Real-world adoption of AI in distribution is uneven. That’s not because distributors aren’t interested in the technology; it’s because many don’t think they’re ready.

But AI readiness isn’t about having perfect data or the “leading” software. Distributors need the right foundation and mindset. It’s about knowing where you are, what’s already working and where to take your first steps.

Here are five signs you may be closer to using AI in practical, meaningful ways than you think.

1. You’ve made progress in breaking down your data silos.

If customer data lives in one system, order data in another, and product data in a third, and none of them communicate, AI can’t see the full picture. If you’ve made progress toward breaking down those silos, AI can connect the dots:

• What’s the customer buying?
• What products are often ordered together?
• Which orders tend to get delayed?

A centralized data platform, such as Microsoft Fabric, Databricks, or Snowflake, provides a single environment to store, organize, and access your data, eliminating silos that limit AI’s potential. By unifying ERP, CRM, ecommerce, and supplier data, a modern platform ensures consistency, enables real-time insights, and creates a scalable foundation for AI applications.

With this in place, you can layer on governance, cleaning processes, and AI-driven tools without having to rebuild your core. It shows your business has moved from ad-hoc reporting to a structured, future-ready data strategy.

2. You understand there is no such thing as perfect data.

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 roadblock, including poor data governance and lack of real-time availability.

This doesn’t mean your data has to be perfect to get started. If you understand this, you’re ready for AI. The nature of data in distribution, with continually changing SKUs, customer information, and supplier catalogs, means perfection isn’t realistic. But you do need data that is good enough to support a use case, and a plan to improve data as you move forward.

Most distributors already have a foundation. Think impact, not scope. If you’re testing AI to automate quotes, start with your 100 most-quoted products. If you’re exploring personalized recommendations, begin with your top-tier customer segment.

Some of the best early use cases for AI are focused on improving your data: deduplicating records, matching inconsistent SKUs, extracting structured fields from PDFs, and more.

3. Your team sees the value in data, and not just your tools.

Many companies invest in dashboards, platforms and reporting tools, but that doesn’t mean their teams are using them in meaningful ways. AI readiness begins when the team moves beyond what a tool can do to the decisions it can drive.

When your teams value accurate, consistent and usable data, they may start:

• Asking better questions
• Flagging data issues for resolution
• Acting based on KPIs

This shows your team wants to solve problems with better data, which sets the stage for AI.

But if instead decisions are made based on anecdotes, reports are emailed but never discussed, nobody trusts your dashboards, and sales/ops/finance all report different versions of performance, then you may need to reset the culture first before investing in AI. The good news is this is not unsurmountable: You can train your team on not only how to use tools but why the data matters. You can also incentivize insight sharing across departments, and model data-driven decision-making. It just has to be prioritized.

4. You’re already using data to make improvements.

If you’re already using data to identify bottlenecks, track KPIs, or drive decisions, you’re not starting from scratch. Here’s what this might look like:

• Your team regularly reviews KPIs and adjusts processes in response.
• They question the “why” and not just the “what,” digging deeper into the data to uncover reasons.
• You’re tracking improvement over time.

In other words, if you’re already looking at the data with improvement in mind, AI will help you act faster and more consistently. It will also help you scale insights from periodic and manual to continuous and automated.

5. You see the value in taking small steps.

You know that AI isn’t an all-or-nothing event. Pilot AI where data quality supports it; the idea is to test, learn, refine, and repeat. Here’s what this looks like:

• Testing AI on narrow use cases, like cleaning up product data or rewriting product descriptions.
• Treating pilots as learning tools, not proof of total ROI.
• Focusing on operational use of AI, looking at how you can save time, improve accuracy or automate repetitive work.

Pilots reduce internal resistance, help you learn how it all works, build a feedback loop, and minimize the risk of investment in AI. When teams see value in these smaller steps, you build trust, momentum and a culture of experimentation that’s important for long-term success with AI. Progress, not perfection, makes AI work.

If you see your business in a couple of these signs, that’s a signal to start. Pick a use case with decent data and measurable impact and then build from there. You’re likely more ready than you think.