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Trusted AI in Finance: When “Good Enough” Isn’t Enough for Business Integrity
As agentic AI and generative technology rapidly advance, many organizations are enchanted by the opportunity to automate, accelerate, and amplify workflows. Yet, beneath these promising capabilities lies a problem that’s particularly acute for finance leaders: Is “good enough” truly sufficient when numbers run the organization—and any error can have costly downstream effects? Recent developments in explainable, auditable AI show that the bar for trust must be set much higher.
AI Trust and Explainability in Finance
Deploying AI within finance is more than simply connecting an LLM to an accounting system. The accuracy of results is paramount, given that finance decisions ultimately depend on the reliability of every figure. The shortcomings of text-based AI models, particularly their tendency to hallucinate or miscalculate without proper grounding, are well documented. Early generative models were often unable to perform basic arithmetic reliably—incomplete training data and fabrications led to misplaced trust.
This episode demonstrates the necessity for audit-ready, fully explainable processes in AI financial tools. Solutions arriving to market now emphasize more than generative output: they prioritize transparency and allow for the auditing of every step, data provenance, and system reasoning.
Integrated AI Systems: Beyond the Model
Performance in finance AI requires more than an impressive model. True business value comes from integrating robust agentic AI with reliable accounting backbones and secure, standards-based data protocols—including technologies like MCP (Model Context Protocol) and A2A (Application-to-Application) standards. This integration ensures that answers are not produced in isolation; instead, models operate only with live, system-grounded business data, vastly reducing error rates and removing “black box” uncertainty.
Agentic layers now empower CFOs, controllers, and accounting teams to close books faster, keep budgets on track, and surface business-critical insights that were previously inaccessible. Unlike generic chat agents, these systems can take meaningful actions, all within the enterprise’s established software and compliance boundaries.
Mathematical Rigor and Tooling for AI Outputs
A marked leap in finance-focused AI is the consistent handoff between automation and mathematical rigor. While LLMs struggle with arithmetic based solely on training data, the newest systems leverage external calculators and trusted computational tools. The result: each output, whether simple addition or more complex analytics, is computational—as opposed to speculative—grounded.
Correctness is non-negotiable. “Close” answers do not cut it when reporting to tax authorities or facing a financial audit; only rigorously validated, fully auditable outputs are suitable for critical processes. Trusted AI now exposes the sequence of decisions through “chain of thought” reasoning which is always available to the user, enabling full transparency and assurety.
Governance, Agency, and Human Oversight
No modern finance AI solution should operate without a robust human-in-the-loop approach. Governance and control panels are now standard, providing finance leaders with granular supervision over what AI can and cannot do on their behalf. AI acts as a doer—executing task automation, anomaly detection, and workflow acceleration—while the human remains the supervisor and ultimate reviewer.
Legacy fears of AI replacing expertise are reframed: with today's talent shortages in accounting and finance, intelligent agents automate tedious data entry and pre-audit checks, freeing up skilled professionals to focus on forward-looking strategic analysis rather than reactive batch updates.
Continual Change Management for AI Workflows
One core insight is the need for continuous audit and feedback. As AI capabilities rapidly improve, decision makers must revisit change management and workflow audits far more frequently than previous technology refresh cycles dictated. Shadow IT—rogue experimentation with external AI systems—remains a risk, making internal visibility and constant communication essential.
Organizations are encouraged to pilot AI for specific, auditable finance functions, rather than attempting broad transformation all at once. Early wins can come from anomaly detection in the general ledger or automated invoice processing, which build confidence and establish transparent value before scaling up.
Selecting and Deploying Best-in-Class AI Tools
The AI models powering advanced finance tools are selected and orchestrated through platform partnerships—combining market leaders (Anthropic, OpenAI, Microsoft) with domain-specific controls and governance. By embedding these engines within accounting platforms, organizations receive both cutting-edge AI reasoning and the contextual constraint necessary for finance-grade results.
Top solutions provide a control plane that ensures only the most accurate, relevant, and compliance-ready outputs make it to the end user. Answers are always grounded in the organization’s own financial data—removing the allure and risk of shadow IT and manual copy-pasting from generic public AI models.
Preparing Finance for Near-Future AI
Continuous readiness is an operational mindset. Decision makers in finance must hold their processes loosely—ready to adapt for true value as capabilities emerge. Early adoption should focus only on tools that directly solve real business pains, such as drastically reducing the days required to close the books, or providing persistent, real-time accounting status updates.
Key policies should always insist on:
AI outputs that are explainable and fully auditable
Human oversight at all critical junctures
Selective, case-driven adoption, and ongoing process auditing to ensure risk remains managed
Conclusion: Building Confidence with Accountable, Auditable AI
The current wave of agentic, explainability-first AI is not about replacing finance professionals; it is about amplifying their strategic importance by eliminating manual drudgery and providing actionable insights with full transparency. Trustworthy AI is already available for finance—when built with auditable chain of thought, grounding in live data, and robust controls for human review. In this era, “good enough” may have once been tolerated; but with accountability, explainability, and audit trails now within reach, finance organizations have new standards for what trustworthy AI must deliver.
Topics Covered in This Episode:
- AI Trust and Explainability in Finance
- Sage Copilot's New Agentic AI Features
- Financial Accuracy and Model Risk Management
- Chain of Thought Reasoning in AI Outputs
- Explainability and Auditability for CFO Confidence
- Human Agency and AI Automation in Accounting
- Shadow IT Risks and Model Integration
- Risk Appetite for SMB Finance AI Adoption
- Outlier Detection and Anomaly Monitoring with AI
- Future-Proofing Financial Processes with AI
Episode Transcript
Jordan Wilson [00:00:16]:
What good is AI if you can't trust it? I think as agentic AI grows at an unprecedented rate, sometimes there's a rush to just do more, create more, but when was the last time you went through and audited your AI? Because if it's good enough, is that good enough? Well, that's what we're gonna be talking about today on everyday AI for our podcast audience, you might not see this, but, we are at Sage Future, and I'm excited for today's show. So, we're gonna jump right into it. So help me welcome my guest livestream audience. We have Jeremiah Edwards, the head of Sage AI. Jeremiah, thanks so much for joining the Everyday AI Show.
Jeremiah Edwards [00:00:59]:
No. Great to be here in San Francisco with you.
Jordan Wilson [00:01:01]:
Alright. So we're gonna get to everything that's new here, at Sage, at Sage Future. But let's start with the big question. When it comes to explainability in AI, when it comes to trust in AI, how can people, whether they're business owners, whether they work in finance, how do you actually get to the point where it's not just a race to do more, produce more, more outputs, but how can you get to that actual trust and explainability piece?
Jeremiah Edwards [00:01:30]:
No. It's super important. I think, focus on correctness and focus on explainability is is a big gap in a lot of AI tooling today. You know, we kind of stopped and said, oh, this it's amazing that the the dog can play piano, but but does it sound good? I think, you know, I would encourage every business user to to look at their AI systems and say, okay. How did it calculate these results? Is it correct? And is that better than the process I was using before? Is it really saving me time? Because, you know, there's a there's a lot of noise out there.
Jordan Wilson [00:02:06]:
Yeah. Absolutely. And and and let's actually rewind a little bit. So and and zoom out. So for those that aren't familiar with with Sage, obviously, a a global player in the game. But, you know, explain a little bit, you know, what Sage is and what you all do.
Jeremiah Edwards [00:02:21]:
Yeah. So Sage makes accounting and back of office software for small and medium businesses around the world. You know, we focus on that accounting function and and give a CFO tools that they need to run the business.
Jordan Wilson [00:02:33]:
Yeah. Absolutely. And, with Sage Copilot, obviously, a lot of new announcements and new capabilities coming out of Sage future. Can you maybe highlight, a little bit what was announced? Because I know there's a lot. Right? But highlight maybe a little bit on the Sage Copilot side, the new capabilities that Sage customers now have.
Jeremiah Edwards [00:02:54]:
Yeah. So Sage Copilot is our user interface for all things generative and agentic AI. And, you know, we released that a couple of years ago, but these days, there's this big transition from generative, you know, kind of chat agents to to agents that can really take action within the software that people are using. So in particular, our finance intelligence agent is helping people close the books faster, keep track of their budgets, and answer questions about their business that they couldn't before.
Jordan Wilson [00:03:22]:
Yeah. And so last year, you know, I had a great conversation, with Aaron. And, you know, at that time, it wasn't that the models themselves, you you know, were the biggest hurdle. But, you know, looking at the models from a year ago to the models that power the technology today, you know, there's definitely been a step change. You know, more agentic by nature, you know, smarter, more capable, etcetera. On the flip side, for, you know, people in finance because, you know, a lot of day to day operations, they start and they end with the numbers. How do you balance that, you know, the the rapid growth, in agentic capabilities even within, Sage Copilot with the potential for maybe getting that one number wrong. Because if you get that one number wrong and that's your starting point, things can go downhill quickly.
Jeremiah Edwards [00:04:16]:
Yeah. No. I think the there's been this kind of obsession for the last three or four years on the model. The model. The model. The model. And and I think that's in one part justified because Anthropic and OpenAI are are releasing some really cool stuff. Every day, every week, it gets a little bit better, a little bit faster.
Jeremiah Edwards [00:04:33]:
But, in order to to get, like, finance bulletproof accurate numbers, it's not just the model. I think there's an entire system of software, that has to come together. And more and more around agentic AI, I think the industry is realizing this and catching up. It's not the model. It's the model plus the agentic harness, plus the tools that it has access to and the data sources that really matter. And the the best AI model in the world cannot cannot do your accounting for you if it's not connected to the best accounting software in the world. Connected via, you know, standard protocols like MCP or a to a. And without that, you're you're you have no hope of getting getting a good answer.
Jordan Wilson [00:05:17]:
Yeah. And and that, I think that's a great point because, you know, you even brought up things like the model context protocol and the a to and the a to a standard. Right? And it seems like there's also for people that maybe don't have a background in AI and machine learning and math, right, like you, but maybe business, business owners in the c suite that wanna be involved and understand how the AI interacts with their numbers. Can you even explain a little bit and specifically, you know, even about the the the math capabilities of these models. Right? Because maybe two years ago, people, you know, you see all these kind of horror stories. People share something like, oh, you know, this model can't add, you know, two plus two, but we're not Class two. There today. Can you just give everyone else a quick update on just where these foundational models are now when it comes to being able to understand the basics that you need to have in finance?
Jeremiah Edwards [00:06:09]:
Yeah. So all all the LLMs in the world were trained on a on a vast corpus of text data. Right? They know how to complete text and and at a very fundamental level, they are translators. They're able to translate from a question to an answer. They run into a lot of trouble because there's a lot of there's a lot of lies on the Internet. I don't know if you knew, Jordan, but I've I've heard. Not everything on the Internet is true. And and errors like that, factual errors are in their training data.
Jeremiah Edwards [00:06:38]:
The the training datasets are so big that that can't be filtered out or corrected very easily. That process takes a long time. So enter enter the dragon. In order to to get correct answers out of these systems, we have to give them access to tools. We have to give them a calculator to do do arithmetic. And without that, you're you're definitely gonna get incorrect results. And I think it can be hard to find the line where, you know, that training data was good and correct and where things start to go off the off the rails.
Jordan Wilson [00:07:08]:
So I do wanna get back to making sure your AI that deals with the numbers gets on the rails. But I do also wanna talk about, a pretty big partnership that you guys announced, with PwC. And in that partnership, you know, the study that came out that said 71% of finance leaders would reject AI that can't explain itself. Right? So talking about this explainability, there's obviously things, you know, at the baseline model level and then at Sage Copilot level. But can you just talk a little bit about the importance of explainability and trust, right, when it comes to the numbers? I know, like, it maybe just seems like it goes without saying, but I want you to say it directly because I think some people need to hear it.
Jeremiah Edwards [00:07:50]:
No. At at the end of the day, the CFO or a or a small business owner is accountable for for the integrity of their books, you know, when their taxes are filed, when an audit comes up, it's it's not Claude that's sitting in the hot seat with the IRS. It's the CFO. And because of that accountability, the expectations that they have of any software system, including AI, are very high. So if it's not completely correct, it's it's not good enough. Close is not good enough. And because of that, I think, you know, it's critically important that that in all of the software that Sage builds, in all of the AI that we bring to market, we think of explainability as a as a first class design principle. And and what what does that mean? It means that every step the AI agent took to answer a question is there.
Jeremiah Edwards [00:08:38]:
It's fully auditable. It's it's right there for someone to see, including data sources, including API calls, and including reasoning that these models can bring on top of that.
Jordan Wilson [00:08:49]:
Yeah. And, you know, even speaking of reasoning. Right? So not to get too technical for our nontechnical audience. Right? But what does it mean now that the, you know, the models used by Sage Copilot, you know, can reason by default. Right? Whereas two years ago, you know, that wasn't really an option for anyone. You know? So what does that mean when it comes to increasing, the trust and increasing the explainability when you can see? Here's how Sage Copilot got to this answer.
Jeremiah Edwards [00:09:20]:
Yeah. So I'd I'll use another term of art. I hope oh, it's approachable. But I think more and more chain of thought reasoning, you know, is is a first class consideration for for anybody using these systems. You know, you're not just calling, an LLM once and getting an answer. I I hope not. You're you're asking it to critique its answer. You're asking it to dig in.
Jeremiah Edwards [00:09:41]:
You're asking it to to look at the other tools available and pick the right one. And and you can look at and hopefully everyone wants to surface this reasoning to users. We certainly do inside Sage. You know, what the model was doing and why it was taking reactions that it was. But that's that's the only way that you're going to get AI that a that a CFO will use.
Jordan Wilson [00:10:03]:
Yeah. And and yeah. FYI, you know, audience, you you you've heard me be the old man shaking my fist on the chair all the time about looking at the chain of thought and, you know, just just FYI. You know, Jeremiah, one of the smartest in the game, said to do the same thing. But also, you bring up a good point, the transparency of being able to look at that inside of Sage Copilot, which brings me to time. It brings me to agency. You you know, for people, you know, I consider myself mid career. Right? But for those people yeah.
Jordan Wilson [00:10:36]:
Yeah. But, you you know, for those people that have, you know, been, you know, in a a CPA for a decade or a CFO for, you know, twenty five years. Yeah. You know? And now they see these agentic models, that can do the type of work that they've kind of built their career around. Right? So I think people look at it two ways. One way, they say, this is great. Right? And I can, you know, check the work inside of Sage Copilot. It explains how it thought.
Jordan Wilson [00:11:06]:
Great. The other people are like, wait. What does this mean for my job, for my time, for my agency, for my expertise? I know this is a much bigger question that we could probably talk about for hours, but how do you begin to unravel what this means for your own experience and for that human agency?
Jeremiah Edwards [00:11:27]:
Yeah. Well, first of all, I wanna acknowledge that this is a universal problem. I mean, as a as a mathematician, as a software engineer, these are problems that I I also face. You know? Wow. That the computer is getting awfully good at what I used to do for a living. Goodness. But, no, I think, within within finance, within accounting, you know, there's there's actually, you know, a shortage of talent these days. Fewer people are coming out of CPA programs ready to jump in and, you know, and help small and medium businesses get that job done.
Jeremiah Edwards [00:11:57]:
And I think AI is kinda rising to meet that challenge and fill some of those gaps. And to anyone who's fearful of that transition or looks at this and is like, I'm not sure where this is going, I'd say you don't have to to eat the whole enchilada at once. I think you don't have to use every every piece of AI out there all at once, and I I don't advise that people do. I think, you can start with task based AI. Process automation, you know, I love to to focus on kind of AP accounts payable for that. People used to have to to look at invoices and manually enter data into the accounting system. No human has to do that anymore. We have tools for that now, and then that's great.
Jeremiah Edwards [00:12:39]:
That is AI. It it, you know, a lot of people don't think of that as AI anymore because because it's been around for a while. We've gotten comfortable with it. But, for anyone who's who's skeptical of that transition, start there. It can save you a lot of time. I think our customers say, you know, it can double the number of invoices they can process every month, which is which is huge. Getting to your point though about, you know, how does that change the nature of strategic thinking within finance and and the role of the CFO, I'd say it frees up a lot more time for, you know, looking ahead instead of looking back. When you're crunched for talent and under a lot of time pressure, often you get into these sort of batch update cycles.
Jeremiah Edwards [00:13:20]:
We have to look at the last quarter, make sure everything's correct, turn it into an update, and by the time you do that, those numbers are out of date. And and I'm I'm hoping, you know, that AI and certainly the AI we build at Sage can help them, you know, be more forward looking and and really kind of meet this dream of continuous accounting where the data is available. The data is available to the people within the business who need it to make decisions. That takes work off the finance function and and allows them to to really focus on on what matters for them.
Jordan Wilson [00:13:52]:
Yeah. And, you know, you bring up a good point because it does seem like as agentic AI becomes more powerful as, you know, layers of intelligence like Sage, Copilot, you know, help it become more explainable and, you know, more utility as well, obviously. You know, you talk about, you know, it will free up a lot of time. But I'm wondering, you know, can you get maybe a little tactical and, you know, so where should people be spending that actual time? Is it, you know, more on the front end? Is it more on the back end? Should we be spending more time? Right? Like like me, I'm a dork. I love reading the chain of thought and, you know, trying to make it a little bit better or get better outcomes. You know, where should people, you know, specifically whether you're they're using Sage Copilot or not, but if they are using agentic AI, in finance, where should those experts be zeroing their time in to make sure that they're getting the most out of the AI output, but also to make sure that they're still keeping those crucial skills sharp? No. Absolutely. I think, let me give a
Jeremiah Edwards [00:14:55]:
very finance oriented answer first. And that and and the short version is is governance and control. You know, any AI system, you know, that that people are working with should give you the confidence you need to, you know, in the data, in the results it's bringing to you. And if it doesn't, don't use it. But the you know, it should also give you a control plane for for deciding, you know, using critical judgment to to decide what to what it should do on your behalf. And if if the human is not in control of the AI system, you shouldn't use it. Yeah. That's another design principle.
Jeremiah Edwards [00:15:28]:
But, ultimately, I think it's about a point we touched on earlier which which is accountability. You know, if if this if the finance function is still accountable for the the financial health of the business, you know, that means tasks are gonna be a lot more review based. Mhmm. AI is is the doer. The human is the supervisor. The human is the reviewer. And you have more time to do that because you're you're not stuck on on kind of, you know, lower level tasks.
Jordan Wilson [00:15:54]:
Yeah. And, you know, a lot of success probably comes from, you know, constantly auditing and improving the people, the process, and the technology. How do you schedule those, you know, feedback loops, right, when the when when the technology does change so quickly. Right? Yeah. How often should you be visiting change management within your financial organization? How often should you be, you know, reauditing some of these agentic workflows that are now possible this year that maybe weren't possible when I talked with Aaron last year. Right? What does that, kind of iteration loop look like in the age of AI everywhere being very impressive? AI moves too fast to follow, but you're expected to keep up. Otherwise, your career or company might lag behind while AI native competitors leap ahead. But you don't have ten hours a day to understand it all.
Jordan Wilson [00:16:54]:
That's what I do for you. But after seven hundred plus episodes of Everyday AI, the most common questions I get is, where do I start? That's why we created the Start Here series, an ongoing podcast series of more than a dozen episodes you can listen to in order. It covers the AI basics for beginners and sharpens the skills of AI champions pushing their companies forward. In the ongoing series, we explain complex trends in simple language that you can turn into action. There's three ways to jump in. Number one, go scroll back to the first one in episode six ninety one. Number two, tap the link in your show notes at any time for the start here series, or you can just go to starthereseries.com, which also gives you free access to our inner circle community where you can connect with other business leaders doing the same. The Start Here series will slow down the pace of AI so you can get ahead.
Jeremiah Edwards [00:17:52]:
No. It's I wanna answer that question two ways. One is because, you know, as a as a a builder of AI systems, I I certainly have opinions about, you know, how it can help save people time. But but I'm also, you know, an AI user. Right? We all we all are. We all wear that hat. Like, I'm I'm using AI to try to, you know, make make my workflows more efficient and also, you know, transmit that to my team. Also learn from my team.
Jeremiah Edwards [00:18:18]:
And I think how you do that effectively, you know, is gonna look a little different in different organizations depending on your risk appetite. But I think at the very minimum, I'd advise that that everyone be looking at AI tools and thinking about how, you know, how that can change the operation of the team. And I I really do love to think about it as the at the team level, and and sharing information is is the the bare minimum here. I think what what naturally arises out of that is some use cases, you know, are are gonna be better than others, you know. And and as a leader, you can identify, you know, which ones those are, which ones you wanna promote within your organization, and incentivize people to, you know, to be creative, to bring those to you. Because otherwise, I think you you end up in a in a situation where, you know, there's there's shadow IT. There's there's people using tools that are kind of outside of your visibility and control, and and that's worse for everybody. Yeah.
Jeremiah Edwards [00:19:16]:
You know, obviously, you know, vendors like Sage are gonna make the the best AI integrated with our accounting systems as we can. But we we exist in an ecosystem and, you know, while we can provide the best solution inside inside Sage Intacct, for example, you know, like, people are going to to look at other solutions and and and use them for different use cases. And so you really have to give yourself
Jordan Wilson [00:19:40]:
the visibility into that and and then advise from there. So you brought up a couple great points there both about shadow IT, right, and which I think is important to, you know, may or maybe let's just do this. Can you explain? Right? Because I think a lot of people think, oh, I can only get the best models if I go to, you know, Claude or OpenAI or Gemini or whatever. Can you explain about how you just bring those best models inside? I think we maybe skipped over that point, but I think it's important to talk about, yes, you still get world class AI capabilities within Sage Copilot.
Jeremiah Edwards [00:20:15]:
No. Absolutely. I think we, you know, we have we have technology partnerships, you know, with with all the best players. We we do use models from from Anthropic and OpenAI and and Microsoft and open source. Mhmm. But, you know, by by integrating them into our AI platform, by adding a layer of control and governance, we're able to to ensure that all the answers that come from Sage Copilot are grounded in real data, you know, and are relevant to to what the user is trying to accomplish. And and that's, you know, that that's really because we have the context within the accounting system and within, you know, the the user identity of of who's using that system, you know, to give them the best data and answers we can.
Jordan Wilson [00:20:57]:
Yeah. Absolutely. And that does, you know, help fight against the shadow IT, which I know is always a big problem. But you brought up something else interesting that I I'd really like to discuss, just the risk appetite. Yeah. Right? So, for I think, obviously, in the enterprise, we're past that conversation for the most part. Right? I think most large enterprises that are moving fast, you know, they've got it, you know, somewhat figured out when it comes to, you know, finance and AI. But I think a lot of SMBs are still maybe struggling with that risk appetite.
Jordan Wilson [00:21:28]:
Right? How much, you know, should we be pushing AI, you know, when maybe they don't have a team of experienced, you know, machine learning engineers that can come in and, you know, help set this up? So with that, for maybe those, you know, small you know, maybe those small and medium sized businesses when they're looking at something like Sage Copilot, and maybe they're still doing it the manual way. Right? And they're saying it might be too risky. How do you address that?
Jeremiah Edwards [00:21:54]:
No. Absolutely. I think it, I approach this this whole decision space very much with the mind towards, like, use the right tool for the right job. You know, you can you can maybe just use chat g p t directly for some functions, you know, ideation, creativity, marketing copy, a first draft, not the final draft. That that's fine. But when it comes to finance and you really do need to to have a higher standard, and I think people are right to to to look at that risk and be like, wait a second. Do I do I want the robot anywhere near my general ledger? You know? And and again, my answer to that is just that, it's not an all or nothing type of thing. I always suggest that that people look for individual use cases, sub functions where AI can bring bring whatever amount of value you're comfortable introducing today.
Jeremiah Edwards [00:22:47]:
And yet more than ten years ago, the first AI that we built at Sage was was outlier detection. It it just combs over every general ledger entry and says when something is anomalous, when something got misentered, or when fraud can pop
Jordan Wilson [00:23:00]:
up. Mhmm.
Jeremiah Edwards [00:23:01]:
That should not be controversial. It's not changing anything. It's not posting transactions. It is simply there as a watchdog, as a stop gap, and that's still running today. And now, like, we can do a lot more obviously with the agentic AI, but even within that system, our outlier detection on the GL is one of the core tools behind our MCP layer, behind our finance intelligence agent that makes it work. That's what grounding means in in the real world. Yeah. You know, we're grounding our agentic answers with real anomalies detected with good old machine learning.
Jordan Wilson [00:23:34]:
Yeah. And, you know, you you said something important there that I wanna point out, you know, doing the best with today's agentic AI. But, you know, obviously, you can't be looking at the past if you wanna be a future forward, a future facing organization that's keeping up with, you know, bleeding edge AI. So, you know, obviously, you can't, you know, lay out the playbook for next year's Sage future. Right? But what should business leaders, you know, on the finance side and otherwise, be doing today, specifically, when it comes to their books, their processes so they can be ready for whatever comes next?
Jeremiah Edwards [00:24:11]:
Yeah. I mean, if you live on the bleeding edge, you get cut sometimes. It's, I'm there with technology and, you know, our our customers are there with their processes and and with, you know, like, all of the changes in this space. So my my best advice is, you know, hold on to it loosely. I think, there are some problems that we can solve and AI is not one of them. AI is bigger than that. It's bigger than you. It's bigger than me.
Jeremiah Edwards [00:24:37]:
And we can orient ourselves around that change and be ready for that change. So I think, my best advice is start using AI that you're comfortable with. Keep an eye on all the AI as best you can, you know, through everyday AI and other other resources. Right? So that you're aware of what's going on in the ecosystem. And then when you do see something that, that's a good fit, you know, when finance intelligence agent can help you close your books Mhmm. You know, ten days faster than you ever could before, which it can. You know, jump on that. Right? Be ready to move in any direction, but only adopt AI that has real value for your business.
Jordan Wilson [00:25:16]:
Alright. So, Jeremiah, we've covered a lot in today's conversation from what's new with Sage Copilot, what's being announced here. We've talked about risks and guardrails and everything in between. But as we rep, what is your one most important piece, of advice? Right? Getting back to trust. And good enough, maybe not being good enough. What is the big takeaway to make sure that, people can have AI that they can feel confident in and they can trust those outputs?
Jeremiah Edwards [00:25:45]:
Yeah. Oh, I I just wanna share that, people shouldn't shouldn't give up on on this. I think there's a lot of skepticism around AI that's arisen from, you know, overinflated promises. But I think, you know, all the AI we build at Sage is built with grounding and with access to real answers and real data. So if you've seen it, you know, some something else hallucinate. Mhmm. That's not good enough. You you can you can maintain a really high bar there.
Jeremiah Edwards [00:26:11]:
There is AI that you can trust out there in the world. It's being built by Sage. And and what does that really mean? It means that you're confident in its answers. As a human, you control what's going on, and that AI is accountable and auditable for for what it's done inside your finance system.
Jordan Wilson [00:26:27]:
Alright. What an amazing conversation. So, we covered a lot in today's show, but there's gonna be a lot more. So, make sure to check out today's daily newsletter, for everything else that happened at Sage. Jeremiah, thank you so much for taking time to join the Everyday AI Show. We really appreciate it. Thank you, Jordan. Alright.
Jordan Wilson [00:26:45]:
So if you miss anything, like I said, make sure you go to youreverydayai.com. We're gonna be recapping everything that happened at Sage Future and a lot more. Thanks for tuning in. Hope to see you back tomorrow and everyday for more everyday AI. Thanks, y'all.