Your AI Strategy Is Failing. Here’s the Fix No One Talks About.

Here’s what I keep seeing when I walk into B2B distributors: the AI conversation starts with excitement and ends with the same problem.

“Wait, why do we have three different descriptions for the same SKU?”

That’s the real blocker. Not technology. Data.

Akeneo just dropped their 2026 report on product data in the age of AI commerce. The numbers confirm what I’ve been seeing for years: 95% of GenAI pilot programs fail, largely due to poor or fragmented data foundations.

Let me break down what this means for $5M-$1B distributors—and what I’d actually do about it.

The Hidden Tax You’re Already Paying

Here’s what bad product data actually costs:

Your employees waste up to 12 hours a week hunting down information across systems. That’s not a typo. Twelve hours. Per person. Per week.

The average business runs nearly 300 software applications. Large enterprises? Over 600. Your product data doesn’t live in one place. It lives everywhere and nowhere.

Gartner estimates poor data quality costs organizations $12.9 million annually. But the number that should really concern you: customer dissatisfaction with product data accuracy more than doubled from 13% to 30% between 2023 and 2025.

And 66% of shoppers have abandoned a significant purchase because of missing or inaccurate product information.

That’s not a data problem. That’s a revenue problem.

Why This Kills Your AI Initiatives

Here’s the part most B2B leaders don’t realize yet.

AI agents and recommendation engines don’t pull from your website copy. They pull from structured data. If your product descriptions are inconsistent, your attributes incomplete, your specifications scattered across ERP, spreadsheet, and eCommerce platform—AI can’t help you.

Worse: it will confidently recommend your competitors.

The report puts it plainly: 85% of AI projects fail because of poor product data quality. Underperforming AI models built on low-quality data can cost companies up to 6% of annual revenue.

For a $50M distributor, that’s $3 million in lost opportunity. Every year.

How I’d Fix This (The Practitioner Playbook)

When I walk into a distributor with messy product data, here’s my actual approach:

Week 1-2: Diagnose the Data Landscape

Before touching any technology, I map where product data actually lives. ERP fields. Spreadsheets (there’s always a spreadsheet someone’s been maintaining for years). Supplier portals. The eCommerce platform. That Access database nobody wants to admit still exists.

I identify who “owns” each system. In most companies, software management is decentralized—departments with budget authority make purchases independently, bypassing IT. You can’t manage what you don’t know exists.

Then I catalog the pain: siloed systems, inconsistent attributes, slow product launches, discrepancies across channels. Usually it’s all of the above.

Week 3-4: Define Business Outcomes First

The mistake I see constantly: companies treat PIM as a technology project instead of a business initiative.

I frame everything around measurable ROI drivers:

• How fast can you launch a new product today vs. target?

• What’s your return rate due to inaccurate product information?

• How many hours does your team spend on manual data entry?

These become your KPIs. Not “data completeness” (too abstract). Actual business metrics your CFO cares about.

Week 5-8: Build the Foundation

This is where most agencies go wrong. They want to boil the ocean—clean every SKU, standardize every attribute, integrate every system.

I start smaller. Pick your top 20% of SKUs (the ones driving 80% of revenue). Get those perfect first. Create the governance model—who can edit what, approval workflows, quality standards.

The goal isn’t perfection. The goal is progress you can measure.

Week 9-12: Enable the Team

Here’s the thing about product data: it’s not IT’s job. Marketing needs to enrich descriptions. Merchandising needs to manage categories. Operations needs pricing and inventory.

I train each team on their piece of the puzzle. I set up dashboards so they can see data quality scores in real-time. I create feedback loops so problems get caught early.

By week 12, you have a functioning system—not a one-time cleanup that degrades the moment I leave.

The Monday Morning Takeaway

Before you invest another dollar in AI, answer three questions:

1. How many systems does your product data live in right now?

2. Can your marketing team update a product description without waiting for IT?

3. If ChatGPT pulls from your catalog tomorrow, will it recommend you or your competitor?

If you don’t like the answers, that’s your real AI strategy. Fix the data foundation first.

The companies winning in AI commerce aren’t the ones with the most advanced models. They’re the ones with the cleanest data.