Michael Fraccaro on AI work transformation

Author: Reejig
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Reejig

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11 mins

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Published

Jul 3, 2026

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Talk to a Work Strategist

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AI work transformation is the leadership test of the decade, and most organizations are still treating it like a technology upgrade. The enterprises making real progress are the ones who have moved beyond AI deployment into a deliberate redesign of how work actually runs. Michael Fraccaro, former fellow and Chief People Officer at Mastercard, is working with CHROs, boards, and executive teams across industries on exactly this challenge.

This conversation, from Reejig's Work Blueprint series, covers the three strategic buckets every CEO should be deciding between, why HR is now in the front row of AI transformation, how to protect the junior workforce pipeline, and why building internal capability is not optional.

What they covered:

 

  • Why AI transformation is a macro leadership test, not a technology upgrade, and what that means for CHRO priorities
  • The three strategic buckets: efficiency and cost, improving products and services, and fundamentally reimagining how value is created
  • Why job architecture is wired for an era that no longer exists, and what Work Architecture replaces it with
  • How to think about junior workforce development when agents are taking on analyst-level work
  • Why the organizations that gave every employee AI access and said "go build" are actually moving slower
  • Why HR must build internal work design capability rather than relying on external consultants

Key takeaway: The organizations winning at AI transformation are not the ones with the most AI. They are the ones who understand how their work runs, have decided what to redesign first, and are building the internal capability to keep redesigning as agents get stronger.

AI transformation is a macro shift, not a technology upgrade

The biggest mistake organizations made in the early wave of AI was treating it like a technology upgrade. Buy the licences, give people access, wait for productivity to go up. That framing is now visibly broken.

As Michael Fraccaro, former Chief People Officer at Mastercard, put it: "This is like the next big shift, right? And with any J curve, the role for HR is, how deep and how quickly do you get your organization out of that J and into the optimization. That's the change and transformation period we're in now."

The organizations making real progress have moved to a different question: are we using AI to cut costs, to improve our products and services, or to fundamentally reimagine how value is created? These are not the same question, and the answer determines everything from who owns the initiative to how success is measured. Every enterprise is deploying AI. Almost none can see the work they're deploying it into.

HR is now in the front row, not the back office

For the past two years, AI transformation sat with the CIO. That has shifted. The work of redesigning how an organization operates belongs to the people function, and most executive teams have now recognized it.

What changed the conversation is the realization that deploying AI without work redesign produces chaos, not productivity. When everyone is given access to AI individually and told to figure it out, organizations do not move faster. They move slower.

As Siobhan Savage, Founder and CEO of Reejig, observed: "They've scaled chaos. Everybody's in the rowing boat, going in different directions. Things are actually slowing down."

Fraccaro described the shift he has seen: HR is now a "front-row seat" in developing and implementing workforce transformation. That means owning the question of how work happens today, which tasks should be AI-led versus human-led, and what the organization needs to look like on the other side.

From job architecture to Work Architecture

Job architecture was built for a world of stable, siloed roles. It is not built for agents, dynamic workflows, or task-level AI deployment. From Job Architecture to Work Architecture is not a cosmetic update. It is the foundational shift that makes AI transformation possible.

Work Architecture maps every department, job, level, role, workflow, task, and subtask, structured for both humans and agents. It is dynamic by design, because the work keeps changing as agents get stronger. Job architecture describes what people are called. Work Architecture describes what actually happens.

Fraccaro was direct about the gap: "There is still a tendency to hold on to what we know and what we've been taught, which is the more conventional approach to job architecture. But our organizations will require us to think differently as we deploy agentic tools to do particular tasks. That's a big shift."

The task-level exercise Fraccaro uses with leadership teams, taking a single role and classifying every task as AI-only, human-only, or hybrid, illustrates the scale of the problem. Do that across every job family in a large enterprise, and the scope of redesign becomes clear. You cannot deploy AI into work you cannot see.

The junior workforce pipeline is a strategic risk

One of the most urgent and underappreciated risks in AI work transformation is what happens to junior workforce pipelines when entry-level analytical work is automated. Many organizations have already started pulling back on graduate and intern programs. Most have not thought through the five-year consequence.

Fraccaro put the question directly: "If some of that analyst work can be done by agents, what do you do as an organization? Do you still double down and think about those roles? What happens five or ten years down the track? Where's your pipeline?"

The Reejig perspective is that the question is being framed too narrowly. Rather than deciding whether to hire entry-level people at all, the better question is: what if agents were wrapped around those people to make them productive faster? Instead of three to five years of structured training, a new hire could operate at a higher level much earlier, because agents are handling the repetitive execution while the person develops judgment, craft, and business context. The goal is to protect the people bench while accelerating how quickly people become genuinely valuable.

Build internal capability, not external dependency

The organizations that are outsourcing their AI work transformation to consultants are solving the wrong problem. Work redesign is not a one-time project. It is an ongoing operational capability that compounds over time.

As Savage argued: "We can't rely on externals. We have to build the capability inside our businesses. This is not a one-time change. This is a forever shift. The iPhone keeps upgrading. Work keeps upgrading. You need the capability inside to keep redesigning."

Fraccaro framed this as an oxygen mask problem for HR leaders: fix your own function first. If HR is advocating for work redesign across the business but has not redesigned its own workflows, the credibility is not there. Leading by example means HR demonstrating task-level AI deployment in its own processes before asking the business to follow.

The internal team model that is emerging in large enterprises includes a small workforce innovation group, working alongside HR business partners, that maps AI opportunity, models agent cost against people cost, and drives redesign at the department level. This is not a consulting engagement. It is a permanent capability.

Executive Checklist: AI work transformation

  1. Decide which of the three strategic buckets you are in: efficiency and cost reduction, improving products and services, or fundamentally reimagining value creation. These are different programs with different owners and different success metrics.
  2. Replace job architecture with Work Architecture. Map every role at the task and subtask level, classify each task as AI-led, human-led, or hybrid, and make that map the basis for AI deployment decisions.
  3. Stop measuring AI success through AI adoption metrics. Measure whether work actually changed: time per task, throughput per workflow, capacity redirected.
  4. Protect the junior workforce pipeline deliberately. Do not cut entry-level programs without a plan for where organizational expertise will come from in five years.
  5. Build internal work design capability. Establish a permanent workforce innovation function rather than repeating consulting engagements every time the work changes.
  6. Lead by example in HR. Redesign HR's own workflows with AI before advocating for the same in the business.
  7. Address culture and organizational trust directly. Employees who do not understand what is changing, and why, will not redirect their time when workflows shift.

Where CHROs and CIOs must partner

CHRO Focus

CIO Focus

Shared Outcome

Task-level work design: what is AI-led, human-led, or hybrid

Agent inventory and governance: what is approved and deployed in the environment

A unified map of which workflows to redesign, in what order, with what guardrails

Junior workforce pipeline strategy and entry-level role redesign

Agent capability assessment: what analytical tasks agents can reliably perform today

Entry-level roles rebuilt around human judgment and agent support, not replacement

Internal HR capability building and workforce innovation team

Engineering and integration support for AI workflow deployment at scale

A permanent, internal work redesign function that does not depend on external consultants

Employee communication, trust, and adoption at the role level

Change logging and audit trail for every workflow and agent modification

AI adoption that employees understand, trust, and can operate within

Executive FAQ

Why is AI transformation described as a leadership test, not a technology problem? AI work transformation requires decisions about value creation, risk tolerance, workforce strategy, and organizational culture, none of which are technology decisions. The organizations treating AI as a technology upgrade are deploying AI into unchanged workflows and finding no return. The leadership test is deciding what work to redesign, in what order, and how to bring the organization through continuous change without losing people or expertise.

What are the three strategic buckets every CEO should decide between? The three buckets are: using AI for efficiency and cost reduction, using AI to improve products and services, and using AI to fundamentally reimagine how value is created. Each requires a different program, different leadership ownership, and different success metrics. Organizations that conflate them end up optimizing for the wrong outcomes, often short-term cost cuts at the expense of long-term capability.

What is Work Architecture and why does it replace job architecture? Work Architecture is the entity model that replaces static job architectures: every department, job, level, role, workflow, task, and subtask, structured for both humans and agents. Job architecture describes what people are called. Work Architecture describes what actually happens, at the task level, dynamically updated as work changes. It is the foundational map that makes AI deployment decisions possible.

Why is the junior workforce pipeline an AI transformation risk? When entry-level analytical work moves to agents, organizations stop hiring for those roles. Within five years, the pipeline of people with the foundational experience needed to become senior leaders and domain experts has dried up. The answer is not to keep hiring for roles exactly as they were, but to redesign those roles so that new hires build judgment and craft faster, with agents handling execution while people develop the capabilities that cannot be automated.

Why should enterprises build internal work design capability rather than hiring consultants? Work redesign is not a bounded project with an end date. As agents get stronger, workflows change again. An organization that relies on external consultants for each wave of redesign will always be behind, always paying for the same capability, and never building the institutional knowledge of how its own work runs. Internal capability compounds. External dependency does not.

How should organizations measure whether AI transformation is working? The right measures are changes to actual work: time saved per task, throughput per workflow, decisions accelerated, capacity redirected to higher-value activity. Licence adoption, prompt volume, and usage metrics tell you whether people are using a product. They do not tell you whether work changed. AI ROI is proved through outcomes, not consumption.

Conclusion

AI work transformation is not a project with a completion date. It is a permanent shift in how enterprises operate, and the organizations building internal capability to manage that shift continuously are the ones that will compound their advantage. The starting point is always the same: understand how work actually runs before deploying anything into it.

Book a demo to see how Reejig's Work Operating System gives your team the task-level visibility to start redesigning work with confidence.

Speakers

Siobhan Savage
Siobhan Savage

Siobhan Savage

CEO & Co-Founder of Reejig

Michael Fraccaro
Michael Fraccaro

Michael Fraccaro

Former Chief People Officer & Fellow at Mastercard

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Siobhan Savage: Hello! How are you, Michael?


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Michael Fraccaro: Derek, how are you? Derek, how are you?


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Siobhan Savage: I am going very well, I'm going very well. How was your trip to Australia?


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Michael Fraccaro: It was wonderful. It was wonderful.


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Michael Fraccaro: I had, Tom Hank… And, and our work. So, a good mix. So, a good mix.


00:00:32.640 --> 00:00:37.549

Siobhan Savage: Amazing, amazing. Is your microphone repeating back to us, or is that my microphone repeating back?


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Michael Fraccaro: I'm getting a lot of echoes.


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Michael Fraccaro: Let's see…


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Siobhan Savage: I love that while you're getting your microphone fixed, where is everybody dialing in from? Welcome, folks!


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Siobhan Savage: I am dialing in from New York. I wish I was heading to the beach like everybody else. Right now, given this weather is so warm.


00:01:05.530 --> 00:01:06.310

Michael Fraccaro: Sure.


00:01:06.490 --> 00:01:11.209

Michael Fraccaro: Still getting a lot of echo. Still getting a lot of the echo, a lot of the echo.


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Siobhan Savage: I love this, we're here to talk about AI.


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Michael Fraccaro: AI.


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Siobhan Savage: And… Zoom is malfunctioning as we speak.


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Michael Fraccaro: Okay. Okay, okay. Nuts, no nuts.


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Michael Fraccaro: It was okay just a minute ago.


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Siobhan Savage: It was! Guys, we just did a tech check, and it was all great, and then suddenly it went crazy.


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Siobhan Savage: And it's even echoing to Germany. We can hear the team are saying here that it's echoing back in Germany.


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Siobhan Savage: And Michael's reconnecting. While Michael's reconnecting, I'll kind of set us up for what we're going to talk about today. So, the work blueprint, the whole design of these conversations, we work with the most complex customers, everyone's on this journey, and what they're trying to kind of get


00:02:21.010 --> 00:02:37.509

Siobhan Savage: together on is, like, where is the world going with AI coming through? What does that mean in terms of our people? What does it mean in terms of how work is designed? And essentially, we're all in this space trying to find this new blueprint for how we will, kind of.


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Siobhan Savage: Organize our organizations, organize what work looks like from our people perspective.


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Siobhan Savage: And we've got Michael, who's coming on, and once he gets his microphone fixed.


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Siobhan Savage: Michael will be coming on, where, you know, for those that don't know, Michael was CHRO for MasterCard for a very long time, you know, very forward-leaning in terms of, you know, his role in shaping Mastercard. He now does a lot of advisory. He's now come on board as Regig's advisor, where he works alongside our customer CHROs, really helping them figure out, like, where the world is going.


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Siobhan Savage: When it comes to designing this new era.


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Siobhan Savage: And what we're typically seeing with customers right now is a couple of key themes that are playing out. So, you have, like, on one side, you have the conversation where every CEO right now and every board are pushing AI. They're pushing the adoption of AI, and it's causing, sort of, like, this ripple effect into our businesses.


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Siobhan Savage: And we want to be driving our organizations towards, you know, becoming agent-first and driving AI workflows, but at the same time, we don't want to leave our people behind.


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Siobhan Savage: And what we're seeing across our customer base right now, and across the industries, is that on one side, there is this, like, drive towards the bold, but we're kind of stuck in that kind of early pilot phase, where customers don't know, you know, their work, they don't know where to go in terms of, you know, what agents to go after, they don't know the workflows.


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Siobhan Savage: And then this really interesting thing that we're starting to see play out with our customers is, once you have designed AI workflows.


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Siobhan Savage: How do we make sure that our people are able to even adopt this new way of working, and adopt that new,


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Siobhan Savage: Agent, and make sure that they're able to actually work in this new way.


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Siobhan Savage: And then on the other side, the CHR role that we're seeing play out right now is there's this big focus on how do we make sure that we don't leave our people behind? So that's where we're really starting to focus on, you know, what is the impacts to our workforce? What is this new blueprint for work? How do we make sure our people are not being left behind?


00:04:46.530 --> 00:04:52.060

Siobhan Savage: what is, sort of, the CHRO, kind of, what are the things that are keeping CHROs up at night?


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Siobhan Savage: And then how do we make sure that we are not, sort of, leaving our people behind? I'm sure everyone here in the chat will also know that there's a lot of worry


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Siobhan Savage: around our employees, like, not knowing actually where work is going to… like, what does that mean for me as an employee, and what's that gonna look like? So Michael has really been working with, you know, top


00:05:17.510 --> 00:05:34.890

Siobhan Savage: CHO, CIO leaders, he does a lot in the academic side of things as well, and he's going to be able to share some of his perspective on some of the watch-fors. The other thing I would say to folks in this room right now as well, there is this new kind of space that's forming within HR.


00:05:35.260 --> 00:05:55.100

Siobhan Savage: Where there's this new team and this new career pathway that's being launched, where folks have kind of come from, like, all these non-traditional backgrounds that we're starting to see play out in our customer base as well. Everything from, you know, folks that were doing learning and skilling and org design, we're now kind of molding into this new team, which is kind of like workforce innovation.


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Siobhan Savage: So, these conversations are really open book.


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Siobhan Savage: everyone can be active in the chat, drop in sort of what you're thinking, any questions that you want to know from Michael, myself, anything that you're kind of trying to, you know, solve right now that we can kind of give you a point of view. And hopefully, Michael, you can hear us, and your microphone is working, and think you're in a good place?


00:06:19.130 --> 00:06:20.750

Siobhan Savage: We can't hear you, Michael.


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Siobhan Savage: It's the… it's one of those things that when, I don't know if anyone's experienced this, when you go to do a big presentation, and then you can't share your screen, and then suddenly the demo doesn't go to plan, it's like one of those things that you're here to talk about, like, big AI, and then Zoom… Zoom malfunctions just before you're doing it.


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Siobhan Savage: while Michael's connecting his audio as well, like, folks, is there any sort of questions that folks have coming into this that they want us to cover? Like, drop into the chat, like, some of the key themes that you're kind of struggling with, like, what's keeping you up at night at the moment? How can we help sort of answer any of those sort of questions for you as well?


00:07:02.970 --> 00:07:18.929

Siobhan Savage: I can tell you that there's a couple of key themes that are probably keeping me up at night right now. We used to worry about, you know, one, understanding the visibility of work. That was, like, the big thing that Regie and I kind of, like, pioneered around, like, understanding tasks.


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Siobhan Savage: Then it became, like, knowing where to go.


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Siobhan Savage: and then what agent, and then how do we build the workflow? My biggest worry right now is, like, even when agents are being deployed into workflows and companies, what customers seem to be doing is, like, launching prompt training for everyone, and expecting that that is enough for our employees to now adopt this new way of working.


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Siobhan Savage: So that's where we're really seeing, you know, that play out, where I'm a little bit concerned that, like, the prompt training is like a… is like a seating for, like, what's coming, whereas if you go and you reinvent.


00:07:55.430 --> 00:08:14.530

Siobhan Savage: how folks work, we need to make sure that people are able to adopt this new way of working and have to have a step-by-step guide of, like, hey, this is not in our company how we pay this invoice. I see Erwin in the chat saying, how are orgs deciding about AI risk, AI costs, and AI uncertainty? Oh, Erwin, that's an incredible,


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Siobhan Savage: point as well. So, one of the big questions that we are getting asked, that we're working on at the moment… So, one, there's, like, if you work for the most regulated organizations, pharmaceutical, insurance, banking, hospitals, which I do.


00:08:27.040 --> 00:08:35.499

Siobhan Savage: there is, like, this one kind of mindset which is like, okay, so AI can do these tasks, but just because it can doesn't mean you should.


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Siobhan Savage: So what we're typically working with our customers is on understanding their work.


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Siobhan Savage: And then we look at the regulations, so if you're FDA regulated, if you're looking at HIPAA from a healthcare perspective.


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Siobhan Savage: There is this other part of, like, deciding which parts should be kept human versus which parts should you give to an agent. So that's where we're starting to see that play out right now.


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Siobhan Savage: Where, like, that whole view around, sort of, AI risk and the ethical component of that, but also, like, should you?


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Siobhan Savage: So, there's that kind of part to Erwin's question, and there's this other part as well. So, I'm sure folks have seen all over the news, like, you've got, like, the Ubers of the world, and others talking about where they've brought in agents, and it's actually been more expensive than their people.


00:09:23.260 --> 00:09:27.249

Siobhan Savage: And a lot of companies are looking at agents as a cost-saving


00:09:27.430 --> 00:09:45.739

Siobhan Savage: component right now. And the thing that I'm working on with the team is, like, so if I understand all of the work that happens, let's say, in your company, and I understand the tasks that happen, I can tell you roughly how much that task costs to do, because I can model it with your people data.


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Siobhan Savage: But then the other part of this is, I can now tell you the cost per token. So if an agent goes and does this workflow, this is typically how much this is gonna cost. So we're in better right now on that, like, and it's kind of, like, ever-changing, because you guys will all know that models are constantly changing how they price.


00:10:02.540 --> 00:10:09.550

Siobhan Savage: and how that works. So, there's this big kind of conversation that's happening at the CEO, CFO level right now.


00:10:09.680 --> 00:10:34.669

Siobhan Savage: around, obviously, the risk that Erwin's talking about, but also that cost as well. So that's something that, like, is going to be really important, that when you think about your role, and how you reinvent and re-blueprint your organization, just because agents can, it's also, like, what is the agent and people capacity of our org, and what is the cost profile? And in some instances, in my company, we've actually, like, not used agents, and we've kept people because it was going to be more


00:10:34.670 --> 00:10:57.540

Siobhan Savage: cost-effective to actually keep our people than it was agents, which sounds kind of crazy compared to the press that you typically read. So, Urban, like, feel free, if I haven't answered that correctly, like, jump in and let me know. Another question here from Melvica as well, would love to know what your thoughts are on Meta's latest move. Oh, this is, like, a really interesting one. So, for folks who don't know.


00:10:57.540 --> 00:11:00.569

Siobhan Savage: Meta basically has now installed


00:11:00.570 --> 00:11:23.289

Siobhan Savage: I don't know how you would describe it in a polite way, but essentially, it's like monitoring all of your employee clicks, it's monitoring everything that you do. There's other versions of this as well, which I'll share in the chat in a second. So I've also seen, like, a deskless version of that as well, so you've got, like, Meta now tracking how their employees work so that they can train agents to now do those tasks.


00:11:23.700 --> 00:11:35.090

Siobhan Savage: And that's basically what they're doing. There's this other part where there's these new technology companies, and I'll share it in a second. What they're basically doing is they're setting up cleaning companies.


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Siobhan Savage: And what they're doing is they're hiring, like, traditional cleaning folks that would go in and clean your house. So, you know, you may be buying a cleaning company to come in and clean your house, and they've got cameras bolted everywhere.


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Siobhan Savage: And what they're doing is they're creating the data footprint, which tells you the work context and how work happens. So when you think about this from an agent perspective, like, understanding how I pay an invoice, you know, on my computer.


00:12:03.050 --> 00:12:16.029

Siobhan Savage: you capture that, but there's this other level that's starting to play out when you think about AI and robotics together, where when you start to see that play out, where you're now getting robots trained on how, sort of, folks clean your house.


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Siobhan Savage: And looking at all the different ways of, like, this is how you clean a toilet, this is how you clean a sink, this is how you hoover. All of the… that is, like, what we would describe as work context.


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Siobhan Savage: It's, like, the pattern of how that happens. So I think… I mean, most large enterprises


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Siobhan Savage: don't want to monitor their employees. They don't want to do that in a way where it's, like, a security risk.


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Siobhan Savage: But there's also this other part where organizations are truly trying to understand how work happens, because they want to be able to, one, understand how the work happens, but secondly, they want to be able to introduce, like, agents within those flows.


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Siobhan Savage: And for folks, like, just a little bit of, like, what we have learned. So, in Rejig, we look at all of the tasks, so think of the high-level task as paying an invoice.


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Siobhan Savage: And then we look at the subtask level.


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Siobhan Savage: Which is where we know, like, the steps that you must take to then pay the invoice. That's incredibly important context. And then there's this third level, which is really important, which is the work context. Think of it as, like, the handoff points, the systems that it goes to. There's that other layer. It's usually, like, that last mile.


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Siobhan Savage: And when you're building agents.


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Siobhan Savage: like, proper agentic workflow agents, you need all three levels. Now, the customers that we work for would not want us plugging in and monitoring how every click happens, but they still want to capture. So in what we do is we have, like, the work contest capture where we prompt you and say, like, this is how we see you work, and you get to, like, prompt it.


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Siobhan Savage: It is really interesting, though, what Melvika is, like, saying, though, because there is employees' perspective that I've seen on socials and just talking to employees and to customers, that there is this big concern.


00:14:07.360 --> 00:14:13.839

Siobhan Savage: Around employees now being used to train models to then out-train themselves out of a job.


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Siobhan Savage: And I think that's a real, like, component, which I don't think the news helps employees trust, and there's a lot of, like, distrust around it. So I think that's where…


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Siobhan Savage: that's where, you know, you're starting to see it, but I definitely think the pattern of what Med is doing is what I'm seeing more broadly in the industry, which is, like, that cleaning company. There was another example of folks, I think in a factory, like, stitching…


00:14:40.090 --> 00:14:57.009

Siobhan Savage: garments, and they had cameras also connected, it's exactly the same form of what Melvika is saying. It's the… it's the viewing of the task in action to then create that context. So, I think, I'm not a super fan of that. I would rather that, you know, you have a…


00:14:57.310 --> 00:15:13.709

Siobhan Savage: specific example, and it's back to Divya's point as well, like, how can you do the task mapping without the time and motion study? Like, the problem to solve is we want to know how work flows. I think there is much, like, safer ways of capturing that work context, either through


00:15:13.950 --> 00:15:34.449

Siobhan Savage: prompting through, like, in our product, it's a prompt which basically says, here's how, you know, you pay the invoice in your company, and it prompts them and asks them a couple of core questions that we want to know, which captures it and then updates the workflow. So that's your, like, enterprise safe-grade way of doing that, where you're not doing that kind of surveillance and still solves the same problem.


00:15:34.450 --> 00:15:59.080

Siobhan Savage: And then the second part is where, you know, one of the things that we've built out as well is, like, I can share a link, let's say, to Divya, and say, hey, Divya, can you just record how you actually, pay that invoice so I capture that without the time and motion study? So that's a… that's a safe, you know, you get to opt in, you double opt-in to that moment, so that's what we're typically seeing, play out as well.


00:15:59.200 --> 00:16:11.319

Siobhan Savage: Michael's got a great question here. Keeping leaders from over-indexing on proving increased productivity as the results of AI, and only focusing on the potential efficiencies rather than value creations.


00:16:11.320 --> 00:16:24.230

Siobhan Savage: capability of AI to do novel things we could never do before. I don't know, Michael, if that's, like, a statement or, like, a question. The one thing I would say that I've seen shift in market right now


00:16:24.230 --> 00:16:38.250

Siobhan Savage: is that you have, leaders who originally were, like, focused on AI because they wanted to cut costs, and typically that was associated with headcount, if I'm honest. I have seen a shift in narrative now at an executive level.


00:16:38.450 --> 00:16:44.010

Siobhan Savage: What I have seen is customers now saying, we want to keep headcount flat.


00:16:44.610 --> 00:16:50.669

Siobhan Savage: And then what we want to do is grow our revenue profile and do, like, more of the thing that makes us money.


00:16:50.850 --> 00:16:57.100

Siobhan Savage: Which I think is a really interesting change, because that's about amplifying the things that help us


00:16:57.360 --> 00:17:13.219

Siobhan Savage: do more of the things that, as a company, make us money and increase velocity, but it's not effectively focusing on headcount reduction. It's like, how do we help, you know, us do more of the things that are valuable, which I think is a really good turn that you're starting to see.


00:17:13.319 --> 00:17:29.920

Siobhan Savage: I think there's also a little bit of political shift that's going to start happening now as well, where employees are definitely a lot more vocal, and I don't know if everyone's seen a lot of the graduations most recently, where I think, you know, a lot of the, you know, the new entrants into our workplace


00:17:30.120 --> 00:17:42.010

Siobhan Savage: they did… everyone said, oh, the kids will love AI, and they'll be our earliest adopters. It's really interesting that they are pretty anti-AI, so anyone who's kind of thinking about AI adoption, you know.


00:17:42.350 --> 00:17:59.760

Siobhan Savage: we're gonna have issues where employees are petrified of this thing, but also pretty empty it, because they've been taught in the media that it's gonna take away all of their jobs. I think the one thing I am definitely seeing, though, is this narrative shift away from cutting headcount to now increase


00:17:59.910 --> 00:18:05.630

Siobhan Savage: Revenue, do more things, without it actually being, you know, an impact


00:18:05.750 --> 00:18:14.589

Siobhan Savage: to our people, which I think is a really promising thing, guys, that is just not cutting through on the press right now. Hey, Michael, I'm just, like, on TikTok Live right now.


00:18:14.590 --> 00:18:17.840

Michael Fraccaro: You're doing a great job. You're doing a great job. I can hear everything.


00:18:17.840 --> 00:18:34.399

Siobhan Savage: Guys, I love that everyone's, like, helping me through this moment by seating me with questions. This is amazing. But I think we've got Michael, I think we've got full technology, we're working. Do you need to have, like, a little bit of, like, a meditation moment, Michael, where you're good, or are we good to get in?


00:18:34.400 --> 00:18:36.090

Michael Fraccaro: Sure, I'm ready to go.


00:18:36.090 --> 00:18:53.479

Siobhan Savage: And guys, thank you for navigating us through the technical challenge. That was very comfortable, because everyone dropped into the chat. We'll try and answer as many of these questions as possible, because they're brilliant. And if I don't get to all of those, I'm gonna follow up individually with everybody, just to, like, answer them as well. But Michael.


00:18:53.480 --> 00:19:04.399

Siobhan Savage: Now that we've got the… the technology doesn't go to plan. That's point number one, right? Like, it's the… everyone's hyping the agents right now, and sometimes it doesn't go to plan. Hi, how are you?


00:19:04.400 --> 00:19:13.040

Michael Fraccaro: I'm great, and it's always great to have the human, you know, working out these intricacies of technology, so, there's still a role for us, for sure, but apologies.


00:19:13.040 --> 00:19:13.650

Siobhan Savage: Agree.


00:19:13.650 --> 00:19:19.179

Michael Fraccaro: Apologies, you were doing a wonderful job there, riffing on all those questions, so, glad to be here.


00:19:19.180 --> 00:19:33.849

Siobhan Savage: It's my time on TikTok Lives that I have never done before. That's maybe a new career for me after this. So, Michael, I had already given everyone context about your incredible career history, your background, you have sat right at the front.


00:19:34.060 --> 00:19:45.269

Siobhan Savage: leading, you know, one of the most transformational, times in history as well, and you're now… I mean, you're a couple of things. You are now in your portfolio.


00:19:45.490 --> 00:20:02.019

Siobhan Savage: career moment where I get to have access, and our customers at a CHRO and CFO and CIO level have access to you. So, you know, getting your expertise here in the room for everyone is incredible, and we're super grateful. Why don't you give everyone a little bit of, like, what you've been up to?


00:20:02.020 --> 00:20:14.790

Siobhan Savage: sort of the kind of conversations that, you know, you're involved in right now, like, where you're kind of… kind of leaning towards, because I think that'll be a really good setting from… I've now, I've been, you know, CHRO, MasterCard, and what are you up to?


00:20:15.290 --> 00:20:38.279

Michael Fraccaro: Yeah, so, so thanks for the introduction. Yeah, so, since I retired from MasterCard, back in January of this year, and really stepped out of the Chief People Officer role in April of last year, so I spent a bit of time just, doing transition work, while I was still there. But in my new, portfolio career, my next season, as it were.


00:20:38.280 --> 00:20:49.910

Michael Fraccaro: I'm sort of dividing my time up in a couple of areas. One is around, advisory and consulting work, so I'm doing some work with Egon Zender, which does a lot of, leadership advisory work.


00:20:49.910 --> 00:20:54.739

Michael Fraccaro: And it gives me access to conversations across industry.


00:20:54.740 --> 00:21:19.739

Michael Fraccaro: even though it may be financial services, industrials, pharmaceuticals, doesn't matter, this topic about AI and AI readiness, continues to come up, whether it's at the board level or at the management operational level. So, spend time, there, and then I'm spending a bit of time, doing some teaching work as well. So I'm faculty at a couple of different universities, so it gives


00:21:19.740 --> 00:21:21.720

Michael Fraccaro: me access to…


00:21:21.960 --> 00:21:46.030

Michael Fraccaro: executives that are in the heart of change and transformation, but it also gives me an opportunity to learn from academics that are doing research in this particular area. So that's really interesting. So, just recently, in the past month, we were doing some work with Harvard, Cambridge, and then just recently in Johannesburg.


00:21:46.030 --> 00:21:47.350

Michael Fraccaro: with Duke.


00:21:47.350 --> 00:22:10.060

Michael Fraccaro: So, so that's interesting. And then the other part is obviously, with startups, and with organizations like Rejig, really, which is at the leading edge, of looking at tools and solutions to help support organizations leading through transformation. So, so that's, in a nutshell, what I'm up to, and I'm really looking forward to this conversation, because I think


00:22:10.060 --> 00:22:33.319

Michael Fraccaro: given the number of people that are on the call, everyone is still eager to learn from each other, but also to take it away from the hype and into real practical implications and ways in which CHROs and executives can really affect significant change and positive change in their organizations during this period.


00:22:33.980 --> 00:22:53.770

Siobhan Savage: Yeah, it's really interesting. The thing that you have, which I really think is great, is the diverse access point from an academic, from real life, being, like, hands-on tools in your role, and now you're advising across multiple different industries. What do you think, like, from your perspective, like.


00:22:53.920 --> 00:22:59.170

Siobhan Savage: When you… when you think about work and this new moment, like, what do you think…


00:22:59.440 --> 00:23:11.679

Siobhan Savage: getting work right looks like in this era. Like, what does that kind of, like, look like, when you're talking to these customers? And the, you know, the CHO is like, what… what is it? Because, like, it's kind of tricky right now, right?


00:23:11.680 --> 00:23:20.169

Michael Fraccaro: It is tricky, it really is tricky, and I think this is, like, the leadership test of the decade, and I think…


00:23:20.170 --> 00:23:36.860

Michael Fraccaro: you know, organizations have had to deal with so much, you know, the pandemic, tariffs, geopolitical challenges, but this is, like, one of the, as I think, one of the biggest leadership tests of the decade, if not the century.


00:23:36.860 --> 00:23:44.100

Michael Fraccaro: And I think there's been, what I've observed over the last 12 months, there's been this interesting arc of…


00:23:44.100 --> 00:23:56.069

Michael Fraccaro: At the beginning, everyone was really looking at AI, like a software upgrade. It was a bit like Y2K, like, this is a big, a big thing.


00:23:56.070 --> 00:24:07.060

Michael Fraccaro: And there was a lot of debate around who owns, AI. Is it IT? Is it HR? And I think that sort of…


00:24:07.240 --> 00:24:21.780

Michael Fraccaro: sort of been, a debate that's been going on. I think what I've seen now, it's sort of evolved from that, and it really is this joint collaboration, with HR playing a really, front-row seat


00:24:21.780 --> 00:24:27.769

Michael Fraccaro: In actually developing and implementing and thinking about workforce transformation, because


00:24:27.770 --> 00:24:31.630

Michael Fraccaro: AI really is a strategic inflection point.


00:24:31.630 --> 00:24:41.379

Michael Fraccaro: And it demands a different level of thinking and, leadership clarity around what is it that you're trying to solve for.


00:24:41.380 --> 00:24:52.269

Michael Fraccaro: And I think the three big buckets I always go back to is around, are we doing this for using AI to cut our costs and looking at efficiency and productivity?


00:24:52.270 --> 00:25:09.159

Michael Fraccaro: That's sort of number one. Number two is, are we looking at AI to improve our products and services? And then number three is, are we looking at AI as a way to fundamentally reimagine, how value is created for our businesses?


00:25:09.160 --> 00:25:14.419

Michael Fraccaro: And I think all of those three things, are equally important.


00:25:14.420 --> 00:25:37.290

Michael Fraccaro: And depending on where you are in your organizational journey, you need to think about, are we doing this to play defense, or is there an aspect here about playing offense, and what do we lean into? And then, what role do we need to play from an HR perspective to really help navigate those conversations, at the board and at the executive team level?


00:25:37.290 --> 00:25:43.640

Michael Fraccaro: So, I think that's sort of the big arc and shift that I've seen over the last, 6 months in particular.


00:25:43.860 --> 00:25:48.289

Siobhan Savage: Yeah, it's really interesting, when I started, like, rejig and was really pushing…


00:25:48.630 --> 00:25:53.700

Siobhan Savage: I had a very low… like, the CHROs weren't really…


00:25:54.210 --> 00:25:59.979

Siobhan Savage: like, I was pushing. Like, I felt like I was pushing the rock up the hill a little bit, like, three, three and a half years ago.


00:26:00.180 --> 00:26:19.919

Siobhan Savage: And the last, probably, 9, 12 months, I have seen this huge recognition that, oh my goodness, this is a HR problem. Because at the start, it was, like, very much so, like, an agent problem, so it sits with the CIO, but everyone's now realized, this is not a tool problem, guys, this is a… work reinvention sits with the people team.


00:26:20.000 --> 00:26:34.180

Siobhan Savage: Like, the tool is just the tool. It's actually about the work, and the reinvention, and the redesign of the work, and this is where I think it's become really important. And the thing that I remember meeting you years ago, you said it was like a COVID moment?


00:26:34.440 --> 00:26:57.789

Siobhan Savage: Where it was, like, the CIO, CFO, CHRO now collaborating in a way that we've never had to collaborate other than that COVID moment, which always stuck with me, because it was, like, that's so true, and I've started to see now this new team being formed within the CHRO's team, like, work innovation, workforce innovation, whatever you want to call it, which is now driving, like, that they are now owning out that whole space, so I think, like.


00:26:57.930 --> 00:27:03.010

Siobhan Savage: I'm definitely seeing what you are saying play out, like, real time across our customer base.


00:27:03.340 --> 00:27:09.059

Michael Fraccaro: It is, and I think the, you know, forums like this, but I think it's getting to that point where


00:27:09.220 --> 00:27:27.559

Michael Fraccaro: HR is really beginning to think about decision intelligence. That's what this is all about. It's like, it's not that, is this gonna happen or not gonna… it's going to happen, but we have to manage the tension between speed, and quality. We need to think about


00:27:27.560 --> 00:27:29.289

Michael Fraccaro: Trust and governance.


00:27:29.290 --> 00:27:40.329

Michael Fraccaro: we need to think about how do we take people along the journey? I mean, there's so many different aspects, and depending on where your organization is and what values you have.


00:27:40.330 --> 00:27:52.120

Michael Fraccaro: the role that HR plays is really helping to facilitate and have those active conversations to land at the right decisions, and I think that's, like, a really big aha moment for a lot of.


00:27:52.480 --> 00:28:01.130

Michael Fraccaro: HR professionals that I've seen, and it's things like, Siobhan, we've spoken about this, and I think the work that you've been doing around


00:28:01.130 --> 00:28:25.250

Michael Fraccaro: just that basic question around, how does work happen? Like, how will work happen in the future? And even, you know, the exercise that I've done with some leadership teams of taking a job description of a financial planning and analysis role, and looking at the tasks, and doing this manual, which you do it automated, and you use your tools, but


00:28:25.250 --> 00:28:28.700

Michael Fraccaro: Even if you do it as an exercise, what tasks


00:28:28.700 --> 00:28:34.080

Michael Fraccaro: AI only, what is human-only, and what is hybrid?


00:28:34.080 --> 00:28:57.140

Michael Fraccaro: And even just thinking about that little exercise on one role, when you're doing that across multiple job families, and multiple jobs themselves, that is a huge scale transformation and change that you need to be thinking about which processes stay, which processes disappear.


00:28:57.140 --> 00:29:07.349

Michael Fraccaro: How do we re-engineer, not just refine what we currently have, but reimagine, what these agents could do? Where does human work start and stop? I mean.


00:29:07.350 --> 00:29:19.760

Michael Fraccaro: there are big questions, and I am encouraged to see that more and more HR leaders and executives are actually taking that journey, that starting path, to


00:29:19.780 --> 00:29:32.600

Michael Fraccaro: basically put, a stake in the ground and be able to think about all of these things, or how the organization is going to… what the organization will look like in the future, and in particular, jobs. So…


00:29:32.770 --> 00:29:33.100

Siobhan Savage: Yeah.


00:29:33.100 --> 00:29:34.210

Michael Fraccaro: It's a big shift.


00:29:34.560 --> 00:29:46.919

Siobhan Savage: And I think Erwin was saying right at the beginning, it's like the risk around AI, too. Like, most folks that are on this call probably work with regulated customers that have, you know, whether you're sitting across healthcare, finance.


00:29:47.100 --> 00:29:52.810

Siobhan Savage: Pharmaceutical, insurances, like, there's actually things that you can't automate and you shouldn't.


00:29:52.810 --> 00:30:11.090

Siobhan Savage: So there's also, like, the practicalities of, like, like, designing the work and understanding it, but knowing where the risk sits, and making sure that you can navigate through that. I think that's also, like, a thing that's becoming very important that we were all kind of jamming on in the chat, like, when you were connecting, and I think… I think, to your point, though.


00:30:11.090 --> 00:30:27.360

Siobhan Savage: I believe that, like, you're even seeing in market the CIO now starting to report into CHRO, so that's a new shift that's starting to happen. We see that across a couple of our customers, and I think it's regardless of the reporting line, I think the acknowledgement is there now that this is not an IT problem.


00:30:27.390 --> 00:30:46.850

Siobhan Savage: Yeah. Like, this is… which is great, right? And for folks that are on this call, Michael, like, you know, I have this, like, rally call to market right now, where, like, this is the greatest career opportunity for all of us right now, to be, like, in this once-in-a-generation change to work, to actively, like, drive and design how our companies will work.


00:30:46.880 --> 00:30:58.630

Siobhan Savage: you know, by being bull, but responsible at the same time, and I think the folks that you and I get to talk to, they're right at the helm of, like, getting to make that decision as a company, right? Like, and as the leaders that are in charge of that?


00:30:59.100 --> 00:31:18.070

Siobhan Savage: And when you think about, like, you know, the CEO, for instance, what is the one data point or, like, the missing link that you think is there for the CEO? Like, when you think about the CEO and how they're thinking this through, what do you think is, like, an issue there? Like, what's the missing link that they're not seeing?


00:31:18.970 --> 00:31:29.209

Michael Fraccaro: I think, I think they're beginning to see it, and I think this is part of the change that I've seen. You know, one is around


00:31:29.520 --> 00:31:42.290

Michael Fraccaro: the board, so they have a responsibility around, you know, how is the board prepared? Are they… are they adequately equipped to be asking us the right questions to… to put that right.


00:31:42.290 --> 00:31:42.640

Siobhan Savage: Hmm.


00:31:42.640 --> 00:31:49.059

Michael Fraccaro: tension level, so I think there's a board and a governance piece. I think the other, big


00:31:49.060 --> 00:32:13.560

Michael Fraccaro: question, I think, on top of mind for CEOs, and it gets back to something you just touched on, but I think risk. Like, there's risk in all of this. There's, thinking about your customers, thinking about data, thinking about governance. I think for CEOs, they're beginning… well, not beginning, they do recognize that this is a big, big shift.


00:32:13.670 --> 00:32:28.349

Michael Fraccaro: And so, this earlier version of, you know, what is AI, this is a big software upgrade, and we're just going to give everyone, co-pilot licenses, or allow people to play with Claude, or not play with, you know.


00:32:28.760 --> 00:32:40.930

Michael Fraccaro: tools and so forth. I think the conversations now is, this is much more than just giving people access to tools. It is a fundamental shift in the way that


00:32:41.040 --> 00:32:48.589

Michael Fraccaro: CEOs are having to think about all of these bigger macro, questions,


00:32:48.590 --> 00:33:03.600

Michael Fraccaro: And as well as bringing the organization along this, this change journey. So even things like, you know, chips and power consumption, communities. So, you know, the discussions last week in South Africa.


00:33:03.600 --> 00:33:14.419

Michael Fraccaro: when I'm talking with mining companies, they're saying, look, hey, there's a lot of opportunity here with AI and with, with automation and robots.


00:33:14.420 --> 00:33:30.620

Michael Fraccaro: But also, we have to consider that in certain markets, there is a requirement in terms of how many people are hired. There are tax implications as well, if you were to automate too much. So there's all these other macro questions around


00:33:30.620 --> 00:33:44.760

Michael Fraccaro: how fast the change, needs to happen for the organization. So, I think this is… the missing link, if you… if you want to go back to that, is around… this is like a macro, transformative moment.


00:33:45.170 --> 00:33:59.790

Michael Fraccaro: And there are multiple touchpoints, and thinking it's just a technology solution that's just focused on productivity misses the whole point around the opportunities that come here, as well as balancing all the risks as well.


00:34:00.380 --> 00:34:10.670

Siobhan Savage: Yeah, and I think, like, Cordell has a really good question in the chat. He said, like, I'm curious how you see HR's role in AI adoption.


00:34:10.699 --> 00:34:25.010

Siobhan Savage: I do not think HR should own… I do not think HR should own identifying the highest value use cases, but it may need to help the business understand how work is actually done, or where AI agents fit, and what has to change.


00:34:25.420 --> 00:34:33.209

Siobhan Savage: in roles and skills and workflows to make the transition work, and I think one of the things I would say before you answer is.


00:34:33.219 --> 00:34:33.539

Michael Fraccaro: No.


00:34:33.540 --> 00:34:43.059

Siobhan Savage: there's kind of two voices of this in market right now. There's the voice where there's a team that's getting stood up underneath CHRO who's, like, building capability internally.


00:34:43.550 --> 00:34:51.900

Siobhan Savage: To do this, think of the architect as the designer of this new world, and then the builder brings together the agents, so that's, like, one rule where it sits under one camp.


00:34:51.989 --> 00:35:10.359

Siobhan Savage: And then there's this other one, which is HR are now just doing the work design, org effectiveness, tasks, owning that whole component, and then the business builds their own agents. Like, what do you think is HR's role in all of this, Michael? Like, where do you see the boundary of what… if you were designing this right now.


00:35:10.360 --> 00:35:10.710

Michael Fraccaro: Yeah.


00:35:10.710 --> 00:35:13.169

Siobhan Savage: a CHRO, what would your team look like?


00:35:13.580 --> 00:35:26.360

Michael Fraccaro: Yeah, I mean, I do think… and these are the conversations that are happening now. It's like, if you are… if you are sitting in the role, and you're advocating, or you're proposing and influencing the business.


00:35:26.360 --> 00:35:48.889

Michael Fraccaro: the business will ask you, what are you doing in your own function as well? How are you creating capabilities within your own function? And I think one of the areas of focus for HR leaders is really doing that. It's like leading by example, and thinking about what kind of roles do I need? How should my team


00:35:48.890 --> 00:36:01.659

Michael Fraccaro: reimagine the work that they're doing. And these are the real examples. So, the point about, looking at processes and looking at jobs and, redesign


00:36:01.660 --> 00:36:16.460

Michael Fraccaro: and job architecture, tasks and skills, and thinking about agentic AI. You know, it's a multitude of, of different touchpoints, and I think there's a responsibility on HR to upskill itself.


00:36:16.460 --> 00:36:22.950

Michael Fraccaro: It's almost like putting the oxygen mask on yourself before the others, you know, this whole safety, you know.


00:36:22.950 --> 00:36:40.530

Michael Fraccaro: call that you have on flights and so forth, it's the same kind of thing. What are you doing for yourself? And so, I do see that's a big factor for HR, in particular reimagining its own workflows and processes, as well as helping shape and influence the rest of the organization.


00:36:41.360 --> 00:36:42.860

Siobhan Savage: I think it should be, like.


00:36:43.530 --> 00:36:48.730

Siobhan Savage: Like, we sh… this is gonna sound really crazy, but, like, we shouldn't rely on external people


00:36:48.990 --> 00:36:51.000

Siobhan Savage: To come in and show us how to work.


00:36:51.130 --> 00:37:07.960

Siobhan Savage: Like, we need a capability inside our own companies to be able to do this forever, because this is not a one-time change. This is a forever shift. Like, the iPhone's upgrading, you're gonna keep upgrading work, and it's gonna keep changing, and, like, so I think, like, wherever it sits, I got a pretty…


00:37:08.080 --> 00:37:15.670

Siobhan Savage: rigid view now that, like, we can't rely on externals, we have to build the capability inside our businesses. Like.


00:37:15.860 --> 00:37:32.240

Siobhan Savage: if I was CHRO and I wasn't doing this right now, I mean, how long will that CHRO be a run for? Just being honest, like, because you can't keep relying on externals to do something. It's like, any shift in, like, technology or advancements in anything, you know, at the start, you kind of…


00:37:32.240 --> 00:37:45.139

Siobhan Savage: figure it out, but then you become, like, okay, this is a COE, like, we gotta build this practice in, and we gotta learn how to do this and build it. So, I do have a pretty spicy kind of take, where if they're not doing it, I'm like, well.


00:37:45.260 --> 00:38:08.159

Siobhan Savage: how you're… why would you expect an external to come in and do that for your company? You should know how to change work yourself. Like, work is the most important thing that you do as a business. It's how you make money, right? It's not like a thing. It's like, it's the most important thing that makes a company actually successful, is, like, based on, like, what you do as a currency of work. So, like, I really do think… and I think it's, like.


00:38:08.160 --> 00:38:14.249

Siobhan Savage: The people that, like, kind of navigate towards these conversations, like, they're the best people, because they're curious.


00:38:14.400 --> 00:38:20.119

Siobhan Savage: they know it's never been designed before, but they're up for, like, that mindset, Michael, you know, like, they'll.


00:38:20.120 --> 00:38:20.470

Michael Fraccaro: Yeah.


00:38:20.470 --> 00:38:26.010

Siobhan Savage: mindset of, like, that agility and, like, learning agility and being able to be creative enough to, like, kind of


00:38:26.240 --> 00:38:39.389

Siobhan Savage: like, figure it out, because no one solved this problem, right? It's… this hasn't been solved yet. Anyone who's promoting that it's completely solved, that's not true. Like, it's still early innings in the sense of an enterprise deploying AI right now.


00:38:39.590 --> 00:38:48.330

Michael Fraccaro: It is. And, you know, the whole… if you think about the theory of any kind of new introduction of technology, there's that J-curve, right?


00:38:48.850 --> 00:39:06.830

Michael Fraccaro: And so, even the adoption of the internet, it took… it took a number of years before people really embraced internet, right? And web browsers, and it shifted the way that advertising, was perceived, and marketing dollars, and all those kinds of things.


00:39:06.830 --> 00:39:30.670

Michael Fraccaro: And the way that we do our work, this is like the next big shift, right? Yep. And with any J curve, the role for, HR is that how deep and how quickly you get your organization out of that J and into the, you know, the optimization is like the period we're in now. That's sort of the change and transformation, period.


00:39:30.670 --> 00:39:54.230

Michael Fraccaro: And so, the more that we can do ourselves, yes, bring in, insights and look at best practices and research and so forth, but at the end of the day, a lot of the responsibility is going to come back on us and internally, how we make the change and the transformation occur. We just need to equip ourselves and our teams with the right data, with the right tools.


00:39:54.230 --> 00:39:56.790

Michael Fraccaro: And the space to be able to do this.


00:39:56.790 --> 00:40:21.789

Michael Fraccaro: And I think the other piece, which we haven't touched on, but I do think it's a really critical one, is around the culture. What kind of organizational culture do you have? Is it one that's going to reward this shift and recognise, or is it one that's going to hold people back, or to some extent, have consequences of people leaning in? And so, the cultural aspect of this is also


00:40:21.790 --> 00:40:45.760

Michael Fraccaro: also a significant one, and particularly when it gets into decision making and what's automated, where's the human decisions and so forth. It's a really important question up front around how you take your organization through this. And bringing people with you and the transparency, I think, is a really important question for the organizations to ask.


00:40:45.760 --> 00:40:46.710

Michael Fraccaro: themselves.


00:40:47.240 --> 00:40:58.469

Siobhan Savage: one of the… one of the things that… when you were talking about the CEO, and you just triggered me to think about it when you said that, so one of the biggest things that I think has been, like, quite hilarious to watch


00:40:58.470 --> 00:41:14.550

Siobhan Savage: So you had all of the CEOs pushing these leadership boards where everyone gets, like, celebrated for their AI usage, and it was all competitiveness, and push, push, push, push, push, push, push, and then you've now seen… you've now seen that that costs shitloads of money.


00:41:14.550 --> 00:41:31.510

Siobhan Savage: Yeah. Then they're like, hold on a second, actually, we're not token maxing anymore. Actually, we gotta be more disciplined in this, so I think there's this leadership, like, issue right now, where employees are like, hold on a second, I thought we were token maxing, now we're not token maxing, like, there's this…


00:41:31.510 --> 00:41:43.670

Siobhan Savage: confusion around, like, what does the company culture actually value? And I think, like, like, that's a cultural thing. It's like, what does the leadership push? Like, are we… are we pushing AI for the sake of AI?


00:41:43.670 --> 00:42:01.319

Siobhan Savage: Or are we pushing a more strategic surgical approach to how we adopt AI? Or is it leadership boards, or… but now they've gone from, like, we gotta build as fast as we can, and it doesn't matter because we get on the leaderboard, to now we're not spending any money, and everyone's got to hold. So there's this, like,


00:42:01.860 --> 00:42:05.839

Siobhan Savage: I don't know, like, you ever watch kids play soccer, and they, like, all run after the ball?


00:42:05.840 --> 00:42:06.690

Michael Fraccaro: Yeah.


00:42:06.690 --> 00:42:14.490

Siobhan Savage: like this, like, or chickens, whatever you want to describe it. There's this thing happening right now in the industry where… and imagine being an employee, sitting in that mess.


00:42:15.300 --> 00:42:27.080

Siobhan Savage: you'd be kind of going, well, hold on a second, what is it? Like, I just literally was like, so, like, is that where you think the cultural part is important? Just, like, with that, like, live scenario that's playing out?


00:42:27.080 --> 00:42:44.190

Michael Fraccaro: Yeah, I think that's a really great analogy, you know, the kids on a soccer field, and, you know, people are running, you know, in different directions, wherever the ball is going. And, you know, and I think the other big questions for organizations as they go through this, which links to the culture one.


00:42:44.190 --> 00:42:56.650

Michael Fraccaro: the conversations I've been having the last 6 months or so, the question that comes up the most is around, you know, how do we develop and grow talent? And particularly as it relates to junior talent.


00:42:56.650 --> 00:43:10.769

Michael Fraccaro: Like, the, you know, the graduates, or the interns, and so forth, and a lot of organizations had very robust, interns and graduate programs, and now if some of that


00:43:10.810 --> 00:43:15.260

Michael Fraccaro: analyst… analyst work can be done by agents, well.


00:43:15.260 --> 00:43:38.780

Michael Fraccaro: what do you do as an organization? Do you still double down and think about, those roles and say, well, look, we're not going to hire, those roles anymore, but what happens, you know, 5 or 10 years down the track? Where's your pipeline, as an organization? So, these are… these are really important questions for the organization, and rather than it becoming a…


00:43:38.780 --> 00:43:44.870

Michael Fraccaro: one or the other, those roles become extinct. Stepping back, pausing.


00:43:44.970 --> 00:43:54.899

Michael Fraccaro: And saying, what are we missing here? Yeah. And should we reimagine, you know, the way that we develop junior talent? Do we do it in a different way?


00:43:54.900 --> 00:44:11.609

Michael Fraccaro: And I think this opportunity about imagination and reimagination, I think it's a really important aspect that we need to create space for leaders and HR to really not just rush and run to where the ball is.


00:44:11.610 --> 00:44:28.759

Michael Fraccaro: but actually to think about strategically, where will the ball be in 5 more steps, or 5 more passes, and actually go there. So, I think that those kinds of questions… I think the other one that we've touched on already is around the role of the leader.


00:44:28.760 --> 00:44:44.530

Michael Fraccaro: And, you know, what… what role does the leader play in all of this? Are they… are they closed? Are they open? And are they creating the right environment with their teams as well for taking them on this journey? So.


00:44:44.530 --> 00:44:48.789

Michael Fraccaro: I think that whole piece around collective wisdom and,


00:44:48.790 --> 00:45:08.369

Michael Fraccaro: you may not necessarily be the smartest person in the room, nor should you be, but how do you bring your team along? Like, even the thing about work redesign, actually, the best person to design… redesign the work is the person doing the job. But do you do it in a way that's non-threatening? Yeah. Anyway, that's a…


00:45:08.370 --> 00:45:24.280

Siobhan Savage: No, and I think… I think, I think it's… I was, I was hanging out with, I was in Seattle last week, and I was hanging out with this awesome chief talent officer, and her question was, like, the business and everyone saying that we don't need these entry-level talent anymore, because agents are gonna do everything, and I was like, that's rubbish.


00:45:24.280 --> 00:45:36.439

Siobhan Savage: Like, that's not true. One, it's gonna cause exactly what you just said, the talent bench issue, but I also, like, flipped it a bit and was like, well, hold on a second, why couldn't we look at it in a different way? Instead of us spending


00:45:36.440 --> 00:45:48.359

Siobhan Savage: three to five years training up our entry-level people. Imagine if we designed the role in a different way, where they enter in, and we wrap agents around them, and suddenly they become experts in a lot more things faster.


00:45:48.360 --> 00:45:51.339

Siobhan Savage: And they become highly valuable a lot quicker.


00:45:51.340 --> 00:46:08.780

Siobhan Savage: Because they have this, like, broader bench of, like, agent expertise around them. Like, imagine how much more value they could bring to the company and then to your customer. So I think, like, there's a lot of doomy, gloomy kind of, like, perspectives that are coming out from the agent companies right now, which does not help us and our jobs.


00:46:08.780 --> 00:46:19.910

Siobhan Savage: But the reality is, like, if we look at, like, the opportunity, I've been started to take, you know, early graduates that have never worked in a company before into my company, and, like, even my company, it's like.


00:46:19.960 --> 00:46:42.689

Siobhan Savage: it's not a MasterCard or these big companies, but the interesting thing is that I'm now testing and road testing, like, okay, what if I built a whole pile of wrapper to, you know, to really enhance the ability of this individual? Because I've got known ways of working, how fast can they be productive? And it's incredible, Michael. Yeah. It's like a… it's such a different, like, view that this would have taken me years to get someone to this, like, strength.


00:46:42.690 --> 00:46:52.919

Siobhan Savage: and I'm able to, like, fast track that, so just something for folks to noodle on, like, I'm trying to figure out, like, what the methodology and, like, how would you scale that and everything else, but it's just another…


00:46:52.920 --> 00:46:59.820

Siobhan Savage: perspective of, like, my kind of live lab testing right now, like, like, does it actually work? But I think it's important because


00:46:59.960 --> 00:47:06.479

Siobhan Savage: you know, especially folks coming out of university, and I think you've got kids that are at that university age that they're quite worried right now.


00:47:06.480 --> 00:47:23.360

Siobhan Savage: about what will happen to them, and what does career profession look like for them. There's a lot of, like, anxiety around that for folks, and a lot of us are parents of, you know, that age group. So I think that's where CHRO, Chief Talent Officer, Chief Learning Officer.


00:47:23.430 --> 00:47:45.700

Siobhan Savage: that point is important, because it's not just protecting the company's capability, but it's the societal as a whole, like, how do we operate in this new world, and what are our kids gonna do, and, like, what does that mean, and how do we build that strength and bench in our company, too? So, I don't know if you think that my thinking's a little bit off, but, like, that's kind of where I'm trying to push customers to, is, like, this different perspective as well.


00:47:46.010 --> 00:47:58.870

Michael Fraccaro: Yeah, no, and look, part of the research coming out of some of these great universities, I mean, they're beginning to look at, you know, is this the death of real ultra-specialization? Because AI can


00:47:58.870 --> 00:48:08.570

Michael Fraccaro: fulfill a role in the future, and as we know, AI systems are good at some of the technical and repetitive knowledge work.


00:48:08.570 --> 00:48:24.890

Michael Fraccaro: but not necessarily good on some of the other aspects which require imagination and storytelling and so forth. And I think the talent that will win in the future are probably those that can combine the specialist skills or the domain.


00:48:25.360 --> 00:48:41.020

Michael Fraccaro: they may have maybe an engineer, but also this multidiscipline where they've also got other aspects or tools in their kit, that they can bring. So maybe the foundation is engineering, but they bring in a marketing aspect, or they bring.


00:48:41.020 --> 00:48:41.400

Siobhan Savage: Yeah.


00:48:41.460 --> 00:48:59.159

Michael Fraccaro: you know, some other capability, but they remain curious and adaptive to change, and that it can be transported across contexts. Because I think it leads to another big question around, you know, our organizations were all built in a 100-year-old model.


00:48:59.300 --> 00:49:00.200

Siobhan Savage: you know.


00:49:00.200 --> 00:49:17.690

Michael Fraccaro: functional and siloed, and we see that in all our employee experience surveys around bureaucracy and so forth. This may be a moment to reimagine org design, and, deploying talent where there is a need, and where those skills match.


00:49:17.690 --> 00:49:28.459

Michael Fraccaro: And so things like your internal talent marketplace and so forth is, like, an example, but that kind of, aspect around you'll need some kind of framework


00:49:28.460 --> 00:49:41.669

Michael Fraccaro: But I think there may be an opportunity to really think differently about deployment of talent and career development that can absorb this creative nature in organizations. So, I think that's another shift.


00:49:41.670 --> 00:49:42.010

Siobhan Savage: Yeah.


00:49:42.010 --> 00:49:43.069

Michael Fraccaro: as well.


00:49:43.300 --> 00:49:47.340

Siobhan Savage: And I think, like, you touched on, like, another hot button of mine, the job architecture.


00:49:47.460 --> 00:49:51.230

Siobhan Savage: Like, we've sleepwalked our way into this, like…


00:49:51.790 --> 00:49:56.759

Siobhan Savage: Wiring of a company, which is designed not for this era.


00:49:56.870 --> 00:50:12.109

Siobhan Savage: And I think, like, that's one of the big points around, like, where we, like, focus is, like, it's not a job architecture, it's a work architecture, and it should be dynamic because of the changes. Like, what's your view? You've probably done many job architecture projects over your career.


00:50:12.110 --> 00:50:12.480

Michael Fraccaro: Hmm.


00:50:12.480 --> 00:50:18.650

Siobhan Savage: Like, what's your view of, like, job architecture and now this new world? Like, how do you see that whole thing playing out?


00:50:19.280 --> 00:50:35.120

Michael Fraccaro: I think that's, like, that's the moment they were in. I think there is still, a tendency to hold on to what we know and what we've been taught, which is the more conventional approach to job architecture.


00:50:35.120 --> 00:50:58.790

Michael Fraccaro: to something which is a little more fluid. And I think that's where the shift needs to be, because I do think our organizations will require us to think differently as we start to deploy agentic tools to be able to do particular tasks and so forth. I think… I think that's a big question around, are we going to do performance management on


00:50:58.790 --> 00:51:08.060

Michael Fraccaro: these agents. Who's going to oversee the agents? And it's a different skill, and it's a different problem that we're going to be solving.


00:51:08.060 --> 00:51:13.789

Michael Fraccaro: that we haven't yet, uncovered it yet, and I think that's a… that's a… that's a big shift.


00:51:13.790 --> 00:51:17.609

Michael Fraccaro: And I think the other one that sort of links to this is that


00:51:17.610 --> 00:51:41.220

Michael Fraccaro: you know, are there winners and losers in this? Or is there going to be a changing in terms of a leveling up? So, the example would be, is AI going to be a non-linear approach so that the real talent in the organizations are going to embrace this even more, and are going to be even more productive than what they were before? But does that create,


00:51:41.350 --> 00:51:56.280

Michael Fraccaro: you know, health and well-being and, burnout issues, because they're just so, so into it. And then for, bottom performers, do they get overwhelmed by all of this change, and they fall further behind?


00:51:56.280 --> 00:52:10.869

Michael Fraccaro: So there's another argument that says, well, if you're in the lower tier, that the deployment of AI in a responsible way could actually lift them up to another baseline in the organization.


00:52:11.240 --> 00:52:27.210

Michael Fraccaro: even thinking about your population and your performance and potential, questions as well, these are really important around the overlay of technology and AI in particular, around how you see this playing out in your organizations as well.


00:52:27.990 --> 00:52:32.270

Siobhan Savage: It's interesting, because Matt put a question into the chat about


00:52:32.330 --> 00:52:50.979

Siobhan Savage: like, RAM and comp and how things will evolve, and one of the pieces of work that I've been working on, Matt, at the moment has been, on one side, the job architecture is kind of, like, usually… it's… it kind of sits half in rewards teams, half in talent teams. It's kind of like, in most… it's kind of split between those two areas.


00:52:51.210 --> 00:52:59.369

Siobhan Savage: One of the things that we… we have built out is we take the job architecture from being this kind of, like, archaic wiring system.


00:52:59.430 --> 00:53:17.559

Siobhan Savage: And then we evolve it into what's called a work architecture, which means, you know, we understand the levels, the departments, the RAM, all the associations with that, but we're blending it with, like, tasks, subtasks, workflow, agent, like, all of the broader pieces that we need as a work architecture.


00:53:17.590 --> 00:53:31.600

Siobhan Savage: What's really interesting, though, is there's a lot of questions coming to me now, and my DMs around, like, okay, what does this mean about reward? How do you pay people differently? And how do you see that playing out? And Michael, one of the things I've been saying right now is that


00:53:31.840 --> 00:53:45.639

Siobhan Savage: most of the, like, agents are at subtask level right now. Like, there's not a lot of areas, like, let's be honest, that are taking out 50% of someone's job. Most of the scenarios are, like, subtask level, like, small agent… no.


00:53:45.700 --> 00:53:58.810

Siobhan Savage: that's today. Now we look at capability like co-work, and when that really starts pushing out, like, a Microsoft co-work that goes out into the enterprise, when that starts being alloyed at scale, you're gonna start to see, like, broader shifts now in work.


00:53:58.820 --> 00:54:06.079

Siobhan Savage: So, that's where the REM conversation comes in. Like, if I make a salesperson 10 times more valuable.


00:54:06.790 --> 00:54:10.220

Siobhan Savage: Because of agents, are they gonna expect more…


00:54:10.220 --> 00:54:33.399

Siobhan Savage: reward for that, if… or in that other scenario, like, if we take X percentage of a rule, does that mean the shift in REM? And I think that's where… I don't think anyone, Michael, has solved that. Have you seen anyone having a strong point of view, to Matt's question? Because I think no one knows, because they don't know how real the AI thing is really going to hit the jobs right now. What's your take?


00:54:33.770 --> 00:54:58.639

Michael Fraccaro: Yeah, I mean, it's a great question. The take really is around, whenever you're looking at compensation design in organizations, you're also looking at, value creation, right? And so, you need to take a step back and really think about what are we incentivizing for, how are we funding, you know, our compensation, is it creating value for shareholders.


00:54:58.640 --> 00:55:07.199

Michael Fraccaro: customers and so forth. So I don't think the fundamental principles shift in terms of good comp design.


00:55:07.200 --> 00:55:10.929

Michael Fraccaro: But in terms of the mix, it might be different.


00:55:10.930 --> 00:55:27.099

Michael Fraccaro: like, in terms of, you know, base compensation versus short-term bonus and long-term incentives. And so I think… I think it is a question that comp committees are asking themselves around where does this go? And when you see some of the,


00:55:27.190 --> 00:55:31.949

Michael Fraccaro: Parabolic, you know, stock prices of certain companies.


00:55:31.950 --> 00:55:32.710

Siobhan Savage: Yes.


00:55:32.710 --> 00:55:48.739

Michael Fraccaro: you sort of think, okay, is this… is this because of hype, or is there, you know, some real value that's being created here? And then how do we reward people for their contribution, to this? And equally, if something goes the other way, is there enough


00:55:48.740 --> 00:55:54.810

Michael Fraccaro: Durability in the existing compensation plans that doesn't penalize people


00:55:54.810 --> 00:56:12.860

Michael Fraccaro: Where they haven't had control over the outcome, there's some other, issue that's occurred. So, it's a bigger question around, you know, reward and incentives and comp design, but it has to be something that teams are thinking about. Yeah.


00:56:12.860 --> 00:56:17.280

Siobhan Savage: I do get asked a lot. I get asked a lot about this with our customer base.


00:56:17.280 --> 00:56:40.949

Siobhan Savage: But I think the… and for everyone's knowledge, when I say REM, I'm talking about, like, reward, that's my accent. So thanks, Steve, for picking that up. We need a translation agent here for my weird accent, but, I think the… I think the job architecture first shift has to be modernized. End of story. Forget REM for a sec… like, reward and remuneration for a second.


00:56:40.950 --> 00:56:46.950

Siobhan Savage: Like, even just modernizing that, Michael, and then shifting it into a dynamic view, so that a company


00:56:47.010 --> 00:56:53.690

Siobhan Savage: Has the infrastructure it requires for decision-making support when it comes to, like, agents, people, work jobs…


00:56:53.690 --> 00:57:13.119

Siobhan Savage: and how they orchestrate work-to-worker, whether you're people or agents, it doesn't matter. Like, that is like a… I think everyone, like, collectively agrees with me on that, that that's, like, a… we need to do that. I think the reward and remuneration component is still to be decided, because it's how much impact… because here's the other thing that I've seen. So…


00:57:13.120 --> 00:57:21.889

Siobhan Savage: When you're out reinventing work and it's changing, you remove tasks, absolutely, but you also introduce new tasks you haven't done before.


00:57:22.140 --> 00:57:35.240

Siobhan Savage: So, people only talk about 40% of this job is now gone with agent. Well, yeah, but did you know that you introduced 20% more new things that we never had to do before? Because, like, those agents don't do the things, and there's all these net new task curation?


00:57:35.560 --> 00:57:55.480

Siobhan Savage: So I also think there's this other part where I think jobs are gonna be less about, like, job cutting and job structures and more, like, evolutions, like Jenga. Like, you take parts out, but you're putting parts in. Like, I think that's kind of the way that I'm seeing it in our data, like, across 25 industries right now. Again, I haven't seen full-fledged…


00:57:56.680 --> 00:57:58.420

Siobhan Savage: true agentic…


00:57:59.060 --> 00:58:05.950

Siobhan Savage: Like, no customer is a lying, no human in the wood. Even in my company, where I can do whatever I want.


00:58:06.620 --> 00:58:19.740

Siobhan Savage: I don't allow no human in the loop. Like, I just don't trust it, it's not strong enough. So while we're in a world where there's human in the loop, we're gonna have this in-between part of, like, that Jenga, like, tasks coming in, tasks coming out, but I do think


00:58:19.760 --> 00:58:34.890

Siobhan Savage: when we start to think about the reward, it will be when you start to see big chunks of change. You know, rapid change across multiple, you know, areas, like, that's where you'll start to see, Michael, probably a little bit of a crisis moment, where folks are gonna go, hold on a second, what do we do?


00:58:34.890 --> 00:58:52.990

Michael Fraccaro: Yeah, agree, agree. I mean, it's another fascinating thing, and as I said, you know, what are you rewarded for? Are you rewarded for, you know, just changing incrementally what you should be doing, or are you really being rewarded for, creating new value into the organization? And so.


00:58:52.990 --> 00:58:58.730

Michael Fraccaro: Yeah, these are… these are great questions, and… and I don't think we're there yet. I think it's sort of take…


00:58:58.740 --> 00:59:15.830

Michael Fraccaro: take this journey on its different stages, and, like, the job architecture question, and thinking about talent, and think about all design… all of these aspects are really important, and in the background, think about the question about reward and incentive and so forth.


00:59:15.830 --> 00:59:26.589

Michael Fraccaro: But I'm not… I don't… I don't hear enough at the moment that it's top of the list in terms of priorities right now. There are just so many other fundamental, areas that people are focused on.


00:59:27.080 --> 00:59:44.799

Siobhan Savage: And think of… speaking of, like, fundamental things, one of the things that I keep seeing play out in customers is probably two different takes, and I want your view of what you think is the best. So, you have Customer A, and Customer A has bought 150,000 seats of Copilot, and has now told their people to go and build.


00:59:46.090 --> 00:59:49.939

Siobhan Savage: And then you have customer B, who has…


00:59:50.250 --> 01:00:01.039

Siobhan Savage: probably taking more of a tops-down approach, where they're looking at the opportunity, and where, and redesigning. Where do you sit? What's the best approach?


01:00:01.530 --> 01:00:03.240

Michael Fraccaro: Huh, yeah, I mean, that's…


01:00:03.440 --> 01:00:17.580

Michael Fraccaro: I always like the… it's a bit of both, right? You need to… I always think around… you need to look at what's the… almost the 90-day, 100-day sprint of things that you… you need to move and mobilize.


01:00:17.680 --> 01:00:20.990

Michael Fraccaro: But you can't lose sight of the future, either.


01:00:20.990 --> 01:00:45.179

Michael Fraccaro: And it's this ambidextrous, you know, leadership capability that is really good and adaptable to be able to focus on the here and now, but also to be not missing the opportunities of redesigning for the future as well. So, I always go in terms of… it's really got to be a bit of both. If you over-rely on


01:00:45.180 --> 01:00:55.309

Michael Fraccaro: just the short-term aspects, I think you miss the opportunities, and then you'll be playing catch-up, and potentially it's a bit too late. Someone else is going to come into that space.


01:00:55.630 --> 01:01:04.270

Siobhan Savage: Yeah. You know what's a really interesting thing I've seen play out right now? In Customer A, where they have ruled out whatever tooling.


01:01:05.050 --> 01:01:06.700

Siobhan Savage: They actually go slower.


01:01:07.040 --> 01:01:07.700

Michael Fraccaro: Hmm.


01:01:07.930 --> 01:01:15.680

Siobhan Savage: Because what's actually happening is they've skilled chaos. So, everybody's in the rowing boat, and we're all going in different directions.


01:01:16.530 --> 01:01:36.000

Siobhan Savage: things are actually slowing down, because actually in big companies, like, ways of working are really important. Yeah. And, like, paying the invoice, like, we should pay the invoice in the same way as a company, because there's velocity. It's like that Formula One pit stop analogy, like, everybody's working and knows their role to play and their task. So I think there's this really interesting thing


01:01:36.130 --> 01:01:59.700

Siobhan Savage: in a lot of the world I've seen is that that probably has a suboptimal effect, but what it did do really well is it got all of the employees feeling that they had a moment and a… and got them opening up to the AI. Where I'm starting to see customer shift is, like, to this B part, where they're going, we want to look at the highest value areas of opportunity and redesign that, but still allow our employees to have this, like.


01:02:00.150 --> 01:02:16.959

Siobhan Savage: moment of, like, innovation where they can be creative and design and do, but, like, not at the skills that we've seen. And again, it came back to my point at the beginning, it was also about cost. Yeah. Because they blew the budgets, because they were spending so much money, as well as just everybody doing their own thing.


01:02:17.430 --> 01:02:34.210

Siobhan Savage: slow things down, so it's… it's gonna be really interesting. I'm looking at the data sets right now to see, like, okay, what is the best way to get your culture of folks wanting to innovate and be creative and get your people adopting and feeling they've got, like, empowerment and ability, while also, like, not, like, causing chaos, and, like.


01:02:34.560 --> 01:02:48.390

Siobhan Savage: kind of Formula One pit stop moment, so I think, like, I'm gonna hopefully have some data to tell that story in a bit when I've got a little bit more game tape on it, but it's just some interesting signals that are playing out in market. Michael, we have ran out of time!


01:02:49.280 --> 01:03:03.620

Siobhan Savage: We have had the most awesome conversation, and, you know, it's been incredible to have you here. There's been so many questions that have went into the chat as well from folks that we'll follow up and make sure that we've got answered.


01:03:03.620 --> 01:03:23.070

Siobhan Savage: Thank you, Michael, for joining us, for your expertise, and thank you to all of our community folks for joining, and your incredible questions, and for saving me at the first 5 minutes. I love you for that. And folks, you can follow Michael on socials. Anything you need from us, just DM us, and we will see you again next week.


01:03:23.270 --> 01:03:24.950

Michael Fraccaro: Thanks, Yuan. Thanks, everybody.


01:03:24.950 --> 01:03:25.860

Siobhan Savage: Ready?


01:03:26.110 --> 01:03:26.890

Michael Fraccaro: Bye now.

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