Why Improving Productivity Starts With Understanding The Work

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0:27David Rice: When a C-suite leader looks across the organization, What they see is an org chart and a process doc. Roll A finishes something, hands it to roll B, B hands it to C. It's clean, simple, and almost entirely fictional. On today's show, I'm talking with Heidi Ferris, CEO at ActiveTrack, about the gap between how leaders think work happens and how it actually happens. The real workflow is the sum of the handoffs, the workarounds, the informal rerouting that never makes it into any document. Heidi calls the good version of this tribal knowledge. It's not the I know a lot about the business kind, but the small daily adaptations that keep things functioning.

1:04David Rice: People make work better every single day, is what Heidi says. The problem is it doesn't really show up in documentation. So today we're covering why the document and process rarely matches how the work actually gets done, the three buckets for deciding where AI belongs in a role, what happens to organizational throughput when one team accelerates and its neighbors don't, and the real signs that a business has moved from AI adoption to AI impact. I'm David Rice. This is People Managing People. And if your organization is measuring AI success by usage metrics instead of what happened to the capacity that it created, this conversation shows you what you're missing.

1:39David Rice: So let's go. All right, Heidi, welcome to the show. It's good to have you. Thanks, David. I'm very happy to be here. Well, where I wanted to start our conversation was, you know, when a C-suite leader looks across the organization today, I think this is one of the common frustrations a lot of people have with leadership at work, right? It feels like they don't really know what goes into the work. And I'm curious, you know, like when a C-suite leader looks across the organization, what do they typically misunderstand about how work actually gets done? I think it's what a lot of organizations are struggling with today as you look across the landscape and everyone's talking about redesigning work or applying AI to work today.

2:18and what leaders see is an org chart and a process doc, right? And, you know, role A finishes something, they pass it to role B and then B to C. And, you know, it's supposed to be a nice, clean handoff like that. And we tend to oversimplify the way work is actually getting done in organizations. You know, an individual in an organization can easily be navigating, you know, dozens of apps, multiple approval processes, many different stakeholders and workarounds in any given day. And the real workflow is the sum of the handoffs, the workarounds, the informal rerouting that people deal with that doesn't show up in a process document or on an org chart.

2:56And it's really very, it's invisible, you know, to the top. And we get disconnected from how work really gets done pretty quickly in an organization.

3:05David Rice: Yeah, I think it's kind of natural, right? Like as you move up the chain, things get reduced to charts and SOPs and dashboards. I think there's an interesting distinction between visibility into employees and visibility into work. They're not really the same thing, right? That's right. That's exactly right. The work itself, you know, is what we care about, right? I mean, look, I think employees instinctively are always, you know, on the on largely are always putting their best effort forward. Right. Like it's not it's more about like how do we help improve work across an organization or understand how work is happening?

3:43so we can then improve work or the humans that, you know, can apply judgment and empathy to the work that AI can't do.

3:51David Rice: And if you're trying to decide where AI should change the way the work is getting done, I'm curious, like, what do you need to understand about the work first? What's really the high value stuff that's going to help shape that? You need to understand the tasks that are being done by each role, right? And be able to bucket them in a way because jobs are collections of tasks. And AI transforms work by changing tasks, not necessarily by replacing entire roles, right? There's certain parts of every role that AI may be beneficial to. And so the first thing is if you can sort of categorize the tasks that any given role does into repetitive, structured, and rule-based tasks, which are ripe for AI, AI-assisted tasks that require rapid data synthesis or pattern matching or something like that, where AI serves as a co-pilot to a human, where there's collaboration happening, and then uniquely human tasks, right, which require empathy and emotional intelligence and judgment.

4:50That's the first step. And most people just skip over that step entirely and just throw technology at every problem.

4:57David Rice: That's interesting you say that, because I was thinking about this earlier, having seen a clip of somebody talking about empathy being the most important thing moving forward. And I think it's something that we haven't taught particularly well. I think we've often thought of it as a trait rather than a skill. One danger with AI is that its usefulness at the task level can sort of distract from asking whether the task should exist in its current form. And there are really different levels of intervention, right? Like you can improve the task, which I think is what we've spent a lot of time doing.

5:25David Rice: You can remove the task maybe, or you can redesign the workflow. You can reconsider the outcome that you're trying to produce. There's a lot of different things here. I think there's a difference between asking like, can AI do this? And asking, why does anyone have to do this? And that second one feels more consequential, I guess. So the easiest things to automate, in a lot of cases, they're not necessarily the most valuable things to change, right? That's right. I think anytime you do an audit, I mean, AI is shining a light on work right now, but this is what you're saying is kind of always existed, right?

5:57We always like, over time we accumulate work debt the same way that a tech stack or a product accumulates tech debt and there are just certain things to your point that like at some point you have to re-evaluate and say why do we even do this anymore right and doing a full audit and then understanding again the different types of work and how you make them better because not everything again like I said is AI is not the solution for every problem we see with work. Sometimes it's redesigning the workflow. Sometimes it's better enablement for people. Sometimes it's the wrong technology altogether.

6:36And you have to really understand how work is happening and what tasks are being done to then be able to kind of pull apart the work and say, okay, this is how we actually improve this. And it may be AI and it may be something else.

6:48David Rice: Let's say AI gives a team 15 % of its capacity back, right? We've seen like a lot of productivity gains and we've seen in some cases where it has made admin stuff easier, things like that. But I'm curious as if people are getting capacity back, like what decision has to happen next? What's sort of the, I've been asking a lot of leaders this lately, like what's your first step in redeploying that capacity with business value in mind? Because if you leave it to employees, it could get pretty interesting. You know, I think the first thing you have to empower someone in your organization to be a decision maker.

7:19And what I mean by that is, like you said, capacity doesn't redeploy itself. So if no one's explicitly accountable for it, it just quietly gets absorbed, right, back into the organization, maybe low value work, maybe disappears into Slack somewhere. You know, like I said, there's that list of work debt that has accumulated over time. And that may not be the most valuable work you want people just defaulting back to. And the second is deciding, again, which buckets it goes into, right? Is our goal cost reduction, right? So do we actually want to shrink the function? Is it reinvestment? Do we want to redirect free dollars back into higher value work?

7:57Or is it expansion? Do we want to actually take on an entirely new project or entirely new work in an organization that's never existed before? Each one has a different financial outcome. Some you tie back to EBITDA, some you tie to growth, some you tie to differentiating yourself in a market. And, you know, if you're only celebrating the adoption metric and never check which bucket that capacity landed in, you're never going to be able to draw a line to the P &L to say, this is how we did it. Because at the end of the day, making work better is one half of the equation, but the other half is showing where that results in your financial profile for an organization.

8:36Yeah.

8:36David Rice: And I couldn't agree with more with what you said there about it becoming low value work i was actually talking with a leader recently in a different media organization we're talking about you know like some of the some things getting freed up and they had somebody who like they were really not impressed with some of the ideas that they had brought to the table and they had started to use ai in their sort of creative planning and that was freeing up a lot of time but this person was then being asked to put it all in these spreadsheets different spreadsheets and it just managed the time is absorbed by managing spreadsheets and i'm like is that a good use of the capacity that was created?

9:11Because I think the AI could probably

9:12David Rice: manage the spreadsheets too, but it doesn't automatically mean like you don't need that person anymore. You know, I think that's the part of the narrative that we get lost in too much, like is headcount reduction. And, but there's a lot of other things that it could become. I don't know. It seems like there's maybe a missing management decision between like, we save time and we created value. Somebody has to decide what that time is for. And I guess my question, my follow-up question would be is, should leaders be deciding how capacity is going to get redeployed before they automate? Like automate to create that capacity so that you can do X.

9:45Yeah, because you should start with your goals, right? Because if your goal is EBITDA to eliminate roles, then you should understand where the biggest opportunity for AI automation exists in your organization first and start there as opposed to if you have a bunch of roles in an organization where 80 % of their time is a work or tasks made up of tasks, again, that require either co-pilot, you know, AI as a co-pilot or a lot of human judgment, you're not going to eliminate those roles. And if you do, you're going to be hiring them back because you realized, oh no, you know, human judgment, empathy, and ability to kind of pressure test what AI is telling me is important, not just I'm going to automate something that is already a really well-defined data-driven process.

10:34Those are very distinct things. And if a role is made up 80%, you know, of those other things, you're not going to get the EBITDA you want anyway. So understanding what your goal is, to your point, understanding what your goal is before you start is very important. Otherwise, you could do a lot of work and never actually hit your goal.

10:51David Rice: Yeah, absolutely. Okay. Right. So AI adoption and AI value, right? One doesn't talk automatically to the other. But I'm curious, like, what do you think leaders should actually measure to know whether AI is creating meaningful capacity and whether that capacity translates into better business outcomes? I mean, the important part is you kind of mentioned it, but, you know, usage metrics, measure adoption, right? Just plain usage metrics, measure adoption. But you need a before and after measure, right? You need to understand what did cycle time, throughput, you know, you name it, look like before I implemented a fix, and that could be AI, right, is what we're kind of centered on today.

11:31And then what did the work look like after, right? Because without the before and after picture, it's really hard to assess, well, how did the new thing that I put in this process actually affect how work is happening? Did it improve capacity? Did it improve throughput? where did I gain something? Right. And, and then where that capacity actually went is what, you know, almost nobody, right. Tracks today or looks at today. And if you can't answer, what is a team doing, you know, with the time that AI gave back, you don't have an ROI story. You just have an adoption story. We just, we just spent money and started using more tools, but we didn't actually tie it to anything meaningful in the company.

12:08David Rice: You just said right there, like, what is the team doing? And I think that's gotta be the focus like individual productivity right like it's very seductive because it's kind of easy to demonstrate like oh this used to take me 40 minutes now it takes me 10 but does the org actually benefit from one person's process getting a little bit faster right like you can become 30 percent more productive without the organization becoming 30 percent more productive and if i finish my work twice as fast but then it still sits in somebody else's queue as a bottleneck for three days like Do we actually improve anything?

12:39David Rice: So, yeah, there's a useful distinction between task efficiency, workflow throughput, and business outcome. I'm curious, like, why is there often such a gap, though, between how leaders think a workflow operates and how it actually gets done? Like, I know we kind of alluded to it at the beginning, but in your experience and what you've seen, you know, kind of take me through it a little bit. Yeah, I think, you know, aside from what we've already talked about, the process doc is a snapshot in time and work is a moving target. it's work is always changing right and so a document you document a process you know we say internally a lot a process document rarely survives the workforce and just what all that means is like it doesn't mean that people aren't doing the right things it just means that in application a process document doesn't show all the nuance people closest to the work make dozens of small adaptations to work every single week right to make it better and if those adaptations very seldom make it back into any kind of document.

13:38And if you start to multiply that across hundreds or thousands of roles and many, many years, the gap between the documented process and the actual workflow widens very quickly. And leaders need a clear, continuous view of how work gets done so that they can be agile in the way they manage organizations in the AI era.

13:59David Rice: Yeah, I can't think of anywhere I've ever worked where we didn't have basically a workaround, a repeatable workaround that we created as employees. You know, the formal process doesn't match the on-the-ground reality. I think one of the risks that we have with AI is like we try to automate the official workflow and we don't actually understand the workaround that possibly, in some cases, it accidentally made the thing function. Like it wasn't part of the initial plan. But if we start to remove those things, you know, how much of the process breaks is what was the question we have to answer. Yeah.

14:33What we don't give enough credit to, I think, as leaders is that people make work better every single day because they think, you know, they're using reasoning, all of these things. And there's a, you know, you go, you're clicking through a process document, you know, as a new hire, that's, that's what you get. You get a process document, you're clicking through like all the steps you're supposed to click through and you run into something and you're like, oh no. And of course, the person sitting next to you or over the queue or on Slack or whatever is like, oh, yeah, you have to hold the shift button down and go to Excel and copy it from here and paste it over there.

15:08And then the whole thing will work again. Right. And we don't give enough credit to the fact that humans figure those things out and they don't exist anywhere in documents. And people call that tribal knowledge. Right. But I think a lot of times when people think about tribal knowledge, they're thinking more about like, I know a lot about work intelligence. That's my tribal knowledge. And that's not the tribal knowledge that really makes an organization function. The tribal knowledge that makes an organization function is all those little nuances and workarounds and how I have to make something work every single day so the business can keep operating.

15:40David Rice: So, you know, that leads to another question, which is like, what happens to workforce planning when AI changes the capacity of different functions? And like we're seeing this at different rates, right? So like for an analyst, it's going to be a lot different than somebody in marketing or somebody in accounting, maybe. Like there's all these different fields that are now applying the same technology, but it doesn't always have the same effect across the board. So what do we have to account for when we think about workforce planning? We actually had a really great example of this internally for our own teams, which is our engineering team adopted AI coding tools and found a way to be very impactful doing it.

16:17You know, they cut our cycle times, they accelerated the commits, right? Our engineering throughput, you know, more than doubled. And that was a win. And we all started to celebrate. And then we quickly realized that now our engineers were able to code faster than our PM team was able to develop requirements for them, right? And so we just shifted the bottleneck from one function in our group to another function. And so as an organization, we didn't actually improve overall throughput. We did temporarily because they were able to get the requirements that were on their plate already done quicker.

16:51But then getting new requirements to keep up with how fast they were able to now develop code was a problem. And so this is where organizations have to look holistically across an organization and understand that all work in an organization connects in some way. And if one department get, you know, accelerates throughput or accelerates, you know, or decreases cycle time or something, their neighboring, right, their neighboring teams are going to have to adjust to be able to keep up with that change as well.

17:25David Rice: Yeah, that sounds like a prime example of like the workforce planning issues. It's not about how many people do we need. It's where's the constraint going to move because it's changing all the time. And the isolated productivity gains like you mentioned, they move the bottleneck that you or you can create new ones rather than eliminating old ones. And look, some of that's going to happen because you can't do everything at once. But this is why there is a sequence to how you should think about things, right? When this should really be a part of workforce planning to the original question, which is you should sit back and think about which part of this should I accelerate first and how long is that going to buy me until I'm going to run into a bottleneck somewhere else.

18:05And being able to have that continuous view of work over a whole organization allows you to understand where those ebbs and flows are going to happen in an organization.

18:13David Rice: When you find a workflow that's consuming more capacity than maybe we think that it should, how do you determine whether the answer is AI or process redesign or some other technology or just simply better enablement? Yeah, I think the question that leaders have to answer first is what's actually causing the drag? You know, if the work is repetitive and well-defined but high volume, right, that's an automation or AI candidate, right? But if the sequence itself is wrong, right, too many approvals, unnecessary handoffs, duplicate steps, that's a process redesign problem, right, that you have to go in and fix.

18:49And if the process and the tools are fine, but the people don't know the best way to do the work or they lack the skill to do the work, then that's an enablement problem. Right. And so the fix has to match the actual cause of why the work isn't as efficient or as good as you'd like it to be. And most organizations default to, again, just buy more tech, you know, without first checking on which of those three things is it actually so I can apply the right solution to it.

19:14David Rice: Yeah, it seems like maybe it was a little bit of an incentive problem here. Like we've allocated AI budgets and executives have made these AI commitments, told everybody to be AI first, right? So then like identifying something as an AI use case becomes a little bit more attractive than identifying it as like a boring management or process problem. I think once you decide to go AI, like you say, AI is strategically important to us. I wonder if there's a danger that like every inefficiency suddenly starts looking like an AI opportunity when really it's, you know, recurrent, like you mentioned some of them there, but like a recurring manual task, just that might need automation, but like an inconsistent task that just needs clearer standards.

19:55That's right.

19:56David Rice: Or, you know, a confusing task needs training. We've got to think about it in these different buckets. I think the way we came out of the gate strong with AI and AI is still going strong. And it's, it is a sea change. Like I'm not like AI is very valuable. It's going to change the way we all work. There is no part of me that believes anything different than that at all. Right. And I think that initially for change management, it's very hard unless you kind of go all in on something. So I completely understand why organizations had a big AI push, because to change the habits and the way people work and to get them to think out of the box, you have to kind of do something, you know, kind of over the top sometimes to get the change rolling in an organization.

20:38But now I think the change is well rolling because now you're seeing, you know, people like, oh, we fired too quickly. We need to hire him back. Or we told we told everyone, you know, token maxing gets you to the top of a leaderboard and you get bonuses for that. And now they're putting limits on spend. Right. And so now there has to be a more thoughtful approach long term to how AI is used in these organizations. And I think a framework that kind of understands roles, tasks, human, human assisted, AI only, like where does AI belong and how are you going to get the long term benefits out of AI now that you've convinced everyone AI is a way to go and you have people using it?

21:19Now, what are you going to do?

21:20David Rice: I mean, it's like it's almost like a game of Sims where you like build the city, you know, it's like your first city was like a, you know, a very clean map. But I think now you've got to build it out into a very detailed, much more intricate and sophisticated city. Your understanding of what's going on in work. You mentioned it there. We're well into it. We're rolling along with it now. And it's changing workflows across functions. HR traditionally owned jobs, talent systems, workforce planning. Operations is very much processes, IT, technology, finance, the money. The whole structure, right? But as it starts changing workflows across those functions, I'm curious, like, who has to be accountable for just redesigning the work itself?

22:09David Rice: Because if everybody's doing it very differently, I don't know as it syncs up the way that it used to. You know, redesigning the work itself needs an owner that has two things most AI initiatives lack, I think, today. Right. One is visibility across the whole workflow, as we've been talking about, not just one functions piece. So you understand where those ebbs and flows are, where work connects. And then the actual authority to change how work is structured. Right. Because without the second part, you're sort of hamstrung. You can point at all the places in an organization, but, you know, without the authority to actually go in and say, hey, we're going to really rethink how work is structured for this function or this role of this department.

22:50That requires some autonomy and authority to be able to go in and do that because functional leaders, right, are very close to the work. And sometimes as functional leaders that are really close to the work, you get tied to a certain way and it makes you very nervous to think about changing how things, you know, things are being done. And so it's not solely an AI or IT team because they don't understand that nuance that those functional leaders have. And it can't be functional leaders alone because they don't understand all the capability that AI can bring to an organization. And so really, it needs to be someone, often a COO type role, right, who owns outcomes end to end and treats the redesign as an actual deliverable, right, across an organization.

23:37And AI is just one way you may solve the problem, not the way.

23:42David Rice: Yeah, I would agree with you. I think we've had a lot of conversations over the last year about sort of the CHRO's remit needs to expand from designing jobs and organizations towards like designing how human and machine capacity combine to produce work. But I think what that's led to for those who have done it is sort of an increase in operations responsibility rather than just like it's still sitting, feeling like you're you're mostly in HR or people operations. Right. Like it is it has become it's all evolved the role into a place where like I don't know if that's the long term solution, to be honest, because I think that the people challenge is big enough as it is.

24:20I agree. I think what what AI brings to HR leaders, which is very exciting in my mind for for HR, is that it actually starts to assimilate people and operations together. You know, a lot of times organizations put HR over on the side and say, well, HR does a thing over here and then we all operate a business over here and HR comes in with compliance things and whatever they need to do when they, you know, they have their own data and we have the data that we run the business on. And now that data has to all kind of be the same view of work, because if you're going to operate in redesign roles, HR obviously has is a key, if not the owner, a key stakeholder in that whole conversation.

25:06Right. And because it has implications throughout, it has implications on on comp and title and structure and all of those things. And so I actually think that AI is very exciting for, while very taxing on one hand, I think it's also very exciting for HR leaders who want to now lean into actually being part of running the day-to-day business as opposed to, you know, a compliance center that sits on the side of everything.

25:35David Rice: You mentioned earlier, you know, like the usage metrics. And I think that there's a lot of that out there. The licenses activated, the weekly active users, the use cases launched, all these sort of things that tell us like, oh, we are using this technology. I don't think that they necessarily tell us that you've moved beyond just adopting and you've changed operationally. What would you say is sort of like the signs that somebody has done that and that the business is now running differently? Yeah, I think, you know, an organization has moved past adoption when leadership or really anyone in the organization can articulate what work was redesigned, what the outcome was and where that's showing up in goals.

26:17Right. And for leadership, that's the P &L for the teams that may be sub goals under that. But actually being able to, instead of saying the one-off like, oh, our CS team now saves, you know, they used to do QBRs and they had to do them manually and collect all the data for a customer and create the deck and everything. Now, you know, we're able to do that in what used to take four hours and five minutes. That's one thing. But to be able to say, okay, but where did that show up in a goal or on the P &L? Because that by itself is not the thing. The thing is, and with that extra time, they were able to increase their book of business by 50 customers each.

26:59And now I need, you know, a one to 75 ratio of CSMs to customers as opposed to a one to 25 ratio of customers.

27:09David Rice: That's where it starts to actually show up in goals and things. So when I believe that those are the key markings of when an organization actually moves from just adoption to impact, which is don't just tell me what you did with AI, but tell me then what you did with the benefit that you got from AI. Yeah, I'm a more and more, as I have these conversations, I'm arriving at the conclusion that like eventually it has to become visible somewhere. It can't just be in a dashboard. It's got to be like your role design, your customer outcomes, organizational structure, skills, whatever it is. But if it's genuinely transforming the org, I should basically be able to see evidence without looking at a metric.

27:48David Rice: Maybe that's the ultimate sign of AI maturity is that we stop talking about AI really because we're talking about how the business operates. Yeah, I couldn't agree with that more. I think that HR and operations are going to partner, and AI is one of many solutions as we dig into the world of how does work get done today. Absolutely. Well, Heidi, that's actually all the questions I have for you today. Thank you for coming on the show and for giving us some of your insights. I really appreciate this. Again, David, thanks so much for having me. Listeners, if you haven't done so already, head on over to peoplemanagingpeople.com, forward slash subscribe.

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