Discover
Collect candidate workloads from process pain, not from technology lists.
Done when: Each candidate names a decision and the person who makes it today.
AI Value Framework (VALUE)
AI Value Framework
From use-case long list to booked benefit, with unit economics attached at every step.
The business problem
AI spending is visible. AI value is not.
Use cases are approved on enthusiasm, then nobody can say what each one costs or who records the benefit.
What you hear inside the organisation
What did we get for last year's AI budget?
Every department sent a wish list.
The model works, but the cost per request keeps rising.
Finance does not accept the business case.
Who it serves and what changes
Your teams, before and after the AI Value Framework
Client teams and what they need
CFO and finance
Benefits they can book
Strategy office
A ranked, sequenced portfolio
Business owners
Clear targets per use case
AI and IT teams
Unit cost limits to design to
The outcome you get
- A use-case portfolio sized and ranked by value
- Unit economics per workload
- A named owner for every benefit
- Tracking against the numbers used to approve it
A worked example
A bank with a long list of AI ideas
The problem we saw
Every department asked for AI. Approvals followed enthusiasm, and finance could not see a return on any of it.
How the framework solved it
We sized each idea by value, cost per transaction and data readiness, named a benefit owner, and agreed how it would be tracked.
The business result
The bank funded a short list it could defend, stopped the rest, and reports realised benefit with the numbers used to approve it.
Illustrative example based on common client situations.
In short
What is the AI Value Framework?
AI Value Framework (VALUE): From use-case long list to booked benefit, with unit economics attached at every step.
AI budgets die when nobody can say what a workload costs per transaction or who books the benefit.
- How it moves
- Each step closes with a check before the next begins
- Owned by
- Owned by the business sponsor with finance as co-signer on the benefit definition.
- You keep
- Sized use-case portfolio, Unit economics model, Benefit charter
How it works
How the AI Value Framework works
Cost per transaction includes inference, retrieval, human review and the data pipeline that feeds it, not just the model call.
Size
Estimate benefit, cost to build, cost to run per transaction and the data dependency.
Done when: Run cost per transaction is estimated before build is approved.
Sequence
Rank on value, data readiness and risk tier, not on enthusiasm.
Done when: Nothing enters build on an uncertified data domain.
Commit
Fund a slice with a named benefit owner and an agreed measurement method.
Done when: Finance accepts the measurement method before build starts.
Realise
Track actual benefit and actual run cost quarterly, and retire what does not earn.
Done when: Every live workload has a current benefit and cost reading.
Is it right for you?
Is the AI Value Framework right for you?
A strong fit if
- You have more AI ideas than budget
- Finance questions the return on AI
- Running costs of live models are growing
Probably not yet if
- You have no AI use cases identified yet
- Value is purely regulatory and not measured financially
Free assessment
Discuss the AI Value Framework 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 Value Framework 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.
AI budgets die when nobody can say what a workload costs per transaction or who books the benefit. VALUE sizes, sequences and then tracks realisation against the same numbers used to approve it.
Version
v1.4
Last reviewed
June 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 VALUE one-pager
Stages, exit gates, artefacts, standards crosswalk and FAQs for AI Value Framework on one printable page, share it with the committee.
- Week 1
List candidate use cases by the decision each one changes, not by the technology involved.
- Week 2
Size each one against a baseline finance recognises, and record what is assumed.
- Week 3
Sequence by data readiness and risk, not by enthusiasm; attach a stop rule to each.
- Week 4
Agree the measurement plan and the unit-cost view before the build starts.
- End of the first thirty days
Sized use-case portfolio
Every candidate with benefit, build cost, run cost and data dependency.
Unit economics model
Cost per transaction broken down across inference, retrieval and review.
Benefit charter
The measurement method finance agreed to before build.
L1
Anecdotal
Benefits are claimed in slides, never measured.
L2
Estimated
Cases are sized, but against soft baselines.
L3
Tracked
Benefits and unit costs are tracked per workload.
L4
Governed
Funding follows evidence, and stop rules are actually used.
Banking & finance
Benefit is booked against loss rates, handling time or capital efficiency, and reviewed with risk.
- 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
Discover
- Use-case long list
- Decision owners
2
Size
- Value case
- Unit economics sheet
3
Sequence
- Portfolio ranking
- Dependency map
4
Commit
- Benefit charter
- Measurement method
5
Realise
- Realisation report
- Retire / scale decision
Cost per transaction includes inference, retrieval, human review and the data pipeline that feeds it, not just the model call.
Stage
Exit gate
Artefacts
1
Discover
Collect candidate workloads from process pain, not from technology lists.
- Signed off by
- Business unit leaders
- Typical duration
- Scoped with you
Exit gate
Each candidate names a decision and the person who makes it today.
Evidence: Use-case register with the decision each one changes
Artefacts
- Use-case long list
- Decision owners
2
Size
Estimate benefit, cost to build, cost to run per transaction and the data dependency.
- Signed off by
- Finance business partner
- Typical duration
- Scoped with you
Exit gate
Run cost per transaction is estimated before build is approved.
Evidence: Sized benefit with the baseline it is measured against
Artefacts
- Value case
- Unit economics sheet
3
Sequence
Rank on value, data readiness and risk tier, not on enthusiasm.
- Signed off by
- Investment or steering committee
- Typical duration
- Scoped with you
Exit gate
Nothing enters build on an uncertified data domain.
Evidence: Funded sequence with a stop rule per workload
Artefacts
- Portfolio ranking
- Dependency map
4
Commit
Fund a slice with a named benefit owner and an agreed measurement method.
- Signed off by
- Investment committee with the accountable executive
- Typical duration
- Scoped with you
Exit gate
Finance accepts the measurement method before build starts.
Evidence: Benefit charter signed by the person whose numbers will change
Artefacts
- Benefit charter
- Measurement method
5
Realise
Track actual benefit and actual run cost quarterly, and retire what does not earn.
- Signed off by
- Finance and the workload owner jointly
- Typical duration
- Quarterly
Exit gate
Every live workload has a current benefit and cost reading.
Evidence: Benefits realisation review with unit cost per outcome
Artefacts
- Realisation report
- Retire / scale decision
Standard
What this framework satisfies
FinOps for AIInform · Optimize · Operate phases
Unit economics, showback and cost anomaly review per workload.
ISO/IEC 42001Cl. 6.2 objectives · Cl. 9.1 performance evaluation
Clause 6 objectives and planning, and performance evaluation in clause 9.
NIST AI RMFMEASURE 1 · MEASURE 2 (metrics and methods)
Measure function, defining what is measured and how often.
Benefit with no owner
If no budget line changes, the benefit was never real.
Ignoring run cost
Workloads approved on build cost alone become the next cost-cutting target.
Portfolio by enthusiasm
The loudest sponsor gets sequenced first and the data is not ready.
Built on
- 1
FinOps for AI
Unit economics and cost accountability for AI workloads.
- 2
ISO/IEC 42001: AI management systems
International Organization for Standardization, management system requirements for AI.
- 3
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 early should run cost be modelled?
Before build approval. Retrieval, review and pipeline costs usually exceed the model call at production volume.
What if the benefit is qualitative?
Then it is stated as a service-level or risk-reduction target with a measurable proxy, and labelled as such.
How do you size AI benefit without inventing numbers?
Sizing uses your own baselines, volumes, cycle times, error and rework rates, and each case is signed by the business owner who will be measured on it.
Does the framework include run cost?
Yes. Inference, retrieval, human review and platform cost are sized alongside benefit, following FinOps-for-AI practice, so a case cannot look positive on build cost alone.
Who owns benefit realisation?
A named business owner per case, with the Realise stage reporting actuals against the committed baseline on a quarterly cycle.
How is the portfolio sequenced?
By data readiness, value density and risk class together, not enthusiasm. Cases whose data domain is not certified are sequenced behind the certification that unblocks them.
Where this sits on the spine
Where this connects
Rest of the library
DFFData Foundation FrameworkAIRMAI Readiness ModelTRUSTAI 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.