Amy Wilson, Product Strategy Advisor at Reejig

Siobhan Savage: Right. Hello everyone. Welcome. Welcome, everyone. To Skills Connect. This is our podcast where we have conversations with the most bold and most responsible leaders in the world. I'm Siobhan Savage. I'm a CEO and also co - founder of Reejig. Today I'm joined by the incredible Amy Wilson. Amy is an expert in all things product strategy, and I'm lucky to call her one of my own advisors at Reejig.

She's a visionary SaaS executive with a career that spans some of the most transformational moments. In enterprise tech. Amy has led the global teams across SAP, SuccessFactors and Workday and scaling complex businesses and championing thought leadership and innovation and talent, workforce strategy, all the things we love to talk about in terms of human capital and management.

She's known for her ability to spot unmet needs, turn bold ideas into strategy and to rally teams to deliver with heart and purpose. And I can tell you firsthand that she has had a massive impact with us in very short space of time. So, welcome, Amy. I. Thanks so much, Shavonne. That was quite the introduction.

You didn't pay me for that. I didn't know. So Amy, you have LED product strategy for SAP for Workday and now your advisory. Jake, from that advantage point where you have worked with some of the most transformational technology companies of essentially HR work tech, I would call it more enterprise tech. I think we talk about Workday and SAP and SuccessFactors as HR Tech.

They're not, these are transformational enterprise systems that the whole businesses and enterprises of the world are built on. And now you're helping us build into what will be a once in a generation change to work and a product. What do you think enter, enterprise HR tech has consistently got wrong about workforce intelligence.

So how do you feel about that move that we're making?

Amy Wilson: Yeah, I think that the term work intelligence and to some degree workforce intelligence is a bit new. So I'd to step back and talk a little bit about workforce planning, which has been this really tough nut to crack in the enterprise space for.

Decades, to be quite honest. Mm - hmm. And the main reason for that is that there's been incomplete data, right? So companies have wanted to be able to predict the future, so that they, their people and their businesses can thrive. But how the heck do you do that, right? Especially if you don't have all the data.

And what we've seen in the past is that there's been. An attempt to boil the ocean. So just so many assumptions, so many variables. It's quite frankly, overwhelming. And as a result, a lot of. Companies, a lot of leaders have just fallen back into just doing headcount planning. Mm - hmm. Just very operational workforce planning and then putting strategic workforce planning to the side and doing a lot of hand wringing over that.

Right. And so, this has been that tough nut to crack and to be honest. The skills push that we have seen over the last years has actually brought a lot of clarity to solving the problem, because at least it has narrowed the focus. Finally, leaders have been able to specifically say, Hey, we're going to have.

A different need for skills in the future. There is going to be a skills gap. There is a skills gap. So how do we solve for that? And the narrowing down of that problem really has allowed a focused sense of innovation and exploration in the industry. That has been really quite remarkable. Hmm.

And so now a lot of companies at least have an idea of where their skill gaps are. That's super helpful, but it's not enough. Yeah,

Siobhan Savage: yeah. And it's focused, as you said. I love the point that you made, it was all about headcounts versus actually strategic. Workforce allocation, workforce strategy, that whole part was quite separate.

And I think to your point, it's not because folks weren't trying it was because the data was the problem. Right? Yeah. And I always hear folks complaining about their systems and complaining about, this is clunky, or this doesn't work, or It's not amazing. But actually if you look closely, most of the time the problem is data.

The systems are not being given the data they require to do the thing that they need to do. And I think that's the part where, over the last, couple of years we've been very focused on, let's solve for that problem, which is the data problem. And, the crazy thing that I found out at the early part of Reejig was, no one knows what anyone's doing.

Yeah. Literally, absolutely. No one, the best companies in the world have no idea what their people are doing. Yeah. And the framework of how we've designed workforce planning is exactly what you said based on headcount, and based on job titles and people. But that's actually not.

Workforce planning. That's not at all that. Yeah, and I think that's

Amy Wilson: counting heads and money and that's it. And so it has no relation to what the company actually needs to do to equip themselves for the future. Yeah. And so the skills focus has really.

Been great and I have a confession to make, which is that honestly, I thought it was the panacea. I thought that was the end game. Yeah. Up until recently and you schooled me, shavonne, that, but it was a huge step forward. It really was. But it's not predictive of the future, right?

Mm - hmm. Because if you are. Looking at what's trending now in job postings. In resumes, then the best you can do is predict next month. But yeah. But more likely you're predicting a couple years ago?

Siobhan Savage: Yeah, I think the only for context for listeners as well, the only reason that Amy and I have had this epiphany moment is because we made all the mistakes.

Ourselves, I spent $40 million building skill centric technology. Right? How much money did you spend? We all did the same thing, right? Yeah. Right. But at that time, that was the moment. That was what was available. That was what was possible. The only reason that you and I had such a deep moment of, huh, was because I had literally learned so many things on the way to getting to this moment.

this wasn't, I woke up one day and decided that I'm going to move towards tasks and work this was a consistent, repeatable thing that I was seeing across our customer base where. People had skills, absolutely. But the jobs and the work and the architecture in which the company that was being built on was just for me, built in the dinosaurs and it wasn't actually relevant for the world that I was in.

And I found exactly the same thing that you said about the data. That was the problem. So I think by the time I'd got to you, I had done about 10 laps around this problem and I'm sure, I was literally Sure.

but yeah, I think, and you recently wrote a piece, which I love on the, so it was the shift of talent intelligence to work intelligence. And it's essentially, and I'll link it here folks for you to have a read, but it was where you argue that skills data alone isn't enough.

What's the most common misunderstanding? What's the common most, the most common misunderstanding you see between skill focused tools and true? Think about the way re jigs doing it from a true work intelligence and how should leaders rethink their investment? Especially now, look at the market that we're in, it is.

Potentially recessionary again. Mm - hmm. We're back into this everyone is freaking out again mode. It feels that pre covid moment where people and businesses, if you are in any form of Nike or selling physical products and you've got offshore development, you are in serious trouble that.

so all of these businesses that are going from they're already having a tough time. Talent intelligence and work intelligence are two very different things for businesses when they're in this moment. Right. So how would you advise leaders on, the difference between the two?

Amy Wilson: Well, so what I was starting to say before was that, the whole point is to predict what do you need to do in the future, right?

And so if you're just looking at what skills are currently trending, then that's going to give you a picture of the past and not the future. And. In order to start to predict the future, you really need to get at the root problem. And the root problem is not skills. Mm. That is a symptom of the problem, right?

The fact that we won't have the skills that we need to do the work in the future. There's that word work, right? Yeah. So the root problem is understanding the work. It's understanding. Who's doing the work, what the work is, what actually needs to get done in order for the business to thrive.

Mm - hmm. And if you don't look at that first and you're just looking at skills in isolation, then you're trying to beat out the symptom instead of going to the foundation. And I'm telling you this, but, this is the revelation that I have had based on.

What you have shared with me with all of your experience.

Siobhan Savage: Yeah. And what was really interesting, the market has such a. Deep impact. So when we started thinking about work, we started thinking about work more as a solution to solve the talent intelligence problem, if I'm really honest, right?

Mm - hmm. So this was pre - chat, GPT and a large language models. This was why are the job matches not great? Well, the ultimate thing we realized was that people have skills. Work doesn't have skills, and jobs don't have skills. Tasks. They have tasks, and you require skills, right? What ended up happening very soon after we built the work ontologies was then the market started to change and the large language models and chat, BT became every CEO was on the radar, right?

And suddenly we realized that another thing that we were getting wrong was that in the talent intelligence side of things, you're looking at jobs and impact to jobs based on skills. And AI doesn't automate skills. AI automates the task. And that was another thing where it was, hold on a moment, I've got this one wrong too.

Because we spent money on building, a model that could predict skills impacts. Right. So then that happened. And then now we're in a market where, especially I've noticed this in the US since I've moved over, there is a lot of operational efficiency, cost optimization. Mm - hmm. Productivity. It's very intense over here in terms of the worker, expectations around work.

It's very different from apac. Right. There is definitely a different vibe between how work happens over here. But that actually signals that what we've seen with these customers is that actually, it's not that they don't care about their people, they absolutely do, but they don't really care about career pathing and things that.

In a market where the CEO is trying to keep the business above water. Exactly. And that they're in this different place and what's become really valuable and why we've been growing so fast, as. Is because we are actually diagnosing the problem exactly the way you said it. We're not looking at the symptom, we're looking at the root cause of what's going on and can we tell you and predict your future based on where you wanna take it and what will be the symptom of that based on your workforce impact?

And then how do we think about evolving your workforce? So that was really where we have noticed this big shift to more. The work intelligence is also not hr. Yeah. Yeah. That's the other thing. Remember when we did this product strategy session, although it

Amy Wilson: matters to hr, it matters

Siobhan Savage: absolutely to HR.

Tremendously. Yeah, absolutely. Absolutely. And I think that it becomes a full business. And the other thing that we noticed was that skills are the HR folks language for describing work. But if you talk to a business leader, they don't really talk about skills. They talk about the things that they need done.

And sure there is the skills that are required to do those things, but it's not the natural language, nor is the job architecture. And I've been coming hard after the job architecture for the last 18 months because when you go into companies and you see how limited they are in terms of understanding the structure of work and jobs that has been where I think there'll be this, big change when it comes to real ai.

And if you think about people deploying agents. That job architecture becomes pointless in a world where we're taking out tasks Jenga, right? That whole idea that we'll take some out, we'll put some on, that's what's gonna be happening. So I think your article around that move and that bringing folks on the journey from, talent intelligence to work intelligence, I think becomes really important, especially today in this world.

Right?

Amy Wilson: I think that, what's been interesting is that the advent of AI being such an important part of the work operating system, so to speak, has really illuminated what the root cause of the problem is, right? Mm - hmm. So maybe we would've, if this hadn't happened in the last year and a half or so, maybe we would've just continued on that.

Isolated skills journey for mm - hmm. For a while and thought, okay, well we're making progress and so on, but now all of a sudden there's. It just doesn't matter because mm - hmm. There's such huge change. There's such, tremendous, opportunity and then also drawbacks in terms of the way we're working and the impact to the workforce.

So yeah, to continue to build the skills that our trending isn't going to help the workforce at all.

Siobhan Savage: Mm - hmm.

Amy Wilson: If AI is taking away all of the tasks and the work that people are doing, with those skills, right? Yeah. So we need to get a lot more strategic and have a much better understanding of what is the work that humans are going to be doing, and what is the work that AI is going to be doing.

And then build out those talent strategies, those talent programs based on what actually is going to be needed, in the future. Yeah. And that's amazing that we have that data now to be able to do that because that is a gift to our workers and to our businesses, to be able to.

Thrive in the future.

Siobhan Savage: Yeah. The other thing as well, the, a lot of the skills projects that had this whole excitement but then sizzled out a little bit, I've seen a lot of folks stepping back a little bit and I think a lot of the, again, comes back down to the quality of data.

if you're fairing skills off a job advert and the job advert is crap. Rubbish and rubbish eye, right? It's, and that was the thing when we started, we were, the data we were getting was terrible and then we were trying to infer skills and we would've the most randomest things that was quiet because it was pulling random stuff out of the marketing part of it,.

and, I built ai, right? So I can say this openly. It's not a magician. It literally needs to know, what are the skills? So that was a big problem as well, that a lot of the skills products also were pulling in not great data. Therefore, as a user, I'm not gonna trust it.

So that was Okay. Another problem. And then the other problem, which is what we spent time talking about you, when you were in your last role. Was this, harmonization, if everyone is using different skills, languages from hiring, from moving, from mobilizing to resource allocation to learning, it becomes honestly, a shit show.

there's just so much confusion and that was the whole emphasis of why you guys went down that road around harmonization, the governance layer, because governance became a very important topic, right? Yeah. Yep. And I think that's where I think customers are, stuck right now.

They're, oh, but our learning team have bought this and, recruitment are doing this. And it's, I truly believe that actually none of this should be skills. This should be one common language of work. So think about your organization needs an infrastructure for it to operate.

Reejig becomes the critical infrastructure that is enabling your work to worker. Orchestration. Mm - hmm. So that is made up of a work architecture, which is the compliance part that we need to have because we know some level of hierarchy for mm - hmm. Unions, EA agreements, et cetera, that we need to know, some level of rem we need to know what that looks.

But then we've got the ontology, which is then essentially the, what is people doing, what are all the tasks and subtask and skills. And I think that becomes. The critical infrastructure, which skills are then being connected to, so we can basically see, well, what are the skills that are required to do this work rather than us.

Pump in data that is job adverts, position descriptions, and pulling out the skills that becomes the inference layer and the enrichment layer of, actually what's happening. And then you should be feeding that in and out of all of the systems or feeding it into a, an SAP or a workday or one of those systems.

Mm - hmm. That was what you and I had started really talking about when we first met as well. Right.

Amy Wilson: Right. Yeah. And what also is really interesting is, in parallel to the skills. Revolution that's been happening in the industry over the last few years has been the advent of agile teams and side gigs.

so even inside enterprises, right? So, we're here, gig economy, but even inside a company. And so I think that this influx of focus on agile teams and side gigs was another omen of what was to come, right? Mm - hmm. We need to get much more agile in terms of how we think about jobs and work and orgs and people, because frankly, our rigid structures do not work at all in the midst of change, and so, mm - hmm.

What we've been seeing over the last, five years or so, is companies bolting on side systems to try and get any visibility or make any sense of what work is actually happening with these side gigs and agile teams. But frankly, it's part of, needs to be part of the core system.

Right? It's foundational because otherwise we're. Lacking visibility and that there's, lacking visibility of what people are doing and accomplishing, in incentives are screwed up in terms of, you're reporting to somebody who cares about something completely different than what you're actually working on.

Yep. So it's just a jumble and, this means that. The org chart, the job architecture, these no longer reflect what's actually happening. No. So we need to blow it up, Siobhan. Yep. Yes.

Siobhan Savage: , I was so polite about it for the whole year. 'cause, we've been, we've built out what we've built out the work architecture, we've built out the work ontology, we have all of this in the work operating system.

We've had that for the last, two years. And we've been really evolving all the work intelligence and I think. I've been really polite about the job architecture, but I've given up now because I'm, I have not seen, and what I'm, I am deep in trenches with customers. I'm not sitting on the sidelines.

Yeah. I learn best when I'm beside a customer with my sleeves rolled up trying to figure out how to solve it, right? Mm - hmm. Because we're never gonna figure this out If I'm sitting with a product team just them feeding back, I've learned that lesson before. The only way that I can figure out how to do this is I gotta have empathy for it.

I gotta know it. I have not seen in one company yet. And I'm talking the biggest companies in the world, not one yet. That the job architecture, the consultants come in, they spend a whole pile of money on this thing. By the time they finish the project, it goes outta date. No one actually knows what to do with it.

everyone's allowed to do whatever they want within systems. There is no connection point in any way, shape, or form to actual work. That's just a labeling of work by HR folks. So, I think the blowing it up is, I actually completely agree. Everyone can go after this job architecture thing.

I'm not doing that. I'm building a work architecture.? Right. That's what we are doing. And I think that becomes the critical infrastructure for building the AI part workforce. 'cause you can put che GPT bots in front of any crap data and it's still crap data.

Right. So,. I think that's where it becomes really interesting. One of the other things that I find really interesting, right? So we're in this, I think 20, 25 year of agent, but we're in this piloty, better testy type world where customers have not skilled across agents.

Now I can see the potential of the agents across major enterprise for sure, and we're not even into full autonomous yet. But what I do see is this problem where. You can build it and they don't come. What? And I think one of the things that, you've built platforms that have been used by hundreds of millions of people.

we're talking, I can't even imagine how many people your software has touched over your career. Mm - hmm. Because you're not only looking at the business leads, but you're also in the hands of your employees too. Right. So when you're, when you're looking at that, how do you design for change? In that level of skill when AI is resha reshaping work.

how do you think, because that's gonna be the thing that's gonna trip us all up. We can roll out all these tools, but if people are not adopting, then the AI thing feels it's not gonna work a little bit for me. I'm a little bit concerned about that. How do you, what have you seen work?

What have you guys been part of before?

Amy Wilson: Well, I'm gonna go in a slightly different direction, which is my greatest hope of what we're gonna see out of HR Tech in the coming years. And that is for a company. And it could be a startup, it could be. An existing large company, but they actually build an HR system of record that supports, at its core a more agile way to organize people and work.

and not as a side hustle, not as, something that happens over here, but that it's at, its. Foundation. Because honestly, as we've been talking to customers about work intelligence and the work ontology and the work architecture, we can do so much, for companies now, but we are still, we still have to.

pump that information in into an old rigid structure. Yeah. Right. So we can help organizations redesign and re - engineer, their processes and their roles and every, we can do all of that, but as long as these changes are being pushed into, rigid existing structures,.

There is a limitation in terms of what an organization can accomplish and what the relevance, quite frankly, of that HR system of record. Right. That HR system of record isn't reflecting the modern way of working. Yeah. And what the company needs to do. So I think it's, I think it's out there.

I think it's gonna happen. Mm - hmm. It's not there yet, but that's really, what I think needs to happen.

Siobhan Savage: Well, you see platforms Ripple and Deal that are all built for the small to medium sized businesses, right? Mm - hmm. That are built with AI first and built, fully connected from day one.

they're not bolted on modules. They're actually one system that is designed for. So, it'll be really interesting to see. Product goes, even the workday. Well, I know they're built AI

Amy Wilson: first, but I don't know if they're built agile work first,

Siobhan Savage: right? Mm - hmm. So, yeah, no, the truth, they're actually people first.

They're people and numbers. You're right, they actually are. That's actually very true. And I think that's where I think the whole space is gonna change because I think that. Right now we're in this period of the pre crazy, where it's we're, I feel the can before the storm of I'm now seeing across all these customers that they're all in phase one is how I would describe it, which is they're all testing and they're all starting to deploy AI and they're using us to figure out what's the best way to do this and how to reinvent.

Right? But very quickly, once agents turn full autonomous and you actually have mature enough technology. Can you imagine what's gonna happen then? How fast this is gonna change? I think it really will only kick in 2026 that we'll start to see that true level of agent operating inside of environment.

and I think that's where it becomes your whole work operating model has to completely change. Right now we're half in the old world. Trying to put her one foot in the new world. And we are seeing that with customers, which is why we hesitated to blow up the job architecture because we didn't wanna hurt their feelings.

And just tell them, sometimes you can, from a product strategy and vision, this more than anything. You can get really vision focused, but some people are not. Most of the population are not there where you are, and you can actually isolate them without bringing them on a journey. So. I feel that's what you're saying is actually really fair because it just becomes an orchestrator.

it's that whole continuous work to worker, and the worker could be an agent, or it could be one of your flex folks, or it could be one of your employees, right.

Amy Wilson: I think it's really good advice to meet people and organizations and leaders where they are. But what's interesting is how quickly where they are is changing, right?

So the. The AI automation and AI workers that are, coming into the fourfold right now are really just illuminating these problems that have been building over mm - hmm. The past decade. Yeah. But right now, or organizations and leaders just don't have a. Choice but to redesign, right?

Yeah. They're getting pressure from the board. They're getting pressure from everywhere, right? The government, I don't, to, yeah. It's everywhere. To come up with more efficiencies, to come up with more velocity and they have to redesign. And so again, we can do so much with what we have.

but. We need, some foundations to change as well, I think. And the other thing too is to recognize is that it's not this one time thing, right? Mm - hmm. This is, it's not just that, oh, AI's here and we need a quick project to figure out what we're gonna do with ai and then next.

Year, everything goes back to the same, right? That is not what we're talking about. We are talking about just increase in change, increase in agility, increase in a focus on what work. Is to be done, jobs to be done, et cetera. And so that's why I feel we really need to blow up how we think about work and jobs, mm - hmm.

In general. And it's something that's going to sustain for the long term, not just, a this year project.

Siobhan Savage: Yep. And I think the AI potential of the job is the tiniest part of the puzzle. Yeah. 'cause actually the magic is the re - engineering. How do you the Jenga blocks?

What are you taking out? What are you adding in? There's this rebuilding that's gonna happen. And what's really funny because I keep talking about Jenga blocks. I keep seeing them everywhere. These massive, New York has all these random, massive Jenga blocks in the city. It's all the buildings, everywhere I go, the universe, you have to stop and play.

Every time I'm well more, I'm just, oh, it's the universe. Tell me I'm on the right path.

It's funny. So you are, you're not just product leader, right? You are also, an early investor. Oh, sorry. You're an investor in early stage companies. What can enterprises, and enterprise leaders learn from high startups approach workforce design, speed, and innovation? So if you look at the way I build this company versus a big company, what do you reckon is, what do you see as the difference?

Amy Wilson: Well, startups and, starting a company, AI first or really anything first right? Is always going to be an advantage because a hundred percent of the people are bought in, or at least close to a hundred percent. Yeah. Or they're peer pressured in or something to the methods and the practices, that have been, incepted.

but honestly this. Advantage doesn't last that long because there's always something new, right? There's always a new technology. There's always something. And so the key is to create a culture that can sustainably adapt and learn, and moves together, right? So it's. It's really about that culture.

And the bigger you get, the harder this gets. So the competitive advantage isn't going to be about any particular tech, but instead those startups. So it's not all startups, but those startups that set up that culture of curiosity. Learning and change from get go. And honestly that is what I've seen from Reejig being, up close and personal and, with some of the other startups that I've worked with, but certainly not all.

a lot of startups are just banking on, okay, some cool new tech and. Now we're gonna be great. Mm - hmm. But that's not how you build a competitive moat. You build a competitive moat by building a team that is going to be able to pivot. So for example, Reejig was all in on talent intelligence.

And the team, because of the way that you built it and the way that you operate, you are able to pivot to work intelligence. Mm - hmm. Right? Recognizing that this is the way of the future. This is mm - hmm. This is how we're really going to be able to crack this nut. And everyone's on board, right?

It's, yep. It's,

Siobhan Savage: well, to an extent. Yeah. Yeah. I think, well, 'cause I went. One in the work ontology direction. But then I also was, also, we're not hiring anyone until you can show me you haven't tried to automate this. So, it's we gotta live and breathe.

Right. What we're doing. Right, exactly.

Amy Wilson: Yeah. Yeah. Yeah. And a lot of our learnings keep leaning, comes through keep leaning on ai, keep leaning on, yeah. New. Well, it's the learnings. Be curious. Yeah. Be curious about, how we can do this better, how we can do this faster, et cetera. Yeah.

Siobhan Savage: Yeah. Yeah. And I think that's where most of my learnings are coming from. Real life practice. Yeah. Because, I'm not a consultant standing around selling services of, here's hey, theory or researchers. There's so many researchers out right now saying to the world, this is what you should do.

They do this in a lab, people, they're not doing it in real life. Yeah. Do what? This is not reality, this is not what

Amy Wilson: happens. Right? Yes, exactly. Yeah. So theoretical and then, historically, the. The way to grow in a software company in the past has been add headcount, right?

add more people, add more developers, add this, add that, and that is not the way of the future. And that's not the way that the best, culture. AI startups are beginning, they're beginning with more of a mindset of how can we do the most with what we have and how can we learn and grow?

And that sounds a little bit scary because that's less headcount, but. When you start to scale that across, solving multiple different problems in multiple different spaces. There's plenty of work for everyone, but it does require a different way of thinking about running a company.

Siobhan Savage: Yeah. And I think the one beautiful thing about that whole experience as well is when you do that, suddenly you unlock capital in the business so you can reallocate budget. And I think a lot of companies right now are, it's not necessarily just about cutting costs, it's about reallocating.

Budget to feed into other things. I was able to do that. Things that we would never have been able to do, people that we would never typically be able to hire at our stage, we were able to do because we had been through the hard bumpy moment of, and now we're in this groove where people, my team just expect it now.

if you're putting up a hire request, you gotta show me, you gotta show me that you have tried. And we've been doing that for, what, 18 months now at least. Mm - hmm. And I think that's where. That's where I think the empathy and the, not building in a lab, but building in real life and building in public was what we started to do without even realizing it.

We were building in public and telling people, Hey, this works, this doesn't work. Or by the way, I built all these agents for, doing outbound and it was really great for three months and then we completely crashed our art binding system because people were just, we were getting nothing back.

So you gotta pull back in the humans. It is this. It's just a learning curve, right? Which no one really, you don't get that unless you try 'cause it's all experimentation. So looking ahead, what is one thing that you hope every HR and every product leader gets serious about in 2025?

If they wanna stay relevant in an AI native world of work, what does that look?

Amy Wilson: So I think it's really easy for. HR folks and really anyone to see the onslaught of AI marketing as hype. Right? And to. Treat AI as a bell and whistle. So should we use AI to write our job descriptions, right?

but that is not what is actually happening and that's what people need to get past. It's not an add - on that you can ignore and then get back to regular business, right?

Siobhan Savage: Mm - hmm.

Amy Wilson: If you are an HR leader, if you are a business leader, if you are a product leader, whatever leader you are worth your salt.

You need to recognize that it is a work disruptor. It's a people disruptor. It's a workforce disruptor. Of course it's an opportunity, so it's a tremendous opportunity. So you need to get serious about how people are going to be affected, both good and bad, and then really get in front of it to shepherd the absolute best result.

Mm - hmm. Be a proponent of understanding what the work is. Be a proponent of understanding what AI tools can be used to make that work more efficient, to amplify people's work, with ai, all of the different things and what. Ultimately what that is going to mean in terms of the workforce you are building.

Yep. And if you're not Amen. Doing that, if you're still thinking that it's a side distraction, you're missing the point.

Siobhan Savage: Yep. Amy, this is incredible. I really appreciate your time. I really appreciate your just openness to, speak about the things that you've learned and give some of your expertise over for everyone.

We'll link, some of your resources in, the channel for folks as well, and we'll make sure. But Amy, thank you so much. It's, and thanks everyone for joining as well.

Amy Wilson: Yeah, thanks everyone. Great to chat and let's go do this,

Siobhan Savage: Amy. Thanks so much. All right.

Amy Wilson: Bye.