Context Readiness Scorecard

Assess your organization's readiness for enterprise AI that actually delivers ROI

Part of the Making AI Work series by Thomas Larson

Why This Matters

95% of AI pilots fail to deliver measurable ROI. The problem? Context readiness, not model capability. This diagnostic helps you understand where your organization stands across the four critical dimensions that separate AI leaders from laggards.

Time to complete: 5-7 minutes | Questions: 16 | Your data stays private (nothing is sent to a server)

1. Data Integration & Accessibility

Can AI actually access the information it needs, when it needs it?

1.1 How well are your critical data sources connected and accessible to AI systems?
Siloed
Some APIs
Partially
Mostly
Fully
Siloed systems, manual exports Partially connected Real-time, governed access
1.2 What is your data quality across key sources that AI would need to use?
Poor
Basic
Moderate
Good
Excellent
Inconsistent, unverified Validated in key areas Continuously monitored
1.3 Can your systems share context (relevant data and state information) in real-time across applications?
No
Limited
Some
Mostly
Yes
Batch only, delayed Some real-time Seamless, real-time
1.4 How fresh is the data available to AI systems?
Days old
Hours
Minutes
Near real-time
Real-time
Days old Hourly updates Real-time

2. Organizational Alignment & Process Readiness

Does your organization understand what AI needs to succeed?

2.1 Do stakeholders understand what AI requires to deliver value?
No
Minimal
Partial
Good
Strong
Confusion/skepticism Some understanding Strong alignment
2.2 Are your workflows and business processes documented?
No
Minimal
Partial
Good
Comprehensive
Tribal knowledge Key areas documented Comprehensive, maintained
2.3 Is there executive sponsorship for AI transformation?
No
Passive
Moderate
Active
Champion
None Interested observer Active champion
2.4 How well defined are roles and responsibilities for AI initiatives?
Unclear
Informal
Defined
Clear
Dedicated
Unclear Some definition Dedicated AI roles

3. System Architecture & Technical Foundation

Is your infrastructure ready to support context-rich AI?

3.1 Is your infrastructure architected to support AI context requirements?
Legacy
Some updates
Partial
Mostly ready
Purpose-built
Legacy systems Modernization underway AI-ready architecture
3.2 Are your APIs documented and standardized?
No
Minimal
Partial
Mostly
Comprehensive
Inconsistent/undocumented Some standardization Well-documented, versioned
3.3 Is scalability planned for in your technical architecture?
No
Limited
Planned
Built-in
Enterprise-grade
Not considered Roadmap exists Enterprise-scale ready
3.4 Do you have observability and monitoring for AI systems?
None
Basic logs
Some metrics
Good dashboards
Comprehensive
None Basic metrics Full observability stack

4. Governance & Quality Assurance

Can you ensure quality, compliance, and continuous improvement?

4.1 Are quality standards defined for AI outputs?
None
Informal
Defined
Enforced
Automated
No standards Documented standards Automated quality gates
4.2 Is there ongoing monitoring of AI system performance?
None
Ad hoc
Regular reviews
Continuous
Real-time
No monitoring Periodic reviews Real-time dashboards
4.3 Are improvement processes in place for AI systems?
None
Reactive
Planned
Systematic
Self-improving
React to problems Regular optimization cycles Continuous improvement loops
4.4 Do you have compliance and audit capabilities for AI?
None
Minimal
Basic
Good
Comprehensive
No audit trail Basic logging Full audit/compliance

Your Context Readiness Results

About Making AI Work

A thought leadership series on closing the AI deployment-to-value gap through context engineering.

By Thomas Larson, Enterprise AI Strategist

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