[AI Series] Transforming Meeting Management for Financial Advisors With Arnulf Hsu
- Quik! News Team
- 4 days ago
- 26 min read
Updated: 2 days ago
Arnulf Hsu is the CEO and Founder of GReminders, an AI-powered end-to-end meeting management platform tailored for financial advisors. With over two decades of experience in B2B enterprise software, he has held leadership roles including CEO, CTO, and board member, successfully guiding multiple startups from inception to acquisition. His previous ventures include SalesDirector.ai, Central Desktop, and Upgradebase, demonstrating a consistent track record of innovation and growth in the tech industry. An alumnus of the University of California, Irvine, where he studied electrical engineering, Arnulf is also an active advisor to early-stage technology companies.
Here’s a glimpse of what you’ll learn:
[2:10] Arnulf Hsu discusses how GReminders simplifies meeting scheduling for financial advisors
[6:00] Deep CRM integrations with Redtail, Wealthbox, Salesforce, and others
[7:42] Why GReminders shifted from a horizontal platform to a vertical focus on financial services
[10:05] How GReminders began adopting AI to power automation and scheduling workflows
[14:07] Compliance considerations for sending AI-generated content to clients
[15:56] Ensuring AI quality by narrowing data scope and citing sources
[18:49] Arnulf explains how his team uses feedback loops to detect and improve AI hallucination issues
[23:52] The emergence of agent-to-agent communication standards across systems
[26:16] The future role of AI in enriching unstructured data for CRM systems
In this episode…
Managing client meetings, follow-ups, and CRM entries can consume hundreds of hours for financial advisors each year, taking them away from higher-value tasks. Even with existing tools, the lack of deep system integration and automation leads to inefficient workflows and missed opportunities. So, how can advisors truly leverage AI to streamline operations without sacrificing accuracy or compliance?
Arnulf Hsu, a seasoned product and technology leader in B2B software, shares how narrowing AI’s focus to specific use cases and deeply integrating with industry-specific systems can unlock massive efficiencies. He outlines how advisors can automate the full meeting lifecycle — from client outreach and scheduling to transcriptions and CRM updates — by implementing AI tools that align closely with existing workflows. He emphasizes the importance of interoperability, targeted data contexts to reduce AI hallucinations, and robust feedback loops to ensure quality outputs. His approach centers on solving advisor pain points rather than deploying generic AI for its own sake.
In this episode of The Customer Wins, Richard Walker interviews Arnulf Hsu, CEO of GReminders, about AI-driven meeting automation for financial advisors. Arnulf discusses how his platform helps advisors recover up to 600 hours per year, the evolution from simple reminders to intelligent automation, and why compliance and data accuracy matter. He also explores agent-to-agent communication, CRM integration depth, and the future of personalized AI assistants.
Resources Mentioned in this episode
Quotable Moments:
"Leveraging AI and automations is really the path to get more time."
“Nobody became a financial advisor to do all this administrative work.”
“If you solve a problem, you make the sale.”
“The narrower you keep the context, the higher the quality output.”
“Prompt engineering is a real thing, and spending time on prompt engineering pays dividends.”
Action Steps:
Narrow your AI’s context window: Limiting data scope to specific client records helps reduce hallucinations and improve output accuracy.
Deeply integrate with existing systems: Connecting AI tools to CRMs and operational platforms streamlines workflows and eliminates data silos.
Collect feedback on AI performance: Gathering user ratings provides insights that help refine prompts and maintain high-quality interactions.
Use AI for meeting summaries and pre-meeting briefs: Automating these tasks frees up advisors to focus on strategic client conversations.
Prioritize use-case-driven AI implementation: Building AI around clearly defined problems ensures the technology delivers measurable business value.
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Episode Transcript:
Intro: 00:02
Welcome to The Customer Wins podcast, where business leaders discuss their secrets and techniques for helping their customers succeed and in turn, grow their business.
Richard Walker: 00:16
Hi, I'm Rich Walker, the host of The Customer Wins, where I talk to business leaders about how they help their customers win and how their focus on customer experience leads to growth. Some of my past guests have included Patrick Hannon of Fidelity Labs, David Perez with Tax Maverick, and Donald Morgan of Independent Wealth Connections. And today is a special episode in my series on artificial intelligence. And today's guest is Arnulf Hsu, CEO of GReminders. And today's episode is brought to you by Quik!, the leader in enterprise forms processing.
When your business relies upon processing forms, don't waste your valuable time manually reviewing the forms. Instead, get Quik!, using our Form Xtract API. Simply submit your completed forms and get back clean, context rich data that reduces manual reviews to only one out of a thousand submissions. Visit Quick Forms to get started. All right.
Today's guest, Arnulf Hsu, is the CEO of GReminders, an AI powered meeting management platform for financial advisors. With over 20 years in B2B enterprise software. He's held roles as CEO, CTO, product leader, and board member. Known for turning customer pain points into successful solutions, Arnulf operates at the crossroads of business, product and tech. He has led companies from start up to acquisition, selling three businesses.
He also advises early-stage tech companies. Arnulf, welcome to The Customer Wins.
Arnulf Hsu: 01:49
Thanks for having me, Rich man.
Richard Walker: 01:50
I love this background of yours. You and I are going to get along. So if you haven't heard this podcast before, I talk with business leaders about what they're doing to help their customers win. How they built and deliver great customer experience and the challenges growing their own company. Arnulf, I want to understand your business a little bit better.
How does your company help people?
Arnulf Hsu: 02:10
Yeah. So thanks. So GReminders is an AI-powered end-to-end meeting management platform built for wealth managers and financial advisors. And so let me talk more specifically about that somewhat abstract statement. Let me talk specifically what that means.
So we look through the lens of sort of a meeting life cycle. Right. And so if you're a financial advisor or wealth manager, you're meeting with your clients maybe a couple times a year. Once a year we reach into the systems of records that they have, right. So there's CRMs.
We understand the cadence that they have with their clients, and we will automatically reach out to their clients to essentially get them to book an annual review or performance review. We continue that process until they actually schedule. So we have an enterprise-class scheduling learning platform where clients are able to select a date and time and so forth. Also provide updates that might exist or might have changed, you know, since the last time they met. Those things go on the calendar.
We then kick off certain workflows and automations within the CRMs or other engagement systems, such as Docupace or precise RFP and so on and so forth. We send out notifications to clients to make sure they actually show up email, SMS, different, different vehicles like that before the meeting, because we're deeply connected to the systems that advisors use. Every day before the meeting, we send a pre-meeting brief. So a nice summary of essentially all of the things that have happened since the last time they met with them could be various meeting notes, could be activities, tasks, opportunities, portfolio performance things like that. Again nice pre-meeting brief If the advisor has additional questions, they can.
We have a genetic I. So we have basically you know think about ChatGPT on top of a client record. Essentially you can ask additional questions. And then once you're in a meeting, we have a note taker product that joins zoom Microsoft Teams in-person meetings, does transcriptions, summaries, tasks, recommends opportunities based on content, recommend workflows that might need to get kicked off, and again, pushing all of that back into the system of record and then help with email follow ups and things of that. So kind of completing that, you know, end-to-end sort of, you know, meeting life cycle.
And so, you know, we call it an end-to-end meeting management platform.
Richard Walker: 04:42
Man, I don't know if, you know, I used to be a financial advisor way back in 2001 to 2004. Oh my gosh, I would have loved to have this because everything I did was in Excel. I had it all planned out on a calendar of who needed what by when, and I had to email it myself and follow up and call. This sounds like an assistant in a box, like all rolled up together to handle all these tasks related to the meetings that. That's amazing.
Arnulf Hsu: 05:08
We are seeing somewhere upwards of 500 to 600 hours saved per year per advisor. And so, you know, if you look at that on a, on a, on a sort of, you know, a business day basis, you're talking about getting 3 or 4 months back per year. And if you're, you know, currently running about 100 clients or something like that, you could probably easily increase that to 130, 150. And you just really get a lot of benefit. And again, you know, nobody became a financial advisor to do all this administrative work.
Richard Walker: 05:45
I know right.
Arnulf Hsu: 05:46
And so, you know, leveraging AI and automations is really sort of the path, you know, to get more time.
Richard Walker: 05:53
Oh, man, that's so cool. So you said you do integrate with CRMs. I presume that's Redtail and Wealthbox and the likes, right?
Arnulf Hsu: 06:00
We integrate with tons of CRM. So Redtail Wealthbox, you know, dominant players sort of a lower end of the market. We integrate with Salesforce, all of the different overlays. So rectify, accelerate, Quiver Elements, Microsoft Dynamics and the overlays like Tamarac Smart Office we integrate with HubSpot, Pipedrive, Activecampaign. So we spend a lot of times sort of on the integration side of things.
You know, interoperable systems is really a big deal in the tech stack, right? So if these, you know, if you want to leverage AI and, and, and analytics across your technology stack, these systems better talk to each other. And if they don't, you have a bunch of data islands that are really not doing anybody any good that that might automate a certain type of workflow. But if they don't talk to their system, you end up doing a bunch of manual stuff. And really, you know, the automations aren't giving you the full benefit that that you need.
And so we spend a tremendous amount of time on deeply integrating, right. Not just single sign on, but, you know, going into different objects, custom objects, custom fields. And there's a lot of nuance in these systems. And while many financial advisors at a, at a, you know, sort of 100,000 foot level kind of operate the same when you get really down into the weeds, you know, there are lots of different nuances there. And so we spend a lot of time making sure that we have the right dials and knobs that can support, you know, slightly different, you know, sort of work practices in those types of things.
Richard Walker: 07:38
So when did you start this? I'm not familiar with when you guys came to market.
Arnulf Hsu: 07:42
So we started in 2020. So about four and a half years ago. So somewhere in that range and we started really more horizontally, right? So we weren't necessarily focused on wealth management or financial services back then. We had a bunch of different customers sort of in, you know, professional services space, and that included financial services, that included healthcare, that included, you know, other professions like legal and accounting and things of that nature.
But around 2022, we wanted to go down a specific vertical. Right. And so we did some assessment of kind of, you know, what our customer base looked like. We had also hired some folks that sort of came out of the financial services space, specifically wealth management, and really sort of uncovered, you know, some needs and some gaps in this particular area. You know, in our opinion, it had been somewhat underserved, meaning that wealth managers and financial advisors were using tools that were for the general sort of, you know, population.
Right. And that's those products are okay. But again, going back to these interoperable systems, they did not integrate with systems like Redtail or Wealthbox or some of these specific overlays. And so we felt there was a gap. We spent the last two and a half years essentially building integrations and supporting workflows that are very specific to financial services.
That's all we do. We live and breathe this industry in space, and we're very happy with the decision. The uptake has been, you know, really good. We're growing 120% year on year and, you know, very happy with sort of where we are and we continue to dig in.
Richard Walker: 09:28
No, that's awesome to hear that, man, because I think I've seen you at T3 once or twice or something, but we haven't really crossed paths. We do very different things, so to speak. So I want to hear how you leveraged AI, because I saw it in 2022, in the middle of 22 with Dall-E, then ChatGPT came out in late November that year, and then by January I said, we're all in. Like everything we do, we're going to be an AI-driven company going forward. What was your view of AI as you built this business, and how did you kind of transform or did you transform?
Were you always AI-driven?
Arnulf Hsu: 10:05
We didn't. So when we started in 2020, you know, I there was not a lot of talk about generative AI, right? And maybe, maybe GPT one. I think GPT two came out maybe somewhere in 21, 22, somewhere in that range. They weren't very good.
There wasn't a lot of talk about it. Most of the AI models were really, you know, machine learning based and, you know, supervised or unsupervised sort of stuff. But it wasn't it wasn't really around generative AI, right? So, so most of the stuff that we were doing at that particular time was really more around automations, right? So, you know, if then this, that sort of stuff.
Yeah. So we started out with client notifications and kind of the name of the company GReminders, you know, is really was around the Google ecosystem and a lot around notifications pre-event and sort of, you know, post-event. We added scheduling. And then, you know, again, we continue to sort of add in that particular area and where we felt that, again, we were looking for use cases. Right.
So it's not necessarily, you know, if you're if you're a business and you're saying, okay, how you know, just adding I like you have to find a use case that makes sense for AI applications. It's not just let's just add AI to something. So find a use case that actually drives real value. And so we were sort of looking for that. And you know, because we're in the sort of calendaring area, what made a lot of sense to us is to, you know, leverage call recordings and transcripts, which we weren't really doing until mid-part of last year, quite frankly.
But we had access to the calendar, right. We had access to all the all the web meeting providers and so on and so forth. And effectively, by adding that component, it really filled out this sort of meeting management platform that we're talking about here. And so it just fit together really, really nicely. And so now that's effectively, you know, part of our part of our value proposition, our kind of value chain.
And then also on top of that, now that you have sort of, you know, this note taker product that does nice clean meeting summaries and, you know, action items, those types of things. We then took that and put that onto the sort of pre-meeting side of things. So pulling a bunch of data together putting together a nice pre-meeting briefs. And then you know now with essentially a Agentic I, which is, you know, kind of like the ChatGPT or the different systems that that might get used in mainstream, you know, get, get applied to the client. Right.
So again, leveraging AI to rather than hunting and pecking into different systems, looking for, you know, certain pages or tabs or you have to search in different places, you just ask a question just like you would, you know, Siri or Alexa or something like that. Again, just makes a lot of sense. And it's very use case driven. So I don't think about it necessarily as just, you know, I for AI sake, but it's use case, you know, where the value lies.
Richard Walker: 13:20
Yeah. You know, I was in an airport a couple of weeks ago and I saw every single advertisement, had the word AI in it, and I don't care what company it was. I mean, it makes sense when Google's talking about it, but I don't know. Does Dyson vacuum cleaners need to talk about it? And by the way, I don't know if they had that or not, but I mean, just trying to make a silly pun of it.
So it told me that it's mainstream. Therefore using it in marketing is over. Nobody should be using it in marketing anymore because the mainstream has gotten their hooks into it. And I agree with you. I mean, it's a tool set.
It's an amazing tool set, but it's a tool set to accomplish very specific things that you're trying to accomplish. You said something earlier that in preparing the meeting brief, and I presume the meeting brief is for the advisor, but does it also get sent to the client?
Arnulf Hsu: 14:07
No. So we don't do that for compliance reasons. And, you know, this particular industry is officially especially is very, you know, compliance and sort of governance centric. And so no, that that just gets sent to the advisor, but it helps them better prepare for the for the session.
Richard Walker: 14:27
Yeah. So I'll mention something else because HubSpot is a CRM we use. They came out with a copilot, and the initial version of it I thought was great. Lately I can't use it. Anytime I ask it a question, it can't find the answer.
And I don't know what happened. But I love that idea of like, oh, I don't want to go search for the records and find the right one and find that one note that was three months ago. I love that idea that you can just say find it.
Arnulf Hsu: 14:51
So a lot of, you know, almost every system will have some sort of agent. And it is very easy for, I think, organizations to say we have an agent, but what is the quality right, of that agent, just like you were talking about. So you can have a check box, but does it actually perform and is it useful and is the output accurate? Right. And so to get that kind of accuracy and to get that quality requires a tremendous amount of time working with the dataset that underlies all of this.
And again, sort of back to interoperable system, just like you would train a human to say, if I ask you this question, you have to go look in this file cabinet, Go look over here. This is where I store this. This is how I categorize something, right? It's the same process, but really in sort of a more programmatic manner.
Richard Walker: 15:50
So that's what you guys are doing. Like you, you say this is where the data lives. This is the only place to get it from.
Arnulf Hsu: 15:56
Yes, depending on what you're looking for. Right. So a lot of it is pointing the systems into the right places and making sure that the naming convention matches with what is actually being asked. And so as a result, again, it's very use case driven. So we look at a lot of the inputs and we look at some of the outputs to make sure that in fact the quality is where we generally need it to be.
And that takes a lot of effort. Right? It's not just go build an agent and pointed at, you know, a raw generic data set and let it loose. It doesn't really work like that. And so the quality, it takes a tremendous amount of time to get quality up.
And if firms you know, if independent software vendors like ourselves and others aren't doing that, you know, you're just not going to get the output that you want. And as a result, you know, having an agent that that, you know, has low utility, well, it's just not going to get used. And I think, you know, you talked a lot about, you know, everybody's talking about AI. And I think that's very true. And you know everybody wants to add AI to their system.
But again, just figure out where the value lies and what the use cases are. And so in our opinion, in our particular field here, especially around meetings call transcripts, summaries, there's huge value add there. Right. Because you don't have to take notes. You get this stuff pushed back to your system of record which you didn't want to do anyways because you didn't want to type it in, and so on and so forth.
So let AI and automation do that for you. Pre meeting briefs again very good application and the agentic I again, it's, you know, work in progress. And I would say our quality is good, but we continue to, you know, enhance that on a daily basis.
Richard Walker: 17:46
Yeah. I think for the average person who might just be using ChatGPT, this is similar to how you write good versus bad prompting. If you write just a basic prompt that says, I don't know, give me feedback on this transcript. It's just going to give you generic feedback. But if you give it ten transcripts and other sources of who you are and say, this is who I am, this is the kind of feedback I want.
This is the role I want you to play, etc. and you give it all this context and be very, very specific gives you the kind of feedback you want now. And so that's kind of what you're saying about you got to plan out where it's going to go to get the right data. So I want to ask you another question, because in those generative large language models, a lot of people understand the concept of hallucination, where the system makes up information that wasn't necessarily relevant to that data point. Right. How do you ensure as a company, how do you monitor for that?
How do you ensure that it's not pulling a different client's information and putting it in the brief for this client, or making stuff up like I don't hold IBM stock? Why do you think I have IBM stock?
Arnulf Hsu: 18:49
So a couple things a prompt engineering is a real thing. And spending time on prompt engineering pays dividends. So that's one. But really number the second thing around, especially around hallucinations, is you got to keep the context narrow. Right.
So the narrower you keep the context the higher quality the output. And so when we have a gigantic AI, it's actually sitting on top of a specific client household or a specific client record. And so the scope of the data set is just narrowed to that particular household or that particular contact. And so we have not expanded the scope of that yet to be sort of firm-wide for the exact reasons that you're talking about. The other thing we also do, which was very important to us, is that we would also always cite the source of where that summary or that sentence came from.
So anytime you ask a question, you might get some statements back. It always tells you, and it would link you back to the source so that you could verify. In fact, oh, this is where it came from. And maybe my input data was wrong to begin with. Perhaps, but at least it's always telling you this is the source of the data.
So you can essentially verify that. Yes, in fact that is what was said or that is the task that was referenced, that sort of thing.
Richard Walker: 20:14
Yeah. All right. So let's get a little bit techie here for a second. In technology companies like ours you have monitoring for server uptime and throughput and database usage and all these things so that you can keep your system alive, keep it high, performing that kind of stuff. Is there a way to monitor these agents, these large language models, these implementations you have for errors or hallucinations if you want.
Arnulf Hsu: 20:41
So a couple of things A is difficult.
Richard Walker: 20:44
Yeah I can imagine.
Arnulf Hsu: 20:46
B you are looking for feedback feedback loops right. So one is we ask customers to rate thumbs up thumbs down right. So very basic stuff that helps give feedback back to us to say hey this was a quality response. This wasn't not a quality response. And so we then look at that feedback and you know, when we get thumbs both on either side of that quite frankly, we look at those we look at that feedback.
And you know when it's thumbs down we look a bit deeper and say, okay, why is that? Did we not have access to that data? You know, as you know, you know, data lives in a variety of different systems, right? And so, you know, we may have access to two of the five systems that they use on a regular basis. And so if that data resides in other systems, we may not have access to it.
So therefore we may not need to go build integrations into those systems. There's also a bunch of development happening. You know, to the extent that that, you know, your audience is following some of this, but because every system is going to have an agent to some degree, there are now standards being developed that are agent-to-agent communication. Right? So one agent is going to be talking to another agent, and another agent going to be talking to another agent.
And then there's a there's sort of agent orchestration. And again this is a very rapid sort of, you know, developed area that's rapidly developing, you know, as these sort of standards come to be. And so, you know, we're spending time, you know, monitoring that. And, you know, to some degree it's sometimes better to be almost like a, you know, quick second mover than a sort of a first mover that you end up, you know, having to re-pool a bunch of different things. But, but feedback, you know, just go back to your original question.
Feedback loop is really very important. And then we also we also monitor ourselves to see you know what. Because oftentimes if the system is not able to give a response again we're looking at why is that the case. Where is that data living. And knowing those systems and where the data resides is very very important.
That's super critical. And again like the narrower the context and the narrower the use case the better the output.
Richard Walker: 23:00
Yeah. So this agent to agent model is something I was predicting at least six months ago. I was telling my wife about it. She's a case manager in a hospital, and a lot of her job is calling on facilities like, hey, do you have a bed available for post care, for nursing, for whatever. Do you have oxygen tanks available?
Can I get a ride for this person? All that kind of stuff, right? And so she's on the phone a lot. Or sending messages, emails back and forth. Why couldn't that job be an agent talking to the agent at the skilled nursing facilities?
Talking to the agent at the oxygen tank facility and just, you know, negotiating with each other and a lot of a lot of the interactions that I think we have on a daily basis are going to become that. And I think, how do we as individuals get our own agent to go book my repair for my automobile or whatever little things we need to do? Do you I mean, you're obviously thinking this way because you brought it up.
Arnulf Hsu: 23:52
So that's all coming, right? So everything you described will come to be. It does take time to work it through, you know, all the various systems that exist, the large technology players. Right. Microsoft, Google, Amazon, Facebook, etc..
They will and they are working on general agents. Right. General assistance sort of thing. But that's probably the most difficult because it has the widest context window, right. It has the widest and it needs to be connected to a variety of different systems and those systems.
Again there's authentication right. There's privacy. There's all kinds of things that need to be dealt with. And those will all get resolved over time.
Richard Walker: 24:39
Yeah.
Arnulf Hsu: 24:40
But once generally systems have agents and there are standards right. So just like, you know, the web standard, the HTTP standard, these types of standards that exist, agents will be able to effectively discover other agents. Right. And then you would authenticate those and then you would have access to your data, you know, in these other systems. Right.
And so all of this stuff sort of takes time. But these standards are, are on their way. You know, anthropic has put forth something called MCP. Google has put forth something called agent-to-agent recently. And so there are a bunch of things happening in this space.
And various vendors are adopting it, as are we as well.
Richard Walker: 25:26
Yeah, it's a really exciting time. It's also highly challenging, right? Because you're if you're running a business, you're asking, should I be building agents? Because I could augment what I do. And I keep coming back to the conclusion of I should buy products like yours, because you're building the agent specific to the need and specific to the systems that are in place.
I don't know if I don't necessarily want to go out and build a bunch of agents, unless it's a custom system I own and I developed. So what is the future for you guys? And I actually I want to ask this in another context. There has been a lot of interest and excitement in your competitors and the whole note-taking idea. In fact, I think it's a disservice to call them note takers because it's so much more than note taking.
Where do you see what you're doing evolving to?
Arnulf Hsu: 26:16
So, you know, kind of what I was saying earlier, I think there's a lot of data, you know, in these unstructured areas, right? Like transcripts and email and so on and so forth. More so than there is in some of the systems of records. And I think systems like ours and others that are, that are playing sort of in this area can help really enhance and enrich, you know, the systems of record. And so I think that's where a lot of this is, is sort of heading, I think, you know, the agentic I and again like the quality needs to improve.
We need to have access to more systems. But again, you know, I think as, as a firm you will tend to gravitate to, you know, some sort of agent which might be your primary first class citizen sort of thing. And then that system will interface with the other systems. So it doesn't really matter, sort of, you know, where you perhaps ask the assistant could be could be in your CRM, it could be in our system, it could be in, you know, some other system. They will essentially all sort of effectively kind of talk to each other over time.
Yeah. But in general, you know, all of this automation and AI is again, like giving you sort of, you know, superpowers in that it really dramatically creates a ton of efficiency. And I think people that over time do not leverage this type of tech, you know, will to some degree fall behind.
Richard Walker: 27:55
Yeah. So I'm curious. I almost don't want to ask this question because I might think there's a bias in it. But do you see AI? Do you ever see AI as a threat to what you're doing?
Because, I mean, obviously you see it as opportunity because you're using it and you're applying it. I see it as opportunity, but I also see it as threat. So I'm kind of curious how you kind of see this fast evolution and transformation that AI is bringing.
Arnulf Hsu: 28:22
I wouldn't say a threat. I mean, you know, we're certainly leveraging it. We see it as a huge lift and a huge sort of opportunity in general. You know, I think sometimes folks in general see, see as sort of a zero-sum game where, you know, AI is coming for employment and those types of things. I don't necessarily take that sort of approach, you know, that this, this, that concept sort of assumes that, you know, everybody has sort of full employment and everybody is doing, you know, all the things that need to be done.
And the reality is, in every company I've ever been part of, there have never been enough resources to do X, Y, and Z. Right. And so isn't. That the truth? Oh my gosh, thanks.
Richard Walker: 29:07
For saying that.
Arnulf Hsu: 29:08
So as a result, you know, I think if automation and AI can take some of the some of the sort of, you know, more administrative stuff off of people's hands, they can start working on higher value things for being more proactive about, you know, client engagement and so on and so forth. So I don't take the sort of, you know, more nihilistic approach of, you know, it's going to, you know, it may affect employment in some areas. Right. I'm not saying it won't. But generally speaking we see it as a net positive.
Richard Walker: 29:43
You know, I've never had somebody say, Rich, your software is going to put me out of a job because it's going to fill out forms for me because who wants to fill out forms? I mean, seriously, it's just like I didn't really want to manage the calendar in Excel and send out five emails to each client over a six month period to remind them of our meetings to collect their data. I didn't want to enter more notes into my CRM. I don't want to do a lot of the things that your product is doing, and I certainly don't want to do the things my product is doing to make it better. So yeah, why don't we eliminate the dirty work and let them do better work?
Man, I got to wrap this up. I'm really enjoying talking to you. Before I get to my last question, what is the best way for people to find and connect with you?
Arnulf Hsu: 30:25
So you can go to our website G com. You can also hit me up on LinkedIn Arnulf Hsu. And if you want to send an email it's arnold@greminders.com
Richard Walker: 30:37
Awesome. All right. So this is one of my favorite questions. Who has had the biggest impact on your leadership style and how you approach your role today?
Arnulf Hsu: 30:46
Oh, there have been there have been a lot of folks that have, you know, impacted sort of how I think about the world. I ask, you know, I, I'm sort of a, you know, product centric individual. So I ask a lot of questions. I ask why five times until I finally understand what the root cause is and, you know, things like that. You know, a couple people sort of that come to mind, you know, famous people would be, you know, folks like, you know, Steve Jobs, for example, you know, very much sort of, you know, product centric individual spent a lot of time on, you know, building beautiful products that that people love and spending a lot of time, you know, understanding, you know, how they operate.
And again, being very detail oriented and constantly sort of iterating on that. So I would say, you know, certainly, you know, somebody famous would be Steve Jobs. You know, another friend of mine, his name was Steve Krauss, also Steve. He's, he was a very he is a very product centric individual and taught me a lot about sort of building product and building product that people love to use.
Richard Walker: 31:55
Yeah. So I'm a product guy too. I've had to learn sales, I learned engineering so I could build product, but I just I love bringing things to life, and I love seeing it in a way that it should be versus what it is, so that you can make it better and better over time.
Arnulf Hsu: 32:09
Well, sales, you know, sales is just solving people's problems, right? Like you don't have to if you solve a problem, you make the sale.
Richard Walker: 32:16
Yeah.
Arnulf Hsu: 32:17
Generally how I think about sales.
Richard Walker: 32:19
You know what? My very first company, when I was 12, was selling a toy for water fights, and it was so obvious everybody wanted it. I didn't have to sell it at all. I just had to have the supply for people to buy. So I kind of grew up with this idea of being an entrepreneur means give people what they want and they'll buy it from you.
It's not even a challenge to sell it. I learned in software it can be quite challenging. And you come from the business to business enterprise sales world, it can be quite challenging to navigate the structure of a company and their initiatives and their timeframes and all of that, that that I think is art trying to figure.
Arnulf Hsu: 32:53
I mean, there is a framework for enterprise selling, right? So it's more difficult to do product-led growth in the enterprise. It's much more easier to do that on the SMB side. But so there is a you know you know, you identify stakeholders and you know, figure out, you know, how the buying cycle works and all of that. But at the end of the day, it's still about solving a problem, right?
Richard Walker: 33:17
It is. Absolutely.
Arnulf Hsu: 33:18
And if you can solve the problem and do it effectively and again, you know, navigate the enterprise sales cycle, you will be quite successful.
Richard Walker: 33:25
Yeah. And with enterprise it's usually at a scale that's quite nice right. All right I got to wrap this up. I want to give a big thank you to Arnulf Hsu, CEO of GReminders for being on this episode of The Customer Wins.
Go check out Arnulf's website at greminders.com and don't forget to check out Quik! at quickforms.com where we make processing forms easier. I hope you enjoyed this discussion. We'll click the like button, share this with someone and subscribe to our channels for future episodes of The Customer Wins. Arnulf, thank you so much for joining me today.
Arnulf Hsu: 33:56
Thanks for having me. Really appreciate it.
Outro: 33:59
Thanks for listening to The Customer Wins podcast. We'll see you again next time and be sure to click subscribe to get future episodes.
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