Insights from Oracle: Activating AI for Smarter Recruitment and Workforce Planning

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Rachelle Graham: So the opportunity for AI within talent marketplaces is phenomenal. It's really one of those areas where the data becomes m- more important than perhaps in some other areas. So it's important to caveat that. I, I think that for me, one of the most exciting opportunities is when all of your data is in one place and how that can really amplify not just that talent marketplace, but the entire talent growth and development pipeline.[00:01:00]

Alex: Talent management, the HR function that finds people, the HR function that develops skills, the HR function that decides who gets opportunity and when, and now increasingly, the HR function being reshaped by AI. From candidate sourcing and screening to skills matching and internal mobility, AI is changing how organizations find, engage, and elevate talent.

But speed alone isn't the goal. The real challenge is activation, using AI in ways that improve quality, reduce bias, and expand access to opportunity without losing human judgment along the way. That's exactly where today's conversation begins. Our guest, Rachel Gram, a strategic solutions consultant and advisor at Oracle, has been working at the intersection of AI, talent strategy, [00:02:00] and HR transformation, helping organizations move beyond experimentation to real responsible impact across the talent life cycle.

Today, we're exploring how HR leaders can activate AI in talent management from smarter recruitment and skills-based matching to internal talent marketplaces, and what it takes to ensure fairness, inclusion, and human oversight remain at the core. Rachel, welcome to the AI+HI Project.

Rachelle Graham: Alex, great to be here.

Thank you for having me.

Alex: We've got a fascinating conversation ahead right after this brief message

Ad: My name is Courtney Scott. I'm a director HR business partner for the Learning Care Group. I've worked in HR for the last 10 years. The SHRM Annual Conference has changed the way that I approach my role personally by expanding my network, uh, increasing and expanding upon my innate [00:03:00] leadership aptitude and capabilities, but, uh, helping me to really leverage my skills as an HR practitioner better and show up stronger for my organization, ensuring that I have an expansive network to lean into through SHRM, uh, for support to help ensure that my teams are able to deliver on what the business needs of them every day.

So one workplace challenge I feel more prepared to tackle through this, uh, conference participation is change management and helping my teams to be more agile and capable to respond to the changing needs of our society and of our business. For someone who's on the fence with attending, pull the trigger, do it.

Alex: So Rachel, it's great to have you here. I really am excited that you're on the show. Obviously, you and I have been on stage together before. Yes. Uh, so it's, it's always good to see you again.

Rachelle Graham: Absolutely. Likewise.

Alex: Uh, wonderful. So one of the things that I wanna talk to you about is, uh, I, I don't know anybody as learned [00:04:00] in the world of AI plus HR as Rachel Gram, right?

You do this on a level that I think no one else really does, uh, s- save one or two other people that I've come across, but you do it at a whole other level and with style, which I love. So talk to me a little bit about what you're seeing in your client base. What is it that you're seeing at the intersection of talent management and AI?

Rachelle Graham: So we're seeing a lot right now, and the conversation is truly shifting as we roll further and further into 2026. I think that what we saw a lot of in 2025 was education, understanding how AI can really intersect into HR, what that looks like, and how it can be delivered to employees. And now in 2026, it's that inflection of how do we drive value out of these features and capabilities, and how do we insert them into a business process overall?

So it's been a [00:05:00] really exciting shift in the conversation.

Alex: From a practical perspective, what are you seeing HR leaders and, and strategists really applying when it comes to that transformation, that shift that you're describing?

Rachelle Graham: So it's about what does the roadmap look like? Mm. How do we develop an AI strategy that's going to focus less on shiny objects, less on features, and more on driving outcomes, which especially when we think about the language that we're using in this session- Mm

around activation versus adoption, it's really about driving those outcomes. So strategists, HR leaders, business leaders, because there is an intersection Whether we're talking about an HR leader or an IT leader, how they're coming together to have these conversations and then saying, "When we look at a business process, where does it make sense to insert AI, and what type of AI should that be?

Is it [00:06:00] good old-fashioned machine learning?" Yeah. "Is it generative AI? Should we be exploring some of the new really exciting innovations around agentic workflows?" And often the answer is all three, so that we can drive that outcome at the end.

Alex: You know, I find that fascinating in large part because it's so, so many people think of one answer as the one answer, and then I'm gonna tuck it away, and then we'll revisit the business process requirement or really revisit that B-BPR down the line and say, "Okay, are we making a sh- a difference," right?

Right. As opposed to really solving for the whole thing and in many ways thinking about, you know, can I actually apply three or four different, uh, approaches here to really make a difference in what we do? Do you find that, uh, clients are surprised when, when you run into that?

Rachelle Graham: A little bit. There's a lot of hype around AI in general, particularly generative and agentic.

We've all come to terms with some of the [00:07:00] machine learning things because we've had access to that for so long that it's often overlooked. But there's a lot of emphasis on the thing, the specific task, and it's important, and part of the role that I play is challenging those leaders to really think about what's the business problem that you're trying to solve?

What's the challenge that you're trying to solve? And once they hear me ask that question, once it's posed to them, most of the time it's an aha moment of, oh, right. I'm here to solve business problems, not just turn on features here and there. And then we start to map out what that looks like.

Alex: Yeah. I, I love the way you couch that 'cause I, I do think that the majority of the world still runs into, "I'm gonna turn on some features for you-" Yes.

as opposed to help me solve the challenge that I'm have, uh, you know, dealing with today." Let me ask you this in a different way. Where is it that you see AI really having an immediate impact [00:08:00] today in talent management? A- and, uh, specifically, I kinda wanna do a little bit of a deeper dive on this- Mm-hmm

to also look at sourcing-

Ad: Sure ...

Alex: of candidates and then also early screening, right? 'Cause i- if you look at our research, for instance, SHRM research, we do an annual survey of talent management professionals and, and particularly talent acquisition, and one of the things that we find is there are those that are- enjoying the, the benefits of AI-based solutions, a variety of different solutions, especially in sourcing, but at the same time also thinking about what it means for their work in, in a variety of different ways.

And then candidly, there are some who are cursing the world of AI in the hands of candidates, right? 'Cause they're getting spammed left and right. So I'd love to hear your thoughts on that. Where are you seeing the immediate impact?

Rachelle Graham: So we're definitely still seeing the immediate impact early in the recruitment funnel when we think about increasing the pipeline and getting candidates into that [00:09:00] pipeline through the application process.

And the reason for that is that we've had that machine learning mechanism of matching candidates with skills for a lot longer than some of the other AI tool sets. Now, I do think that there's been a reluctance to roll out the skills matching, uh, for a lot of organizations because they're getting hung up on skills as it relates to job architecture.

But what we're seeing is that organizations that break those apart focus on the candidate skills specifically, irrespective of what's going on with your job architecture, that they can increase that candidate pipeline sometimes up to, I've seen data points on organizations that I worked with, up to 63%.

So that was a manufacturing organization. So we can take a look at that industry and say, well, clearly [00:10:00] there was likely a lot of optimization and automation opportunity there.

Alex: Mm-hmm.

Rachelle Graham: But I've also seen it in tech companies-

Alex: Okay ...

Rachelle Graham: where they tend to be a little bit more forward-thinking with their automation, and by enabling some of those capabilities still saw an uplift in candidate pipeline.

Uh, the number was 17%.

Alex: Okay.

Rachelle Graham: So at the end of the day, we don't wanna get spammed. You mentioned that. Yeah. That's incredibly important. But when we're actually looking at matching candidates to the skill set of that specific job posting, then what we actually see is that that candidate is more highly qualified.

They're a better fit for the role. They're a better fit for the culture in the organization. There's also AI capabilities where we can suggest other types of jobs that maybe they weren't int- initially interested in- Mm ... [00:11:00] and then drive them into the application process that way. So yes, it's about increasing pipeline, but it's also about increasing the quality of the candidate and ultimately increasing Uh, the long-term productivity and performance of that candidate, i.e.,

employee

Alex: You know, it's so funny 'cause you make me think about a, a term that I coined recently, and I hate to say that 'cause it sounds so immodest, pardon my French. But one of the things that's, uh, that stands out is, um, i- in talking to talent acquisition professionals and/or talent management professionals, one of the things that stood out was sometimes they feel like they're being skill-phished.

Mm-hmm. Right? Kinda like catfishing. You've been catphished, you know? But in reality you've been skill-phished 'cause you think you have a skill set from a candidate, and then you get four weeks in, you get five weeks in and, or even three months in, and you start to see they don't have any of the skills you thought they did, right?

And some of that is about the, the way you're assessing it, but some of, uh, some of that is also about the [00:12:00] throughput in your funnel, right? And so I, I, I, I guarantee you that the solutions you're talking about certainly help a lot of those people from being skill-phished, as, as I like to think of it.

Rachelle Graham: Yeah, so fortunately I haven't been catphished.

Alex: Yeah. Um-

Rachelle Graham: But we've

Alex: all watched the show, right? No, absolutely. Yeah. Absolutely. It's, uh...

Rachelle Graham: Uh, so what I can say about that is s- certainly we can use AI to enhance the matching. That's also a great segue to talk about and think about the fact that AI is never going to replace recruiters. It's never going to replace hiring managers.

It should augment them. Eventually, I think that we'll start to, uh, detour into having agentic applications and agents coach them, but for where we're at right now, this is just about augmentation. The humans, recruiters, managers, are the people who should be making those decisions, and I think that that's incredibly [00:13:00] important to remind ourselves of, that we're not looking to replace people.

We're just looking to make their jobs a little bit easier.

Alex: You know, do you find people-- Some people are on the other end of the spectrum, though, and they wanna over-automate. They wanna go about over-automating everything and getting to the point where they get as, as much of a final answer as they can.

Rachelle Graham: So absolutely. And I think that boils down to the age-old issue that we have of at some point, everything becomes a performance management conversation. Misuse of any tool eventually becomes a performance management conversation, and AI falls into that as well. We do need to be able to have some, uh, monitorability, for lack of a better term, of how people are interacting with AI tools, and some observance around that so that we can say, "Is this generating the outcomes that we're looking for?"

And you'll know that pretty quickly once you start to understand [00:14:00] what the quality of those hires are

Alex: People think that AI is actually the thing that's pushing organizations away from resume and skills. But in reality, we've been talking about that for almost 20-plus years, right? Anybody who's been around organizational strategy knows that.

AI is just the tool that helps us advance that in many ways, or one of the set of tools that help us advance that in many ways. I guess my question for you is, do you think that the AI, if done right, does a great job at that? Or do you think, for instance, that it's helping even create better quality of hire, as you know, you, you mentioned, right?

But as well, greater long-term potential with a firm?

Rachelle Graham: I absolutely do. Because when we look at hiring for a skill set rather than hiring on exclusively past experience, we get people who are more adaptable. We get people who perhaps they're more curious, perhaps they're lifelong [00:15:00] learners, and it opens up the door to, you know, there's this conversation of how can AI boost inclusion, and it's giving people opportunities that they may not have based on the static resume of the jobs that they've done in the past.

Mm-hmm. It brings the whole person to the table of the skills that they've acquired in their experiences more holistically. And I'll share a story with you, uh, around that, is that I worked with an organization where a manager had candidates, this was within the IT department, and had an abundance of candidates, ended up hiring somebody who had a late career degree, had never worked in IT before, but yet had all of the core skills that the manager was looking for.

And they went through an assessment process where they could vet [00:16:00] whether they had the curiosity and the technical skill foundation-

Alex: Mm-hmm ...

Rachelle Graham: and led to the hiring of that person. That person quickly, within a year, outperformed a career IT professional and ended up becoming, uh, elevated within that IT department very, very quickly and becoming the right-hand person of that manager.

So a really nice anecdotal story of when we focus on the skill, when we focus on the person, we can have better longer-term fit, uh, and growth potential.

Alex: One of the things that strikes me is, and you've, I'm sure, no doubt have heard this repeatedly, right, is when it comes to AI, especially in the early sel- uh, selection process, the talent acquisition process, talent management process, is fairness, right?

And ensuring that you've got fairness in, in the system, that it's not living in the world of bias. But one of the things that struck me is a lot of people- still think that the notion that you don't share the [00:17:00] algorithms, that you don't share the secret sauce behind a lot of these tools is some form of bias creeping in, or is a, a representative piece of, of bias creeping in.

And in reality, what I, what I've learned is that secret sauce is also what helps accelerate and/or eliminate some of the biases that we know, we know exist out there, right? Humans more than anyone, anyone else produce bias in many ways, right?

Outro: Yes.

Alex: What are some of the guardrails that you think can really make sure that we're not introducing lack of fairness in the development or the use of some tools?

But what do, what do you see in that space, right? 'Cause y- you've been around it a long time, so.

Rachelle Graham: So such a hot topic.

Alex: Yeah.

Rachelle Graham: And I'm really glad that you led with, "Humans have bias," because that's absolutely... Anytime this question comes up, my mind immediately goes to, "But we already have bias." Now, can AI amplify that?

Sure, right? That's the reality of the world that [00:18:00] we live in because AI is only as intelligent as the humans behind it, and those humans inherently have bias. So how do we help remove some of that? You mentioned that algorithms are black boxes, and to ex- certain extent, yes. But I do believe that vendors should be able to share with you what are the data points that are included in that algorithm that it's going to be looking at as part of that screening process.

And I say screening deliberately because I don't think that AI should ever be making decisions about the selection of a candidate. So when you speak about guardrails, first and foremost, AI shouldn't be making decisions on the behalf of a human. Mm-hmm. Every decision made about a person should be made by a person, and I believe that very, very firmly.

We've had knockout questions in the talent acquisition space for decades. Mm-hmm. [00:19:00] So that's not new. That's also not AI.

Alex: Yeah.

Rachelle Graham: Right? There's a difference between how we're leveraging automation and how we're leveraging AI, and I think it's important in this recruiting space to not confuse the two. So continue to use automation, but then when we're thinking about how a candidate moves through a pipeline, your people, recruiters, leaders, they need to be the ones that are making the decisions.

That's your biggest guardrail. Understand what data points are included as part of that algorithm, and product developers from your vendors should be able to share that information with you so that you can validate that it's not PII. Mm-hmm. That's important. That's another guardrail that you can put in place.

Hmm. But I also think that when used appropriately, that AI can actually suggest candidates that may have been overlooked by a manual review And it can help bubble some of those candidates [00:20:00] up to the top based off the skill set, like we were talking about a moment ago, based off of perhaps an internal candidate pool that you may not have been considering, and that could help bolster an internal mobility program.

So there's a lot of great untapped opportunity with AI, but at the end of the day, your humans have to be put in a situation to be human, to make decisions. It won't always be perfect.

Alex: Yeah. You know, I, I, I love those guardrails in particular, and I've heard you s- you know, share them a couple times, uh, over the years.

But one of the things that strikes me is it's, it's really practical. It's actually very easy to do. Mm-hmm. And I love the first one, which is always, if it's a decision about a person, it should be made by a person.

Rachelle Graham: Exactly.

Alex: Right? Uh, to me, that's a, that's a great one. Um, I, I do also wanna highlight the, the po- the, the notion about how it surfaces candidates that you might not normally see.

Rachelle Graham: I also think that there's a narrow lens [00:21:00] around what the definition of AI is in the recruitment space- Mm-hmm ... oftentimes. And there can be an over-indexing around the machine learning, and I'm using air quotes- Yeah ... the machine learning mechanisms that are the underpinning of screening and selection conversations, because there's so much other AI to be able to tap into in the recruitment space alone, that I do think sometimes recruitment teams, talent acquisition teams, will get paralyzed by, how do we know?

Is it going to enhance bias? Is there an issue with fairness around this screening and selection? And say, "We're just not going to do AI in any capacity," when there's so much other opportunity. So that could surface itself as generative AI.

Alex: Mm-hmm.

Rachelle Graham: You know, where are recruiters really spending a lot of administrative time?

Mm-hmm. It [00:22:00] could be around email and text thread creation. If they're doing that on a one-off basis, that's an extraordinary amount of time that, that could be bought back to them. Is it around creating job postings every single time that a requisition needs to be opened? Mm-hmm. That's inefficient. Yeah.

Is it around, you know, trying to comb through interview notes, or whatever the case may be? That generative AI alone is easier to execute on, easier to deploy, easier to get through legal- Mm-hmm ... and has an incredible amount of impact on how a recruiter spends their time, freeing them up to focus on conversing with candidates, understanding do they actually have the skills that we need, are they a cultural fit, which all goes back to some of the outcomes we said we were looking for, which was quality of hire- And long-term performance

Alex: Yeah.

You know, I, I think [00:23:00] about that too, and I, I have yet to meet a recruiter that didn't actually think that generative AI in particular was really good for pipeline nurturing as well.

Rachelle Graham: Yes.

Alex: Right? It's-- I mean, something about being able to automate that communication with your candidates, but then at the same time identify the ones who didn't work out for this one role, but may be a great opportunity in a different spot.

I've seen, uh, just as an example, I know Booz Allen Hamilton has used it to expand the way that they think about their pipeline and the pipeline nurturing that happens to the point that in the federal contracting space, they've actually been able to, what they call reapply skills, and they move it from one part of the pipeline to a different pipeline and say, "This is, this is where you could really do some damage for us.

You could really work out really well for us and be here long term."

Rachelle Graham: I love that.

Alex: Yeah. So one of the things that, that I'm gonna highlight is, 'cause we've talked a lot about talent acquisition, right? I, I, I, I gravitate towards that, but there's also this notion of skills marketplaces, talent marketplaces, and really the internal [00:24:00] mobility that happens with them.

And for me, the, the gold standard in that is really professional services firms 'cause they are actually commoditizing that and making it so that they take those skills, build a project team around that, and apply those skills, sells- sell them and propose them for any kind of contract, right? Um, I've seen groups like McKinsey do it as an example.

I've, I've even seen some of the inner workings of h- of how they do that, and I'd love to get your th- thoughts around what are you seeing in terms of AI helping accelerate that?

Rachelle Graham: So the opportunity for AI within talent marketplaces is phenomenal. It's really one of those areas where the data becomes m- more important than perhaps in some other areas.

Mm-hmm. So it's important to caveat that. I, I think that for me, one of the most exciting opportunities is when all of your data is in one place and how that can really amplify not just that talent marketplace, but the entire talent growth [00:25:00] and development pipeline. And so when we think about if you're an organization and your core HR data, your recruitment data, talent management data, and ideally even your learning management system, when that all sits in one place, now you have this rich database of everything that touches that talent pipeline start to finish.

And what that allows you to do is use AI in, I, I keep going back and calling it the old-fashioned machine learning, to match job openings to your current employees. Now, as part of that, you're also going to wanna have a communication strategy. Mm-hmm. So how are we letting people know that these opportunities exist is part of that.

Communication has to be paired with the actual product and technology of AI. And then there's the layering of different flavors of AI, if you will. And the talent marketplace [00:26:00] is a great example of where I see agents being immensely impactful. So if you think about, there's this one use case that I, I really adore, where an employee comes in, they're looking for a new opportunity.

They want to upskill, have new experiences, new challenges. The system matches them with a job opening. They take a look at it and they say, "I don't know if I'm quite ready for that." Now, talent management sits in that same place. They have the ability to go in, create development goals. That agent is going to serve them up learning paths and courses that they can interact with, and they can create an entire plan for how they're going to prepare themselves for that next step.

And that next step doesn't have to be climbing a ladder. It may be just a lateral-- I shouldn't say just. It may be a lateral move that's a better fit for them for where they're at in their lives in that moment. [00:27:00] The real value with the agentic workflows is that in six months, in nine months, that agent can autonomously come back to that employee and say, "I see that you've finished your goals.

I see that you've completed those learnings. And now I see that there's a new job posting for that same opening that you were originally looking at. You should go apply." And that's when we move from that companion into that space of now AI as a coach. It's helping me figure out what I need to do, where I need to go, and how do I actually execute on it.

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Alex: So when you think about what we're seeing, right, and you're-- let, let's pretend you're talking to a new HR leader or somebody who's, uh, really wanting to jump into the adoption of AI responsibly in the world of HR or talent management.

What are the first two or three steps that you'd recommend for them?

Rachelle Graham: So two steps that I would highly recommend. The first is if you're looking to activate AI, understand what is available to you first. Mm-hmm. Uh, which seems pretty straightforward, but sometimes it's we have to go shop And you don't always have to go shop.

Mm. Many vendors are including this as part of their subscription that you are already paying for. Mm-hmm. So you really wanna know and understand, what do I have available to me today? Now, don't take it at face value. You'll [00:29:00] want that vendor to be able to explain that to you. What type of data is it looking at?

And we really need to have explainability in the different variations of AI. But once you understand what's available to you, you'll know, where can I go? What can I deploy? Does it map to any of the business problems that I'm really looking to solve? Mm. If the answer is no, then you can go shopping, right?

But perhaps the answer is yes, and it will be a much faster, easier deployment of AI. The second thing is my experience over the years is that HR leaders, and that could be domain leaders- Mm ... talent acquisition, talent management, uh, y- you know, even payroll, everybody wants to do their own thing. Mm. And their technology that they're using, they've, they've kept a protective, uh, armor over it.

[00:30:00] When we start to think about activating AI, we really, as HR, need to become more collaborative with IT for a variety of reasons. And when we think about that activation, your IT orgs probably already have a plan. They may have tools that are already deployed out to your organization. I can't tell you how many times I've asked HR folks, "What AI are you using today?"

And they tell me, "Nothing." Mm. And then when I talk to IT, I say, "What AI tools are you using today?" And they tell me all about their projects ongoing with Copilot, their private instance of ChatGPT, you know, all of these different projects that they're either working on or already have deployed. So it's important as an HR leader to know and understand the true landscape of the technology and tools that are [00:31:00] at your disposal.

Mm-hmm. And when you are thinking about this, you also have to be mindful of employee experience. Yeah. So when your vendors are offering something, is it interoperable with whatever IT is doing? Because what you don't want to do is have all of these different usages of AI, because that becomes overwhelming for a worker.

They don't know where to go, how to use it, and, and what will end up happening is that they won't use it.

Alex: Sure.

Rachelle Graham: So you, you wanna understand what IT is doing, you wanna understand what your vendors are doing, and do those two things connect in order to solve your business problem?

Alex: One thing that strikes me is as you think about this, right, what are some of the common mistakes that you see, or what are some of those really fatal mistakes that you see, for instance, HR leaders make?

I mean, you alluded to it with not, you know, connecting, with not having that, that, uh, approach to talk about AI or even working with IT. [00:32:00] Uh, but I'd love to hear your perspective.

Rachelle Graham: So it definitely ties back to focusing on tasks and not outcomes. When we are chasing shiny objects, when we are focusing on features and functions versus what is the problem that we're trying to solve, you may deploy AI.

You may accomplish that. You will accomplish that, right? Because a lot of times it's we're turning on a feature and off we go. Will your people use it? And if the answer is no, then you're never going to get to that outcome. And so we really have to start thinking about that differentiation between, uh, adoption versus activation, about driving an outcome in terms of solving a business problem.

And we are seeing a little bit of a change in the way that [00:33:00] HR leaders are thinking about that, but it, it is a change, and I will say that most of 2025 was spent having conversations about why we shouldn't be focusing on a feature or a task and helping people understand that this really does need to be focused on some sort of strategic objective, or we risk that, that the project overall will fail.

And there's numerous data points out there, studies done around many of these AI projects failing. So we wanna be hyper-focused on what is this doing for the business and how are our people going to use it.

Alex: So I'll ask you one last question, and it's a little bit of a curveball. I didn't let you prep for it in any way, shape, or f- or, or anything like that.

If you were to predict where you will most likely see AI assistants pop up in the HR space for CHROs, specifically for the role of CHRO, where do you [00:34:00] think it's gonna be?

Rachelle Graham: Workforce planning.

Alex: Workforce planning?

Rachelle Graham: Yes. So that was a fast answer. Uh- I like it. Yeah ... because I think that the opportunity to make an impact on workforce planning for a CHRO is untapped.

It's something that most, I'm gonna call them legacy systems, SaaS platforms, have struggled to really deliver into the HR organization. Now, don't get me wrong, they have tools. They have, um, cloud applications that are focused on that, but the reality is that it oftentimes is owned by finance. And so many HR leaders, CHROs tell me that they want more involvement, but that it's challenging, and the existing cloud applications haven't solved the problem.

And when you bring in autonomous agents, which is wh- [00:35:00] where the future is headed, and we'll start to see the emergence of that more so in mid 2026, mid to late 2026, and the ability to orchestrate behind the scenes into the cloud platforms that already exist, 'cause we need that data, and serve it up in a really simple, easy way with those automated nudges to coach that CHRO along the way around what that workforce plan needs to look like, about the skill sets that they're lacking within the organization.

AI is a great example, uh, though we need to more clearly define what type of AI do we actually mean, depending on the job role, but that's a, a different question to tackle. Yeah. But I see really untapped opportunity around workforce planning.

Alex: Rachel, it's been a real pleasure always. Anytime we get a chance to share a stage, I really enjoy it.

I owe you a whole ton of Dunkin whenever I, whenever I make it back up to Massachusetts. [00:36:00] Thank you so much.

Rachelle Graham: Thank

Outro: you, Alex.

Rachelle Graham: Appreciate it.

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