Introduction: AI isn’t magic — it’s math
The rise of AI-powered tools has sales teams expecting predictive brilliance from the click of a button. But underneath that smart summary, auto-tagged deal, or forecasted close date is one truth: AI is only as good as the data it’s trained and triggered on. And most teams are flying with corrupted inputs.
Good AI needs clean data. Great AI needs consistently structured data.
What bad data looks like in sales
Reps forgetting to log calls
Deal stages updated once every 2 weeks
No summaries or next steps recorded after meetings
CRM fields half-filled, never updated
Different reps using different labels for the same thing (e.g., “contract sent” vs “doc shared”)
Now layer AI on top of that. The result? Garbage insights that feel smart but actually mislead your team.
How to clean up your workflow before adding AI
1. Define what “healthy” deal data looks like
Build a checklist:
Last meeting recorded
Summary or action items present
Contact roles mapped
Forecast confidence entered (even manually)
This creates a baseline that AI can reinforce — and surface exceptions when something’s missing.
2. Use automation for structure, not shortcuts
Instead of asking reps to remember fields, build workflows where:
Calendar events trigger meeting capture
Summaries are auto-generated, but editable
Follow-ups are drafted, but always reviewed
That’s structure — not laziness.
How Hexa makes this automatic
We built Hexa’s engine to clean as it goes:
AI tags meeting summaries with keywords like
Objection,Urgent, orBlockedThose tags populate CRM fields automatically
Reps are prompted to review — not recreate — what already happened
Managers see health scores based on completeness, not just activity
The result: a clean pipeline, fed by real behavior.
Final Thought
Bad data is like bad diet: it won’t hurt on day one, but you’ll feel it when the quarter ends. If you want AI that works — clean the pipes first.
