{"id":10606,"date":"2026-05-21T12:50:58","date_gmt":"2026-05-21T16:50:58","guid":{"rendered":"https:\/\/distributionstrategy.com\/?post_type=tribe_events&p=10606"},"modified":"2026-06-17T16:56:46","modified_gmt":"2026-06-17T20:56:46","slug":"06-17-26-tlp-fixing-the-data-problem-for-ai","status":"publish","type":"tribe_events","link":"https:\/\/distributionstrategy.com\/event\/06-17-26-tlp-fixing-the-data-problem-for-ai\/","title":{"rendered":"Fixing the Data Problem for AI"},"content":{"rendered":"\n
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Technology Leader Panel<\/p>\n\n\n\n

Fixing the Data Problem for AI<\/strong><\/h1>\n\n\n\n
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watch Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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The most ambitious AI strategy cannot outperform the data feeding it. Here’s how distributors are fixing the foundation.<\/p>\n<\/div>\n<\/div>\n<\/div><\/div>\n<\/div>\n\n\n\n

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Every AI initiative in distribution eventually runs into the same wall: the data. Inconsistent product information, unreliable customer master data, siloed transactional systems, and uneven governance make it difficult for distributors to move AI projects from pilot to production. The most ambitious AI strategy cannot outperform the data feeding it, and many of the AI investments distributors are making today will underdeliver until the data foundation is addressed.<\/p>\n\n\n\n

In this Technology Leader Panel, we examine what it actually takes for distributors to fix the data foundation AI depends on. The discussion covers where data quality breaks down across product, customer, and transactional systems, how to prioritize cleanup and governance without stalling AI progress, the role of MDM, PIM, and modern data platforms in AI readiness, and how leading distributors are sequencing data work alongside AI initiatives so the two reinforce each other rather than compete.<\/p>\n\n\n\n

Key Takeaways<\/strong><\/p>\n\n\n\n