So what exactly are facts in 2026? Reclaiming time, truth, and trust in the AI workplace
We’ve all been there: you ask your AI sidekick a simple question, and in return, you get a stream of absolutely confident… nonsense.
Sometimes it’s only subtly off. Other times, it’s a complete fabrication, peppered with ‘references’ that don’t even exist when you attempt to just read the source. Either way, your time just evaporated.
You start clarifying. Rewording. Providing context. Trying again. And again. Until you’re second-guessing everything that comes back. Because you know, under the hood, that most of the AI tools you get to play with right now are just making it up.
So how did we get here? How did you wind up arguing with an AI tool that you paid for to help you achieve more? And more importantly, where do we go next?
We’re working in a facticity crisis
There’s a reason even the best AI outputs feel like a gamble: they are. In late 2025, audits of top-tier platforms such as ChatGPT, Gemini, and Perplexity found factual error rates ranging from 35% to over 70%. That includes fabricated dates, broken links, made-up legal cases, and completely misattributed references.
You’re not imagining it — these systems are very good at sounding authoritative while being completely wrong.
And that’s a dangerous mix. Because when AI sounds right, it feels trustworthy. Even when it’s not. That’s how AI hallucinations slip into strategy documents, grant applications, legal advice, product briefings, and government reports. That, and the fact that a lot of us humans aren't really thinking about what’s being put in front of us anymore.
Worse still, it burns time. Correcting a confidently wrong answer costs you more effort than doing the task manually. Review, recheck, rewrite. And that’s before we talk about risk — from embarrassing missteps to significant reputational damage and poor decisions.
Governance is catching up fast
AI governance isn’t optional anymore. At least not in a workplace.
At the end of 2025, the Australian Government formalised strict standards for AI adoption across public agencies. Every AI use case must now be registered, audited, and assigned a named accountable owner. Systems must be tested for accuracy. Datasets must be traceable. Outputs must be verifiable. Because if an AI recommendation influences a policy decision, a contract, or a citizen's outcome, the paper trail matters.
The implications for business are immediate. Ungrounded, black-box AI will fast-track ISO 9001 headaches. How can you certify a process if you can’t trust the accuracy of its outputs? How do you verify a decision if the AI’s logic is opaque? Deploying generative AI just because it exists is a really fast way to create governance gaps that can’t be closed later.
AI doesn’t make you smarter — unless you stay in the loop
The promise of AI is speed, right? More done with less. Utopia is just around the corner (maybe). But the data is telling us a different story.
Teams relying too heavily on AI are already reporting the opposite: missed details, slower outcomes, and increased rework. Employees spend hours interpreting, correcting, or second-guessing AI-generated content. Worse, studies show prolonged use of generative AI can reduce critical thinking, attention, and even basic recall.
That doesn’t mean ditch the tools. It means you need to design better workflows.
Human-in-the-loop isn’t just a safety check. It’s a cognitive safeguard. Because if we offload too much thinking, we stop learning, stop noticing, and stop questioning - even when the AI gets it wrong.
Grounded AI is where the value lives
Here’s the good news. The AI landscape is shifting fast, and there’s a better path forward.
Grounded AI systems don’t invent answers. They retrieve and summarise information from trusted, real sources — like your own internal documents, datasets, or compliance manuals. You control what they know. You define the inputs. And they give you traceable answers with visible evidence.
This isn’t just about dashboards and automations. This is about rethinking how your organisation makes decisions, shares knowledge, and reduces waste.
AI shouldn’t sit outside your workflow. It should enhance it. Done well, grounded AI becomes a value-add delivery system — connecting your team to the right facts at the right time, with less digging, duplication, or delay.
And here’s the best bit: the infrastructure is affordable. You don’t need to build a giant platform or have a large team of software developers. With the right partner, you can deploy secure, source-bound copilots that respect your data sovereignty, align with your process, and prove their worth from day 1.
This means your people get to spend more time doing the fun stuff, meaningful work — insight, consulting, design, leadership, installation, customer outcomes — instead of fighting a losing battle against missing files, stale data, or confusing AI guesswork.
From fatigue to flow: getting ahead of the curve
Right now, many teams are stuck in AI fatigue. The novelty has worn off. The risks are obvious. And the gains are feeling increasingly elusive.
But that’s a design problem — not a technology problem.
By getting specific about your workflows, your knowledge sources, and your expectations, AI stops being a distraction and starts becoming infrastructure.
The result? More accurate outputs. Better decision-making. Faster delivery. Fewer meetings. And more time delivering the work itself.
We’ve moved past the hype cycle. AI isn’t magic, and it isn’t malevolent. But it’s also not optional. Which means now is the time to take control of how your operation uses it and how your teams benefit from it.
We’re helping innovative organisations do just that. With fact-anchored systems that combine human expertise and AI tooling into measurable outcomes. So your next investment in AI doesn’t just make you frustrated - but actually faster, and better.
Three sources worth your time
The Australian Government’s 2025 Policy on Responsible AI Use (digital.gov.au)
The MIT/Harvard Your Brain on ChatGPT EEG study (media.mit.edu)
Guardian comment on the BBC AI Accuracy Audit, Oct 2025
