The race for AI
Departments across organizations are adopting AI tools at record speed, and companies are lining up to pour money into advanced engines promising higher performance. But in the midst of the gold rush, a crucial question is often forgone: how do we know if the information coming out of an AI tool is accurate, and what are we doing about it?
An AI can only be as good as the data it’s trained on: if it has insufficient, inaccurate, inconsistent, or outdated information, the AI will do what it can with what it has. Ultimately, the risk that organizations are facing is adopting AI too quickly without checking what’s fueling it.
Automating mistakes at scale
Think of AI as an engine: even the most powerful one won’t run well on bad fuel. AI works the same way—it’s only as good as the data it draws from. When the data is outdated or inconsistent, the AI amplifies those issues. Unchecked, it can create confusion, duplicated effort, and erode trust by magnifying bad data rather than fixing it.
This is the era of ‘Workslop’: content and information multiply faster than teams can verify them. Without proper governance, the convenience of AI can backfire by creating duplicated effort, contradictory information, and ultimately, organisational confusion. In other words, automation without verified knowledge wastes time more than it saves.
The costs of poor knowledge management
When information is scattered across systems or becomes outdated, mistakes proliferate, and the financial and operational cost is significant. According to Gartner, poor data quality costs organizations an average of $12.9 million a year. Beyond dollars, the hidden cost is broken trust: employees waste hours piecing together information and over time, lose confidence entirely in the tool. With AI in the mix, the stakes are higher. Automation multiplies your knowledge and makes it painfully visible as every inaccuracy or outdated fact gets amplified. What was once a data-quality issue becomes a trust issue.
The shift
The next frontier in AI isn’t in raw performance, but in trust. We see it more and more, even outside of the workplace: ChatGPT hallucinating fake citations, a video of rabbits on a trampoline that fooled the internet, and Google’s AI Overview recommending using non-toxic glue to have cheese stick better to pizza. It’s becoming increasingly difficult to know what information to trust, and how to tell real from fake.
And that uncertainty doesn’t stop at the workplace door. If AI can mislead in the public domain, the same risk exists internally when company AI tools start drawing from outdated, duplicated, or poorly verified data. The consequences stop becoming mildly amusing (at best) — they’re costly. That’s why the real differentiation for organizations isn't how much AI they use, but how well they can govern and verify it. If a company is aiming for success, accuracy, reliability, and accountability should be their AI’s performance metrics. In other words, AI governance trumps AI power for companies who want to use AI strategically.
By embedding human expertise into the AI workflow, subject matter experts can regularly validate and refine the AI’s knowledge base. Verified knowledge ensures that every output is reliable, not just fast. It transforms AI from a content generator into a strategic partner that organizations can depend on. But an AI is only trustworthy when it has an ongoing relationship with the humans that manage it.
Building trust in your AI
- Centralize and clean your knowledge: consolidate scattered files, eliminate duplicates, and make sure that outdated content is archived or updated.
- Establish verification workflows: review key data, validate content, and flag inaccuracies before they become part of the AI’s knowledge base.
- Implement governance rules: define what sources AI can ingest, how often knowledge should be reviewed and updated, and who is responsible for oversight
- Foster a culture of accuracy over speed: encourage employees to prioritize verified information over quickly-generated content
When these principles are in place, AI becomes a partner rather than a liability: a tool that amplifies verified knowledge, drives productivity, and builds organizational trust.
Ultimately, AI isn’t a replacement for human judgment. It should behave as other tools do, as an extension and enabler of knowledge and skill. And just as an organization’s people are only as good as the information they rely on, an organization’s AI is only as good as the knowledge it’s built upon.
Building trust into your AI architecture means building structure and feedback into every stage of how information is created, verified, and shared. Treat knowledge as a living system: one that requires care, governance, and collaboration between humans and machines.
When feedback loops connect subject matter experts to your AI’s outputs, your knowledge base becomes self-correcting. When governance defines what data the AI can draw from, results become more consistent. And when your culture values accuracy over instant output, you elevate quality and reduce risk.
Learn how Happeo turns AI from a chaos multiplier into a trust engine through its Knowledge Engine here.
