An Executive Diagnostic for Production-Grade AI Systems
Purpose
This checklist helps organizations determine whether they are ready to deploy AI in a way that is reliable, defensible, and sustainable—particularly in the Kenyan and African operating context.
AI should not begin with tools.
It should begin with readiness.
HOW TO USE THIS CHECKLIST
- This is not a scorecard for marketing
- It is a go / no-go diagnostic
- “No” answers indicate risk, not failure
- Proceeding without readiness leads to predictable collapse
SECTION 1: PROBLEM & STRATEGIC CLARITY
☐ We have a clearly defined operational decision we want to improve
☐ The decision occurs frequently enough to justify AI
☐ We understand the cost of a wrong decision
☐ AI is being considered to solve a real business constraint, not to signal innovation
☐ We have validated that automation alone is insufficient
If 2 or more are unchecked → Stop. Strategy work is required first.
SECTION 2: DATA READINESS
☐ We know where the required data currently lives
☐ Data has a clear business owner
☐ Historical data exists in sufficient volume
☐ Data quality issues are documented and understood
☐ We can trace data from source to decision output
If data ownership is unclear → AI should not proceed.
SECTION 3: INFRASTRUCTURE REALITY
☐ The system is designed for intermittent connectivity
☐ Infrastructure costs are understood and sustainable
☐ The architecture supports retries and reconciliation
☐ AI components can fail without collapsing the full system
☐ The system can scale gradually, not aggressively
If failure modes are undefined → Expect operational instability.
SECTION 4: GOVERNANCE & RISK
☐ AI decisions can be explained to non-technical stakeholders
☐ Audit trails are built into the system
☐ Human override is possible where required
☐ Regulatory exposure has been assessed
☐ Accountability for AI outcomes is clearly assigned
If decisions cannot be explained → The system will eventually be rejected.
SECTION 5: ENGINEERING & ARCHITECTURE
☐ AI logic is separated from core business workflows
☐ The system can be modified without re-architecture
☐ Monitoring and alerting are designed from day one
☐ Performance degradation can be detected early
☐ Retraining or recalibration processes are defined
If AI is tightly coupled to workflows → Long-term evolution will be costly.
SECTION 6: OWNERSHIP & OPERATIONS
☐ An internal owner exists for the AI system
☐ Knowledge transfer is planned, not assumed
☐ Documentation will outlive the original developers
☐ Operational playbooks exist for failures
☐ The organization can run the system without the vendor
If ownership is external → The system is a liability, not an asset.
INTERPRETING YOUR RESULTS
Mostly checked
You are likely ready for production-grade AI.
Mixed results
AI may be viable, but foundational work is required first.
Mostly unchecked
Proceeding with AI will likely result in wasted spend and system failure.
A FINAL EXECUTIVE NOTE
AI readiness is not about intelligence.
It is about discipline, structure, and ownership.
Organizations that delay AI until foundations are ready:
- Spend less overall
- Move faster later
- Avoid rebuilds
- Gain durable advantage
Those that rush AI early pay for it repeatedly.
HOW THIS SHOULD BE USED ON YOUR WEBSITE
Placement
- Linked from: AI Strategy & Engineering
- Linked from: Why Most AI Projects Fail in Kenya
- Used as a pre-sales qualification asset
Format
- 1-page PDF
- Optional gated or ungated (recommended: lightly gated)
- Clean, executive design
- No sales copy
WHY THIS ASSET IS POWERFUL
This checklist:
- Filters unserious prospects
- Educates the market without pitching
- Positions Graph Technologies as a strategic authority
- Creates inbound conversations at a higher level
It quietly says:
“If you are not ready, we will tell you. If you are ready, we can build something that lasts.”
