AI Readiness Assessment for Regulated Industries

AI programs struggle when data is ungoverned, processes are undocumented, and the organization lacks the necessary structure to support AI in production. Felaris provides robust data governance, business process optimization, and the foundational support that AI requires, or fixes these issues when an AI investment has stalled. With twenty-five years of experience in ERP implementations, data migrations, and compliance remediation in regulated industries, we understand that the data challenges that hinder AI programs are the same ones that can derail ERP initiatives.

Felaris specializes in business process optimization through master data management assessment and implementation, data quality measurement and profiling, data lineage and traceability—crucial for AI auditability in regulated industries. We also focus on establishing data ownership and stewardship models, conducting data architecture reviews for AI/ML workloads, and enhancing metadata management. Our approach ensures that AI data governance aligns with compliance obligations such as FDA 21 CFR Part 11, EU MDR, and SOX ITGC.

AI applied to undocumented and unmeasured business processes can lead to unreliable outcomes that are challenging to validate in regulated industries. Felaris optimizes business processes by accurately mapping how they operate, measuring critical metrics such as cycle time, error rate, and decision points. Furthermore, it standardizes practices across sites and business units while ensuring robust data governance. This approach helps identify suitable candidates for AI integration, focusing on high-volume, rule-based decisions that have sufficient data and measurable outcomes.

The five-dimension evaluation encompasses: data (quality, governance, architecture), process (documentation, measurement, standardization) for business process optimization, technology (infrastructure, integration, scalability), people (skills, roles, change readiness), and governance (policies, risk, oversight) tailored for regulated industries. This includes gap analysis, AI use case prioritization, and a remediation roadmap.

Felaris applies the same assessment discipline to stalled ERP programs, focusing on business process optimization through root cause analysis. This includes examining data quality, process misalignment, governance gaps, vendor misfit, and organizational resistance. The recovery roadmap emphasizes prioritized remediation and effective data governance to prevent recurrence, especially crucial for regulated industries.

Ensuring data integrity in accordance with FDA 21 CFR Part 11, navigating the EU AI Act risk classification, adhering to ISO 13485 process documentation, implementing SOX ITGC controls for AI-generated outputs, applying GAMP 5 Category 5 validation for AI/ML in GxP, and aligning the NIST AI RMF with existing NIST CSF controls are crucial for success in regulated industries. 

Organizations that require executive-level ownership of AI initiatives, while focusing on business process optimization, can benefit from engaging a fractional Chief AI Officer (CAIO) to steer the AI agenda at the leadership table. The data governance and process documentation disciplines applied here mirror those that Felaris employs in effective ERP strategy and implementation.

In the realms of medical device manufacturing, pharmaceutical and life sciences, and regulated manufacturing, businesses often emphasize data governance and business process optimization. This is particularly crucial for warehouse and distribution operations as well as industrial manufacturing involving growth-stage companies in regulated industries.

Organizations in regulated industries, such as medical devices, pharmaceuticals, CDMOs, manufacturing, and distribution, with revenues ranging from $50M to $500M, are increasingly focusing on business process optimization. This includes those with operations in the US, Caribbean, or Europe, particularly where AI has become a key topic on the board agenda, but no one at the leadership level has ownership of data governance.