The Critical Role of AI Auditing

As summer winds down, AI remains the hottest buzzword.

AI has not gone fully autonomous yet; it still relies on human interaction to run efficiently.

Questioning AI, not just consuming it

Today, many rely on AI tools like Perplexity, ChatGPT, Claude, and others to get quick answers. These tools are powerful, but here is the catch: you cannot take their output at face value.

Just like you would double-check a colleague’s report, AI-generated insights need to be reviewed and audited to ensure accuracy.

Primary and Secondary Research

Even with limited knowledge, AI users need to understand one key fact: the quality of research and data used to train these models is critical.

At Cohres Ltd. , we have firsthand experience in conducting research and providing data to help our clients build internal AI models. What truly matters is the research methodology behind that data.

If it is primary research (gathered directly through interviews, phone calls, expert conversations, focus groups, etc.), the approach must be transparent and rigorous. Furthermore, when relying on secondary sources, it is just as important to evaluate how the information was collected and whether the source itself is credible.

Connecting to the Source

You can spend countless hours organizing data, but if the sources are not credible, the effort is wasted.

That is why it’s essential to understand where the data comes from credible sources ensure the information is reliable and valuable for training AI models.

Training The AI Model

Think of an AI model like a child, you cannot assume it understands unless it has been trained properly. That means breaking data down into the simplest form possible, so the model can process it effectively.

From my experience working with tens of thousands of data points to train AI models, the key is simplicity and clarity: structure data so clearly that even someone with no prior knowledge could follow it.

And when in doubt, enrich it with synonyms and variations, removing any room for ‘what if.’

AI Trained—What About the Results?

Once the data has been used to train the model, it is crucial to test it across different scenarios to see the results it produces. This ensures the AI delivers accurate and reliable outputs for the user.