How to Measure AI Readiness: Inside the Senticit Intelligence Score

"Are we ready for AI?" cannot be answered as asked. It bundles data quality, security posture, governance maturity, and organisational capacity into one question, so the answer is always a shrug or a sales pitch.

The Senticit Intelligence Score (SIS) exists to decompose it. It is a 0–100 measure across four weighted dimensions, and it is deliberately unflattering — a high score should be hard.

The four dimensions

• Data foundation. Do you know where regulated data lives, who can reach it, and how it is classified? Without this, every AI deployment is an undocumented data-flow change.
• Security posture. Identity, endpoint, and monitoring maturity. AI expands your attack surface faster than your headcount grows.
• Governance. A written AI use policy, an accountable owner, an approved-tool inventory, and vendor diligence on record.
• Operational capacity. Whether anyone owns the outcome after the pilot. Most stalled AI programmes fail here, not technically.

Why a single number helps

Not because it is precise, but because it is comparable. One number lets you:

1. Show a board direction of travel across quarters instead of a wall of findings.
2. Compare business units on the same basis.
3. Tie remediation spend to a measured delta rather than to a vendor's alarm.

Beneath the number, the dimension scores are what drive work. A company at 62 with a strong security posture and weak governance needs a policy and an owner, not another tool.

Reading your score honestly

• Below 40 — deploying AI on regulated data is premature. Fix data classification and identity first.
• 40–65 — safe to pilot in bounded, non-regulated use cases with human review.
• 66–85 — ready for production use with monitoring and documented governance.
• Above 85 — the constraint is usually capacity and change management, not controls.

The score is generated by Meridian Baseline™ and kept current by the rest of the Meridian Suite. You can run the assessment and get your own score and dimension breakdown, or read the practical NIST AI RMF rollout if you would rather start from a framework.