#AIReady? You don’t have to read Microsoft’s 10Q (though you should) to know that 2024 will be the year of the Enterprise AI Pilot. And while exciting it’s not all smooth sailing. Our customers – particularly in Pharma and Finance – are hitting roadblocks with Gen AI. I’ve had four conversations in four weeks with SVPs sweating syntactically right but semantically incoherent AI systems.

Why? “Garbage in, garbage out.” Poor input context on data design, poor quality from insufficient data modeling drives underperformance. There’s no quick fix – AI is a black box, and old-school root-cause tools like Lineage and Provenance by nature, don’t work.

So, what’s the antidote? They are avoiding the AI graveyard by modeling with intention. The common roadmap looks like this:

– Centralize your VIP datasets: The core data of your organization – customers, products, vendors, attributes. They are needed enterprise-wide and chances are, they need to harmonized across systems.

– Model your world with Ontologies: Give your AI the context it needs to make sense – to you.

– Set the rules with a Governance Model: Define what your teams can and can’t do.

– Stay compliant with Semantic Policy Enforcement: You’re on the hook if inputs or outputs break regulations. A semantic approach ensures GDPR, HIPAA or compliance from input to output.

– Cache your results: Every interaction feeds back into your enterprise context. This is what will train your AI to fit your enterprise.

– Then pick your LLM and build! This is the most competitive market in decades.

Grab your popcorn and enjoy the ride. Plan right, and you can nail these challenges and make Gen AI work for you. Let’s get #AIReady. #DataGovernance #DataArchitecture #Semantics How are you making your data #AIReady?