CASE STUDY
Structured AI Readiness Assessment Delivers Board-Approved Roadmap and 12 Prioritized Use Cases for a Mid-Market Manufacturer
Key Results
| Use Cases Identified | 12 AI use cases prioritized by business impact and implementation feasibility |
| Data Readiness | Data readiness score improved from 34% to 88% following remediation recommendations |
| Time to Roadmap | Board-approved AI implementation roadmap delivered within 6 weeks |
| False Start Avoidance | Estimated 18 months of misaligned AI investment avoided by addressing infrastructure gaps first |
SERVICE AREA
INDUSTRY
CHALLENGE
Board pressure to implement AI with no data foundation, no governance, and no clear starting point
A mid-market manufacturing company had observed all their competitors’ announcements regarding Artificial Intelligence (AI), and now they are being asked by their Board of Directors to do something similar. The leaders want to take action, but internally, the reality is that no one knows where to begin, what the data looks like, or if the current infrastructure can support the ideas that have been presented. The two prior internal efforts to scope out an AI program both ended with competing priorities, architectural discussions, and no actionable result. The problem was structural. Data was fragmented among multiple legacy Enterprise Resource Planning (ERP), Plant-Floor Systems, and Departmental Spreadsheets, which had never been identified as a single asset. There was also no existing data governance function, no clear ownership of key datasets, and no shared definition of what “Readiness for AI” meant for the company’s systems and processes. Without this basis, any AI investment would be ultimately wasted. The company required an objective outside assessment that could honestly identify where the company currently stands, define readiness for the use-cases that make business sense, and provide a roadmap that leadership could take action on without having to re-examine the beginning premise each quarter.
SOLUTION
End-to-end AI readiness assessment covering data maturity, infrastructure, use case prioritization, and phased implementation roadmap
SCIGON conducted an in-depth evaluation of the client’s enterprise data infrastructure, software and hardware technologies, operational systems and organizational competencies using a structured assessment methodology for AI readiness. Interviews with stakeholders representing Operations, Finance, Information Technology, and Executive Leadership helped identify several high-priority business problems that AI might help solve and set out to find solutions.
Data maturity was evaluated based on four criteria: availability, quality, accessibility, and governance. SCIGON conducted a detailed analysis of major sources of data, tested transformation pipelines, and assessed the gap between what the data could support and what each of the prioritized use cases required. When gaps were found, SCIGON proposed specific remediation recommendations along with estimated time frames for completing the necessary fixes. This gave the company a complete picture of what would have to take place prior to deploying AI.
The results included a list of twelve prioritized AI use cases ranked by business impact and feasible implementation, a data readiness scorecard with a remediation map, recommended infrastructure to build a scalable AI platform, and a phased implementation plan that received approval from the board as part of its next review. The assessment produced a credible, realistic pathway from where the company was at the outset to where it wanted to be moving forward.
