How AWS thinks about architecting the AI-enabled workforce

Siobhan Savage: Hello, welcome, folks!

 

Siobhan Savage: We're just gonna get a little minute to get everyone in and settled. Welcome, everybody.

 

Siobhan Savage: Hopefully everyone's got a nice, full drink, cup of tea, whatever one needs at this time during the day.

 

Siobhan Savage: I think we're having a backstage Ben coming in entrance, he's gonna be making… oh, here he is! Ben, I was like, I think you're making your magical entrance. I should have got my drum roll ready.

 

Siobhan Savage: Welcome, Ben, how are ya?

 

Ben Schreiner: I'm wonderful. It's great to be here.

 

Siobhan Savage: It's great to have you, and welcome to everyone online as well. These are the sessions that we bring the biggest, the smartest, the most energizing minds together to really kind of figure out what's going on in this new world of AI, and we're really, really fortunate to have Ben with us. So Ben is the head of AI and Modern Data Strategy at AWS, and a little bit of background text, we hosted together

 

Siobhan Savage: Rejig and AWS hosted, like, 50 leaders together in New York.

 

Siobhan Savage: And we were working through

 

Siobhan Savage: some of the biggest problems when it comes to, like, trying to figure out what to do with AI, how to think about, you know, redesigning AI workflows, and I got to see Ben speak, and I made my team very quickly.

 

Siobhan Savage: make, like, a beeline for him, because it was such an energizing room. He got everybody up and going, got them so excited, and also took away the nerves, which you typically see with a lot of folks when it comes to AI. So, we really were so appreciative for Ben to agree to come and do this live with everyone. So, Ben, thank you so much for coming here live to talk with everyone, and I'm really excited about this conversation.

 

Ben Schreiner: You're so very welcome. I had so much fun the last time we were together, and I wouldn't miss the second chance.

 

Siobhan Savage: I love it, I love it. So, one of the things I think a lot of people are a little bit, sort of, unsure of is, and you get to have this lens that not many folks in the world get to have, what is actually happening around AI strategy? You know, when companies, and you get to sit right at the forefront of, you know, not only building, but talking to all the leading, you know, customers in the world, like, where are we actually at? What's happening? Is it a bubble? Is it not a bubble?

 

Siobhan Savage: You know, what's going on?

 

Ben Schreiner: You know what? The typical consulting answer, it depends. It depends on where folks are in… in their…

 

Ben Schreiner: you know, assessment of this technology. You know, we at AWS certainly believe this to be, you know, a transformational technology that's going to impact, you know, every industry and potentially every role. You know, has some…

 

Ben Schreiner: potential to take advantage of AI, and what I've seen, and what we see, is probably 3 different buckets, right? Logically speaking. You have some folks that are still in the foundation-building bucket.

 

Ben Schreiner: that maybe haven't even gone to the cloud yet, right? And so there's a fair number of traditional industry that hasn't done that. I think Andy Jassy says it's somewhere between 15 and 20% of the workloads are in the cloud, so that leaves a lot.

 

Siobhan Savage: It's mental.

 

Ben Schreiner: of legacy, if you will. If you think about it, there's lots of companies that aren't startups like yours that have this legacy and sometimes baggage holding them back.

 

Ben Schreiner: There's another group that's been actively, you know, testing and learning as fast as they can, and then you have, I'd say that the smaller of the three, probably, let's call it 10-15%, that are running ahead, right? Many startups who, you know, like yours, that are ahead of the curve, that see the opportunity, don't have the baggage and are able to be

 

Ben Schreiner: more nimble, and that's probably only, again, 10-15% of what we see, but, but…

 

Ben Schreiner: The conversations, again, depending on where… what bucket you're in, are very rich, there's a lot of curiosity, and there's a lot of…

 

Ben Schreiner: fear associated with the conversation, depending on who you're talking to and what. And so, those are the three groups, and again, I think everybody has an opportunity right now. It's just a matter of

 

Ben Schreiner: Meeting people where they are, and then figuring out a plan on how to go forward, ideally, as fast as possible.

 

Siobhan Savage: Yeah, and I think it's one of the things that we've talked about with your team more broadly is, like, I want to build a billion-dollar company with under 100 people. I want to test that, right? I want to see, is it actually possible? And to be… to be fair, in your world, what you've just said, like, the big enterprises that are already pre-established, that have been operating for 4 years, for them to kind of go backwards and, like, reinvent, it's a lot harder than someone like

 

Siobhan Savage: me, who's day zero, whiteboard, like, what could we do, and how could we build everything from scratch? So, like, that makes, like, a really fair, you know, point of view of, like, actually where everyone's at.

 

Siobhan Savage: The other thing I find, and I'm not a technical finder, but I've built an AI company, right? And I think that there is this really interesting moment where, at the beginning, this AI moment was very much so about technical folks.

 

Siobhan Savage: And now what we're starting to see is this kind of, like, democratization, where normal people like me are starting to actually embrace and understand what's possible. And I think the work that we're doing alongside your team is, like, really opening my mind to, like, hold on a second, someone like me?

 

Siobhan Savage: can build something like this, that this is truly possible in this day and age, and that part of this movement, I think, is so exciting, right?

 

Ben Schreiner: You're describing what I like very much about AI is the empowerment.

 

Ben Schreiner: It can give people. Like, I believe this tool, and it certainly works for me, and I shared in some of my slides when we were together, right? It's allowed me to tap into my creativity. It's allowing you to do things that

 

Ben Schreiner: you don't have the technical skills and experience writing C and code and so forth, but you can actually put things together and move your ideas forward, or start to visualize your ideas in new and different ways.

 

Ben Schreiner: And that, to me, is, again, for everybody listening, that's…

 

Ben Schreiner: Why people are so excited, right?

 

Siobhan Savage: Yep, agree.

 

Ben Schreiner: But it's what can we as the humans do with the new toy or tool?

 

Ben Schreiner: To help us be more effective, more efficient at serving, or doing, or making our lives easier, whatever it is, whatever task we're trying to accomplish, can we use the new tools in ways to help us do more? And in your case, right, without having to necessarily scale up the headcount?

 

Siobhan Savage: Yeah, and I think in this new era of work, like, we at Regi truly believe that this opportunity belongs to builders, and the definition of a builder is not an engineer.

 

Siobhan Savage: The builder is, like, me, is you, is, like, anybody, and that, for us, that is so exciting. And I think that's, to your point, I think, one, boards look at this and kind of think, oh my god, this is an amazing opportunity to accelerate and amplify business, but I think the message that I really kind of try and focus on with folks that spend a lot of time talking to me about the fear part that you mentioned, is that, like, if we could turn that fear into, like, this culture of

 

Siobhan Savage: opportunity, and if I can, then we can teach normal, everyday people like me to be able to harness and amplify themselves with AI, like, this is gonna change everything in terms of, like, how work gets done.

 

Ben Schreiner: Yeah, and that's been our goal, just internally, as we use it. We want every employee to be more productive with the tools, right? And if you have that mental model, and you have a culture where people have the freedom to learn, they're expected to learn and use new tools, and to continue to sharpen their skills, like, these are…

 

Ben Schreiner: I have zero doubts. These are skills that everybody needs to acquire, right? Because they're new tools, and if you figure out how to use the new tools, you're going to be more effective and efficient.

 

Ben Schreiner: I will say this, right, we, the human race, right, that's… change isn't something we all look forward to, and many resist, but I think this is an opportunity, so that growth mindset and having some curiosity, but I think as leaders, too, and we talked about this earlier, this is a leadership moment.

 

Ben Schreiner: Right? Like, this is a big leadership moment, where you have to set a direction and a vision for your company, an expectation for your people, and if you do that, right, you can make the culture change that may be required. You're… you're creating a culture in your company, and some larger organizations have a culture, maybe that's been…

 

Ben Schreiner: codified by process, by paper, by, you know, we've always done it this way. And those are the ones that are going to struggle the most adapting

 

Ben Schreiner: to changing expectations.

 

Siobhan Savage: Really, I think, you've commented, and you just mentioned, right, our ability, anybody's ability to be more creative and to build.

 

Ben Schreiner: It's just been, like, the barrier to entry is just almost eliminated, because you can write English words and start to see things that are backed by code, right?

 

Siobhan Savage: Nope.

 

Ben Schreiner: you didn't have to write any, and that's a very new and powerful thing, and I mean, Vibe Coding's only existed for 12 months, right? It's still very, very new. But it's incredibly exciting to be able to get

 

Ben Schreiner: Business people, who are not engineers, to visualize, experience, possible solutions to their problems.

 

Ben Schreiner: and to accelerate that solutioning, right? And that's really what I'm most excited about, is AI helps speed up that process.

 

Ben Schreiner: And it means we'll be able to solve more problems faster.

 

Siobhan Savage: Yeah, one of the things that we have seen, and don't know if it's sort of your view on this, but

 

Siobhan Savage: when the whole AI moment started, everyone bought tools and said to all their employees, now go, go and do this, and then everyone was expecting that, like, productivity would go off the charts, and… and nothing really kind of, like, happened, and everyone's view was, oh, okay, this whole thing is not working. And my take on that is, like.

 

Siobhan Savage: Well, one, no one has a clue how to use this thing, we haven't actually enabled these folks to do it. And also, like, if you're in a rowing boat, and everyone is allowed to row in a different direction.

 

Siobhan Savage: you don't really go far, right? So you actually have this empty velocity.

 

Siobhan Savage: Which is, like, the whole promise of AI is velocity, and if we do that… so a lot of what we talk about is… and my brain's thinking about this because your point about the leadership.

 

Siobhan Savage: is really important. Like, the leadership expectation right now that you're not just gonna throw out, you know, ChatGPT or Copilot across the business and expect suddenly the business to redesign work and actually make AI workflows and have everybody kind of doing things in different patterns, that that doesn't work.

 

Siobhan Savage: what are you seeing, kind of similar themes to that, that are kind of big no-goes that folks need to kind of watch out for? That, you know, what have you seen over your travels when you're talking to leaders that, like, things they got wrong, that they kind of wish they didn't spend millions of dollars on, if they knew how to do it in a different way? Like, what have you seen

 

Siobhan Savage: Around leadership.

 

Ben Schreiner: You know what, it's… it's… it's interesting. I think…

 

Ben Schreiner: I think just rolling it out, I talk to some companies, and I'm like, what… what's your AI strategy, or more importantly, what's your…

 

Ben Schreiner: business strategy that's AI-enabled is a better way to say it. I'll be a little controversial, being the head of AI business development, but I don't think you should have just an AI strategy, because that often gets delegated to the IT team to go deal with that. And if companies

 

Ben Schreiner: treat AI as an… as just any other IT project, it will not meet expectations.

 

Ben Schreiner: The other thing, too, I'd like to say is… is… I believe everyone, even if you roll out the tools you mentioned, or any tool, I think every person, without any training whatsoever, can get some incremental benefit.

 

Ben Schreiner: Your ability to measure that, we can debate, right? But everybody can write better emails.

 

Ben Schreiner: Everybody can ask it to summarize a document. Most people are using it to summarize meetings. I love not having to take meeting notes anymore. Those are all AI tools. People are using them and getting a great deal, I would argue, of benefit from that. And so, that to us.

 

Ben Schreiner: That's incremental, right? And back to the… there is value, are you measuring it well, or effective, or efficient? You know, perhaps not. But you're still getting value, and I don't… I don't know that that's actually deniable.

 

Ben Schreiner: But are you transforming your business? Which is a very different set of expectations, which does require you to rethink work, or how people work, or… I joke a little bit that if you use AI to automate a process that doesn't need to exist.

 

Ben Schreiner: you really are wasting time, right? Like, this… I mean, we have our big user conference, right, which is… was in, you know, the beginning of December, and it's called reInvent. Very appropriately named for what we're talking about today is… is this is the opportunity

 

Ben Schreiner: to reinvent how your company works, how it organizes. Because most, who aren't a startup, have… a legacy. Have…

 

Siobhan Savage: Yep.

 

Ben Schreiner: have come together, and you want to make matters worse, many out there have been a part of a merger and an acquisition. Now you take two sets of problems, and you double it, right? Because you have two different ways of doing things. You have two of everything, that has to get sorted through, and I've been through many, and it's… it's complicated, right?

 

Ben Schreiner: And if you don't simplify.

 

Ben Schreiner: You don't get those synergies, you don't get those efficiencies, you don't get the progress that you had hoped.

 

Ben Schreiner: And we have a leadership principle inside of Amazon that's invent and simplify, and we're amazing at invent. Like, we love to innovate, we have tons of builders that think that way, and sometimes we have to remind ourselves that it says and simplify.

 

Siobhan Savage: Yeah.

 

Ben Schreiner: Because it's the simplified part that's actually harder than inventing something new to solve a problem. Simplifying something that's complex, and as soon as you start getting, you know, more than probably two handfuls of employees, as you can attest, things start to get more complex.

 

Ben Schreiner: It does. And they do for everyone, right? Yeah.

 

Ben Schreiner: You know, the folks that get things wrong don't have the vision.

 

Ben Schreiner: Don't have clear metrics to know

 

Ben Schreiner: whether it's working or not. You know, but I do think everyone right now should be in learning mode, and so no matter how you're going about it, you should be learning, right? And so there is some benefit, it just may not be, as tangible or quantifiable.

 

Ben Schreiner: But right now, everybody, if I can implore anyone listening, like, you should be using these tools on a regular basis to learn what they're good at.

 

Ben Schreiner: what you can get better at, and sometimes what they're not good at, right? Knowing what they're not good at is just as valuable as, you know, the things it can help you with.

 

Siobhan Savage: Yeah, it's a really good point, because the point that you were saying is, like, just because you can invent it doesn't mean you should. We had a little bit of that in our company, like, and in the scheme of our… we work with the top big enterprises, right? But when you think about even just as a use case.

 

Siobhan Savage: I put a lot of pressure into my team, like, we've got to drink our own champagne, and actually design our ways of working, and what we find was, like, just sprawl of, like, everybody trying.

 

Ben Schreiner: Yeah. And what we ended up doing was, if we can't measure that this is actually.

 

Siobhan Savage: bringing some value to the business, then we shut it down. And one of the things, and I don't have the exact stats, but I'm going to text you after this, so I did ask my CTO, we moved, I think you know we moved and transitioned over to Bedrock.

 

Siobhan Savage: And we can…

 

Ben Schreiner: By the way.

 

Siobhan Savage: Thank you. We actually looked at the team of what we would have hired to do the thing that we're now doing on this, and it would already be at a team of 10 people.

 

Ben Schreiner: Wow.

 

Siobhan Savage: Yeah, I'll send you it after, because it's a really, really interesting, use case, because of the way that we're designing what we're doing. And I think that's where I'm like, okay, anytime you ever have a doubt, if you measure how it happened before.

 

Siobhan Savage: And how it happens after the fact, and if you can then start to see, like, true outcome shifts within your business, or true value that is connected to revenue, then I think, like, I'm like, okay, double down, like, keep going, team. Like, keep more of that, more of that. But there is this kind of, like…

 

Siobhan Savage: constant, like, triaging of, like, yes, yo, yes, no, and, like, tidying up, shutting down, and, like, that… we're really good at inventing and have lots of great ideas as a team, too. And right now, we're in, like, we're gonna scale this thing really fast right now, so discipline is, like, super important, and I feel like a lot of our customers are big global enterprises.

 

Siobhan Savage: they have to have that, like, you know, work happens in a certain pattern because it's always been done, and if you come in and stick a spanner in that, you know, you're gonna cause that big problem, so you can kind of feel where a lot of our customers, shared customers, are at right now on their journey of, like, do we dabble over here, or do we truly start thinking about reinvention?

 

Ben Schreiner: And those big companies, right, they have those processes in place

 

Ben Schreiner: because they've had to scale things, right? They've standardized so that they get a repeatable outcome, right? Like, all… the whole mental model still is valid.

 

Ben Schreiner: it just doesn't… it's not very easy to adapt, right? And that's really where the struggle is, and that's really where these tools can… can potentially help.

 

Ben Schreiner: Is finding, and I think I've told you this in the past, right, looking for areas of your business where you spend a lot of time.

 

Ben Schreiner: Right? Like, where is it time? And if you can find places where you spend a lot of time and see if AI, you know, can help reduce the amount of time it takes to do something, that's great, right? You get instant benefit, you save time, right? Like, you mentioned, you're building more with fewer developers because you've changed your process.

 

Ben Schreiner: The challenge for most is, then what do you do with the time you saved?

 

Ben Schreiner: Right? And that's the metric, again, that most aren't measuring. But ideally, it's, I'm able to see more customers, or I'm able to…

 

Ben Schreiner: do the next feature faster, or I'm able to, you know, reduce the number of meetings I have that are internal, and now, you know, focus more on external, you know, value-added things. So, and that's really, again, when I say leadership moment, it's great to have these tools, but we as leaders actually have to change

 

Ben Schreiner: We have to know what they can do, and the potential, but then we have to start changing expectations, right? That's…

 

Ben Schreiner: That's the hard part.

 

Ben Schreiner: You can give somebody a tool, and if you go, alright, last year you did 10, now I need you to do 100,

 

Ben Schreiner: humans, us, we're gonna have an allergic reaction to that. Even if we have the tool, and know the tool can help us do it, we're still gonna have

 

Ben Schreiner: a human reaction to our boss or our leadership saying, you now have to do 10X.

 

Ben Schreiner: And that's… that is that leadership moment where we have to… we have to bring people along, and that's where this learning and building their confidence, so that that adjustment isn't so visceral, or so… such an allergic reaction, because if anything, you want them to go, no, I can do more, give me more, give me more. Like, that's…

 

Siobhan Savage: And then pay me for it.

 

Ben Schreiner: Yeah, well, yeah.

 

Siobhan Savage: Exactly right.

 

Ben Schreiner: Exactly right.

 

Siobhan Savage: Because that's the next thing, right? It's like, if you turn the whole world into builders, and if everyone's, like, 100X,

 

Siobhan Savage: it kind of defeats, and this is one thing at Regique that we're trying to figure out, that we're like, in a world where we do build a billion-dollar company with 100 people, how does it completely structurally change

 

Siobhan Savage: how reward works, right? Like, and this is, like, I don't know how to answer that, trying to figure it out thing, but it's going to be really interesting to see how does work shift, and how to, like, how does the social contract of work

 

Siobhan Savage: change in a world where, like, truly AI starts to happen. And we're, like, super focused. One of the things that you said, which was really interesting, because it's validating something that we've been talking about with customers. So, we go into a lot of customers, and very large global enterprises, and they've done these kind of performance theater of, like, AI. So, they've brought in consultants, done these big things, haven't really moved the needle

 

Siobhan Savage: on a lot, like, small pockets of stuff.

 

Siobhan Savage: But what we're starting to see with customers is that when we actually looked at, like, what actually were you doing, that a lot of it wasn't really connected to actual things that bring the business value. So, like, we kind of say to customers, like, you need a mental motive, like, think of it as, like, a decision-making support, where if does this task and workflow help you make more money.

 

Siobhan Savage: Does it help you, like, remove low-value work that costs you money?

 

Ben Schreiner: Right. Does it help you avoid risk, which costs you money? Yep.

 

Siobhan Savage: Does it remove no-joy work from your people? And, like, again, that's linked to money, because if your people stay longer, because you make it a happier place to work, you save money. So a lot of what we're kind of pushing customers to say is, like, we're not going to go after everything.

 

Siobhan Savage: We're gonna be really strategic, and we gotta make sure that anything we chase has to fit into one of those buckets. The one thing that we're doing with other customers, so one of our customers is really obsessed with being the most creative company in the world.

 

Ben Schreiner: Wow.

 

Siobhan Savage: And they believe that will help them make more money.

 

Siobhan Savage: So now we're actually defining, like, what tasks and workflows are connected to creativity. So think of, like, creating new products, like, just more innovation, and, like, can we prove? So everything that you're saying is really validating the model that we're seeing with customers, that at the leadership level, if we can help make good decisions on where do they go, but then we have to measure the outcome shift, so

 

Siobhan Savage: If you've got salespeople that need to book 5 meetings a week, you want to see them book 7.

 

Ben Schreiner: Yes, I love that you're saying that, if…

 

Ben Schreiner: I really hope we can stop using the term proof of concept.

 

Siobhan Savage: Yep.

 

Ben Schreiner: And it truly should be proof of value. Yep. To your point, what value do we think this is going to create? And then, can we put a test in place to see that, yes, it does? But then, you said something earlier, too, about scale.

 

Ben Schreiner: Which is really important, right? Because you can do something in the lab.

 

Ben Schreiner: that looks good, but then when you roll it out, it doesn't quite work like it did in the lab, right? And then, therefore, it doesn't actually solve the problem that you were trying to solve in the first place. So you've got to make sure that your proofs

 

Ben Schreiner: are grounded in reality? Like, do you have access to the data that AI's gonna need to do the… Not just the test set of data, but actually all the data that you're gonna need.

 

Ben Schreiner: But proving value, I think grounding and working backwards, as we call it, that's what our mechanism is here at AWS, working backwards from

 

Ben Schreiner: The… the problem or the value proposition is… is… It should be your why.

 

Siobhan Savage: Yep.

 

Ben Schreiner: You have to have a why, and I think you alluded to this and stated, like, just giving everybody the tool and seeing what happens, something's gonna happen, right? Like, but it won't be orchestrated or organized. I do think you need to…

 

Ben Schreiner: Help build people's confidence, and… and empower to go, like, here, like, we want you to think about, like, you know your job better than anybody else.

 

Ben Schreiner: Right? Like, so, use these tools to figure out how you can do your job better, right? Like, versus a top-down or a centralized, we're gonna do all the innovation over here, and then we're gonna roll that innovation out to everyone. I think it's a little bit of both, right? Like, you need some…

 

Ben Schreiner: some center of excellence that's really able to go deep and make sure that the things you're building are secure, are scalable, are going to hold up to the test of time, and if you're in a regulatory environment or have to deal with payments or anything, like, that you stay compliant and out of jail, that'd be nice.

 

Ben Schreiner: But then… But you don't also want to say, alright, like, you don't… and I'll say this to everyone, right? Like, if you don't provide the tools to your people, they will start using this to do their…

 

Siobhan Savage: 100%.

 

Ben Schreiner: They will.

 

Siobhan Savage: shot it. They'll screenshot.

 

Ben Schreiner: Your data's flying out of your.

 

Siobhan Savage: Totally.

 

Ben Schreiner: if you haven't given them the tools. And so, I think this is an important, again, leadership moment where you need a feedback loop to learn from each other. We have something called, and there are several

 

Ben Schreiner: podcasts on, like, how I AI, right? And we have meetings where we just share, and here's the crazy thing. I've had people develop stuff, and like, here, look at it, and I'm like, what do we do with it? Like, I don't even know where to start. Like, show me what you did, right? And have it be a little more of a tutorial, and make it a little more practical. And that's where we've seen some…

 

Ben Schreiner: some…

 

Ben Schreiner: benefit where somebody created something, and now 3 other people are using it, right? Now you're… now you're starting to scale out the innovation, and it doesn't all have to be trapped inside engineering or in this small group. But you've got to have that… that sharing mechanism, or you're just…

 

Ben Schreiner: You know, you're gonna have a suboptimal, you know, experience.

 

Siobhan Savage: Yeah, there's two questions in the chat.

 

Ben Schreiner: Go ahead.

 

Siobhan Savage: I'm gonna chuck them into the room for us, because I think they're really good. There's one from Mark.

 

Ben Schreiner: Where he's asking about, like, they're all chatting on the channel about what we call the work map.

 

Siobhan Savage: And he's asking for more definition, so the clarity of… just for folks to know, like, what we do at Regig is we basically give companies an out-of-the-box view of work. And when we say work, we're talking task, subtasks.

 

Siobhan Savage: we basically give you, like, the map of your company so that you can then, you know, GPS your way around and figure out where is the best place to do it. And like me and Ben were talking about, like, we're directing you to, like, things that are worth

 

Siobhan Savage: value to the company, like, does it save me money? Does it reduce risk if we were to, you know, use AI for this moment? So, imagine that that's, like, step one. And then Doug is asking a question, Ben, where basically, when you get to that point.

 

Siobhan Savage: How do you decide what is, like, leader-directed?

 

Siobhan Savage: versus Team Discovered? Like, how do you, like… because I've got a cheeky perspective on this, that, like, there's, like, definitely a point of view

 

Siobhan Savage: that top-down… if you're working in a factory, and, like, one of my best experiences of 2025 was I got to go to the AWS Formula One

 

Siobhan Savage: experience, and it was really cool. And I sat there, like, drinking a glass of champagne, right? And I couldn't believe that I got to go to this thing, and I was looking at the pit stop of the team, and I was thinking, like, how do I build that in my company? Like, the best companies in the world, like, how do they put a team together to do that? Because that was, like, so inspirational for me. I was like, holy moly, like, how do I do that in, like, team?

 

Siobhan Savage: And you can't just have everyone randomly deciding what they want to do in that pit stop moment. That has to be a very clear, leadership-approved.

 

Siobhan Savage: optimized moment of, like, speed and accuracy and energy and, like, in sync and chemistry with the team. So, like, there's, like, that side, but then there is your… what you've described also on the other side is that localized team.

 

Siobhan Savage: How do you make the decision on what and when to do what… which one? Like, what do you think about that? Because Doug's question's really good, and it feeds off Mark's question, too.

 

Ben Schreiner: Yeah, I think it starts with… how well… Can you describe the task?

 

Ben Schreiner: If you can describe the task with excruciating detail, because it's repetitive, and how often does the task have to happen? That's also equally important.

 

Ben Schreiner: If it happens once a year, if it happens once a year, don't build AI to do it, right? Like, it's just that level of effort's probably not worth it, right? So you want volume and repetition are the areas that I'd draw your attention to. The other thing is, is it clear

 

Ben Schreiner: What information is coming in, and what information needs to go out, so that you know what

 

Ben Schreiner: what, when you have the complete dataset in order for AI to go do it.

 

Ben Schreiner: And then you have a test to make sure AI did it correctly, right?

 

Ben Schreiner: You can define those three things, the inputs, the what needs to happen, and the outputs. That, to me, is ripe for automation, and it's a high-volume

 

Ben Schreiner: activity. So, for example, at many small-medium businesses, they get invoices that have to get entered into their accounting system, right? And that process is usually manual.

 

Ben Schreiner: Ideally, you don't have to do that, right? You would just take a picture with your phone, you'd send it in, and the system would figure out and categorize it correctly and do everything, and if it didn't know, it'd come back to you, the human, going, hey, we have a look, because

 

Ben Schreiner: Because it didn't follow the rules, right?

 

Ben Schreiner: It was an exception to the rules that you've given me. And this, and it's always been this way with AI, it's all about patterns. If there's a pattern there, then AI is going to excel at that, because it can follow the pattern, or can figure out the pattern.

 

Ben Schreiner: where high judgment is required, or where there are conflicting things, or differences of opinion. I'll tell you a perfect example. We had an organization that had a help desk, they had manuals, they had,

 

Ben Schreiner: a relatively complex set of machinery that needed to be fixed, and one manual was said to do this, and another manual said to do something that conflicted with the other one. And it was like…

 

Ben Schreiner: which one?

 

Ben Schreiner: is right.

 

Ben Schreiner: And the answer was, you go back to the humans who've been doing this job for 10 years, and they know which one of those is right.

 

Siobhan Savage: Yep.

 

Ben Schreiner: But the rest of the humans had no idea that conflict had existed, because very few had ever read both manuals to realize at that level of detail that there was a conflict. And so some of what you're describing, right, is using the tools and finding tools that can help you define, and map

 

Ben Schreiner: really allow you to zero in on those high-value, high-volume, areas. And then, the opposite is true. If you have low volume, highly complex, right, those are the places where, actually.

 

Ben Schreiner: You want to spend more energy, and you want to free up time so that the humans aren't making rushed decisions.

 

Ben Schreiner: Right? And that is, again, where I think you can start to optimize where are we spending our time.

 

Siobhan Savage: Yeah, and if it's something, like, connected to pain in your pupil, which is, like, super high risk, if you get it wrong, you're screwed.

 

Ben Schreiner: Oh my gosh. Nothing worse than messing with people's comp.

 

Siobhan Savage: you avoid the… anything connected to customer data, and this is where I think, like, there's the out-of-the-box tools that are great for, kind of, like, low-risk, high-value, repetitive work, and then you have complex…

 

Siobhan Savage: Reinventing a call center is not something that you're going to do on an out-of-the-box model. You're gonna design that, you're gonna build that with your team, you're gonna keep that custom on bedrock or whatever, do you know what I mean? Like, you're gonna be very specific.

 

Siobhan Savage: And that's where I feel like there's these different points of, like, one is, like, a leader, SI, build it, design it, like, really… and a lot of the stuff that I've seen, I mean, my expertise, just so you know, is, like, workforce optimization.

 

Siobhan Savage: So my brain really thinks clearly about velocity, and, like, I've been trained to do that. So for me, like, AI is this, like, golden goose.

 

Siobhan Savage: I'm like, I can't believe this! Why is no one adopting this? It's amazing. But the reality is, when I watch customers right now, and I get to sit in, like, the coolest rooms right now, I just like listening to folks think through these things, and I get to, like, see patterns of what's happening. And, like, just because, like, you sleepwalked your way into that process.

 

Siobhan Savage: this moment of AI is also this opportunity to, like, clean the slaves, like, start again, because a lot of why you did stuff was just because it was the way that you did it, and you know, you said the point at the start, once you hire 10 people, 20 people, 30 people, each one of those will incrementally add another layer of task or subtask into a process, because that's just what humans do. Busy work kind of inflates things, and I think that's where it becomes, like, really, really interesting.

 

Siobhan Savage: And then Juliano in the chat as well, the chat's really good here, by the way, Juliana's also said, and hopefully I'm pronouncing that correctly, that there's this other element that I'm starting to see, which I think will be a big thing later this year, is like, let's say I go and redesign her to pay this invoice.

 

Siobhan Savage: By the time we get to the end of the year, these models will be completely evolved and upgraded, and I'm gonna probably have to reinvent it again.

 

Siobhan Savage: Because there's this evolution of just the capability, how fast the AI is growing, and the potential of new access, and, you know, right now, we're kind of subtask level agents. Really few are at this, like, what they, you know, the dream state of, like, agent full end-to-end, but I think that's where it's this constant evolution.

 

Siobhan Savage: of work is gonna be in play, where it's not like, I'm gonna do a transformation program, and then I'm gonna redesign it, and then, oh yeah, like, that's it, done, wash your hands, and on to the next. It's this always evolving and shifting. Like, how do you see teams and leaders

 

Siobhan Savage: staying in this new mindset mode? Like, how do you… like, because it's not like, oh, we're changing it and it's done, right?

 

Ben Schreiner: No.

 

Siobhan Savage: What's your kind of take on this?

 

Ben Schreiner: Yeah, we're trying to… and again, the technical folks have been at it for a while, with… with agile development and trying to be more iterative,

 

Ben Schreiner: We need the business teams to also adjust their thinking on how problems get solved, and not let the pursuit of perfect get in the way of good, and really being able to assess risk. Like, is this good enough?

 

Ben Schreiner: to learn from, and if we iterate fast, it's better to get some value now versus zero in wait. And so having an iterative mindset, these models are going to continue, and everything is going to continue to get better, and I think as we build and architect.

 

Ben Schreiner: you're gonna need a couple of things. One… one, you're gonna need to have a mental model that

 

Ben Schreiner: what you're building isn't forever, and that's not how we used to build things, right? You know, the software creators before you, you know, built things to last, and they would just add features, and it would just grow and become this big monolith, right? And gone are those days, right? And I think people can appreciate that.

 

Ben Schreiner: But ultimately, again, that mindset of, I need to build into my process, The ability to adapt.

 

Ben Schreiner: Which means I need a checkpoint to validate, is this still working?

 

Ben Schreiner: Right? And if not, I need a feedback loop to go, why not?

 

Ben Schreiner: And… and then I need to adapt…

 

Ben Schreiner: to the changing, you know, whatever, market, customer need, you know, could be compliance, like, whatever happens to be, but we've talked about this in the past. It's my fundamental belief that how companies compete

 

Ben Schreiner: is going to change in the AI era, and it will be predicated on how well do you know your customer, and how fast can you adapt to their needs.

 

Siobhan Savage: Yep.

 

Ben Schreiner: Because those two things are going to be critical components to being successful. And so, you have to have those feedback loops, but then you have to be able to adapt, and not all companies

 

Ben Schreiner: Have that as a superpower.

 

Ben Schreiner: It's an opportunity. Some cultures are real resistant to that kind of change, and it's really just trying to, again, back to leadership to go.

 

Ben Schreiner: we need to change how we do things. And if the leadership team, if the board is like, no, you need to change how you do things, now you've got the reason.

 

Ben Schreiner: And now you have to go figure out the how, and partner with people who've done it before, like you've done these…

 

Ben Schreiner: these maps, it can help people better understand, like, where to spend their time. We've helped people figure out how to get their data into some place and leverage AI to start to get some insights to give them that point them in the right direction. So there's people who've done this before, and it can help accelerate your learning, but ultimately it comes down to you, your organization, and what do you want to be?

 

Ben Schreiner: known for in 2 years. Not 10 years, like, 2 years. Like, what are you gonna be known for?

 

Siobhan Savage: And what action are you going to take? Because I think I got kind of sick of…

 

Siobhan Savage: Did everyone talk about insights?

 

Siobhan Savage: last year, it was like, guys, come on, like, it's like, can we get some, like, let's go, like, let's get this party started. And I think I have felt, I don't know about you, but there's an energy in the market right now, especially in the US, where it's like, okay, 2026 is action time. Like, there is, like, a feeling from a leadership perspective that, like, these transformation theaters around

 

Siobhan Savage: programs and pilots is now, like, no, how do we operationalize this and change our operating model to enable this? So I love that you're leaning in on that, because we're starting to see a lot of that play out. One of the things that's really helped, and I've seen this play out with my team, so when we get brought in, it's… customers have either… they're kind of at all different stages. They're like.

 

Siobhan Savage: thinking about it, have tried and failed, kind of don't know what they're doing, like, they're all at different stages, but they all have the same kind of consistent recipe for us, where, like, they need to understand their work, they need to know what AI to use, they need to know how to reinvent

 

Siobhan Savage: the actual workflow for an agent, so we create these, like, AI workflows, and we used to do these kind of, like.

 

Siobhan Savage: transformation programs where we would, like, make it a lot more serious and, like, you know, sponsorship and all of this stuff. What we have found, as we've shifted this probably in the last maybe 9 to 12 months, is we've moved into a different operating model where we're driving customers and seeing a completely different result. So we've moved from this, like, strict

 

Siobhan Savage: Transformation-style thing into, like, stealth change management.

 

Siobhan Savage: So what we're basically saying to customers is, like, there's so much of this air and energy around, like, big transformations, whenever actually, if we can reinvent from the grind up.

 

Siobhan Savage: and actually do this kind of stealth thing, so by week four, if our customers haven't… they've made a commit to us that they're deploying three AI new workflows by the end of that, and we prove impact, like, into production, not shadow AI workflows. Like, truly in production, moving, we can see, like, how work was before, how work was after, and prove the value of the investment in the agent themselves, right? We are seeing, like, this appetite for this type of way of working

 

Siobhan Savage: is very much so what the customers are liking from us right now. And I don't know if you see, from your perspective, like, what… why do you think

 

Siobhan Savage: that the leaders are now in that, like, we're definitely, like, seeing, like, this appetite for, let's go like that, versus this big, kind of, theatrical… like, what's your take on that? Because we're starting to see it play out across the board now.

 

Ben Schreiner: I'm glad to hear you are seeing it, and I hope everybody's taking note, right? So many who are in leadership positions of great big organizations,

 

Ben Schreiner: have seen many transformation projects before the AI transformation one, and most of them came up short.

 

Ben Schreiner: or failed for numerous reasons. And so, what you're describing, I love, because you're…

 

Ben Schreiner: building their confidence. Yep. This is a trust thing. It gets back to leadership and trust, right? And so, the leadership knows change needs to happen, and most organizations' change management practices are woefully

 

Ben Schreiner: under…

 

Siobhan Savage: What's the nicest way to say it? It's a safe room! It's a safe room!

 

Ben Schreiner: Yeah, I mean, again, especially for those change management agents, it's a hard job, right?

 

Siobhan Savage: But yeah.

 

Ben Schreiner: And I tell everybody, I've told you this, right? I talk about AI every day, all day long, and the AI, the tech stack, the tech bits, is the easy bit.

 

Siobhan Savage: Yep. The people… Completely agree.

 

Ben Schreiner: culture is the harder bit, and I fully understand that.

 

Ben Schreiner: And it's real, right? Like, the culture change, the shift is the harder bit. And I think, ultimately, that's…

 

Ben Schreiner: That has been those leaders' experience. So they have scar tissue, the cuts were deep, you know, they spent lots of money, wasted lots of time, didn't get the result, maybe didn't get their bonus that year. Like, I mean, there's some history and baggage there on

 

Ben Schreiner: on the word transformation. And so, I think your approach of showing

 

Ben Schreiner: genuine, real progress that's measurable, where they can see a before and after, without having to boil the ocean. That's the other thing I'll say is a misconception, right, is you have lots of folks come in and go, oh.

 

Ben Schreiner: And we see this, like, people will admit, raise their hand right away, that their data is all over the place.

 

Siobhan Savage: Thank you.

 

Ben Schreiner: absolute mess, right? And lots of colorful ways you could describe it, but you get my point. The data is all over the place.

 

Ben Schreiner: And they've been told, because of the insights and the analytics and the big data, you know, talk for the last 10 years, that they have to go solve that before they can have any benefit from AI. And it's simply, simply not true. Like, you can get benefit now.

 

Ben Schreiner: It just might be incremental, or it might be one workflow, or one area, right?

 

Siobhan Savage: Hmm.

 

Ben Schreiner: Where we see the biggest bang in the North Star that you want is you do want to have your data accessible. You want a layer of intelligence, and you want to create this holistic environment, but you can't start with that, right? If you start with that, you're…

 

Ben Schreiner: you're gonna follow the same, you know, way of big transformations before. Yeah. So your approach…

 

Ben Schreiner: You have to have that North Star, which you guys do, right? You're like, no, like, this is what good looks like when you're all done, but we're gonna start with these three, right? But those three all get you towards the vision, right? And so, I think you need that

 

Ben Schreiner: both? Because if you don't have the vision, then how do you know those first couple projects are heading in the right direction, right? You've got to have some North Star.

 

Siobhan Savage: And it has to be accessible for, like, everyday people.

 

Siobhan Savage: is, like.

 

Siobhan Savage: And Anna's put it into the chat, and Anna, you're gonna poke the bear with me a little bit on this one, because I have, like, a pet peeve around prompt training, and just, like, basically what I see across the board is, like, everyone

 

Siobhan Savage: rules out a new way of working. The AI movements come, team, everyone do your prompt training, and then they suddenly expect employees to know how the hell they're supposed to work.

 

Siobhan Savage: Like, how do they actually complete their new workflow? Like, prompt training is not the answer. The answer is that we need to actually show people, it's not change management, have the common courtesy to give people a view of what their workflow looks like now. Like, really simple. If your job is to pay an invoice at Rejig, here is the steps that you must take now to pay that invoice. Like, as simple as that sounds. And you know what? No one's doing that, and it's not.

 

Siobhan Savage: Like, it's like, how do you expect people to, like, adopt? And then they don't want to ask, because then they feel like they're, you know, inappropriate, and then they get more afraid and more afraid of AI. So I think there's this moment with HR people, and like, I think there is this opportunity for HR to step in right now. Like, how do we turn traditional HR practices into this

 

Siobhan Savage: like, workforce innovation squad, and they sit half in IT, and they sit half within HR, because

 

Siobhan Savage: To be clear, IT don't know how to do this. They know the tools, but they have no idea about work.

 

Siobhan Savage: Right. The closest capability to work is actually HR folks. They're the closest in times of, like, skill set of, like, a central place, and you see, like, skilling and capability teams, and there's job architecture teams. These are all teams that basically sit in the business that have more of the skills than probably most, and I think, you know, the session that you ran was, like, SVPs of, like, the biggest department, you know, companies in the world, and it was HR, and they are sitting in this world where

 

Siobhan Savage: the CIO is saying.

 

Siobhan Savage: we need to do this, we need to figure out how, and there's this coming together moment, kind of like, one of our… one of our CHRO customers said it's like a COVID moment. CFO, CIO, CHRO have to buddy up now, because this is not one department, this is, like, a whole department, and I think the moment where

 

Siobhan Savage: the… this…

 

Siobhan Savage: tooling moment actually becomes a workforce innovation moment is when they're all together, and I think that's where the opportunity… but, like, if we are simply rolling out tools to the company, and telling people to do prompt training, and then we're all cranky because the work hasn't got faster, I think we, like, actually have to get our shit together a little bit on that one, because it infuriates me, and then all the people feel like they're stupid.

 

Siobhan Savage: And that they're left behind, and that they, you know, I fear the AI. So, like, we're really kind of a bit feisty on that one, and I think…

 

Siobhan Savage: You know, like, a lot of… a lot of…

 

Siobhan Savage: folks that I talk to, it's a courage thing. It's like that they need… you need to take the time just to give them that moment. You know, like, let them have that moment, right?

 

Ben Schreiner: I think… I think we need to lead with empathy. We have to acknowledge the fear that exists.

 

Ben Schreiner: And we need to listen.

 

Ben Schreiner: you know, this is something new to learn, and everybody learns differently, but I think with encouragement, and you mentioned it, right? HR, there should be incentives, they should make it fun, there should be games or prizes or whatever. We'd run… we call them hackathons, which is probably poorly named for business people. Hackathon's definitely something a little…

 

Siobhan Savage: Agree.

 

Ben Schreiner: IT-oriented, but you can have innovation days, right? Call it whatever you want. But where you step people through solving a problem, and they type the things in, and they see what it does. I did one, and I had the aha moment, where we had somebody walk me through

 

Ben Schreiner: Using English words only, Creating a chess tutorial application.

 

Ben Schreiner: Right? That's what it was. It was a simple… I love chess, and I'd love to learn more about it, but that was the… that was the problem I was solving, and I started with English, like, hey, this is what I'm trying to… this is the problem I'm trying to solve, and it… it stepped me through, okay, I want beginner, intermediate, and expert. And then the… the answer, and you'll love this, you know, was in a… in a terminal window with all letters, like, back from the 80s, and I'm like, no, no, no.

 

Ben Schreiner: I wanna…

 

Ben Schreiner: I want a web UI that looks pretty, and then all of a sudden I had a chessboard, and I can move pieces.

 

Ben Schreiner: And I didn't write a line of code.

 

Siobhan Savage: It did it all itself, and it allowed me to go.

 

Ben Schreiner: A, see what's possible, B, I did it with English words, and then I was able to go, okay, how… how could I now… now that I've…

 

Ben Schreiner: I've built something, right? How can I now think about my job, right? And that was a real innocent way to just follow the bouncing ball, right?

 

Ben Schreiner: And we've all learned to read somehow, right? And so this is that moment where you don't want it to just be a free-for-all. You want people to feel safe to ask questions. And here's what I'll say to everybody who's started to use these tools.

 

Ben Schreiner: You are gonna have good days and bad days.

 

Ben Schreiner: You're gonna have days where you're like, oh my gosh, this was the coolest thing ever. I didn't think it could do it while I am falling off my chair. And then you're gonna have days where you are trying to get the damn thing to do exactly what you want, and it refuses to do what you're saying.

 

Siobhan Savage: And you have to realize that these things are gonna continue to get better, and you're gonna get frustrated, but it doesn't mean…

 

Ben Schreiner: That they don't work, that they can't add value, but it does mean you found

 

Ben Schreiner: an area where they're… they're maybe not as strong, and that's a valuable thing to figure out. But please, everybody listening, don't get frustrated when you hit one of those roadblocks, because you're… you're inevitably going to. The other thing I say, too, is these things are amazing.

 

Ben Schreiner: It appears, at things you don't know anything about.

 

Ben Schreiner: And if you're an expert in something.

 

Ben Schreiner: It's a lot easier to see when they get it wrong.

 

Siobhan Savage: Yeah, yeah, yeah. Right?

 

Ben Schreiner: And so most of us ask questions of AI for stuff we don't know.

 

Ben Schreiner: Right? And we're now getting pretty dependent, some folks.

 

Ben Schreiner: on those answers, and taking them as it's the right thing. And I would say, again, if you haven't done the job of putting your corporate data married up with one of these big models and created a solution so that you have the checks and balances in place.

 

Ben Schreiner: then we need to keep our critical thinking skills and double-check that it is the right answer. We still need to validate. There's plenty of press about people taking things and just…

 

Ben Schreiner: Using it as their work product, and getting a quite…

 

Siobhan Savage: Are they charging for it. Yeah, yeah, indeed.

 

Ben Schreiner: You can read up, I won't point any fingers or throw any.

 

Siobhan Savage: Either will I! Either will I!

 

Ben Schreiner: You all can do your own research, but just, I mean, again, please, like, double check, right?

 

Siobhan Savage: Yeah.

 

Ben Schreiner: Make sure that what you're doing is authentic. These things are incredibly powerful, but we, the humans, still need to be in the loop, making sure and validating. Those checks and balances are absolutely critical.

 

Siobhan Savage: Yeah, I think… I think there's just this incredible moment in time where anything is possible from a rethinking perspective. It kind of opens up this other kind of first principle, day zero, like, when it comes to work redesign, when it comes to just how you as an individual

 

Siobhan Savage: do your work. Like, look at me, like, I'm a CEO that, like, I use AI. I have no clue about a whole pile of stuff, and the point you made is so true. If you trust it too much, and you send it to the board, and then you have to go, oops, sorry about that, like, it's like a… it's a real thing to just factor in whenever you're operating, right?

 

Ben Schreiner: I told folks last night, right, I mean, AI is amazing at a first draft of just about anything, right? And we tell people often, especially in the developer community, right, if you're letting AI write code for you, or do anything for you, you should treat it like a new hire.

 

Ben Schreiner: Right? Like, would you… would you let a new hire put your board prezo together and send it for you, without checking it, right? Like, and the answer is obviously no, you would never do that as a human being. You'd never trust

 

Ben Schreiner: you know, somebody junior in the organization to do that for you. And so, at the end of the day, you know, I think you need to have that same mental model, right? Check the work.

 

Ben Schreiner: Make sure it's good enough.

 

Ben Schreiner: refine it as needed, and here's the other thing I think we're all learning, Sabine, is we're learning to ask better questions.

 

Ben Schreiner: Right? And back to prompt engineering. You ask enough questions and you get bad answers, you get better at asking questions, right? And giving the AI tools what we call context, right? So it understands where you're coming from and what you're trying to accomplish, and if you give it better context, and not just

 

Ben Schreiner: a straight, flat answer, you'll find that you get a richer, A, dialogue between you and the system, and B, better outcomes and outputs.

 

Siobhan Savage: Yeah, one of the things that I… I'm kind of crap at is the prompting. So I figured out this little hack, where I basically just talk, and I kind of give it the context and what I'm trying to do, and then ask it to create me the perfect prompt for it.

 

Siobhan Savage: And I have found, for someone who's not technical, that that's, like, the quickest, like, way to get something really valuable. Like, the difference between when I was, like, trying to remember how to prompt and all of it, now I just kind of talk and just say, like, this is what I'm trying to do, and why, and context, and, like, I kind of share, and I don't want this, and I do want this, and then please create… well, I don't… I stopped saying please when I read that Sam Altman said that it was pointless, being polite to

 

Siobhan Savage: a.

 

Ben Schreiner: It was costing them.

 

Siobhan Savage: It's costing them money?

 

Ben Schreiner: Let's be clear why he said what he said. It was costing him money. But I still say please and thank you, I do.

 

Siobhan Savage: Well, you see, I thought, like, if I get to the point where I'm cracking the whip with, like, an agent, at what point am I gonna, like, bring that into my human day-to-day with my kids and with people at work? If, like, you forget to… you get so used to not saying please and thank you, you're gonna get to the point where you just brood. So, like, it was funny, but even in the prompts, like, I was like.

 

Siobhan Savage: just basically, like, voice dictating, like, what I'm trying to do, and the quality of my prompt and the output now is, like, so much better.

 

Ben Schreiner: It's a way to learn, right? If you're reading over the prompt to see what it did, it can help teach you

 

Ben Schreiner: again, this is a dialogue, like, we're learning how to speak a new language, if you will, right? And it takes some iteration, so I'm glad you found that…

 

Siobhan Savage: I did. And I taught my 7-year-old, so my little 7-year-old, Indy, she's dyslexic, and she struggles with, like, reading and writing and things that you would imagine just with that going on with her. So I taught her to do the same thing, watching a 7-year-old say, for context, and actually…

 

Ben Schreiner: Oh, good for you, good for her! She's gotta feel so empowered, I bet.

 

Siobhan Savage: Oh my god, it's amazing! So last Sunday, and I've been working a lot, so I'm a bit, like, of a distant mother right now, which is kind of crap, but just the reality of, like, my business is going very well, and things are good, so I got, like, first world problems. But they are, like, kind of… the two girls are, like, pottery, so I've taught them how to do this.

 

Siobhan Savage: And we live in an apartment, and they're like, on a Sunday, we're gonna go and design a business, and they're sitting prompting and asking for ideas. They sat up in the lobby and ask the guys at the lobby, like, are we allowed to stay? They go down for an hour, and they make 20 bucks.

 

Siobhan Savage: selling things, and I'm like, shut up! Like, that is, like.

 

Ben Schreiner: It's the entrepreneurs, they got.

 

Siobhan Savage: Why?

 

Ben Schreiner: Hey!

 

Siobhan Savage: Right? So imagine, like, all of our children…

 

Siobhan Savage: are gonna be AI first. Yes. Like, imagine… imagine if we adopt this skill, and then if we teach and create, like, flexibility in our homes where we also are seen to do it, like, imagine what their careers are gonna be like. Like, I can't even imagine if a 7-year-old can make 20 bucks in an hour.

 

Siobhan Savage: Like, it's crazy!

 

Ben Schreiner: I was talking to somebody last night, and he, with his kids, vibe-coded a game.

 

Ben Schreiner: And then the whole family played the game.

 

Siobhan Savage: I love.

 

Ben Schreiner: We're having adjusted.

 

Siobhan Savage: I love that.

 

Ben Schreiner: And they were about their kids' age, maybe a little older, but not in middle school yet, right? And I'm just like, how wonderful as parents to start exposing and peaking their curiosity. Like, that's what you want for your kids, is to actually learn to love to learn.

 

Siobhan Savage: Right.

 

Ben Schreiner: Solve problems, to be able to identify a problem, and be able to learn how to…

 

Ben Schreiner: Solve problems, incredible skills that will pay them huge dividends throughout life.

 

Ben Schreiner: And then for the rest of us, who aren't going to be AI native, because we've been on this planet for a while, this is an opportunity to tap into our creativity, because folks who didn't learn how to code can now do things that they couldn't do before. And that, to me, like, I think…

 

Ben Schreiner: these tools are going to tap into our creativity, both on the job, and potentially outside of the job, right? Where you've got a side hustle, and you're like, I'm gonna build a mobile app, and I went and described it in English, and now I have an app on the App Store, and two people have downloaded it, and then you realize that

 

Ben Schreiner: Alright, now you need to learn how to market, now you need to learn how to create awareness, and now you need to learn how to run a business, and I… I think…

 

Ben Schreiner: Tapping into that creativity as a human race is nothing but good for us.

 

Siobhan Savage: Yeah. I think one of my 2026 big goals

 

Siobhan Savage: like, you know, when you're building a company, it's like, you tend to have to have revenue targets and all the things that you know, how it works with startups, but I was like.

 

Siobhan Savage: on the other side, like, I've got this, like, be really bold, but also be really responsible. I don't want to be on the side of history that's harvesting people out of jobs, just for the record. Yeah. I want to be on the side of history that was, like, part of, like, a completely different thing, and for us, like, one of my personal goals is, like, how do I use everything that I now know, and how do I, like, with all due respect to the technical folks, like, they've got this in terms of the knowledge, but how do we, like.

 

Siobhan Savage: create a world where that opportunity has access for anybody? Like, and that we can create, like, builders, like, anyone can be a builder, and I think a lot of, you know, what we did together in our partnerships with AWS is all about building the builders. Yes. Like, how do we create that world where, like, anyone, and I think

 

Siobhan Savage: your very, like, from a, like, heart perspective and empathy are very similar to my vision of, like, like, the human is, like, the critical component here. Like, obviously we want to make things faster, and we've got to prove value and stuff, but the reality is, like, there's this crazy opportunity.

 

Siobhan Savage: For, you know, everybody.

 

Ben Schreiner: Quality of life. Quality of life, yes, right? We… we say often and believe, right, that part of our role is to help democratize

 

Ben Schreiner: access to these tools, right? The cloud, like, you used to have to have a data center before you could do anything meaningful with technology, right? And gone are those days. So if you just look at the founding of AWS and kind of how we came to be, it really was trying to

 

Ben Schreiner: create access to these incredibly powerful tools, and not just be for those Fortune 50, you know.

 

Ben Schreiner: that can afford it, right? And so, democratizing is absolutely something that we feel very strongly about, but also training, right? So, making training… I heard today that the UK is going to train every, you know, citizen, right? Or make it available…

 

Siobhan Savage: Trump trainers!

 

Ben Schreiner: Make it available, but it's… but it's 20-minute nuggets, right?

 

Siobhan Savage: Yeah.

 

Ben Schreiner: you get to learn how to build your confidence, but I do think

 

Ben Schreiner: access is important, and I do think, like, we need to help people understand… and I've had two conversations this week with people I used to work with, not technical at all, no AI, they don't do it on a regular basis, and I said, please.

 

Ben Schreiner: just put one of the assistants on your phone, and just start asking the questions. Like, just get… just get started. Ask it to… you know, you're going on a trip, like, ask it to… to plan your itinerary, or give you restaurant reviews, or, like, just start…

 

Ben Schreiner: asking it questions so that you can start to get more comfortable. And as you do, you'll get more and more sophisticated, and you'll learn what it can and can't do. But, like, to me, I can't stress enough.

 

Siobhan Savage: Bye.

 

Ben Schreiner: Nobody's got more than a two-year head start on, you know, on you, right? Like, and anybody, right? And… and…

 

Ben Schreiner: this stuff is changing so fast, right, that if anybody says they know all of it, I would argue that perhaps they might not be telling the full truth.

 

Siobhan Savage: Don't buy whatever they're selling.

 

Ben Schreiner: It's just… And here's the other thing, just from a very empathetic point of view, like.

 

Ben Schreiner: There's so much change going on.

 

Ben Schreiner: that it is overwhelming, or it can be overwhelming, and we just need to give each other some grace and go, just learn. Learn as much as you can, learn as fast as you can, but right now, like, that's the big moral of the story, is start using it. Find people who can help you learn faster is a great way to accelerate what you're trying to do, but have a goal in mind, like your approach, like, there has to be value, or…

 

Ben Schreiner: Or you shouldn't do it, like, otherwise you.

 

Siobhan Savage: evening.

 

Ben Schreiner: Time, and nobody has extra time.

 

Siobhan Savage: Yep, agreed. I know we're at time, but I've got one more quick question for you. Love it. So we've talked a lot about where we're at today, and kind of, like, the coming up, but, like, where do you see the big… like, what's the next big thing coming that we gotta keep an eye on? Like, what are we all not sort of aware of that we should be thinking through when it comes to AI?

 

Ben Schreiner: So, we made some announcements this summer, and I love the company I work with, because we're always thinking ahead of the problems that are going to be. And certainly we're reactive when we take customer feedback. We're always trying to anticipate, and often, we're trying to solve our own problems, because that's where we're at in the learning curve.

 

Ben Schreiner: We have a vision of there being, you know, millions and billions of agents doing varieties of work, and that creates solutions to certain problems. It also creates new problems, right? Which is orchestration, monitoring, authentication, how do you evaluate when the agent

 

Ben Schreiner: is no longer doing what you wanted it to do? Like, how do you know when it's not? Like, how do you… how do you double check? So, there is a monitoring, governance, security, like, all of these agents… I mean, if you…

 

Ben Schreiner: watched any news about ClaudeBot or MoltBot now. Like, woo, it's great, like, look what it can do! And then, all of a sudden, like, an hour later, it was like, somebody took over my machine and is sending out emails on my behalf, and I'm like.

 

Ben Schreiner: what did you think was gonna happen, right? Like, so… so we have to go forward, like, with security.

 

Ben Schreiner: data protection, like, scalability, like, there's some just must-haves when it comes to running an organization of scale and size that are just expected, right? And we can't lose sight of those things in the AI area, the AI era, they'll still hold true.

 

Ben Schreiner: And so we're trying to make it easier to evaluate models, evaluate agents, be able to see which ones are better, what the cost profile is, so that you can constantly be adjusting and evaluating. Authentication, right? So it's… you and I authenticate when we go in the machine. Well, when the machine's talking on the machine, who's authenticating?

 

Siobhan Savage: Yeah, true, true, true.

 

Ben Schreiner: What should it be allowed to do, and on whose behalf?

 

Ben Schreiner: Right? So, cascading that rights and data protection, you know, beyond just the human is really important. So, I think at the end of the day, we're going to solve a lot of things, and then we're gonna learn that there are new problems. I don't think there'll be fewer problems in the future.

 

Siobhan Savage: meaning.

 

Ben Schreiner: we're wonderful at finding new ones, right? And so, those are the challenges that are before all of us.

 

Siobhan Savage: I love it. I love talking to you. I love your energy, I love just where your brain goes in these conversations, and I love that, at a, like, at a moral level, we're actually very in sync. So, Ben, thank you. I know how busy you are.

 

Siobhan Savage: I'm so grateful for the opportunity to get you here to talk live to everyone. For the folks that are on the Peloton or walking the dog, thank you so much

 

Siobhan Savage: Keep going, you can do it!

 

Ben Schreiner: Exactly!

 

Siobhan Savage: Speed, it's up to 1.5!

 

Ben Schreiner: There you go.

 

Siobhan Savage: To everyone online as well, thank you so much, folks. My team have put in the channel as well. If anyone wants to, you know, find out more information about Rejig and what we're doing, or get access to some free data to do some job analysis, let us know, we can send that over as well. And thank you to everyone. See you later, Ben!

 

Ben Schreiner: Always a pleasure, cheers, thanks for having me.

 

Siobhan Savage: Take care! Bye, team!