Jasmine Jaco grounds Boston Scientific’s AI decisions in real work

Siobhan Savage: Welcome, Jasmine. Thank you so much for joining us today on the Work Blueprint. We're so happy to have you.

Jasmine Jaco: Oh, super thrilled to be here. Thanks for the invitation.

Siobhan Savage: For those who don't know, the awesome Jasmine, she is the VP of many things. That's like org design, HR strategy. What? Tell me everything that you do.

Jasmine Jaco: I am the VP of organization transformation and HR strategy.

Jasmine Jaco: So on the org transformation side that's like largely AI org transformation which includes work redesign as well as role and organizational redesign. And then the HR strategy piece is looking at our HR function. And part of that is to determine how do we continue to build the HR

Jasmine Jaco: capabilities required to lead AI transformation across the organization?

Siobhan Savage: Wow, what an exciting move in your career. How does one get a job like that?

Jasmine Jaco: Well, when I think about my career journey to this point, it's been pretty nontraditional and certainly not linear, which I think is the case for a lot of people who have found themselves in this, in this space. But looking back, I think there is a pretty clear through line.

Jasmine Jaco: So I spent most of my career in management consulting, leading large scale global transformations of all flavors functional, enterprise, digital operating model. But they were always anchored to two things really business strategy and value delivery. Along the way, I got really interested in and passionate about experience design.

Jasmine Jaco: So customer experience, employee experience, product experience. I launched a couple of startups fundamentally new customer experience propositions, and then later built an experience innovation consulting practice. And then 2022 happened. And ChatGPT really fundamentally changed the conversation.

Jasmine Jaco: I think for a lot of us, and very quickly, our work became about imagining and designing the next generation customer and employee experience powered by then gen AI and increasingly, of course, now agenda AI. And what was really interesting to me was that this wasn't

Jasmine Jaco: just about designing a better experience AI was really starting to fundamentally change work itself, what people do, how they do it, and ultimately how you redesign organizations around this new reality. And that's what really drew me into the AI work design space.

Jasmine Jaco: So when I had the opportunity to bring all of those threads together at an organization with a mission like Boston Scientific, I couldn't pass it up for me. It combines everything I'm interested in and experienced in transformation, anchored in business outcomes, putting people and their experience at the center, and then really

Jasmine Jaco: the opportunity to innovate and build something fundamentally new.

Siobhan Savage: That is incredible. We have so many people now talking about one. How do I sort of get my business into sort of really taking AI work design like more seriously, rather than just kind of the sporadic way that it's happening in their companies? Because I don't know if you've kind of experienced this and being an ex

Siobhan Savage: consultant, you know, you've got a good sense of it, right? So what we've seen across customers is they've all been kind of running towards the fire in every department. Everyone's off doing their thing. They may be hiring in some of their partners, they may be doing some of it themselves. And we're not really seeing it being treated like a forever. Like you can't be a transformation.

Siobhan Savage: Transformation like it's a forever change. It doesn't this doesn't have an end date, right? And what we're seeing with customers is everyone starting to realize that now they're starting to realize that they need to build proper capability and infrastructure into their companies to be able to do this forever, not just like to add it like a project.

Siobhan Savage: So how did you get to the point where, you know, you've seen this play out on both sides, but how do you see this playing nine industry where AI work? Design becomes like a true capability within our organizations. Well, I think it becomes a true capability

Siobhan Savage: in a in a couple of ways.

Jasmine Jaco: One is that it needs to be prioritized at the enterprise level. And I think that's taken some time for some organizations. I think those who are there now are there now because they are continuing to invest heavily in AI, and most are still not seeing

Jasmine Jaco: a material return on that investment, like hard return to the now. So so this focus on technology by itself, I think more and more organizations are realizing that's not getting them where they need to be. So I think the first thing is that it needs to be prioritized at an enterprise level.

Jasmine Jaco: I think then the second thing is really understanding how to build the capability internally and scale it within the organization. So capability is people process and technology, right. So, you know, how do you build from a people perspective?

Jasmine Jaco: A, you know, a pool of resources that have the right skills and experience to redesign work from a process perspective, how do you build a repeatable methodology and iterate and improve, you know, as you go? And then from a technology perspective, how do you AI power

Jasmine Jaco: this work in AI work design? And certainly platforms like Reejig, you know, deliver a lot in that regard. And then once you, you know, as you're building that capability, I think it's also learning how to where to start and how to scale. So the where to start piece for me

Jasmine Jaco: is and this was a learning for me as I joined Boston Scientific about a year ago now, is don't try to boil the ocean. Don't don't tackle the entire enterprise at once. Pick one part of the business that matters. Go deep, learn design, iterate, and then show the outcome.

Jasmine Jaco: Right. You know, it's it's one thing to talk about why this work is important and how you do this work. It's another thing to get in and do it and show the value. Show how when you when you redesign work itself, you get more value than when you just implement a new technology. And then the second piece I would say is figure out

Jasmine Jaco: a an appropriate service delivery model. We at Boston Scientific, and I would imagine many other organizations don't have enough resources skilled in this place, in this space, to deploy to every enterprise initiative. So how are you going to to to tier that service delivery,

Jasmine Jaco: you know, deploy your resources to your top most critical enterprise initiatives, then provide advisory and coaching services to that next level and then for the lower complexity initiatives. You know, equip them with the methodology with reusable deliverables and assets,

Jasmine Jaco: with a technology platform so that they can do more of this work themselves. So I think it's a combination of building the capability people process technology starting somewhere to learn and to and to prove the value, and then creating a delivery model that enables, you know, the organization appropriately.

Jasmine Jaco: It's it's from what you've been saying, we're seeing this. I mean, as you know, we work with the most complex works in the world, right?

Siobhan Savage: Like we really focus on that sort of enterprise. And you're part of the beginning. People are just getting there because a lot of the times customers had to go through a whole pile of governance to even get to the point of risk and compliance, to start working and deploying AI. No, they're passed out part. Everyone starting to deploy these tools.

Siobhan Savage: But what we've seen is it's not just about the tool, it's about the work itself. I would say most of the problem that we're seeing is about work. You know, the thing I've been talking about a lot recently is, you know, the workflow is going to be the core unit that we all care about because the agents

Siobhan Savage: I mean, I was very, very, very first started the business, my business on OpenAI, by going to Claude because of cloud design and Claude code. And just this week moved back to OpenAI. So what's happening is the cheaper the models, the higher performing models,

Siobhan Savage: people are going to trade these agents in and out because you'll see like there's just it's just there's no one can lock this thing done yet. Like it's not it's not done and dusted. But what will be the same as the workflow because you're just switching the models, right? So what we're starting to see now as customers have like kind of stalled out a little bit, then they've kind of

Siobhan Savage: thrown it up against the wall hoping something will stick. Neither at the point where they're realizing that this wasn't an agent thing. This was actually a work problem, which is why people like you are so critical now within the organization, because everyone is starting to realize that AI work. Design is a really important part of this puzzle,

Siobhan Savage: and it's about, you know, focusing on where do you go first, where, where, where offers the highest value of opportunity, what's even feasible in your company? What will be the what did you call it like the value realization out of that? Like what do you expect to get out of that? How do you get your people to work in the new way?

Siobhan Savage: How do you measure the ROI and then who builds it? Like so that's kind of what we're seeing across all of these customers is really this like focus on building out that practice. And when you're when you're thinking about. So obviously we've been working together on a lot of the work.

Siobhan Savage: Why do you think the work part like Task subtasks talk us a little bit about like what does that help you do? Why do you think that's so critical? Would love your take on sort of how we ended up getting to know each other in the first place? Or I think, first of all, I think

Siobhan Savage: until now, most organizations have started with the technology.

Jasmine Jaco: Most leaders understand or believe that, you know, AI is about the technology. And so you saw a few years ago a lot of focus on buying tools, driving adoption. You know, measuring usage of those tools. I think, again, you know, what I was saying earlier that companies, you know, are continuing

Jasmine Jaco: to increase that investment and still not seeing the return that they want and expect, which is leading them to ask why and to start to think about what else do they need to do to to really drive impact. And I think that's where, you know, AI work design comes in. And when you do so, so when you're when you appreciate that and that you need

Jasmine Jaco: to actually redesign the way work gets done, not just give people new tools to do today's work, you know, better, better or differently. I think it's then that you you need to take it down to that level of granularity, because if you look at, you know, a job title or a job description, for example, that tells you where someone sits

Jasmine Jaco: in an organization, it tells you at a very high level, you know what their broad responsibilities are, but it doesn't tell you exactly how work it's done. It doesn't. It doesn't tell you the tasks and subtask. It doesn't tell you what systems and data are involved or where the friction is, or even if the work that's supposed to get done actually

Jasmine Jaco: even gets done today, if people even do it. And I can actually give you an example of this that we experienced at Boston Scientific in terms of why getting down to the task and subtask matters. Let's say you want to leverage AI to help your sales team like we did, drive more sales.

Jasmine Jaco: You can look at job descriptions to see what your sales reps are doing. You can conduct a handful of interviews to talk to them at a high level about where they spend their time and let's say, okay, sales reps spend a material amount of time on planning for and conducting customer calls

Jasmine Jaco: and then documenting those interactions in a CRM. So great. So we identified opportunities to build agents that accelerate that work, make that work better. And based on our initial assumptions, we estimated we could free up like four hours a week for each of our sales reps. Sounds great.

Jasmine Jaco: But then when we actually decompose the work to the task and subtask level and actually really engaged people who do that work every day, we learned a couple of things. First of all, the call preparation actually requires information from a number of different sources and sometimes even information that our sales reps

Jasmine Jaco: just individually had on their their phones that we hadn't counted for. So the agent that we designed initially didn't fully equip the sales rep for the customer conversation. Second, and this was really important, we discovered that a lot of our sales reps aren't even really consistently documenting those meetings in the CRM today.

Jasmine Jaco: So now we have two different value problems on the front end. We haven't automated enough of the workflow to really deliver the experience and the value we expected. And then on the back end, we claim time savings for work that in some cases is not even happening. And in fact, if if we had implemented those new AI workflows

Jasmine Jaco: that required that documentation, we would have actually been adding time rather than frame capacity, which undermines the whole value case that we put together. So that's why getting down to that task and subtask level really matters, because it tells you how work actually happens, and you have to have that to design workflow to, to identify

Jasmine Jaco: the right AI solutions and to build a credible value case. Yeah. And it's so true.

Siobhan Savage: So when we first started right at the beginning, we learned really quickly high level task not enough. You ought to go subtask. And then right down to that work context of like the handoffs, the kind of key things that are kind of hidden, unless you know that you need to know about to your point about like

Siobhan Savage: how things are actually happening, because that is really important. The high level task data is interesting. If you are thinking about like, what is the potential impacts to these jobs? That's kind of like where it's enough. But honestly, what we've seen with customers is that is not enough. Even when you give them like this job has got X amount of percent,

Siobhan Savage: that is not helpful because as soon as you sit in front of the business leader who owns this work, they're going to drill, drill, drill, drill really quickly into that. And I think what you're seeing is there's a great thing that's happening that everybody's not talking about tasks, which is great. You know, like we started right out in the very front of that moment. But I think the customer needs to know that subtask and work context

Siobhan Savage: becomes supercritical, because if they go out onto onto the onto the business and talk about that high level and aren't able to go down to that next level, they're going to look a bit silly because any of the business leaders will want to know, like right down into that, a subtask step level to find exactly what you find in terms of that diagnosis of like the problem.

Siobhan Savage: So I think that's a really, really great example as well. One of the things that I'm sort of So how do you think, you know, you've analyzed work, right? And how do you make decisions on if, you know,

Siobhan Savage: Boston Scientific has millions of tasks that are happening? So where do you go to like triage? Like what are we talking about? Makes a good bet. Is it money? Is it feasibility? Like, if you've got this big bucket of tasks that you could go after, how do you bring it down to think about

Siobhan Savage: the ones that become worth it for the company to even spend time on? Because to your point, there's not a lot of there's not a lot of people to go and do this work and change the work. So you've got to be like laser focused right on where you guys are going. That's right. And we learned early that, you know, just because you you can automate or augment work in a particular area

Siobhan Savage: doesn't mean that it's, you know, strategic.

Jasmine Jaco: It's a strategic or valuable enough investment to make. So we absolutely learned that early. And I think there's a number of data points that you need to make really sound investment decisions. First of all yes okay. The AI potential right. How augmented or automated is this work. But then start asking how many people do this work today?

Jasmine Jaco: How frequently do they do this work? How much time do they spend doing this work? And then to your point, I think into things like, you know, what are the risks and talent implications associated with automating or augmenting this work? You know, ultimately, how much value to the to the enterprise,

Jasmine Jaco: not to the individual, to the enterprise is there in investing in AI. And how expensive is the AI solution that you would need to be building? And I think you need all of those signals again to make good investment decisions. And and, you know, again, a platform like Reejig, I think that's

Jasmine Jaco: a lot of the value that you bring because it's really challenging to be able to otherwise surface all of those insights at an enterprise level in a large, complex global organization. Otherwise you're making those decisions without data. And so, you know, you're basically guessing has been my experience.

Siobhan Savage: The one that, you know, that we've been working on with the team is like the token costs. So I think it's like for in my mind, like after going through like a lot of these, it's like, what is the actual work? What like how many people do the work, what's the effort level? And like sort of how long does that happen?

Siobhan Savage: How often what's the cost associated with that. Because you can model that. Then it's like what agent can actually do this task because like an AI potential score and some random data is just not super valuable. It's kind of like, well, what agent can't actually do it within what Boston is allowed to use? Because you can't.

Siobhan Savage: You got to remember that you can't go off chopping and adding more complexity. So you've got these already pre-approved, you know, your cyber team, then a really great job of to make sure you're safe and secure as a company. So hiring Max out the tools that we've already got. And then it becomes like, is it even feasible?

Siobhan Savage: So feasibility I've spawned to be a big thing. Like just because you can doesn't mean you're going to be lied to. You know, like most most enterprises are like, oh yeah, we want to completely reimagine this whole world. And they do these reimagination processes, and then most of it has to stay the same You can't do these like there's certain things

Siobhan Savage: that you, as much as you would love to or just you just can't like the big companies run in a certain way for a reason. A lot of it can be changed, but like there's stuff that just can't be. So there's like feasibility. And then this final part, I don't know if you've seen my social media post, but we got a we got an award from OpenAI for spending 10 billion tokens,

Siobhan Savage: which is like, I didn't even know that was a thing. And then I felt really embarrassed I kind of preach this for a living, but yet I have spent whatever $10 billion worth of tokens are, and I'm getting an award. And if I'm getting an award, it's probably not a good thing. Right? So now I'm like, okay, token cost is not critical,

Siobhan Savage: especially when you start to bring in things like coworkers because they start maxing out the whole like they, they consume like cloud code is possibly the most and design most expensive consumer of anything because it takes the longest way to get anything done,

Siobhan Savage: So it's like, how do we help organizations like feel like, no, which one is like like less expensive in terms of models, but actually in some instances it's cheaper for people to keep doing it than actually have agents. So we got to help people figure this out pretty quickly. So that's the one that we've been working on is that. And that's where we believe

Siobhan Savage: now we're at a point where we feel like there are the dimensions and high decisions should be made so that they're good and fair. The only other one thing that we've seen, and I know we've talked about this before, there's also tasks you have to protect and don't remove because they're high. You build capability and expertise in your org.

Siobhan Savage: So this is one of the ones where we had Pioneers Club, where a lot of the folks in the room were talking about like, how do we make sure that we keep and protect those tasks? Because if the models are not always right, you need people to check that work, and they need to have expertise to know that it's not correct.

Siobhan Savage: So like, what's your take on that?

Jasmine Jaco: Absolutely is my take on that. And I think, you know, a lot of people are talking about impacts to especially early career talent pipeline. And I think it's exactly what you said. And I think it's for the reason you said, is that, you know, and this is important, I think, from a work design perspective as well.

Jasmine Jaco: I also don't think we're spending enough time really factoring into work design, the amount of time and the steps required for people to monitor AI output, apply judgment really critical, critically evaluate

Jasmine Jaco: those AI outputs and to remember that they remain accountable for those outputs. So that's an aside, but I think exactly to your point, in order to be able to do that effectively and have that judgment, you have to have grown up in that space and built those capabilities. So absolutely, that is a consideration for us as well.

Jasmine Jaco: When we think about not just what can we automate or augment, but what should we based on all the factors that we've talked about? Yeah, we even had a whole session as a leadership team and I am so sick of receiving claw docs. There are ten pages long.

Siobhan Savage: Oh my God, I can't cope anymore. I literally like push them back. Now I'm like, until you can write me a one page rapid that helps me. If you want me to help you make a decision on something really important, you should be able to concisely tell me what's the problem. Like, why does this problem exist? What have you tried? What's your like?

Siobhan Savage: What's your take in terms of recommendation. And then who needs to approve this? Is it me? Because I want to, I work fast, I want to be able to help these massively long pages that you get from Claude. That's just so much waffle. The amount of that that must be spinning around every future enterprise right now is like horrific. And then the other thing that we've, like, shut down.

Siobhan Savage: I put a big rule on this internally, we're not allowed to use AI for our social media post. We're not allowed to use it for our branding. We are completely cutting all of that out of our way of operating. You can use it for ideation, and you can use it for structuring your thinking and checking you. But like, we're not doing that anymore. It has to be real.

Siobhan Savage: It has to be like everything is just looking the same to me now. I like even even the claw design. You can see everybody looks the same. So we're like, there's just these things. No, I've got to the point in the business that it's like, before we start really scaling this, like we're making decisions now of like, who are we? What matters to us?

Siobhan Savage: And I think major enterprises have to do the same, you know, like like get really clear on like where do they not want it to go as much as how much they do want it to go places, because that matters, especially when it connects to your brand, or if you look after patients Like there's certain things that I think they should be really careful

Siobhan Savage: about protecting, because I reckon you'll end up seeing the folks that really, like, lean into like human front end experiences, whether it's more time with patient, more time with customer, better kind of experiences for people, I think they will do much better as a business. I don't think automating the whole company is the right plan, and that comes from

Siobhan Savage: someone who literally gets paid to automate your company. Right? So I know that sounds a little bit. And I think it's, you know, I mean, absolutely not when it's customer and brand facing because to your point, everything is starting to look the same.

Jasmine Jaco: It's being commoditized. Right. So how do you how do you authentically, you know, create those customer experiences and create those brand experiences? I think, you know, the converse of that is for internal communications. Absolutely. We should be using it. Why spend the hours it used to take to build a PowerPoint presentation?

Jasmine Jaco: Focus on the content that you're trying to convey. You know, leverage AI to do it for you faster. But then to your point, again, this is where applying judgment and being accountable for outcomes, you know, you're still you still need to use AI to deliver what you need to deliver. And if that's a one page summary versus ten pages of details

Jasmine Jaco: and graphs, then you still need to be able to do that effectively. Yeah. And it's these are the real things that everyone's going to have to deal with. Right.

Siobhan Savage: It's the it's the real consequences of I mean, I have rolled out as much AI and pulled back as much AI, like in my own company. So when you think about like I have thrown a whole pile of stuff out there, pulled it back as much as I'm deploying and keeping, which tells you like that it doesn't all go to plan.

Siobhan Savage: And that's why I'm so open about this stuff with customers. Because like, like there is like some thought has to go into like, and part of me thinks I should build that into the product. Like, should we be recommending stuff to protect their company? Like don't touch stuff that connects to customer and patient, like, would that be helpful as a feature? Maybe.

Jasmine Jaco: But I also think part of this is in the kind of human element of how you roll this out. So for example, I was talking a minute ago about I don't, you know, I'm helping my teams be really mindful about how do we make sure we design into workflows enough time and the right steps for people to monitor that output, for people to apply judgment. Right.

Jasmine Jaco: For people to really scrutinize, iterate where they need to iterate. Because in this speed right now to, you know, to to capacity savings and productivity, those those steps in that time can be easily compressed or skipped. And so I think it's really important to do that. But then I think from a change management perspective,

Jasmine Jaco: we also need leaders who recognize that and ask those questions like who value, you know, people sharing their learnings. Like, you know what? I developed this and it was crap. So I went back to the drawing board and now I have this right. And to have that be part of the culture and part of what gets recognized

Jasmine Jaco: alongside the, you know, the wins. So my brain is like literally sketching right now on my product.

Siobhan Savage: So you just said something that's really interesting. So, you know, in the work architecture and Reejig, you've got you've got Cantu lenses, you've got all of the work where you can go by like task or whatever, but then you've got the architecture, which is the wiring of the company like a wonder. Is there like a scenario where because I can see,

Siobhan Savage: I can see certain jobs that are going to change with a lot more of that review time because the volume of outputs getting so much higher. And I'll give you an example. In my team, we've completely changed the old model from, you know, product managers, designers, engineers, front end, back end. We know I have builders and these are like well did like the candidate

Siobhan Savage: come from a design background can also build or they're an engineer and they can also act like product. They're these like kind of AI powered, but they're never really coding. These folks are probably spending 90% of their time reviewing code. And the problem that we have is that most of the code. We're talking like a thousand x outcome, like puts compared to what they used to build.

Siobhan Savage: Which means one of the things we screwed up massively when we first moved to that model is we didn't factor in. The job would change significantly to the point that it's like mostly a reviewer of the of the job. So I wonder, is there going to be this massive consequence for like managers

Siobhan Savage: that they know I need to have, like all of this extra time for review cycles? Because the amount of like volume that's now coming out from, because there is like I have so much reviews to do. That's why I get so cranky when you send me like a seven page doc. I'm like, guys like, seriously, like concise this thing. Tell me what you're actually trying to say.

Siobhan Savage: You know, all the fancy words that Claude uses as well. And you can just as soon as I open it, I'm like, I'm right, I'm done. I'm not like, you've already failed. Take it back. I'm saying no. But on a serious note, what you're saying is really interesting because I wonder, have we completely forgot that all of these jobs will also like

Siobhan Savage: just because of the increase of, like, stuff that's happening underneath us, our manager is now going to have to have like this other part of their like their day job, which is like a lot more review because there's so much more that they have to check because the speed of which goes up. And that's a really good point.

Siobhan Savage: We should be triggering that in our architecture to say that when a workflow changes, it's not only just changing the individuals that do this, but it's also changing. That's actually really interesting because we definitely seen that in reading.

Siobhan Savage: Like I can tell you, our QA team have struggled, and Mike and our CTO and Ronnie have struggled because there's just so much volume. And that's where like things go wrong because you've got so much volume. And if you let a little bug in there and you got to go and find that thing, and if you don't know how to find it or where to find it from,

Siobhan Savage: because you've really so much unraveling, that is like pretty tricky. And we've been caught out a few times and that we've got so much better at it now because we slowed down to speed back up again. And everyone's kind of like being reset. But it was just one of the things that we learned was we didn't actually tell the people in the team that like, no, your job is change because we didn't know, like and I would say,

Siobhan Savage: if you've got all these lawyers that are now using like Harvey's or lagers or these other tools, these lawyers are working in completely different ways, nor that they've never had to work and having to review legal contracts, you know, like really important things. And I wonder what the consequences will be now in terms of like like that side.

Siobhan Savage: So it's just a really sorry, I brought a standard rabbit hole, but my brain was like thinking about you were saying something that's going to have to get built into our product, which we haven't really thought about, because you will start to see, see that whole thing play out from a, from a like lessons. So what are some of the kind of key things that, you know,

Siobhan Savage: you probably thought coming in that you've probably changed your mind on like like I'm pretty open so we can better tell some tricks. Like I'm messing up everything, I think.

Jasmine Jaco: Well, we talked about one of them. I think in the early days, everybody was off to the races to automate and augment So we already talked about the fact that just because you can automate or augment doesn't mean that it's the most valuable or strategic place to invest.

Jasmine Jaco: I think the second thing that I learned in building this capability and this practice, is that work design becomes real when people can see it. So, you know, work to me, work design is actually has been harder to explain to leaders than other aspects of AI transformation. Leaders generally understand the value of AI technology.

Jasmine Jaco: They understand whether they invest in it or not. Sufficiently, they tend to understand the value of change management and AI upskilling. But work redesign is still really a new muscle, and I found that the best way, the most effective way to build that understanding is to first

Jasmine Jaco: anchored in value and second show rather than tell. So again, pick a real area of work, do the work, show the outcome, show what you're able to achieve that you wouldn't have been able to achieve just by implementing technology alone. Because I probably spent too much time when I first started. You know, sharing thought leadership and all the research,

Jasmine Jaco: McKinsey studies and otherwise building this compelling narrative about why work redesign is important and, you know, that can create interest. But proof points create belief. So that's the second thing. And then I think the third thing would be that we really need to start valuing progress over perfection.

Jasmine Jaco: Some of us I think this discipline is new. Nobody has all the answers yet. You know, we've we've got to be comfortable with experimenting, seeing what works, what doesn't build a living methodology that gets better over time. And I think as part of that, transparency really matters to, you know, share the successes for sure,

Jasmine Jaco: but also be comfortable saying, like, we haven't solved that yet. Yeah, we're working on that. So I think those are some of the lessons. I like the progress of the perfection, because that falls right into the camp that we see.

Siobhan Savage: And like we've got like the most beautiful customer base that I could ever imagine where there's so much patience around, like us in terms of because everyone knows that it's new. And as long as we're committed to helping them solve it and we involve them in solving it like they are genuinely good people

Siobhan Savage: who also are kind of at that point where they realize that, you know, every two days it's changing, you know, like it's that rapid. So but I do think that there is an expectation some folks, you know, like our deciding to pick certain workflows for whatever reason.

Siobhan Savage: And it's like the worst starting point And then what's happening is that it's taking too long and then they're getting in trouble. And then it looks like the whole thing's a waste. So it's it's like definitely like start somewhere, build capability. I do believe that the way to learn in this world is not by like studying.

Siobhan Savage: It's by doing, you know, like I really practitioner led all the way. I think research and those McKinsey studies whatever like they're interesting to prove your point. But actually the, the real value is in like hands on keyboards or like get folks.

Siobhan Savage: Actually I hosted this like event with Microsoft and we had like 50 senior execs from the HR domain in the room. And we were all talking about this topic. Right. And then I asked them, like, put your hand up, how many people in here have actually built an agent? Two people. Wow. Right.

Siobhan Savage: So there's a whole pile of like that is a problem that like, how can you understand if you don't have your hands on it? Like even just like a day of, like taking yourself offline with your team, hosting something to just get you comfortable enough that you can, like, wing it at least.

Siobhan Savage: Like, don't I mean that you know enough, but also like it just gives you more empathy for like how fast things will change. And for teams like I get the kind of a bit of a high horse prompt training like prompt training is interesting, but it doesn't help people work. You know, it doesn't change how they work and doesn't teach them how to do the task.

Siobhan Savage: It actually gives them a high level perception of like a way of, you know, like prompting something and how the technology talks, but it doesn't actually, like change their work and get them to that point. So I think a lot of a lot of that like progress, getting people moving, learn and iterate as you go, give feedback.

Siobhan Savage: Like that's what we have learned. I mean, like even from when you started with us, like the evolution of where we're at on reaching side is nuts. And that's because we have been passionate group of customers who want to be like, right at the front of this. They know that it's really important, and they feel that they're responsible

Siobhan Savage: for both helping the company be really bold and reinvent. But also they don't want to leave their people behind. So they want to get this data so that they can understand what the consequences will be when it comes to workforce impacts as well. And that has become a huge topic across all of our customers now.

Siobhan Savage: And how do you see the work architecture? You know, we've talked most of our time about sort of AI and opportunity and everything else. So when you think about the architecture itself and you go and make those changes, how do you then think about workforce impacts, you know, and like what that means to like not leaving your folks behind

Siobhan Savage: or and I think, you know, having that common or shared work architecture is really critical to that.

Jasmine Jaco: I think, first of all, it's important before I get into the workforce implications, I think it's important from an investment and a value perspective a common architecture will keep each of your business units

Jasmine Jaco: and regions from looking at their own work differently, redesigning basically the same work multiple times, building AI solutions that basically accomplish the same objectives. So there's a lot of duplicative investments. And to me, a shared architecture

Jasmine Jaco: helps you, like invest once and realize that value over and over again. So that's one piece. But I do think from a workforce planning perspective, it's also really critical because it gives us a common foundation for understanding workforce and implications at an enterprise level. So, you know, if you think about the sales example that I shared earlier, like,

Jasmine Jaco: I don't want to just understand how AI is changing the work of a sales rep in one business or one region. Like, I want to look across our entire commercial organization and see where similar work is being automated or augmented, you know, across business units, across roles, across geographies. And when we have that common architecture that region gives us,

Jasmine Jaco: we can start to see those changes in aggregate, how much work is shifting, which roles are being reshaped, which skills are becoming more or less important because from workforce planning perspective, the question isn't just what is this agent or suite of agents mean for the sales team? It's what all of these changes that we're making to commercial work

Jasmine Jaco: collectively mean for the workforce, you know, over the next 3 to 5 years. So that's a fundamentally different, you know, level of insight than I think we've had historically when each of our divisions and regions have been operating separately. I mean, we're not there yet in terms of really taking full advantage of

Jasmine Jaco: of these skills and workforce implications and integrating them into some of our larger systems to really evolve our strategic workforce capability. But we certainly see that in the near future, and we're working toward that. Yeah. And it's.

Jasmine Jaco: I've seen probably the last six weeks this like urgency on it now. So don't know what's happening. But there's definitely like a lot of attention now to like okay, now I spoke in most of our time about this AI impacts our AI opportunities.

Siobhan Savage: Now let's look at the impacts because we've got a scenario play. And as you know we've been working with your incredible team to, you know, get feedback on like what are the things we want to tell our executives. Like what are the things that we need to start thinking for to plan through to make sure that, you know, you're able to manage your workforce

Siobhan Savage: through this massive, like once in a generation change to work. So you have an incredible amount of people online who usually when they come to these calls, they're really interested in probably starting they've just started this department in their own company or they're trying to.

Siobhan Savage: What are some of the things that you could share to folks listening now that would help them really position this new way of AI work design to to build this team, to build this, you know, the capability. How what would you say are the best sort of things to do?

Jasmine Jaco: Let's see a number of things. I think first, like I said earlier, start small but go deep. You know, pick a meaningful area of work, do the work, demonstrate the value. Don't try to solve for the whole enterprise on day one, and then use that first effort as a proof point and to learn what it takes to to do AI work design while in your organization.

Jasmine Jaco: The second thing, and this is been critical for us is design cross-functional, not enzymes. So we have a Boston Scientific designed an AI transformation pod structure that brings together people from different disciplines into one team who are responsible for work and or design, AI, technology and data solution.

Jasmine Jaco: So the builders, AI enablement, business leadership and subject matter expertise and program management. So you need all of those perspectives working together from the very beginning to drive real outcomes. You know, you don't want the tech built in one place work being designed somewhere else. And then change management and enablement brought in at the end. This should be one team.

Jasmine Jaco: And then I think the third thing is, but it's it's build the the capability internally as you go and then design it to scale. So again I talked about people process technology and service delivery and trying to figure out how are you going

Jasmine Jaco: to deploy your scarce resources in the in the most effective way.

Siobhan Savage: It's such an exciting time. It really is to, to to be in our career right now. And, and it's such a needed thing. And everywhere I turn, these AI work design teams are being set up across all of our customers because everyone realizes this is not a one time change. This is a forever change to work.

Siobhan Savage: So I think for folks listening in, it's like really like focusing on this moment. I think we've got to make sure that we're proving the value, that value realization, opportunity case to the business. You know, where we're really super clear on on how to describe that. So after this, what we'll do is we're going to put together a little playbook with everything that Jasmine has been saying,

Siobhan Savage: because there's so much context here, Jasmine. And what you're saying where we can put this together is a bit of a playbook for folks when they're starting to think about this themselves. So, folks, you'll be able to receive this, you know, from Reejig after just to make sure that you can really kind of take what you're saying. Because if you just listen, that's like millions of dollars

Siobhan Savage: worth of consulting hours you've just booked because it's so much experience of both being you've done it for many years on one side consulting, but now you're deep in org also in real life doing so. The lessons here are incredible. Jasmine, thank you so much for for all of your wisdom. Thank you for joining the Work Design Blueprint show.

Siobhan Savage: We're so grateful to have your learnings and thank you for going on this journey together.

Jasmine Jaco: Absolutely. Thank you. It's been a great conversation.