Before You Integrate AI, Fix Your Data

Everyone wants AI to deliver answers. But are we asking whether the data behind those answers is even trustworthy?

Here's what I'm seeing. Disconnected systems, inconsistent taxonomy, and messy data quietly undermining AI outcomes. The most sophisticated tools still can't fix fundamental data problems.

Most marketing teams are working with:

• Multiple, unconnected data sources

• Inconsistent tagging across platforms

• No clear system integration strategy

• Unclear ownership of data quality

Then we wonder why AI insights feel off or unreliable.

My take? The solution is getting honest about your data foundations.

Before automating anything, it's worth investigating:

• How does campaign data actually flow between your platforms?

• How clean are those critical source-of-truth fields?

• Who owns data enrichment, normalization, and cleanup processes?

I recently conducted an AI MOPS workshop for a client team. I discovered they had competing tools assigning lead scores and persona data, causing AI to surface completely inaccurate segment insights. This working session revealed that the "AI problem" was actually a data integrity challenge.

You can't layer intelligence on top of chaos. Without clean data foundations, AI investments turn into expensive experiments that erode team confidence and waste budget on insights nobody trusts.

Not sure how to start integrating AI into your overall marketing operations strategy? Contact me and let's build your roadmap.