AI Readiness Assessment¶

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Purpose: Evaluate your organisation's readiness to successfully implement AI initiatives. Identifies strengths and gaps across six key capability dimensions: Strategy, Data, Technical, People, Process, and Use Case readiness.

At a Glance

Time to complete: 2-4 hours (workshop format recommended)
Who should participate: IT, Data, Business, and Executive stakeholders
Output: Readiness score (1-5), gap analysis, and action plan
Related tool: Interactive Readiness Scorer

Assessment Information¶

Field	Details
Organisation/Division
Assessment Date
Assessor(s)
Scope	Enterprise / Division / Team / Project
Version	1.0

How to Use This Template¶

Assemble your team - Include representatives from IT, data, business, and leadership
Score each criterion - Use the 1-5 scale, discussing evidence as a group
Calculate averages - Compute section and overall scores
Identify gaps - Focus on criteria scoring 1-2 (critical) or 3 (moderate)
Develop actions - Create prioritised remediation plan

Workshop Format Works Best

This assessment is most effective as a facilitated workshop where different perspectives can be heard and discussed.

Scoring Guide¶

Score	Rating	Description
1	Not Started	No capability or awareness
2	Initial	Ad-hoc, individual efforts
3	Developing	Some structured approach, inconsistent
4	Established	Consistent, documented practices
5	Optimized	Continuous improvement, industry-leading

Section 1: Strategic Readiness¶

1.1 Vision & Leadership¶

#	Assessment Criteria	Score (1-5)	Evidence/Notes
1.1.1	AI strategy aligned with organisational strategy
1.1.2	Executive sponsorship for AI initiatives
1.1.3	Clear AI vision communicated to staff
1.1.4	AI included in strategic planning
1.1.5	Budget allocated for AI exploration/implementation
	Section Average

1.2 Governance & Ethics¶

#	Assessment Criteria	Score (1-5)	Evidence/Notes
1.2.1	AI governance framework in place
1.2.2	Ethical AI principles defined
1.2.3	AI risk management integrated
1.2.4	Accountability structures for AI decisions
1.2.5	Ethics review process established
	Section Average

Strategic Readiness Score: ___/5

Section 2: Data Readiness¶

2.1 Data Availability¶

#	Assessment Criteria	Score (1-5)	Evidence/Notes
2.1.1	Data inventory/catalogue exists
2.1.2	Required data identified and accessible
2.1.3	Sufficient data volume for AI use cases
2.1.4	Historical data available for training
2.1.5	External data sources identified (if needed)
	Section Average

2.2 Data Quality¶

#	Assessment Criteria	Score (1-5)	Evidence/Notes
2.2.1	Data quality standards defined
2.2.2	Data quality monitoring in place
2.2.3	Data cleansing processes established
2.2.4	Data lineage documented
2.2.5	Data quality issues remediation process
	Section Average

2.3 Data Governance¶

#	Assessment Criteria	Score (1-5)	Evidence/Notes
2.3.1	Data ownership clearly defined
2.3.2	Data classification scheme in place
2.3.3	Data sharing agreements established
2.3.4	Privacy controls implemented
2.3.5	Data retention policies defined
	Section Average

Data Readiness Score: ___/5

Section 3: Technical Readiness¶

3.1 Infrastructure¶

#	Assessment Criteria	Score (1-5)	Evidence/Notes
3.1.1	Compute resources available (GPU, cloud)
3.1.2	Data storage and processing capacity
3.1.3	Development environments established
3.1.4	Production deployment infrastructure
3.1.5	Network connectivity and bandwidth
	Section Average

3.2 Tools & Platforms¶

#	Assessment Criteria	Score (1-5)	Evidence/Notes
3.2.1	ML/AI platforms available
3.2.2	Data processing tools in place
3.2.3	Model development tools available
3.2.4	MLOps capabilities established
3.2.5	Monitoring and observability tools
	Section Average

3.3 Integration¶

#	Assessment Criteria	Score (1-5)	Evidence/Notes
3.3.1	API infrastructure in place
3.3.2	Integration with core systems feasible
3.3.3	Real-time data pipelines available
3.3.4	Security integration (SSO, IAM)
3.3.5	Vendor integration capabilities
	Section Average

Technical Readiness Score: ___/5

Section 4: People & Skills Readiness¶

4.1 AI/ML Skills¶

#	Assessment Criteria	Score (1-5)	Evidence/Notes
4.1.1	Data scientists available (internal/external)
4.1.2	ML engineers available
4.1.3	Data engineers available
4.1.4	AI product managers available
4.1.5	AI skills development program
	Section Average

4.2 Data Literacy¶

#	Assessment Criteria	Score (1-5)	Evidence/Notes
4.2.1	Business users understand data concepts
4.2.2	Leaders can interpret AI outputs
4.2.3	Staff can identify AI opportunities
4.2.4	Data literacy training available
4.2.5	Data champions identified across business
	Section Average

4.3 Change Readiness¶

#	Assessment Criteria	Score (1-5)	Evidence/Notes
4.3.1	Change management capability exists
4.3.2	Track record of successful technology adoption
4.3.3	Staff openness to AI/automation
4.3.4	Training and support structures
4.3.5	Communication channels effective
	Section Average

People Readiness Score: ___/5

Section 5: Process Readiness¶

5.1 Delivery Capability¶

#	Assessment Criteria	Score (1-5)	Evidence/Notes
5.1.1	Agile delivery practices in place
5.1.2	Cross-functional team capability
5.1.3	Product management practices
5.1.4	User research and testing processes
5.1.5	Quality assurance practices
	Section Average

5.2 Operational Processes¶

#	Assessment Criteria	Score (1-5)	Evidence/Notes
5.2.1	IT service management mature
5.2.2	Incident management processes
5.2.3	Change management processes
5.2.4	Monitoring and support structures
5.2.5	Continuous improvement practices
	Section Average

5.3 Compliance Processes¶

#	Assessment Criteria	Score (1-5)	Evidence/Notes
5.3.1	Privacy impact assessment process
5.3.2	Security assessment process
5.3.3	Procurement and vendor management
5.3.4	Audit and compliance processes
5.3.5	Risk management integration
	Section Average

Process Readiness Score: ___/5

Section 6: Use Case Readiness¶

6.1 Specific Use Case Assessment¶

#	Assessment Criteria	Score (1-5)	Evidence/Notes
6.1.1	Use case clearly defined
6.1.2	Business value quantified
6.1.3	Success metrics identified
6.1.4	Stakeholder support confirmed
6.1.5	Risks understood and manageable
6.1.6	Required data available and suitable
6.1.7	Technical approach validated
6.1.8	Resources committed
	Section Average

Use Case Readiness Score: ___/5

Overall Readiness Summary¶

Dimension Scores¶

Dimension	Score	Rating	Status
Strategic Readiness	/5
Data Readiness	/5
Technical Readiness	/5
People Readiness	/5
Process Readiness	/5
Use Case Readiness	/5
Overall Average	/5

Readiness Radar¶

Use the scores from each dimension to create a visual radar:

Dimension	Score	Visual
Strategic	/5	`████░`
Data	/5	`███░░`
Technical	/5	`████░`
People	/5	`██░░░`
Process	/5	`███░░`
Use Case	/5	`████░`

Interactive Assessment

Use the Interactive Readiness Scorer to automatically generate your radar chart.

Readiness Level Interpretation¶

Score Range	Readiness Level	Recommendation
4.5 - 5.0	Optimized	Ready for complex AI initiatives
3.5 - 4.4	Established	Ready with minor capability building
2.5 - 3.4	Developing	Address gaps before major AI projects
1.5 - 2.4	Initial	Significant foundation work needed
1.0 - 1.4	Not Ready	Begin with awareness and strategy

Gap Analysis¶

Critical Gaps (Score 1-2)¶

Area	Current Score	Gap Description	Remediation Required

Moderate Gaps (Score 3)¶

Area	Current Score	Gap Description	Improvement Actions

Recommendations¶

Immediate Actions (0-3 months)¶

#	Action	Owner	Priority
1			High/Medium
2
3

Short-term Actions (3-6 months)¶

#	Action	Owner	Priority
1
2
3

Medium-term Actions (6-12 months)¶

#	Action	Owner	Priority
1
2
3

Next Steps¶

Review findings with leadership team
Prioritize remediation actions
Develop capability building roadmap
Re-assess in 6 months
Proceed with AI initiatives when ready

Assessment Sign-Off¶

Role	Name	Signature	Date
Assessment Lead
Business Sponsor
Technical Lead