Episode 158: How to Successfully Lead the Return to Office (Interview with Philip Arkcoll)
In this episode of the Digital HR Leaders podcast, host David Green dives deep into the current dynamics surrounding the return to office.
Joining David in this enlightening conversation is Philip Arkcoll, CEO and founder of Worklytics, renowned for their expertise in workforce analytics. Together, they explore:
The pros and cons of remote and in-office work;
The challenges in transitioning back to the office and successful mitigation strategies;
Understanding network density and social capital metrics;
Balancing employee privacy and data insights;
Applications of generative AI in collaboration and productivity;
The future of Organisational Network Analysis (ONA);
Gain valuable insights into striking the right balance, fostering employee trust, and creating a productive work environment in this thought-provoking episode.
Support from this podcast comes from Worklytics. You can learn more by visiting:
www.worklytics.co/DigitalHRLeaders
[0:00:03] David Green: Today we are going to be delving into a topic that has gained significant attention in recent months, the return to the office. After a period of remote and hybrid work arrangements necessitated by the pandemic, it seems as if the pendulum is swinging back the other way, with numerous organisations re-evaluating their strategies and considering the benefits of bringing employees back to the office. This shift has sparked a renewed conversation around the role of the physical workplace and its impact on productivity, collaboration, serendipity and innovation. With a multitude of factors to consider, from employee preferences to the organisation's goals, finding the right balance becomes essential. Therefore, as HR and people leaders, it's crucial for us to understand the associated implications, challenges, and opportunities.
To help put some clarity on this complex topic, I'm delighted to welcome Philip Arkcoll, CEO and Founder of Worklytics. Today, Phil and I will be discussing the various aspects of the return to the office, from understanding employee preferences and managing expectations, to implementing effective communication strategies and creating a productive and inclusive work environment. We'll explore the challenges faced by organisations in transitioning back to the office, the best practices that successful companies have adopted, and the role of analytics and technology in enabling a smooth and data-driven approach. With that, let's kick off the conversation with a brief introduction to Phil himself and Worklytics as an organisation.
[0:01:54] Philip Arkcoll: So I'm Philip Arkcoll, I am Co-founder and CEO of Worklytics. We are a workplace insights platform. We help organisations access and analyse anonymous data on work and collaboration from common collaboration tools, so Office 365, email, calendar, the Google suite of tools, Drive, Slack, Zoom, you name it, and we're helping organisations leverage that data to improve employee experience, drive outcomes in employee satisfaction, engagement, understand the day-to-day life of employees, avoid things like burnout, and understand collaboration patterns as well. We do a lot of organisational network analysis, and I think we'll talk about some of that today.
[0:02:40] David Green: Yeah, looking forward to it. And clearly, as you help organisations wade through all their collaboration data, there's so many different platforms that people are using now within organisations to collaborate, aren't there? I mean, back in the day, it just used to be email, but there's so many different tools as you highlighted there though, and I suppose maybe it shows that people are just collaborating more and more intensely than they used to, perhaps.
[0:03:04] Philip Arkcoll: Yeah, I mean I think obviously remote work and hybrid in general forced adoption of a lot more tools. I think we saw a number of organisations go into the remote work period at the beginning of the pandemic with just email and calendar, and they didn't have great chat tools, they didn't have great project management tools, priority lists, shared tools that allow asynchronous collaboration, and you've seen an explosion of adoption in all of those platforms. And in particular obviously, Zoom, very heavy usage of Zoom and things like Teams to stay connected. Those have been critical and I think they've been fantastic. They've allowed teams to continue to get work done even when we're not together.
[0:03:46] David Green: So, Phil, last October we collaborated on an article for myHRfuture around how the future of work is going to be really focused on where work gets done. And since then, we're seeing that many companies, notably some of the most prominent technology companies in the US, are really being a bit more forceful now about mandating return to office. There's a lot of research out there, and more research every week really, around the notion of returning to office being helpful maybe for organisations that are looking for benefit from things like innovation and collaboration, and maybe onboarding perhaps new employees as well and helping them build their networks within the company. But obviously, as CEO of Worklytics, and someone who's looking at this data day in and day out with your team, what are your views on this endless debate that we seem to be having?
[0:04:40] Philip Arkcoll: Yeah, I think you're right, it's been a really fantastic period from a data, people analytics perspective, so much change over the last few years, and we've been working with organisations through pre-pandemic and then the initial move to remote work, and all of the permutations. People thought that they could come back to the office, and then the pandemic peaked again, and people were sent back home. I think a lot of organisations committed to being remote long-term, and now you've seen some of them had to go back on that. So, it's been really interesting from a data perspective to see the major changes in ways of work that all of these configurations cause.
We really see points of inflection in how teams collaborate and communicate each time there's one of these major shifts in work, and we're definitely seeing, particularly among tech organisations, a push to return to office at the moment. And I think a couple of things are driving that change. And the first is essentially when organisations first moved remote, I think a lot of people were surprised by how well it actually worked. We had been in person since forever in most cases, and people had formed strong relationships with their teams, with their managers, with the rest of the organisation. There were already lots of projects kicked off, initiatives already running, teams already formed, and so people found, I think, that they could get things done while being at home, they could still stay connected, they still had those strong relationships to get the information they needed, and they enjoyed the additional focus time. They could kind of hide away and get heads down a little bit more at home to get things done, they enjoyed not having to commute, in many cases, a couple of extra hours of work a day.
I think what we've seen over time from a data perspective, and from the experience and what we've heard from a lot of leaders and organisations is that over time, as things evolved, new projects kicked off, new team members joined organisations, organisations restructured over time, there's been external pressure from potential economic downturn, and a lot of that change has driven changes in the internal networks of organisations, and we see from a data perspective that over the long run, you start to see a bit of a decay in the cohesion of networks inside of organisations. So, there's still a lot of collaboration and you pointed out all the tools that people are using to stay communicated, but we see a narrowing of those networks. You spend a lot of time working with your direct team, the people you need to get things done, but you have less and less time to work with the people outside of that direct network. And I think that gave leaders a sense of potentially a decay in culture, decay in strength of the network in the organisation, people are less aware of what's happening outside of their teams.
I think the second factor that we see as a driver in this as well is for managers, remote work can be particularly challenging. It's very different being a manager or a leader in a remote scenario versus in the office. You can't walk in and glean how people are doing, just sort of passively manage by being there and joining conversations and diving in when you need to; you have to be a lot more proactive, you have to schedule meetings over Zoom and reach out to people and have better tools to coordinate asynchronously. So, it's challenging for leaders to do that on a continuous basis, and I think there was a bit of a sense of a loss of control from some managers, potentially a bit of inexperience in managers as well, struggling to keep those connections, to keep those bridges with their teams, and a loss of a bit of a sense of cohesion from that perspective as well. So, middle management and leaders, I think, have also pushed to have a little bit more facetime back in the office.
[0:08:47] David Green: So, if we're thinking about organisations more and more now transitioning to return to office or spending more time in the office, what are some of the main challenges you're seeing these organisations face when they're trying to transition their workforces back into the office?
[0:09:03] Philip Arkcoll: Yeah, so this is something that we're working on a lot at the moment. Obviously, a lot of companies in the last six months encouraging people to come back, trying to figure out what the right configuration is, do they have anchor days, do they have a minimum number of days that people come back. Broadly speaking, we've seen a few things from a data perspective.
One is, we saw collaboration overhead rise quite a bit. When organisations went remote, you had to have more recurring meetings, more check-ins to proactively drive communication and connection across the organisation. And counterintuitively, I think you would have expected that when people returned to the office, you'd see sort of a dip in collaboration, that it would go back to normal. But Michael Arena, I think, recently pointed out that there's a collaboration avalanche, I think he terms it, and we see that from a data perspective as well. And the underlying reason for that, we believe, is that when remote work started, people instituted a new way of working. Just from a personal experience, we're on Zoom a lot more, we're connecting a lot more over all these tools all the time, and that is a layer of workflow that is instituted in organisations now.
Come return to the office and a little bit more facetime, that way of working is still there, but you have all of the serendipitous connection that you get in the office, all of the people that you wouldn't have otherwise connected with when you were remote. And now you're face to face, you're seeing them at lunch or at the water cooler, and so you get another rise in collaboration. So, we definitely see what Michael Arena reported, a lot more collaboration, an explosion of collaboration, people I think struggling to find time to do focused individual work outside of outside of that challenge.
Then, I think the other challenge that we see is an issue with in-office density. So, what happened in the remote period is a lot of people moved out of big cities into places where cost of living was lower, for example, they didn't have to be in San Francisco, New York, or London. And now with return to the office, a lot of those people have settled outside of the cities and they don't want to move back. And so, we've looked at that pretty closely. What we see is if you look at people's collaborative networks, who they email, who they meet, who they chat with, who they most work with, we're seeing in many cases they're only together in the office with 20% to 30% of the people that they actually work with. As a result, they're on Zoom all day in the office and working with people on the other side of the country or on the other side of the world, and I think that causes a bit of frustration when you return to the office in that case.
I think very related to that, the ways of work have changed substantially and one simple example of that is that there just aren't enough meeting rooms anymore. There are just a lot more one- or two-person Zoom calls where you're chatting to people somewhere else and offices aren't configured for that. They're open plan, people want to just have their heads down in an open plan space, they don't feel comfortable taking calls, etc. And so, we hear a lot of people struggling to find the right space to get things done and preferring to work at home as a result of that. So those are major issues I think a lot of organisations are contending with, and they're seeing in surveys, in some cases, negative feedback about the return-to-office programme, and I think reducing some of that friction will help resolve some of that reticence to return.
[0:12:38] David Green: Yeah, it's interesting. It's almost like there needs to be a bit more intentionality around if you're having an anchor day, for example, it's, "Okay, well, let's not book five hours of Zoom calls with various members of the team on those days". And maybe another way that it can help organisations is around things like workplace design as well, so looking at the data that you've got, but then actually bringing their survey data in as well, and I think that's where the power of the two different data sources come in. So, you can actually start to think about how we design our workplaces for the future because as you said, the way we work has changed, and we're probably not going back to the way we were pre pandemic.
[0:13:20] Philip Arkcoll: Yeah, I think that's right. As we see cases of organisations that are handling return to office well, they're thinking about this density challenge, and we've seen people use data to implement, I think, a bunch of interesting programmes to try to deal with some of these challenges. So for example, the density challenge that when you come into the office, you're surrounded by the people you actually work with, not people that you don't work with. We've seen a few customers design or redesign their floor plans and their seating plans in the office around people's collaborative networks, so that when you come into the office, you're on the same floor, in the same quadrant or space, where the people that you most work with are in the office. And so that makes sure that you're getting a lot of value over that time.
We actually combined sentiment data from surveys around whether people supported these new hybrid ways of working, how they rated them, whether they thought they were working for them, and we found density was absolutely critical in that. If you come into the office and you have 20% to 30% density with your peers, you rate hybrid work as a lot lower. If you have 50% density, then you're far more likely to rate that it's a success, that you are able to connect people with people that you work with when you come into the office, and you're getting value for the one-hour commute or the two-hour commute that you have to add.
Then, I think you're right, space design, key component of that as well. We're seeing a lot of organisations use this data to reconfigure spaces, reconfigure how they use open-plan spaces, put in a lot more phone booths, one- or two-person call rooms that allow somebody to either focus and find a quiet space to get work done, or to be on those Zoom calls with more distributed teams across the organisation.
[0:15:21] David Green: How can organisations go about understanding their network density, and what are some of the data points that they should look at, and how do they differ when you're looking at network density within a team but maybe between teams as well that maybe need to collaborate and innovate together?
[0:16:43] Philip Arkcoll: Yeah, good question. This is something we spend a lot of time on and we deliver data sets for organisational network analysis and work with customers to analyse this data. And I think the metrics that matter differ by the question you're trying to answer, the particular problem you're trying to solve. But broadly speaking, we look at people's close or strong networks, who are the teams, parts of the organisation that you spend the most time with, and what is the size of that network? Is it too large, such that you might struggle from over-collaboration or keeping in touch with that many people; is it too small such that you might be isolated? We see a lot of isolation like that in more remote organisations.
Then we look at weaker connections, what are your connections across the broader organisation; do you have visibility into what other teams are doing; potential for cross-pollination of ideas in other departments? And I think key to that is bridging connections, not just connections within your team, or your department or your role, but what do they look like between your team and other teams, between your department and other parts of the organisation. And then we might look at it from a seniority or leadership perspective as well, do you have connections with your manager, with your skip-level manager, with leadership? And those are strong predictors of retention, of high engagement and of career mobility as well, of the probability that you're promoted if you do have those strong networks and you are able to maintain and build them over time.
I think one example is, for instance, we have looked at sales teams and what drives performance on sales teams, shortening sales cycles or increased target completion. And there we found that people's very close, intimate networks really matter. And for example, we've looked at networks in Slack direct messages or Teams direct messages, who are the people that you have one-to-one conversations with, and if people in sales positions have direct connections with marketing, with product, with customer success, people that they can reach out to to better understand customer problems, solutions that they can leverage and bring into customer calls. Those connections are really critical and strong predictors of success in sales.
So, it's an incredibly rich space and I think interest in this space has blown up over the last two to three years in particular, and it's been fascinating to see.
[0:19:16] David Green: Yeah, and I guess at this point, we might want to talk about some of the privacy elements of this because, number one, you're not looking at content, you're just looking at who connects with who, how often, and who are the bridging connections maybe between different teams, because they could be vital to getting work done. I don't know if you want to talk a little bit to the kind of privacy and how the data surfaces with people in the organisation?
[0:19:39] Philip Arkcoll: Yeah, good question. I think a lot of people, that's the first thing they think about before they venture into this space. It's clear that there's a lot of value in this data. We're seeing an explosion in the use of it, particularly over the last few years when there's been so much change, really helping a wide variety of organisations understand how they function, improve employee experience, improve how they work and get things done.
But at the same time, there is fear among employees that this data will be used for nefarious purposes to monitor people or to count the number of emails they send and use that as a weak proxy for productivity, and I think in some cases that is founded. It's ridiculous that those types of use cases would be considered. But I don't think that that necessarily means you need to throw out the baby with the bathwater, I think you could have both of those things. You could gain the value from this data while still protecting employee privacy and ensuring that you have employee trust.
So, we implement a technology layer, a proxy that sits on customer infrastructure and filters out all PII, all identifiers, or any personally identifiable information before it comes through to Worklytics for analysis. We also filter out all content. We're only interested in flow of information in organisations, not the body of any content whatsoever, and that is filtered out at the source as well. Then the data is aggregated up to the group or department level. And so you're looking at broad trends across large groups of people inside of these organisations. And so then I think by doing that, you can balance getting value from this data while still maintaining trust, protecting employee privacy, and that's absolutely critical.
I think another thing that's critical is ensuring that employees see value from the analysis of this data. We've seen a lot of successes when organisations present these insights back to their teams, to the organisation. There's appetite for understanding how ways of work and connection and networks impact working organisations. I think right now, there's an opportunity for a win/win. A lot of employees want the additional flexibility from hybrid and remote work, but there's this fear around, are things still getting done in leadership; is there still connection, etc? And I think if we can use this data, I think it's the perfect data set with which to understand that problem, that we can allow more flexibility and things can still work, we can maintain those connections if we implement the right strategies to do so. If you can deliver that and use the data to do so, it's really a win/win for both employees and for organisations.
[0:22:34] David Green: What are some of the other social capital metrics or network metrics that you would recommend analysing to measure success of return to work efforts, or maybe success of overall approaches to hybrid?
[0:22:47] Philip Arkcoll: Yeah, again, I think it depends on the problem. And you're right on your previous point around trying to identify win/win problems that you can focus on with this data; that's where we see these projects be most successful. I think one of the areas where we've seen a lot of traction is in diversity, equity, inclusion, and belonging. Use of this data is one of the strongest use cases for it, and it's been a particular challenge for underrepresented groups during the pandemic for a few reasons. In particular, it tends to be harder to build your networks in organisations. And so if you're doing hiring in this space, you're bringing in people from diverse backgrounds, do they have the opportunity to build those networks in their teams, in the broader organisation, with leaders. And those networks are critical for allowing you to succeed in the organisation, for retention, for promotability, and really driving value in those organisations.
I think there's been a really strong use case around using this data to understand the potential for that broadly across your organisation, identify hidden bias in the organisation. It's one of the most exciting applications of ONA data.
[0:24:14] David Green: So, I mean the interesting thing, I speak to, as you do, speak to a lot of different organisations and occasionally someone will say to me, "We want to do ONA, what use cases should we use it for?" which is definitely not the right question. It should be, "I've got this problem I'm trying to solve", and then, "we think that the network data or collaboration data can help us with it". What advice would you give to a people analytics leader or an HR leader that's looking to use this, and how should they start?
[0:25:34] Philip Arkcoll: Yeah, I think you're spot on and I was listening to a recent interview with Rob Cross, where he was asked a similar question. And you're right that ONA sometimes is the shiny new thing. We've all seen those network diagrams that really look exciting and fantastic, and so we do, from time to time, get people who approach us and they say they want to do ONA and they're trying to figure out what problem to apply ONA to.
First of all, we will say that those diagrams, while they look good, inferring anything from them is really like reading tea leaves. You need to really dive into the data to understand what's going on in the diagrams, maybe help sell the project internally, but they're certainly not the focus of what ONA should be. And then I think, you know, you kind of alluded to it, what is critical is starting with a problem, starting with something that you want to solve in your organisation, whether that is leadership development, understanding managers' spans of control and layers, understanding your latest survey results and what the underlying drivers are because you're seeing higher burnout and higher attrition. Those are kind of core problems that are very important to organisations right now. And there are a number of ways in which ONA and network data can help solve those problems.
So, figure out what your top one, two or three priorities are and we always redirect people to do that, what is critical to your executive team, to your C-suite in your organisation; what are the top priorities in your organisation from a people perspective; and how can ONA help drive those priorities, rather than coming at the problem from the reverse? And when you do that, I think you're applying it to a key problem that everybody cares about. They see the value of a lot more visibility into understanding that problem and being able to create new and better metrics around it, and those are the projects that are the greatest successes in this space.
Unfortunately, in some cases there have been a few too many of those cases where people start with ONA and try to retroactively fit that to a problem, but as the space matures, we're seeing more and more people taking on these projects in the right way and starting with a real problem.
[0:27:59] David Green: How do you foresee the practice of organisational network analysis evolving over the next five years, and maybe if there's a balance between active and passive ONA as well?
[0:28:10] Philip Arkcoll: Yeah. So, I think the space is still pretty young, as you know, but we've seen it mature a lot in the last few years because there have been just so many applications to it over all of the changes that have occurred in organisations. What we're starting to see is this type of data, these metrics, things like focus time, things like how much time is my organisation spending in meetings, what are the productive meetings versus less productive meetings, what does network size look like, are we siloed and isolated? We're seeing those metrics being bubbled up to the executive level and being reported to the C-suite on a regular basis in quarterly business reviews or monthly business reviews, along with other HR data on hiring and promotions, etc. So, it's becoming part of a core toolkit and KPIs that the organisation uses on a continuous basis to understand where things are, where there are potential challenges that need to be focused on throughout the org. So, I think it will continue to rise in importance. We've started to see that, and we're seeing a lot more of that over time, and I think over the next five years, you're going to see a lot more maturity.
Then secondly, related to that, I think is getting insights out further and further down to the individual contributor level in an organisation. Right now, ONA is driving value at the HR level, maybe at the HRBP, maybe some senior leaders, but you're going to see it over time drive value at the manager level and ultimately at the individual contributor level, how can you understand your networks and the impact that those have on your potential success; how should you develop them over time to get things done in your organisation? And so, that will be an interesting trend to monitor over the next five to ten years.
[0:30:04] David Green: And, Phil, maybe a bit early to say, but are there any specific applications of generative AI that you see in the realm of collaboration and workforce productivity, or any that you could foresee in the near future?
[0:30:19] Philip Arkcoll: Yeah, obviously it's still a very nascent space, incredibly exciting, I think a bit of hype right now, we're at the peak of the hype cycle, maybe starting to trend down. But I do think, to the previous point I made, you're going to see this value being delivered all the way down to the individual contributor level, how do you combine all of these different data sets together, your active listening, your passive listening, what you have in your HRIS system, all of these other datasets, into a recommendation that you can make to somebody that provides value to what they're doing, helps them do a better job, helps them focus more and get things done.
I think combining these datasets together and generating recommendations on the fly that feel very relevant to your particular context as an individual, they understand where you are in the company, what your work life looks like, what your day-to-day life is like, and could be very focused in on your problems and your challenges, and so very relevant in making suggestions. We're not there yet, but I think that's where things will evolve and we're looking forward to that day.
[0:31:33] David Green: And this is the question we're asking everyone on this series, Phil, what steps can HR leaders take within their organisation to humanise the work experience, and by all means talk about it from the perspective of the sort of data sets that you're helping organisations understand better?
[0:31:51] Philip Arkcoll: Yeah, good question. I think to my previous point, what can be dehumanising is applying blanket policy changes or blanket structural changes, treating employees all as a single amalgamous mass that all have the same needs, the same ways of working, the same day-to-day context of what they do. And network data and technologies like AI and better data sources from other places as well combined together help you layer in the context of every unique individual, the value that they provide, what their context is in the organisation, and the context around them, their broader team, etc, so you can provide solutions, and recommendations and policy changes that are better suited to the needs of each individual.
I think the more we can use data to do that, and the less we use single, blanket models for what people want or what people need, the more we are able to humanise and add value to individuals.
[0:32:58] David Green: Well, I can't believe the conversation has already come to an end, Phil. Thank you so much for being a guest on the Digital HR Leaders podcast. Can you let listeners know how they can stay in touch with you on social media, and also find out more about the work you're doing at Worklytics, because I know you share a lot of the data that you're collecting from an aggregate level with some of the insights, certainly something I've been interested in throughout the various stages of the pandemic, and I think has really helped track some of the big trends during that during those few years?
[0:33:29] Philip Arkcoll: Yeah, thank you, David, really enjoyed the conversation. So, if you want to find out more, we do share a lot on LinkedIn. You could follow our newsletter, we share out a monthly newsletter with the latest insights that we found or techniques in ONA or workplace analytics, so definitely check that out. You can visit our website as well, worklytics.co. We are, as part of this series as well, offering a free meeting effectiveness analysis that we plug into companies' calendars and we anonymously analyse meeting patents across the organisation to identify bottlenecks, identify where potentially parts of the organisation are excessively meeting, have a lot of overhead and make active recommendations. So, if you're interested in that, we recommend that you take advantage. The first ten companies, as I said, we're offering that for free to try out what that looks like.
[0:34:27] David Green: Brilliant. Well, we'll make sure that we get the details of that. I know there is a dedicated link, I think, from the podcast, which actually takes people to that landing page so they can find out more about that offer there. So, Phil, it's always a pleasure. No doubt we'll bump into each other at a conference at some point in the near future, I suspect. But yeah, thank you very much again for being a guest on the show.
[0:34:50] Philip Arkcoll: Yeah, thank you, David. It's an honour and a real pleasure and look forward to seeing you in person soon as well.