AI drafts emails and summarizes meetings, yet few companies trust it with quarterly numbers. That’s because the model has to guess what your business terms mean, and a smarter model still guesses.
BIRD-Interact holds AI to the true enterprise standard: a simulated colleague asks vague questions against typically messy databases with no documentation and scores the result. A plausible but wrong number scores zero. AtScale ran the full benchmark on six leading AI models, both on their own and with a semantic layer. Turns out, the semantic layer moves accuracy far more than switching models does. Knowing what’s needed for a true semantic layer is critical to AI analytics that work in the enterprise, and not just in the lab.
Join AtScale Co-Founder and CTO Dave Mariani, Chief Marketing & Strategy Officer Mark Palmer, and industry analyst Andrew Brust (Founder & CEO, Blue Badge Insights). They’ll go through the results model by model and explain what they mean for how you buy and build enterprise AI. They’ll also preview a new AtScale capability that tells your team whether an AI number is right before anyone acts on it.
What you’ll take away:
- The four ways business questions trip up AI, and why none of them is a reasoning failure
- How adding a semantic layer improved accuracy three to five times more than switching models, and a low-cost, open-weight model nearly matched the frontier models
- The four fixes that start to close the gap to 100%
- A method for scoring AI accuracy on your own data, metric definitions, and questions before anything reaches production
- Andrew Brust’s independent view on where semantic layers and open standards like Apache Ossie fit in your AI stack
