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However, tech teams are all too aware that speed does not always equal success.
In fact, too fast can actually hamper eventual progress.. Lead Data Strategist at Databricks.
If a model hallucinates and gives incorrect answers which are then acted upon, there could be huge repercussions.
For example, anemployeecould look to find out which deals they are able to offer tocustomers.
Additionally, if incorrect information is provided and supplied to stakeholders, reputation and trust could be damaged.
Not only is the data key, but the models being used too.
Particularly for organizations who are heavily regulated, but it is a massive consideration for all.
So, a key challenge is ensuring that a model is reliable, consistent, and safe.
Getting this step wrong could mean that organizations find themselves dealing with thefalloutfurther down the line.
Technical teams know this, but communicating this to the C-suite can often be a real challenge.
There is, and it lies in AI experimentation.
Experimenting in this way ticks the box for both the C-suite, and technical teams.
For technical teams, they have more control over the pace and quality of the roll-out.
However, for experimentation to be truly effective, theres a few things technical teams need to verify of.
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