Making AI Work for People and Business – Smart Cookies with Nicole A. Donnelly

 

In this episode of Smart Cookies, Nicole A. Donnelly, founder of AI Smart Ventures, joins Derek to share lessons from training more than 20,000 people on AI. From hands-on learning and practical implementation to measuring ROI and Return on Heartbeats (ROH), Nicole explores how organizations can accelerate adoption while making room for human creativity, curiosity, and connection.

Key Takeaways

• Training alone is not enough. People need a live lab session alongside formal training: a recurring space where they can bring real work problems, see how colleagues are using AI, and experiment without consequence. Most organizations will not dedicate one hour a week to this. The ones that do see the fastest adoption.
• Sharing is the real catalyst. The average worker is not part of communities where AI use cases get shared. When organizations create internal spaces for that sharing, adoption accelerates faster than any communication plan or training curriculum alone.
• Start with the pain, not the technology. Nicole’s approach is to map the internal or external customer journey, identify the biggest bottleneck, apply AI there first, and then move to the next one. Proposals, customer service, and newsletter production are common starting points. Strategy follows a 12-month project roadmap with two-week implementation sprints.
• ROI and ROH both matter. Return on investment and return on heartbeats. Measuring success means tracking both time saved and workplace satisfaction. Surveys before and after every training session, usage data, NPS, and time-to-value metrics all tell part of the story.
• The human in the loop is a choice. AI could do more than most organizations allow it to do. Nicole chooses specific points in her delivery where humans remain: reading every survey before entering a room, reviewing every piece of content before it goes out. The question every organization should answer is: where is your magic, and where will it always be human?
• Dismissing people is the myth worth retiring. Using AI usage scores as the primary factor for layoffs discards knowledge, creativity, and experience that cannot be recovered. The organizations cutting aggressively are often trimming something that was bloated, not unlocking something new. Discovering the magic in existing people and multiplying it with AI is the stronger play.
• Curiosity is the skill that matters most. In three years, the people who will thrive are the ones who stayed curious. Not the most technical, not the most senior, but the ones willing to keep asking what else is possible. That applies to individuals navigating their own careers and to organizations deciding what kind of workforce they want to build.
• You do not need a developer to start. Workflows and SOPs are where every non-technical organization begins. Any domain expert can describe their process, ask AI how to improve it, and evaluate whether the results are good. Technical expertise helps at the edges. At the center, it is the person who knows the work best who drives the value.

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If you’re working through these decisions in your organization, we’d be glad to continue the conversation with you. Connect with KG Digital

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Full transcript

Derek: All right. Welcome back to Smart Cookies. Last month in Seattle, our panel told us that giving people AI is the easy part. Getting them to use it well every day is the really hard part. The Smart Cookies podcast is designed to spotlight strong leaders, translate complex topics into clear business insights, and build community and real discussions around that.

And today I’m blessed to have Nicole Donnelly here, who has trained more than 20,000 people on AI. Nicole, welcome, and super excited to have you here. And even before we started our official conversation, here we go, dialoguing and brainstorming and talking about all the things happening in the AI world.

Nicole is an incredible partner and brings a lot of insights into this space. But let’s get to know you a little first before we jump into AI. How did you get from some of your earlier ventures into what you’re doing in AI now? You’ve got a fantastic origin story that could probably take up the whole conversation, but I’d love to hear a little bite. So how did you get to where you are right now?

Nicole: Yeah. So my first company was Baby Legs, leg warmers for babies. They’re in the MOHAI in Seattle right now, and that’s where their resting spot is memorialized. But what I learned from starting my first company, that was a product company, is all of the systems and process. And we were so good at being efficient with systems and process. We had a server closet, and back in 2007 people would take their VoIP phones home, you know the ones that sit on your desk, and they could work from home because I wanted it to be friendly for the kind of life that I wanted to have and be able to be there for my daughter when she was sick. I wanted that for my whole team too. So you could bring babies to work if you’re breastfeeding, bring dogs to work, whatever. As long as it works, that’s fine. And part of that is technology. I wanted to give the same opportunities to everybody on my team. We see that so much now, but in 2007 you could work from home, and when Seattle had a huge ice storm, we were one of the only businesses that stayed open because everybody had phones at home and they could remote into our server. And that was just before Christmas when we’re shipping out all of our last orders, and we were able to still keep things running even though the whole city shut down.

That was something I learned in my first business. I started it when I was about twenty-seven, and I just had a natural knack for it. Maybe it’s the German in my genes, always wanting to be really efficient.

But then there’s also the human experience. And with AI now, I’ve been using AI since 2015 in my marketing agency, which I started after. We’ve been using AI operationally, using OpenAI since before ChatGPT came out. And now that it’s broken wide open, we can show other people how to use it in really practical ways that will improve the human experience of work. And that’s one of the things that Microsoft talks about: people who use AI more at work have more joy, and they experience a better work day and work environment, and they have more positive sentiment towards their employer the more AI that they use. So it behooves the employer to make sure that your teams know how to use it really well to make their job easier. The parts of the job that they hate, they can give more to AI, and then they’re gonna have more satisfaction and more loyalty.

Derek: That’s interesting. When we think about some of the narratives around AI, there is a lot of energy on both sides of the conversation, let alone the adoption side of it. Is there something that you’ve learned or applied that has really helped people that might be in the headspace of negativity or objection to AI in their day-to-day? Something you’ve seen work to help bring people along?

Nicole: Absolutely. We do AI training with companies: training, implementation, and AI strategy, and there’s always an element of training that has to go with this. Our out-of-the-box class is 10 weeks long. And over these 10 weeks, we see people progress a little bit more and get more excited as they learn the things that are relevant for their job. The longest it ever took was for an accountant. It took her 10 weeks to get really excited about AI and see the possibilities for herself. But most people within those first three weeks can see the possibilities and how it can change their workday and even their home life too, because we know people are using it for personal uses already.

Any chance you can give people a little bit more ease, they’ll be happier to adopt. To experience that, you have to share ideas, you have to share how you’re using it. And I think you and I are part of these groups where we’re sharing all the time. You hosted an event where people are sharing these ideas, but the average everyday worker is not part of those groups.

They are not around people that are sharing how they could use it in their role. And so one of the things that we do during our training is we have a lab session as well, just like you would in university. Our labs are experimental, and people can bring their real-life work issues to the lab. We do them internally every Wednesday from two to three PM.

We often invite guests to our lab. We had my friend’s daughter who does marketing at her company. She came to our last lab, and we showed her how she could do things in her role as a marketer. We showed her what we were doing, and then we take the content and use it for our own purposes too. Our structure for creating content is our labs. And any company can do that too,

Derek: Yeah.

Nicole: because then you can put that content out to the rest of your team, and they can learn. Our whole team attends labs, and when we facilitate classes with companies or organizations, we facilitate a lab session along with it every single week outside of the training. Because training gets boring, and something we’ve learned is Zoom training live or in person is definitely most effective. Self-serve, nobody finishes those self-serve trainings.

Derek: Yeah.

Nicole: Yeah. But live they will. You need to have lab support to really have deep implementation and adoption in your company. And even companies like Microsoft struggle with that usage, and that’s what you were mentioning earlier, or what the Gates Foundation mentioned. So yes, everybody’s using the tools, but their usage is not as high as it should be. And if you had a lab session every week that you could go to and see what your colleagues were doing, then you would see all this cool stuff that you could do too.

Derek: That’s super interesting. If I reflect back what I’m hearing, sharing is a catalyst for change. I think a lot of organizations think about all the communication plans and all the bringing people along, and like you said, all the training. But if I’m hearing you right, it’s just getting that sharing to happen that creates positive inertia. Is that right?

Nicole: It does. And we see that with every company that we work with. The labs are usually where the lights come on and people get the most value. You can learn tactical stuff in a lesson, in the courses, but in the labs it’s more hands-on. People are able to get their real problems out, and they’re able to see how others are using it, so they’re inspired.

And then you know who in the organization is a super user or a half user, and then the people on the teams can ask that person because they know that person will be able to help them. This is a gap that we have bridged in our work, even in marketing for years: this communication gap.

The lab is the place to experiment, to be wrong, to figure it out, and it’s a safe place to do that. And most organizations will not dedicate this one hour a week to allow people this experimentation. And with the speed of change of AI, that’s necessary. And the amount of time that people will save by experimenting together is astronomical. I don’t even think we could calculate how many hours are saved because of that cross-pollination.

Derek: Where do people usually get time back? Are there any themes? Are there certain workloads? What is the low-hanging fruit, what are the first places you see people get time back?

Nicole: I have a list.

Derek: Yes.

Nicole: Looking at my phone right now.

Derek: I love it.

Nicole: Let’s see. I can tell you in the last three days, since the evening of October sixth at nine thirty PM, the things that AI has done for me: daily rundowns in my inbox at seven thirty, eleven, and three PM, and it texts me and sends me draft replies. The draft replies are in my voice because it is in my inbox, so then I can say, “Okay, go ahead, send that, change that,” whatever it is. It will schedule meetings for me. It can click on Calendly links and find everybody on the team. So that was one of the first things I had it do. We needed to coordinate with a couple people on the team and schedule through someone else’s Calendly link. Did that for us just fine. Found our matching time.

Derek: Wow.

Nicole: Coordinating time takes

Derek: Forever, yes.

Nicole: And it did this so fast. It did it in seconds. So all the replies drafted. Oh, payment receipts, doing spreadsheets of payables and receivables, what I’ve paid, what needs to get paid. It drafted my newsletter. So we have a newsletter that we send out every week, and the AI drafts the newsletter, and then I go in and improve it. Our newsletter is an aggregation, it’s this assembly item that used to take two days to do, and I will read

Derek: Enter.

Nicole: on different AI stories. And there’s an opening paragraph about what we did in labs the previous week. We feature the best, most read article on our website every week. And there’s several sections. Anyway, it does this assembly item so fast, and then I go in and edit because I still sign my name to it, so I wanna make sure that it is

Derek: Thank you.

Nicole: and that’s important to identify: where humans exist in the process. So newsletter is a really big one that we do every week, and now it just takes a couple of minutes to run.

Derek: But the list is so long. We already see some themes, and the fact that you’ve got this long list is incredible. And I think you step into something really sensitive here that I’d love to get your thoughts on: the human in the loop, right? You were just talking about sending out this important insight that is coming from the aggregation of your brain and your team and all the great things you’re doing. And while AI is still helping that, you’re the human in the loop to make sure it’s the quality and level you want.

How do you talk to people that are worried about AI replacing them? How do you approach that with teams? How are you thinking about this from a people, process, technology framing?

Nicole: First we have to talk to leadership and put some measures in place with leadership around what they are okay with allowing their team. So let’s say I have a certain amount of work I have to get done, I have certain KPIs I have to hit. Can I get Fridays off if I’m making those, and I’ve created enough room in my schedule because I’ve automated things? So we encourage Fridays off, earlier days off. And some people want to apply the additional time to more human activities, to visit clients in person, which is a novel concept that doesn’t happen so often. Every company has their own culture and their own way to deal with it.

So we talk about what the options could be. We were doing a strategy day with a company in New Orleans, an engineering and construction company that does these big complex projects. We had everybody from the field team and also the people in the office in our session together because we wanted to get everyone’s input for the different areas of work. And all the employees, without influence of the boss, said, “We want to spend more time with our customers. We want to get more face time with them. We want to go out and visit their sites more,” and that’s where they saw the opportunity for how AI could help them out. So sometimes it’s asking the team what they want and what they would do with that extra time. Sometimes it’s, “I’d go for a walk in the middle of the day.”

Derek: Interesting.

Nicole: Everybody has their different answers.

Derek: What I appreciate is that you’re framing it through the lens of trade-offs that are almost more human-centered, and not, “Oh, I could be more productive by stamping out more widgets.” You’re saying, “Hey, what if that opened up…” Like you said, you can go for a walk, you can have a more flexible day, or you can go actually talk to more people. You can go out and be with your customers or be with your colleagues, right? I think there’s so much power in just how you frame that question.

We’re here to talk about scaling, so maybe we’re finally at that point in the challenge of how people think about this. One of the things that our organization struggles with internally and also has to help large organizations navigate is that scaling looks different depending on the size of your company and how you think about it. How do you strategically think about how scaling works for organizations? And I’d be interested in what it means for you as an organization, and how you think about it when you advise all these incredible companies and governments and different organizations.

Nicole: So we start with either an internal customer journey or an external customer journey, and we look at what takes the most time that people maybe don’t like to do. So with one company, this also speaks to the being replaced by AI question too.

Derek: Yeah.

Nicole: One of the companies we work with had customer service that was in multiple languages, so they had multiple humans who could speak the different languages. It’s called Save a Life, and we have the case study on our website. But they had people in customer service, and because AI can speak all these different languages and the majority of questions can get answered by AI, they were going to have all these people loose. And one of their operational issues was customer service: being able to answer the phone and speak and answer questions in all these different languages about potentially complicated topics that even a doctor would need to answer, or maybe it’s scheduling a class. The range was really broad. AI can get trained on all that information, so they took the people from customer service and put them in different areas of the company. Customer service got so efficient they only need one person in there now. Because at some point you’ll want to get to a human, but AI is endlessly empathetic, and so in that case, that was where they started because customer service was their biggest bottleneck.

And every team, every organization knows the thing that they don’t like to do that’s taking a lot of time. For us, we started with proposals. We’d have to do proposals, and as you and I were talking about earlier, it’s so custom for every organization. Every organization has a different tech stack, so how do you handle proposals? They have to be exact to the customer. And with what we do with AI, it’s so different for every company right now. There’s no cookie cutter model. And so we broke down that process and we applied AI and automation to it. So when we work with organizations, we look at where the biggest pain points are and figure out how to first use AI and automation for that particular pain point, and then we go out from there, and then we pick off the next one and the next one and the next one.

So when we do our strategies, we’ll have 12 months of projects, and because it’s usually so quick to implement these things, one project doesn’t typically go on for more than a couple of months at most.

Derek: Okay.

Nicole: The proposals, there’s testing and everything that happens, but usually we plan for two-week sprints, and in two weeks we’ll have had something built, and then it’s testing and refining after that.

Derek: How do you help organizations measure? What does success look like? You’ve already answered some of that, but are there other tactical places you go, either by industry or some theme of just, we’re still dealing with organizations here that want to see an ROI. How do we address that?

Nicole: Okay, I love this question because there’s ROI and there’s ROH.

Derek: Oh.

Nicole: I don’t know if you’ve heard of ROH. My daughter knows ROH at this point. You probably do too, but return on heartbeats. So there are ROH measurements and ROI measurements. We do surveys, and we ask questions, including questions around workplace satisfaction.

We actually ask people the things that they don’t like to do about their job, the things that they wish AI could do. So we go in and do a survey when we work with customers, and we do surveys throughout. If we’re doing training, we do it after every training session. We do it every week. And we do it before every training session, just a quick little survey so that we can get those numbers. Because sometimes it is usage. If you’re using something like Copilot or Claude, you can see everybody’s usage on your team. And so you might measure in usage. You also may do NPS.

Derek: Yeah, Net Promoter Score.

Nicole: You might do NPS. Every organization has their KPIs. There’s soft KPIs, and there’s the ones of time to value for a customer. So how long does it take us to get through the proposal process? How long does it take us now? And then when we implement, how long does it take us? And some of these things, like newsletter production, newsletter production took two days before AI tackled it. This was like five years ago. And then once we brought AI into it, it was a couple of hours. Well, now it’s down to a couple of minutes, and then it’s ready for human review.

Derek: That’s brilliant.

Nicole: There are measurements for everything, and they could be emotional or they could be tangible.

Derek: Okay, I’m gonna ask you potentially a really hard question. Three years from now, we’re having this conversation again. What are we talking about in terms of what AI is doing? Look into that looking glass for me. What do you see? Answer at any elevation you want.

Nicole: I think three years is really hard.

Oh, I did this exercise. We did a visioning exercise

Derek: Oh, okay.

Nicole: when I was looking three years out and what I have to be doing in my life. Because AI can do so much, it still is so much about that relationship part. Technology is going to be able to handle a lot, and I think we are going to have more room for creativity. I think it’s very important that we encourage our young people to be curious so that they don’t lose out on life experience because it’s all automated and they don’t have to do the work. And that’s because I’ve got a daughter who’s twenty-two, and she’s very curious. I’m not concerned about her future with AI. She’s good. But I do get concerned with some of the people we have coming in for internships. They are not curious enough.

So for people like me, I’m curious, so I will learn all the things. I will automate as much as I can. I will have more time to watch Dancing with the Stars with my friend,

Derek: I love it.

Nicole: and take dance classes if I want to. I think there are big organizations that are shifting this way too, where we should be able to get everything done in four days. And I think you will see that the time we have to do a lot of this work is shrinking. And then once we get to that really compact amount that AI can handle, and it’s not gonna go beyond that because we really need humans, by that time, I think it’ll be so condensed that we’ll know where the human value really lies.

And I think it’s going to change the way that people need to be educated and the way that we need to work with our teams too. And this is a really tumultuous time, if you think about it, because so much work is getting condensed and eliminated, and that’s good news if you’re a curious person. If you are not a curious person, in three years, it could feel disastrous. And that’s where a lot of the fear comes in, ’cause people are saying, “Oh, I’ve got all this knowledge,” or subconsciously, “I’ve spent forty-eight years of my life learning all this stuff, and now AI can just do my job?” Well, yeah, and you have all this experience, so what can we do with it?

I think we will see matriarchal AI models that speak to a different type of world. Everything is patriarchal right now. We will see more well-rounded, better storytelling. I think we’re going to see a creative progression, and not just tactical, but humans have the opportunity and the space in their brains to be more creative too.

Derek: As a parent myself, that word curious really resonates with me because it does create fear for me of how am I helping this next generation. Before I even think about my customers and folks that are adults trying to navigate this, but as kids, what skills do they need, right?

And landing on that curiosity, I think, is powerful. And something that if I had to answer my own question, which I won’t do nearly as eloquently as you, I think a lot about AI as being a member of your team. I think there’s a thread of that, that in three years we’re talking about, “Oh yeah, I’ve got five colleagues that are these AI agents, these AI capabilities,” whatever words you want to use. But it’s not gonna replace you as the leader and your human team that is being amplified. Oh yeah, we’ve got our human team, and we have our virtual team, right? I think it’s something in that, and it’s at an incredible depth depending on what sphere we’re in and the application of it.

Nicole: Yeah, because we already have that now. I have Jenny and Andrea on my team. She has Tammy, and then they have people under them. We’ve got a whole org chart of all the agents and the things that they’re doing. And Jenny and Tammy are ones that we text with. We just gave them those names. They have little pictures and everything.

And so yeah, if we think about the work that could be, then it’s also going to be up to us to choose the work that is not gonna be done with AI. So in our proposal process and the survey process, there is a point at which a human looks at everything. And even in our delivery of strategies, there’s a point where it’s human. It doesn’t have to be. AI could do everything, but we choose very specific points in what we deliver where humans are there.

So if I go speak to an organization, do a workshop or something, I will read every survey answer and question that they write. I wanna get a feel, an intrinsic feel for the people that I’m going into the room with. I want to be able to answer their questions, and I want to be able to feel the resistance in the organization or any of the things. So for me, it’s important as a human that I read every survey. And I could just put the surveys into AI and have it give me a summary, but I wanna see that Bob loves AI and built all these agents, but Joe is resistant, and I wanna see their roles, their departments, all of that.

I want to understand it intellectually, so when I go in the room, I know what I’m facing. And then when I’m delivering things to a client, I am signing my name to it, so I absolutely will review everything. Even if AI has reviewed it ten times through every lens that we give it, every proofreader and all of that that we have created, I still look it over. And that will be an important part too: where is your magic? What are you good at? What do you care about, and where will it always be human for you?

Derek: I love that. In that vein, when we’re casting into the future, what’s an AI myth you’d love to retire? What’s a narrative out there that you’re ready to make disappear?

Nicole: Okay, there are some people that I know who place little value on the human part, and they just wanna automate everything, cut costs, and be done with it. I think there’s tremendous value in humans, and if you dismiss all your humans in an organization, you have lost something important. I think we need to discover the magic in our humans and leverage that instead.

And there’s so many stories in the media where companies, big companies, are making these huge cuts because they say they’re more efficient. I think they were just bloated. I think it’s a big myth. I think it’s just press. I think it’s this fear factor. There’s a very large organization that I know people who work there who are making the decisions to lay people off, and they were laying people off who did not use AI as the number one factor because they can see the usage. And so they have a scorecard, and they consider different things, but AI usage was the number one factor.

But that doesn’t consider the knowledge that you’re losing when they go out the door, the creativity that you might be losing. And yes, maybe there’s some fat to trim, but there’s also value in every person’s experience, especially the ones like us who have been in work for a long time.

We have decades of experience, and we can think about how to leverage AI more because of our context or just differently. You and I will have different conversations with AI, and we’ll use it differently. And so if you think about how you can multiply that in an organization, you can harvest so much magic and so much information, and you can really leverage it to grow ’cause we talked about scaling.

We need all of this human magic to grow so much more than if we just implement AI and dismiss the people.

Derek: I love it. And I think, having our organization do a lot in the security space, it’s easy to gravitate towards fear as your driver. And here we are having a similar conversation of how fear can be a deterrent to all the incredible opportunities that could come from adoption and expanding its footprint.

Nicole: Yeah. But I think something that I am taking away from this conversation that’s so compelling is just: what’s the lens that we look at it through? I have to say I come back to, okay, it’s gonna save time, it’s gonna make us more efficient, we’re optimized. But you’ve really opened up this, well, what if you…

Derek: Yes, that might be a byproduct, but what if it actually, the words you used of it unlocks the magic, it unlocks the higher degrees of personalization. I wish I could have conversations like this five times a day, right? But I have so many other things I’m supposed to do, and now you’ve got me encouraged to go, “Okay, how can I get rid of some of those things so I can have more conversations with Nicole?”

I wanna make sure you get the last word here. If you’re a small business owner or you’re a multi-generational organization, and maybe you’re not even in the tech industry, but you’re afraid. You don’t know where to start. How would you encourage that leader to take the step in to either embrace just starting to use AI or go a little further with it? What would be your encouragement, and where could they start?

Nicole: Yeah. I love this question because I just came back from Australia and New Zealand, and when we go to Seattle, San Francisco, or talk to people from those places, we might forget that not every company has a technical implementer. Not every company has a developer. Most companies don’t have developers, don’t have technical expertise, and so they are the ones that are afraid.

I had a lot of conversations about this, and it really made me think because our team has no coders or developers. We hire if we need some extra special technical resource, or like you going for security and making sure that everything is SOC 2 compliant. I know the words, I know what’s supposed to happen, but

Derek: But that’s why we have partnerships.

Nicole: Yes.

Derek: We cannot do it all, especially with the breadth by which AI is impacting all of us.

Nicole: Yes. I know my lane, and we’re really good at making AI accessible to everyone on a team. There’s a team that we’re working with in Boston right now. They have 40 people. Two of them have built agents. One is technical. They have one intern who’s a developer. I said, “What are you gonna do when that intern leaves? How are you gonna handle this? You’re not gonna know how to fix or edit anything.” And so we talked a lot about documentation, but it doesn’t take a developer inside of a company to do the work. The common everyday person, there’s so much we could do with AI. We have an article on our website at AI Smart Ventures on where to get started. Because we have done upskilling for people who think they’re at the end of their career in their sixties, as well as college students, right?

Derek: Interesting.

Nicole: Across the board. And our team, if we have to train a technical team, I do have a trainer I bring in for that because he’s a programmer, but the majority of the work that we do is for non-technical people. We work with chambers of commerce. Never a developer on a chamber of commerce team. Usually there are two people in the office, if they’re lucky. Sometimes it’s one. We worked with economic development agencies, also small teams, never a technical person on their team. We work with a lot of advertising and marketing agencies. They don’t have technical people on their teams. Maybe they have a web dev that’s a little technical, so you might get one. But typically the people that we work with are not technical, and so I don’t want people to be intimidated by that idea. Everyone can define the work that they are doing and write workflows and SOPs. That’s all you have to know to start: that workflow and SOP, and then we can have a conversation, or you can ask AI, “How can you help me with this workflow? How can I automate this? How can I use AI to help me in my work?” And it’s this collaborative conversation, and for you, the expert in your domain, could be accounting, could be customer service, it doesn’t matter, but the expert in the domain is the one that can drive the conversation and look at the results and see if they’re good.

But we typically help people that are not technical. There are more non-technical people. But if you’re in Seattle, you don’t see that. If you’re in San Francisco, you don’t see that. But on my travels, when I was talking to all these business owners, one has a tea company that’s been around for thirty years.

Derek: Oh, I love it.

Nicole: They sell to all the fancy hotels in their area. They have no technical people. They’re a big business where they are, and they have a brand that’s been around forever, and that’s who we invited into one of our labs: the daughter of the owner.

Derek: Went to the labs. Okay.

Nicole: Yeah, so she could meet the team. And once she had a thirty-minute conversation, she said, “Oh, I see where I can start. I see the possibilities for me and my role and how this can really help me out.”

Derek: I love it.

Nicole: And that’s where I’m lucky because I’m in these conversations all the time. But if you are working at a tea company, you’re not. So workflows and SOPs is where you start.

Derek: Thank you, Nicole, for coming and having this conversation. Our intent here is to have these real discussions, and I think you brought some emotion and some incredible practicality to AI. And to be able to end in that place where we’re talking about a tea company, right? Everyone wants to talk, and I find myself here on the West Coast in Seattle, and it’s always about tech. But there are so many incredible businesses out there across the globe, and we’re all facing down these paradigm changes as humans, and then what it does to our organizations, what it does to our customers or our products.

So thank you. Thank you for coming on here and being a wonderful member of our community, a wonderful partner, and I just always appreciate our time together. So until next time. Thank you, Nicole. Thank you to all of our Smart Cookies listeners, and we’ll see you soon.