The Support Automation Show: Episode 17

In this episode of The Support Automation Show, a podcast by Capacity, Justin Schmidt is joined by Zachary R Wahl, Chief Executive Officer at Enterprise Knowledge. They uncover the role of knowledge management and knowledge graphs for organizations, the impact of AI on knowledge management transformation, and how companies can successfully apply these tools to leverage their business.

Listen now!

Justin Schmidt: Welcome to The Support Automation Show, a podcast by Capacity. Join us for conversations with leaders and customer or employee support who are using technology to answer questions, automate processes, and build innovative solutions to any business challenge. I’m your host, Justin Schmidt. Good afternoon Zach Wahl. How are you doing?

Zach Wahl: Good, Justin. Thanks for having me on.

Justin: Where’s this podcast find you?

Zach: I am in Arlington Virginia right outside of DC here in the States.

Justin: Oh, love it. We’re here in St. Louis, Missouri, so we’re both enjoying beautiful fall colors and jacket weather. Good stuff. Zach, tell me a little bit about yourself and then Enterprise Knowledge and your journey to co-founding that business and what Enterprise Knowledge does.

Zach: Sure. I’ll start with the present, again, @ZachWahl, I’m the co-founder and CEO of Enterprise Knowledge. We are at present the world’s largest dedicated knowledge management consultancy pure services firm. How did I get here? I’ve been in the workforce for nearly a quarter-century. The hairline has cut back, the beard has become gray and I’ve gotten some cool experience along the way, but virtually, all of that journey has been heading towards where I am now, though it was never as deliberate as I think most people make it out to be.

I have degrees in Environmental Science and Political Science, so of course, I’m doing knowledge management. I started my career right at the dawn of web services. It was ’98 when I entered the workforce and I remember my senior year in college right before then, one of my professors turned to me and said, “Zach, have you browsed the world wide web yet?” I turned to him, “No, professor, what is that?” That gives you a sense of what was happening when I came in.

The tech boom, an exciting time, but a lot of companies were just trying to figure out how to manage their knowledge, how to manage their digital knowledge, and most of them didn’t even know the term knowledge management. It’s relatively pervasive at this point but back then, organizations were just trying to get their website to work. I entered at a pretty exciting time where though I didn’t know any code and I wasn’t particularly technical in any sense, I found that I had a particular skill for translating between those who did know how to code and did know the technology and those that had the business problems and the business challenges.

I created the niche for myself in KM before I even knew what KM was. Worked my way up through my organization, built a practice around knowledge management. Back in 2013, co-founded EK with my business partner Joe Hilger. We’ve now grown it to over 80 full-time experts in the field and it’s just been a blast.

Justin: That’s awesome. I’m particularly excited about this conversation, Zach, because having been at the Genesis of what the web and then eventually SAS and now AI has brought to the workplace, and specifically to knowledge management, I think you are in the top 0.1% of the world on authority to speak to this stuff. I think we’re going to have a great conversation.

Zach: Well, cool, Justin. Thanks for setting the expectation. Got it. I’m on it.

Justin: To kick things off, this is called The Support Automation Show and I typically start every interview asking the guest what support automation means to them, and right off the bat here, we’re getting into why I’m so interested to talk to you because a lot of times we discuss the handling a service desk or a help desk ticket depending on whether you’re helping an employee or a customer, but a lot of times the work that goes into managing those tickets comes from managing the knowledge within an organization. I’m curious, when you hear support automation, what comes to your mind?

Zach: I think before anything else, it’s about getting people solutions to their problems as quickly as possible. That doesn’t necessarily mean it’s as straight a line as possible, it means that it is fast and that it is complete. I think a lot of people sometimes mess that up. They say this is just about saying somebody calls, they get the answer right away, or if they self serve, we deflect that call, whatever it might be. I think it’s more than that. I think it’s about consistency and completeness of questions answered, of problems solved.

Justin: That rapid resolution, the shifting left and the self-service, and again, I’m going to use self-service as this bit of maybe an umbrella term thinking everything from HR requests to IT service desk, all the way to getting RMAs and trying to find your account balance if you’re dealing with customers. In all these instances, and this is something we find just in our business, and I’m curious to your point of view on this too, oftentimes that knowledge is scattered all over the place.

You have documents, sometimes literal paper documents, sometimes it’s a cloud drive, you have mission-critical data in an application that may or may not have an API to make that data accessible somewhere else, and then you’ve got the tacit knowledge or tribal knowledge as is sometimes called just inside people’s heads between their ears. When you’re working with a client, how do you approach rounding all that up in all those different places and turning that into something actionable for them?

Zach: Let’s first start by talking about the definition of knowledge management.

Justin: Yes. Perfect.

Zach: When we’re talking about KM, we talk in terms of people, process, content, culture, and technology. I can go through all that in detail, but to jump to the content piece, we at EK take a very broad definition of what content is, or really what knowledge is for an organization. Traditionally, a lot of organizations have defined that in terms of two things, as you said, the tacit knowledge, what’s in people’s head, what’s their expertise, what’s their experience. Then secondly, all of the organization’s unstructured knowledge. The files, the documents, the records that tend to be floating around different applications and frankly, share drives.

We go broader than that and also include all of the organization structured data, all of the stuff that’s hidden within applications, all of those rows and columns of content that are within their CRMs and their financial systems and their people systems. The reason that we do that is because you can’t really master an organization’s knowledge until you have all of those materials accessible, findable, discoverable, and connected. I think that that last piece is really what we’re talking about here. The ability to connect all of those different pieces of material as I technically call it, all of an organization’s stuff regardless of where it is.

To answer your question, how do we go about figuring out where an organization’s stuff is? Well, it’s a little bit of detective work. I think that when we first get to know an organization, they tend to underestimate how much stuff they have and how complex it is. We went into one organization recently and they said, “Yes, we’ve got these seven different repositories where all of our content is, and we really want to get down to two or three.” We nod our head and we smile and we think seven, that probably means they’ve actually got about 21.

Six months into the initiative after we had gone through the full audit, we had identified at over 100 different places at an enterprise level, at a division level, and then at a regional level where content was being housed, some structured, some unstructured some officially blessed by the organization, quite a bit ad hoc created by a smaller group. When we go into an organization, that’s an expectation. That’s why people hire us, or one of the reasons is to help identify all of those hidden places where good knowledge goes to die because the right people can’t get to it.

Justin: It’s just this piling up of opportunity cost and lost time searching for all that stuff and asking around and getting stale knowledge or even worse, completely inaccurate.

Zach: You’re spot on there. What we’ve found through our various different research is that depending on the organization, 20% to 40% of a knowledge worker’s time is wasted because it’s spent looking for information, waiting for answers, or recreating knowledge that existed within the organization but of which they were unaware. You can start doing the math on that pretty quickly and realize that an effective knowledge management transformation, digital transformation, whatever you want to call it, can yield massive ROI, can pay off within a year if done correct.

Justin: Yes. You’re talking about, depending on how you’re accounting for it, up to a third of your APEX costs just fixed which is miraculous.

Zach: Well, then you get to add in the CapEx too, right? Go back to that organization I just mentioned. If they were managing over 100 different systems, consider the administrative burden, consider the licenses that they don’t need to be paying because they had replicant systems. You can justify one of these transformations pretty easily once you start really getting to know the organization and recognizing the inefficiencies.

Now, I still don’t think I answered your original question, how do we actually do this? When we go into an organization, we like to talk about things in terms of a hybrid model, so top-down bottom-up. Top-down, we want to run interviews and workshops, and focus groups with the business, with IT, we really refer to it as a bit of a bingo card. We want to talk with people in all of the different offices in all the different regions, we want to talk with people in all the different functions, and we want to talk with people in all different levels of hierarchy, and all different levels of tenure. If we can complete all the squares in that, I guess, four-dimensional bingo card, we tend to discover a lot of the secondary and tertiary stores of content knowledge that the organization may be hadn’t indexed or wasn’t aware of.

Then from the bottom-up perspective, we approach it from the other perspective. We’re actually looking at the content itself. What tends to happen is when you find some content, it’s linking to other content or it’s referencing other content, so you find one thing and it’s talking about these three other stores of knowledge or three other pieces of information, and then we get to say, “Well, where are those?” Then the organization says, “Oh, yes, we forgot to mention”, or, “Oh, gosh, I don’t know. We should probably help you figure that out.” That’s how you start leapfrogging from one source to many.

Justin: That was excellent, by the way. Thank you for all that.

Zach: Sure.

Justin: You said something in the very beginning that you did a little bit of record skip in my head because in my conversations for this show and then just also as doing marketing for Capacity, I haven’t heard this before and I want to double-click on it. You said culture?

Zach: Yes.

Justin: Like the content pieces, that’s the first the lowest hanging fruit on the knowledge management tree, content makes a ton of sense. Culture is a very, very interesting one. How do you guys look at that? What’s the EK lens at which culture is a part of KM?

Zach: Yes, perfect. You can’t succeed in a KM transformation without understanding the culture of an organization and without having people want to share their knowledge or feel rewarded for sharing their knowledge. When we’re looking at culture, first of all, we’re assessing what the natural culture of an organization is. Is it internally competitive or is it internally collaborative? Is the leadership of the organization modeling the right knowledge-sharing behavior? Are they rewarding it? Are they incentivizing it? Or is it sink or swim, everybody, each person for themselves sort of culture that’s being modeled?

There are impediments sharing knowledge within an organization, and there tend to be three big ones, and they all have a cultural element to them. The number one reason people don’t share knowledge within the organizations is because it’s hard. If your systems aren’t designed and your processes aren’t designed to share knowledge, people generally aren’t going to do it. They’re going to take the path of least resistance, they’re going to send something as an attachment in email, or they’re going to stick something on a hard drive.

The number two reason is because knowledge is power, and as long as an employee holds on to that knowledge, they have the power. They’re essential and they won’t get fired. The number three reason is that they’re afraid of getting in trouble. Maybe they share something that their boss doesn’t like, or that is counter to how the organization wants people to be talking about how they do the thing.

You can pull that apart and you can see for all three of those reasons, there’s a cultural element to it. There’s a human element to getting people to want to share knowledge, to be unafraid to share knowledge, to feel rewarded for sharing knowledge, to feel that they can become more powerful within the organization by being seen as the person who shares, who collaborates, who innovates, through the sharing of knowledge. That’s really how we look at that lens.

Justin: You mentioned a few things when we’re scheduling this conversation.

Zach: Yes.

Justin: KM, knowledge management we just covered, the second thing you mentioned was knowledge graphs. Is that also, in your view, a multifaceted construct where you’ve got technology and connections there, the people that connections there? I would love for you to double-click on the concept of knowledge graphs, how EK views that, and where you find enterprises are deriving value from managing it properly.

Zach: Yes, this is pretty exciting, Justin. We’re having a lot of fun with this and I’m really proud that EK is at the forefront of this work. We’re really putting this into production for a lot of our clients. Knowledge graphs, graph databases, what we’re really talking about here is taking a graph database and applying an ontology against it. An ontology might be a new term for some of your listeners. It’s effectively a three-dimensional model of relationships within the company, so a really simple one is Zach works at Enterprise Knowledge, Enterprise Knowledge is expert in knowledge management, so we can infer that Zach is an expert in knowledge management.

When you take those relationships, subject, predicate, noun, you can build a web for all of the knowledge within an organization. You can break down barriers across different repositories of knowledge, different applications and systems, different types of knowledge, and you can include in that the unstructured, the structured, and the other types of knowledge that exist, including your people themselves.

What this results in is, if architected properly and instantiated in the right application, be that search or a navigation tool or a chatbot, or whatever else, an ability for the organization to do a few things. One is to apply some natural language processing against that and have the average business person or the average customer or client, ask the plain language questions and get answers that are assembled from multiple sources within the organization.

Another is taking findability to the next level. Findability being my favorite made-up word in our industry, but it really basically means that somebody knows what they’re looking for and we help them get it as quickly as possible. With a knowledge graph, you can take that a lot further. Somebody can have an inkling of the question that they’re trying to get answered or can get a sense of what they’re looking for and a graph database will help them discover new knowledge within the organization.

I go looking for a simple answer to a question that I have. I get that quickly, and then the tool also says, “Now, here’s the learning module that you can watch in order to become more proficient in this, and here are the three primary experts within the organization on this topic. Here’s some data from our CRM that backs up the answer that we just got you, and here’s a course that you can sign up to take,” so on and so forth. You can go to each of those sources and then you can discover additional knowledge branching from those different points. A knowledge graph is going to help you discover things that you didn’t know to go looking for.

Justin: That is only something you can do after that discovery of where all that knowledge is and what is it, right? The knowledge graph and the depth of those connections, recommendations, and related concepts that you just described there, none of that’s possible without the legwork upfront of mapping everything out and doing the discovery on the KM situation within an organization itself.

Zach: Totally, but there’s a big yes and there, and that yes and is it’s not just about knowing where the content is, it’s about ensuring the content is right, and so here’s the second big challenge to making knowledge graphs work for the organization. It’s not just about knowing where the stuff is, it’s about ensuring that that stuff is accurate and correct.

What we tend to find is most organizations are managing about five times more stuff than they should. Four out of five documents are old, obsolete, outdated, just plain incorrect, or duplicate, or near-duplicate. This is very much the garbage-in garbage-out sort of thing. You can build the coolest ontology knowledge graph that anybody has ever seen but if you’re just connecting your users to bad information, then it’s worthless, and that’s one of the big issues that a lot of organizations are encountering.

Justin: Yes, it’s a unique challenge I think in modern era where the creation of content is so easy, right?

Zach: Yes.

Justin: It’s ephemeral but also not, right? Slack is a good example of this, right? I’m sure anyone or Teams, but let’s pick on Stewart and Benny off here. You can have a conversation on Slack about the same thing with four different people five different times, and then you’ve basically got like 20 sort of key-value pairs there on, what is it then the knowledge you needed on that. Good luck sort of searching for that, right?

Zach: Right.

Justin: Remembering the context in which it was delivered and then you’re like, oh, okay, imagine I should probably go check X, Y, Z document or database or whatever, and then that contradicts the 20 conversations you just looked up in the history and it is an issue. This is where things get really exciting for me because the even concept of creating like the ontological map of knowledge graph in an organization isn’t possible thought of technology, managing all this becomes really fascinating when you layer artificial intelligence on top of it which was the third piece of your statement that we were going back and forth with before the show and is also most relevant to us as Capacity being an AI-powered support automation platform.

This is the spot of the conversation, I was mostly looking forward to talking to you about. Where do you see AI fit into this puzzle? How are you all approaching it and what do you think the future holds?

Zach: We’re doing AI, it’s just probably not the AI that most people pictured when they were watching Back to the Future for the first time, right?

Justin: Right.

Zach: Self-driving cars, yes, they’re on their way but that’s not what we do. What we’re really talking about here is more of the Alexa or Siri kind of AI, so there’s this concept of Black box AI where the tool just does something and gets you the answer, and you don’t know how, but what we’re talking about is explainable AI. The graph has the logic that you can very much trace and understand and see where these answers came from or why you’re getting the recommendation or how something was assembled, and so in our world of knowledge management, this is the greatest value the organization has. They don’t just want the answer, they want to know the how and why of that answer.

Explainable AI it’s if done correctly for an organization, of massive competitive advantage, massive productivity enhancer, and frankly, I don’t see it going away anytime soon. I think it’s how the business will be done for the most mature organizations.

Justin: 100%, I agree. Yes.

Zach: Now, what’s happening today, I think a lot of the work that we’re doing presently, it is exactly that. It’s just being done in a very agile fashion, so a lot of organizations have not yet achieved an Enterprise Knowledge AI or enterprise AI solution as we call it, but they are choosing data sets, they’re choosing small segments of an enterprise oncology to focus on in order to solve very pointed and specific business challenges, so over the next couple of years, what we anticipate is those six-month or year-long projects that we’ve done to help organizations prove that this works will grow to the enterprise level.

The technology’s going to get better, it’s already pretty cool, but more than that, these solutions will become more enterprise-level for organizations. They’ll truly be able to not just ask the question that the tool is trained to answer, but really ask a much broader set based on the enterprise based on the business.

Justin: Right. The thing that always comes up when people think about AI and making decisions and helping us make decisions is bias, and bias in AI and NLP models can really, and just any sort of machine learning model really, but oftentimes there’s when people say bias, I think sometimes they mean one thing but it really is another. Bias in terms of something like, I don’t know, resume screening. The fear is that the model will be biased against particular ethnicity or gender or whatever it is. There’s sort of an emotional impact of bias and there’s also just the mechanical like this is what it mean, the data was overweighted.

How can enterprises manage that? Is this the kind of thing is the answer simply like you do the fundamentals at KM, build your knowledge graph correctly, and we can minimize the impact of bias, or is this a separate sort of thing that you have to do along the way on the process?

Zach: Yes. If we’re talking about your latter definition, this is manageable by the organization with the right methodology and the right approach, and again, it’s about not just relying on your existing models and your existing content. If you are relying on all of your existing staff and your existing thinking, then you’re going reinforce the biases that already exist within the organization, and some of them are there for a reason, they might be right.

Justin: Right.

Zach: Some are obviously not and are hindering the business from performing at the level and frankly, making these tools work in a way they should, and so a big part about this is about challenging assumptions, not reinforcing the wrong or right assumptions in the past, asking the question of each one, is this the way the business should be operating? Is this the way we should be answering these questions? Not just, is this the way we used to answer the question?

I actually think that you can look at this as an opportunity. If you’re going through the process of redesigning your information, architecture, your taxonomies, your ontologies, your remapping knowledge within the organization, you get the opportunity to ask those crazy big questions that say, should we be doing it this way? Should we be thinking about it this way? Is this the way our customers want to get answers? Is this the way our employees want to learn? What a cool opportunity to get to work with an organization, to ask those questions and come to the right answers. That’s one of the reasons I love my job.

Justin: If you were to give one piece of advice to an executive or an executive team, who’s starting to look at this problem in their organization. By this problem, I mean, they have scattered knowledge, people are wasting time not talking about biasing but just knowledge management in general. If you were to talk to an executive of a leadership team who’s just getting ready to look at this, what would be your piece of advice on where to start?

Zach: Yes. Start with the problems you have today and the way you want them solved in the future. A lot of times this is hot stuff, right? That’s what I mean a lot of organizations are calling us and saying, we need a knowledge graph, or we need AI, and that’s great, that’s wonderful. We need to help those organizations also say why they need the knowledge graph, why they want AI, what problems they’re trying to solve and so it seems so basic, but a lot of organizations are just missing that why right now?

Who needs it? What do they want to do with it? What are the outcomes that you’re seeking for the organization? How are we going to measure the success of this transformation for you? Those basic questions, a lot of folks think they know the answer to, but when they compare their notes with the person sitting next to them within the organization, there tends to be a lot of, well, a lack of alignment, and so oftentimes we’ll go in an organization, the first thing we’re going to do is help with those use cases, help with the personas, help with that alignment to ensure that everybody’s swimming in the right direction.

Justin: Yes. I bet those are some electric early meetings.

Zach: They certainly challenge our ample facilitation skills.

Justin: [chuckles] Yes. There’s a few things that can get derailed like the realization that a leadership team is not aligned on something, right?

Zach: [laughs]

Justin: If you have a meeting agenda and that happens 15 minutes in, it’s an hour-long meeting, you can forget about the other 45 minutes being productive, but this is- you’re right and the staff tells something that we tell people often and that is, I have this – I pick a microcosm of something – process that needs to be automated. It’s like, okay, maybe, but evaluate what it is, you ask why, evaluate what it is you’re trying to accomplish, map out the process in the first place, and you may find that they are just simple efficient organizational management 101 or process design 101 things that you could do to make a drastic impact on what you’re doing, and then either A, you may not need the tool you were looking at. In the first place, or you’re going to set yourself up for a hell of a lot more success with the investment you’re about to make, right?

It’s really amazing to me that we have gotten so good at solving so many problems. We have developed some of the craziest technology that to even someone 50 years ago would look like absolute magic. Give an iPhone to someone in the 1940s and their brain is going to ooze out of the side of their ear.

Zach: How about the 1990s?

Justin: Exactly, but so many of the problems that this technology is solving are just rooted in fundamental human behavior and organizational skills that are just hard to maintain as a group of people working towards a common cause grows, right?

Zach: Yes.

Justin: What’s the next big thing that you feel leaders should be looking out for when it comes to knowledge management? Is it just the AI getting better, is the digital transformation wave mostly complete and it’s the second– I’m just curious, where do you think the broader future of knowledge management is going?

Zach: Sure. Again, it goes back to our definition, people, process, content, culture, and technology. It’s the effective melding of all five of those things across the entire enterprise. Even 10 years ago, the technology wasn’t there. A lot of organizations knew what they wanted to do with it, but the technology wasn’t capable of doing it, from a tagging perspective, from a management perspective, from an enterprise search perspective of bringing everything together structured and unstructured, certainly from a graph perspective of assembling content and making recommendations and from an NLP and machine learning perspective of talking with you and giving you the answers.

All those concepts have existed for a long time, but the technology is finally there. From a KM perspective, what the organization would be looking at would be are we prepared to leverage the technology that is finally ready for us? For most organizations, the answer is not yet. It goes back to the point that I made about content. Most organizations don’t have their content house in order, they need to clean up their content. Most don’t have the appropriate tags or structure on their content. Most don’t have the appropriate governance in place around their content or their systems to manage all of this effectively.

They might put a cool technology in place, without all of those foundational elements of knowledge management, they’re not going to be able to realize the ROI on it that they’re seeking. It’s not going to do the thing that they wanted to do, at least not right or well. Where most organizations should be and most leaders in those organizations should be asking what I would say are three core questions. One, do we have the right foundational elements of knowledge management in place to achieve AI, to achieve the value out of the technology?

Two, do we have the right organization or organizational elements in place to lead that and to sustain it? This isn’t anybody’s part-time job. To do this well, it needs to be focused and dedicated. Then, number three, do we actually have a clear understanding, like I said, of the problems that need to be solved so that when we are ready from both the foundational perspective and the technology perspective, we can say go. We can say let’s solve this problem first. Let’s prove that we solved that problem with the right measures so we can plant the flag of success and go solve the next three problems.

I think organizations are getting there. The maturity around KM and all of the things that we’ve been talking about here, Justin, are certainly better than they were a few years ago and I’d like to think that EK has had a healthy hand in that, but there’s some work to be done and that’s obviously where we come in.

Justin: This has been an amazing conversation, Zach.

Zach: Thank you.

Justin: I can’t thank you enough. Let’s end with our little quick fire round here. What’s the book you most often recommend to people?

Zach: From a work perspective, I’m a big fan of The Trusted Advisor, I think that it’s–

Justin: It’s a good one.

Zach: I’ve read all the management books and all the consulting books, and I believe that to be one that holds a lot of truth and is very actionable in how it gets across. That would be my recommendation from that side. I guess from a fun perspective, I’m reading The Last Days of Night by Graham Moore right now. That’s a pretty good one. I don’t think I have– I guess East of Eden would be my favorite book of all time. There you go.

Justin: Yes, that is a classic. You write about a lot of management and consulting and business books. It’s interesting that when you find one that clicks, it really clicks. Do you know what I mean? Otherwise, you get excited while you’re reading and maybe jot some notes down, but then things fade, but certain ones do stick. That’s why I love that question because you end up getting this collection of books that do stick and it makes for a good resource for people. I add every answer guests ever give me to my reading list and eventually get to it, so– [crosstalk]

Zach: Really, what I’ll say is, I can tell you, in my opinion, what the differences between the good ones and the bad ones. The bad ones tell you who to pretend to be and the good ones tell you how to be authentic and who you are.

Justin: That is very well put. What’s the best productivity tip you’ve ever received?

Zach: [chuckles] It’s really simple. I take my email and I keep things unread that need to be done. Every morning and at the end of every day, I check every unread email that I have. My type-A checking off the list is triaging those things and saying, hey, the ones that can be done in five minutes come first, the ones that can be done in an hour come next, the ones that take a day, I need to plan for, and the ones that take a week, I need to block time for, and it all starts with an unread email.

Justin: I love it. We do a webinar once a year of this giant listicle of productivity tips and stuff. With the two or three years we’ve done it, I refuse to move this out of the first, is literally the first thing I do on the webinar is find a system to manage your email and become that system. Don’t stray from– If you say I’m going to check my mail in the morning, in the afternoon, if you commit to Inbox Zero at the end of every day, whatever it is, commit to it and go all in because there’s nothing that can derail a day like a rabbit hole of email.

If there is one website, blog, LinkedIn group, Slack Community, Facebook group, or before 2020 when we used to get together in person a lot real-life community that you could recommend for someone who’s interested in solving these problems, what would it be?

Zach: Here in DC, there’s a KMI, Knowledge Management Institute’s community practice and group. We used to host the meetings in the before times, I hope we will again in the future. For the KMers out there, for the practitioners, that’s absolutely a great one. It’s on LinkedIn as well. You can join it virtually in advance of ideally rejoining in person.

Justin: Love it. Finally, if you could take anybody out for a coffee or a cocktail depending on the time of day to pick their brain to be a better leader, who would it be?

Zach: I think I would need to go with Jeff Bezos, not necessarily because I agree with everything he has done, far from it, but he’s built something that is incredibly powerful and–

Justin: Can’t argue with the results.

Zach: There were a lot of years of failure there. He stuck with it. I think that that’s pretty incredible. I would love to hear that story firsthand.

Justin: 100% agree. That would be an hour of time very well spent. Well, Zach, thank you so much for coming on The Support Automation Show. Where can people find you, Enterprise Knowledge, and learn more about you or your company?

Zach: Yes, www.enterprise-knowledge.com. We have a free and open knowledge base of thought leadership, literally hundreds of blogs and white papers and videos. You can also find me on the Knowledge Cast podcast either via our site or online.

Justin: Love it. Thank you so much for coming on The Support Automation Show, and you have a wonderful afternoon.

Zach: Thanks, Justin. Thanks for having me.Justin: Cheers. The Support Automation Show is brought to you by Capacity. Visit capacity.com to find everything you need for automating support and business processes in one powerful platform. You can find the show by searching for Support Automation in your favorite podcast app. Please subscribe so you don’t miss any future episodes. On behalf of the team here at Capacity, thanks for listening.

Related Content