AI Readiness Assessment
Most artificial intelligence pilots never reach production. Not because of the technology, but because usable data, governance or a business case to sustain the investment are missing. We evaluate how ready your organisation is to take AI to production and build the roadmap to get there.
The assessment reviews eight fronts that determine whether an AI case can move from demonstration to operation: from data quality to who answers when the model gets it wrong. It turns the gaps into a roadmap prioritised by business value and agrees it with the areas. It does not evaluate tools: it evaluates the ability to reach production.
Know which cases are viable
Which of your ideas have the data, process and value to justify the investment.
Close the right gaps
What exactly is missing — data, platform, talent or governance — before scaling.
Invest with a business case
Each initiative with the value it returns and the effort it demands, not with a promise.
One surveys and maps the route. The other executes it.
These are two different services and it is worth knowing which one you need before contracting. The assessment does the first information-gathering exercise, builds the roadmap and aligns it with the business areas. The consulting practice executes that roadmap.
AI Readiness Assessment — this page
Surveys, maps and aligns. It surveys candidate use cases, data maturity, platform, talent and governance; measures the gap against recognised frameworks; builds the adoption roadmap and agrees it with the areas. It runs two to four weeks depending on the size of the organisation. It does not implement, does not configure and does not operate anything.
Artificial Intelligence — execution
Executes the roadmap. It takes the roadmap into production: solution design, data engineering, model implementation, integration with the processes and continuous operation. It works just as well if you already have a roadmap of your own.
Eight fronts that decide whether AI reaches production.
An AI case fails at its weakest link, not at its average. We review each front with the same depth and rate it by maturity level.
Strategy and use cases
Which business problems you want to solve with AI and which of them justify the investment against simpler alternatives.
Available data
What data exists, where it lives, at what quality, and whether it can be trusted to train or feed a model.
Platform and infrastructure
What compute, storage and integration capacity you have today and what would be needed to run in production.
Talent and skills
What the team can do, what is outsourced, and where a critical dependency on one person sits.
Governance and ethics
Who approves a case, who answers for an automated decision, and what responsible-use criteria are defined.
Security and privacy
Which personal or sensitive data is involved, how it is protected and what the applicable regulation requires.
Process and adoption
Whether the process AI will touch is documented, and whether the people who run it are ready for the change.
Value measurement
How the return will be measured and who answers for it once the case is operating.
Six phases, without interrupting your operation.
The assessment rests on interviews, review of data sources and documentary analysis. It does not train models or intervene in your systems.
Scope and preparation
We define which areas, processes and data sources are in, and agree read-only access.
Business interviews
With the areas proposing use cases, to understand the real problem behind each idea.
Data and platform review
Available sources, quality, governance and technical capacity, checked against what the interviews said.
Gap analysis
Comparison against NIST AI RMF and ISO/IEC 42001, front by front.
Use-case prioritisation
Each candidate with viability, effort and expected value, to decide where to start.
Business alignment
Presentation of the roadmap with leadership and the areas, so priorities are agreed.
What your maturity is measured against.
The evaluation does not rest on the judgement of whichever consultant shows up, nor on a vendor catalogue, but on public and auditable frameworks your team can consult and your auditor will recognise. Those frameworks sit alongside the law that decides what data a model may use: Habeas Data (Ley 1581) in Colombia and the LFPDPPP in Mexico.
NIST AI RMF 1.0
Evaluation framework. The AI risk management framework: govern, map, measure and manage. It orders the maturity rating.
ISO/IEC 42001
Evaluation framework. The AI management system standard. It is the structure an audit or a corporate client will ask for.
ISO/IEC 23894
Risk guide. AI-specific risk management: bias, explainability, model drift and data dependency.
OECD AI Principles
Responsible-use guide. International reference for transparency, fairness and accountability in automated systems.
EU AI Act
Regulatory guide. Classification by risk level. It applies if you operate or sell in Europe, and it sets the regional regulatory direction.
MLOps maturity
Operational guide. What separates a model in a notebook from a model in production with monitoring, versioning and retraining.
The frameworks are public and verifiable: anyone on your team can consult them and check the rating we deliver. That is the difference between an auditable evaluation and an opinion.
What it costs to scale before you are ready.
These risks do not show up in the pilot. They show up the day the case reaches production and touches a real customer.
Pilots that do not scale
The demo works on hand-prepared data and collapses when it meets the real data of everyday operation.
Insufficient data
Halfway through the project you discover the information needed does not exist, is incomplete or cannot be used.
Decisions with no owner
The model gets it wrong and nobody defined who answers, how it is reviewed or how it is corrected.
Regulatory exposure
Personal data or automated decisions with no legal basis and no traceability, in a tightening regulatory environment.
Spend with no return
Platform and licences get bought before knowing which case justifies them.
Vendor dependency
Knowledge stays outside and changing platform means redoing the work from scratch.
What it costs you in time, said upfront.
An assessment that does not state the commitment it requires ends up delayed. This is what we need from your side to deliver on time.
Two to four weeks
Two weeks in organisations of up to 50 employees; four between 51 and 300. The timeline is agreed before starting.
Scheduled interviews
Sessions with the areas proposing use cases, with IT and with whoever answers for the data.
Read-only access
Queries against the platforms and existing documentation. At no point is a configuration modified.
A single point of contact
One person coordinating schedules and access. It is the factor that most affects hitting the deadline.
Whatever documentation exists
Inventory of data sources, privacy policies, current architecture and results of previous pilots.
A closing session
The presentation of findings with leadership and the areas involved, where the roadmap priorities are agreed.
When it makes sense and when it does not.
It makes sense if…
Your organisation has pilots that never quite scale; you are about to set an AI budget and need to justify it; you handle personal data and want to know your exposure; or you want to know which of your ideas are viable before hiring anyone.
Probably not if…
You already have the case defined, the data ready and what you need is to build it: go straight to the Artificial Intelligence practice. Or if the underlying problem is the quality of your data: that is measured by the Data & Analytics Maturity Assessment.
What you gain from the assessment.
Cases prioritised by viability
Which ones have the data, process and value, and which are better discarded before investing.
Explicit gaps
What exactly is missing in data, platform, talent and governance before you can produce.
Regulatory risk contained
What the applicable regulation demands of the cases you want to run, before you are exposed.
Investment backed by figures
An economic case per initiative, with effort and expected value.
Defined governance
Who approves, who answers and how an automated decision gets reviewed.
A comparable baseline
A starting point that lets you measure progress next year on the same criteria.
Why this evaluation and not a proof of concept.
The difference is not in showing the technology works: it is in knowing whether your organisation can sustain it in production.
Evaluation, not demonstration
A pilot proves the technology. This assessment evaluates whether there is data, process, governance and talent to operate it.
Recognised frameworks
The comparison is against NIST AI RMF and ISO/IEC 42001, not against whichever consultant shows up.
Business language
Each gap with its cost of inaction and the use case it blocks.
Vendor independence
The roadmap is not shaped by the platform that would suit us to sell afterwards.
No interruption to operations
Interviews and document review. We do not train models or touch your systems.
Continuity into execution
If you decide to proceed, the roadmap connects with the Artificial Intelligence practice without starting the survey again.
As-Is, To-Be and the plan to get from one to the other.
Every assessment closes with the same structure, whatever the practice: where you stand today, where you need to be, what separates the two states and in what order that distance gets closed.
Current state — As-Is
The starting point surveyed with evidence, not declared in an interview: what exists, how it operates and how far it sits from what the business needs.
Target state — To-Be
Where the organisation needs to get to, defined with the business areas rather than imposed by the consultant. It is the benchmark everything else is measured against.
Gap analysis
Every difference between the As-Is and the To-Be, with everything required to close it: technology, processes, people, governance and budget. No gap is stated without what it demands.
Risk matrix
Each gap rated by probability and business impact, so priority does not depend on who pushes hardest but on what it costs to leave it open.
Work plan
The concrete sequence to reach the To-Be: what comes first, what it depends on, how much effort it takes and who should answer for each front.
Business alignment
The plan is presented and agreed with the areas involved. A roadmap signed only by IT does not survive the first quarter.
How what you receive is structured.
The central deliverable is a report with a fixed structure, designed so leadership reads the first pages and the technical team works with the rest.
Executive summary
Two pages: readiness level, the three gaps that weigh most and what decision each one calls for.
Maturity by front
Rating of the eight fronts with the gap made explicit against NIST AI RMF and ISO/IEC 42001.
Use-case inventory
Each candidate with viability, data required, effort and expected value.
Data diagnosis
Which sources exist, at what quality, and what is missing to sustain the prioritised cases.
Adoption roadmap
Prioritised initiatives, with the gaps to close before each one.
Immediate actions
What can be corrected or started without a project or additional budget.
What you receive at the end.
- Current state (As-Is): readiness to take AI to production, rated across each of the eight fronts against NIST AI RMF and ISO/IEC 42001.
- Inventory of candidate use cases, prioritised by feasibility and value.
- Diagnosis of the data sources that underpin the prioritised cases.
- Target state (To-Be): the data, governance and operating conditions the first production case demands, agreed with the business.
- Gap analysis between the As-Is and the To-Be, with each gap, its evidence and everything required to close it: data, platform, governance and capabilities.
- Risk matrix: regulatory, privacy and model exposure, rated by probability and business impact.
- Work plan to reach the To-Be: an adoption roadmap with the gaps that must close before each initiative.
- Governance recommendations: who approves, who answers and how it gets reviewed.
- Immediate-impact actions, executable without additional budget.
- Executive presentation for committee and leadership.
- Alignment session with the business areas involved.
About the AI Readiness Assessment.
What exactly do you evaluate?+
How does it differ from AI consulting?+
Do we need data ready to do this?+
Do you train any model during the assessment?+
Who should take part on our side?+
Is it useful if we already have pilots running?+
Does it cover regulatory compliance?+
How often should we repeat it?+
Know what you are missing before investing in artificial intelligence.
Book your AI Readiness Assessment and get an adoption roadmap prioritised by value, with the data, platform, talent and governance gaps made explicit.
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