328. Driving agentic transformation: How AI is rewriting the playbook on large-scale change

Speaker A: We observe all these jobs change, but they change in ways that are cool. You spend less time making a PowerPoint deck and crunching, you, uh, know, figuring out some data irregularity and spend more time coaching that initiative owner on how to make the procurement initiative more impactful. That's cool. And so I think what we found at all levels of our Transformation office, from the most junior person all the way to the Chief Transformation Officer, him or herself, the role is different, the tools are different, the underlying core job of changing humans is not different, but you go about it in a different way. And that way is generally more fun and exciting and allows people of all 10 years to have more impact.

Speaker B: For McKinsey and company, I'm Sean Brown and welcome to Inside the Strategy Room. That was Chase Covington, co author of our recent article, the Agentic Transformation Redefining the Economics of Change. And in today's episode we discuss how transformation workflows are ripe for agentification and how deploying agents allows a Transformation Office team to focus more on the human side of change. Coaching, generating ideas, pushing for value, and the change management itself. The kind of work that makes for an even more successful transformation. Chase is a partner based in our New York office and a leader in our transformation practice as well as our strategy and corporate finance practice. Chase, welcome.

Speaker A: John, appreciate the introduction.

Speaker B: And Tomer Slaney is a partner in our New York office and an expert in change management and long term change success. Tomer, it's great to have you here. Thanks

Speaker A: Sean. Good to be here.

Speaker B: So in your new article you note that the Transformation Office, the very function charged with driving massive change across organizations, is also proving to be one of the slowest to adopt AI technology. So Chase, or why is there this somewhat ironic lag?

Speaker A: Yeah, that's kind of the dirty secret, right? I think, uh, what we observed is a lot of transformation offices or PMOs or integration offices, whatever variant name you want to put on that we're relatively slow to identify, uh, despite often pushing agentic solutions onto an organization or at least espousing to do so. And, and I think that, you know, to some degree the core reason for that is they're just busy, really, really busy with the day to day. There's always a ton of work in a PMO or a transformation office or a growth office, whatever it might be. And so the insight for us was how do we get those group of clients take a step back and really think through how those workflows can be different? And when we did that, what we typically found is A lot of the grunt work in a transformation office can be identified in a way that enables the people in that office to operate at a higher level. So you're not producing a rapport, you're producing a change outcome in the enterprise. And that's really the key to the whole truly. To the article we wrote, what is powerful about identifying a transformation office.

Speaker B: Thanks, Jason Tomer. Could you talk a little bit about where AI fits into the broader context of transformations and where you see it creating the most value?

Speaker C: Yeah, absolutely. So when we talk about AI and transformation, really think of it as an entire range of how do you identify the functions in the transformation office? How do you elevate the work that's being done and the level which is operating? There's also the entire notion of how do you identify the transformation, how do you make things available to your participants, if that is copilots writing business cases plug into ERP systems to populate, um, financials and impacts, tying it to board documents and all the way, uh, into transformations really being the, or AI being the catalyst for transformation. So the reason for change for many of the organizations is preparing themselves and rethinking how they remain competitive in an agenda fighter, in an AI forward environment, going forward.

Speaker A: And maybe, Sean, I add one thing here. Just as we think about what is an agentic transformation, a year ago, two years ago, I think we were getting lots of inbounds from clients and maybe we want to do an old school transformation or I want to do an agentic transformation. Those are somehow separate things or separate ass, maybe uh, even separate approaches from our side. But I think what we've seen over the course of the last 24 months, that coming together. The analogy I give you is if you were to tell somebody you're listening to digital music versus just on Spotify, of course you're listening to music or the vinyl. And so the concept of an agentic transformation is just all transformations need to be using agentic levers because that's just one of the most defining technologies of our time. It'll be like saying, hey, I'm using the Internet for my transformation in 2005. Uh, of course. Right. So I think we will reflect that language throughout here. I don't think there's in our minds as much a distinction between an old school transformation, an agentic one, because agentic is just a really powerful lever to execute change at scale in a business that we're working into, all the, all the efforts that we're doing.

Speaker B: Thanks. And just to dig into that, a Little bit more deeply. Chase, does the concept of agentifying the Transformation office make it more accessible to more companies or does it make it more straightforward? For example,

Speaker A: it makes it more accessible to more companies because the human resource load required to stand up an effective transformation office is lower. It doesn't mean it's any more difficult in terms of executing change, but just the standing up, the basics. We find the barriers to entry there a little lower. I think what's more interesting though is less, hey, more companies can do this now because you don't need as many people in the to. That's less interesting. What I think is more interesting is in any company that's using a to, your TO can reach more employees. And if you reach more employees, you can embed change more granularly into the organization, which our research shows dramatically increases the odds of success. That I think is actually the more interesting conversation. Right, uh,

Speaker B: got it. So, Tomer, how is the identification of the Transformation Office changing what the TO actually does? Is there a market difference in the typical activities that those in the TO are spending their time on? And what, what does a typical Transformation Office week look like?

Speaker C: Yeah, so if we think about sort of what that catalyst for change was and why to identify, we went and looked at some teams and we have the research here. One example team that we looked at, four person team, how are they spending their time? And 80% of that time is spent on reporting, creating materials, preparing for meetings, doing the follow ups. A lot of the stuff that is both very time consuming, also very condensed into a certain time of the week, but also very repeatable, very predictable, very manual as. So Chase said that's the grunt work of the transformation. But it's stuff that you need to do. It's stuff that you need to do and you need to do correctly and you need to do well. Because a lot of the change management occurs when there is trust in the system, in the transformation. This is the kind of stuff that you need to do to maintain that trust. The materials that are produced are correct, the stuff's on time, the decks are ready. And through both the amount of other tasks that these teams were doing, the people staffed on these ToS, the people running these ToS, just getting more exposure to AI tools, whether it's cursor or take your pick, and looking at other initiatives in the transformation, saying, hey, these are the things that are being identified in the company starting taking a lot of that initiative, saying, hey, we can make our own lives easier. There's also a bit of the tension uh, where longer running transformations towards the back end of the horizon. The expectation is that these transformation offices get smaller and they get more efficient. But in fact, what we actually see is they actually start getting access to more and more data sources. As these transformations progress, we create the more capabilities, we feed in more data, which is actually increasing the time, uh, required to do reporting. Which squeezes out more of the elevated discussion that Chase was talking about, like creating the insight, driving the change, creating the narrative. So expected to be doing more with less and getting more of the grunt work to boot.

Speaker B: So does this help the transformation office move more quickly or just focus on, as Chase alluded to, more of the change management, which in turn then helps the organization move more quickly.

Speaker C: Within a given week, you are going to move more quickly from when data is available, until materials are ready, until insights are starting to be published, until we start looking for the answers to the questions raised by the data, the transformation will still move at the cadence that we expect it to move at. Transformations tend to be regimented, moving at a weekly cadence between the meetings in preparation for executive committees, board meetings, those sorts. What we are allowing these teams to do, what these teams are actually allowing themselves to do with these tools is front run a lot of the grunt work early when data is available, spend more time on the insights, on the coaching, on the, on the actual human side of the change management in preparations for these meetings or these big, um, events where decisions can be made, where things could be rolled out.

Speaker B: Right. And so as you see more of this agentification of the transformation office in your work, what are some of the early hiccups you've experienced?

Speaker A: I'll give you one. It's kind of a funny example, but it's emblematic. Uh, I think the technical term is cognitive surrender, which is a really scary way of saying you just let the AI chatbot, uh, do your job. And we actually saw some of this really with more junior colleagues, colleagues of ours, or even clients, or it's, hey, this chat bot can create something that looks, at first blush, pretty good. It's formatted really well, it's got color scheme and it's kind of structured and, and that's neat. Um, but then when you review it, it actually turns out to be slop. And so the pitfall is, how do you create the right moments for human in the loop to ensure that the output is high quality? How do you treat the agents you're creating is almost like junior colleagues, but still keep the reviews as the information moves up the chain. So to Speak so that by the time it's going in front of the CEO, you're not embarrassed by, you know, some AI slob.

Speaker B: Thanks. And Tomer, what kinds of challenges have you seen as teams have moved to identify their processes?

Speaker C: Yeah, no, like, um, speaking of in front of the CEO, similar to that, sort of the cognitive surrender, there's also the attention surrender. If you know that something else is paying attention, you can multitask. And we actually saw one of these teams develop a good way to turn meeting transcripts into like lists of actions. So at the end of many of these meetings, we will make sure that we know who's doing what is next steps. And one of these chatbots, which was looking at the list of actions, picked on a bit of sarcasm in the meeting where the Chief Transformation Officer asked, uh, a participant, do you just want the CEO to call every single employee and tell them to do their job? And the, the chatbot picked up an action CEO to call every employee in the company, tell them to do their job. Which, you know, luckily, you know, it was still very much a human in the loop, but that would have very much, uh, unattended, been sent to the CEO and tell him to go call everybody in the company and make sure they do their job.

Speaker B: Yes, that's a great example and not one that I think the CEO would have appreciated. But I think the lesson is to pay attention as technology actually accelerates the transformation. So Tomer, on that point, can you talk a little bit about the cadence, the pace of the transformation office as it agentifies and how agentic impacts the pacing? Because every week there's a lot of information that's gathered, synthesized, and then reported on. How does that really increase the metabolic rate of a typical transformation?

Speaker C: Yeah, happy to talk about what we think about as a typical transformation. Everyone's going to be slightly different, different dates, different periods. But generally speaking, um, we are going to be working in a cadence where there is the ability to elevate discussions and decisions to an executive committee every week. That's sort of some of the magic of the transformation, of driving the rigor and the decision making and the cadence which allows us to drive change apace. And um, to do that, we have to prepare for this meeting every week. And there's a whole cascade of meetings and new data pull. So data is pulled every week. We then look at the famous week over week change. The nature of this means that from the minute the data is pulled until m. The key meetings happen. It's when we tend to crunch all of that what we call the grunt work, the repeatable, predictable manual work that needs to happen tends to happen between that data pool and the executive meeting. And you can move the data pool around the week, it won't materially change the way that that crunch happens. But unfortunately from when that data pull happens until the executive meeting is when you actually want the best, the best time to spend time with the data for the insights, for the actual decisions that you need to drive from that week. When you do that with last week's data, you always get the questions like is this data changed? And that's used very often used as a way to not make decisions and not drive change. So we've created this structural limitation or structural ceiling as we've called it, that we compress all of the uh, the TO team's time between when we're doing the data pool and when we're having the meetings where we need to be using that data and not leaving time for, as Chase mentioned, that elevated thinking and discussion and coaching and teeing up the decision and the strategic so what's, or the big insights from that, from that same data pool, which is unfortunate because come Friday and Monday when the teams continue doing some processing, they come up with all these insights and those might be either stale or the data might have changed by the following Tuesday or whenever you pull the data, which, which case they, they spend a lot of time just bridging back to last week's data and saying, is that still valid? By that time they're crunched again with the data meeting happens. And there's also the, you know, just that sense of like, we know that we need to have that discussion, but the, just the data wasn't ready because we were so busy updating things.

Speaker B: Thanks. Tomer and Chase. Earlier you alluded to this idea that agentification could also be applied in a project management office, for example. So in your client work, are you seeing this agentic approach being deployed across other major change programs?

Speaker A: All of it, all of it, whether it's a transformation, whether it's a turnaround, whether it's a merger integration, whether it's a diagnostic arval, whether any sort of change program, broadly defined. I think this is super relevant for now. The exact tools and how you think about what the objective of the PMO or the transformation office or the strategy, whatever that changes. I think for sure how you configure the setup of the, of the office and the tools that you're deploying, but the concept of using these tools to take the people in that office and make them more effective. That's, that's pretty universal.

Speaker B: So I'm curious to touch on what the experience is for the people in the organization who are then driving the change. What is their interaction like with this agentified Transformation Office and where does the human in the loop remain critical there?

Speaker C: Yeah. So first of all there's the folks inside the TO and think the change management there is also very important if you are an individual leading or setting up a to realizing some of the design choices you'll be making and deploying these tools are the change man that you'll need from the, from the Transformation Office team. Right. The Transformation Office team is also a group of individual who, individuals who will uh, have to adopt some change. There's a big risk of, you know, call it false positives or just the fact that the tools can create so much more data and that will, that will actually do the inverse of what you're looking for. That can actually go and create a lot more work for the TO Team. If you, if you sort of don't set thresholds or guardrails of like, I want to know about every change from last week and I want a little speaker note next to every little thing that change or any time that somebody's updated a forecast and not update the KPIs, I uh, want to know if that's an error or there was an error before that they're fixing or if that's now if that's, you know, if that was intentional. The fact that you, that you can now go and look at thousands of initiatives and tens of thousands of KPIs and impacts can actually drive a lot more work for your T O team. Right. So there's also the change management, uh, element in there. Many of us have probably received, even if they are well put together, AI generated content from our teams, clients, assistants, you know how to spot them, you know what those little icons next to all the bullets look like. There is some, there is some hesitation. The bar for this being useful is higher. Just generating more and generating more often to more people is really the solution actually. And a lot of the things we'll be talking about in the Human in the Loop, most of what we're seeing from best practice is these tools generating content and insight for the TO team. And the human in the loop is still not just um, doing quality fact context checking. They are also uh, applying some common sense of does everything need to be sent out? Does this need to be personalized? Uh, especially when it comes to executives and While setting up an entire different structure to run transformations in every function I think will backfire. You are, as we said, crunched the same executives coming to the meetings from the bus. And if they are not getting what they need from answers, insight from the TO will likely, at some point, once they get hammered a couple of times, stand up their own functional capacity within their teams to generate that. Those, um, individuals are prime candidates for getting some of this automated stuff. They are very much an extension of the transformation office. So if you've already got those as far as change agents in your organizations, those folks would be sort of the prime candidates where we do see the benefit of sending more automated communications.

Speaker A: Can I just put a super fine point on this? Don't use your AI bots to talk to the business that sucks. The purpose of a transformation office is to drive change that's human to human because when your TO is interacting with the quote unquote business, they're pushing for value, they're counseling, providing feedback. No agent's going to do that. So use your agents to make your TO people better, not create more work for folks.

Speaker B: Got it. Thank you. And Tomer, can you talk through the areas of work where you would say identification really has the most impact?

Speaker C: Yeah, so we started talking about, so, uh, those data, the data pools, the creating, creating the materials, creating the slides, creating the first pass of things. I think we've, we've discussed that quite a bit. There, there is the uh, offloading of the weekly task. I go check this, go update that, go send, you know, send this into that depository, do the data cut. One thing that we've actually seen teams push on and create a lot of value out of is once these materials been created, right, the emails have been sent, etc. Uploading everything and letting your agents are creating agents that will go and check this week's materials, this week's uploads, this week's downloads versus other things. And they tend to catch and they are able to go through just so much more than your team can. They can go through every last slide on every last appendix and identify, hey, is there a place where this week's insights contradict last week's insights? Is there a place where the data that we're showing in the transformation is contradicting the data we showed in the MBR last week? Is there a place where, um, we saw a change in initiative that we expected to see a change in the dependent initiative and we didn't see happen? So as these agents get trained better in a single Transformation context. This is the kind of work that makes this team do better, not just do more and do faster. So we're seeing the whole spectrum of that work being identified from pulling the data, doing the admin work. Um, we're seeing agents being trained right now on meeting attendance and agendas. So if last week we agreed that this is the action, this person needs to come speak to that, has that person been invited to the meeting. We have somebody sharing their change story, somebody being recognized. If there's an expectation there in the meeting, it was usually a to's team to go and make sure that we have the right agenda and we have the right people and these people have been invited, et cetera, et cetera. Now, as Outlook mcps allow folks to even go and say, hey, have the invites actually been extended to the right people for the right meeting? Or have we sort of missed that? We put them on the agenda, but not the invite and show up at the meeting like, where's Jenny? I was like, oh, oops,

Speaker A: maybe jumpy. Tomer

Speaker C: yeah,

Speaker A: I think the risk here is if you're new to this, you're going to have your TO teams create a whole lot more stuff. The decks will be three times as long. All that that's not that helpful. When I counsel clients on it, I use it same length deck, sharper insight. So use the tool to get to that. It's easy to create a lot of slop, uh, but that's generally unhelpful. So keep that in mind when you think about what work moves to agent versus stays with human.

Speaker B: And so Chase, what are some of the ways that you help make sure that that happens in your teams?

Speaker A: Honestly, the simplest way to do it is have someone pitch to you the material they've created because it will quickly sort out what's like, hey, I've actually thought about this. Or I just type something in and hit enter twice and, you know, emailed it. So have it almost Shark Tank style pitch and ask a little bit of kind of probing questions. Do you actually understand where that data point comes from? Or am I going to use this in counseling someone from the business? Those questions typically pretty sharply get to the meat of what you're trying to communicate, not the kind of fluff that that goes around it.

Speaker C: I've got one, I've got one rule on my team. You can use any tools you would like as long as they're sort of approved to use by our risk, the client's risk. But if you put something on a page, it's Yours the same way you would never, you would never say, you would never respond to when you get the information out of Google, right? It is an unacceptable answer for somebody to say where you know who wrote down and say cursor. That sort of is a. Use anything you want, but if it's on a slide, it's your materials. And that is the rule for every, every single person on any one of our teams.

Speaker B: Makes a lot of sense. And Tomer, you just mentioned the word risk. So there's the risk of accepting what you get from an agent without questioning it. There's also the risk that information within the TO might leak outside of the organization or even within the organization. So the TO by its nature is privy to very sensitive information. And I wonder how you help keep tabs on that from an agentification perspective. And how does using centralized tracking software, including our uh, wave transformation platform help to mitigate this risk?

Speaker C: So earlier days of all of this identification many material creations updates were driven by extracts. A team would extract data the same way they would for everything else and feed it in pre identification there was the common sense of saying hey, this actually has the word confidential in the title. This kind of sense of information that we would not like to share and that didn't make it into the materials. That control is not necessarily built into any vibe coded agent that somebody's come up with or an uh, automated update. So we start by putting rules in place as you're downloading, extracting information, there's a certain way to do it. But more, more recently, you know, especially for the transformations using wave, the Wave AI Team M is built to start building something called Wave apps which allows, allow this is, it's actually designed for this. It allows you to work with data in wave A. You don't keep extracting data, right? Even extracting that data onto shared drives. As you said there's a potential, there's quite a bit of sensitive data in these, in these uh, transformations right. So we have eliminated that need to go and just make weekly huge data extracts which has everything and all the everything and all of it in it in one place and plug straight into your performance infrastructure. But also those wave apps have the ability to set your visibility to match an audience that you're preparing something for so that you've built in the guardrails that you're not doing that. And we are generally speaking seeing that if in the past tech teams were building applications and building software, those tech teams have been trained on guardrails risk control as this is being More democratized. And we're seeing everybody vibe code from every function, the guardrails, the uh, sort of the mindset of the risk has not caught up.

Speaker B: All right, so we talked earlier about how identifying the transformation office frees it up to focus more on the human to human or change management aspects of the transformation. But why use agents to achieve that? Couldn't you just add more human capacity in the first place? Or is there really an ideal size for a transformation office and you can't make it much bigger than that?

Speaker C: Can a TO be too big is maybe good. Provocative question. I think the way I would approach that is saying that the size of the TO should be driven by the number of work streams, stakeholders and interactions that TO has. And it's very much sort of the coaching and that is the big driver of more work. Right. Making a bigger team to make more slides. You're going to fall into the trap that you need people to read more slides. You'll have a team that, you know, you'll have to then grow a team to read the slides so very much on. If you think about the core function of the TO before you start expanding it, and whether that is distributed at the work streams or the bus, or whether they're as in the, in the tier with coverage to those work streams from coaching, that is very much the value added work. That is um, the work of talking through the valuation, problem solving, how to move initiatives forward, unblocking um, problems in the delivery. If you are just putting in more people to look at more data sources to generate more data fast forward, you're going to need even more people in the future because the data and the transformation only goes one way. There's always more of it. As we looked at the gentrification and we're back to that same four person team that we looked at earlier in implementation, that team developed four main agents that they were using from materials creation, the admin, the checks. And by doing that over a period of time, actually in a period of time where the team was expected to get smaller, what they were able to do is one cut down the team without traditionally, historically they were probably uh, offloaded that work into more work for fewer people, they were able to offload a lot of that work into the, into the agents and they were able to free up an entire individual's worth of time. And at the same time they reduced more than that amount of capacity. And all of the functions which we consider the value adding functions, whether that is refilling the pipeline or thinking through the Niche end cases of valuation and what can be done about these and how do you actually make these progress and how do you make these work? Uh, and how do you refine business cases? They were able to do more of that and do more on the inside creation at the same time. So do more of the valuable stuff, do less of the less value stuff and shrink the team at the same time.

Speaker B: Chase anything you'd add here?

Speaker A: Yeah. M. My take on this is TOS, PMOs, iOS and IMO are generally pretty small and should be. They shouldn't be cottage industries. I think most folks intuitively understand that um, taking a small group of making it a little bit smaller doesn't really make a lot of money for your enterprise. That's not really where the value comes from. Where the value comes from is having that small group of people spend time on what matters, which is coaching, providing feedback, pushing for more value, doing interesting analysis. That's the beauty here. Uh, look, if one takeaway from this conference could be hey, you could do your TO stuff with a smaller team. Yeah, sure. Although I think that's honestly pretty pretty limited in terms of value creation. The value creation here is at being a better TO

Speaker B: indeed. And it gives those executives in the TO a much better handle on how things are going and a much better ability to connect with the individuals who are actually going to be driving the change. Right.

Speaker C: And get you happier to members. Right. Like let's be real folks. Join a to drive change, not to make reports. They'd rather be doing that.

Speaker B: Absolutely. And so Tomer, can you perhaps take us through a deep dive into some specific examples of how agents are being used in the TO right now? What does it look like in action?

Speaker C: One is the uh, I talked about the daily control control quality control. Right. Every night and this has been trained so refined every night we have an agent that runs on a transformation. That transformation plugs into your performance infrastructure or you can give it a download. This was built originally with just wave download or with just data downloads and it goes through all of the plans in your transformation and says hey, what changed yesterday? And if this changed, am I expecting other changes? And there are changes that we always know are interlinked. Is there a case where an element has changed and the domino effect of follow up changes did not happen? And can we actually catch that now when somebody is still sort of in the mindset of oh I'm m updating this now I know what this is about. The same person is still on that, on that, you know, on that task at the moment and catching those early. So it then went through prioritized list on magnitude of changes, what is the number of follow up effects we wanted? Right? So especially if you have dependent initiatives, uh, you can, you can tell your, your transformation infrastructure will know the initiative one moves, two, three and four dependent cannot start before one finishes. It will check all of those and really tee up the Transformation Office to be able to reach out to the individuals. And it does a couple of things from also a change perspective. One, it tells people that the Transformation Office is actually on top of it, that they care. It allows the Transformation Office to reach out to folks when they see a change before they have to explain a variance. So I'm going to put myself in your corner, right? I will help you make that change before it makes it into the executive materials. Let me help you out here. It creates a lot of goodwill between the Transformation Office and the participants. And this is the one where I'd caution that you can actually create a lot of too much information for the office. So very much. One where you want to control the filters and the sensitivity and the prioritization because you don't want to pick up every 2 cents of change and say go explain that. So that's one sort of good example where I think has been probably the most well received. It was the one that created the most additional work upfront for the team, um, one where they had to train it quite a bit to get good. But when they started seeing that secondary benefit of the kind of relationship it allowed them to create with the work streams, with the folks, with the participants, actually create a lot of excitement within both the Transformation Office and the work streams that this is going on and this is happening.

Speaker B: And Tomer, do you create that sort of filtering, if you will, upfront as you're identifying the to, or do you sort of see the results come in and say okay, we've got to dial this back because we're getting too much detail here, it's not helpful.

Speaker C: Well in this, in this instance it was very much dial it back. And in most instances we've seen it's been the dial it back. And transformations are each one's of a different size and different magnitude. One um, thing that we built in very early was prioritization. So even if you have a list which is too long, you start going from the top. At some point you get into stuff which is meaningless and you stop. So even if the data exists and you know and the expectation is not that you check everything off the list every day that can work. You don't have to spend all your effort playing around filters.

Speaker B: And so can we talk now a little bit about creating all these PowerPoint decks? I think everybody would love to hear how agents are helping with that aspect of the work.

Speaker C: So deck drafting is the first place that most teams go to. It's the biggest time constraint on a team. It's the one thing that when you get wrong, you get wrong. It's um, if you look at the weekly work plan of every transformation office, this tends to be. It's the data pool analysis draft. The deck that goes into is the most repeatable, predictable, grunt, worky work that a transformation office does. Go and take this data, uh, put it in these materials, update the comments. Um, and I very, very purposely calling it deck drafting and not deck making. Um, the first part before you actually start bringing the insight and the context and the analysis into the decks and is where these agents have been doing very well. Uh, especially when you're in the back end of a transformation, you've got 50 weeks of decks to look at, you have a lot of data to look at. You can actually train an agent to do a pretty good job of doing most of the work upfront. It does still require you to go through every single page and every single insight. You will also like the teams get to know week over week where the agent does a very better job than not where there are still holes in, in the insight. What it does very well, though the teams have never done as well is it will also go through and read with a fine co, fine tooth comb last week's deck and the week before and the finance reports and your uh, revenue reports and it will flag, not necessarily change, it will flag with their inconsistencies. And then as you start thinking about drafting the um, prep notes for your chief transformation officer or for your um, executive, uh, members, participants in the meetings, you can actually start preempting saying hey, by the way. And this differs to that. Either we know why and you shouldn't hear the talking points or this may come up in the meeting. You should probably be able to talk that and explain it. So not only does it draft it a bit faster and takes off a meaningful amount of the grunt work and the work the team doesn't love doing, it actually does also that back to the change manager, create a lot of goodwill with executive participants, uh, specifically by pointing out to them where there might be quite a need to do some explaining.

Speaker B: And chase if we call out this importance of keeping the transformation human how do you ensure this automated drafting of the key outputs doesn't impact on that in terms of to members being really sure of their content and truly owning it?

Speaker A: Most of these agents we're going to talk about are pretty obvious. Like it's obvious, hey, we should use AI to like make some of these decks we present. Like yeah, of course you don't need McKinsey to tell you that you uh, probably already doing it. But what I suspect you're going to encounter is your team members won't really know what they're saying. That would be that cognitive surrender because ah, uh, someone else that. And so I think like many of these agents in the to it's obvious you should use them. It's not hard to configure them but to make sure they actually create the value that we're talking about is relatively difficult. So you know, how do we, how do we, how do we go about that? It's all about being thoughtful about creating human in the loop moments. Right. When do we have a review? Who does the review? How does that review cascade through the members of the Transformation Office team? I think that's really critical to be thoughtful. It's different in every context. I don't know, there's not a five point framework for it. We can give you principles but it is different in every context. But it requires real active thought. It's not the afterthought.

Speaker B: Super. And Tomer, you mentioned earlier this idea of using agents to conduct consistency audits. Can you dive into that a little bit for our listeners and what it actually looks like.

Speaker C: Yeah, I think we spoke about this one quite a bit. Agents that are able to make sure that week over week things are not dropped and we're consistent week over week that if we add a action to follow up on something from three weeks ago, it actually shows up on the agenda. If there's somebody invited to the meeting, they're actually showing up there. Inconsistencies with MBR decks. It does require you to feed in quite all of this information depending on like Chase said, sort of politics in the organization does the to actually have access to all of these different decks and functions. But when the teams do, there's a real unlock here that just prevents the derailing of conversations of oh, you're saying this but my chief revenue officer said something very different than yesterday's revenue review or the CFO showed me a different number for this last week in the MBR and you start having discussions about bridging information rather than about doing something about Gaps and misses and there's a real benefit to that here. I think we've already spoken quite a bit about how that comes to happen but effectively just if you have the more information to feed it, feed it information and don't automate the emails.

Speaker A: The consistency audit's a big one. This is where the most junior people on the to spend the most of their time and it's kind of this, this detective effort. The degree to which we can provide tools to make this not faster because like that actually is to make this more insightful and accurate. That's really powerful.

Speaker B: So before we wind up our episode, um, let's talk a little bit more about some of the key takeaways. Tomer, what. What would you want to call out for our listeners as they think about agentification in their Transformation Office?

Speaker C: So as you think about what you want to identify, we've just had some examples like this is going to be true for everything. Not just a transformation Office is understand what you're doing and how it fits in before you start doing anything. Don't automate for the sake of automation. So get to grips with what are the actual workflows, what is the team actually doing, what is the flow? As we, you know, we had a chart up there saying hey, uh, here is when you pull the data, here is this meeting that turns into that meeting. Actually have a good understanding of what that looks like. Set up the rules of what the agents can do and condu so that governance right uh, before you start building the actual agents. Secondly, as you think about roles, responsibilities, capabilities, orchestrators, we used to say that you want good project owners in the Transformation Office analytical capabilities. So start rethinking that. So if you would like these folks to not just run but also innovate and build as you're staffing your TO team. Even from the front it's hard to come and staff data scientists to come and do the work and do it to the to uh, all the success that we have seen has been organic but it's honestly been fortunate that we've just had the right profiles in the right tos and they got to feel the pain, they got to see sort of what is their grunt work, what does it take and then sat down and could actually reimagine what it would look like if they could automate, quote unquote for their own benefit. Invest in the change management both within the to Like I said, you can actually create quite a lot of work and resistance in the TO and be upfront about it, say that, you know, we're going to do something and it's going to be bumpy or there's somebody on the tier wants to do something, there's two or three other folks who are going to live with the consequences and be upfront and saying this is going to be a learning process and we're going to have to iterate and we're going to have to see what we get and the first one's going to be clunky and we'll improve. And lastly, when it comes to the automated emails, when it comes to, as Chase said, if it's on the page you made it, uh, it's very much make sure that you build that trust in the system. Make sure that everything that comes out of, out of your TO is of the same standard that you have been used to. Don't, uh, put slop out there because if you start eroding the trust in the to, you start eroding to the trust in the transformation programs and that domino effect is very unhealthy.

Speaker B: Thanks Tomer and Chase. Are there any particular AI platforms that are really well suited to identifying the Transformation Office?

Speaker A: The vast majority of the stuff we observe in a transformation Office can be done by open source models. And open source model is getting quite good for me. It's less of uh, like, hey, go use this model. That's actually not it. I think it's less, to be totally honest, what is the platform to use because they're all pretty reasonably good and more about how you customize the workflow to work in your context. That's where I see failure modes. I don't see failure mode on oh, we picked the wrong model.

Speaker C: The other thing, maybe just to point out there, is consistently using the same models. So things that were built for one solution and trained on one set of decks do well when you keep the same LLM running behind them. Actually when teams have gotten, we now have access to a newer fancier LLM have actually um, seen a regression in what they get because a lot of this is knowing what to expect, right? You have planned your workflow around. I'm going to do this, the model is going to do that. I'm going to get X. If X changes materially because you've changed your model, you might actually go backwards in the time requirement it takes from you. So as Chase said, the open source models tend to do exactly what you need here. But also don't keep upgrading because you're going to keep changing what you're getting as an output. Maybe not what you expect to get.

Speaker B: So before we end, can you share a story of the most inspiring outcome or impact that came out of your work with teams that identified the TO of a client that you worked with? Maybe, Tomer, we can start with you.

Speaker C: Yeah, for me, it was actually very much around the experience of the folks on the team. Right. For. For me, it was a team on, you know, year two of a transformation with implementation and having a, uh, you know, having a sit down over lunch with the team and they're saying, actually we're a lot more excited about the work that we're doing. We feel like we are doing better work because we are able to engage in stuff that, you know, this is why we joined the team. This is what we were hoping for. And realizing that a lot of that is possible because they are not spending 45 minutes of every hour copy pasting materials from data dumps into presentations.

Speaker B: There you go. Thank you, Tomer and Chase, you get the last word.

Speaker A: You know, my thought on identifying a TO is similar to my thoughts broadly on identification. You know, there is. When we first started doing this, I thought to myself, oh, man, does this mean we no longer need all these junior people? We get rid of all these? Nope, that's not the case. That's not what we observed. We observe all these jobs change, but they change in ways that are cool. You spend less time making a PowerPoint deck and crunching, you, uh, know, figuring out some data irregularity and spend more time coaching that initiative owner on how to make the procurement initiative more impactful. That's cool. And so I think what we found at all levels of our transformation office, from the most junior person all the way to the Chief Transformation Officer, him or herself, the role is different. The tools are different. The underlying core job of changing humans is not different, but you go about it in a different way. And that way is generally more fun and exciting and allows people of all 10 years to have more impact quicker. So that, to me, is my down arrow on what the agentification has done for tos, and I suspect we see that in other areas of corporations as well.

Speaker B: Wonderful. Tomer, Chase, thank you so much for taking the time with us today. This is a great conversation.

Speaker A: Thank you.

Speaker C: Thanks for having us.

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