Scope
Agree the business units, systems and decisions in scope for scoring.
Done when: The scope names decisions, not departments.
AI Readiness Model (AIRM)
AI Readiness Model
One honest picture of what your organisation can actually run with AI today.
The business problem
Big AI ambition, uneven ability to deliver it.
Pilots get funded, demos impress, yet little reaches daily operations because nobody measured what the organisation can actually run.
What you hear inside the organisation
We have many pilots and nothing in production.
The board asks for our AI strategy every quarter.
Is our problem the data, the people or the platform?
Every vendor says we are ready.
Who it serves and what changes
Your teams, before and after the AI Readiness Model
Client teams and what they need
CEO and board
An honest picture before investing
CDO, CIO, CTO
Priorities by dimension, not by hype
HR and capability
Where skills are missing
Business sponsors
Which use case can start now
The outcome you get
- A scored level across data, platform, governance, talent, process and value
- The gaps that block production, ranked
- A roadmap matched to your real level
- A first use case the organisation can sustain
A worked example
A government entity with several AI pilots
The problem we saw
Leadership believed the organisation was ready. Pilots impressed in demos and then stalled with no owner or data access.
How the framework solved it
We scored data, platform, governance, talent, process and value on evidence, not opinion, and showed governance as the real blocker.
The business result
Budget moved from new pilots to fixing ownership and data access, and the strongest pilot became a supported service.
Illustrative example based on common client situations.
In short
What is the AI Readiness Model?
AI Readiness Model (AIRM): One honest picture of what your organisation can actually run with AI today.
Ambition is rarely the constraint.
- How it moves
- Each step closes with a check before the next begins
- Owned by
- Run jointly by the CDO or CIO office and the business sponsor of the first workload.
- You keep
- Scored maturity profile, Blocked-workload map, Sequenced roadmap
How it works
How the AI Readiness Model works
Levels run from Ad hoc, Repeatable, Governed, Scaled to Self-improving. Most enterprises score unevenly, high on platform, low on governance.
Evidence
Score each dimension from artefacts and systems, not from self-assessment alone.
Done when: Every score cites the artefact behind it.
Gap
Compare current level to the level the target workloads require.
Done when: Each gap is tied to a workload that is blocked by it.
Sequence
Order the moves so each one unlocks the next rather than running in parallel silos.
Done when: The first ninety days are fundable and owned.
Re-score
Repeat the scoring each two quarters so progress is measured, not asserted.
Done when: Movement between levels is evidenced by new artefacts.
Is it right for you?
Is the AI Readiness Model right for you?
A strong fit if
- You are planning or resetting an AI programme
- Pilots are stuck before production
- Leadership wants evidence before more budget
Probably not yet if
- You already run several AI workloads at scale with clear ownership
- You want a tool selection only
Free assessment
Discuss the AI Readiness Model with our team
A working session with our senior team. We review what fits your situation, what does not, and where to start.
A focused conversation
We listen to your situation and goals.
Fit review
Which parts of this framework apply to you, and which do not.
A written view
Clear next steps, whether or not you work with us.
Go deeper
Applying the AI Readiness Model in practice
Open only the part you need: how it starts, what your team keeps, maturity levels, sector rules and the evidence a reviewer will ask for.
Ambition is rarely the constraint. AIRM scores Data, Platform, Governance, Talent, Process and Value so the roadmap matches the level you are at, not the level of the case study you read.
Version
v2.0
Last reviewed
July 2026
Family
AI Operating System
How this was developed: Built from delivery practice on Saudi and international engagements, then reconciled clause by clause against the published standards below. Revised when a standard is updated or a gate proves unworkable in the field.
One-pager
Download the AIRM one-pager
Stages, exit gates, artefacts, standards crosswalk and FAQs for AI Readiness Model on one printable page, share it with the committee.
- Week 1
Name the two or three workloads the assessment must serve; readiness in the abstract is not useful.
- Week 2
Collect evidence per dimension from the systems themselves, not from questionnaires.
- Week 3
Score, then write the gap statement behind every level so the number can be argued with.
- Week 4
Convert the gaps into a sequenced plan with owners, dates and a re-assessment point.
- End of the first thirty days
Scored maturity profile
Six dimensions with the evidence cited for each score.
Blocked-workload map
Which planned use cases are gated by which gap.
Sequenced roadmap
Ninety-day and twelve-month moves with owners and exit criteria.
L1
Ad hoc
Capability is personal, not institutional.
L2
Repeatable
Some pipelines and reviews repeat reliably.
L3
Managed
Platform, governance and delivery are standardised.
L4
Scaled
New workloads reuse the platform without new exceptions.
L5
Optimising
Value, cost and risk are measured and tuned continuously.
Banking & finance
Model risk management practice raises the Governance bar before any customer-facing workload.
- Who reviews it
- SAMA, plus PDPL and NCA controls
- Systems in scope
- Core banking, cards, KYC and AML systems, credit bureau feeds, the general ledger
- Where it lands first
- KYC and AML review, credit decisioning support, complaint and dispute handling
1
Scope
- Scope statement
- Interview plan
2
Evidence
- Evidence log
- Dimension scores
3
Gap
- Gap matrix
- Blocked-workload list
4
Sequence
- Ninety-day plan
- Twelve-month roadmap
5
Re-score
- Trend view
- Updated roadmap
Levels run from Ad hoc, Repeatable, Governed, Scaled to Self-improving. Most enterprises score unevenly, high on platform, low on governance.
Stage
Exit gate
Artefacts
1
Scope
Agree the business units, systems and decisions in scope for scoring.
- Signed off by
- Programme sponsor
- Typical duration
- Scoped with you
Exit gate
The scope names decisions, not departments.
Evidence: Scoped assessment plan with the workloads in scope named
Artefacts
- Scope statement
- Interview plan
2
Evidence
Score each dimension from artefacts and systems, not from self-assessment alone.
- Signed off by
- Assessment lead with each dimension owner
- Typical duration
- Scoped with you
Exit gate
Every score cites the artefact behind it.
Evidence: Evidence pack per dimension, not self-declared scores
Artefacts
- Evidence log
- Dimension scores
3
Gap
Compare current level to the level the target workloads require.
- Signed off by
- Executive committee
- Typical duration
- Scoped with you
Exit gate
Each gap is tied to a workload that is blocked by it.
Evidence: Scored radar with the gap statements behind each level
Artefacts
- Gap matrix
- Blocked-workload list
4
Sequence
Order the moves so each one unlocks the next rather than running in parallel silos.
- Signed off by
- Programme sponsor
- Typical duration
- Scoped with you
Exit gate
The first ninety days are fundable and owned.
Evidence: Sequenced remediation plan with owners and dates
Artefacts
- Ninety-day plan
- Twelve-month roadmap
5
Re-score
Repeat the scoring each two quarters so progress is measured, not asserted.
- Signed off by
- Assessment lead
- Typical duration
- Each quarter
Exit gate
Movement between levels is evidenced by new artefacts.
Evidence: Re-scored profile showing movement against the first baseline
Artefacts
- Trend view
- Updated roadmap
Standard
What this framework satisfies
Google MLOps maturityLevels 0–2 automation criteria
Levels 0-2 absorbed into the Platform and Process dimensions.
DCAM / DAMACapability scoring for the data dimension
Capability scoring model for the Data dimension.
NIST AI RMFGOVERN 1 · GOVERN 2 (structures and accountability)
Govern function evidence feeds the Governance dimension.
ISO/IEC 42001Cl. 4 context · Cl. 5 leadership · Cl. 6 objectives
Clause 4-6 organisational context and objectives feed Process and Value.
Self-scored maturity
Scores based on opinion inflate every dimension except the one that blocks you.
Roadmaps without gates
A twelve-month plan with no exit criteria becomes a status report.
Scoring the tools
Platform maturity without governance and process maturity produces expensive pilots.
Built on
- 1
MLOps and LLMOps maturity practice
Published maturity levels for automation of the model lifecycle.
- 2
DAMA-DMBOK and EDM Council DCAM
Data management knowledge areas and capability assessment.
- 3
ISO/IEC 42001: AI management systems
International Organization for Standardization, management system requirements for AI.
- 4
NIST AI Risk Management Framework 1.0 and Generative AI Profile
National Institute of Standards and Technology, Govern, Map, Measure, Manage.
Citations describe the parts of each standard the framework answers. Bilytica is not an accreditation or certification body.
In Saudi Arabia
What this family has to satisfy before a release in Saudi Arabia
The AI operating system frameworks carry a workload from a readiness score to a governed release. These are the Saudi checks each stage is built against.
AuthorityWhat it requiresWhat we buildWhat you keep
SDAIA, PDPL
Purpose, lawful basis and disclosure recorded before the first personal-data field is used.
Diagnosis and assurance stages block a build that has no recorded basis.
Processing register entry attached to the release record.
NCA ECC
Authorisation and containment for anything the system can do on its own.
Agent operations defines action classes, dual control and a tested rollback path.
Containment test result and an authority matrix signed by the business owner.
NDMO data management controls
No workload builds on an uncertified data domain.
The data foundation gate certifies ownership, lineage and quality thresholds first.
Signed domain certificate referenced by the release.
Where it runs
Training, evaluation and serving run inside Saudi Arabia on your infrastructure, with any hosted component documented as a boundary.
Arabic, as it is spoken here
Arabic quality is a release condition: evaluation sets include Saudi Arabic content, not translated English.
FAQ
Frequently Asked Questions
How is this different from the online scorecard?
The scorecard gives an indicative read in minutes. AIRM scores the same dimensions against evidence and produces a funded plan.
Who needs to be involved?
Data, platform, security, risk and the business owner of the first workload. Scoring without risk present is the usual failure.
Can we skip it if we already know the gaps?
You can start delivery in parallel, but the sequencing decision still needs the scored view to avoid building on an uncertified domain.
How long does an AI readiness assessment take?
Timeline agreed with you, covering interviews, evidence review and a roadmap workshop that ends with a sequenced plan rather than a score alone.
Is the score self-assessed?
No. Every dimension is scored against evidence you can produce, contracts, tests, controls, releases, which is why the Evidence stage sits before the Gap stage.
What does the re-score stage add?
It turns readiness into a tracked measure: the same dimensions are re-run after the roadmap's first gates so progress is proven, not asserted.
Which reference models sit behind it?
Google's MLOps maturity levels, DCAM/DAMA for data capability, NIST AI RMF for risk functions, and ISO/IEC 42001 for management-system expectations.
Where this sits on the spine
Where this connects
Rest of the library
DFFData Foundation FrameworkVALUEAI Value FrameworkTRUSTAI Assurance FrameworkDELIVERAI Delivery FrameworkAGENTAgent Operations FrameworkSOVEREIGNSovereign AI FrameworkDMMData Management Maturity ModelDPAData Platform Architecture FrameworkDQFData Quality & Observability FrameworkDECIDEAnalytics & Decision FrameworkDGOVData Governance Operating ModelSDCFSaudi Data Compliance FrameworkAQSArabic Quality StandardKSA-READYIn-country Deployment Framework
Topic cluster
Explore the topic cluster
Curated links across our Saudi hubs, sector pages and long-form blog research.