Me, Myself, and AI Episode 902

Solving Real User Problems With Generative AI: Slack’s Jackie Rocca

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Artificial Intelligence and Business Strategy

The Artificial Intelligence and Business Strategy initiative explores the growing use of artificial intelligence in the business landscape. The exploration looks specifically at how AI is affecting the development and execution of strategy in organizations.

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Like many product leaders in the technology space, Jackie Rocca took a somewhat circuitous path to that role. After beginning her career in management consulting with Bain, she earned her MBA at Stanford and then worked at Google, where she helped launch YouTube TV. Now, she serves as vice president of product at Slack, where she focuses on the collaboration platform’s Slack AI product.

As a product leader, Jackie had continually heard from users that they were experiencing a common challenge: It was a struggle to keep up with the pace of information and prioritize where to focus their attention and energy. So she looked to AI as a potential source of solutions and is now leading a team that’s focused on launching AI-driven features to address user pain points. The Slack AI team’s work is already helping customers take advantage of the wealth of knowledge within Slack exchanges by providing features such as channel recaps, thread summaries, and the ability to ask questions to surface information that’s embedded within conversations.

On this episode of the Me, Myself, and AI podcast, Jackie describes how her team approaches new product design in the generative AI space and offers up some predictions for what lies ahead.

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Transcript

Shervin Khodabandeh: How can generative AI help us collaborate more effectively at work? Find out on today’s episode.

Jackie Rocca: I’m Jackie Rocca from Slack, and you’re listening to Me, Myself, and AI.

Sam Ransbotham: Welcome to Me, Myself, and AI, a podcast on artificial intelligence in business. Each episode, we introduce you to someone innovating with AI. I’m Sam Ransbotham, professor of analytics at Boston College. I’m also the AI and business strategy guest editor at MIT Sloan Management Review.

Shervin Khodabandeh: And I’m Shervin Khodabandeh, senior partner with BCG and one of the leaders of our AI business. Together, MIT SMR and BCG have been researching and publishing on AI since 2017, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities and really transform the way organizations operate.

Sam Ransbotham: Hi, everyone. Today, Shervin and I are talking with Jackie Rocca, vice president of product at Slack. Jackie, thanks for taking the time to talk with us. Welcome.

Jackie Rocca: I’m so excited to be here.

Sam Ransbotham: Let’s get started. I think most of our listeners, many of our listeners, likely use Slack, but let’s fill everyone in. What is Slack?

Jackie Rocca: Slack is a collaboration platform. It started out as a really effective way to message and work with your colleagues, and it’s really expanded over the years to include a lot of your work tools that are accessible from right within Slack. So it’s a great way to work with your team, with your colleagues. We actually have external functionality — you can work with customers or contractors. And we are really looking to make it the hub of all of your AI tools and bring AI into that conversational interface.

Sam Ransbotham: All right. You mentioned AI. Let’s go: What’s going to be AI-related? I heard “communication tool.” Where’s AI in that?

Jackie Rocca: I lead our Slack AI product area. We are building native functionality. We are looking at ways that AI can be used to address top user problems that we’ve been facing for years. Whether you’re overloaded with all the communication and tools that you have in Slack, or if you’re just trying to find that knowledge across your enterprise, we are using AI to solve some of those top user pain points. Because we are a collaboration platform, we’re also a place that a lot of your AI tools are being built into Slack today. We actually have more than 10,000 AI-powered tools that are available through applications or third-party developers.

Sam Ransbotham: All right — 10,000! Let’s go through ’em all, right? Start at the top.

Jackie Rocca: A lot of them as well are private to specific organizations, but some of them are public, available to download today, too.

Shervin Khodabandeh: This is quite interesting. Tell us more about generative AI. I have to imagine you are either doing a lot or thinking about doing a lot with generative AI. Can you tell us a little bit about that?

Jackie Rocca: Yeah, so we are very excited about what’s happening in generative AI. First of all, we’ve been using AI and machine learning for many, many years — if you think about our search functionality or when you’re recommended different channels to join. So that foundational element has been there for many, many years, like many products. But, as many of you know, the industry in this space with LLMs and generative AI has really taken off in the last few years. I was on parental leave when a lot of the things happened with ChatGPT, and I was so excited about how some of these capabilities could help us take new approaches to some of the user problems that we’ve been facing for such a long time.

We’re really taking the approach of starting with those user pain points and constantly trying to think through “How do new advancements in technology help us take a fresh approach to solving those issues?” rather than starting with the technology itself and saying, “Oh, generative AI is out there; let’s throw the spaghetti at the wall and see what sticks.”

The features that we have launched and available today are around channel recaps, thread summaries, and search answers. And let me talk through a few of those. So on channel recaps and thread summaries, this is really about getting up to speed quickly in channels. So often, there’s just so much conversation back and forth, and whether you’re away from Slack for an hour or a day or you’re coming back from vacation, it can be overwhelming to catch up on that conversation.

So, what we are offering, and what’s available now for customers, is the ability to summarize that content as you want it.

Shervin Khodabandeh: Mm-hmm.

Jackie Rocca: And then there’s the ability to go back to the source as well, to validate that, or even dig in deeper, if you want to get more information. So that’s really around channel summaries and thread summaries as well.

Another great use case that we’re seeing is, let’s say there is some sort of technical incident, so maybe your service is down or having issues, and every single moment really counts in those situations. Maybe that subject matter expert just got paged in the middle of the night. Rather than have that person have to spend minutes and minutes catching up on everything that’s happened so far, they can request a quick summary and jump into that conversation and contribute to the solution immediately.

And also, you can get a summary at the end and say, “This was the root cause, and this is what the resolution was.” So it’s been super valuable — so many different use cases.

And then the second category is around search answers. Especially for companies who’ve been on Slack for a long time, there’s so much conversation in there. People talk about projects, about people, about topics, about company policies. If you think about all of that knowledge that really lives in Slack, how can we make it more accessible and easier to use?

So with Slack AI search answers, you can simply ask a question. You know, “What is Project Gizmo? I haven’t heard about that.” Or maybe I don’t want to ask my colleague or disrupt their work. You can just ask Slack AI to get up to speed on different topics. You want to learn about company acronyms: So many of us have been in meetings at your company and somebody throws something out there. Or you’re a new hire, and rather than ask your manager these 32 questions that you have, just ask Slack AI; it’s sort of your trusted companion that you can rely on.

Shervin Khodabandeh: Super valuable. Yes. The ones you mentioned are user-facing, as in, “I’m a Slack user; I’ve been gone for some time,” or, “I’m on 50 different channels, and a lot’s going on.” And some [messages] are incidental to what I care about, or some are really, really critical, and then somebody says, “Hey, why aren’t you responding to my Slack?” And I’m like, “I don’t know; what channel are you?” or, “What are you talking about?” And so I get that it’s super valuable. Are there also use cases where the generative AI creates messages on my behalf?

Jackie Rocca: Yeah. We are focused first on really keeping the human in the loop and making sure that you are always in control. We know that Slack can be a really sensitive place. There are a lot of private conversations. People are in a work context, so they really want to have control, and it’s really important how they show up. Our experiences are very guided and situational, but we want to make sure that we are not creating and posting things on behalf of our users without them having really the agency to make that choice and send that message.

Shervin Khodabandeh: Yeah. I love that.

Sam Ransbotham: You have so many users of your product. How do you go about introducing some features like this?

Jackie Rocca: Yeah, and I’d love to talk through some of our core product principles because this has really guided us here. One of our product principles is “don’t make me think,” which in the generative AI space for us means Slack AI should not require our customers to be prompt engineers or experts in the field. It also means it shouldn’t require training. So we really want to have situational and guided experiences. Let’s surface some Slack AI answers for you when we think it’s appropriate. You know, this channel is really busy; let’s surface a summary that might be really valuable to you at that moment. We don’t require you to figure out how to write that prompt so that you get that great answer. We’re doing that work for you.

A second principle that we’ve also really leaned on is “prototype the path.” So, you know, there’s a lot of potential with generative AI, but we know that accuracy and relevancy and, frankly, latency as well are really important to the user experience, so we’ve been prototyping for the last several months so that we can, again, not make users think, and we can do all that work for you to make sure that we are creating a great experience. And we’re shipping those experiences that we have high confidence in, and we’re continuing to work on those things that maybe need a little bit more refinement.

Sam Ransbotham: How long does that take? Is that a short process, long process — 15 minutes? Twenty minutes?

Jackie Rocca: More than 15 or 20 minutes. One of the big challenges as a product leader working in the generative AI space is balancing time to market with getting that experience 100% right. We know that generative AI is not 100% right, so we need to kind of find that middle ground. We know that there’s a ton of customer value here, and we feel great about the experience we’re shipping, but we’re going to continue to iterate and make it better over time.

So, for me personally, that’s been a challenge because I always want to have things as close to perfect as possible, but we know it’s also important to give people that value sooner. So it’s a balance for us. We have spent many months to try to get that experience the best it can be, both from the features that we’ve already launched, as well as new things that we are already prototyping to have as fast follows down the line.

Shervin Khodabandeh: And I have to imagine the experimentation loop or the test-and-learn loop is quite fast with all the users, right? I like your principles on humans in the loop and all that.

You mentioned it’s a balancing act, and I could imagine there’s so many things you could be doing with AI and gen AI, right? And so even if those principles are always followed, you could be incredibly ambitious in so much with gen AI or mildly ambitious. And so how do you navigate that path? How far do you go? How fast do you go?

Jackie Rocca: It’s a great question. And we debate this all the time as a team when we talk about prioritization and what to build and when to launch. For us, again, we are really trying to focus on, what are those top user problems that we think generative AI can help fix? And we’re prioritizing those first. There are some table stakes features that we could absolutely — and probably will — build within Slack AI. So things around helping you compose messages. Slack primarily is a text-based tool, although we do have video capabilities through — we have a meetings product called Huddles — but it’s still primarily text-based.

The top pain points are around, how do I create focus and reduce some of the noise? How can I better find information and leverage the knowledge archive that I have in Slack? So we are really trying to put more of our energy toward solving those things that we’ve heard repeatedly from customers, and hopefully we’ll have some opportunistic things as well, especially as we go throughout the year, where we can also address things that are really cool and delightful that we certainly want to build into the experience but maybe don’t hit that very first set of features that we’ve launched.

Shervin Khodabandeh: That’s a great answer.

Sam Ransbotham: Yeah. The generative part of that, I mean, I think that everyone probably, at first blush, is going to say, “Hey, can it type some texts for me?” But I think that really isn’t where Slack is competitively different, in some ways. And what I mean by that is that you’ve talked about the history of being a communication channel, and that, when you hear “communication channel,” you think about typing messages; you know, that’s the production of messages. But there’s so much organizational communication that’s happening in these tools, and you’ve got that rich history of that. That strikes me as what’s critical and differentiating here versus — I mean, there’s nothing wrong with generating text, but the other part seems unique.

Shervin Khodabandeh: Well, there might be some things wrong with that. Well, actually …

Sam Ransbotham: OK, you can push back. I accept that. Go ahead; keep going.

Shervin Khodabandeh: Well, I mean, I think that’s the point that she’s making, right? Because there is clearly a huge pain point on what is going on in the organization and “What should I know?” and all that, which makes a ton of sense, but then there is something personal about Slack. And now, you’re going to have to wonder, “Is it Shervin that’s typing it, or is it Shervin’s agent that’s typing it?” And maybe at some point, we will be in that world where, you know, we get messages and texts and all that, and you wonder, who wrote this? Is it the person or is it the person’s agent? And I don’t know. I mean, I think that could be what’s wrong with this — is that at some point, we just sort of abdicate the whole thing and let AI just do everything, which is not, I think, where we want to go.

Sam Ransbotham: I think I was reacting to how nice the first use cases were, though, because, actually as a … I mean, I’ll tell you, it will surprise nobody here that I’m an inbox-zero kind of person. I don’t like any little red alerts up there; I clear them as soon as I can. And that’s actually … I struggle with Slack because of that, because at any point, there’s so much coming through that I can’t satisfy my inbox-zero needs there without 100% attention. And what you’re offering is a very different way of solving that pain point for me, so that seems very appealing.

Jackie Rocca: Absolutely. I mean, me personally, I can’t imagine my life without some of the Slack AI features. And I have the privilege to be able to also have the things that we’re still prototyping and haven’t launched yet, but the way that I consume information has changed. We think about this sometimes in different tiers of messages and channels. So today, everything has an equal priority. In general, a message is a message, whether you are in that channel with the CEO of your company or it’s a social channel with your colleagues. We do offer channel sections, which is a great feature, if folks haven’t checked it out. It’s a really nice way to organize your channels.

But can we do more there? Can we think through, what is sort of a Tier 1 channel or conversation? And those are things that you want to read every message for, and you probably won’t get a summary unless maybe you’re on a parental leave or gone for an extended period of time and there’s just so much there. But there’s a huge group of sort of these Tier 2 conversations that you want to know what’s happening, and you want to stay in the loop, but you might not need to read every single message, and maybe you don’t want it to show up in Slack every single moment of the day, or it’s just really fast moving: “I kind of just want the TL;DR [too long; didn’t read] of what happened when I was out.” So we’re really trying to think about things in these different buckets of use cases and prioritization and help make that easier for our users to consume.

Sam Ransbotham: Hey, Shervin, I just want you to know you’re always Tier 1 for me. I read everything you send, so I would never summarize you.

Shervin Khodabandeh: Well, I don’t have to summarize you because your messages are so terse, it feels like it’s been written by a physician, not by a college professor.

Sam Ransbotham: All right. So actually, I was thinking about, as you’re talking about this, talent mobility and changing workforce, and that seems like a big role here as well, that as we’re having people leave jobs, retire, after having people shift jobs back and forth … How did you reconcile the need to sort of capture all that information with “Does information go with a person?” How do you reflect that?

Jackie Rocca: Yeah, one of the early premises of Slack was how it’s different than email. So with email, if that person leaves the organization, that knowledge is really lost. With Slack, one of our founding perspectives was really that Slack should be this searchable log of all communication and knowledge. That’s what “Slack” stands for. So we have this persistent place that it’s really the organization’s knowledge.

You post something in a public channel, you happen to leave the company — well, the knowledge about what you had worked on and the project and the people and things like that can still persist, and you can still get value out of that. Or if you’re a new hire joining the company, you can still learn about what had happened — the learnings, the experts that you should connect with, etc.

We actually think that building AI on top of that is a really great connection point to make those experiences more valuable, because we have all that knowledge, all of that insight that we can pull on and just make it more valuable. It all already exists today, but maybe it’s not fully tapped to its potential, or it’s not easy to make sense of or get you that quick answer that you’re looking for. And we aim to make that experience better.

Sam Ransbotham: This all sounds amazing. What’s hard about it?

Jackie Rocca: There’s a lot that’s hard about it. We know that generative AI is not perfect. So we have been spending a lot of time trying to get the best prompts that we can create on behalf of our users so we get the right detail of information. We’re not sharing an answer when we’re not certain that we’re providing sources back so people can reference the source content easily. So there’s a lot of work to be done to make that experience better.

We also are really laser-focused about the user experience. So we have played around with, how does the summary display itself? What’s the correct entry point? How do we make this feature and functionality obvious so, when it’s valuable to a user, they know it’s there but not so annoying that we’re surfacing it all the time? For example, we talked about those Tier 1 channels; maybe summarization’s not appropriate there. So we’re continuing to do a lot of work to make sure that that user experience is great, both from the actual “How do you find Slack AI, and when does it surface to you?” as well as the experience when you get that response back and making sure that the information is relevant, accurate, and well sourced.

Shervin Khodabandeh: You’re leading the charge on AI and gen AI in Slack. Tell us about the team. What’s the team like?

Jackie Rocca: We actually formed this team through a group of people that was prototyping on the side, really just trying to build experiences in our spare time — a lot of nights and weekends, just really passionate about how this can help solve these core user problems using new technology. So it started there, and then as the year progressed, as we got more momentum behind these prototypes and got feedback internally and externally, we’ve actually put a more sustainable team structure in place and started hiring against the goals that we had set more officially. But, you know, everyone is very excited about the future of AI.

We’ve been able to attract and retain just an incredible team — everything from, obviously, engineering, machine learning team, design; that user experience is so important. Data science is critical to how we think about generative AI, as well as all of our go-to-market teams, just really trying to hear customer feedback; how do we bring this to market?

Shervin Khodabandeh: I don’t know if you can answer this question and, if not, that’s fine, but I’m curious what’s under the hood and whether that’s a pre-trained model that’s sort of available, or is it [that] you are training your own models, given the uniqueness of the domain.

Jackie Rocca: So, what I will say is that the space is changing very fast, and we’re open to different options and, again, super laser-focused on what’s best for our users.

Shervin Khodabandeh: That’s a good answer.

Jackie Rocca: So we are evaluating pre-trained foundational models. We have things that are machine learning models that we’ve had in Slack for a long time. We are actually part of Salesforce, for folks who might not know, and Salesforce has an amazing AI research team as well. And there’s obviously open-source models too. So we are evaluating everything, and I do expect this space to change over time. But we have taken a really deliberate approach to how we’re thinking about data and trust.

So we have built an architecture where your Slack data does not leave Slack. Everything is hosted within our virtual private cloud, our VPC, so that when you’re using AI in Slack, you can be confident that your data — your really sensitive company data — is not leaving Slack’s infrastructure and that we still abide by all of the compliance and enterprise-grade security capabilities that we have in Slack today.

Shervin Khodabandeh: Of course. Yeah.

Sam Ransbotham: What’s exciting that’s coming next? What are the AI developments that you’re pretty fired up about?

Jackie Rocca: There’s so much to be excited about, both at Slack and things that are happening outside of Slack with foundational models and other products. I can’t wait to see what the next year brings. We are continuing to iterate on different experiences in Slack.

We are constantly getting feedback from our customers, trying to stay rooted in user problems to help define our road map, but we’re also listening to what’s happening outside as well, because the space keeps changing, keeps evolving, and it’s hard to predict one year to the next what’s going to be possible. But we’re also really trying to stay grounded. I’ll be the first to admit that I don’t have that crystal ball of how things will evolve in the next three to five years, but I’m really excited to be here for the ride.

Sam Ransbotham: Tell us a little bit about your background. How did you get interested in this? How did you get to be able to do these things?

Jackie Rocca: I’ve been at Slack for about five and a half years now, and it has been an amazing and fun ride. I led our product-led growth team for a long time, so our self-service group, and just really, again, was passionate about the space. I’ve heard the same user problems or user needs be referenced time and time again and was just sort of a self-learner to say, “OK, we’ve got this new technology; can we take a fresh approach?” And I also did have the privilege of coming back from parental leave, so I could really kind of clear my head from the day to day and think a little bit bigger about the opportunity space.

Before Slack, I spent six-plus years at Google, specifically on YouTube, and was fortunate to be on the launch team for YouTube TV. So I was a product manager over on YouTube TV, which was an incredible experience. When I joined, we were, you know, on the whiteboard trying to figure out “Should this thing have three tabs or five tabs?” and “How do we think about what channels to put into the service?” So it’s really exciting being part of that journey to launch YouTube TV and see it scale for a time.

Before that, I got my MBA at Stanford, and then also I have my beginnings as a management consultant, so I started my career at Bain in Los Angeles. It’s been a ride. I think a lot of people who end up in product management have had this indirect path to get there. I don’t think I knew what product management was when I was in college or in my 20s. So I’m really grateful to have found Slack, to have found product management, and, more recently, to help lead through this sort of AI revolution for the team and for the company.

Shervin Khodabandeh: That’s an amazing background.

Sam Ransbotham: The self-learning part of that’s really interesting too because I think that’s a shift that’s happened over the course of even since we started this podcast, Shervin. At the beginning, we were talking about a machine learning model, and very few people I think woke up on Saturday morning and played around with some machine learning model. I think practically everybody has gone and screwed around with a lot of the generative tools that are out there. And that’s a fundamentally different way that people are learning about these technologies, and I think what you just said echoes a lot of that.

Jackie Rocca: And I think there’s really two sets of skill sets to think about. There certainly is the technology side — what’s possible, what’s unlocked. And it’s important to understand and, hopefully, if that’s not someone’s expertise area, they can connect with others who maybe can work between strengths and weaknesses. But I think there’s equally the opportunity to think about, how can it be applied? So, what would that user experience look like? What specifically are we trying to solve? Because I think it’s exciting to just try a bunch of things or experiment with generative AI and what’s happening with LLMs — which is great, and again, I’m not going to discourage people from playing around. But when you’re really thinking about, “OK, well, what am I going to build? What am I going to ship to customers?” the technology is part of it; that’s kind of the backbone. But I think what will really set different products and services apart is, what value do they ultimately provide?

Shervin Khodabandeh: Yeah, you need the left brain and the right brain, and a very cross-functional team. So now we have a segment called five questions. Did they tell you about that?

Jackie Rocca: No.

Shervin Khodabandeh: Wonderful. That’s how we like it. So I’m going to ask you five questions. Just give us the first thing that comes to your mind in rapid-fire style. What do you see as the biggest opportunity for AI right now?

Jackie Rocca: I think the biggest opportunity is finding true product-market fit and solving real user problems.

Shervin Khodabandeh: What is the biggest misconception about AI?

Jackie Rocca: Let’s come back to that one.

Shervin Khodabandeh: OK. What was the first career you wanted?

Jackie Rocca: I wanted to be a doctor.

Shervin Khodabandeh: Me, too. When is there too much AI?

Jackie Rocca: That’s a great question. I think we are very far from too much AI. Perhaps that’s when you can’t tell if AI or a human made something, and we’ve kind of gotten so far down the path and away from that human component that maybe that’s a little bit too much AI. But …

Shervin Khodabandeh: Aren’t we there yet, I mean, already?

Jackie Rocca: I think we are in the very, very early days.

Shervin Khodabandeh: What is the one thing you wish AI could do right now that it can’t?

Jackie Rocca: I’m excited for AI to get more into the robotics space and actually take care of physical tasks for me. I am a new-ish mom, and there’s so much stuff that burdens my life that I would prefer to have that time to either spend with my family or at work.

Shervin Khodabandeh: Yes, yes.

Jackie Rocca: So I’m excited for some of the physical, maybe robotics opportunities to come up.

Shervin Khodabandeh: I love that. A little bit less cerebral, a little bit more physical please, right? All right: What is the biggest misconception about AI?

Jackie Rocca: It’s a tough question. I think [it’s] the fact that any of us really knows what the future looks like. But I think we’re all trying to learn and be humble along the way. There’s probably a misconception that we can create that 2030 product strategy and probably know exactly what the future brings.

Shervin Khodabandeh: Mm-hmm. By the way, this is a hard question. Sam, what is your answer to this?

Sam Ransbotham: The misconception thing?

Shervin Khodabandeh: Yeah.

Sam Ransbotham: I think my answer would be more sort of people not understanding how limited these tools are. It’s sort of like AI can do all these things, and LLMs can do all these things, and it’s just a Bayesian update of the probabilistic next word. And there’s a lot less thinking behind it than there is just a pattern. I think that would be my misconception.

Jackie Rocca: I think it’s a hard question because I think there are very divergent perspectives out there today. And so there are so many opinions that it’s hard to say that there is this perspective out there, because I would argue that there isn’t a clear, consolidated, agreed-upon sense of what the future looks like.

Shervin Khodabandeh: Yeah.

Jackie Rocca: So everyone’s probably going to be a little bit wrong.

Shervin Khodabandeh: That’s an ill-posed question to begin with, right? It seems like it, right? Like, I would say that it’s going to replace humans; that for me is my biggest misconception.

Although — I know this is off-topic, but — like, what you said about the probabilistic next letter or next word — it’s not obvious that that’s not how our brain works, by the way, right? It’s a whole bunch of …

Sam Ransbotham: Yeah. We just could be doing a better job of it; we’re just better trained.

Shervin Khodabandeh: I mean, it’s not obvious that that’s just not how our brain works.

Sam Ransbotham: It could be, yeah.

Jackie Rocca: A fun conversation is, what kinds of things that humans do could AI not presumably do in the future?

Shervin Khodabandeh: Yeah.

Jackie Rocca: I think a lot of things around experiences, and, Shervin, you just mentioned that more of that human element hopefully makes us unique. And then I think it’s that those big, visionary things that happen that we maybe aren’t able to predict, like going to the moon or, you know … will AI kind of think through those experiences or just these big breakthroughs that happen that I want to say only humans can really dream up: these —

Shervin Khodabandeh: Stroke of genius, right?

Jackie Rocca: Yeah. These strokes of genius.

Shervin Khodabandeh: Yeah.

Jackie Rocca: But maybe we’ll be wrong.

Sam Ransbotham: Yeah. We’re not the probabilistic machines that Shervin thinks we are.

I’ve really enjoyed learning more about Slack today. I mean, I think increasingly people are using communication channels like Slack at work, and these tools like Slack really fit that distributed, asynchronous work world that we’re in. But calling it a communication tool is just not right and far too narrow, and I think you’ve really shown a lot of the opportunities that we have here. Thank you for taking the time to talk with us today. Thanks.

Jackie Rocca: Thank you so much. It was a pleasure.

Shervin Khodabandeh: Thank you.

Sam Ransbotham: Thanks for listening. On our next episode, Shervin and I get some AI-fueled fashion advice from Jeff Cooper, senior director of data science at Stitch Fix. Please join us.

Allison Ryder: Thanks for listening to Me, Myself, and AI. We believe, like you, that the conversation about AI implementation doesn’t start and stop with this podcast. That’s why we’ve created a group on LinkedIn specifically for listeners like you. It’s called AI for Leaders, and if you join us, you can chat with show creators and hosts, ask your own questions, share your insights, and gain access to valuable resources about AI implementation from MIT SMR and BCG. You can access it by visiting mitsmr.com/AIforLeaders. We’ll put that link in the show notes, and we hope to see you there.

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