What happens when trusted product data becomes more than just information stored in a PIM? For DwyerOmega, it becomes a powerful driver of operational efficiency, smarter decision-making, and faster product launches. Discover how the global manufacturer is using Akeneo MCP to transform product information into actionable Power BI insights, strengthen technical product descriptions with AI, proactively identify data quality issues, and accelerate the onboarding of newly acquired product lines in as little as 40 minutes.
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In Part 1 of this series, we explored how DwyerOmega rebuilt its product data pipeline using Akeneo MCP Server, creating a stable, self-updating connection between Akeneo and the systems its teams use every day.
With Akeneo remaining the governed source of product information, the next question was what DwyerOmega could do once that trusted data became easier to access and activate.
The answer is already taking shape across the business.
With the MCP-powered pipeline in place, product management and marketing teams get a familiar, filterable view of catalog quality in Power BI. The dashboards show whether products meet their targets across areas such as:
Users can filter by product manager or product model, see where current content does not meet its target, and export a focused list of products that need attention. The dashboard is intentionally read-only.
When asked whether edits in Power BI push back into the PIM, Cyrus Grimes, Director of Product Content Management at DwyerOmega, was direct: “No, this is just a report.” Updates are still made in Akeneo or through coordinated loading documents handled by trained PIM users.
That combination gives teams convenient access without weakening control. In one example, a manager used the dashboard to show a product owner exactly where content and image targets were misaligned.
A Product Manager was able to understand exactly where he needed to go. When he clicks this, he gets this list of product series, and he can just export that and work on those directly. He didn't have the tools to do that before.
The product owner could then defend the targets or improve the data, working from a clear list of affected products rather than a broad request to fix completeness.
Before this visibility existed, inconsistencies could surface through customer complaints, manual website checks, or one-off investigations. Power BI gives DwyerOmega a more proactive way to find missing or misaligned information before it becomes a downstream problem.
Because the company is in an active acquisition phase rather than a maintenance phase, data quality work has historically been easy to push to the back burner. The dashboard makes that work visible and assignable. Cyrus plans to extend it as acquisitions become a smaller part of daily operations, including new status views and additional objects.
Two further ideas are in early exploration rather than production: a data hygiene or “policing” view that would compare records across the source system, SAP Commerce Cloud, and Akeneo to surface synchronization gaps, and an AI-assisted workflow that reads product data issues reported in a team chat channel, checks Akeneo, and proposes corrections through MCP with human approval retained.
The operating model is clear: automate repeatable discovery and preparation, then keep people in control of consequential updates.
DwyerOmega is also using a multi-phase, AI-assisted workflow built in Claude to improve product descriptions. Many technical products have capabilities documented in specification sheets and manuals but represented online by only the small set of popular variants loaded as PIM variants. Older descriptions were often thin.
“We had some product descriptions that were pretty weak. Literally just a paragraph, and kind of just marketing fluff.”
The workflow uses Akeneo product records to locate the relevant manuals and specification sheets. Local optical character recognition, run through a local language model, extracts the technical content, followed by quality control. The process then builds structured, capability-led descriptions and merges them with useful elements of the existing copy before product managers review and load them back into Akeneo.
Preserving the existing content matters. Cyrus reports that the approach has helped keep organic search traffic stable and growing despite broader industry declines.
The most immediately valuable use case is the acquisition workflow, and Cyrus names it as the one with the greatest potential.
Newly acquired businesses do not organize or sell products the way DwyerOmega’s established ecommerce catalog does. Some products are highly technical, expensive, or configurable across an impractical number of permutations.
“Not everything is easy to add to your cart and sell there. They might be extremely expensive or very technical, and we do not want the customer to just spend $10,000 and be like, ‘I got the wrong thing’.”
To support these products, DwyerOmega created a sales-assisted product family where the call to action is contacting a salesperson rather than adding to cart, while the product information is still fully managed and published.
MCP-powered workflows chain several systems together. A web scrape of an acquired company’s product series loads supporting assets into SharePoint, mirrors images into AWS for web delivery, creates the asset in Akeneo with the URL populated automatically, and builds the product series with a placeholder variant and basic information.
We're able to get the assets, the product images, spec sheets, manuals, and load this simple product model into Akeneo in 40 minutes. We've got 82 of them so far. It's been going pretty fast, and we've only been doing it for a couple of weeks.
The workflow also reduces a common acquisition tradeoff. DwyerOmega does not have to wait for every commerce decision to be finalized before making newly acquired product information useful to customers and sales teams.
Across these use cases, DwyerOmega is building an operating model in which Akeneo remains the authority for product information and MCP reduces the friction between that source and the systems where work happens.
The practical benefits are already visible:
The value of governed product data grows when people and systems can use it without introducing another layer of inconsistency. For DwyerOmega, Akeneo remains the trusted product foundation, while MCP makes that information easier to access and put to work across the business. What started as a better way to connect systems is becoming a more flexible operating model for product information.
Our Akeneo Experts are here to answer all the questions you might have about our products and help you to move forward on your PX journey.
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