Slowing Down to Speed Up

Rob: In our industry, like many others, the pressure to move faster has never been greater. New technologies like artificial intelligence promise greater efficiency, but they also raise new questions about trust, collaboration, and how we make good decisions. As organizations race to keep pace with change, perhaps the real challenge isn't simply moving faster, but knowing when it's worth slowing down.

This is In Plain Cite, a podcast exploring the biggest questions and trends facing medical publication and communication professionals. I'm your host, Rob Matheis, President and CEO of ISMPP.

Today we're joined by leadership coach, Michael Siller. Together, we'll explore why slowing down can actually help organizations move faster and how leaders can navigate complexity without sacrificing rigor or trust.

Let's get into it.

[End of Intro]

Rob: We were really pleased to have you join us for our keynote presentation at our European meeting of ISMPP in 2026. And I just wanted to, um, take some time and chat with you today about some of the topics that you had covered during the meeting.

Michael: Oh, wonderful.

Thank you so much, Rob, for having me here on this podcast. And obviously, I'm very happy to dive back into the whole topic, which was very exciting.

Rob: Yeah, we were pleased to have you. Your discussion was perfectly timed, I have to say. And it's perfectly timed because when I look at our industry, we're really in a state where we're being asked to do things faster and faster and faster, more efficiently, with, um, you know, less economics, and so on and so forth.

And at the same time, we're not quite sure how to get that done. You know, we have this pressure happening. So I felt we could just start by talking about that paradox and how that actually fits together. How do we speed up and slow down at the same time?

Michael: I mean, it's, on the one side, like the call for more efficiency, for more productivity, and on the other side, also about academic rigor.

It's half a paradox. And so if we try to find a solution to solve this, to find new ways of dealing with that, then the way to go is, at first, we need to slow down. We need to slow down to be able to reflect, figure out, okay, what is actually the problem that we're trying to solve here?

For example, AI is revolutionizing the whole way we're working. We're trying to keep working the same way, with the same assumptions about working, without ever questioning ourselves how we should be doing that. In history, we had to slow down before in order to be able to speed up. For example, when the railroad came into place in the UK, all of a sudden we had to synchronize our watches because every single town had its own time zone, basically based on its own sundial.

So you had differences between London and Plymouth of about 10 to 15 minutes. And if you have one single railroad track, then obviously this heads toward a catastrophic collision if you don't synchronize the time. That's why it's about slowing down in order to speed up, and that's somehow the same way we need to deal with this in organizations.

Rob: So it sounds as though we represent publication and medical communication professionals, largely, who are really just trying to get medical science into the hands of healthcare decision-makers, and they're being asked to speed up. They're being asked to be more efficient, to make sure that medical evidence is in the hands of stakeholders as quickly as possible.

But what I hear you saying is that if we're not careful about doing that, then there could be some misses. For example, not synchronizing watches could create a disaster down the road. Perhaps not thinking about other stakeholders, trust, as you'd mentioned, responsibility, I think, could have almost disastrous results.

Michael: Yes. I mean, if you just put more pressure on people in solving this issue, applying AI, then sometimes we totally miss the point. Because sometimes we feel like, "Oh, people are not applying this. They're not trying hard enough. We need to get different people in." But probably they are already trying as hard as possible.

I think the underlying human dimensions we need to take into consideration. But basically, what we're asking people to do in most places is reduce the workforce or reduce work for people like them. So the work that they love doing and what they are experts in, what they've been trained to do, they need to start reducing.

And that kind of fear obviously comes up. It's always holding them back because they know if they learn how to apply this very fast, they are actually cutting their own jobs. And if we don't address this kind of issue and learn how to upskill people and help them redefine their own roles in the organization, which are changing as well, and which also has to do with their own identity, then we will not get this kind of commitment to apply this in their organization and in their own roles.

And so there's a big job for HR, but also for leaders, to foster this kind of transformation, which has not just to do with bringing in new software and saying, "Apply this and do this faster," and so on. No, it's a whole new way of thinking about the roles they were doing, to always have the human in the loop with the AI, but which obviously also changes the whole role in which they are then working.

Rob: Yeah. Very good points. I speak to a lot of our publication and medical communication professionals, and what I hear from them as a general use case is, "You know, Rob, I've been told just to make sure I put AI into my goals and objectives. Rob, I've been told this year I have to somehow use AI."

Without a lot of direction, without a lot of strategic thought, without a lot of training, upskilling, as you'd mentioned before, it sounds like that could lead us down a dangerous path if we're just telling people to march in a certain direction without giving them the scaffolding to help them get there.

Do you see that across the industry, and do you think that's a potential problem within our publication space?

Michael: Yeah, I totally agree with you on that point. It's so new, and so a lot of people just don't have a clue how to implement it, how to bring it into the organization. And I guess, like in all of the industries out there, there's still a big uncertainty about how to make this really profitable.

And you're totally right. They're just throwing it in there. Obviously, not all of them, but a lot of organizations just say, "Yeah, use AI." And every different department uses it differently. They don't have any kind of standards that are synchronized yet or any broad alignment on this.

And so you create different departments, each in their own bubble, where they're using it in a certain way and another department is using it in a different way. So not much learning happens between them. And they basically also develop their own systems for how to use it, but that can lead to more friction than it actually helps develop the organization.

Rob: Yeah, and talk about repeating the past, right? I mean, we see this happen in our industry over and over again, where you have different parts of organizations trying to adopt new technologies, and they're doing it in silos.

So we have a common effect where we'll have some of our biopharmaceutical companies implementing different aspects of technology in different ways across two different departments that sit two rows apart within an office building.

So with that type of thing happening, it's very possible that we have inefficiencies despite the fact that what we're going for is more efficiency to begin with.

Michael: And therefore, my call again for slowing down to speed up is because, in the first moment, you need to slow down. You need to bring people together to agree on certain aspects of how to move forward, how to make sure that you keep learning and keep developing this.

And if you don't slow down in these moments, then everyone's just getting busier and busier, but not getting more effective. If you slow down in these moments, then you can have a strategic advantage.

Rob: Yeah. Yeah. It makes good sense, but I'm gonna ask you the hard question.

Is there a way to do both? Is there a way to slow down and speed up? Because our folks are being asked to at least appear as though they're speeding up. And even if they're not necessarily speeding up while they're slowing down, is there a middle point that you think they can achieve?

Michael: I think that aspect about slowing down is already speeding up.

Rob: Mm.

Michael: And that's somehow the confusion in our thinking sometimes, because very often we think more is more, and more is more, and more is more. Like if we keep working harder, more hours, we feel like that is more—that we're becoming more productive. Um, but we know that if we push performance to a certain point where we go into more stress and more burnout, then people all of a sudden are out for months, and they won't be able to do any kind of work.

And so, by taking a step back to look more at your own resilience and building that up, you already help yourself solve more complex problems. I really strongly encourage organizations to slow down and create these kinds of safety zones where people know that they can take a step back because they're aware that it leads to better problem-solving.

Then that's the slowdown that actually speeds you up already.

Rob: So you know what's interesting about that, Michael, is that it leads us into a conversation on metrics, right? Because at the end of the day, our folks are being measured on productivity. So how many abstracts did you produce? How many manuscripts did you produce?

I think what I can gather from our discussion is that it may not just be a quantification at the end of the day, or, "Did I use AI in the generation of my manuscript?" but, "Did I find a more responsible way to do it? Did I find a way that, down the road, isn't gonna create a compliance concern?" And perhaps there are ways that we could build those measurement tools into our objectives and make those acceptable to our leadership.

Is that kind of the direction you're going with this?

Michael: I definitely think so. I totally agree with you because if, down the road, you produced more papers than were requested, but actually they don't apply to the academic rigor that's required, then what are they good for?

I mean, trust is the key point there, and therefore it's important to really look at that aspect—that even while applying AI and being as fast as possible, you still need to adhere to your ethical standards and the academic standards that are necessary.

Rob: It's funny to hear you say that because I think most people, when they think about artificial intelligence and other digital advancements, are thinking, "Gosh, this multiplier is gonna help me do so much more, so much faster, so much more efficiently."

But if we get it wrong and we have an error in the sprocket somewhere, we're also gonna multiply that error or that inaccuracy a lot more quickly than we could have in the past.

Michael: Exactly, yes.

Rob: We certainly work in a profession where these are dynamics that we face every day. On a regular basis, we're being asked to consider new products, new technologies that come our way as publication and medical communication professionals.

We work in an industry where we have, obviously, our industry partners, and we have our agency partners. We work with publishers and academia and so on, and everybody seems to have a different way that they want to go about getting medical science into the community.

And I think sometimes our professionals are really challenged to make hard decisions to determine—we have shiny new toys that come along almost every day. Do you have any advice when our folks are contemplating new tools as to how they might evaluate them for utilization and ultimate impact?

Michael: Yes. I mean, if there's so much noise there, it draws away from the focus, and focus is one of our key elements in driving productivity.

If we can work in a focused way on one aspect for an extraordinary amount of time—like the idea of deep work, working for 90 minutes, for example, on one thing—it gets us way more productive and also more fulfilled.

It's not just that we're more productive, but we also feel more fulfilled if we can work in a focused way on something. There was research out last year, I think in Germany or across Europe, showing that we are interrupted in the workspace every four minutes.

We get an interruption—some kind of ping, some kind of email, some kind of call that comes in—and we know that we need seven to ten minutes to dive deep enough to become productive at what we're doing.

Now, if we're distracted every four minutes, how can we get to that point where we are productive? So adding more shiny toys distracts us even more from becoming focused and adds more stress and more noise to what we're doing.

Therefore, slowing down to reduce this kind of stress helps us reduce this kind of noise, helps us focus, be more productive, and also implement AI where it's necessary and where it's really useful.

Rob: That's a great point. The only thing is, I would disagree with some of that research. In the course of this 15 minutes we've been recording, I probably got pinged about 20 times, I think.

So it's certainly a busy time for us. Yeah... reports.

So I'm gonna completely switch gears on you, Michael, and talk about Tetralemma.

You introduced us to Tetralemma in Europe, and for those who weren't there with us, can you tell us what this is, what it means, and then we can try to apply it to what our folks do for a living here?

Michael: Of course. Very often in organizations, when it's about making a decision—where it's about speed versus academic rigor—we find ourselves within the polarities of a paradox, where we think we need to decide one over the other, like either speed or academic rigor.

Also, when we're under stress, we're very often put on blinders. Our stress mode narrows our view, and we think in black and white, all or nothing.

Very often with AI, that's also the case. We trust it or we don't trust it.

Now, the Tetralemma is a tool that comes from systemic consulting and was developed by Matthias Varga von Kibéd, which forces you to look beyond this dilemma. It opens up two more fields.

One field is the both/and. So you have this, which could be speed versus academic rigor, and it immediately asks you to think about the both/and. What could the both/and be?

It's basically what we blind ourselves to, what we don't look at during a discussion. In an argument, I might say, "No, it's all about speed," and you might say, "No, it's all about academic rigor."

And we get into this fight, which becomes more of a heated conflict between the two of us, while we blind ourselves to the aspect that might actually be both/and.

Then there's even a fourth field, called the neither/nor. We need to think about this neither/nor field as a solution that can come to us when we ask, "In which context does this problem show up?"

Every problem only shows up in certain contexts. In other contexts, it doesn't.

For example, if you think about speed and academic rigor, on the one hand we need to get more publications out, and on the other hand they need to meet the necessary academic rigor.

Sometimes we forget that maybe we need to bring in someone else from the organization in order to solve this. Let's bring in finance people, or let's bring in more investors. Maybe then we can get more people so that we can have both/and.

This helps you think outside your box, beyond the dilemma of this or that, toward both/and and also neither/nor.

Rob: I think you're really speaking to the heart of our listeners who are sitting there thinking, "Well, how does this apply?"

But every day we're trying to make decisions that seem as though they're either/or, A or B. And what I hear you saying is, well, maybe there are some other options. On a day-to-day basis, our folks are being asked to decide, should we rush that out to a medical congress, or should we slow down a bit and hold it off and make sure we get into a top-tier, peer-reviewed publication down the road?

Now, not in every scenario is it possible for us to be able to do one or the other, or both, or neither, but it certainly does open us up to think about, well, are there other stakeholders that maybe we should be asking about this? Are there other folks that we should be thinking about? And are we thinking way outside the box beyond our own organizations and the outside community?

And I hear the tetralemma mindset telling us to really just try to open our field of consideration.

Michael: Yes. I mean, we're always talking here about a complex topic. We're in a complex field. And so sometimes it helps us to get better solutions if we draw the circle smaller about stakeholders or about timelines, or sometimes we draw it bigger.

You know, very often we just talk about facts and figures and bring all the content in there and have dialogues about that, but we very often forget the social aspects, right? So we're sitting in a room together, and there's the big CEO who's saying, "This is gonna be the solution. We're gonna be doing this," and everyone else shuts up. No one says what they're actually thinking about.

So there's just this, but the other perspective is not even there because no one is okay saying that. No one feels the psychological safety in the room to be able to voice that. And so, therefore, you need to develop and figure out different strategies for how you can also bring this opposing idea into play to be able to bring a new solution to the table.

Because if no one opposes it, it also means that everyone nods in the room, but at the end of the day, there's no commitment to follow through.

Rob: Yeah. They're all very fair points.

So, as we're chatting here, I'm thinking to myself, a lot of people are listening to this and thinking, "Well, this is good, Rob. This is all well and good. We enjoyed the conversation. But you gotta ask him about AI. You gotta have him deep dive into AI."

I don't know that we're gonna get into that necessarily fully today, but my question for you is—and I think, if I remember correctly, we had talked a little bit about Theory U at our London meeting—and I just wanted to see if you wanted to help us understand that theory and how it may apply to AI for our publication professionals.

Michael: Mm. Okay. For sure.

Theory U was developed by Otto Scharmer, senior scientist at MIT. Basically, years ago, with the beginning of digitalization, he realized that we are constantly, collectively producing results that actually nobody wants.

You know, looking at the world right now, you see polarization increasing over time. We have more wars now than we had before, and still somehow it looks like we're not getting smarter. We're not learning from the past. Learning from the past is not good enough anymore.

So, basically, he developed a process for how to start looking differently at things. He said there's this mode of seeing and paying attention to the world, which he calls downloading. That means looking at the world through our old patterns, using strategies from yesterday to solve the problems of today or tomorrow.

And that's something we need to let go of first. Let go of that and be able to see things with fresh eyes. Then, probably, redirect our view to what we have not paid attention to. That could also be the both/and solution or the neither/nor, right?

Then he calls something presencing. In his point of view, we've lost a lot of contact with ourselves, with our physical being, and we just try to solve everything with our brains. You and I are constantly working with lots of smart people, and so the brain is obviously very important.

But there's more to that. Having this connection to our physical selves and also to Mother Earth helps us develop new ideas and new solutions that he calls crystallizing—you let them emerge, and they become new prototypes.

So, therefore, what it would mean for publication professionals is letting go of the way things have always been done. Not thinking, "Okay, we've always done it this way, and now we just put an AI patch on top and it's gonna be better," right?

You need to develop a new way of working together, or even a new way of working altogether. The human plus AI becomes better AI, or AI plus human becomes a better or more efficient human. But what does this exactly mean? How do we need to relate as human beings to that so something greater can come out?

Again, it's the slowing-down process in order to figure out the meaning and the way to relate to it—what we can trust, what we need to pay close attention to in order to cooperate well together.

This is a slow process, but it's definitely very fruitful and helps us move forward to generate, collectively, more solutions that help us create more of what we want and less of what we don't want.

Rob: Yeah. It's such an interesting way for us to bring this around full circle, Michael.

Because we started this conversation around slowing down, and now here we are talking about putting a Band-Aid or a patch of AI on things, being asked to do this at a very rapid rate by our leadership.

And at the same time saying, "Well, nobody's really taking a step back and asking, do we have trust? Have we looked at the ecosystem? Have we changed the way in which we do things to accommodate AI?" Instead, we're doing Band-Aids and patches and just trying to move forward.

If I can take away a central message—which I think I hear you saying—it's that speeding up really does mean slowing down a bit, getting the ecosystem straightened out, understanding what your objectives are, and then purposefully moving forward once you understand the environment you're working in and what you're trying to achieve.

Does that sound accurate to you?

Michael: And we should never forget this is an iterative process. It's not something where we put a five-year plan down and say, "Okay, now it's all done."

It's something that we need to develop and co-create together. Therefore, it's very important to always have a participatory design in any kind of change process that you're trying to implement, where you bring all the different stakeholders to the table and develop this together.

So it's a big challenge for leadership because, all of a sudden, you really need to let go of certain levels of control and practice this shift that I talked about in my keynote as well: moving from the expert to the explorer.

We were talking about experts. They're hired as experts. They're paid as experts. They were trained as experts. But being an expert alone is not good enough anymore. You also need to become the explorer, where you let go of your own truth from the past and explore the unknown.

Rob: That's all for today. Thank you all for listening. Please take a minute to subscribe to In Plain Cite on your favorite podcast app. Share with your colleagues and rate our show highly if you liked what you heard today.

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In Plain Cite is a production of ISMPP, the International Society for Medical Publication Professionals. 

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