Dataset · AI Governance

AI readiness question bank and scoring dataset

The full question bank behind our AI readiness assessment — every question, the dimension it belongs to, its weight, the evidence it asks for and the band thresholds — published so organisations can run the assessment themselves or adapt it.

11 August 2026SJAIN Tech AI Practice, SJAIN Tech Risk & AssuranceDataset

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Why publish the question bank

An assessment that cannot be inspected is hard to trust and impossible to repeat. Publishing the questions, weights and evidence requirements lets an organisation run the readiness review without us, compare results over time, and challenge any weighting that does not fit its regulatory context.

Using it well

Answer against evidence, not intent — every yes should point at an artefact
Keep the question identifiers when you adapt wording, so history stays comparable
Re-run quarterly; readiness decays with staff, policy and data-source change

Limitations

Weights encode our delivery experience rather than a published benchmark, and the bands recommend next steps rather than predict outcomes. Sector regulation may justify raising the governance weighting before you use it.

Data dictionary

Field	Type	Notes
question_id	string	Stable identifier for tracking answers over time
dimension	string	One of the seven readiness dimensions
question	string	The question as asked in the assessment
weight	number	Relative contribution to the dimension score
evidence	string	Artefact that substantiates a positive answer
band_thresholds	string	Cut-offs for fix, pilot, scale and optimise

Licence and citation

Free for internal assessment and academic use with attribution to SJAIN Tech. Commercial redistribution or resale as an assessment product is not permitted.

Cite as: SJAIN Tech (Sjain Ventures Ltd), “AI readiness question bank and scoring dataset”, 11 August 2026. https://tech.sjain.io/research/ai-readiness-benchmark-dataset

More research

The Enterprise AI Readiness Index: seven dimensions that decide whether a pilot scalesA working paper describing the seven-dimension readiness model behind our AI readiness assessment, how each dimension is scored, and why governance and data foundations — not model choice — separate pilots that reach production from those that stall.A practical AI governance framework: control points from intake to retirementA reference model that places governance controls at the six points where an AI system changes state — intake, data access, evaluation, release, operation and retirement — with the artefacts and owners each control requires.The automation economics of document-heavy workflowsA costing method for document-heavy processes that separates handling cost, exception cost and assurance cost, showing why automation business cases built on average handling time alone consistently overstate savings.

Related capabilities

AI Readiness AssessmentA structured audit of data, systems, skills and risk posture before you commit budget.Security AssessmentPosture assessment against a recognised control framework.VAPT CoordinationManaged testing with accredited partners and verified remediation.AI StrategyA board-ready plan linking AI investment to specific processes, numbers and timelines.Workflow AutomationRouting, approvals and hand-offs automated across teams and systems.Document AutomationExtract, validate and file documents without manual keying.

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