In the rush to implement AI, automation, or even just cleaner dashboards, one thing keeps tripping up teams: basic definitions.
Take something as “simple” as sales.
Ask 5 departments what it means — you’ll get 7 answers.
• Gross or net?
• Before or after rebates?
• GST included or not?
The problem isn’t technical. It’s semantic.
And in big orgs, that difference adds up — in misaligned goals, confused reporting, and poor decisions.
Some argue that AI can just “figure it out” if you give it enough context or rules in a markdown file.
Sure. But if you’re already going to the effort of encoding logic, why not do it in a semantic layer that’s reusable, efficient, and shared across teams?
Semantic models aren’t old-school. They’re how you scale shared understanding.
Especially in orgs where “one version of the truth” needs to outlive any one tool, platform, or trend.
Before you automate, define.
Before you ask “what’s working,” agree on what “working” even means.
We’ve been working through these exact challenges, interesting sharing of experirnces you can find at EDNA Builder and a KnowCode platform built for smarter teams, not just faster builds.
Top comments (0)