The biggest barrier to AI success isn't AI
We’re in what’s being dubbed the ‘year of AI ROI’. Four years on from the AI boom - ignited by the launch of ChatGPT - many businesses now believe that they’re AI-ready, using successful early-stage chatbot and copilot rollouts as evidence.
Yet there is a significant difference between experimenting with AI and embedding it across complex business processes and operations.
The real barrier to this transformation sits beneath the models themselves. While most AI outcomes-related conversations focus on model performance, GPUs, and compute capacity, organisations are increasingly realising that it’s their data infrastructure holding AI projects back.
Up until now, businesses have got away with operating successfully despite their disconnected file environments, inconsistent data governance, and information spread across multiple repositories.
This is because historically, data has been accessed sporadically, mostly by human employees who could compensate for shortcomings when data lacked context or wasn’t where it should be.
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