Latest / The Adults In The AI Room / Earley AI Podcast Episode 65: David Hartley and Dave Blatt - Turning AI Hesitancy into Opportunity

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Earley AI Podcast Episode 65: David Hartley and Dave Blatt - Turning AI Hesitancy into Opportunity · The Adults In The AI Room

• 0:00
  SPEAKER_01: Okay, well, welcome to the Early AI podcast.
• 0:03
  SPEAKER_01: I'm Seth Early and I'm your host today.
• 0:06
  SPEAKER_01: Today we're going to be talking about some of the biggest misconceptions in AI, especially in industries like accounting, finance, and mid-market business operations.
• 0:15
  SPEAKER_01: There's a lot of noise out there about what AI can do and can't do.
• 0:19
  SPEAKER_01: And here we're here to separate the myths from reality.
• 0:23
  SPEAKER_01: And joining us today are two experts who've been at the forefront of AI-driven transformation.
• 0:28
  SPEAKER_01: They are with Anders, which is a top 100 CPA firm that does advisory and tax and consulting.
• 0:36
  SPEAKER_01: And we have David Hartley, who is a seasoned CPA who spent his career bridging the gap between finance and technology.
• 0:44
  SPEAKER_01: He's a key advocate for how AI can enhance, not replace, traditional accounting roles.
• 0:50
  SPEAKER_01: So, and I see you're both listed as Dave.
• 0:52
  SPEAKER_01: So David Hartley.
• 0:54
  SPEAKER_01: So we're going to disambiguate the Dave.
• 0:55
  SPEAKER_01: So David Hartley and then Dave.
• 0:57
  SPEAKER_01: I'll say David and Dave.
• 0:59
  SPEAKER_01: And we also have Dave Blapp, who is a leader in AI automation and analytics.
• 1:04
  SPEAKER_01: He has a background in large-scale digital transformation.
• 1:07
  SPEAKER_01: Dave now focuses on helping mid-sized companies level the playing field using AI to compete with bigger players in the market.
• 1:14
  SPEAKER_01: David and Dave, welcome to the show.
• 1:17
  SPEAKER_01: Great.
• 1:17
  SPEAKER_01: Thanks, Seth.
• 1:18
  SPEAKER_01: Nice to uh see you and uh have the opportunity to talk with you and your listeners.
• 1:24
  SPEAKER_01: Thank you.
• 1:24
  SPEAKER_01: Thank you guys for your time.
• 1:26
  SPEAKER_01: So, what I want to do is I want to jump in and you know, a lot of times AI is being portrayed as something that's going to replace jobs entirely or is only accessible to large enterprise organizations.
• 1:39
  SPEAKER_01: David, from your experience in the CPA world, what are the biggest myths that you hear about AI and accounting?
• 1:46
  SPEAKER_03: Yeah, Seth, uh great to be with you today.
• 1:49
  SPEAKER_03: Uh both of the things that you highlighted are things that we see both in the CPA world and in our middle market, lower middle market client base.
• 1:57
  SPEAKER_03: Is a lot of regardless how, and communication is a big part of this for leaders, but regardless of how much leaders communicate, there is always we we've seen so many organizations where the employee base, the team members are just completely 100% confident that the reason AI is being embraced is to eliminate their positions.
• 2:19
  SPEAKER_03: And so that that's kind of a major starting point.
• 2:22
  SPEAKER_03: There's like all the technical aspects, but at the end of the day, it's about users using the tools.
• 2:26
  SPEAKER_03: And that is definitely a significant obstacle that we see.
• 2:30
  SPEAKER_03: And I think in the CPA world, I think there's there's a little bit more acceptance because if if you do sort of traditional accounting type work, there's a lot of things that aren't necessarily fun about that, but have to get done.
• 2:42
  SPEAKER_03: And I think there's a lot of people that say, man, there's 20, 30, 40 percent of my job that I really don't enjoy doing.
• 2:48
  SPEAKER_03: If I could give that stuff to AI, I'd be a happy camper.
• 2:51
  SPEAKER_03: And so I think we're seeing more of that a kind of interest from you know a CPA firm accounting point of view, because there is that you know substantial opportunity there.
• 3:01
  SPEAKER_02: Yeah.
• 3:01
  SPEAKER_02: All right.
• 3:02
  SPEAKER_02: Seth, Seth, I'll I'll add in and just to add on what uh uh David was talking about was just this adoption and this uh fear of the unknown.
• 3:11
  SPEAKER_02: And if individuals would just, you know, find ways to experiment and practice with uh some of the tools and be introduced to it at a simple level, at an individual level, we'll see more of this adoption in the mid-tier space.
• 3:25
  SPEAKER_01: So and and to add on to that, in the mid-market companies, they can be hesitant because they think it's too complex or too expensive.
• 3:32
  SPEAKER_01: So, how do you address those concerns of those type of you know, following on from what you're saying, uh to help them see AI as a practical tool rather than being overwhelmed with the challenges?
• 3:43
  SPEAKER_01: Yeah.
• 3:44
  SPEAKER_02: Absolutely.
• 3:45
  SPEAKER_02: Uh, these uh mid-tier uh firms either um either are afraid of AI, mainly because of this hype in the news, uh, you know, several reasons.
• 3:55
  SPEAKER_02: There's this hype in the news of using this language that is foreign to them, large language models, rags, GPUs and CPUs, and it's just that foreign language that they're just you know hesitant to do that.
• 4:09
  SPEAKER_02: And then we've all seen the technology hype, uh, where uh you know many technologies come and go, but uh the ones that get talked up a lot fail to materialize significant business or adoption.
• 4:22
  SPEAKER_02: So the Bitcoin or blockchain, metaverse, you know, you name some of these, they just haven't gotten the adoption.
• 4:30
  SPEAKER_02: So, you know, in the mid-tier space, especially, they're gonna be a little bit more hesitant.
• 4:35
  SPEAKER_02: They don't have sometimes full-time IT staff uh to support it.
• 4:39
  SPEAKER_02: So they're gonna look at it uh with a little bit more skepticism, but they honestly have the most to benefit.
• 4:46
  SPEAKER_02: If you think about their space, they probably have they have often less ERP systems.
• 4:51
  SPEAKER_02: And so they're doing things a little bit more manual, you know, at times live, you know, working through Excel to run their business.
• 4:58
  SPEAKER_02: And the impact of of uh these tools on you know an employee base of uh of a hundred or five hundred employees versus um you know a hundred thousand or a hundred thousand employees, it can be more proportionally impactful.
• 5:14
  SPEAKER_01: That's a great point.
• 5:15
  SPEAKER_01: And so when you start getting past those misconceptions and you start getting around the the hesitancy, how do we actually apply AI in ways that make a difference for those mid-market companies?
• 5:27
  SPEAKER_01: So, Dave, can you walk us through some real-world AI application uh applications in mid-size industries like construction or manufacturing?
• 5:37
  SPEAKER_02: Yeah, yeah, absolutely.
• 5:38
  SPEAKER_02: Let me sort of break it down a little bit so that when we do talk to our customers, we try to demystify this or take this really large topic called AI and break it up into some things that they can think of.
• 5:51
  SPEAKER_02: So we uh we try to break it up into four areas.
• 5:54
  SPEAKER_02: Think of individual productivity, team-based productivity, enterprise, the entire company and organization, and then often new markets.
• 6:03
  SPEAKER_02: So I'll just break that down a little bit.
• 6:05
  SPEAKER_02: So the examples and the things that we see are mid-tier uh customers really adopting easily in the individual productivity spaces, areas of the co-pilot, the uh chat bots, the tools that can help them, of course, create content, but also collaborate better, spend, you know, just less time having to review things because they could have that personal assistant help them and guide them.
• 6:31
  SPEAKER_02: Now, there's definitely things to watch out for because it's not a complete solution, as you know, but there's some great tools that support the individual in their ability just to do their job.
• 6:42
  SPEAKER_02: And then from a team perspective, great examples.
• 6:45
  SPEAKER_02: Many of these mid-tier spaces, uh, especially in construction, they do a lot of contracts.
• 6:50
  SPEAKER_02: Uh, they have subcontracting the work out, and they have to deal with the terms and conditions and the fine print.
• 6:56
  SPEAKER_02: And it can, you know, the human can read it, but there's often something that they might miss.
• 7:01
  SPEAKER_02: So it's great to have an assistant that can review that document, highlight things that are inconsistent with their company, just as an example.
• 7:09
  SPEAKER_01: That's a great example because you're not depending, you're asking the AI to review that specific document and to answer questions about the information in that document or to compare documents.
• 7:21
  SPEAKER_01: And you're not, and you're less likely to have a hallucination when you're getting a very specific piece of information.
• 7:27
  SPEAKER_01: And of course, that's also gets into ways to apply AI internally in the organization.
• 7:32
  SPEAKER_01: But that's a great use case and example.
• 7:34
  SPEAKER_01: You had also mentioned a flooring company that improved a scheduling system using some simple automation tools.
• 7:40
  SPEAKER_01: Do you want to share that story?
• 7:42
  SPEAKER_02: Yeah, yeah, sure.
• 7:43
  SPEAKER_02: You know, as as we start talking to clients and just providing some education around AI, helping them understand that it doesn't have to be this large, arduous uh you know, big data analytics, clean up all your data, get the security in place.
• 8:00
  SPEAKER_02: I mean, which are all important things.
• 8:02
  SPEAKER_02: But while those happen, you could be doing some small process automation with them, which is in part AI as well, learning and capabilities.
• 8:12
  SPEAKER_02: So instead of uh taking floor plans and architectural design and having to manually click through those and map out the measurements proportionally to that design, let AI help you with that.
• 8:26
  SPEAKER_02: Scan that in, optical character recognition, OCR can help evaluate that and give you know 75 to 80 percent of that first pass clarity on the measurements of a very large building that can come in many different formats.
• 8:41
  SPEAKER_02: So helping uh this customer through the ideas of that automation so they don't have to manually do that or have their employees sit through the boredom of having to do that every morning.
• 8:52
  SPEAKER_02: We can leverage AI tools for that.
• 8:55
  SPEAKER_01: And was that using a specific application, purpose built for that task and that use case, or was that using a large language model uh and asking uh questions and prompting it?
• 9:05
  SPEAKER_02: What was the uh purpose was built for for them specifically to evaluate their specific kind of floor plans that they get from their from their wonderful and David, you you focused on integrating AI into advisory services.
• 9:19
  SPEAKER_01: How does that this shift enhance the value you bring to clients?
• 9:22
  SPEAKER_01: And what are some success stories?
• 9:25
  SPEAKER_03: Well, I think the you know, from our perspective, what we're you know, the things that we're trying to do, and then also we're active within associations and other things in terms of what CPAs are doing with with AI.
• 9:36
  SPEAKER_03: And I think, you know, probably some of the biggest examples are just teams becoming more productive by leveraging AI.
• 9:43
  SPEAKER_03: So it's almost like you used to get this much output out of an advisor or a consultant.
• 9:47
  SPEAKER_03: And as a result of them using these tools, now they're able to actually focus on the things that deliver more value to clients and are the things that they really need, as opposed to spending a lot of time doing documentation.
• 9:59
  SPEAKER_01: And you know, and there's a lot of things meeting summaries and so on.
• 10:02
  SPEAKER_03: Yeah, I was just gonna say that actually, that that's such an easy one for both firms and companies that haven't seen this to experiment with AI note takers because you just subscribe to them, invite them to a meeting, and all of a sudden they start doing these amazing things.
• 10:17
  SPEAKER_01: It is amazing.
• 10:17
  SPEAKER_01: Yeah, I totally agree.
• 10:19
  SPEAKER_01: And we use large language models in our information architecture work.
• 10:23
  SPEAKER_01: And we have found we have a uh tool called VIA Virtual Information Architect, which I haven't really mentioned on the show before.
• 10:29
  SPEAKER_01: So that's that's kind of a new thing.
• 10:31
  SPEAKER_01: But what that does is it takes methodologies that we've built over time and embeds them as prompts into using an LLM.
• 10:38
  SPEAKER_01: So we feed content in.
• 10:40
  SPEAKER_01: We actually uh can see a 10x improvement in our processes, right?
• 10:45
  SPEAKER_01: So I think you know, for consultants and service providers out there, you have to use these tools because they are no-brainers, you know, and as you say, the meeting summaries, they can produce action items, they can produce follow-ups, there's all sorts of wonderful things they can do with those prompts.
• 10:58
  SPEAKER_01: And for our work, it's given us a factor of you know 10x improvement in doing and deriving things like taxonomies and metadata and content models and processing content and so on.
• 11:09
  SPEAKER_01: So, for businesses that are still on the fence, what are the key takeaways they should consider when they're thinking about AI adoption?
• 11:15
  SPEAKER_01: And Dave, you can continue that thought if you'd like and hear from Dave as well if you'd like.
• 11:20
  SPEAKER_03: Yeah.
• 11:21
  SPEAKER_03: Um, well, I I think, you know, as I mentioned before, that communication piece is so important.
• 11:26
  SPEAKER_03: So there's like all the technical aspects that are really important, but especially in our client base, the communication is important and just the awareness stage.
• 11:34
  SPEAKER_03: A lot of companies kind of rush to a solution without necessarily spending time to socialize these concepts with team members and letting them know that, hey, we're doing this to make you more productive and make the company more successful, not to replace you.
• 11:48
  SPEAKER_03: And I think a lot of those early on things are critically important because that really sets the tone to whether people are going to embrace what you're giving them or basically stonewall it and try as best they can to get around it.
• 12:01
  SPEAKER_03: So, Dave, I'll let you go from here.
• 12:04
  SPEAKER_02: Yeah, I think to just help the organization, many examples to start small and uh not make it so daunting.
• 12:12
  SPEAKER_02: So think of every client, every customer that's actually a growing company is hiring employees.
• 12:17
  SPEAKER_02: And uh, if you can on board them a little bit faster by understanding that knowledge base of who's the individuals I can talk to about this particular topic, what have we done in the past as a firm that I can use as a source of information?
• 12:31
  SPEAKER_02: Just getting new employees these tools like a uh assistant chatbot, that they can ask it questions.
• 12:37
  SPEAKER_02: Um, those are really easy to implement.
• 12:40
  SPEAKER_02: You can keep the information curtained so you don't have to do a very large security review and a very large data cleanup just to get some of the company policies and knowledge sources into a place that uh the chatbot or AI can can support.
• 12:56
  SPEAKER_02: And so sometimes doing these small little things will help that adoption because they just comfortable with it.
• 13:03
  SPEAKER_01: Yeah.
• 13:04
  SPEAKER_01: And and continuing that, but when you're helping mid-sized companies implement AI, you talked about going to policy databases or knowledge bases.
• 13:11
  SPEAKER_01: What are the biggest data challenges you see?
• 13:13
  SPEAKER_01: And how do you start to help address those or solve those?
• 13:17
  SPEAKER_01: Yeah, absolutely.
• 13:18
  SPEAKER_02: I mean, like anything, like any large company or mid-sized company that knowledge base.
• 13:24
  SPEAKER_02: Yeah, yeah, it's gonna be all over the place.
• 13:26
  SPEAKER_02: And so it, but you can move faster in the mid-tier space.
• 13:30
  SPEAKER_02: You just organize the the the team members just you know and identify where that sources of data is and information and and it can be reviewed a little bit faster.
• 13:40
  SPEAKER_02: Um, but I think all the companies have this issue around that.
• 13:44
  SPEAKER_02: And so it's it's the security and access, both limiting that private sensitive information and doing the scans on the file stores, whether it's uh SharePoint or others, where you store these information and assets and data.
• 14:00
  SPEAKER_02: So the security aspect of it, and then just the cleansing of it and making sure it's the most recent, you know, because we can all see models that hallucinate.
• 14:09
  SPEAKER_02: I could be working on a draft document and it could interpret my draft as something that I've already completed.
• 14:15
  SPEAKER_02: Sometimes we've done, you know, proposals where it interpreted that we had done those implementations.
• 14:22
  SPEAKER_02: So we help, of course, educate the fact checking that you want to do as an individual when you're working with these tools, the security aspect, and just the cleanup the data.
• 14:31
  SPEAKER_02: But you can start small, and then as you get some of those wins, it will be a catalyst to doing more projects and a bigger effort.
• 14:38
  SPEAKER_03: And Seth, just one thing to add I was actually visiting with a client last week, and and this con this topic came up regarding data, data quality.
• 14:45
  SPEAKER_03: And the answer was, and I think this is fairly typical of smaller companies, which is we really don't have the concept of master data management defined within the organization.
• 14:55
  SPEAKER_03: And then also our systems have been around for a couple of decades.
• 14:58
  SPEAKER_03: And 20 years ago, the fields were used for different things like today.
• 15:02
  SPEAKER_03: So that that's a real challenge in the the mid-tier and lower middle market, is just it's it's just not clean.
• 15:10
  SPEAKER_03: So they they go out and they get these tools, and that's another nuance of our client base, which is a lot of times they're not they may be architecting some things, but they're not really building things.
• 15:18
  SPEAKER_03: They're mostly finding the right tools that exist and then bringing them into their environment.
• 15:23
  SPEAKER_03: And of course, you can find a great tool, but if your data is poor, the tool's not going to be able to do its job.
• 15:28
  SPEAKER_03: So that that's a real obstacle and with a lot of the clients we work with.
• 15:32
  SPEAKER_01: Yeah.
• 15:32
  SPEAKER_01: And and you know, one of one of the next things I was going to say is, you know, how important is it to get that data foundation right and you know, before diving into AI.
• 15:42
  SPEAKER_01: And then what are those critical steps they should take first?
• 15:47
  SPEAKER_02: Yeah, I think it's obviously critical.
• 15:50
  SPEAKER_02: At the same time, it can't be paralyzing.
• 15:52
  SPEAKER_02: So we do try to start small with that example of a personal assistant where you can take a subset of the information and data and start on that.
• 16:02
  SPEAKER_02: And then we can architect bigger and bigger tools and capabilities and analysis from that data so that they feel that they get some quick ROI, quick win, but yet can build and start doing a foundation of data cleanup and management.
• 16:18
  SPEAKER_02: And it also sort of builds in the importance of it.
• 16:21
  SPEAKER_02: If they start seeing and the executives and any all employees experience, hey, I can ask this in simple, you know, common language, what is the latest process policy or capability for a particular area?
• 16:35
  SPEAKER_02: Um, they're more in tune to be like, okay, I see the value in getting that data cleaned up.
• 16:39
  SPEAKER_02: And uh then it can carry some initiatives forward.
• 16:42
  SPEAKER_02: We just don't want it, we just don't want it to die on the vine before it ever gets started.
• 16:46
  SPEAKER_01: Absolutely.
• 16:47
  SPEAKER_01: And I think that you know, you you make a good point there is that you can't get overly hung up in data cleanup and have to try to clean up everything.
• 16:55
  SPEAKER_01: You really want to say, what's the content and the knowledge and the information I need for this specific use case, and then test that, right?
• 17:03
  SPEAKER_01: So that you know you're getting the right answers.
• 17:05
  SPEAKER_01: Because as you know, if uh people go in and they start using a tool, they're getting the wrong answers, then that very quickly dissipates any trust that they would have.
• 17:15
  SPEAKER_01: And then once you lose trust, then you lose adoption because people say, Oh, it's just not giving you the right information.
• 17:20
  SPEAKER_01: It's really important to say, stay focused on that use case and say, what is the information I need?
• 17:24
  SPEAKER_01: Then test, right?
• 17:25
  SPEAKER_01: Make sure that it's getting it right.
• 17:27
  SPEAKER_01: So, and to your point, have you seen instances where small AI implementations led to that immediate return on investment?
• 17:34
  SPEAKER_01: Are there any examples that might inspire businesses to take that first step?
• 17:38
  SPEAKER_01: Again, something that has a clear ROI.
• 17:40
  SPEAKER_01: You know, there's a soft ROI when you're doing things like summarizing meetings and action items, anything you can point to that was a hard ROI from a small implementation.
• 17:50
  SPEAKER_02: Yeah, yeah, absolutely.
• 17:52
  SPEAKER_02: And just a couple that we've mentioned, uh, just the support you get from a personal assistant as you onboard new employees, literally sending you time to time on that.
• 18:01
  SPEAKER_02: And then, you know, outside of this one customer that we're working with, they still get a lot of manual orders coming in for their products through email.
• 18:11
  SPEAKER_02: And just instead of rekeying that in, uh being able to auto-recognize hey, here's the email coming in with an order, what the form looks like, getting that to auto-process with a review in there.
• 18:24
  SPEAKER_02: We're able to, on an annual basis with the the studying of the time and stuff, save at least two to three FTEs just on a very simple use case that that we all know in from a technical perspective that it's relatively easy to implement.
• 18:39
  SPEAKER_02: Yeah.
• 18:39
  SPEAKER_02: But from a business perspective, they have that fear, that that concern of, you know, is it going to be accurate?
• 18:45
  SPEAKER_02: How is it going to work?
• 18:46
  SPEAKER_02: Do I really see the the orders going in and sort of you know starting that small project just to get them going?
• 18:52
  SPEAKER_02: And it just feels more impactful in the mid-tier space because they may have been doing this in a lot of our clients who have done doing this scheduling or order entry for 20 years the same way.
• 19:03
  SPEAKER_02: Right.
• 19:04
  SPEAKER_02: So it's a it's a it's really enlightening and it's such a fun thing to see this transformation when the light bulb goes on.
• 19:11
  SPEAKER_02: Right.
• 19:12
  SPEAKER_02: Um, we're getting great conversations about, you know, what is this thing with AI?
• 19:16
  SPEAKER_02: And it's getting them over the hump of actually executing and starting small.
• 19:22
  SPEAKER_02: So um, you know, that's that's what you know, I we we talk a lot about is like don't make it too arnerous and a big effort to get those quick wins and those small wins because that will carry it forward.
• 19:35
  SPEAKER_03: But and yeah, I was just gonna add, you know, one of the things we have to be to answer your question, one of the things we have to be careful with is a lot of times some of that immediate ROI will be headcount savings.
• 19:45
  SPEAKER_03: And in our minds, it's like, okay, you've got somebody performing something and we can free up two FDEs.
• 19:50
  SPEAKER_03: That doesn't necessarily mean that you cut your headcount by two, but rather those people have a long to-do list of things that they would love to get to, but they never can because they're always doing these manual things.
• 20:01
  SPEAKER_03: So that that's got to be part of the conversation as well, which is there's value being created here and it's freeing up hours, not necessarily that we can let people go, but more so there's all these other things the organization wants to do, and now we are going to be able to get to.
• 20:15
  SPEAKER_03: So part of our conversations and business cases, we have to focus on that as well.
• 20:20
  SPEAKER_01: Absolutely.
• 20:21
  SPEAKER_01: That's a great point because you know, work will always expand to take up the time allotted.
• 20:25
  SPEAKER_01: So if you can reduce some of that lower value repetitive work that is a time suck, then you get to those higher value activities and tasks.
• 20:34
  SPEAKER_01: When I we look at the the clear ROI, easy to demonstrate and measure use cases, we call those the excuse cases, right?
• 20:43
  SPEAKER_01: Because those are the ones that give you the excuse to do something bigger and something broader and to clean up the data.
• 20:49
  SPEAKER_01: But you need that first reason to to justify it.
• 20:52
  SPEAKER_01: And that's really uh what you're saying is make those quick wins available, make sure people understand them, make sure they trust the system, you're getting the right results.
• 21:00
  SPEAKER_01: So I just want to get some final insights.
• 21:03
  SPEAKER_01: If you were advising a business just starting to explore AI, what is the very first practical step you would you would take?
• 21:10
  SPEAKER_02: Yeah, I'll I'll take this one and maybe David, you can follow it up.
• 21:14
  SPEAKER_02: But um it's it's the first and most important is just education and awareness and getting the same leadership team on the same page with what it is, demystifying it, helping them understand that you can implement some of these excuse projects.
• 21:29
  SPEAKER_02: I love how that you uh coined that, and um get them awareness of how to get started and that it's not just it could be multiple things simultaneously without overwhelming them.
• 21:40
  SPEAKER_02: So, like I had said, you can do some small AI initiatives without a very large data cleanup exercise.
• 21:48
  SPEAKER_02: So get started on some of the quick wins is little little personal productivity and team based collaboration and productivity with an easy chat bot or a one particular process that we can.
• 22:00
  SPEAKER_02: Help them with the, you know, we educate them and then give them a roadmap that you can get started and make sure that there's some hard R ROI in that first initiative so that the in the mid-tier space, it's so impactful to see that bottom line results because it is their money, their budget.
• 22:20
  SPEAKER_02: Uh, they seem to feel it more.
• 22:21
  SPEAKER_02: And honestly, they get more excited about it because they can play with the the enterprise level, the big, the big dogs, right uh, with these kind of capabilities, you know, customer engagement and better processes.
• 22:34
  SPEAKER_02: So they can really make an impact.
• 22:36
  SPEAKER_01: Yeah.
• 22:36
  SPEAKER_01: And I'll tell you, the the improvement that these tools enable is X in terms of what you can achieve in a services engagement.
• 22:44
  SPEAKER_01: So, what would it cost a million dollars, Stephen just a few years ago, could be done for a hundred thousand or two hundred thousand dollars today.
• 22:51
  SPEAKER_02: Yeah, I'll I'll I'll add on that is that you know, we also help them take notice as well, because not often is such a big technological leap.
• 23:01
  SPEAKER_02: And what I mean by that is okay, some technologies that will automate a particular process is something you might use at work or one thing you might use at home.
• 23:10
  SPEAKER_02: But like the internet, where it impacted everyday life of you and me and how we do things at home, um, as well as as we work.
• 23:20
  SPEAKER_02: And that's what I think about, you know, I don't want to be cliche, but the AI revolution, like the internet revolution, because it would because at home we're adopting these tools so fast, it will be mainstay, much faster than some of those other hyped technologies that I mentioned earlier.
• 23:38
  SPEAKER_01: Totally, totally agree.
• 23:39
  SPEAKER_01: David, do you want to add uh something to these uh final thoughts?
• 23:43
  SPEAKER_03: Yeah.
• 23:43
  SPEAKER_03: So Dave mentioned getting the the leadership team together and organized and having a consistent view.
• 23:49
  SPEAKER_03: Completely agree with that.
• 23:50
  SPEAKER_03: Small wins, completely agree with that.
• 23:52
  SPEAKER_03: The thing that I would add is that communication plan that you have to start intentionally communicating with your team members about what you're doing, why you're doing it, or else that level of distrust will be so high by the time you get to that person's area and actually ask them to do something, they'll be completely intimidated andor see a whole lot of ulterior motives of why you're doing what you're doing.
• 24:16
  SPEAKER_03: So to get really everybody excited and on board and not going to be barriers to adopting these rules, that communication aspect is super important to do up front as well.
• 24:27
  SPEAKER_01: That is a very, very good point.
• 24:29
  SPEAKER_01: And I think that you want to get ahead of it.
• 24:32
  SPEAKER_01: You want to get ahead of people's assumptions or presumptions or fears and you know, ulterior motivations, and you want to really say, look, this is really to help the organization and you know keep up with the competition.
• 24:43
  SPEAKER_01: We're speeding up the information metabolism of the entire organization.
• 24:47
  SPEAKER_01: And it's not just that we're doing this, that the competition is doing this.
• 24:51
  SPEAKER_01: So this is a matter of staying competitive and being able to adopt these in our individual work processes as well as across the organization.
• 24:59
  SPEAKER_01: So those are really great uh points.
• 25:02
  SPEAKER_01: Dave, David, thank you so much for your time today.
• 25:05
  SPEAKER_01: This has been a terrific session to get those insights and those perspectives on business transformation, especially in this marketplace and at this time.
• 25:13
  SPEAKER_01: And to our listeners, thanks for tuning in to the early AI podcast.
• 25:17
  SPEAKER_01: Be sure to subscribe for more expert conversations on how AI is shaping the future of business.
• 25:23
  SPEAKER_01: Until next time, keep innovating and making smarter decisions with AI.
• 25:28
  SPEAKER_01: Thank you uh very much for your time, Dave and David.
• 25:31
  SPEAKER_01: And you can find uh each of you on LinkedIn, Dave Hartley, D-A-V-H-A-R-T-L-E-Y, and then Dave Black, D-A-V-E, B-L-A-T-T.
• 25:42
  SPEAKER_01: So that's those are the LinkedIn.
• 25:44
  SPEAKER_01: And then the company is Anders C P A, A-N-D-E-R-S-C-P-A.com.
• 25:50
  SPEAKER_01: So thank you again for your time.
• 25:52
  SPEAKER_01: We really appreciate you having having you on the show.
• 25:54
  SPEAKER_01: Great, thank you, Seth.
• 25:55
  SPEAKER_01: It's enjoyable.
• 25:56
  SPEAKER_03: Thank you.
• 25:57
  SPEAKER_01: And thanks to Carolyn and uh for her work in uh producing the show, and again, thank you to all of our listeners.
• 26:03
  SPEAKER_01: Have a good day, and we will see you next time at the Early AI Podcast.
• 26:08
  SPEAKER_00: Visit Early.com to find links to the full podcast on all audio platforms to listen on the go.
• 26:14
  SPEAKER_00: Thank you for watching.
• 26:16
  SPEAKER_00: Contact us today at infoearly.com.