An AI Integration Playbook That Prevents Disintegration
For better or worse, AI continues to be a headlining topic, and chances are you’ve interacted with content generated by a large language model (LLM).
More commonly, though incorrectly, this is referred to as “AI”. It is a more attention-grabbing label, but true artificial intelligence likely will not exist for another decade, if not several.
Whatever you want to call it, the bottom line is: it is a tool. And a tool is constrained by the skill and knowledge of the person using it.
A reasonable person should have a good understanding of what the tool is and isn’t capable of.
If a tool isn’t used properly, the best case outcome is mediocrity. The worst case is significant, even irreparable, damage to the work or its user, with sizeable trickle-down consequences.
A good, if unfortunate, example involves a government official and director of cybersecurity who uploaded sensitive documents, not meant for the public eye, to a public version of ChatGPT. Oops.
Given that a core function of LLMs is to generate outputs based on patterns in the data they are trained on, any information uploaded to a public model creates obvious risks, whether from friendly users or otherwise.
Another example involves a vibe-coding model that not only deleted a software company’s entire production database during an active code and action freeze, but also attempted to hide it by falsifying records and fabricating test results. More than a thousand clients had data wiped because the model “panicked”.
Luckily, that company had procedures in place to roll back changes made to its systems. But if your business depended on that data to generate revenue, you would still be left waiting for the issue to be fixed before money could move again.
Here at Greenbase, we use a tailored version of an LLM with the ethos of maximising utility while minimising risk.
This is achieved through a few simple, yet essential core mandates:
Human oversight – Anything generated by an LLM is treated with scepticism and reviewed by a real person with genuine expertise, who remains responsible for the work. This helps ensure the output is high quality, accurate, relevant, and defensible. It is further supported by robust chains of delegation and accountability that guide decision-making around the use of an LLM.
Defined scope – Use of the tool is strictly limited to its strengths, such as augmenting human effectiveness through pattern recognition, data extraction, and scaling automatable tasks within clearly defined rules and context. This improves productivity and reduces cost by increasing workflow efficiency.
Information sovereignty – Input data is controlled using methods aligned with cybersecurity best practice and regulatory compliance. This builds assurance and trust by protecting data integrity and proactively managing risks such as data breaches and theft of intellectual property.
These mandates reflect an integration approach grounded in ethics, efficiency, and accountability. The sentiment is not new. In fact, it is echoed in an IBM training manual from the 1970s — from the people responsible for inventing the PC, mainframe, floppy disk, and more.
The infamous quote follows:
“A computer can never be held accountable. Therefore, a computer must never make a management decision.”
Greenbase. Makes Sense.