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.
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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.
Instead of initiating daunting, multi-year data projects, the most practical first step to unifying customer profiles is to focus on fundamentals. Prioritize automated data integrations for list building and implement rigorous list cleaning and tracking from day one to avoid manual errors.
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.
The best initial use for AI in marketing operations is automating high-volume, low-complexity "digital janitor" tasks. Focus AI agents on answering repetitive questions (e.g., "Why didn't this lead qualify?") and cleaning data (e.g., event lists) to free up specialist time for more strategic work.
Before implementing AI, organizations must first build a unified data platform. Many companies have multiple, inconsistent "data lakes" and lack basic definitions for concepts like "customer" or "transaction." Without this foundational data consolidation, any attempt to derive insights with AI is doomed to fail due to semantic mismatches.
A major hurdle for enterprise AI is messy, siloed data. A synergistic solution is emerging where AI software agents are used for the data engineering tasks of cleansing, normalization, and linking. This creates a powerful feedback loop where AI helps prepare the very data it needs to function effectively.
Before deploying AI across a business, companies must first harmonize data definitions, especially after mergers. When different units call a "raw lead" something different, AI models cannot function reliably. This foundational data work is a critical prerequisite for moving beyond proofs-of-concept to scalable AI solutions.
The traditional marketing focus on acquiring 'more data' for larger audiences is becoming obsolete. As AI increasingly drives content and offer generation, the cost of bad data skyrockets. Flawed inputs no longer just waste ad spend; they create poor experiences, making data quality, not quantity, the new imperative.
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.
According to Salesforce's AI chief, the primary challenge for large companies deploying AI is harmonizing data across siloed departments, like sales and marketing. AI cannot operate effectively without connected, unified data, making data integration the crucial first step before any advanced AI implementation.
