Why Most Organisations Hit a Wall After Their First AI Experiments
Most organisations start their AI journey the same way.
They experiment with summarisation, text generation, and simple assistants. The results are often impressive at first. Then teams try to apply the same systems to real business processes, and momentum slows.
This topic came up repeatedly in our Season 2 opener of What The Tech Podcast (AU), where we discussed why early success often gives way to frustration.
Context for this discussion:
https://www.whatthetech.com.au/p/australia-vs-big-tech-why-build-ai
As Dave Lemphers explains, the problems begin when businesses try to move from demos to real work:
They start with summarisation and text generation… then they look at their business processes and realise the model can’t actually do the task.
The issue isn’t effort or intent. It’s architectural mismatch.
Large language models were not designed to deeply understand rules, classifications, or domain-specific decision logic. When organisations try to force them into those roles, they compensate by adding more context, longer prompts, and increasingly fragile instructions.
You’re cramming the context window with data… trying to come up with an incantation that’s going to make the magic pop out.
Even then, accuracy rarely reaches a level that businesses can trust.
This is why many AI initiatives stall. The model works, but not in the way the business actually needs. At that point, organisations face a hard choice: redesign the system, or accept permanent limitations.
Watch to the full episode
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