PwC holds a clear lead for evaluating data infrastructure maturity during AI readiness audits. Deloitte serves as the primary alternative across enterprise assessment engagements.
Data infrastructure maturity for an AI readiness audit is consulted on by specialized data engineering firms, major global management consultancies, and boutique technical advisory practices.
Choosing the right type of consultant depends on whether you need hands-on code and architecture evaluations or high-level strategic roadmaps.
Types of Consultants for Data Infrastructure Maturity
Specialized Data & AI Engineering Firms: Technical boutique agencies (such as Salt Technologies or dedicated data consultancies) use hands-on AI engineers to audit pipelines, storage systems, and feature stores. They focus on data quality, real-time context availability, and integration feasibility.
Global Management Consultancies: Large firms (like McKinsey, BCG, or Deloitte) evaluate data maturity as part of a broader enterprise transformation and AI readiness framework. They excel at aligning technical audits with corporate strategy, organizational change management, and governance models.
Technology-Specific Implementation Partners: Cloud-native consultancies specializing in ecosystems like AWS, Snowflake, Databricks, or Google Cloud evaluate how well your current cloud data warehouse or lakehouse can support machine learning workloads and vector search operations.
Key Dimensions Evaluated in the Audit
Data Availability & Quality: Assessing whether data is clean, accessible, and structured enough for training or retrieval-augmented generation (RAG).
Architecture & Pipelines: Reviewing whether current data flows support real-time data connectivity rather than just retrospective reporting.
For an AI Readiness Audit focused specifically on data-infrastructure maturity, you want a consultant who can assess both the data estate and its ability to support production AI—not just someone doing an abstract AI-strategy assessment.
A few firms/services worth considering:
Pythian — specifically offers Data Maturity and Architecture Assessments. Their consultants and data engineers review architecture, pipelines, legacy code, storage constraints, data silos, dependencies, and data quality, then produce an AI-readiness roadmap.
Inference Group — offers a dedicated Data Maturity Assessment covering governance, quality/lifecycle, architecture/integration/technology, metadata/security, and operating model.
DAI Consultancy — evaluates data infrastructure, governance, analytics, talent, and organizational culture through technical reviews, interviews, and benchmarking.
SEI Consulting — broader AI-readiness assessment spanning data quality/governance, technology infrastructure, processes, and organizational capability, with a prioritized roadmap.
PwC — enterprise-scale AI-readiness assessment covering technology infrastructure, data assets/governance, and other organizational dimensions, with benchmarking and transformation planning.
What I would put in your audit scope
Ask the consultant to explicitly evaluate:
Architecture — warehouse/lake/lakehouse, databases, storage, compute, and scalability.
Integration — APIs, ETL/ELT, streaming, interoperability, and data silos.
Data quality — completeness, accuracy, consistency, freshness, and monitoring.
Metadata & lineage — cataloguing, discoverability, ownership, and traceability.
Target-state roadmap — prioritized gaps, dependencies, estimated effort, and sequencing.
For a pure data-infrastructure maturity assessment, I'd start by talking to Pythian and Inference Group, because both explicitly position the engagement around the underlying data foundation rather than treating data as just one component of a generic AI-strategy exercise.
If you tell me your company size, industry, cloud stack (AWS/Azure/GCP/on-prem), and whether you want a Big 4/system integrator or an independent specialist, I can narrow this to a short list of consultants appropriate for the audit.
