On this episode of the AI For All Podcast, Andrew Louder, Founder and CEO of Louder Co, joins Ryan Chacon and Neil Sahota to discuss how businesses and startups can leverage AI. They talk about buying an AI solution versus building your own, getting started with AI, managing expectations, dealing with reluctance internally in your organization, new revenue models, and the future of AI in the workplace.

Andrew Louder is the Founder and CEO of Louder Co. He started Louder Co to make an impact on his clients and their communities. His company has helped generate over $400 million in profit gains for over 35 organizations across industries from construction to healthcare, all by leveraging AI to optimize their operations and improve productivity and efficiency.

Louder Co is a boutique consultancy started in 2016 to help companies increase profits through artificial intelligence and better operations.

(00:42) Andrew Louder and Louder Co
(00:55) Buying AI vs building your own AI
(03:42) Getting started with AI
(06:12) Managing expectations for AI
(08:08) Dealing with reluctance about AI
(11:31) Industries benefitting from AI
(14:28) New revenue models enabled by AI
(17:14) How startups can use AI
(21:12) Effort needed to integrate AI
(24:33) Future of AI in the workplace
(26:45) Learn more about Louder Co

- [Ryan] Welcome everybody to another episode of the AI For All Podcast. I'm Ryan Chacon and with me as always, or most of the time, is our co-host, Neil Sahota, AI Advisor to the UN and Founder of AI for Good. Neil, how's it going?

- [Neil] I'm doing all right. As long as I can avoid thunderstorms.

- [Ryan] We also have our producer Nikolai here who will jump in with some questions throughout, I'm sure.

- [Nikolai] Hello everyone.

- [Ryan] Before I introduce our guest today, our conversation is going to be focused on how companies can determine if they should buy or build AI solutions and tools. What advantages AI is going to be bringing to organizations to give them a competitive advantage, as well as how AI can actually be like a secret weapon for startups that are looking to scale.

To discuss this, we have Andrew Louder, the CEO and founder of Louder Co. They are a company that helps companies increase profits through artificial intelligence and better operations. Andrew, thanks for being on the show.

- [Andrew] Thank you all for having me.

- [Ryan] So to kick this off, Andrew, I wanted to ask you when it comes to adopting any type of tools, new solutions, especially on the technology side, there always is this debate between whether to build it yourself or to buy something and deploy it internally for your organization.

How is that thought about in the AI world? How do you approach that challenge when asked or proposed by companies that you all work with. Just take us through that thought process. What should our audience be thinking about at a high level?

- [Andrew] It really starts by addressing what problems are in the business at that time, and really what the solution is entailing.

Essentially if you're looking to build out something that doesn't exist already, maybe you're wanting to create a competitive differentiator in your marketplace. If you're, you have a unique problem set, or you operate within a niche industry, and you're wanting to seek opportunities for AI to solve that problem, I think it really begins with some level of research to say, hey, what might be out there currently?

And if you do some research on what might be solving that problem out there and realize, hey, it's, maybe it doesn't exist already. Maybe it would require some kind of Frankenstein solution of different tools and API integrations, whatever. You might come to the determination of, okay, it doesn't exist in the marketplace, at least not in something that's quick and easy to attain.

Then it might be worth the while, maybe the juice is worth the squeeze to proceed forward and building something that hasn't been done before. And, of course, many of you know this already, just building something out is going to take time, money, energy, blood, sweat, tears, hiring some resources or finding a development shop to bring this thing to life.

Whereas on the buy side, especially when I talk about it, I'm looking at it from those easy to attain AI solutions that can be grabbed off the shelf. They're relatively inexpensive, relatively quick to implement, and more than likely are targeting some kind of operational table stakes meaning things like marketing, finance, accounting, legal, things that virtually every business needs to operate.

And so it's not, because everybody has the access to those off the shelf tools, it doesn't necessarily create a unique advantage besides the fact that it's a, it becomes an arms race in a way with your competition. And whomever can leverage that AI best, whomever can leverage it first, whomever can leverage it in their day to day lives to drive ROI, those are the companies that are going to be winning.

- [Neil] It makes a lot of sense. I hear from a lot of companies though, that they just not even clear on how to get started or didn't even know what questions to ask. Given your experience, how does the process tend to start?

- [Andrew] Here's what we guide our clients through. And it really starts with two key questions, Neil.

Number one is what are your biggest pain points or challenges in the business? And number two, what are those tedious tasks or manual processes that are driving inefficiencies? And to me, that's where you begin. And it turns into a pretty good brainstorming session. You start gathering these problem sets that perhaps as business leaders, we didn't even know really were in existence in our business at that time. And it's a simple really in what, how we guide our clients is we just start with a long laundry list of what those pain points are. We start looking at them, we start just doing this keep, kill, combine analysis on that list.

And then we say, okay, let's prioritize these based off impact. And impact can be relative. It could be a cost savings, could be productivity gains, could be a revenue gain, depends on just what that problem is. And once we've identified that, we've got we've listed out by impact, we can then start mapping it to solutions.

Sometimes the solutions could be old school fixes, could be process improvements, could be a job task analysis, whatever that might be. And then a lot of times, though, it could be an AI solution. A lot of the clients we're helping are saying, hey, we need an RFP tool that can help us. We need a contract management tool that can help us. We need a bills forecasting tool. And so we start identifying what those AI solutions could be to those problems. And that kicks off a deeper analysis on which one of these will drive the most bang for the buck, what are the most quick wins, what's going to drive that those high impact, low effort initiatives, that's your low hanging fruit, that's what you would start with.

You start building out your roadmap based on that grading scale. And then going off and doing, we have a well, a pretty methodical four phase approach to selecting an AI system. Starting with the requirements gathering, doing some research and the actual selection of the tool, doing the implementation, which is the configuring, the training of the model, and then going through change management, which is often overlooked, yet incredibly important to ensure your people are aware of the changes, they're trained up on them, and they're utilizing the tools at its fullest capability as soon as possible.

- [Nikolai] Is there any managing of expectations?

- [Andrew] I'd say high expectations and even unrealistic expectations. Meaning some, I've been in so many conversations where they envision a world where email comes in, and it triggers this step, which then creates this report that then feeds over to this organization that then runs the analysis.

And so it's this thing, it's eye in the sky vision of AI. Basically, you push a button, and it runs the business end to end. And so there's that level of expectation setting of look, we're talking narrow AI, right? These are tools that are geared towards solving very specific problems.

So there's that. And then there's also a timing expectation on how quick it is to implement these tools and how quick it might take to train them. We need to level set them on. And then on top of that too, I think there's a level setting on the effectiveness of the tool. I think when I go and talk to various different leadership groups on the topic of AI, the moment they see AI isn't 100 percent correct, 100 percent of the time, oh, okay, it's wrong. I'm moving on from this. This lost all my trust. I'm moving on. And so there's also this level of expectation setting that we do that says, look, if AI can get you 60, 70, 80 percent of the way there, and it's cutting off 10 to 15 hours a week per person on your team, wouldn't you want to take that? Right? Now that comes with the human validation and verification and all that that comes with AI that I think often also gets overlooked, and we like to highlight that. And so if AI is only 80 percent level of confidence or accuracy, then it's up to the humans, and it sounds weird to say, but it's up to the humans to come in, validate, and verify, and run with it from there, right?

- [Ryan] Yeah, it sounds like there's a trade off for sure. And I'm curious in that same kind of line of thinking, when you, let's say, work with an organization who wants to adopt tools and solutions on the AI side of things, but maybe the end user is going to be interacting with it the most, or it's going to be affecting their job the most, is more resistant potentially to it, how do you approach, or how do you advise companies to approach that discussion to talk about the competitive advantage that AI can really give to that individual organization? Now, I imagine it's, that competitive advantages varies depending on the business but just generally speaking, I'm sure there's people listening who have said, look, I'm ready to adopt, but I have some people internally who are still resistant to it. How can they approach that conversation?

- [Andrew] There's a few different ways, but I think it starts with doing some kind of inventory of your people. Identifying who on your team might be a little more reluctant than others, those that you identify as being reluctant or either having a negative attitude or mindset toward the change.

That's one thing versus maybe somebody who it takes them just some time to evolve and adapt the technology. Those are two different approaches, I think. But if you're having, usually what I propose is this, like I do the inventory, identify those people that might have some challenges, it might require one on one dialogues, one on one training, some extra handholding to at least present them with the opportunity to come along for the journey.

Now, if they're unwilling to either buy in or unwilling to learn it, just being an unwilling participant in this then that's another difficult conversation that likely needs to be had.

- [Neil] There's a lot of organizational change management involved. Forget even AI, but a new process or a new tool.

That's a pretty big change, right?

- [Andrew] Yeah, and one of the most effective things we've done, it seems so simple and obvious, but it often gets overlooked, is doing a pilot. You pick out your tool, you can, you get it configured, you prepare it, you run your scenario based testing, but then you set up a pilot, you get a sampling of the stakeholder group that's going to be utilizing it, you train them up on it, you give them access to it, and you say, hey, use this for the next couple of weeks. As you're using it, take notes, contact us, feed us information that can help with configuration or workflows or whatever it might take, so that then we can build this thing out the right way. And ultimately, after that pilot, what we find is that those folks that were participating in that pilot become very positive influencers for the change in the company. So the naysayers may be like, hey, I heard you were using such and such, right?

How's that been going for you? Oh man, it's been awesome. It's saved me so much time. I've been able to close more deals, whatever, right? So they start getting the conversation, the positive flow going, and they become the positive influencers to that change. And also as people start adopting it and they come across questions, they become almost like your center of excellence in a way for any questions, concerns, comments they may have for the tool, leading some training efforts as well. And so it gives you, it provides a lot of benefits from and basically double checking the tool, making sure it's working as you have it set up to them also be positive change agents for the company.

- [Ryan] Are there any industries that you feel are more primed to see a bigger advantage from adopting AI?

And then in the same vein of that but a little slightly different from a job perspective, like characteristics of a job, are there any type of elements there that you feel like will be the ones to see the biggest benefit of adopting AI into, from an industry perspective and or a job perspective?

- [Andrew] From an industry perspective, it's those that are usually stuck in the past.

And if you've been in our business and touching so many different industries as we do, that oftentimes promotes itself to be the law firms. They're very old school in how they approach things. Also more like the blue collar industries like construction, also very stuck in kind of the old ways of doing it.

And so those companies in those industries that are able to utilize AI to create advantages will just widen that gap between themselves and their competition. That gap becomes so great. And both of those industries oftentimes have hard pressing margins. And so being able to gain an advantage that can increase their margins, give them more padding, be more competitive in the marketplace would likely yield more wins.

Like for example, there's a law firm client we helped that, they're a criminal defense law firm. They go through an e discovery phase where they have to comb through two to three terabytes of electronic data to find evidence for their clients. We put in an AI tool that can help them do that.

So they went from about thousand work hours, people hours, doing it the old fashioned way of combing through it all manually to the AI now can comb through everything within eight hours. The team then validates, verifies, reruns the model. End to end, we're seeing results of about 40 hours per case.

You're going from about a thousand hours per case now to 40 hours per case. What does that mean for that law firm? Now they've removed a huge bottleneck to their growth. They're able to go take on more clients. Oftentimes the joke I hear about this story is like don't they lose the billable hours?

They weren't billing those hours. A lot of times it's baked in, but now they're looking at different ways to flat fee, do some flat fee billing as it relates to that backend stuff. And so now they have additional revenue streams, profit streams, and now they're able to grow without needing to add expensive administrative people.

And so top line growth without impacting costs, that's, those are major profit gains.

- [Neil] It's interesting you bring up the legal services like law firms because five years ago that's what they were saying, right? They weren't interested in some of the stuff, and they're worried about the billable hours, and the clients put a lot of pressure on them into fixed fee billing and that changed the nature, right?

A lot of the big firms especially hired like MBAs to basically run it like a business, become more cost efficient. Are you seeing any other industries that are maybe going through a revenue model transformation like this, where suddenly being more operationally efficient and tapping into some of these AI tools is going to be a boon?

- [Andrew] ChatGPT takes dead aim at a lot of professional services firms. However, those that are able to leverage it to potentially expand their reach, take on more clients, be more aggressive out there, they're the ones that are going to win out. Those that embrace it, learn how to gain, build revenue off of it.

And look, we're doing it ourselves as Louder Co. We're utilizing AI in a number of different capacities. We're adding more and more clients and not necessarily needing to add a whole slew of new consultants to take on that added capacity. And so absolutely for that professional services aspect, if you're not one of those firms, it's going to roll over and die as a result of this, then you've got to grab it by the horns and figure out new revenue streams, new ways to attack it. Because it's not going away. And look, another thing to take note of for your listeners that have, are not potentially aware of this, Microsoft is on the verge of releasing their Microsoft Office Copilot system where they're basically taking generative AI, ChatGPT like technology and attaching it to the productivity suites and leveraging the power of your company's data and files to generate tons and tons of new productive outputs.

If you haven't seen the videos on YouTube, check them out. They're pretty game changing, showing things like Excel tables of quarterly financials, and you can ask it things like what were the top three trends from last quarter, what were ways to increase net income, and Microsoft Teams is utilizing, is going to have it as well.

So, you can join a meeting late and say, what have I missed? It'll run you down. And so virtually everyone that's on the Microsoft Office platform that's willing to pay the extra 30 per month per user as was stated just last week on the new pricing model, they're going to, their day to day lives are going to change completely, right?

And this, I've been sharing this news to different business leadership groups where data management is going to become incredibly important. So that you have tools like Copilot that are leveraging your source of truth as it relates to data, as it relates to files. You don't want it to run off of version 33 of a document, right?

And so as business leaders, we're going to need to really hone in on how Microsoft is leading the charge on this. Google has their version of it for Workspaces as well. And what all this means for how we need to manage and implement our data for the future.

- [Ryan] How do you view the impact AI tools can have on startups that are looking to get off the ground, looking to scale. Obviously there's some stuff we already talked about of being able to be a copilot to the work that they do, automate different kind of more menial tasks and things like that, but just broadly speaking, why should startups really care about AI and be maybe some of the bigger advocates for driving that adoption?

- [Andrew] I think from an operational standpoint, they have a phenomenal opportunity to start at the ground level with these tools that have AI baked into them. And so they don't need to go through the hardships and change management, like potentially these other firms that are more established, more people, and set processes, that sort of thing.

So I think there's a lot of value to be gained in just starting clean, starting fresh, and knowing exactly how to model this out and utilize it going forward and look, especially for startups that, they're strapped for cash. They're constantly out there pounding the payment for investment dollars.

I think leveraging AI solutions would let those investment dollars go even further. And so you might start seeing, some of these calculations that, their burn rates, et cetera, hopefully they're able to go further as a result of leveraging AI solutions, I think that can guide them along.

Perhaps it delays the hire of a couple of different folks that would be producing a lot of overhead related tasks. And so I think there's a lot of cost savings to be had. There are a lot of efficiency gains to be had there. And even, it might even help them make better decisions. I think a lot of times you see these entrepreneurs, these founders of startups may be making poor decisions, maybe emotional decisions that if they can turn to generative AI or another AI tool to help them along the way, you can, it can uncover things that potentially they weren't able to see on their own. And so it could certainly derisk some of that decision making, add efficiencies, save costs, that sort of thing. So lots to be gained out of it.

- [Ryan] Being able to do more with less is a big kind of message there that this can enable for those companies who are bootstrapped, who haven't raised money yet, but looking to still grow without having to scale their resources, maybe a core of, as quickly as others have to allocate money into kind of more strategic ways than feeling like they have just all these different things that need to be done that maybe AI can come in and actually do for them, maybe better, and give them insights and information they didn't have to make, like you said, those better decisions.

- [Andrew] And there's tools out there that they can leverage for things like creating a positive customer experience. One I love harping on is a tool called Fin by Intercom, and Intercom has been around for a long time in the world of customer service chatbots, but now Fin is leveraging ChatGPT like technology where in the past to create a chatbot, you had to think of all kinds of variations in the questions and the ways people ask things onto decision tree mapping and inevitably just takes, inevitably somebody asks a question that wasn't mapped and now you've got an issue. But with a tool like Fin, it creates a really positive customer experience because you just upload your knowledge base, and now that customer can free flow and ask the question any number of ways as they have been with ChatGPT and other generative AI solutions, and it produces the answer based on what's in that knowledge base. So the customer experience goes up. You don't need to have people manning the system during working hours, you don't need to have customer service reps to a certain degree, right? You can obviously gauge in terms of how far you can go, but it's a rather inexpensive tool for a rather valuable part of the business. So I think as a startup, if you're looking to grow, you really should keep AI in your mind.

- [Neil] What about especially for startups, the ability to integrate tools like Fin. How much like effort does it take to integrate that in? And then what do they have to do from like a maintenance standpoint?

- [Andrew] So a tool like Fin can be implemented rather quickly. On the most basic scale, likely within an afternoon. Other tools that I'm seeing out there gain a lot of traction, Jasper is one that's been around for several years now in the marketing content creation space. They've taken it leaps and bounds further just here in the last six months.

It's great. I've been hearing CMOs that say we've got marketing teams of five that are producing content as if they were marketing team of 15, right? And so, yeah, these tools are just driving these crazy productivity gains and they're not very complex in terms of integration and implementation.

Jasper, you could be up pretty seamlessly and quickly. I think it's just a matter of getting your branding right, getting people acclimated to it, get them going. That could be done also virtually over an afternoon, over a few days, over a week, perhaps tops. But then you're up and running and going.

Others become a little more complicated. There's, let's say an accounts payable tool. There's one out there called Stamply. It's really powerful.

They say, hey, that we can be live in an hour, but if you really want to leverage the full strength, then it's going to take some time to map your general ledger codes, your, you build out your workflow so that the automated processing of those invoices gets done right away and quickly.

And then you've got to test it and run some various validation screenings, that sort of stuff. So it varies by complexity, but there's a lot of tools out there now that are very inexpensive and very quick to implement.

- [Neil] So some are plug and play and some requires a configuration depending on how adaptable you need it to be for your specific business.

So it sounds like for the most part, it's low impact, right?

- [Andrew] High impact in terms of the output, but low effort. Low effort. And that's basically what we call the low hanging fruit quick wins. We grade it on based on impact and effort and everything that's high impact, low effort, you got to go after and get it. And look, many of us have been through those massive, maybe it's an ERP implementation or CRM implementation, takes 12, 18 months, 24 months, and they're so painful. That's not really what goes on now with these AI solutions.

They're relatively quick. On the highest end, it might be a five to six month implementation, but even then, look, I've had, since leaders are like what if we pick out a tool and in a year from now, there's a better one? I say switch over. The pain of switching over won't be nearly as high as it was. Many years ago, a lot of these systems now you can pay monthly subscriptions on so you can cancel any time.

And of course, it's a case by case decision. But I'm throwing that out there because there really should be a bit of a paradigm shift that just because you pick a tool one day, you're married to it for several years, I think that thinking really should go away.

- [Ryan] Let me ask you something before we start to wrap up here is if we're looking at this kind of heading into the future, more companies adopting tools, getting better models, getting better, where do you see the future of AI in the workplace? Just generally speaking.

- [Andrew] I'm going to give it three years and that's probably very conservative. It's going to be attached to virtually every system and application we're using today. Generative, layering on generative AI, I think the easy solution, I haven't heard yet what QuickBooks is doing, but can you imagine having generative AI attached to your QuickBooks and the power of that. So I would anticipate that just from an operational table stakes situation, in three years, it's going to be touching each, every application we use, every business process we're working within. And to what degree, I think there's a lot of regulatory things that are being worked out. A lot of decisions being driven by the government and other private agencies in terms of what it might mean from, I don't know, from a copyright standpoint or who owns what or how far can we go.

There's still a lot of that being determined. But if I'm looking ahead, and I'm looking at where these tools are going, a lot of this being driven by the productivity tools we're using today, I think line of sight, it's pretty easy to think here in the next three years, every application we're using is leveraging AI and look, with every innovation, there are leaders and there are laggards, and I think, in the past, laggards have been able to be laggards and survive just fine, but I would be very worried in this new age that being a laggard, being left behind, missing out on profits and struggling to keep up with those that are leveraging AI. So if you're generally speaking a laggard, I would ask, start considering some shifts, turn to some people that maybe are not laggards that can help pull you along, but it's time you really start considering it as a benefit to your business.

- [Ryan] Neil, any last questions or points you want to bring up?

- [Neil] I think it's been a great conversation. I think Andrew, if people want to learn more about you and your company and maybe seek your help, what's the best way for them to do that?

- [Andrew] Go to louderco.com. There's a button on there to schedule a call, a Zoom conversation with me, if you want to get started on artificial intelligence or other operational style projects. One thing we've been offering lately is an AI workshop where we present the latest on AI. It's an AI 101, very non technical, getting you up to speed on what's new in the world of AI. Real life applications, you're able to see them in action, while then also rolling into a rapid assessment where through the course of the workshop, we're able to pull out those opportunities, those problems and map them to AI opportunities, initiatives, put them in that quick win matrix and build out that roadmap. So over the course of a morning or an afternoon, you can have your whole AI future mapped out and be ready to rock and implement those solutions. So if anybody's open to talking that through happy to show them what that looks like and see a kind of highlight the power of that for their business.

- [Ryan] Andrew, thank you so much for your time. Really appreciate it. Great focused conversation today on how AI can really power businesses of all different sizes going forward. Really appreciate it and excited to get this out to our audience.

- [Andrew] Thank you for having me.