The impulse to "add AI" is common, but workshops exploring it must first ask "where do we have good, clean data?". Without a solid data foundation, AI ideation is futile. The first innovation step might be improving data collection, not implementing machine learning.

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.

Waiting for perfectly clean data stalls AI adoption. Instead, deploy AI agents to execute tasks. Their diligence and consistency in handling information will progressively clean underlying systems of record as a byproduct of their work.

Providing too much raw information can confuse an AI and degrade its output. Before prompting with a large volume of text, use the AI itself to perform 'context compression.' Have it summarize the data into key facts and insights, creating a smaller, more potent context for your actual task.

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.

A powerful and simple method to ensure the accuracy of AI outputs, such as market research citations, is to prompt the AI to review and validate its own work. The AI will often identify its own hallucinations or errors, providing a crucial layer of quality control before data is used for decision-making.

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.

To get better results from AI, don't ask for the final output immediately. Instead, prompt the AI to first provide a detailed process. This allows you to review and debug its logic, then instruct it to execute each step for a more accurate outcome.

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.