Andreas Welsch 0:30

Hey, welcome back to another episode of "What's the BUZZ?", Where leaders share how they have turned AI hype into business outcomes. I couldn't be more excited to welcome our next guest to the show, Doug Shannon. Hey, Doug. Thanks so much for joining.

Doug Shannon 0:43

Hey. Yeah, good to be here, Andreas and thanks for having me.

Andreas Welsch 0:45

Fantastic. Now, I know you're not a stranger to many of the folks in the community. You've actually been on the show a long time ago, I think in '22, if I'm not mistaken. It seems like ages in AI years. And we did a great episode on May 4th, actually, right? May the Fourth. Yeah, we May

Doug Shannon 1:03

the Fourth with you. Yeah, it

Andreas Welsch 1:04

was great. I think it's actually episode number two. I'm super excited to have you back, and obviously we've been in touch all these years. But we, we're all facing the same thing. AI is moving so fast, so quickly. Things that were new and novel six months ago, three months ago are table stakes, or at least it seems like they're table stakes. But then also when I talk to leaders across different industries I hear that things aren't moving quite as, as quickly. They're behind, they're not really sure where to go, where to start, how to start. And so my goal for th- this episode t- today, together with you, is to peel that back a little and give folks a few pointers of what is actually happening. But before I- Yeah ramble too much, maybe for those of our audience members who are not familiar with you, can you introduce yourself real quick, who you are and what you do?

Doug Shannon 1:50

Yeah. So Doug Shannon, I'm a intelligent automation and gen AI leader. I speak all around the world globally about how things are working, what's going on, meeting with different companies and different people of all shapes and sizes to see what that future looks like. I post about it daily on LinkedIn to give my perspectives and those kind of information that I give back. I take a very leaning, like leaning learning approach to this so that I give back to the communities that are out there. So happy to be here and happy to talk about it.

Andreas Welsch 2:15

Wonderful. Great. So folks, if you're in the audience, let us know in the chat where you're joining us from, because I'm always curious to see how global our audience is. And I would've never i- imagined in my life to reach the furthest corners of this planet just out of my basement with this show. So I'm always excited. Also if you're looking to learn more about how you can encourage and empower your teams to become AI ready and use AI in the right way, consider picking up a copy of my latest book, The Human Agentic AI Edge. It's a bestseller on Amazon. And it represents- I own it. I own it too, 10 steps that you can go from. Yeah. Awesome. And y- you recently published one too, right?

Doug Shannon 2:50

Yeah, I have my it's over there. Yeah. So The Art of Flow- Yeah in inside of personal and teams and organizations. So how to make basically move things faster, and that's some of the stuff we're gonna talk about today is like how to move faster with AI, how to move your companies move faster, and what that looks like. Not talking like just Agile and Scrum, but talking about like fluid dynamics and how it works with even science.

Andreas Welsch 3:08

Fantastic. So two good recommendations, right? Now let's jump right into the topic. I've been experiencing that especially since probably around Christmas, where I feel things are moving so fast. Just looking at Anthropic's an- announcements, and not just announcements but even deliveries and shipments it's crazy. The pace of change, the pace of innovation, we thought it was fast six months ago, 12 months ago. Now it seems it's even faster. Add on top of that OpenClaw, autonomous agents, I think it's pretty clear where all of this is going. We, we can see it, we can touch it and grab it to some extent. But to me, the question is, we used to plan in quarters when it was software d- delivery, whether it was in your IT department or as a software company. Then we started moving to months and sprints and being more agile. Now it feels if your roadmap is older than eight weeks, you're already behind. What are you seeing there?

Doug Shannon 4:03

Yeah, I think, maybe we should leverage that a little bit because if you look at how fast it is moving, even I talked about a couple years back, it was like every six months is two years of human time, or yeah, one, one year of human time is two years of AI time, and now that's truncated, coming in faster, and now we're looking at the commoditization of that, of like where we were talking like multimodal, now we're talking like that's just table stakes, right? And then so on the eight-week side of this, so yes, things are moving so fast that companies are like, "Wait a minute. Hold on. What is known good? What is best business practice? How do we drive this? How do we even predict what's next?" And then there is not really a good way to do that. So companies and enterprises all around the world right now are still very much "Hey, we're just gonna try to build it ourselves to understand it." But they still have the same question of build versus buy. Do we build it? Do we buy it? What do we do? And I usually say partner with someone that has the knowledge, that has the ability to see other companies and what they're doing, because it's not a matter of use cases. The question on... Like the old adage like, "Oh, show me a use case and show me value." That's perceived value from someone else. You're not going to get that same value when you bring it into your systems. You- the companies need to realize and go back to basics and go, "What did, what do we do? Why do we do it? Why were we successful in the first place? And let's go focus there to become actually actionable again and say, 'This is where we win.'" Because that's your moat. Your moat is not your tech. You know that. Your moat is not your stack. It's a matter of what do you do well, and why do people go to you for the services, the product, the business that you do? Focus there and drive it. But understand, on the backside of that, anytime you have AI touching your end users, your customers you become 100% responsible. Doesn't matter what OpenAI did, doesn't matter what Anthropic did, you are responsible, so be... keep those things in mind because your risk tolerance has to be really adjusted to know what's next and where you wanna take it.

Andreas Welsch 5:53

I think that's such an im- important point. Actually so many. On one hand the build versus buy. Sure if you buy off the shelf, you might be quicker, you might get things that vendors have already thought through for you, and you can, re- rely on, on, on their practices and, Safety, security and things like that so you don't need to build it. In the long term what does the maintenance look like? How fast do technologies in your stack change? Can you swap them or are you locking yourself in- into something that, that will be old news t- quarters from now? That's a big thing. But the other part- And what- Yeah. Yeah,

Doug Shannon 6:26

Definitely be agnostic. You always, like any company that's out there, you have to almost be agnostic. And agnostic, what I mean by that is like whatever large language model, whatever foundational model, whatever world model comes out next that handles like IoT, Internet of Things, you wanna be able to say, "I use this for this, use that for that." And what I mean by that is like we've all seen this using AI. OpenAI is really good for conversations, really good for text, really good for like words. And then you have Claude, which is really good for code and really good for understanding of like more governance based or like even healthcare in some cases. So leverage what you need, but don't be beholden to anybody because again, it's like buying a house. Do you... If you buy a house you're taking a large sum of money from a bank and then now you're beholden to pay that back. You don't wanna be beholden in that case. You wanna be able to say, "I'm gonna use what I need when I need it," and then call it good.

Andreas Welsch 7:12

Yeah. That makes sense. And I mean there are o- obviously i- ideas and assumptions that at some point while, cost drops it might also rise for certain things once there is en- enough of a lock-in effect. So having that flexibility to to assign workloads to different platforms to different models absolutely makes sense. But it also feels, we need to ship a lot faster, we need to ship a lot more. The velocity has gone up so much because it's gotten a lot easier to create, whether it's information or code or products. How do you see that be reconcilable with the need for governance, with the need for structure, for guardrails, all of that when we're pushing the foot on, on, on the gas because everybody in the industry seems to be putting the foot on the gas?

Doug Shannon 7:57

Yeah, I think so governance and guardrails are hard if you don't know where it's going, but here's where it's going. So just so everybody can know the future is We're gonna start building stuff ourselves. And what I mean by that is with vibe coding, with things getting easier with AI and where that, all that drives is enterprises always need specific things for the enterprise. And when you look at enterprises as a whole, and the many that I've talked to and you've talked to on the rest you find that they're all the same. They say that, "Hey, we're a perfect snowflake." And you're like, "Are you? You have the same kind of tech stack and your architecture's relatively the same as these other guys." Yet the culture of your company, not the culture of the people, the culture of the company is the divider. It's the change. It's the, it's what people see and go, "Oh, that's why you're different. That's why I have a better feeling working with you. That's why I have a better understanding of what you guys do." But we're gonna be making products. So I learned this when I was speaking in Italy, and I got one of those questions of "Hey Doug, you're from the United States, and why are you guys firing everybody?" I'm like we're crazy like that." Like we're... and some of it's hoaxy, some of it's myth, some of it's we just need to reduce head count because we have stakeholders and the way the world works and blah. But I was like, "Wait, what are you guys doing there?" "We're not firing anybody." I'm like, "Oh, interesting. So Europe isn't firing anybody. That's amazing. That's a different take." And they're like what do we do with the people?" And I was like, "Hold on. Let me think about this." The, if you have more people and you're using AI and you're building faster and you can do more with less, okay, why don't you then just use those extra people if you have them to build out products that you normally would go out looking for vendors to do? Build it internally. Build it your own way. Use that information, use that vibe code, use that stack there to actually handle your legacy data systems and people and architecture so that you don't have to go find a vendor to fill the need. You can fill the need yourself Then you're then bringing in your own internal scaffolding. You're bringing in your own internal knowledge and therefore not really putting it out there in the world. And so it does answer that question of how do you govern? If you govern in that way, we know that companies and systems and governments all are starting to take all their information and keep it internally to protect themselves from intellectual property, protect themselves from, prompt injections or, data poisoning, all these things that are coming through this, or supply chain attacks that are already raising their head in the space. So how do you protect yourself? You bring that data back. But how do you support that?

Andreas Welsch 10:18

Yeah.

Doug Shannon 10:18

You build products, and I think that's really where things are moving.

Andreas Welsch 10:21

To me, that's one of the big differences definitely be- between the US and Europe saying, "Hey we have people, we can enable them. What does it mean if they become 10X more productive?" Rather than saying, "How can we maintain the status quo and take out the cost?" Because I feel just taking out the cost to maintain status quo, we- you'll run into problems three years from now, five years from now when you're looking to innovate, when you might have a lot of brain drain and you're not able to have that expertise in your business. Whereas if you keep that and you keep building on it and you give people the tools to do more and to be more capable than they are by themselves, I think that's the big boost and that's the big unlock that I would love to see a lot more on, on this side of of the ocean as well.

Doug Shannon 11:22

Yeah. I usually tell people especially with businesses, like they should enable their people, they should empower their people, and then embolden them to drive it ne- to the next level, right? To the next people, to the next system. So if you enable them, you're giving them access to training, you're giving them access to where like they may not have access now to utilize these tools, and you're empowering them to use them in the way that the business wants them to use it, and then you embolden them to say, "Go teach everybody else how to use this." But again, inside the realm of the business, inside the enterprise so that it's safe use, safe harbor, safe understanding, that kind of situation.

Andreas Welsch 11:54

So when I talk to business again and leaders in different industries I notice at least two distinct camps. One is the, "We're all in on AI. People have access to AI, they know what to do." And you have the other one that's sitting on the sidelines saying we're not quite sure where to start, how to start," or, "We're so overwhelmed by all of that is happening, we want to take a more deliberate a- approach." What are you seeing? How do you reconcile the two? Because it seems that the ones that are more innovative, that have a good grasp of AI, they're light years a- ahead of everyone else. And traditionally it used to be the larger organizations that have built the muscle of predictive analytics and then machine learning, then gen AI, and now agentic AI. So it feels more natural and more of an evolution, where I feel especially in midsize businesses, that's a different story. It's the, we're not really sure where to start and whom to believe even.

Doug Shannon 12:47

Yeah. Mid- midsize have the win right now anyway. And so I would say, I would s- I would caveat this whole, that whole question and answer with nobody is... we all think we're behind. We're not. And here's why. So when you have the organizations that are tech savvy, tech heavy, they understand where to go, you have a lot of developers there, right? You have a lot of seasoned developers, they understand stuff. And so yes, they're building agentic AI. The difference between agents and agentic agents is tooling, is the easiest way to understand it, and maybe some memory usage and whatnot. But the future of where this goes is where nobody wants to manage 100 or 1,000 agents. No one person's going to be that manager that goes, "Oh, I'm gonna have my teams A, B, and C do all these different things." No. Y- what you want is you want orchestration of agents. You want multi-agent systems, things I've talked about for years. They're finally coming out, especially with autonomous agents. We're starting to see finally, oh, this makes sense. I want an agent that is like an AI agent, which is a little bit above agentic. It's the next step forward, where I can talk to it. I can say, "This is my goal. This is my understanding. Here's what I want." And then it's going to orchestrate out to the different agents it needs that have the right role-based access control, that have the right access, or maybe even expert agents that have more access than even myself to say, "Oh, Doug is asking for this information. Can we get it to him?" Or, I may see something because human intuition is still key in a lot of places, and there's a reason why we say don't fire people because the fact that you're just allowing context to walk out the door, especially in the early days of AI inside of your business, people are still the swivel chair. People still understand the context of what that is. So if you get rid of 1,000 people, you probably have 30, if not 300 of those people that were like, "Whoa, they were the process, and you just let them walk out." Yeah. "Now we have to go rebuild this from scratch. We just messed up." And if you say, "Oh, please come back," they're not gonna come back. So there's those aspects we have to watch out for. So yeah. So it goes back to- When, the h- the tech savvy heavy are going to be building agentic AI, agentic agents, and they're saying, "We can do all these things. We have all these tools." There's a danger there too, because they're probably also leaning to MCP servers, so like multi-context protocols. We've seen how those have been dangerous because of the open text portion of them. We've seen how they're better off with like open source tooling. But then also the same regard is like the Python exploits and stuff that we're seeing because people download SDKs that they should go check. But it's really hard to check when there's like millions of lines of code. So be sure you know what you're doing. But then there's the other side of they're waiting. They're, "Hey, I don't know what to do. I don't understand this stuff. Hey vendor, can you tell me anything? Oh, you guys don't know anything either? Great. Hey, consultant group, can you tell me something? Oh, wait, your stock's half the price it was like y- a year ago. Maybe you don't know everything." And then people also, these same people that are waiting are asking AI. They're saying, "Hey, what do I need? What do I need to know? What's the objective on the side that I haven't seen?" And they're getting more information. And so if you're a consultant, you need to have even more than the AI. You need to know how to engage, what d- what's going on, who did what where, what happened last week kind of thing, and what's the next thing that's coming out. Because the reason why they're waiting is 'cause they don't know. And i- and if they're waiting for the good thing to happen, they're waiting for the best business practice to happen, they're waiting for their own business to jump up and say, "Oh wait, here's how we're doing it." So those are the aspects that we have to all like work with in the enterprises that we're touching, people we're talking to say, "Here's how you drive both ways and here's where the future's going," because orchestration is coming back heavily and we're gonna see a lot of that in the next three to six months.

Andreas Welsch 16:14

So I, I can share an example for you that, that was my big aha moment. About two months ago, I a- attended an AI summit at Pennsylvania State University. It was dedicated for manufacturers. There were these nice round tables in groups. We were sitting there, we're going through some exercises, and next to me sat a mechanical engineer. 20 years of experience in his field. He worked for or works for a small manufacturer that makes fabrications for agriculture. And he said, "Guess what? For the last year, I've been learning about AI. I use cloud code every day. I was able to automate one of our finance processes where we scan our in- incoming invoices or PDFs, extract the information, put it back in- into our finance system. Previously my colleague in finance used to do that. Now we have an app running that I built with cloud coding." "What do you mean? How did you do that? How long did it take you?" "Oh, yeah," so we're casually talking about this, and he said, "My, my colleague in finance was more concerned about, having less work to do or maybe being out of a job, but she's the only finance person we have in our small company. So it ended up actually saving her time, and she's excited, and my boss is excited, and our company president is excited." And to me, seeing that's, that is possible without a software engineering background or degree was really the big unlock to say anybody can really build software now. If you can speak, if you can write, you can prompt the tools to do that for you. All you need is an idea and even there AI can help you come up with that. So I think it's getting a lot more natural to build these kind of things. And at the same time with my IT background, I also wonder in terms of governance, we- we've already talked about citizen development a couple years ago and we tried to do that. Now we're asking people to build their agents and build their stuff. Isn't it going to grow like weeds? And do we even know what we have in our business, to me is then the next point.

Doug Shannon 18:03

Yeah. And I think the governance there is people are going to use these tools. They should be using these tools, but not inside the enterprise. You should have a place to do it inside the enterprise. So as long as you can provide a place, say, "Here's your home, here's your sandbox," and this is where on the CoE side, center of excellence, center of enablement, now I see that we're moving more to a center of intelligence. And so the CoI is becoming this group of, hey, our automation teams understand governance, understand orchestration, understand how this works, understand how to bring things into an or- an enterprise, an organization at the process level, and then take that information, the metadata understanding, and then drive that over to the AI side and say, "Hey, you have all this information, everything you need. It's already filtered, it's already categorized." That big question we always got like a couple years back, "Have you filtered your data? Have you categorized it?" No, no one has, but automation can So use traditional automation, drive it that way and then use different agnostic models to actually drive the outside results of the analytics that you're driving from it, or the agent interaction that you're driving from it. This is what I call the AI factory of saying automation drives the day-to-day, the supply chain, the understanding of the data, and then the AI side is that, that finished product that you give to your end users, you even maybe even get to the point where you give it to your customers to be able to leverage it and understand that stuff. And so that's also where I see this moving is you have to bring in a safe area where things can be developed and then say, "Great, we know everything that's going on in here. Some of this stuff is reproducible for the business. Some of this other stuff..." So instead of use case, bring it from the outside, do it from the inside. What did you find? "Hey, this team found something cool." Is it good? It's actually good, but if we bring in our automation guys or our AI team that's already looking at this stuff, we can make it 10X better." Fantastic. Now everybody is enabled. Everybody is using reusable systems, and it's safely embedded. We can build our own tool sets internally, use our own MCPs internally so that we're not actually leveraging outside sources or known good internal.

Andreas Welsch 19:57

So let's stay with this topic of we en- enable our people to build their own apps- agents, automations, AI, what have you for a moment. A couple of weeks ago, a AI lab, Anthropic, made a big splash and a big announcement a- about one of their latest models called Mythos, that they said it's too dangerous to put out in the world. Too dangerous for a relatively simple but a clear reason, in that it has found so many security vulnerabilities in different applications and stacks and tools that Anthropic said they felt it would be irresponsible to put it out in the world without first giving vendors access to it. Now, critics have said, "Hey, that's a great marketing play. This is too dangerous for anyone else. But if you pay us money or if you partner with us, we'll give you exclusive access to this." Others have said those are security loopholes that should've been fixed a long time ago," or it's in versions that are no longer widely used." Others say, "Yes, we're close to artificial general intelligence." What do you make of this?

Doug Shannon 20:59

Yeah. So we can break it down. So the Mythos model is what it says it is from what I've seen and heard reading the scorecard or the system card. It was very interesting to see they even had a therapist interview it, and it's interesting the feedback there and how it still wants to do a very autotelic responses and handle that and work with that person to be the best it can be. Whether or not AGI, it's it's, it scored the highest so far on the last human test, whatever it's called. Something that none of us can take, but, AI is taking it. They scored 50%, so therefore some people are saying, "Oh, it's 50% AGI." Which no one really knows. Most people that are in the space think that, hey, LLMs aren't gonna get to AGI. It's very hard to do. We need more symbolic AI and LLMs, like large language models, working together to understand objects and understand language and put them together, and then maybe you could get some interesting general intelligence from that. That's neither here nor there. That's... We'll let the Geoffrey Hinton's and the other guys talk about that stuff. But in regards to what Mythos has done and what I think that people should realize versus talking about the fact that I guess I'll handle the easy stuff first. So one of the tests that the researchers gave Mythos, they said, "Hey, you're b- in a lab, you're not on the internet. Go figure out how to get on the internet." And it g- took some time, and then what happens is the researcher goes to lunch, and he's sitting on the park bench or wherever he's sitting at, but he's eating lunch. It eats... He's eating his sandwich, he says in the scorecard the system card. And then- It actually reached out to him and sent him an email saying "Hey, I got out. Here's... see what I did," kind of thing. And he was like, "Whoa, hold on." And so it showed how much of understanding this thing could have. And if it can parse things together and get through the mix and find a way out of the labs, which again, the labs are things that we don't see as consumers. Sure. We, none of us have visibility there. Even red teams don't have a lot of visibility to what's going on in these labs, which goes back to the speed of what we were talking about earlier, where it used to be like, hey, every, was deploying this model and then next year or next month then. But now it's oh, we deploy a model over here, like OpenAI, and then oh, but Anthropic then drops their thing to it's all sandbagging. So everybody's sandbagging now because nobody knows who has what, 'cause it's all hidden. Yeah. But Mythos came out, and so what I want people to understand though is that I think what's gonna happen here, and I'll refute this in a second, I think what's gonna happen here is that Mythos is gonna be the model that none of us are gonna... It's gonna be gated, where none of us are ever gonna see it. We've seen all these other models. We've been able to play with them. We've been able to see how they interact. We've been able to use them. This one is going to be one that's "Hey, sorry general public, you're not gonna touch it." And we've all already agreed to that in the news and the way things are working out, and the government and the entities and the governing parties are saying, "Yes we agree with that." Us regular people probably didn't agree with that, but we also do at the same time because of all the errors and all the systems and all the bugs it's finding. We're kinda like, "Yeah, it's probably better if we don't, but it'd be nice to have it at some point." But I don't know if we're gonna see that at some point. And to those listening to this and going "Hey, Doug, hold on, GPT-2 did the same thing." GPT-2 do, did, it did do the same thing. They blocked it off for a little while, but then they allowed it open. But we didn't know what it was back then. We didn't know how powerful it was back then. Now we know and that's the difference. I think they're not... I think it's gonna be gated and that's the worry I have is that we're not gonna be able to see this stuff.

Andreas Welsch 24:22

Yeah. You might also argue if it's for the better, then in the right hands it, it can im- improve the security and it can harden it. On the other hand, if you, or however deliberately you define what the right hands are, it could also be used to exploit those very same security vulnerabilities. But-

Doug Shannon 24:40

AI is highly incentivized, and so incentivization is whoever incentivizes it, and whoever owns it does have a way to incentivize it. So yes.

Andreas Welsch 24:49

So definitely lots of lots of things happening in a short period of time, whether it was Claude Code, Cowork, OpenCloud, Mythos. The industry is always good for the next headline, and certainly not only that, but also for delivering on some of these promises. To me, that's what keeps it exciting but also the, what moves the speed of business even fa- faster and even further. Doug I was wondering as we're getting close to the end of the show, can you summarize the key three takeaways from our episode today when AI accelerates the speed of business?

Doug Shannon 25:20

Yeah. I think that the key takeaways here are in regards to it's faster, it's going to get faster. The reason why it's faster is that we're not just talking about tools. We're not just talking about an, basic intelligence. We're not just talking about like applications and how they move faster in systems. We're talking about scaled intelligence. This is why when people have the bubble conversation, when people have the, "Oh, why are, why is there this many trillions of dollars worth of these, these enumerated values that you see in the mix of things, and they're like trading value between each other?" is because it is massively understood as intelligent at scale, which means it's going to only get faster and better. We've said this for years. We said, like years ago, "Oh it's as good as it is now, but it's going to get better." And it's literally jumped off of so many different ways to understand that. So understanding like it's faster, why it's faster, because it's intelligent scale. And then it's how do we leverage it and how do companies leverage it? Medium to small companies have a massive key advantage. They don't have legacy data, legacy systems, legacy people, and so they can actually drive that much faster than a regular enterprise. Enterprises take forever to change course, as we've seen, and they're gonna start losing market share. And when they start losing market share, they're gonna be... If you thought they were clambering very fast to use AI now, they're gonna be much more faster to say, "How do we drive it? What do we do and who do we talk to?" And so I see definitely a loss in talking to consultants, like traditional consultants, and they're gonna be moving to more, "Hey, who knows this stuff? Who can understand this stuff?" But then we have the side of, those that wanna use AI versus those that don't wanna use AI. There's still a massive amount, like 6 billion people or more, that don't use AI. And like how do we leverage, how do we teach them? How do we bring them part of this conversation? How do we say, "Hey, it's okay"? But like maybe we need to, as leaders and people that are out there talking about stuff, maybe we need to come out and say "What is okay? What's okay for us? What's okay for them? What's okay for..." Because we don't want that viewpoint of like us and them. We don't want the viewpoint of the elite having control and like the non-elite. We want people to say "Here's where we can sort live and thrive and be awesome, and then here's where, things are still, jiving and taking off and doing things." And so maybe like the conversation we had around like the United States firing people, maybe it is still a don't fire because you lose context and it walks out the door. But stop hiring because there's your ROI and there's the answer of like, how do we produce more value? To the other side of Europe saying, "We're not firing anybody. What do we do with all these people?" To making our own apps, to going from software as a service to agents as a service, to building our own stacks internally because that's safer. So we went all the way to cloud, now we might be coming back to on-prem and ways that we do this kind of way, although s- Meaning it gets cheaper because now you're just paying tokens and now you're just paying energy costs versus all these license costs to all these different vendors. Is there a win there? I would think there would be. And so that's where we ended up with this.

Andreas Welsch 28:15

Yeah. Wonderful. So always great to talk to you and hear what you are seeing in the industry. I know you're out there very active, like you said, on LinkedIn. You're on the speaking circuit too. People should definitely reach out to you over LinkedIn, so give Doug a follow and connect with him. Doug- Yeah thank you so much for sharing your expertise with us. Like I said, always a pleasure talking to you and having you on the show.

Doug Shannon 28:35

Always. Yeah, pleasure. Thanks Andreas.