From Academia to Action: Turning Research into Executive Impact | Stephanie Woerner | 613

How do you create cutting-edge thought leadership and research that serves the needs of your stakeholders? Today I speak with Stephanie Warner, Principal Research Scientist and Director of the MIT Center for Information Systems Research, also known as CISR. In this conversation, we're gonna talk about the tension between the academic publication cycle and the membership organization's needs. We'll talk about Scissor's annual research calendar and how GenAI nearly broke the model. We'll talk about building relationships and trust between the sponsoring organizations and the research center. And finally, we'll talk about the process of spotting the questions bubbling up in the business world, keeping a pulse on your audience, and then being able to design and deploy a solid study within a year and report it out to your members. I'm Bill Sherman, and you're listening to Leveraging Thought Leadership. Ready? Let's begin. Welcome to the show, Stephanie.

Thank you, Bill, for having me. I am delighted to be here.

So let's start with a level set question. Okay. What is MIT's CSER? What does it do? And what is your role?

So CISR stands for Center for Information Systems Research, which is why we use the acronym CISR. And we are a research center in the Sloan School of Management, and we've been around for 50 years doing research on how businesses get value for their investments in information technology and digital. And you would think that after 50 years, we would have kind of gotten, you know, answers to this, and we have, but technology is always changing. And so we're finding that companies are trying to stay on top of that technology and they're trying to figure out, are there different ways of getting value? What are the ways that customers are now interfacing with that technology? So You know, we've got 6 research scientists and we do research projects every year on these topics.

So you're inside of Sloan at MIT.

Yes, we are.

And there's academic work that goes within Sloan, right? And there's an academic sort of pace and standard of publishing and research. And then there's the pace of business, especially in technology. How do you reconcile those two? And are there tensions? And if so, can you give some examples?

So there are huge tensions. I mean, if you are doing an academic research project, you could figure that could be easily 3, 4 years by the time you collect the data, do and you've got to do a literature review and you've got to do all kinds of stuff around that. And you're also teaching and you've got committee work. So what we do and what is so different about our research center is we are research scientists, which we have the same qualifications as the faculty, but we're not on the faculty track. But that gives us a lot of freedom. To choose our projects, choose our topics that we are going to do research on. And because that's all we do, we actually can go after it really in a concentrated manner. And, you know, also we can scope things differently than you would in an academic project. And so When I look at an academic project, I am thinking about something that can be done in a 9-month to 12-month cycle because our research cycle at the Center for Information Systems Research is annual. And that also means that you learn, you might take a big project, but you're going to tackle it in smaller chunks. And so that's another big difference between an academic project. They're gonna put the whole thing out there and kind of start biting it, you know, just tackle the whole thing at one time. Whereas we're going to say, what's the first thing that we do? What's the second thing that we do? And scope the project so that we are delivering insights to our audience along the way. But it is a huge tension between the two because business is going faster. I mean, I say I'm on an annual, I gotta tell you, businesses on 6 months, 3 months. I mean, fast.

I got to solve a problem in a couple of weeks. What do we do? Right. How do we do?

And we're not that fast. But that's one of the big tensions. And I would have to say that being in the Sloan School of Management keeps us kind of attached and interacting with academia. But the way that we're doing research is very different in terms of the kinds of projects that we do and the time frame that we do them in.

And if I build on that, I think there's something implied as well on academic research where the academic has a lot of freedom in self-direction in terms of what areas of interest they have, what they're going to pursue. Et cetera, and they don't have to ask the question from a pure research perspective often, how will this be used and how does this serve my audience in the same way? They may be following an interesting question and an interesting thread.

That is absolutely true. I have members, I have 75 consortium members who are all interested in getting value And honestly, my projects have to reflect that. And so I, sure, there are questions that are interesting, but they're certainly not relevant to impact. They're not relevant to how a corporation or an organization does their business. And so I actually have a, you know, a more narrower you know, set of projects that I could do. And, and, but I think they're interesting. I think they're fascinating. So I don't find that to really, to really, you know, keep me hemmed in. I think that it's a, it's a good lane to be in because in part, a lot of those academic research projects are going to be doing something that's interesting, but is it really going to change the way that work gets done, that behavior gets done, whereas that's one of the things I'm trying to do and trying to have all of our research scientists do, which is change practice.

And I think you've touched on the heart of one of the differences in the academic-practitioner divide is that question of not only how am I creating new knowledge, But who is it for and who can use it today? Or when we, as soon as we have answers, right? Because otherwise it gets published in a journal, might be get taught in classes and that at best it's a slow cascade of knowledge in an academic research piece rather than here you've got a demand and you're saying, hey, I'm thinking of these research topics on the, on the, our research calendar for Scissor, are we aligned? And you can get immediate feedback of, hey, we love this, or explain why you want to put time there.

Absolutely. And sometimes, sometimes the results are going to be directly applicable right now. I mean, I can think of projects that we did on IoT, the Internet of Things, where you could start saying, well, yes, we're starting to use that right away. We have a research project right now that I'm working on with Peter Weil, my colleague, on platforms. And we're bringing in an idea that we thought about a couple of years ago that we think is going to be interesting. It's sort of becoming relevant now, but I think that because we've kind of been working on it sort of simmering in the background that now it's ready to, to come out. And I think that our sponsors, our members are going to be ready to hear about it because sometimes you have an idea and people aren't quite ready. You got to kind of prepare the ground and give them the early research so that they can start to think about, well, what are the technologies I need to put into place? How can I start to get value? And then you can say, and now look what you can do because you've been doing these kinds of investments.

This is why I love the work that you're doing so much, because it fits exactly with one of the working definitions that I use for the practice of thought leadership, which is peering around the corner into the future, separating the signal from the noise. And identifying who needs to hear about it today, deciding what the next steps and whether it's a call to education or call to action those people need, and then bringing that insight in an accessible way, maybe leading them step by step at first until the time where, you know, in your case, a technology or an approach is ready for prime time. But it's this ongoing service to your audience and helping them always feel prepared rather than behind the 8 ball. Now, you can't always achieve that.

No.

But that's the aspiration.

Absolutely. That's the aspiration. And it's also drives how we write about the topics that we write about. You wanna be able, I love a good literature review, but honestly, my members are not going to really— they're not going to go for it. And the need to get to the nugget of what's important. I mean, a lot of academic work really looks at interesting details, but sometimes— but a lot of our executives, they're in a very fast-paced world. And those details are not going to make as much difference as that central idea that we're trying to get out to them. And we're trying to write it in such a way that gets them excited about it. And, you know, so that they can get it and understand it right away.

Well, and what you're promising is the intellectual and academic rigor, but packaged in a way that will be accessible and actionable to them and presented in a way that they don't have to wade through specialist academic language.

Yes. I want it to be actionable. I find that thinking about, can I create a framework that— I don't wanna say it simplifies, but what it does is it pulls out the important pieces and puts it in, you know, lays it out in a way that an executive can say, oh, this is what I need to do first. This is what I need to do next. This is where I have to do two things at one time. And giving them an idea of what the impact on performance is, because that's another thing that really distinguishes the Center for Information Systems Research, which is not just talking about interesting technologies, but Also, what's the impact on performance metrics like innovation or profitability or revenue growth? And, you know, how do you get to there? And how do we think about what each of those things, you know, how do we measure them? You know, and some of them are quite easy, you know, profitability, we know how to measure. Revenue growth, we know how to measure. Innovation, we've come up with a measure that I think we've used for 15 years. And it makes a lot of sense to executives. And so we can talk about, well, here's a, a measure that you yourself, you could dashboard this for yourself if you wanted to. I use it in my research, but it's also something that's useful to an executive.

So let's go a little deeper on how you're communicating, right? And the modalities that you use to communicate findings, right? Often executives are pressed for time, you know, and they look and they go, yeah, I don't need an article length. I don't need a book chapter. How are you communicating? And are you using short talks? Are you using sort of like the executive summary one-pager? What tools do you use in the toolbox to get them to realize, hey, I need to double-click on this and listen.

So, and this is good because in our latest iteration of what we offer to our members, we've actually made some changes because of that need for speed, that, you know, the compression of time. And so we do a couple of things. One is every month we do a 3-page research briefing. And it's surprising to me, but 3 pages is often too long. And so for our members, the 3-page research briefings we make available to anyone. For our members, what we have started doing in the last year is a 1-page, very quick description of the research. We call this a talking point, but mostly what I want is 3 to 4 actionable points from the research, which is, here's a research briefing. You can go read it if you want to. Here is the 3 to 4 actionable things you can do as an executive. And we also include the graphic. So I want something that's about 200 words with a great graphic and things that someone can work with. So that's, so you have research briefings, you have talking points, and then you have to start thinking about sometimes talks work. Actually, what's surprising is a lot of those don't have to be quite as short and snappy as, say, a talking points, because people, as they're sitting in a talk listening, they have ideas and then they want to bounce them off. And, and so those talks, I can do a talk on one of my, my latest book. I've told people I could do the 10-minute interpretive dance if you would like, but I could also do a 3-hour workshop. And they're just very different. You have very different reasons for doing each of those. You know, the 10-minute is, I just need you to have a gloss so that I can get to my next point. Whereas the 3-hour workshop is, I'm not going to talk for 3 hours. We're going to have a conversation. I'm going to give you some ideas and some frameworks, and then we're going to bring in your context and we're going to talk about your context and why this matters and how can we get to creating a common language so that you can share this with your colleagues and you have an idea that everybody is on the same page. And so I think that if you look at CISR, again, research briefings, talking points, talks themselves, workshops. And then one of the last things that we've started to put into place for our audience, for our members, is something that we're calling Hot Topics, where we're going to find something that's new, something that is being researched, Maybe it's not baked yet, as I would say, but it's really fascinating. You know, right now there's a real need for understanding GenAI, and give them a 45-minute talk and then 15 minutes of Q&A. And we find that a lot of people like that, like right after work or right before work, and it gets them thinking about it. So I have to really introduce a lot of modalities. Depending on what we're trying to accomplish and what kind of time the audience has.

So one of the thoughts that comes through my mind, and I don't know if you've explored it, but I could see for your members as well short-form audio podcast summary. And here I'm thinking of some of the— The Economist does excellent podcasting, okay? And it's not only a daily episode, but then they will do themed topics as well for subscribers. And they've got tracks on technology and that. I'm not only a personal fan, but from a making ideas accessible job, they're doing some of the best work in podcasting. I could see, hey, I don't have time to read a 3-page paper, but if I could listen to a 10-minute conversation between the researchers, with actionable items while I'm on my workout or something. I could see that totally in your lane.

That would be so cool because we do actually record the research briefings. They come out at about 15 minutes or so.

Perfect. You can use the asset.

And then what you could do there is say, oh, okay, so we've read the research briefing. Now let's do a 10-minute back and forth or maybe an interview with an executive. Just 5-minute, 10-minute.

Exactly. You could put that on it. You could also use the audio as a pre-read for a workshop so that they don't have to read the 3-pager, but they can just put it in the queue. There's a lot of ways to use that asset. Anyway, you mentioned GenAI, and I want to go to GenAI, but I want to ask a couple questions that are different than how a lot of people approach GenAI. You had researching queue. That you were working on as GenAI exploded. Can you tell and share the story of how that impacted, you know, research projects for the year that were already baked, and then everybody wanted to know about GenAI?

I have to tell you, February of 2023, my phone started ringing off the hook because people had gotten past kind of the Christmas, the holiday season, and now they're hearing about large language models and OpenAI and how exciting it is. And I had members asking, well, what is this? What does it mean? How's it going to change the way that we work? And I had to, you know, say, I don't know. This was not— when we decided to do our research project, this wasn't part of the landscape. And so You know, first thing I did ask is, what's the problem that you're trying to solve? And most of them could say, not sure. It's like, okay, experimenting is good here. You can do an experiment and you're going to be fine. But at that point, it really became clear that we had to start adding questions about AI into every research project that we were doing. And unfortunately, you know, when we got to the Annual Research Forum in 2023, we still had to say, next year, we will start having results next year because, I mean, we're fast, but we're not that fast. And this is one of the reasons that we thought about those hot topics, which was, okay, I have somebody who's actually been doing something. It's not baked, but it's interesting. And so let's just let them hear what a researcher is doing. It's not going to say, how you're gonna get value and what you're going to do, but it's just gonna kind of lay out the landscape. And what we found is my colleague Barb Wixom was doing a lot of research on AI, but really more traditional AI. And so what became clear as we started doing the research up to the Annual Research Forum in 2023 was that a lot of the capabilities that you needed for traditional AI were also going to be important for GenAI. There were going to be other things that you had to think about also, but we could start at least saying, we don't exactly know, but here is what we do know. You are going to need good data, and here's what it takes to have good data. You're going to need, um, some data skills, and maybe you're not going to have directly machine learning, but you're going to need to be able to ask prompt engineering, which I think is probably right now really important. My hypothesis is probably in 4 to 5 years that actually may not be important. That may be baked into all of the offerings that are out there. And that there are issues around governance that you need to be thinking about and think about them now because everybody's experimenting now. You're not losing ground by laying some of these things out into place. This year we're gonna have a lot to talk about. I have some research on an AI maturity model. Barb Wixom is doing research on, you know, the importance of your data, and she calls it data liquidity. My colleague Nick Van Der Mullen has done a case study on using AI in terms of identifying talent. And so I think that we're starting to build out that portfolio, but Oh my gosh, we really— I mean, we kind of had to think, okay, we're going to add questions to every single research project that we're doing because this is important.

If you're enjoying this episode of Leveraging Thought Leadership, please make sure to subscribe. If you'd like to help spread the word about our podcast, please leave a 5-star review at ratethispodcast.com/LTL and share it with your friends. We're available on Apple Podcasts and on all major listening apps, as well as thoughtleadershipleverage.com/podcast. And I think what becomes important as well is CISR as an ongoing entity has built institutional relationships with your members over, you said 50 years, right?

50 years. Yes. I'm doing a gala this year.

Fantastic. Congratulations. So, but with 50 years, not only do you have a chance to build relationships between individuals, but truly embed between organizations. And so when something new comes up and, you know, everybody picks up the Magic 8 Ball, looks at the answer and says, answer uncertain, ask again later. And in your case, ask again about next year's research. You know, there's still trust of we'll give you what we can in the time that we can, right?

That is exactly right. We are very lucky One is people know that when we do an event, we're doing an event because we're creating a community. We're bringing our members together and creating a community. We have, you could either call them Chatham House rules or Vegas rules. What is said in the room stays in the room. Please do not, you know, we're relying on you and, you know, building a base of trust here. And it's that ongoing, that long, you know, set of relationships that we have. The other thing it does help us is we can go to our members and some of our executives and say, what are you doing? And they'll say, they feel comfortable saying, we don't know. And they know that we're not going to go out and say, oh, so-and-so company doesn't know what they're doing, but they can say, Here's what we're looking at doing. Here's where we're piloting this. And then we can say, oh, well, we've heard of other companies that are doing this, and they feel comfortable and trust us so that they can tell us. It's very much earlier in the sausage-making process, and they're willing to let us see into that process where I think that a lot of other companies that would want that kind of information, they don't trust that it's going to be treated well and with confidentiality.

And that skepticism, whether earned or unearned, impacts the conversations, impacts the clarity, impacts the quality of advice and insights that you can share, right?

Well, you know, it's not your average slide that you can But if you look at my slides, I have footnotes on my slides. I mean, I don't expect people to read the footnotes, but if they are interested, it's going to tell them how big's my survey, what was my p-value, what kind of analysis did I do. And people, that helps them trust what I've put in the big letters.

And, and there's, there's a tension there, right? Of people don't need to read all of the methods and the data analysis and look at the statistical analysis, but they want to know they can trust someone actually did it, who knew what they were doing. And having worked on a deck this week with a presentation that quite literally had, you know, statistical footnotes and p-values on them. You know, in small print, but they're there. You know, you want to ask further questions, go ahead. That's a statement of confidence. And also what in my mind is moving towards data-driven thought leadership. And I know you have a love for datasets. So, and making datasets come to life. So. How do you do that? How do you turn a big dataset into something that people go, oh yeah, that's, that, that's something I have to pay attention to.

So I, one is I think you get trained by talking to all of these executives by, of figuring out, well, what are the good questions? And that's gonna drive a lot of what you start with, which is What are, you know, there's the what, which is okay, GenAI, it's there, but it's like the next, the important is what's the how, how does it get done? And so I look at a dataset and I think, well, how is this going to, what are the ways that I can start to surface a story? What's the story that I think is the important story here? And it's informed by interviews that I've done. It's informed by being in workshops that I've done with senior executive teams where I listen to the kinds of problems that they're grappling with. And that gives me ideas about, oh, how can I create a construct? What are the variables that I think theoretically— now, I would never say theoretically in a talk. But honestly, I'm—

You have to avoid the T word.

Yeah, you do. You avoid that T word. But I'm trained to think about what are good theories that would inform and how would they make me think about a great construct that an executive would find really compelling and actionable. Could they actually do that? You know, we have one framework where we talk about What is digital transformation? And we say digital transformation is really doing two things at one time. You're working on operational efficiency and you're working on customer experience. And that's why digital transformation is so hard because you're being asked to do two things at one time. It's kind of like, you know, patting your stomach and rubbing your head, or is it the other way around? And, and I think that that is how You know, you say that and an executive goes, oh, that's what I was trying to do.

And well, and if you add a visual of a 2x2 matrix, you know, a researcher and a consultant's best friend, and you say you're stuck in this lower left quadrant and that's why it's driving you bonkers. You need to move on these two axes. Let's talk about what you need to do in both places. That solves the problem of trying to walk left and right at the same time.

Absolutely. And I can't tell you how many executives, business people I've said, this is what you're trying to do and this is why it's so challenging. But if you can do it, look at the financial impacts. Look at the impacts in terms of your new systems. Look at what you can do with your data that you could not do before. You are perfectly adequate, although I think, you know, in another 5 years or so, what is adequate now is not going to be good enough then. But you know, you're, you're really relying on great people. Whereas if you can get systems in place, you could then leverage those people and they can do things that you would not have even imagined. And that's part of what we're trying to do is let's reimagine. And I think that part of when I look at data is in a dataset. And again, as I've said, just a nerd here, give me a large cup of coffee and some loud music and a dataset and I very, very happy. And, but that's what I'm trying to pull out is what are those constructs? What's important? What really is going to be meaningful? And let's put the other details to the side.

Well, and you've mentioned a couple things here and that I wanna sort of pull together from an actionable perspective for our listeners, and I'll flip back to you to comment as well. One is if you have a dor— dataset. The storytelling is absolutely, absolutely essential. If you can't distill that story, the data are saying, into something that's clear and compelling and explains the why should I be paying attention. If you can't do that in 20 to 30 seconds, you've lost your audience before they'll tune in. Then on the front end, It's about defining what it is, your operational definitions. And I know we're going nerdy here, but if you don't define what you're measuring, you got problems. And then what hypotheses are you testing? In my mind, as I look at thought leadership and GenAI, GenAI is, and its successors will be able to answer many, many common questions that are already within its body of knowledge and its training model. What the human mind will be able to do is to ask and identify questions that need to be asked for community, frame hypotheses, and define how are we going to test them, and then bring those insights back to the community, which is exactly what you're doing. At CISR and have that duel.

Yes, that is exactly what we do. And, you know, we've been also, as part of what we're doing, also we're bringing not just the insights themselves, but also you kind of show the method. Here's what we thought, here's what we did. And then you start to talk about, well, how do you become more evidence-based? How do you get to data-driven decision-making? And you can say, well, this is— look at how we did a research project. That is data-driven and evidence-based decision-making. And I think that it helps to show how we've done it as well as just tell them how we've done it.

Modeling evidence-based. Is absolutely essential. So evidence-based thought leadership is also a thing, right? And so from my perspective, it becomes the question, how do we make this accessible? And you may not be able to start down the journey of it and be doing primary research day one, right? But you can certainly move to evidence-informed. And not be in conflict with what's out there and what has been researched and known, rather than, hey, I've got an opinion.

Absolutely. And trying to do that makes you realize, well, what do you have to do with your own organization so that you can start to move there? And sometimes it's one leader saying, I'm gonna make decisions based on that data. And that means people go, oh, that data's important. We better start pulling it together. And then from there, you build outwards so that all of your data, all of your systems start to come together to give you that base for using evidence as part of your day-to-day operations.

So I want to ask you a question, Stephanie, based on your work with your members. Mm-hmm. Thought leadership works well when you're tuned into your audience and answering the questions that either they're urgently asking or they need to know about but don't know to ask. What insights have you learned working with your members that others could apply? To be able to help them see around the corner for their audience and bring back the answers their audience needs.

I think one of the things that was, I think, a revelation for me was that I have skills, they have problems, and they also have experience. And so to really look around the corner, it's a kind of a melding of the two. And so it means that I have to listen as much to what their lived experience is and think about the issues that they are struggling with. And then at that time, I can bring what my my expertise is. I think that you don't get to looking around the corner if all I say is I have this expertise. It's much more of a dance. So I feel like we're good at predicting what's coming, but that's not just because of what I know myself. It's really that interplay between You're in the field, you're, you're getting stuff thrown at you every day. Now, let's— and the other good thing is, is talking to a member of CISR, like a research scientist, is they get a chance to actually reflect back on what they're doing because we're asking them a question that allows them to synthesize their experience. And so, I think this, you know, moving ahead is not just based on MIT CSER. It really is because we have this consortium of members who want to share with us and are really very generous with their time and with their knowledge. And because to really see what's happening around the corner or, you know, 3 years ahead, it's not typically based on one company's— it's really based on, I've talked to 20 and this is the problem that 20 of them are facing. Therefore, this is something we should be looking at.

That ongoing dialogue with the community and respectful dialogue. You are not coming down from the mountain with the tablets and the insights that you've received. It's really, what don't you know? Or what do you want to know? And maybe I can go out and scout ahead, right? That's an entirely different relationship. And it requires ongoing effort and communications. I, 100%. 100% agree. And I'm, I'm thrilled and delighted by the stories that you've shared, Stephanie. This has been an amazing conversation. I want to thank you. But before we wrap up, I have one last question. Okay. So I want to ask you where you learned about the practice of thought leadership. What helped you down this journey?

It's a long journey for me. When I came to Scissor, I was excited about getting a chance to work with data and do, you know, work on the guts of a project. But after a while, probably a couple of years, you start realizing The guts of the project are just the start of it. That's actually not what the end goal is. The end goal is to make a difference. The end goal is to have impact. And I think once you realize that it's, that's the goal is impact, it's then, okay, well, what do I need to do? To actually help make that happen? Who do I have to listen to? Who should I be interviewing? How should I be changing how I communicate? Things like that. And so I would not say— and, and as, say, someone who was very comfortable with the guts of the research, to stretch and to try to communicate in a different way and to ask different kinds of questions was hard. I would not say that it was an easy— that was not an easy process for me. And I would say that it would have taken a good solid 5, 6, 7 years to really start to have inklings. And, uh, so that's my And I'm only in the middle. I gotta tell you, I'm only in the middle of that journey.

So the way that I look at thought leadership is it's a practice like meditation or yoga. You come at it, you don't, you know, get the badge of accomplished thought leader and off you go. It's how do I serve my audience? How do I get better at the work that I do to bring ideas into the world and make impact. Because ideas for ideas' sake doesn't change the world, and that's what we're out to do.

That's what we are trying to do is, yes, change the world, have impact.

Stephanie, thank you for joining us today. This has been amazing.

Thank you, Bill. I have really had a great time, and much like our interviews, this has really given me a time to reflect.

That's such a gift. Thank you. Okay, you've made it to the end of the episode, and that means you're probably someone deeply interested in thought leadership. Want to learn even more? Here are 3 recommendations. First, check out the back catalog of our podcast episodes. There are a lot of great conversations with people at the top of their game in thought leadership, as well as just starting out. Second, subscribe to our newsletter that talks about the business of thought leadership And finally, feel free to reach out to me. My day job is helping people with big insights take them to scale through the practice of thought leadership. Maybe you're looking for strategy, or maybe you want to polish up your ideas, or even create new products and offerings. I'd love to chat with you. Thanks for listening.