- Home
- Data & AI Services
- AI Data Readiness
AI data readiness
AI data readiness assessment
Find out whether your data can support the AI use case you have in mind before you invest in it. Konsolute assesses data quality, coverage and permissions, then sets out the preparation and testing needed for forecasting, machine learning, Copilot or AI assistants.
WHAT THIS HELPS YOU ACHIEVE
Evidence for whether the data can support the intended AI task
Make the decision to proceed, prepare further or change direction around a defined use case, with the gaps and responsibilities visible.
Assess suitability before investment
Test the data against the actual task, rather than assume a large data estate is ready for every AI use.
Identify preparation work
Expose gaps in quality, history, labels, content structure and source coverage before a pilot depends on them.
Design permission-aware use
Establish who can access source information and how permissions must apply in the proposed AI workflow.
Define a meaningful evaluation
Agree baselines, test cases and acceptance evidence before interpreting a model score or generated answer as success.
OUR AI DATA READINESS SERVICES
AI data readiness services for predictive and generative AI
Assess a selected use case, prepare a priority dataset or knowledge corpus, or establish the evidence and controls needed before a scoped pilot.
AI use case & data requirements
Define the decision, prediction or retrieval task and who will use its output. Establish the data needed, where people review results and the evidence needed to judge whether a pilot works.
Data discovery & quality assessment
Profile relevant sources for availability, completeness, consistency, provenance and freshness. Check coverage against the intended task, recording source owners, access constraints and known limits.
Forecasting & ML data preparation
Review historical records, target definitions, labels and candidate features. Check that inputs would exist when a prediction is made, and keep training, validation and test data properly separate.
Knowledge & retrieval readiness
Assess documents and knowledge for a retrieval-based AI assistant. Review authority, duplication, structure and currency, then set extraction, chunking, metadata and citation requirements.
Permissions & data use controls
Map source permissions, sensitive information and intended users to the proposed processing path. Assess access to prepared data, indexes and test material, including permission changes and deletion.
Evaluation & pilot readiness
Define representative test material, baselines and acceptance criteria. Prioritise remediation and set out what the pilot must prove, including failure scenarios, ownership and conditions for wider use.
CAPABILITIES & DELIVERY SCOPE
Different AI tasks need different data evidence
The assessment separates the requirements of prediction, model training and retrieval, then connects each to data preparation, access controls and evaluation.
Task definition & acceptable use
Translate the opportunity into a specific task, output and user. Define how results inform a decision, where people review them and which outcomes are unacceptable, and record the simpler baseline AI should beat.
- Task, users and operating context
- Baseline and acceptance questions
- Human review and failure consequences
Source inventory & coverage
Identify the sources and records the use case needs. Assess ownership, provenance, sampling, missing values and freshness, and record where data is unavailable or unrepresentative, so readiness is never judged on volume alone.
- Relevant sources and provenance
- Coverage and quality evidence
- Access constraints and evidence gaps
Prediction data, targets & leakage
For forecasting and supervised learning, review the target, labels, identifiers and history. Check for leakage and whether features exist at prediction time, and choose test periods that suit the task.
- Targets, labels and feature availability
- Time and entity leakage checks
- Task-appropriate training and test design
Knowledge preparation & retrieval evidence
For retrieval-based assistants, assess authoritative content, versions, duplicates and extraction quality. Test whether relevant passages can be found and cited; good retrieval alone does not prove answer quality.
- Authority, currency and content extraction
- Chunking, metadata and provenance
- Representative retrieval questions and citations
Permission boundaries & sensitive content
Map access from the source through copies, embeddings, indexes and the consuming application. Identify where authorisation must be applied and tested, including revoked access and deleted content, rather than relying on ingestion permissions.
- User and workload access models
- Prepared data and retrieval boundaries
- Permission change and deletion scenarios
Repeatable preparation & lifecycle
Define cleansing, labelling and content preparation as repeatable, versioned steps. Assess Fabric or Azure processing against source constraints, and set the refresh, quality and ownership needed to keep prepared data useful.
- Preparation rules and versioning
- Refresh and quality validation
- Ownership and lifecycle dependencies
Evaluation sets & acceptance criteria
Build an evaluation plan around real tasks, including ordinary cases, edge cases and gaps in information. Measure retrieval separately from answer quality, or accuracy separately from generalisation, and agree criteria with owners.
- Representative test material and holdouts
- Task-specific metrics and baselines
- Failure scenarios and human assessment
Readiness decision & pilot roadmap
Summarise the evidence, unresolved risks and preparation required. Identify which assumptions a scoped pilot must test and the conditions for proceeding, pausing or changing the use case, with owners and cost drivers recorded.
- Evidence-based readiness findings
- Prioritised remediation and validation
- Pilot scope, owners and decision gates
TECHNICAL DESIGN DECISIONS
Resolve suitability, access and evaluation before scaling
A readiness assessment should expose the limits of the available evidence and the work needed to test the intended use responsibly.
Quality for the specific task
A dataset can be fine for reporting yet inadequate for prediction or retrieval. Define the history, context and coverage the intended output actually needs.
Information available at the right time
Check that predictive inputs and test splits reflect real operation. Information created after an outcome can make a test look far better than future performance.
Access beyond the source
Account for copied data, search indexes, generated outputs and test material. Test the permission boundary as built rather than relying on the source being secure.
Readiness and production approval
Separate data findings from approval to deploy a model or agent. A pilot still needs to prove behaviour, integration and operating controls before wider release.
OUR APPROACH
How we deliver an AI data readiness assessment
Six stages take one AI idea from a defined task to tested data evidence and a clear decision: proceed to a pilot, prepare further or change direction.
Define the AI task
Identify the users, intended output and business decision, with baseline and acceptance questions.
Assess the relevant sources
Map data or knowledge sources, provenance, ownership and availability constraints.
Profile suitability and access
Assess quality, coverage, temporal context and the permissions needed across the proposed workflow.
Prepare representative material
Produce the agreed sample datasets or knowledge preparation assets with documented assumptions.
Design and test the evaluation
Validate selected preparation or retrieval assumptions and define representative metrics, test cases and review.
Agree the readiness decision
Provide findings, remediation priorities and the scoped pilot or further preparation needed for the next stage.
Assess.Prepare.Decide.
WHERE AI DATA READINESS CAN HELP
Start with the AI use case and its data dependency
Readiness is assessed against a specific task, rather than treated as a permanent label for the whole organisation.
- Assess whether historical records can support a defined forecast or predictive task
- Identify label, target or feature gaps before model-development work begins
- Review document quality and retrieval coverage for a knowledge assistant
- Validate how source permissions must carry into prepared data and retrieval
- Create representative evaluation questions, expected evidence and failure cases
- Prioritise data preparation and define the acceptance gate for a scoped AI pilot
See it in practice: before rolling out Microsoft 365 Copilot for a central government organisation, Konsolute assessed and prepared its data, permissions and governance so staff could use AI safely.
WHAT WE DELIVER
Readiness evidence and a practical route to the next decision
The agreed scope defines the assessment depth, sample preparation and evaluation assets to be handed over.
Use case and source baseline
Task definition, source inventory, provenance, ownership and relevant access or data constraints.
Suitability and control findings
Quality, coverage and task-specific findings, with permission requirements and explicit limitations.
Preparation and evaluation assets
Agreed sample datasets, content preparation rules, test material, metrics and baseline criteria.
Remediation and pilot plan
Prioritised preparation tasks, owners, evidence gaps and decision gates for the next scoped stage.
FAQ
AI data readiness FAQs
Forecasting, retrieval, data leakage, permissions and next steps.
It assesses whether the data or knowledge needed for a selected AI task is available, suitable, understandable and accessible under the right permissions. Findings identify preparation and evaluation work, with evidence and limitations rather than a blanket claim that the organisation is AI-ready.
No. Predictive tasks may need historical observations, targets, labels and features available at prediction time. Retrieval-based solutions need suitable source content, retrieval behaviour and permission-aware access. Evaluation and preparation are designed for the task.
They overlap. Copilot readiness focuses on Microsoft 365 content, such as oversharing in SharePoint and Teams, permissions and sensitivity labels. This assessment covers any AI use case, including forecasting, machine learning and custom assistants built on your own data.
Not necessarily. We assess the relevant sources and existing platform against the use case. Fabric or Azure engineering may support preparation, but a platform migration is not automatically required before a focused assessment or pilot.
We profile relevant records or content and compare them with the task’s requirements. Missingness, consistency, provenance, freshness and representation are considered, with findings reviewed by owners. A general quality score is not treated as proof that data supports every AI use.
Leakage occurs when training or evaluation uses information that would not be appropriately available in the real prediction scenario. We assess temporal and entity overlap, feature timing and test design so validation is more representative of the intended operation.
We review authority, versions, structure, duplicates, extraction and source coverage. Preparation and test questions assess whether relevant evidence can be retrieved and cited. Retrieval quality and generated-answer quality are evaluated separately.
Not necessarily. A retrieval-augmented solution supplies selected source information to a model at query time. Model training or fine-tuning is a different workstream with its own data and evaluation requirements. The readiness scope makes that distinction explicit.
We map source access through preparation, indexes and the consuming application, with users and workloads considered separately. The proposed access mechanism, supported platform features and deletion or permission changes need validation before broader use.
The work can identify data and retrieval problems and establish evaluation criteria, but it does not guarantee every answer is correct. The solution still needs testing, appropriate handling of insufficient evidence and human review for the agreed operating context.
The assessment can define a next-stage pilot, and agreed sample preparation or technical validation can be included. Model development, application integration and production deployment require an explicitly scoped delivery workstream and acceptance criteria.
We compare the evidence with the defined task and acceptance questions. The outcome can identify prerequisites for a pilot, further data preparation or reasons to pause or change direction. Unresolved risks and decision ownership remain visible.
The agreed outputs include use-case and source records, suitability and permission findings, preparation or evaluation assets, and a prioritised next-stage plan. Ownership, assumptions, limitations and the evidence required for progression are documented.
TAKE THE NEXT STEP
Which AI use case needs better data evidence?
Tell us what you want the AI solution to predict, retrieve or support, and which information it depends on. Konsolute will help assess suitability, access and evaluation needs before you commit to the next stage.