AI Readiness Scorecard
Overall Readiness Score
out of 5.0 — [MATURITY LEVEL]
Pillar-by-Pillar Breakdown
Scores across 5 operational readiness pillars
Data Infrastructure
[SCORE] / 5.0Data siloed across multiple systems with no unified schema.
No data lake or warehouse; manual ETL processes; inconsistent naming conventions.
Implement cloud data warehouse with automated ingestion pipelines.
Process Maturity
[SCORE] / 5.0Core processes documented but not standardized across teams.
No process mining in place; tribal knowledge dependencies; limited SOP adoption.
Deploy process mining tools and establish standardized SOPs.
Technology Stack
[SCORE] / 5.0Mix of legacy and modern systems with limited integration.
Duplicate CRM instances; manual data transfers; no API layer between systems.
Consolidate platforms and build integration middleware layer.
Team Readiness
[SCORE] / 5.0Staff comfortable with existing tools but limited AI literacy.
No AI training program; change management not formalized; skill gaps in data roles.
Launch AI literacy program and hire/upskill data engineering roles.
Governance & Ethics
[SCORE] / 5.0Basic data governance policies exist but not enforced consistently.
No AI ethics framework; data quality monitoring gaps; compliance automation absent.
Establish AI governance board and implement automated compliance checks.
Maturity Level Definitions
Ad-hoc processes; no formal AI strategy or data infrastructure.
Some structure emerging; isolated pilots; basic data practices.
Documented processes; centralized data; AI use cases identified.
Measured and controlled; active AI deployments; governance in place.
Continuous improvement; AI embedded in operations; data-driven culture.
Priority Matrix
[QUICK WINS HERE]
[STRATEGIC PROJECTS HERE]
[FILL-INS HERE]
[AVOID OR DEFER]