Before launching any ABM campaign, prioritize data hygiene. In large enterprises, it's common for a single account to exist under multiple names. This 'dirty data' can make 40-50% of an uploaded account list unmatchable in ad platforms, wasting significant budget and effort.

Leaders often believe their data is adequate until they attempt to deploy an AI agent. The process quickly reveals years of inconsistent or missing data from sales teams, forcing a critical data hygiene cleanup that should have happened long ago.

The company's initial attempt to build an AI Sales Development Representative failed because CRM data was too inaccurate. They realized that any AI application built on faulty data is wasted effort, leading them to focus on solving the foundational data problem first, as AI cannot discern data quality on its own.

AI models for campaign creation are only as good as the data they ingest. Inaccurate or siloed data on accounts, contacts, and ad performance prevents AI from developing optimal strategies, rendering the technology ineffective for scalable, high-quality output.

Companies struggle to get value from AI because their data is fragmented across different systems (ERP, CRM, finance) with poor integrity. The primary challenge isn't the AI models themselves, but integrating these disparate data sets into a unified platform that agents can act upon.

Before building sophisticated AI models, Personio invested heavily in data hygiene. They deduped their Salesforce instance, where one-third of data were duplicates, and spent months cleaning their prospect database. This foundational work is essential for making subsequent AI initiatives accurate and effective.

Many companies fail with AI prospecting because their outputs are generic. The key to success isn't the AI tool but the quality of the data fed into it and relentless prompt iteration. It took the speakers six months—not six weeks—to outperform traditional methods, highlighting the need for patience and deep customization with sales team feedback.

At Zimit, the CEO halted lead generation upon finding one inaccurate contact in the CRM. He argued that flawed data renders all subsequent marketing and sales efforts useless, making data quality the top priority over short-term metrics like MQLs.

Before launching any AI-driven outreach, focus on foundational data hygiene. This includes deduplicating accounts and contacts, clearly classifying records (customer, prospect, partner), and ensuring leads are correctly associated with parent accounts. AI rushes its work and cannot navigate these basic data flaws.