Why Most LLMs Won't Work for Enterprises
Harish Alagappa
Nov 27, 2025
Most LLMs predict words, not outcomes. Discover why enterprises need expert-taught AI that delivers accuracy and auditability instead of probabilistic guesswork.
In July 2025, Deloitte Australia delivered a A$440,000 (US$290,000) government report on welfare compliance. Hidden within its pages: phantom citations, fabricated references, and confident-sounding nonsense — errors widely attributed to AI ‘hallucinations’ that slipped past one of the Big Four’s quality controls.
A sharp-eyed academic caught what Deloitte's reviewers missed which led to a partial refund, jokes on the internet, and a stark reminder that even the world's most sophisticated consulting firms can be fooled by their own AI tools.
This case study is the Enterprise AI crisis in miniature: technology that sounds brilliant but is prone to inventing facts, systems that boost productivity until they torpedo credibility, and the dangerous gap between what LLMs promise and what enterprises actually need.
The LLM Problem: Probability Doesn’t Equal Understanding
Large Language Models work like sophisticated autocomplete engines. They've analyzed billions of text fragments and learned to predict what word should come next based on patterns. It's impressive technology until you realize that prediction isn't comprehension.
Think of it this way: an LLM is like a brilliant mimic who's watched thousands of medical procedures but never went to medical school. They can describe surgery perfectly. They might even sound convincing. But would you let them operate?
This fundamental limitation manifests in two critical ways. First, hallucinations: those confident-sounding fabrications that caught Deloitte off guard. Second, model drift: as base models are updated behind the scenes, responses can change over time — turning yesterday’s approved answer into today’s potential compliance risk.
Why That Fails Enterprises
The Deloitte incident wasn't an outlier, it was inevitable.
When a financial services firm processes loan applications, "probably correct" isn't good enough. When a healthcare system triages patient inquiries, approximations can be catastrophic. When a government relies on consulting reports for policy decisions, fabricated citations aren't just embarrassing, they're dangerous.
The stakes are too high for statistical guesswork. Every misclassified risk creates liability. Every unexplainable decision undermines trust. Every inconsistent output multiplies training costs as teams scramble to correct AI-generated errors.
The real cost isn't the technology, it's the human overhead required to babysit it.
Traditional LLMs also create governance nightmares. How do you audit it? How do you prove compliance when you can't explain the reasoning? How do you maintain consistency when the same input might generate different outputs?
If Deloitte, with all their resources and expertise, can ship AI hallucinations in a government report, what chance does your enterprise have?
The Braigent Way: Expert-Taught Intelligence
What if AI could learn like an apprentice instead of guessing like a gambler?
Braigent's expert-taught "Teach-Test-Trust" model flips the script. Instead of training on internet-scale randomness, it learns from your experts' actual decisions. Your best underwriter teaches the system once. Your senior compliance officer codifies their judgment. Your top customer service manager shares their decision framework.
It uses expert-curated examples, a no-code teaching interface, and a workflow you can deploy in minutes — not the weeks or months traditional fine-tuning requires. Braigent doesn’t guess statistically like a general LLM. It applies your encoded expertise at scale, using your examples and reasoning as ground truth.
The Three Pillars of Enterprise-Ready AI
Expert-Powered: Efficiency Through Teaching
Braigent learns from expert-curated examples that you provide. No massive datasets. No months of training. Just efficient, no-code teaching that transforms domain expertise into scalable intelligence that you can deploy in minutes.
Encode Your Judgment: Consistency at Scale
Capture your logic once, then watch it work infinitely. Every decision reflects your standards, your rules, your expertise. Time savings compound as the system handles routine decisions while maintaining perfect consistency. This isn't about replacing experts, it's about multiplying their impact.
Ship AI You Can Trust: Governance Built In
Every decision includes explainable reasoning and complete audit trails. You know why the system made each choice because it shows its work. Risk mitigation isn't an afterthought, it's the foundation. Compliance teams can sleep at night knowing every output is traceable, explainable, and aligned with policy.
The Future of Enterprise AI
The question isn't whether AI will transform enterprises, we’re seeing it happen in real time. The real question is if all enterprises are using AI, where will the competitive advantage come from?
LLMs have their place. Creative tasks, brainstorming, general assistance, these are perfect use cases for probabilistic models. But when accuracy matters, when consistency is critical, when governance is non-negotiable, enterprises need something different.
They need AI that learns from experts, not the internet. AI that applies judgment, not statistics. AI that explains its reasoning, not its confidence scores.
The Deloitte incident won't be the last time an enterprise gets burned by hallucinating AI. But it doesn't have to happen to you.
Ready to move beyond the LLM lottery? Explore how Braigent's expert-taught approach delivers the accuracy, control, and trust your enterprise demands.
Teach Once, Trust Forever.