schiavone
I think you're going to see a bit of a theme. So let me just sort of say that I think as we begin to integrate AI, I think the first easiest measures are going to be like productivity efficiency measures.
- [28:42]
Michael Stelzner
So how much time did it take you to do it?
- [28:44]
Lauren Schiavone
Yeah, exactly. Time saving or like quantity. Right. Like, so maybe you're just cranking out content all the time, but I think over time you're going to want to see things like for example, in the proposal one, you're going to see that your proposals are better and you're getting to more yeses is right. So for example, in the proposal one I would start with are you getting to like a high quality proposal faster? Right. But then you also want to look at like, are you getting to an even higher quality proposal faster? That leads to more yeses.
- [29:12]
Michael Stelzner
Yeah. So is your close rate higher, for example?
- [29:14]
Lauren Schiavone
Right, exactly. Yes, exactly.
- [29:16]
Michael Stelzner
Yeah.
- [29:16]
Lauren Schiavone
And I think like, same on a brand tone of voice, right? Like you're maybe you're going to want to start with like, can we produce faster content or can create, can we create more content? Like always keeping like a high standard to that content and over time, like, is this content closing better? Is this content creating more leads? Right? Like the things that are important to your, to your business.
- [29:36]
Michael Stelzner
I really love this because you're right that most people, when they think about AI, they think about productivity improvement, right? They think about either they're getting something done more quickly or they think that they're able to do something they've never been able to do before. So that's like, like maybe enhancing their skills. Right? And then if you layer in there's creative side of this too, right? Like, is your output more creative? And maybe there is a way to quantify that. Like if you compare the new proposals to the old proposals, what would you or a peer say about the quality of the output? Right. Maybe there's a way to come up with a scale to measure these kind of things subjectively and just kind of see whether or not people feel like this is better. And even subjectively, are you getting more high fives from your boss? Are you getting more accolades from them? I mean, these are the kind of things that if you don't kind of think about them in advance, Right?
- [30:31]
Lauren Schiavone
Yeah.
- [30:31]
Michael Stelzner
Then when they come, you won't even recognize that that's actually an enhancement, Right? That's coming from what you're doing.
- [30:36]
Lauren Schiavone
Yeah. You made me think of something on the proposal, right? Like, one thing you could do is you could go into ChatGPT and you could say like, hey, like, create a scorecard on how well this proposal delivers on this client's need. Here are the call notes, right? From our call.
- [30:51]
Michael Stelzner
Yeah, there you go.
- [30:52]
Lauren Schiavone
And you know, it could come up with like some sort of like, scorecard and you could like put in the old proposal. Right. Or different proposal. And then you could put in like, you know, the AI enhanced one.
- [31:01]
Michael Stelzner
Yeah.
- [31:01]
Lauren Schiavone
Driven one. Right. To sort of see. Because like, one of the things I like to do with ChatGPT is to like be a grader, right? Is to evaluate just like a reminder that like, it can create any sort of scorecard or evaluation tool and it could help you, right. Objectively understand if something is better.
- [31:19]
Michael Stelzner
When you're running a pilot, which is kind of what we're talking about, or an experiment, at what point do you declare that it is no longer an experiment?
- [31:27]
Lauren Schiavone
So that's A good question. I think ideally after 90 days, right. Like things go really well and we could talk about if they don't go well.
- [31:36]
Michael Stelzner
You should definitely talk about that too. Yeah, we'll talk about that.
- [31:39]
Lauren Schiavone
Things go well, you've met your very clear success criteria and then it's about is scaling in and yeah, happy to talk through sort of the, the right way and approach to, to do that.
- [31:49]
Michael Stelzner
Well and we're going to definitely get into the scaling side of things. But let's just say hypothetically, you crushed your objectives and it's 30 days in. Do you ever advocate just calling it a win or instead of waiting for the full 90 days?
- [32:03]
Lauren Schiavone
Yeah, I think that's a great point. Like it is so obviously meeting your success criteria and making things things better, faster. Right. Then in some ways there's possibly no reason to, to wait. Just I think my advice would be, is like torture test it a little. Think about sort of maybe the outlier use cases so you're not, not thinking about something that like could happen that just hasn't come up in the 30 days.
- [32:29]
Michael Stelzner
Yeah. Okay. So there's always going to be situations where the experiment does not fail, because an experiment by its very nature, that's an expected outcome with a lot of experiments. So what do we do if it doesn't work?
- [32:40]
Lauren Schiavone
Yeah. So experiments can and honestly should. If you're experimenting, you know as much as you should that they are going to fail. The best thing to do is to not get discouraged. You've got to view this as a learning opportunity. You're going to want to spend some time assessing the root cause. So things like, was the process chosen not as well suited for AI as you initially thought? Were the tools or data inadequate to deliver the results? Did the team have the necessary resources or training or support? And maybe there are unexpected barriers that arose. You know, what you see is that often it's not the AI capability itself that's the issue, it's how you're approaching it or applying it. So you've got to identify the root cause and assess if it can be fixed and whether or not it's worthwhile to run the experiment again. And if you still believe that there's a significant amount of value in the experiment, don't be afraid to run it again. And then just other piece of advice I'll give you is be incredibly transparent. Transparent and sharing these learnings back with your leaders. Transparency with leadership, particularly during transformation efforts, is going to accelerate progress. And to your earlier point, Mike C Suite leadership wants to help they know that AI is the future. They know they need to get on board. So it might also be something that, if you can speak transparency to leadership, they can really help you solve very quickly.
- [33:55]
Michael Stelzner
Yeah. And I'll add my thoughts on this. Like, we use the experiment methodology throughout almost everything we do inside the company. Everything starts as an experiment. If it fails, it is not a failure on the individual necessarily. There's always an opportunity to learn from it. Right. And if you think about all the greatest technological innovations, they often started as something completely different, you know, so a lot of what happens with experimenting is you discover something along the way and that leads to a new experiment and then ultimately it leads to the output.
- [34:24]
Lauren Schiavone
Right, exactly.
- [34:25]
Michael Stelzner
That's mission critical, that people understand going into that this is an experiment, it's going to go for this many days, this is what success looks like. And then if it doesn't happen, well, then we can decide to abandon, modify or try again. Right?
- [34:40]
Lauren Schiavone
Yeah.
- [34:40]
Michael Stelzner
So, okay, once we've actually got a couple of successful experiments under our belt with various departments or individuals, what comes next?
- [34:48]
Lauren Schiavone
So, three steps to think about. Step one is understand what I call what needs to be true to draw to deliver scale. So you want to start by identifying what's required to scale the pilot effectively. You want to ask yourself who needs needs to be aligned? Which stakeholders or departments do you need support from? What resources are required? Do you need budget? Do you need tools? Who needs training? Right. Which people, which organizations, functions, levels? Are there policy updates needed? You know, hopefully your AI council has at least drafted a bit of an AI policy. You'll just want to take a look at that and see if you need to make any changes or edits. And having these, this clarity will really help you. So then second, you're going to want to build the case and secure leadership buy in. So to scale something, it can require alignment and support from broader leadership. And to get leaders on board, you really want to focus on demonstrating the impact of the pilot and the potential value that at scale. So that's where it's really important to do things like show the ROI of the tool, what do we think is possible. And so that's why the baseline metric that we talked about in establishing your experiment is, is going to be very important. You're going to want to be able to articulate what could be the improvements in time, cost savings, or, you know, know, better outcomes. My next piece of advice is to leverage existing tools as much as possible. So to move quickly, consider maximizing the capabilities of your existing tools. That are already improved. So for instance, if your organization already has a ChatGPT or enterprise license, focus on how to get the most out of that investment. This really makes it much easier. Yes. From leadership, while also simplifying training and upskilling. Today, what I see is often organizations are just scratching the surface of what LLMs like ChatGPT can do. So, so much opportunity here.
- [36:33]
Michael Stelzner
I just want to add something in there and hopefully you'll remember where you were.
- [36:36]
Lauren Schiavone
Yeah.
- [36:36]
Michael Stelzner
We use Asana across our entire corporation and we ran a little pilot with a small part of our operations team to see whether or not the upgraded version of Asana, which has all these AI integrations into it, were valuable. And we ended up employing it across the entire company and there are tools that your company already uses that probably have an upgraded version of it that includes AI capabilities. This would be an obvious, logical place to experiment. Right?
- [37:03]
Lauren Schiavone
Yeah, that's a great point and something I always like to talk to companies about in like the assess phase. Right. Like what are the current technologies you're using? Do you understand? What are their a high capabilities? Do you understand their AI roadmaps?
- [37:14]
Michael Stelzner
Right.
- [37:14]
Lauren Schiavone
Because like, there's probably something next to, for Asana. So what is your process to like be ahead of that. Right. And also then integrate that into your processes?
- [37:21]
Michael Stelzner
Yeah. So I know you were on a roll. I don't remember where you were. I hope, I hope you do too. But we, we were talking about scaling, right, so.
- [37:27]
Lauren Schiavone
Oh yeah, yeah, yeah. So another important part about, about scaling or securing leadership IAM and is, you know, making sure you're also developing an AI roadmap that shows your intent, sort of of how you want to experiment in areas over time. Leaders will want to sort of see the big picture, not just, you know, one experiment. They'll want to sort of see how this one experiment fits into the, the broader efforts. And then the last step is simply to train, upskill and optimize. Right. You want to make sure that you're documenting how the new process works. You want to make it as simple as possible for folks to learn. You know, maybe that's telling them very clearly what is the prompt or giving them guides or templates. And of course, you know, you can use AI to, to help you do those things. The more simple and user friendly, the, the easier it's going to be for folks to adopt.
- [38:12]
Michael Stelzner
What I love about what we've talked about so far is how structured it is. And it's really refreshing to hear that you can take all this experience that you have learned through your experience working at really, really big brands and bring it to allow us to inject, if you will, some innovative new technology like AI into the workforce and into all the things that we do. But I also know that there's another layer here, right, which is culture and accountability and all that kind of stuff. And this is one of the big struggles that I'm finding right now with my team. It's one thing to deploy something like this across the other entire company, it's another thing to actually get them to use it.
- [38:58]
Lauren Schiavone
Yeah, I think you're totally right. And like people often say, like this is going to be the hardest part called change management, which to me essentially boils down to culture, communication and accountability. And this is going to, to your point, be the crux of sort of do you get to transformation or not? A couple things. I like to think about it as we think about culture because I really believe the culture that currently exists and most corporations is not going to be the culture that's going to be successful in an AI driven future. You've got to let go of your ego, right? The pace of AI advancement just simply means no one can be the expert at everything. And leaders are going to have to let go of the expectation of having all the answers and instead embrace humility and curiosity. We've also got to shift from competition to collaboration. So, right, let's face it, Mike, we can be competitive with our peers at work and the same is true for our employees. And that approach just isn't going to work for us in the future. Success is going to come when teams openly share their discoveries, learnings and insights, which is going to build collective intelligence. Right? Because you're going to use the tools slightly differently than perhaps I am, based on our own sets of experiences. And then you're going to have to challenge the status quo. Right? What got us here is not going to sustain us into the future. So organizations need to act bravely and question everything, assumptions, processes, and really be willing to reinvent themselves in a way that also fosters continuous learning. Like continuous learning. It's got to be a priority, it's got to be embedded into the way organizations operate. Right? AI is moving at such a rapid pace and then the last thing I like to say, and probably most important is organizations are going and individuals are going to need to adapt what I call an AI first mindset. So this means looking at every task through the lens of how could AI enhance this? This mindset is going to be key to driving habit change, driving Habit change is really hard. But when people start, start naturally thinking about AI's potential in their daily work, they're just going to discover more opportunities to use it. Mike, any builds for you on culture before we talk a little bit about communication?
- [41:02]
Michael Stelzner
No, keep going. I mean, because I'm loving everything you're saying.
- [41:04]
Lauren Schiavone
Awesome. Okay, so let's talk about communication. You're going to need to have clear, consistent communication as part of this transformation. You're going to need to be transparent. You want to overly share the work of the AI Council, the vision, the goals, how it ties back to business goals and the work underway. Transparency is going to build trust and clarity which is going to make it easier for folks to embrace change. And let's be real, people have real fears and concerns about this work that you're going to want to address. They're going to want to address them often. You're also going to want to celebrate wins. So publicly highlight success stories from teams using AI effectively foster knowledge sharing. Right. Whether that's Mike, to your point, in your sort of team meetings or other channels, you just want to sharing what folks are learning and how they're using AI. And really you want to make sure that it's a two way communication. Right. It's not just leadership talking to the employees. You want to encourage feedback and questions at every step. The employees are going to need to feel heard in this process and they're going to be more likely to engage when they feel like they're, they're being heard. Any thoughts for you on Mike on communication before we talk? Accountability?
- [42:09]
Michael Stelzner
No, keep going. I'm loving this.
- [42:11]
Lauren Schiavone
So the third component, sort of change management, as I think about it, is accountability. So throughout my career I've learned that you can expect to get what you measure and what you reward. So you want to think carefully about how you measure and reward AI integration and transformation within your organization. So for example, you might want to integrate AI into leadership business metrics or scorecards. Integrating AI into key business metrics in which leaders are measured on right will hold leaders accountable to driving transformation. My preference for metrics are outcome based, so focused on driving results. Results like cost savings enabled by AI or productivity improvements delivered by AI or even business growth enabled by AI. You might want to start by using what I call activity based metrics like AI tool usage amongst the organization or percent of organization trained. But my advice is to quickly transition into outcome based AI metrics. And that's really because spending time with AI tools does not simply translate to business value. We know that your organization could have high usage rates of AI but struggle to show any meaningful business impact. So when you hold leaders accountable for outcomes rather than activities, of course they'll focus on implementing AI in ways that truly matters for your business. But listen, leadership is not the only folks that need to be held accountable. AI accountability cannot just live with leaders or AI champions. It needs to be integrated with all employees. Every employee should have AI related responsibilities in their work plan. You're of course going to want to tailor that to their role and their level and it's going to look like different for every person. Maybe it's personal upskilling and adopting a new AI enhanced process, or maybe leading an experiment or maybe identifying new areas of AI integration. If only your designated AI person is held accountable, everyone else will view AI as someone else's job, someone else's problem. Trust me, I've seen it too often employees will say, oh, AI is his or her problem, or AI is an IT problem. It's not my problem, right? Instead of taking personal ownership for the change. So while it's valuable, of course, to have AI champions or members of the AI Council, they need to be enablers of the change, not solely responsible for it. So the level of responsibility, of course, is going to vary by role, but real transformation is going to happen when every person understands their role in moving the organization forward with AI.
- [44:44]
Michael Stelzner
Lauren Schiavone this has been really amazing. There's going to be people that are going to want to connect with you. What's your preferred social and then also if they want to work with you, where do you want to send them?
- [44:56]
Lauren Schiavone
Yeah, awesome. The best way to connect with me is via LinkedIn. It's Lauren Morgenstein Schiavone. And then of course, you're always welcome to send me an email to Lauren at WonderConsultingLLC. If I can be of any assistance to you in your AI journey, please don't hesitate to reach out.
- [45:11]
Michael Stelzner
Thank you so much for sharing your insights with us today.
- [45:14]
Lauren Schiavone
Yeah, thank you.
- [45:15]
Michael Stelzner
If you missed anything, we took all the notes for you over@socialmediaexaminer.com A32.9 be sure to follow this show on your favorite podcasting app. And if you've been a longtime listener, would you give us a review on whatever platform you're listening on? And if you want, share this with your friends. And do check out our other shows, the Social Media Marketing Podcast and the Social Media Marketing Talk show. This brings us to the end of the AI Explored podcast. I'm your host Michael Stelzner. I'll be back with you next week. I hope you make the best out of your day and may AI help you become more successful. The AI Explored podcast is a production.
- [45:55]
Lauren Schiavone
Of Social Media Examiner.
- [45:59]
Michael Stelzner
If you're serious about learning more about AI and marketing, I'll see you at Social Media Marketing World 2025. Go to social mediamarketingworld.info and secure your spot today.