AI Hype vs. AI You Can Trust: What You Should Know
It feels like every week another product shows up claiming to be built with AI at its core. In many cases, that just means someone connected their software to a generally available language model and called it innovation. But when you’re dealing with financial data, trust isn’t something you can tack on later.
That’s why it caught my attention when I noticed a link inside Sage Intacct pointing to their Responsible AI page. It wasn’t marketing copy. It was a clear explanation of how Sage approaches AI in a field where accuracy, privacy, and trust actually matter.
What stands out about Sage is that they’ve been thinking about this for years. Their AI-driven outlier detection feature launched back in 2020, well before the recent flood of products trying to add “smart” capabilities. From the beginning, their focus has been on building tools that make accounting more reliable and easier to audit, not just more automated.
On their Responsible AI page, Sage highlights a few principles that show this is about substance, not form:
- Transparency: being clear about how their AI models work and where they’re used
- Data confidentiality: keeping customer data secure and completely separated
- Security by design: building protection into every layer of the system
- Global compliance: staying aligned with GDPR, CCPA, and the upcoming EU AI Act
- Ethical use: making sure AI supports people rather than replaces them
That is what responsible use of AI should look like. It is not a trend. It is a continuation of the same trust and accountability that have always defined good financial systems.
AI Hype in Finance: A Reminder of What Happens When Trust Gets Lost
There are plenty of examples of what happens when companies rely too heavily on AI systems without enough oversight. One accounting automation tool missed a 2 million dollar liability because it could not interpret a contract amendment, and the team assumed the system could not be wrong. They ended up scrapping it and rebuilding their review process from the ground up. (Rooled: Burned by AI – How to Rebuild Trust in Financial Automation After a Failure)
Another case, covered by The Fintech Times, showed how outdated or incomplete training data can cause financial models to make bad recommendations that look confident until they cost real money.
The lesson is simple. In finance, AI should help people make better decisions, not make the decisions for them. This makes an important distinction between AI hype vs. AI you can trust.
Why Substance Wins
What stands out about Sage’s approach is that it’s built on experience, not urgency. In the ERP world, they’re probably among the first to bring meaningful AI into the product, but it’s clear this hasn’t been rushed. They’ve been at it for years, shaping technology that builds trust, protects data, and genuinely improves how accounting work gets done.
That’s a sharp contrast to what we’re seeing from a lot of other vendors right now, who seem to be scrambling to bolt on whatever plug-in or language model they can find just so they have something to show. Sage’s work feels different because it’s steady, thoughtful, and backed by the kind of depth that only comes from doing the hard work early.
In an industry built on trust, Sage’s Responsible AI work shows that credibility doesn’t come from how fast you move, but from how carefully you build.
Let’s Build Financial Systems You Can Trust
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