In Brief
AI without data is just hype.
That’s because without clean, connected and contextual data, AI is just a buzzword. It can’t deliver reliable insights and help you make better decisions.
Before you dive into AI-powered automation, prediction, and optimization, strengthen your data foundation. Clean, structured data could be the difference between results and wasted effort.
Here’s why data matters to AI, where distributors typically go wrong, and how you can fix it.
Fivetran research found that nearly half of companies delayed, underperformed, or failed AI projects despite major investment in AI and data centralization. The survey highlighted poor data readiness as a leading roadblock to AI execution. That includes poor data governance and lack of real-time availability.
That’s not just a technical problem, according to the report. It also increased risk for these businesses, with 38% saying they incurred increased operational costs due to AI project failures. Another consequence: reduced customer satisfaction and retention.
Investing in AI without a solid data foundation is like buying a car but never putting gas in the tank. AI can’t generate reliable insights from nothing. It learns from both historical and real-time data to forecast demand, improve pricing, and automate product recommendations.
Distributors need AI-ready data to drive consistent logic, smarter automation, and more meaningful predictions. Messy data is caused by:
Many distributors think their systems are too messy to even start. That’s a misconception. The bottleneck isn’t usually the data. It’s a matter of prioritizing the effort. Here’s why:
The technical challenge isn’t as overwhelming as many distributors think. Cleaning and preparing data for AI can be done in weeks to a few months.
The real barrier is usually internal. Distributors may not see AI-ready data as urgent enough to move up the priority list.
Taking the first steps is key. After distributors decide AI-ready data is important, they usually find the path to AI readiness more manageable than expected. Most distributors already have a foundation to start from.
Good news: AI can actually help clean distributor data – standardizing messy descriptions, comparing product codes, and quickly identifying duplicate or inconsistent records.
Most distributors don’t need to start from scratch. And the goal is not perfection. Here’s where to start:
Establish a modern data platform.
Before diving into inventories, cleanups, or governance, distributors should first establish a modern data platform to serve as the foundation. Solutions like Microsoft Fabric, Databricks, or Snowflake provide a centralized environment where all critical data sources can be ingested, stored, and made accessible in real time.
A platform approach ensures that data from ERPs, CRMs, spreadsheets, and external sources are unified and available for analytics and AI models, rather than stuck in silos. It also sets the stage for scalability - allowing distributors to layer on governance, cleaning workflows, and AI applications without needing to re-engineer the foundation later. Starting with a robust platform reduces complexity, improves accessibility, and accelerates the path to AI readiness.
After you’ve established the foundation:
Inventory your data sources and gaps.
You need to understand what you have, where it lives, and how complete and useful it is. You also want to know how accessible it is.
Standardize and clean key data sets, starting with high-impact areas.
Focusing on the areas that matter most, like top-selling SKUs or most active customers, will drive immediate improvements. Remember that AI can perform better than humans in translating messy data into clean, structured data.
Put governance and data ownership in place for sustainability.
If bad data keeps coming back, you won’t get anywhere. Establish naming conventions, approval workflows, change tracking and audit processes.
Pilot AI where data quality supports it.
Use cases may include using LLMs to rewrite product descriptions, matching duplicate customer records post-acquisition, or powering a support chatbot.
And, finally, partner with an organization like ProfitOptics that knows your industry and can build a modern platform to turn your data into a powerful tool. That will help you avoid a generic system that won’t scale with your business.
When data and AI can work together, you’ll get:
These results come from structured data pipelines feeding an AI engine. Because AI can only transform when the data is ready.
Want to be AI-ready? Reach out today to learn how we can help.
