Track Record
The pattern repeats across industries
These were not manufacturing clients. We are not going to pretend they were. What repeats is the failure: an organization could not agree what its own numbers meant, so nothing built on top of them held. Here is where we have seen it, and what fixing it looked like.
ERP Program Management Uplift
Any manufacturer mid-way through an ERP cutover knows this one. The status reports disagree because the underlying fields were never defined the same way twice.
Challenge
A large software firm had no reliable read on visibility or transparency during a company-wide ERP upgrade to SAP S/4HANA. The initial assessment went straight to Project Management Information System (Jira) data hygiene and reporting, because the reporting was only ever going to be as good as the definitions beneath it.
Solution
Three-phase roadmap (Visibility, Transparency, Reliability) delivered over nine months with the internal PMO team: Jira data cleanup supporting portfolio reporting rollup, a new operating and interaction model improving hand-offs, and enhanced reporting dashboards for executive-to-delivery visibility. The engagement extended.
AI Application
Real-time Jira automation, workflow-triggered handoffs, predictive bottleneck flagging (2-week advance notice)
Results
Three-Phase Roadmap Delivered
Predictive Bottleneck Flagging
Executive Reporting Confidence
eCommerce and Product Data After an Acquisition
The post-acquisition version of the same problem. Two companies, two product data models, one catalog that has to ship.
Challenge
A Midwest school system supplier, post-acquisition, needed an assessment and a roadmap covering people, process, and technology to guide the CIO.
Solution
A roadmap covering technology platform assessment, recommendation, and selection; systems integration architecture design; program management across 8 internal and vendor teams; portfolio visibility; and an Agile operating model across software delivery and IT support. The eCommerce and PIM work landed six months faster than the original plan, because the product data model got settled before the platform did.
AI Application
Micro-agents automating vendor coordination, AI-powered status reporting, weeks-instead-of-months product data migration
Results
Implementation Acceleration
Teams Coordinated
Portfolio Visibility
New Organizational Operating Model
Stand up a new internal group, in any industry, and the first fight is over what it is actually for. Naming the demand is the definitional work.
Challenge
A Fortune 1000 insurance firm faced system integration problems following multiple acquisitions. Leadership launched an internal consulting group, but demand was unknown and other leaders had not bought in.
Solution
Demand-based operating model featuring lightweight discovery identifying high-priority initial services, prioritization framework for service sequencing, skill set identification for service delivery, and a scalable model adaptable to emerging demand.
AI Application
Demand forecasting agents predicting service needs, automated prioritization by business impact, knowledge transfer automation for scaling
Results
Demand-Based Scaling
Fixed Overhead Cost
Faster Knowledge Transfer
Standard work before AI work
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