In most companies with Salesforce, there comes a time when someone asks:
“Wait, why haven’t we been documenting our Salesforce fields and data definitions?” or “Why don’t we have a data dictionary for Salesforce?”
And now with AI adoption on top of everyone’s minds, more people (especially Execs) are starting to ask:
“How are we going to trust [AI model/tool] if we don’t trust our metadata that it’s supposed to learn from?”
And almost every time, the first instinct is the same. Someone will:
Most experienced Salesforce owners have learned from this and know… a Salesforce data dictionary in a spreadsheet only works in theory, and fails every time in practice.
It’s the method itself that’s broken, not their discipline or effort.
And as AI-driven Salesforce projects have accelerated, a lack of correct metadata documentation becomes the major blocker to data quality, governance, and AI-readiness/adoption.
Here’s the typical pattern we see across Salesforce admin, RevOps, and data teams:
|
Step |
Reality |
|
Export fields from Salesforce |
Good attempt at first step |
|
Add business meanings & descriptions |
Value-add… initially |
|
Try to track changes over time |
Pain begins |
|
Ask everyone to refer to the (outdated) sheet |
Adoption fails |
|
New fields get created/updated, but it’s not added to the spreadsheet |
Dictionary falls behind |
|
Nobody trusts the sheet |
It dies quietly in Google Drive |
Spreadsheets fail not because teams don’t care, but because Salesforce (and teams) change too often and too fast for a static tool.
Salesforce orgs evolve constantly:
On top of this, tribal knowledge and documentation gets lost because:
One admin on their own can create 100+ metadata changes a month. Add developers, consultants, marketing ops, CPQ, integrations, etc., and you’re looking at hundreds to thousands of changes per quarter.
No spreadsheet can keep up with that.
Most organizations underestimate the hidden tax of spreadsheet data dictionaries:
|
Problem |
Impact |
|
Out-of-date definitions |
Wrong reports, confused users |
|
Duplicated fields |
Dirty data + technical debt |
|
Tribal system knowledge |
Risk when people leave |
|
Slow onboarding |
Weeks instead of days |
|
No change history |
Audit & compliance gaps |
|
Manual upkeep |
Dozens of hours monthly |
|
No trust in documentation |
People skip it altogether |
It’s not just inefficient… Spreadsheets quietly erode data trust, slow your business, and hold back transformation.
Yesterday, a data dictionary was “nice to have.”
Today, AI has made this mission critical, and rapidly exposing teams/leaders that have ignored their data dictionary for too long.
Salesforce AI, Agentforce, Data Cloud, copilots, LLMs, and predictive models all rely on understanding the business context of your Salesforce data.
If your field definitions live in a static spreadsheet:
AI needs updated and correct metadata intelligence to get the right context… and spreadsheets can’t provide that. To successfully adopt AI, you need:
It’s cool to see how many forward-thinking teams are moving to automated metadata platforms with:
That’s the bar now. And it’s what’s required for AI-powered businesses.
Arovy takes everything teams are trying to do manually, and makes it automatic, accurate, and intelligent.
Instead of a spreadsheet, you get:
This will allow you and your AI initiatives/ LLMs to fully understand your Salesforce environment.
If you want to:
Then it’s time to see what automated metadata intelligence looks like.
Stop maintaining Salesforce documentation by hand. Start building a data foundation your business, and your AI strategy, can leverage for success.