Midmarket B2B/B2C Company
eCommerce and Product Data After an Acquisition
Two companies, two product data models, one catalog that had to ship. The platform work landed six months early because the data model got settled first.
Results
Implementation Acceleration
Teams Coordinated
Portfolio Visibility
An acquisition is a merge conflict
Two companies had each built a product data model that worked. Post-acquisition there was one catalog to ship and two definitions of what a product was, how it was categorized, and which attributes were mandatory.
The CIO needed a roadmap across people, process and technology. The sequencing question sitting underneath it was whether to pick the platform first or settle the data model first, and that choice determined most of the timeline.
The model before the platform
The roadmap covered technology platform assessment, recommendation and selection, systems integration architecture design, and an Agile operating model spanning software delivery and IT support.
The decision that carried the outcome was settling the product data model before committing to the platform. A platform selected against an unresolved model encodes the disagreement into configuration, and the cost of that surfaces during migration rather than during selection.
Coordinating eight teams
Program management ran across eight internal and vendor teams, with portfolio visibility across all of them. Vendor boundaries are handoff boundaries, and handoffs are where definitional drift re-enters a program that had otherwise agreed.
Where the AI went
Micro-agents automating vendor coordination, AI-powered status reporting, and product data migration compressed from months to weeks.
The migration speed is downstream of the model work. Migrating between two agreed models is a mechanical problem. Migrating between two disputed ones is a negotiation conducted one record at a time.
How it ended
The eCommerce and PIM work landed six months faster than the original plan.
What repeats
After an acquisition, the catalog is where two definitions collide in public. Settling the data model before selecting the platform is what buys the schedule back, and the saving shows up in migration rather than in selection.
This was not a manufacturer, and we are not going to pretend it was. What carries across is the failure, not the industry: an organization could not agree what its own numbers meant, so nothing built on top of them held.
The same failure elsewhere
Fortune 1000 Software Product Company
ERP Program Management Uplift
A company-wide SAP S/4HANA upgrade with no reliable read on its own status, because the reporting was only ever going to be as good as the definitions underneath it.
Fortune 1000 Insurance Company
New Organizational Operating Model
A new internal consulting group with unknown demand and no buy-in. Naming the demand was the definitional work.
Where do your own definitions stand?
Twelve questions, and a note on what each bad answer costs you downstream.
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