Ep 541: AI & Trust: When 98% accuracy won’t cut it and how Sage can fix it

Topics Covered in This Episode:

1. AI Trust Issues with Financial Accuracy
2. Sage's 7-Billion Parameter Model Training
3. Sage Copilot's Accounting AI Accuracy
4. Transparent Trust Labels in AI Usage
5. Financial Leaders' Trust in Sage's AI
6. Sage's AI Factory Safety Measures
7. Sage's Industry Collaboration for AI Accuracy
8. AI Implementation Strategy in Accounting

Keywords:

AI trust, 98% accuracy, business leaders, Sage Future Conference, Atlanta, trust in AI, Sage Copilot, accounting software, global software company, Newcastle, North America headquarters, CFO, finance team, financial reports, forecast, budgets, credibility, financial accuracy, creative accounting, large language models, ChatGPT, task-based AI, accounts payable automation, invoice reading, data science, AI development, neural models, conversational interface, GPT billions of predictions, generative AI, deterministic AI, billions of documents, fine-tuned models, accounting expertise, AICPA partnership, AI factory, automated machine learning, observability, model drift, hallucination detection, Sage AI factory, industry trust signals, safety mechanisms, customer by customer basis, Sage trust label, transparency labels, trustworthiness, ethical AI, responsible AI, AI safety, AI innovation, industry standards, problem-solving, financial trustworthiness.

Podcast Transcript

Jordan Wilson [00:00:14]:
What if good isn't good enough? Right? I think it's something that business leaders are constantly thinking about when it comes to AI. They're like, hey. If we get this right most of the time, let's go ahead and roll this out to the entire organization. And sometimes that might be okay. Right? If you're doing strategy, creative work, content production. But what about your books? What about your finances? Sometimes being 90 or 95% correct could be bad. It could be a recipe for disaster when it comes to your business's AI plan in 2025 and beyond. That's why today I'm excited to talk a little bit about trust in AI and how well, if you're watching, our video, our livestream, you see I am at, the Sage Future Conference here in Atlanta and how Sage is really helping increase everyone's ability to trust their AI.

Jordan Wilson [00:01:16]:
Alright. I'm excited for this conversation. I hope you are too. What's going on y'all? My name is Jordan Wilson. I'm the host of Everyday AI. This is your daily livestream podcast and free daily newsletter, helping everyday business leaders like you and me not just learn what's happening in the world of AI, but how we can actually leverage it to grow our companies and our careers. So make sure if you haven't already, go to our website at youreverydayai.com. We're gonna be recapping, today's conversation and a whole lot more.

Jordan Wilson [00:01:42]:
But like I said, you can probably see a little different setup here. I am in Atlanta at the Sage Future Conference, and I'm excited to welcome our guest for today, Aaron Harris, the CTO of Sage. Aaron, thank you so much for joining the Everyday AI Show.

Aaron Harris [00:01:55]:
Thanks for having me. Really excited to be here.

Jordan Wilson [00:01:56]:
Yeah. On the road in

Aaron Harris [00:01:57]:
your hometown actually. But for, you know, for those of our audience that maybe don't know Sage, tell us what Sage is. Sure. Yeah. So Sage is a a global software company that focuses on accounting, HR, payroll, manufacturing, sort of all of the things that a you know, finance and accounting team needs to run the back office of the business. So we're actually a British company. We're, we're headquartered in Newcastle, England, but we've got offices all over the world. And and our, you know, our headquarters here in North America is is obviously here in Atlanta.

Aaron Harris [00:02:26]:
It's company has been around for a while. We've we've been building accounting software for more than forty years now. The company that I cofounded that sort of got acquired in, we started more than twenty five years ago. So we've been doing this for a while. We're not as well known in The US, because US is a huge market, and there's there's there's sort of more players in this market. But if you go to The UK or Spain or France or Germany, like, some of those countries, we're a bit of

Jordan Wilson [00:02:51]:
a household meeting within the accounting industry. Yeah. And let's just kind of skip to the end. When it comes to the intersection of of AI and accounting, why is, you know, sometimes being 90 or 95% accurate, why does that not work?

Aaron Harris [00:03:08]:
Yeah. So, I mean, there's there's so many ways to address that question. I think the the first way to start is that, you know, look at the CFO in a company and the finance team. That that CFO trades on trust. Right? Their their job is to create confidence, not only within the business, but with stakeholders, whether it's investors, creditors, right, that you can rely on the accuracy of the financial reports that that they're issuing. Internal stakeholders can rely on the forecast and the budgets, right, that are being provided. And the minute that CFO puts something out that, you know, has a mistake in it, they're gonna lose that credibility. They lose that trust.

Aaron Harris [00:03:52]:
And so the bar is incredibly high. You yeah. One of the things that's kind of interesting in the mindset of a CFO and a finance team, if if I'm a penny off Mhmm. In the the basic equations of accounting, they will hunt that penny down, right, for days until they find it. They may not ever give up until they find that penny. So 99%, yeah, that's not gonna cut it. Right? It's gotta be perfect.

Jordan Wilson [00:04:16]:
Yeah. And not only that. Right? And I'm sure many of our audience can relate to this. Large language models by themselves. Right? So if you're using something like ChatGPT, not always the best at math.

Aaron Harris [00:04:28]:
Right? Yeah. No. Not at all. Not at all. Yeah. In fact, you know, large language models are trained. I mean, this is what makes them so amazing. They're trained to be creative.

Aaron Harris [00:04:35]:
Mhmm. We don't want creative accounting, and we certainly don't want them doing math. Right? So, you know, in the way that we build AI, this is something that I often tell audiences. One of the first rules of building trusted AI is not to use AI when traditional development will work better. And so if you want AI to do math, we're gonna give AI a calculator to do that math with.

Jordan Wilson [00:04:56]:
Mhmm. And, you you know, I wanna get more into this this piece of trust, but, you know, I saw your, keynote. And when you were showing some of the things on screen, I'm like, wait. I need that. Especially when it came to Sage Copilot. So can you explain a little bit for, maybe our audience that doesn't use Sage in your AI offerings, what the heck, like, how can you do that with such high accuracy, and, you know, help people kind of close the books, you know, much faster. You know, I think you said originally it was like two to3 weeks, and now down to like two to3 days.

Aaron Harris [00:05:32]:
Yeah. Yeah. We're still at we wanna get rid of it. Right. Right. It's a relic. Right? It's archaic. We wanna get rid of this thing.

Aaron Harris [00:05:39]:
So we we think about this in in waves. Right? In the way that we build and deploy AI. And and the first wave, we call task based AI. This is AI that you don't necessarily see at work. So the first thing that that, we we built, in the world of accounts payable automation was was AI to read and categorize an invoice. And my first conversations with the data science team was, okay. There's there's a bunch of models, right, from big players that are built just to do this. Why are we not using one of those? Mhmm.

Aaron Harris [00:06:12]:
And, you know, what they had to convince me of was that those models just weren't good enough. Mhmm. Right? They were about 80%, seventy five, eighty % accurate. But in addition to that, you know, that wasn't an even that wasn't evenly, right, deployed accuracy. They really struggle to find the total on an invoice. Right? They might be 30 or 40% accurate on that. Turns out it's kinda hard to find. And so, ultimately, we had to go build our own models, models plural.

Aaron Harris [00:06:39]:
Right? We've got five models just you know, some of them are looking for the total. Some of them are checking the work of the other models to make sure that, yeah, you really did find find the total. So, you know, it ended up being dozens of models. Now, you know, the the the limitation of this approach is that you can't really interact with that AI, and that AI has to be very, very carefully orchestrated, scripted. You know, it does what it's told to do exactly the way it's told to do it. We can't really get to the next level of automation until you can interact Mhmm. With AI. And so that's the big breakthrough with large language models that sit behind Sage Copilot.

Aaron Harris [00:07:16]:
Like, now you can be directive of the way AI works and sort of how it gets its job done. And so that's you know, it's a it's a huge breakthrough, and it it it's really a change of psychology, if you will, in the way you design the product. When when we are in that task based phase, you know, a lot of lot of customers don't even realize how much AI is actually operating behind the scenes.

Jordan Wilson [00:07:38]:
Yep. Right. They don't realize that until they're confronted with a conversational interface. And so that, like, totally changes the way you design. You've got to design for confidence now. Yeah. And and how did we get to the point where, you have a product like Sage Copilot that can accurately, you know, take advantage of, you know, the the powers of generative AI yet work in a more almost deterministic, right, like like way. But, you know, I saw that, you know, there's been billions of predictions, millions of of documents that you've used to to get here.

Jordan Wilson [00:08:14]:
So Yep. Without, you you know, going into because I'm sure we could talk about this part for hours. Yeah. But how did we get to the point where, yes, you can feel confident as one of the global leaders in the space to say, yeah, you can go use AI for some of your most important financial tasks. Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on GenAI. Hey. This is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft, and NVIDIA have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead.

Jordan Wilson [00:09:01]:
And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use GenAI. So whether you're looking for ChatGPT training for thousands or just need help building your front end AI strategy, you can partner with us too, just like some of the biggest companies in the world do. Go to your everydayai.com/partner to get in contact with our team, or you can just click on the partner section of our website. We'll help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on GenAI.

Aaron Harris [00:09:39]:
Yeah. So I I think there's there's two parts to that quest or two answers to that question. But but the, you know, the first, I guess, I will get back to is how do you design the product? Mhmm. Right? You sort of have to like, you have to be credible and believable with your customers. And so if if I stood in front of, you know, our customers today and say, you know, don't worry. It's a % accurate. Like, you know, we've got it. They they wouldn't believe at all.

Aaron Harris [00:10:02]:
Right? And so you've got to adapt the experience. You've got to design around this this understanding that, hey. We're gonna get that large language model to be incredibly accurate. But what we're gonna do is we're gonna over index on understanding, okay, have we met that level? And if not, you know, how do we engage a human properly to to to review the work? And so that that is such a huge, huge part of it. But the other part of it is that, again, the off the shelf models is, right, as good as they are, as magical as they are, as powerful as they are, they're not quite good enough, right, for for what we needed. Yeah. We need a large language model that knows, like, in-depth, you know, with expertise how our products work. It needs to be an expert at our APIs.

Aaron Harris [00:10:47]:
Mhmm. Right? So in in the process of completing a task, it's probably gonna write some code, on the fly, right, to use an API. And so, like, it's not gonna work if it hallucinates in the process of of, you know, building that API request. And so all we found was these off the shelf models are pretty brilliant, but, you know, two problems. First, as brilliant as they are, they still make mistakes. But second, you know, they're incredibly expensive. Sure. Right? So if we wanted to go about, you know, not just operating these models, but sort of get into the world of building one of these gigantic large leg language models, I'd never get the budget to do that.

Aaron Harris [00:11:22]:
Right? What? A hundred million dollars?

Jordan Wilson [00:11:24]:
A lot of money to train those. Yeah. Trillion parameters cost a lot.

Aaron Harris [00:11:28]:
Yeah. Yeah. And so, you know, fast forward two years and the the, you know, the efficiency and the capability of, of of, you know, fine tuning these models has increased rapidly. Right? So so costs have come down, efficiencies come, is increased, but also the tools available to companies like us have gotten better and better and better. And so we took you know, so so GPT, as you mentioned, right, trillions of parameters. You know, GPT four, we think 2,000,000,000,000 Mhmm. Probably. We started with a model that's 7,000,000,000 parameters.

Aaron Harris [00:12:00]:
Mhmm. Alright? And we fine tuned it from there. And when you're fine tuning, like, you can sort of, like, slough off the stuff that you don't want it to do. Right? We we we train our model to, like, not accept toxic prompts. Right? We we train it to be pleasant in the way that it interacts. And then, you know, if the conversation is not about accounting, then, we don't wanna have that conversation. So we can get

Jordan Wilson [00:12:23]:
it down to a 7,000,000,000 parameter model and we can fine tune it to be really, really, really good at these accounted tasks we wanted to do. You know, it's interesting because I'm sure there's a lot of people in our audience specifically, you know, CFOs, people who work in finance that maybe their first or one of their first interactions with, you know, AI, they were probably saw result and they're like, I'm never gonna touch it again. Right? Because some of the earlier right? Even if you say something like GPT four or some of these, you know, earlier trillion parameter models, they couldn't do basic math. Right? So, I think even a lot of people that I talked to, they kinda wrote off, you know, hey. We're not gonna use AI in this department anymore. But it sounds like the, one of the models that you have powering your Sage Copilot. I mean, it sounds like it has like a like a PhD in in in CPA. Right? Like, talk a little bit more about how you were able to address that trust issue by essentially going through and training this 7,000,000,000 parameter Yeah.

Jordan Wilson [00:13:23]:
Model to become an expert. Yeah.

Aaron Harris [00:13:25]:
And I wanna talk about that first experience too. But but, so what we've done is we've taken that base model, and then we've we've trained in all the product documentation and sort of loads of material around that product document documentation about best practices and how products work, you know, all the developer code. Mhmm. We've trained it on accounting textbooks and accounting exams. We've trained it on content that helps it to to sort of understand and speak in the vernacular of, you know, accountants and financial analysts. And one of the things that's super exciting that that we announced today is we're partnering with the AICPA, which is the industry association that accredits CPAs. They're now going to make their, you know, professional content available to us to train into the model. Mhmm.

Aaron Harris [00:14:10]:
Now it's a proof of concept. You know, we're gonna be conservative as we are when it comes to AI. So I can't sit here and say, like, this is exactly what we're predicting. But I think it's an incredible signal that, you know, the accounting industry, which, you know, early on, like, you know, the headlines were saying accountants aren't gonna exist anymore. Right? I think it's incredibly interesting that the accounting industry is is not just sort of embracing AI. They're contributing to the development of of AI models. But I I wanna kinda come back to that first experience because it's so critical. You're absolutely right.

Aaron Harris [00:14:43]:
If a CFO uses, our our Copilot and their first their first experience is it makes a big mistake Mhmm. They'll not like, we won't get another chance. I grew up in in Silicon Valley, probably like a lot of people on your in your audience. And, you know, the mantra in Silicon Valley has always been move fast and break things. Mhmm. But when when you're building this kind of AI in this industry, we've got to, like, have a completely different culture. So I talk about I it's kinda pithy, but, like, accept humility, embrace embrace responsibility. Like, we have to have a different mindset.

Aaron Harris [00:15:20]:
We can't rush AI to to our customers. If they have

Jordan Wilson [00:15:24]:
that bad experience, they won't come back. So you've talked a little bit on how you were able to, increase, accuracy. Right? By creating and and fine tuning your own model, trained on everything that anyone in in finance, CPA, etcetera, really cares about. But what about on the back end? What about, you know, observability, like traceability? You know, how does, you know, Sage Copilot and some of the things that you announced today, specifically kind of this trust label. Yeah. How does that addressed it?

Aaron Harris [00:15:56]:
Yeah. So so one of the things that we had to do very early, is, you know, we had to build our own infrastructure for machine learning. So I I kinda like to come to compare this to the early days of software as a service. So if you go back, you know, to the to the very earliest days, you know, all of us, you know, Salesforce included, and the the other pioneers that are kinda still around, We we had to set out an objective for our developers that we would release a new version of the code on a weekly basis. We would upgrade every customer to that to that next version of the code automatically, so everybody would always be on the same version of the code, and we had to do this without disruption. This sounds normal now. Like, twenty five years ago, that was not normal. Like, that was an incredibly provocative thing to say.

Aaron Harris [00:16:46]:
So if you fast forward, when we started building AI, I had to give the engineers a different mission that was even more provocative, I believe. It's like, I don't want a weekly release. I want you to automate the training of this AI and detect when it's improved enough and then automatically update the version. But it gets worse, mister developer. Like, you need to be able to do this on a customer by customer basis. Right? So we're gonna have some big models that are trained, you know, from from the collective. But a lot of what we do, we need to train from the individual customer on a customer by customer by customer basis. Mhmm.

Aaron Harris [00:17:23]:
So you've gotta build this infrastructure that can automate all that, but do it in a safe way. So we we built all the automation. You know, this is why we've got tens of thousands of models in production today. But what we also did was we built in all of these safety mechanisms, all of these controls. So, you know, we've got systems that detect model drift. Right? And launch a process to get a data scientist involved. We've got a couple of, safety mechanisms that detect hallucination. This is where we've actually gotten some of our patents on this.

Aaron Harris [00:17:53]:
So we call this whole thing the Sage AI factory. And, you know, if you see me talk to our customers or analysts, right, partners about AI, I'm invariably gonna talk about the Sage AI factory because I think it's so important to understand behind the scenes, like, how does how does the factory work? Right? How does this stuff get built? And how do I know that you're taking steps to make sure that it's safe? So I'm curious. How many, total organizations you have using, kind of the the AI Copilot features inside Sage right now? So we have tens of thousands Mhmm. Using, Copilot, in in various in various places around the world. Mhmm. We started small. We started we started with, you know, sort of small businesses that have simple accounting needs. We started with small accounting firms that that tend to serve those businesses, and we started on sort of the early capabilities.

Aaron Harris [00:18:46]:
And and over time, we've expanded it to more products, you know, more countries, but we've also sort of gone into now more sophisticated businesses. So we launched, early access for Sage Intact, at, about about, six six or eight months ago. And, I guess, the thing that I would wanna reinforce here is we're being very deliberate about how we open that to more to more customers. Right? We're just we're so we're so careful

AI [00:19:14]:
Mhmm.

Aaron Harris [00:19:15]:
To not have that first bad experience. And And so we're gonna be very deliberate in the way we roll this out to more customers over time.

Jordan Wilson [00:19:21]:
Yeah. It seems like a super strategic rollout and obviously makes sense when, trust is paramount. Right? And you can't like you said, you can't have someone have that first bad experience, where something goes wrong because the stakes are so high. Yep. You know, so I'm I'm curious throughout the iterations of of Sage Copilot and then, you know, obviously, another wave coming, with what was announced here at Sage Future. What were maybe some of the, initial, maybe, obstacles that you were able to overcome? And then maybe what do you think is is next, right, in terms of not like, hey, what's what's the exact product roadmap, which I know you guys did lay that out a little bit. But in terms of, trust, in terms of reliability, what have you guys already been able to overcome and then what's next to overcome? Yeah.

Aaron Harris [00:20:07]:
Yeah. So so if you'll forgive me, I'm gonna tell a story. Please do. And and I promise it's setting me setting up for the answer. So so I've been talking about, you know, Sage Intacct, and I was one of the cofounders there. So twenty five years ago, we just started the product. We just launched the product. Our biggest objective or the the biggest obstacle to to getting customers to buy the product is that they were not willing to put their data Mhmm.

Aaron Harris [00:20:32]:
In the cloud. And, you know, we sort of scratch our heads at that now, but sort of you have to imagine how new this was at the time, and we would we would use all the arguments you'd expect. We can spend more on security than you can. Our livelihood depends on keeping your data safe. You trust a bank with your money. Why not trust us with your data? Like, it just wasn't working.

Jordan Wilson [00:20:51]:
Yeah.

Aaron Harris [00:20:52]:
And so what we fund what we ultimately ended up doing, right, I think it's pretty clever. We put a button in the product that said, see my data. And when a user clicked that button, we would pop up a window that had a webcam in the data center pointing at the the server that had their data on it. Now we only had one server at that time with customer data, so, you know, this is pretty easy to do. Then this is this is actually pretty awesome. It actually kinda worked. Thankfully, we reached a point, where it was less needed when we turned it off. Now the reason why we turned it off, and I I promise again, true story.

Aaron Harris [00:21:26]:
We had a technician in the data center who was doing some cabling and doing some maintenance, sort of bending over and moving around a bit. Probably should have been wearing a better belt. And, you know, sales rep chose at that point to click in front of a prospect. Right? You know, show my data. Right? And and they got the full transparency. The full transparency. Full transparency.

Jordan Wilson [00:21:46]:
They saw the data in them some.

Aaron Harris [00:21:47]:
Right? And so, like, this is kind of like an old story about building trust and, you know, how, you know, how trans transparency roles plays into that. Yep. Today, it's still true. If you talk to companies today and they're evaluating your software and they know that AI is a big part of the offering, they want to know that you're they're gonna you're gonna keep your data safe. Right? They're gonna wanna know that they can trust the technology. And the problem with AI is the industry and and the the technology is moving so fast that sort of the regulatory environment around it hasn't kept up. Mhmm. So we can't rely on a lot of sort of external signals.

Aaron Harris [00:22:26]:
Right? That that you can trust the AI. We we do use some and I'll get into that. So what we determined was, well, we need to put a button in the product. Mhmm. We're gonna put it in each AI feature where a user can click it. And what's gonna happen is instead of that webcam, where we pop up what we call the Sage trust label. Yep. And in that trust label, this is kinda like a nutrition label.

Aaron Harris [00:22:48]:
We're gonna be super transparent about, okay, what are the models that we use to build this? Are we training our own models? You know, if so, how do we use your data in the training of those models? You know, what are the steps we're taking to keep your data safe? What are the safeguards in place to defend against, you know, issues of bias or other ethical concerns? And we're, you know, we're putting it in a nice easy to read format wherein and if they wanna learn more, we give them a button where they can click and go out to read all of our our AI commitments, kinda solving the the same problem, really. So we, yeah, we announced that today. We're we're encouraging other vendors in the industry to kinda follow suit. We know we can't wait for for the regulatory bodies to to catch up. So we've got to plow ahead with something we think simplifies this and sort of signals to the customer, here's why you can trust us.

Jordan Wilson [00:23:37]:
What do you think is going to be, or or or maybe this is it. Right? Because I think every single, you know, company that that's trying to, you know, put out responsible, trustworthy AI products, there's always that that that hurdle. Right? Is with the trust label, is this something that you think is gonna be not like the last hurdle because there's always innovation, there's always, you know, new capabilities. But is this gonna be one of those, you know, those hurdles that after you get over it, you're like, wow. This made quite a difference for people in finance to be able to trust AI in their data.

Aaron Harris [00:24:12]:
I think it's gonna help a lot. I mean, I I I think that, you know, the first thing that it's gonna do is it's gonna get the CTO off the phone. Mhmm. Right? You know? I I so, you know, when you get a customer who's evaluating your software, and we've we have millions of customers, by the way. Right? We've got 4,000 people in sales. If if we get a sophisticated customer that's got a question about AI, like, they're gonna ask me to get on the phone and talk to that customer and explain it. If if we've got this trust label, it just makes it easy, right, for people to understand and evaluate. So I think that's critical.

Aaron Harris [00:24:43]:
I don't think it's gonna go away. I mean, I think it's gonna evolve and change. You know? Is this the moment where we solve the the issue for good? You know, is this you know, is there gonna be a point where I can turn off that button the way I turned off the webcam? Mhmm. Yeah. I don't know. But I I guess we're we're this everything is happening so fast with AI. It's progressing so fast. And I think we all need to be a bit honest Mhmm.

Aaron Harris [00:25:07]:
Right, that that there's gonna continue to be lots of reasons to not trust AI. Right? You know, it's just broader than just the accounting field. So, you know, I think we've gotta have this mindset that this isn't you know, this is one step in the journey, and we're gonna have to continuing you know, continue evaluating and looking at, you know, okay. How are people feeling about trust in AI? And what are the new things that are causing them to not trust it? And we're gonna have to just keep adapting as we go. Mhmm.

Jordan Wilson [00:25:34]:
So, Aaron, we've we've talked about a lot in today's conversation. So, you know, everything from trust and transparency to even how Sage is not just, you know, building their own models, but how they're showing their work, to to customers on how it's being used and how it's being implemented. But, you know, as we wrap up, what do you think is the one most important takeaway for those people whether they're, you know, a a CPA, whether they're a a CFO at a huge organization? What's the one biggest takeaway that you want people to know from, you know, Sage future here when it comes to trust in AI?

Aaron Harris [00:26:09]:
So I I I think, you know, the one thing is the future is the future that we build. Mhmm. And sort of taking on the responsibility to build that future is is pretty serious. And so that's, you know, why I'm having this big conversation with you. What we've learned through all of our conversations is the biggest signal of trust is the company behind the AI. Right? I I, like, I'm not a sophisticated person maybe. I don't know how to evaluate this, but, hey, that's a brand that I trust. And it it's gotta change your mindset.

Aaron Harris [00:26:39]:
Right? You've gotta be transparent. You've gotta be credible. You've got to be willing to admit that, hey. AI is not foolproof. It's it's it's gonna make pro it's gonna have problems. And then you've gotta make these commitments that you publish and you've gotta stand behind them.

Jordan Wilson [00:26:55]:
I think that was such an insightful look into not just what's happening, here at Sage and their, Copilot and everything happening at, Sage Future, but also just the industry as a whole. I think it's important for people to hear, yes, authenticity and transparency are just as important as productivity gains and everything else that you get from AI. So, Aaron, thank you so much for taking time out of the very busy Sage Future Conference to join us. We really appreciate it. Oh, thanks for giving me

Aaron Harris [00:27:24]:
a chance to talk about my favorite topic.

Jordan Wilson [00:27:26]:
I love it. I love it. So there was a lot happening in this conversation. Maybe you missed, a golden nugget that Aaron just dropped on us. Don't worry. We're gonna be recapping it all in our newsletter as well as everything else that was announced here at Sage Future. And it's not just if you're a CFO or a CPA, even if you're a small, medium sized business owner, a lot to know that was just announced. It's all gonna be in our newsletter.

Jordan Wilson [00:27:47]:
So thank you for tuning in. Please join us back tomorrow and every day for more everyday AI. Thanks, y'all.