Earley AI Podcast – Episode 39: AI Disruption and Economic Opportunity with Kristina Francis

Transforming Workforce Development Through Technology: Addressing Skills Gaps, Systemic Barriers, and Pathways to Quality Employment

 

Guest: Kristina Francis, Executive Director at JFFLabs

Hosts: Seth Earley, CEO at Earley Information Science

             Chris Featherstone, Sr. Director of AI/Data Product/Program Management at Salesforce

Published on: January 18, 2024

 

 

In this episode, Seth Earley and Chris Featherstone speak with Kristina Francis, Executive Director of JFFLabs at Jobs for the Future, a nationwide nonprofit reshaping education and workforce systems for inclusive economic progress. With two decades spanning database administration, software development, and leading a 120-member development team for the Department of Defense, Kristina brings a unique perspective on AI's role in workforce transformation. They explore misconceptions about AI-driven job displacement, the critical importance of addressing systemic barriers in education and training access, and how technology can enable pathways to quality employment for millions facing economic barriers.

 

Key Takeaways: 

  • AI technology serves as an enabler for workforce development, but millions still struggle to access quality jobs, education, and true pathways to economic advancement.
  • Generative AI's explosion has made it clear that technology must enable equitable access, affecting how people learn, acquire skills, and navigate career opportunities.
  • Addressing the widening wealth gap requires confronting systemic barriers in policy, practices, and systems that disproportionately affect communities based on gender, race, and geography.
  • Quality jobs encompass more than wage increases—they include learning and development access, benefits, social capital, and supportive networks for long-term career growth.
  • Personalized learning and career discovery tools integrating assessments can help individuals navigate changing job markets and map skills pathways more effectively.
  • Public-private partnerships, on-the-job training programs, and employer investment in employee skills development are essential for regional workforce transformation.
  • Creating an "all hands on deck" moment requires mobilizing innovators, school systems, employers, and capital to provide support systems regardless of zip code or background.


Insightful Quotes:

"How do we get more innovators, school systems, programs, and employers to get on board and provide the support and systems needed so that everyone in our communities is able to discover and navigate through our system to achieve their highest potential?" - Kristina Francis

"We see AI technology as an enabler. There are millions of people that are struggling to access quality jobs, education and training, and true pathways to economic advancement. We see a widening gap in access and outcomes, particularly when we look at gender, race, and other areas." - Kristina Francis

"I would tell myself not to be afraid of the unknown and to not expect people older than me or people who are managing me to have all the answers. We're all the time—people have the opportunity to use their voice and what comes uniquely to them to help a situation." - Kristina Francis

Tune in to discover how technology can transform workforce systems to create equitable pathways for millions facing barriers to quality employment and economic advancement.


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Podcast Transcript: AI Disruption, Workforce Development, and Economic Inequality

Transcript introduction

This transcript captures a conversation between Seth Earley, Chris Featherstone, and Kristina Francis about AI's impact on workforce development, exploring misconceptions about job displacement, the critical importance of addressing systemic barriers in education and employment access, and how technology can enable equitable pathways to quality jobs for millions of Americans facing economic challenges.

Transcript

Seth Earley:Welcome to the Early AI Podcast. I'm Seth Early.

Chris Featherstone:And I'm Chris Featherstone. And we're really excited to introduce our guest today. And we're going to talk about a lot of very topical issues around AI disruption and job replacement, workforce development, wealth gap, economic inequality. I know that's getting into some, you know, areas that people are kind of radioactive, but we're going to talk about them anyway. We're going to dive in. Alternative ways to achieve success in this technology landscape that's changing so quickly. So our guest is a seasoned professional who's had two impact— two very impactful decades that cut across management consulting, software development, engineering, and cybersecurity. Beginning in the database administration work at the American Institutes for Research, she's progressed from an individual contributor to leading a team of 120 developers for the Department of Defense. In 2016, she changed her career a bit, marked by founding of a consulting company, angel investing in women-owned tech ventures, and diving into workforce opportunities. Currently, she's the executive director for JFLabs at Jobs for the Future, and our guest offers a unique perspective on the, on the present and future of workforce and education, emphasizing innovation, disruption, and anticipating the implications of emerging technologies. Christina Francis, welcome to the show.

Kristina Francis:Thank you so much for having me here.

Seth Earley:Yes. So I wanted to start off you with, know, what are the things that you hear or see? What are the big misconceptions about AI and job replacement or disruption? You know, we talked about in our intro call, we talked about how there's a mismatch between skills and jobs. But let's just kind of get your perspective on what do you think people are not understanding at this point? What do you think the misconceptions are about disruption.

Kristina Francis:Absolutely. Well, again, thanks for having me here. I just had Thanksgiving, and I think I talked about AI in so many different facets. And it was amazing because we had 4 generations at the table. My grandmother was there, my mother, myself, and my 16-year-old daughter and 15-year-old son. And so, you know, when you talk about misconceptions, so one thing I do want to just hit on, and you kind of noted it just around the work that we do at Jobs for the Future, You know, we see AI technology as an enabler. But a couple of things that we talk about a lot is that there are millions of people that are struggling to access quality jobs, education and training, and true pathways to economic advancement. There have been barriers in the work that people are doing across workforce and education around policy and practices and systems. And we see a widening gap in access and outcomes. And as you mentioned, kind of wealth-based opportunities, and particularly when we look at gender, race, and other areas. And so I want to just to put that out front because that's the lens that I typically look at the work we're doing. And at JFF, we believe that technology is a great, one of our greatest potentials to help achieve our North Star, which is in 10 years getting 75 million Americans who face barriers into a quality job. And for us, that's not just wage or salary increase, but that's learning and development access, access to benefits, access to social capital, people to support them, et cetera. And so we've always had artificial artificial intelligence on our, it was one of our bullet points on our strategy around insights and the work that we're doing. And we'd begun research in the area around this technology in the labor market. But with the explosion, as we'll call it, of generative AI about a year ago, I guess now. Yeah, it's a year now. It became really clear for us that we needed AI to enable our North Star and that it affects everything that we do. And within workforce and education, it affects how people how people learn, how people acquire skills, how people navigate, how people make decisions about their pathway forward.

Seth Earley:Yeah. So I want to just dig in on something that you said because I think it's really important about how AI both enables and potentially exacerbates some of the issues you're talking about. So for example, you know, if people don't have the skills today and they're locked out of the economy or they're locked out of certain types of jobs, AI can enable them to get those skills or to do things they couldn't do before, give them new kinds of capabilities. But if they don't have access to AI, then it might actually make things worse. Can you kind of give me your perspective on that?

Kristina Francis:Yeah. I think, you know, there's a couple of ways to look at it. And we're really looking at everything we can, you know, looking at it from different angles around what we see in terms of opportunities. So we did a listening tour with about 100 different individuals across the country, really trying to get their sentiment on what's happening with AI. And we actually use the term generative AI to be able to dig into this particular moment because we've always been using AI. We just haven't called it what we called it. And so we wanted to really dig into that. We'd hear from people in the workforce development system or education system, policymakers, employers, job seekers, and workers. And I think one of the things that came out, which is really like you just said, is the access piece and that it was a tool that we definitely see could have implications in the labor market. But it really is just as every other technology, you know, if it could exacerbate. And so we actually had, we have a report that just launched and it's available on our website. But one of the things that I'll just put out there is that the things that, you know, I'll talk about kind of the concerns and then what we're excited about.

The concerns are what you said. It's like, if we have a widening access— if we have a access gap that's widening between those who can access and those who can't and if we see biased systems or if these technologies have biases built into them, which we know are, we will perpetuate systems that have existed for quite some time that were barriers. And so what we want to do is really think about how do we use this and I put air quotes around use this or how do we incorporate it in ways that can be beneficial? And so some of the things that, you know, if you think about opportunity, you know, I mentioned quality jobs. So there's quality work that people— that people are doing. There's this whole notion of are jobs going to be eliminated? I think as we look at this history of technology, there's going to be changes and disruptions. There will always be jobs. I think we— I agree with a lot of the research that's come out that says that there will be augmentation, there'll be shifts, but it's really thinking about how do we give people the information, the skills, and pathways to being able to access that?

But I think it goes beyond that. We have in this country particularly significant skills mismatch. And even without generative AI, we have a mismatch between people with the skills and the ability to do a job and how job descriptions are written and how skills are designated for that role. And so we're very excited about some of the opportunities we're seeing through AI-enabled tools to be able to match those people with open positions and the skills that they have so that even though a job description may say I need you to have a bachelor's degree or four-year degree or I need you to have these five skills that are written out in a certain way, we can actually match the skills of the person using AI and large language models to be able to say, actually, this person has the same skill set, they're just written a different way or it's a different version or it's a different title. And so you can match people with opportunities in a different way.

We're also very excited about personalized learning. So if you think about kind of the career discovery navigation. One thing that we always talk about, when I talk to someone and I say, have you worked with a career navigator or counselor? And most people say no. And when they do, they don't necessarily have a good experience because that individual is overwhelmed. They may have, you know, hundreds of individuals that they need to serve. And so we believe that there's an opportunity through personalization and using these technologies to be able to meet people where they are. So if I can integrate an assessment, I can integrate skills or knowledge of something and I can help you create or show you a pathway and then continue to adjust as you interact and talk, that gives people the visibility into their choices and an opportunity to make informed decisions. That is something that I wish I had when I was 16 years old or even when I was 21 years old. To be able to just even visualize and see what opportunities are available. And so we're very excited about some of the tools, some of the companies that are thinking about those things.

Chris Featherstone:I want to ask you about that. So what— when you say personalized learning, what does that mean exactly? Like, is that— are you talking about like an AI tutor that works with you one-on-one or are you talking about something else?

Kristina Francis:Yeah, I think it could be any or all of those. So when I think about personalization, I think about it in the sense of meeting people where they are. So if I'm a learner and I'm trying to understand something, you can use conversational AI to be able to interact with me in a way that helps me get to the root of what I'm trying to understand or helps me to learn in the way that I need to learn. Everyone learns differently. Some people are visual learners, some people need to read, some people need to hear, some people need to practice. And so being able to integrate those types of mechanisms through technology is what I'm talking about in terms of personalization.

But I also think about it from a career navigation standpoint. So if I'm trying to understand what's my next step, what job should I go into, what skills do I have, what skills do I need, I can integrate assessments. I can integrate what you've already done. I can integrate your resume or your work history. And I can use that information through a conversational interface to help you understand what your options are and what you might want to consider. And then as you make decisions and you continue to interact, the system can learn from you and adjust and provide you with more relevant information. That's what I mean by personalization. It's really about meeting people where they are and providing them with the information and the support that they need in the moment that they need it.

Seth Earley:So I want to go back to something you said earlier about the skills mismatch. And I think this is really important because I think a lot of times when we talk about workforce development, we talk about training people for the jobs of tomorrow. But what you're saying is that there's a lot of people who have the skills today, they just can't get matched with the jobs because of the way that job descriptions are written or the way that skills are articulated. Can you talk a little bit more about that?

Kristina Francis:Yeah, absolutely. So I think, you know, if you look at the labor market today, there are millions of open jobs and millions of people looking for work. And yet we have this disconnect. And a lot of it has to do with how we've structured our systems. So if you think about a job description, a lot of times job descriptions are written in a way that's very prescriptive. It says you must have a bachelor's degree, you must have five years of experience, you must have these specific skills in this specific way. And what that does is it excludes a lot of people who could actually do the job but don't meet those specific criteria.

And so what we're seeing is that there are tools that can use AI and large language models to be able to look at a person's skills, look at their experience, look at what they've done, and match them with opportunities even if they don't meet those traditional criteria. So for example, if someone has been working in customer service for five years, they have a lot of skills that could translate into other types of roles. They have communication skills, they have problem-solving skills, they have the ability to work with people. But if a job description says you need a bachelor's degree in business administration, they're going to be excluded.

But if you can use AI to look at their skills and match them with the requirements of the job, you might find that they're actually a really good fit. And so what we're excited about is the opportunity to use these technologies to break down some of those barriers and to help people find opportunities that they might not otherwise have access to. And I think that's really important because when we talk about economic inequality and the wealth gap, a lot of it has to do with access to opportunity. And if we can use technology to create more pathways into quality jobs, then we can start to address some of those systemic issues.

Chris Featherstone:So I want to ask you about the employer side of this. Because what you're describing sounds great for job seekers. But what about employers? Are they willing to rethink the way they hire? Are they willing to look at candidates who don't have the traditional credentials? What are you seeing in terms of employer attitudes?

Kristina Francis:Yeah, I think it's a mixed bag. I think there are some employers who are very forward-thinking and who are really trying to figure out how do we access talent in new ways. And they're willing to rethink their hiring practices. They're willing to look at skills rather than degrees. They're willing to invest in training and development. But I think there are also a lot of employers who are still very much stuck in the traditional ways of doing things. And so I think one of the challenges is how do we get more employers to shift their mindset and to think differently about talent?

And I think there are a few things that can help with that. One is public-private partnerships. So if you have workforce development organizations working with employers to help them understand the talent that's available and to help them think about how they can hire differently, that can be really powerful. Another is on-the-job training programs. So if employers are willing to bring people in and train them on the job, that can open up opportunities for people who might not have the traditional credentials. And I think the third thing is just investing in your existing employees. So rather than always looking externally for talent, how do you invest in the people you already have and help them develop the skills they need to move up within your organization?

I think those are all things that can help shift employer attitudes. But I also think there's a role for policy. So if you have policies that incentivize employers to hire in different ways or to invest in training and development, that can also help drive change. So I think it's going to take a multi-faceted approach, but I do think there's momentum building around this idea of skills-based hiring and really thinking differently about how we access and develop talent.

Seth Earley:So I want to shift gears a little bit and talk about the equity piece of this. Because I think one of the things that concerns me is that as we deploy these AI technologies, if we're not intentional about equity and inclusion, we could actually make things worse. So can you talk a little bit about what JFF is doing to ensure that as we think about AI in workforce development, we're thinking about equity from the beginning?

Kristina Francis:Yeah, absolutely. So equity is at the core of everything that we do at JFF. And I think when we think about AI and workforce development, we have to be really intentional about how we design and deploy these technologies. And I think there are a few things that are really important.

One is stakeholder engagement. So we need to make sure that we're including the voices of the people who are going to be most impacted by these technologies. That means job seekers, that means workers, that means people from communities that have historically been excluded or marginalized. We need to make sure that they're at the table as we're designing these systems.

Two is we need to be really thoughtful about data and bias. So if we're building AI systems that are going to be used for hiring or for matching people with opportunities, we need to make sure that the data we're using is representative and that we're actively working to mitigate bias. Because if we're using historical data that reflects historical patterns of discrimination, we're just going to perpetuate those patterns. So we need to be really intentional about the data we use and how we use it.

Three is we need to think about access. So who has access to these technologies? Are they available to everyone or are they only available to people who can afford them or who have internet access or who have the digital literacy to use them? We need to make sure that as we deploy these technologies, we're thinking about how we make them accessible to everyone, not just to some people.

And then four, I think we need to be thinking about accountability. So as we deploy these systems, how do we make sure that they're working the way we intend them to work? How do we measure their impact? How do we make sure that they're actually creating opportunities and not creating barriers? So I think those are all things that we need to be thinking about as we move forward with AI in workforce development. And at JFF, we're really trying to be leaders in this space and to make sure that we're centering equity in everything that we do.

Chris Featherstone:So I want to ask you about the policy side of this. Because it seems like a lot of what you're describing requires not just individual organizations doing the right thing, but also having the right policy frameworks in place. What role do you see for policy in all of this?

Kristina Francis:Yeah, I think policy is critical. I mean, I think policy can either enable or constrain innovation. And so I think as we think about AI and workforce development, we need to have policy frameworks that are supportive of innovation but that also ensure that we're protecting people and that we're creating equitable systems.

So I think there are a few areas where policy can play a role. One is around data privacy and security. So as we're using AI and as we're collecting data about people, we need to make sure that we have strong data privacy and security protections in place. People need to trust that their data is going to be used in ways that are beneficial to them and that it's not going to be misused or exploited.

Two is around funding. So I think there's a role for public funding to support workforce development initiatives, particularly those that are focused on serving populations that have historically been underserved. And I think there's also a role for funding to support the development and testing of new technologies and new approaches.

Three is around regulation. So I think we need to have some guardrails around how AI is used in hiring and in workforce development to make sure that it's not being used in ways that are discriminatory or that create barriers for people. But we also need to make sure that regulation doesn't stifle innovation. So I think it's about finding the right balance.

And then four, I think there's a role for policy in terms of education and training. So how do we make sure that people have the digital literacy and the AI literacy that they need to navigate this changing landscape? How do we make sure that educators and workforce development professionals have the training and support they need to use these technologies effectively? I think those are all areas where policy can play a really important role.

Seth Earley:So I want to ask you about the future. When you think about 5 or 10 years from now, what does success look like? If we get this right, what does the workforce development system look like? What does opportunity look like for people?

Kristina Francis:Yeah, I think if we get this right, I think we have a system that is much more personalized and much more responsive to individual needs. I think we have a system where people can easily discover opportunities, where they can easily navigate pathways, where they can access the training and support they need when they need it. I think we have a system that is much more inclusive and that creates opportunities for people regardless of their background or their circumstances.

I think we have employers who are much more willing to hire based on skills rather than based on credentials. Who are willing to invest in their employees and help them grow. I think we have educational institutions that are much more responsive to labor market needs and that are creating programs that are aligned with what employers are looking for.

And I think we have a system that is much more equitable. Where the barriers that exist today—the barriers based on race, based on gender, based on geography—are significantly reduced. Where people have access to quality jobs with good wages, with benefits, with opportunities for advancement. Where people have the support and the resources they need to succeed.

I think that's what success looks like. And I think we have the tools and the technologies to get there. But it's going to require intentionality. It's going to require collaboration. It's going to require investment. And it's going to require a real commitment to equity and inclusion. But I think if we do those things, we can create a workforce development system that works for everyone.

Chris Featherstone:So I want to ask you about some of the challenges. What keeps you up at night? What are you most concerned about as you think about the future of AI and workforce development?

Kristina Francis:Yeah, I think my biggest concern is that we don't move fast enough and that we miss this opportunity. I think we have a moment right now where there's a lot of excitement and energy around AI and around what's possible. But I worry that if we don't act quickly and if we don't act intentionally, we're going to end up with systems that perpetuate the problems we already have or that create new problems.

I worry about the access gap widening. I worry about bias being baked into systems. I worry about people being left behind. And I think the challenge is that technology moves so quickly and our systems—our education systems, our workforce development systems, our policy systems—they don't move as quickly. And so I think we need to figure out how do we create systems that are more agile, that can keep pace with the speed of technological change.

I also worry about the human element. I think as we deploy more and more AI and automation, we need to make sure that we're not losing sight of the human relationships and the human support that people need. Because I think at the end of the day, people need connection. They need mentors. They need coaches. They need support. And technology can enable that, but it can't replace it.

And then I worry about the investment piece. I think we need significant investment—both public and private investment—to make this vision a reality. And I worry that we're not going to see the level of investment that we need. So those are the things that keep me up at night. But I'm also really optimistic because I think there are a lot of people and organizations who are committed to getting this right and who are doing really important work in this space.

Seth Earley:So I want to ask you about JFFLabs specifically. What are you all working on? What are some of the projects or initiatives that you're most excited about?

Kristina Francis:Yeah, so at JFFLabs, we're really focused on testing and incubating new ideas and new approaches. And so we have a few different focus areas. One is around personalized learning and career navigation. So we're working with a number of companies and organizations that are building tools to help people discover opportunities and navigate pathways. And we're really trying to understand what works, what doesn't work, what are the best practices, what are the pitfalls to avoid.

Another area we're focused on is around skills matching and hiring. So we're working with employers and with workforce development organizations to test new approaches to matching people with opportunities. And we're really excited about some of the AI-enabled tools that are out there that can help break down barriers and create more inclusive hiring practices.

A third area we're focused on is around AI literacy. So we're thinking about how do we help workers, how do we help educators, how do we help employers understand AI and how to use it effectively. Because I think as these technologies become more prevalent, it's really important that people understand what they are, how they work, what their limitations are, and how to use them in ways that are beneficial.

And then we're also doing a lot of research and thought leadership around AI and the future of work. So we're trying to understand what are the implications of these technologies for the labor market, what are the opportunities, what are the risks, and how do we navigate this transition in a way that is equitable and inclusive. So those are some of the things that we're working on. And I'm really excited about the potential to create new models and new approaches that can then be scaled and adopted more broadly.

Chris Featherstone:So I want to ask you about the role of regions in all of this. Because it seems like a lot of workforce development happens at the regional level or at the local level. What role do you see for regions and for local communities in shaping the future of workforce development?

Kristina Francis:Yeah, I think regions are critical. I mean, I think every region has its own unique economic ecosystem, its own industry mix, its own talent base. And so I think solutions need to be tailored to the specific needs and opportunities of each region. And I think there's a lot of innovation happening at the regional level.

I think one of the things that's really important is bringing together all of the stakeholders in a region—so employers, educational institutions, workforce development organizations, community organizations, policymakers—and really thinking about how do we create a coordinated approach to workforce development. Because I think too often these entities are working in silos and they're not talking to each other and they're not coordinating their efforts.

I think there's also a role for regions in terms of being testing grounds for new approaches. So if you have a region that's willing to try something new and to be innovative and to test new models, that can create learning that can then be shared more broadly. And I think that's really valuable.

I also think there's a role for regions in terms of creating sector partnerships. So bringing together employers within a specific industry and working with them to understand their talent needs and to create training programs and pathways that are aligned with those needs. I think that kind of demand-driven approach can be really powerful.

And then I think there's also a role for regions in terms of investing in infrastructure. So making sure that people have access to broadband, making sure that people have access to training facilities, making sure that people have access to transportation so they can get to work. I think those kinds of investments at the regional level are really important for creating opportunity.

Seth Earley:So we're getting close to the end of our time here. But I wanted to ask you, what question should I have asked you that I didn't ask you? What's the question that you wish people would ask you about this work?

Kristina Francis:That's a good one. Because I think we talked about quite a bit, and I appreciate being on an AI podcast where we can come down to the human aspects of the work. And you guys, both of you just mentioned your families and individuals in your families. You're able to see this opportunity and challenges and visualize people in your family that may be impacted positively or negatively by the systems that we have set up. So I think a question that I would ask, and this is probably more for the audience, is how do we create an all hands on deck moment at this time? You know, how do we get more innovators to, you know, put their ideas on paper and start testing? How do we get more school systems, programs, you know, states to test and test new models or integrate new models? How do we get more employers to do the same? And then how do we get money and capital to flow in the communities regardless of where it's coming from to make sure we can provide the supports and systems needed so that everyone in our communities, regardless of their gender, their color, their zip code, no matter the support that they get from their families or not, are able to navigate, to discover, to navigate and move throughout our system so that they can achieve their highest level of potential.

Seth Earley:That's awesome. I know we're running a little bit out of time here, but I did want to ask you, what do you do for fun?

Kristina Francis:This is fun for me, Seth. Okay. No, I, I roller skate. And so I have started roller skating post, uh, not rollerblade. I rollerblade too. I actually rollerbladed before the pandemic but started skating because I want to roller skate like Usher. If you don't know who Usher is, you can look up videos. He is the smoothest roller skater ever. Um, and I've also picked up the saxophone. So, oh wow, that's, that's new.

Seth Earley:Is music— do you play other instruments?

Kristina Francis:No, I played the saxophone in 4th grade, 3rd and 4th grade, and then had to stop. And, you know, in pandemic, I went back to what did I love that I gave up and bought myself a saxophone and just enjoy that sound.

Seth Earley:That's wonderful. I missed out on music myself. And, you know, I know it's never too late, but the question is, how do you juggle all of those things together? You know, I still try to skateboard a lot, but I feel like I'm going to have a hip replacement soon.

Chris Featherstone:Yeah.

Seth Earley:Here's one other question I have for you. If you could go back and talk to yourself when you first graduated college, what kind of advice might you give yourself? Or in retrospect, with this lens, this experience, what would you tell yourself?

Kristina Francis:You know, I, I would tell myself not to be afraid of the unknown and to not expect people older than me or people who are managing me to have all the answers. Right. I think a lot of, a lot of times we lean on others to help navigate. And I think we're all the time, people have the opportunity to use their voice and what comes uniquely to them to help a situation. And oftentimes we silence our own thoughts in ourselves. And so I would encourage myself to be even more curious than I was. I was very curious and still am an optimist. But, you know, kind of pushing the status quo more earlier and working, not being afraid to work with others from the beginning.

Seth Earley:Where can people find you, Christina?

Kristina Francis:Um, LinkedIn is my preferred social media, so Christina Francis on LinkedIn. And then if anyone wants to send an email, kfrancis@jff.org. And happy to speak to anyone about the work that we're doing, or even to challenge some of the assumptions that we have.

Seth Earley:Wonderful, wonderful. We'll have that in the show notes as well. Well, it's been Fantastic having you. I really enjoyed the conversation. Thank you so much for joining us today.

Kristina Francis:Absolutely. Thank you both. I appreciate it.
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