PM Today Podcast: Automation, Project Efficiency, And The Future of Work

QRA future of work

In our latest podcast, our Associate Editor, Amy Hatton, talks to Jordan Kyriakidis, Founder of QRA, and Theoretical Quantum Physicist turned Entrepreneur! Together, they discuss how automation will affect the future of work, the power of technology to drive strategy, relationships and working performance – and when we should (and shouldn’t) embrace technology as an enabler.

QRA is the company behind QVScribe. This powerful, innovative software perform powerful requirements documents analysis, identifying risks, errors, and ambiguities so that these can be eliminated consistently and permanently at the project design stage. To see the technology in action, head over to the QRA website, where you can book a free, personalised demo to show you how QVScribe empowers teams with a foundation for confident project development by identifying ambiguities and inconsistencies.

Crucially, QVScribe also offers Word and Excel plugins – extremely useful for all those project and PMO practitioners out there who are grappling with the risk of human error that so often derails the best laid project plans and budgets. This generates requirements documents that express exactly the right intent from day one of project planning, gearing project teams and PMOs up to deliver more efficiently and avoid costly errors and rework right from the start of the project.

As well as discussing QVScribe’s capabilities, Amy and Jordan consider how automation has the power to free project managers up from lower manual tasks, enabling them to focus less on day-to-day drudgery and more on the strategic impact they have the potential to deliver across the portfolio.

Stream the podcast below to explore the fascinating world of AI, machine learning, automation, and how it can enhance – not negate – the wonderful world of human endeavour and achievement.

Amy: Okay, so hi, everyone, and welcome to this, the latest episode of the PM Today podcast. My name is Amy Hatton. Some of you – most of you, of course – know me as the Associate Editor of PM Today. And I’m here in rainy, cold, Southwest London today. But I’m very happy to be talking to my lovely new friend, Jordan Kyriakidis, who is the CEO of QRA, which is a very exciting tech disrupter based out in Nova Scotia.

So, we are really in different parts of the world today. Jordan is going to be talking to us today about the issue of automation. I think this one is going to be really fascinating for you guys out there. We’re going to be talking about the future of work the role that automation is going to play in that. And in particular, we’re going to be looking in depth at the role of automation in having an impact on the efficiency of product design, and project delivery. I think we’re going to have a lot to get through today. So, Jordan, thanks so much for joining me. It’s lovely to have you here.

Jordan: Thank you very much, Amy, it is a pleasure to be here. And it’s good to speak with you from, like you said Nova Scotia, where there’s a light dusting of snow here, outside my window!

Amy: Oh, wow. We really are in very different zones. So, it’s always exciting to connect in that way. So, Jordan, QRA looks to me like a very, very interesting organisation, I confess it is one that I think will be new to our readers. If I’ve understood correctly, you have been going for about 10 years now. And you come across very much as a very exciting tech disruptor. Can you just start by just setting the scene for our listeners – by just telling me a little bit about yourself, why you started QRA and your vision for the future of the company just to give us an idea.

Jordan: Sure, I’m happy to do that. It is a bit of an odd start to us. I call myself an accidental entrepreneur, I didn’t set out intending to do this, I kind of fell into it. My own background is actually in theoretical physics. And in fact, before I founded, and started running QRA, I was a Professor of Theoretical Physics for about 10 to 15 years. And my specialty was quantum physics and quantum computing. And this whole thing started by an industrial contract I had on computing by an aerospace company. They were having computational problems, and they thought quantum computing was a new thing, that they’d start looking into it and investigating if there was anything there. That contract went well. But what we found after a couple years of working with them, I started understanding what their actual engineering problem was – and their business problem was.

They thought they had a simulation problem, but the problem actually ran much deeper. The problem was that the systems were getting so complex that the way they were verifying and validating them had to actually change. And so, we looked at the root cause, and it wasn’t the simulation or design. The root cause of most of the problems happened much earlier in the process when they were writing the requirements and the specs – when they first captured and were refining the intent of what it is they wanted to build. Refining is an important part because these things change as the project or product develops, either from new engineering input or from customer input. And at that point, we said, “Okay, we got a company here, there is a business need here. And so we should spin out a company and start”. What I thought was going to be an eight/nine-month contract that maybe I’ll put a graduate student on turned out into something quite different. So, I was a bit wrong about that. And here we are, ten years later, still solving the same problem.

Amy: Wow, Jordan, thanks. Well, I have to say, I feel very privileged because I’ve never interviewed a theoretical physicist before. So thank you for that. Let me just pick up on that last sentence because it really resonates with me. You were talking about the fact that even after 10 years, the same stubborn problems still exist. And that as far as I gather, is what your technology, QVScribe is looking to address. But before we come on to QVScribe and what it actually does, and its capabilities, just hone in a bit on that problem for me, from the perspective of the project management and delivery cycle. To put some context around that question, it’s really interesting that you describe yourself as an accidental entrepreneur.

Because you are no doubt aware that many project managers in the space are accidental project managers, they fall into the role with no formal training. They’re trying to find their way with the tools they’ve got available to them. And constantly they’re facing issues with efficiency, delivery, rework, that kind of thing. So, when it comes to automation and the kind of technology that you’re dealing with, how do the capabilities of automation impact on the project management and delivery cycle from the product development stage right through to the delivery iteration?

Jordan: Well, it’s a bit of the predicting the future here. And we can look towards the past to help us guide and help us inform what’s going to happen in the future. And, you know, with this rise of automation today that you mentioned, I really feel that we are at a strategic inflection point now in how the industry is progressing. And in almost all previous strategic inflection points, what happens to the role of the human, I think of it as the human moving higher and to the left, meaning that they are moving higher up – that they’re not involved in as much detail – those are being automated away.

But they play a more supervisory guidance role. And they move earlier on at the beginning of the project. So many, many years ago, centuries ago, humans were tool makers. Then they move in to being designers. And now we’re moving into the age of intense driven development, where the most important part now is to discover and refine the intent as the project evolves. So, questions like, “What do we actually want? Why do we want it? What properties of the system and what behaviours are mandatory and what’s prohibited?”. And really uncovering those, and refining them as the project goes continually is going to be the most important impact I think on the human and the role of the project manager is pretty clear. That’s basically their bread and butter today.

Amy: Yeah, Jordan, I think it’s a very exciting and potentially almost scary environment that we’re living and working in now. So let’s just bring the picture wider again, for a moment before we start to talk about QVScribe and the capabilities that it has. You’ve already talked a bit about the scene of automation and the role that it plays in our working lives and in project management, in particular. Bringing it out wider, looking at society as a whole, just give me a bit of a layman’s view of some of the technical capabilities that are coming out of this and what excites you about them and how you think it’s going to impact – not just on our working lives, but on our whole future as a human race? Are we really going to start to see, for example, self-driving cars, on a on a mass scale – that kind of thing. What’s your vision for the future?

Jordan: So to answer your question about self-driving cars, the answer is absolutely yes. There’s no question about that. Just like trams and trains have become automated. It’s going to happen. It’s only a question of when, not if. But speaking a bit more generally, I think what we see is – on the infrastructure side, and on the product development side – is that the design, the build, and the test are all becoming automated now, all at the same time. And the transitions from one phase to the other are also becoming automated and becoming autonomous – that’s different than automated. For example, for building something, we’re seeing the rise of additive manufacturing, and 3D printing. Manufacturing is becoming automated. In many aerospace and automotive companies, for example, they’re having the use of auto code generators, where you design the system, you push a button and software gets pooped out the other end. And the software’s embedded deep into the manufacturing process right now. So, the build part is being automated. If we look at the design, we’re seeing technologies such as Generative Design where the user puts in the requirements they want, and the design of the system is just being designed by AI. And the human just makes sure that all the parameters are set.

In semiconductors, Design Automation has been very mature. Humans don’t design chips and boards anymore. A machine does that. And that’s spread out to other things to other industries as well. So, design is also being automated. So, we have build, we have design and the next one is test. And test is also becoming automated with the rise of adaptive algorithms that can deduce what tests to run catch errors and having decision making becoming autonomous as well. So, these three things, the whole design build and test cycle, is being completely autonomous. And as the human recedes from the cycle, what happens is that the iteration between design build and test is going to speed up – like a thousand-fold. It’s just gonna start going faster and faster and faster. So, the question now is: what is the role of the human in all of this? As these products and systems become more complex, we really can’t spec out everything.

We can’t have a giant work breakdown structure of everything we’re going to do in the beginning, because we just actually don’t know. And this process of product development, it really starts to resemble something more like evolution, as opposed to a complete full down spec, the properties are going to emerge. You know, some companies are already started talking not about requirements, but about “desirements”. We don’t know exactly what we want. But here are the general properties, we believe are important. And as we go on in the development, we’re going to refine these concepts a bit more. And so, the question now is: what is the role of the human in all this? From the level of the manufacturer or the builder and the project manager as well. And I think the key role there, or the key question is: how can we ensure we get the product we need, when one, we don’t even know what we want in great detail, especially early on. And two, we are deploying autonomous agents that are able to process information, really millions of times faster than humans, who can now act on the world and execute cycles of design build test, without any explicit human input?

Amy: Yeah, you’re absolutely right, Jordan, I don’t think there’s much awareness of the impact that it could potentially have on the project management and PMO community. I don’t know much about QVScribe, and I’m going to invite you to tell me about it in a moment. But it strikes me that the big headlines here are: costly errors, rework, inefficiency, failure to deliver what the project or product is aiming to deliver. Would I be right in thinking that those are the kind of issues QVScribe is addressing? And are you able to tell me a little bit more about the technology and exactly how it works and what it does?

Jordan: Yeah, I sure can. So that is the problem that QVScribe solves. And it aims to solve it right at the root cause – that when these types of errors first are inserted into the project, try to catch them right there. I like to think of our solution as being able to solve a problem by pressing Backspace on your keyboard, as opposed to recalling a fleet of vehicles to fix the problem. So right in the earliest stages that you possibly can. So the product we have in market today is QVScribe. And really, the core use cases are three. One is measuring the quality of your requirements. It’s actually analysing your requirements using natural language processing, and giving you a measure, a score from one to five, on every single one of your requirements, so you know the quality of it. And, number two, it identifies similar requirements in your document or corpus or module that can indicate either redundancies or contradictions in your requirements. When you get to very complex projects, you have thousands upon thousands of requirements. And it’s very difficult for a human to tell that requirement number three, is contradictory with requirement number 14,023. But a machine can actually figure those out. And third, is ensuring the consistent use of terminology and units and dimensions in your systems. So those are the core use cases. So, if we take one, take the first one of measuring the quality of requirements. So, we measure each requirement according to 16 distinct problem types. And these are things like, well, simple things like incomplete sentences, obviously, they should be complete sentences, but also things like immeasurable quantifiers. You’re specifying something that actually can’t be measured. So how do you know if you’re going to get it correctly or not? Or if you have nonspecific temporal phrases saying something should be done immediately. Speaking as a physicist, there is no such thing as immediate. Do you mean a nanosecond? Or do you mean a second? Or do you mean a minute? Depending on the project, each of those things can be immediate. Other examples are universal quantifiers. The system must do X in all circumstances. Really? All circumstances? What if there’s, something comes in smashes the system to bits. And there are many examples like that. Another big one is probably the most common one, which is also very simple to fix, a spec should be atomic. It should require one and only one thing, because if you have multiple requirements, the system should do this, unless it’s in this state, in which case, you should do this. But if it’s got this feature, then do this. That’s probably three or four different requirements, those should be broken up into separate units. Because then when you test you can test each requirement individually. Negative imperative is another one. It’s difficult to test the absence of behaviour as opposed to presence and behaviour. So, these are the sorts of things we measure the quality of, and we’ve come up with a score from one to five for each of the requirements.

Amy: Okay, and just bring that to life a little bit for our listeners, because on the face of it, if we talk about, for instance, looking for inconsistencies and a requirements document. Does it really matter? If you use one piece of terminology in one place and one piece of terminology in another place or whatever it might be? Can you give us any kind of either hypothetical or real-life example that will paint a picture of the impact that QVScribe can have?

Jordan: Sure, yeah, I can give that a shot. First, to answer your question, yes, it does matter if people use different terminology to mean the same thing. What we see many times is, different groups in a company use the same term to mean different things. And sometimes they use different terms to mean the same thing.  And more importantly, they don’t know that they’re doing that. The source of a lot of rework comes to simple things like that. So, I’ll give you one example. We were analysing some requirements specs for a country space agency. And they were designing a lunar rover. And in this one set of requirements, they use the terms light lunar rover, lunar rover, rover, and vehicle. And sometimes they’d use the same terms even within the same requirement, they’d talk about the rover and the vehicle. And it wasn’t exactly clear if the vehicle refers to the rover or if the vehicle refers to another vehicle that was there with the rover. And so you can imagine now that if different groups interpret those requirements, and these are projects that take years to go, and that can be in different countries, they can even speak different languages, having an understanding that actually, no, vehicle and rover mean the same could actually be a huge problem down the line, and will take maybe even some rework, and design rework to actually fix it later on. Whereas for a tool, like QVScribe, literally when someone’s typing in the word vehicle, something could basically be an indication saying, “Oh, you used rover before? Do you mean rover? Or do you mean vehicle? Figure out which one you want and pick that one.” So now it’s consistent everywhere for all time. And if anyone ever changes it, you get a flag right away.

Amy: Yeah, I think that that makes a lot of sense. The tiniest detail can cause the biggest disaster if you’re not aware of it. And for our listeners, it might be helpful to draw that back to an analogy that we use very often in the PMO space, actually. And it’s this business of: how are people running their project portfolios? Believe it or not, even across multimillion pound portfolios, quite often they’re doing it on a spreadsheet. And not only are they doing it on a spreadsheet, they’re doing it on a hundred different spreadsheets, and they’re all being sent to each other all the time. All it takes is for somebody to accidentally put the wrong decimal point in the wrong place. So, if you’ve got something like QVScribe, that is able to go through and give you that absolute confidence that you have that absolute consistency that is going to inform all the stakeholders, in the same way with the same facts and the same requirements. I can see how what might appear to be a very, very, very small risk, if mitigated early in the process, prevents a very, very, very big disaster down the line. Have I understood that correctly?

Jordan: That’s correct. Yeah. And I’ll add to that a bit. But first, I want to add another little plug saying that QVScribe is available as an add-on to Excel. So even if you’re writing all your specs in in a spreadsheet, QVScribe can help you with that as well. But back to your point, you’re quite right, these little things can actually cause a huge problem. There is a famous example of a spacecraft that was out in space. And it was a collaboration between two countries. One country used expected metrics, one used imperial units, and the spacecraft expected one set of units but the commands it received were in another set of units and it kind of veered off and went into deep space, never to be heard of again! a simple communication error there. Very preventable.

Amy: So, what’s really strikes me about what you’ve said about QVScribe’s capabilities so far…there’s a couple of things that strike me actually. One is, it seems to me that there is a very valid place for technology like this in the project management space. And it also seems to me that, put it this way, if there’s anything on the UK market at the moment, like QVScribe, I most certainly haven’t come across it. I think it is exciting to see new innovations in technology coming into this market. I don’t know if you’re aware, but the UK technology market in particular is very saturated.  So, this is, to me something very fresh, that’s cutting through all that. The second thing that comes through very strongly is the labour-saving benefit. I don’t know if you’re aware, but there is a real damning statistic out there that most project managers estimate they spend half of their time on rework.

Jordan: Yeah, that sounds about right!

Amy: Yeah, and the disadvantages of that are, I would have though, obvious. We don’t need to spell them out here. But it seems to me that there’s also a number of strategic benefits, behind capabilities of the kind that QVScribe offers, I’m thinking about things like being able to deliver a more accurate expectation to your sponsors and your executives as to what the end product or service or whatever it is, is going to look like – he benefits it’s going to bring. But I’m also thinking about areas like, for instance, let’s home in on risk management, for example. Now, this is quite an interesting one for me. Because usually, I’m a big believer that managing risks is something that is very dependent on human judgement. But it does seem to me that certainly in terms of accuracy, in terms of applying universal parameters, drawing out things early in the requirements process that may conflict with each other, that sort of thing – there’s a big role for that in the whole risk management cycle. Just talk to me a bit about how QVScribe might help to mitigate possible risks within a delivery cycle? And also, can we rely on automation to get involved in risk management? I was talking to someone about this just the other day. Is the risk of automating risk management, a bigger risk than just doing it on a human basis? If you see what I mean, if you see where I’m trying to go with the question?

Jordan: I do see what you mean, it is a very “meta” sort of a thought! But I’m afraid I’m not going to disagree with you too much. Unfortunately! I generally like to be more controversial, but in this case, I actually do agree with you. And our approach to technology is not to replace the human, but to enable the human to do their job better. And so one of the things that I’ve seen in project management and in engineering, especially, is that a lot of high value people are being frankly wasted doing low level drudgery. And so if we can elevate them, and have them think about the strategic stuff, and let the automated systems do what they’re actually good at doing, and have the humans do what they’re good at doing, that’s a great scenario. Assessing risk is a key human role. And to properly assess the risk, you have to understand what the intent is, and what the system is doing. And having accurate specs and accurate requirements is a prerequisite for accurately assessing the risk. And so, if you don’t have that, you’re not going to do a good job in assessing the risk. The role QVScribe does is to provide a very effective interface between all the automated systems that are being used in design and test and building, and the humans who need this information in order to make proper decisions. Humans are moving up and to the left earlier in the process. So they are now becoming more the facilitators and the conductors of the situation and understanding what’s actually going on. So, I do believe risk assessment is key. You know, another thing I see that is very dangerous with technology right now, especially AI is, I’m on the Canadian government panel, the Advisory Council for AI, advising the government on what sorts of policies they should have for AI, both in industry, education and in research. One of the problems AI has today is it doesn’t assess the reasons why something happens. And when you’re assessing risk, especially, the underlying causal connections are important. What data gives you – what AI gives you – is the correlations between the things. They don’t tell you, what’s the cause? What’s the effect? It doesn’t tell you why, it doesn’t tell you what to do. It just tells you what is. And so, this idea of justification is very important. So, I really see the human having a very important role to play. And QVScribe is being more of an enabler for the humans, project managers, to be able to do their jobs better. They don’t have to work at the very low level to make sure that the specs are correct, that they’re unambiguous, right? They have more important things to worry about than that.

Amy: Sure, sure. And if we’re if we’re going to be getting into the into the realms of “meta” questions, or philosophical questions, let me throw another one at you. Just looking at that whole area of AI machine learning: if machines have the capability to learn, do they have the capability to help us learn and improve as well? I’m coming at this from a continuous improvement perspective. Again, something that’s very important to project managers. And in my experience, project managers are really passionate about what they do. They know what they do is making a difference to people’s lives, and they want to do it as well as they possibly can. Is there a role for AI and machine learning in the continuous improvement cycle? Does technology have the ability to tell us how we can improve as human beings?

Jordan: In general, I take your point, and I agree with it. These AI systems of machine learning are in fact very well suited to doing something like that. You know, one thing we do know for sure, without any question whatsoever, is that autonomous systems and AI can – and do – affect human behaviour. So, as you’re learning continuous improvement, it’s really a question of not just gaining knowledge, but increasing skills and change management and behavioural change. And we do know that these systems can affect human behaviour. So things like Khan Academy is a good example in the in the teaching space. And there’s the rise of online learning where it’s tailored to the individual. So, as you’re learning something, the system is helping you to learn. It sees where you’re getting tripped up, it sees where you’re getting better, it has knowledge of what are best practices. So, it can guide you into having more intentional practice to improve the areas that you specify you want to improve upon. So, I think AI systems actually are quite well suited for that.

Amy: Yeah. And it’s interesting to me as well, just going back to this message that you’re giving about the role of technology and moving people up and left. I love that analogy. That’s very clear. I can see it in my head. And I think it’s worth tying that back to one of the biggest conversations that’s going on in the project management – and particularly the PMO – space, which is around: how can we demonstrate the value of the PMO to the executive? Because fundamentally they need to sign off our budgets. We need their permission to deliver the projects, and typically there is a disconnect between executive boards, and PMOS and what they’re delivering. And I wonder if this whole process of moving the human up and left, automating the laborious stuff, and giving them essentially a chance to shine at what they can really do well, and meaningfully, will also help PMOs to promote that conversation? Because there’ll be spending less time scrabbling around trying to get the documentation, right. And that will give them more time to engage in the strategic conversations and the strategic discussions. Is that a fair assumption for me to make?

Jordan: It is, yeah, and we’ve been surprised by some of the things that we’ve seen. You know, reporting is extremely important – much more important than I ever thought! Reporting is actually communication. It’s not just getting your documentation in order, it’s actually communicating what’s going on to other people – both laterally, and above to the executive. There’s two points I want to make here. One of them is that, oftentimes, if someone tells someone else: “Oh, these requirements are poor, here’s why right here, and here’s some examples”, they tend to not believe them, or they say: “Oh, what are you what are your motivations?” And they can rationalise it away. But somehow, if an automated system produces the report, that gives a score of three out of five, or two out of five, all of a sudden, it’s like: “Whoa! We’ve got to pay attention to this! It’s two out of five!” Somehow, there’s a semblance of objectivity in the machine. And it kind of provides cover to the PMO to say: “Look, it’s not just me saying this, here’s a system that we have, that’s telling us this.” But another one is really getting visibility, especially if you talk about requirements and specs. Typically, today, these are done with a small group, squirrelling away in the office and working away and trying to develop these, talking to people – and gathering or eliciting all these requirements. Then they have a quarterly review meeting. where everyone sees everything all at once. And the executive doesn’t have visibility for that process, right. They only know how much money they’re spending on these people and these review meetings.

But something like QVScribe could give them a dashboard and tell them: “Look you’re writing requirements at a rate of like, a few hundred a day. And this is the actual score, and this is how oftentimes we have to rewrite them to fix them. And so, you have a problem here in the early stages. This project is scheduled to last like five years, six years, seven years. And already in the first year, you’re having all these issues that are just going to snowball and compound.” And so, it’s a way to better communicate the value of these sorts of things early on, because they see actual data and actual statistics on what’s actually happening right on the ground level.

Amy: Jordan you’ve absolutely hit the nail on the head. And the whole issue of reporting in and getting that reporting to the right level is…it’s a pain point that we’re trying to crack all the time. I think it’s largely a cultural thing as well. It’s down to the project managers or the delivery teams not understanding the executive perspective, the executive not understanding their perspective. It kind of leads me into thinking about human relationships and the way that we collaborate with each other at work. And obviously, we all know that is very much changing – because of the pandemic, because of the capabilities it’s shown us we have, that we perhaps had already, but didn’t even know how to harness. So, the ability to collaborate remotely, the ability to work anytime, anyplace, anywhere. As automation develops even further, and the capabilities become more sophisticated, how do you think it’s going to influence the role of people in the workplace? Or perhaps more specifically, how do you think it’s going to influence the way in which we work together?

Jordan: Yeah, that’s a very interesting question. It’s something we’ve been thinking about a lot. And I think it’s going to emphasise certain skills, and de-emphasise other skills. And the most important skill that’s going to rise even more than it has ever before, is the role of communication. Especially communication in the written word, and capturing your thoughts and writing them down, and making sure that what you write down accurately conveys what you want to say. It’s not how eloquently you write, but how clearly you write. And that needs to be a lot more intentional. Every organisation sees this. When you’re in the office, a lot of the information gets transmitted just by osmosis. You see two people talking, you kind of overhear them, because they’re on the desks next to you, and you say, “Oh, that’s and interesting conversation, I’m going to insert myself in it”. Now, if they’re having a conversation over Zoom, you’re not even aware that exists. So, all this fortuitous knowledge that gets transmitted throughout the company now doesn’t exist anymore, because there are all these kind of closed windows. And so, you need to be a lot more intentional about what you communicate – and be a lot more thoughtful. And that also means that empathy is a lot more important now – like you mentioned different perspectives. Well, that is an empathetic viewpoint.

If you’re talking to the executive, you need to understand what their concerns are, what their anxieties are, what they value. It may be different than what you do, it’s not that it’s wrong or right, it’s that it’s different. So, you need to communicate something to be of value to them, right. And so, understanding how we communicate to each other, much more intentionally and empathetically, I think, are the two key factors, that this rise of hybrid working, and remote working, and technology in general, is going to spread throughout all the companies. Another I’m not sure if it’s a feature or a bug, but another characteristic of technology and automation and being remote, or hybrid, is that communication now happens asynchronously. So, you write something, you communicate something to me, it’s not intended that I respond directly in real time, as opposed to a face-to-face communication. I may look at it later that day or the next day. It’s, asynchronous, we have this communication.

So, that means I can’t read your body language. I don’t know when you’re saying this, if you’re angry about it, or kind of joking about it. And so, that’s kind of what I mean earlier, when I say communication in the written word is now much more important, because we don’t have all these cues. And it’s not real time. If I misinterpret it, I don’t have you telling me: “no, no, that’s not what I meant. I meant actually this other thing” I’m only looking at your text, I’m formulating a response. And I’m writing something back. And so we could be veering off in different directions right from the beginning, without even realising it, until maybe weeks pass until we realise we can sync up again and align ourselves. So, these are the things that we need to be much more cognisant of.

Amy: And I think it’s fair enough to say that the pandemic has opened up a tolerance for that kind of empathy that you’re talking about. We’ve almost got a perfect storm now, haven’t we, of the technology that facilitates this anytime, anywhere working, the empathy that understands that we need to appreciate that people are all working in different ways and at different times. And it’s made us all a bit more relaxed as well. Everyone sympathises we love it when a small toddler comes in and disrupts things or whatever. So it’s almost as if the technology is making us more human in the workplace.

Jordan: Yes, I agree with that very much. It’s one of the things that we had to evolve (just like every company, we’re pretty typical in this with the pandemic), is, how do we actually conduct ourselves now that we’re all remote? And the first thing we tried is to mimic the stuff that we do when we’re in the office in person – to mimic it in a digital way. And that didn’t really work. We’re kind of…basically, it’s like putting a surface veneer on something that is fundamentally different. And then we realised, okay, being on Zoom, having these virtual meetings, is not the same as being in the office.

It’s got advantages, and it’s got disadvantages. So, we need to focus on the advantages, and try to mitigate the disadvantages, instead of trying to mimic the previous environment, because that’s not going to work. And one of the things is, in some ways, being in Zoom is more intimate, because you have a much narrower view of someone, but you have a deeper view. So, you’re looking at them in their home environment, while all their life is going on behind them.

And you see them in a different light. You see him as a human being now. So that is advantageous. The disadvantages, if we have a meeting, maybe it doesn’t quite go the way we want it to. But then once we click, Zoom is over and then we’re gone. You’re just gone completely. We don’t have that follow up, you know? In the office, maybe we’d go for coffee, and I would say, “actually, what did you mean, what you said, I didn’t quite get it?” And you kind of continue on. You have a more gradual closing of the conversation where Zoom is very abrupt. So, there are pros and cons. You have to just deal with it.

Amy: You’ve given us loads of food for thought today. Jordan, thank you so much. It’s really fascinating to get your view on the future of work. And to find out more about what QVScribe does as well. Just let me ask you, I asked all of our experts this on this podcast, with all of those passionate, young project managers coming into the workplace forging their careers, if you had one piece of advice for young professionals entering the workplace at this time and moving into the future of work, what would it be? What’s your top tip?

Jordan: I would say the most important thing is to focus on core skills development, and less on career progression. Think about what sort of life and lifestyle you want. Don’t worry about what kind of job you want. And the development of core skills is really important. It’s more important, I think, then finding your passion – I think that’s generic advice, I actually think it’s pretty bad advice. Focus on, what sorts of lifestyle and life do you want?

And what core skills will get you that? Focus on those core skills and be so good, they can’t ignore it. They can’t ignore you! There’s a great book by Cal Newport. I would say, Read Cal Newport’s book: “So good, they can’t ignore you”. Maybe that would be the way to say it. And one extra bonus tip, I would say, For God’s sake, put down your phone and go walk in the woods, that will also do wonders.

Amy: Love that. Jordan, thank you so much. I think that’s great advice. And just moving on to  QVScribe, and QRA generally, just give us an idea of where our listeners can go to find out more. I’m sure they’re going to be fascinated to find out more. So, your website, I believe, is

Jordan: That’s correct. So go there, you can find anything you want. And if any of your listeners want to shoot me an email, I love geeking out about this stuff. You can just email me at

Amy: And is it possible to get a demo of QVScribe? Is all of that available on your website as well?

Jordan: Absolutely, you can definitely you can definitely do that.  We’re very proud of our tool and want to share it with everyone.

Amy: Brilliant. Fantastic. Well, Jordan, I think that’s probably about as much as we’ve got time for today. It has been every bit as fascinating as I was hoping this conversation was going to be. So, thank you very much for joining me. And I very much hope that you will be able to join us again, at some point.

Jordan: Sure, happy to come back on and chat. This was a very enjoyable, I had a lot of fun. Thank you, Amy.

Amy: My pleasure. My pleasure. And so just to remind our listeners once again that to find out more about QVScribe and about QRA as a company, you can go to, where you can find out everything you need to know, make contact, connect with Jordan and geek out about things as he so eloquently invites you to do. And of course, just a reminder that you can follow everything to do with PMToday at our website,

Don’t forget to connect with us on social media as well. And I always welcome new connections on LinkedIn, from people who want to geek out with me on project management issues, which I equally enjoy! So, all that remains is for me to, once again, thank Jordan for joining us. I really hope that all of our listeners have enjoyed this podcast as much as I’ve enjoyed recording it today. And we will see you again next time on the next PM today podcast. Bye bye.

Amy Hatton
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