Service
Data Quality & AI Readiness
A practical assessment for teams that see potential in AI, machine learning or analytics but need a useful and responsible starting point. I connect the intended decision or workflow to the available data, ownership, quality, risk and acceptance criteria across EAM, ERP, CRM, MES, documents, spreadsheets or other business systems.
Call when You see AI or machine-learning potential but need to identify a useful, feasible and responsible first use case.
What I do I connect operational decisions and workflows to the available data, assess quality, ownership and risk, define acceptance criteria and compare realistic use cases.
You receive Prioritised list of use cases, data and process gaps, risk and guardrail checklist, pilot recommendation and explicit go/no-go criteria.
Typical starting points
Concrete issues this work can handle
The service is useful when the issue is specific enough to inspect in real systems, reports, data samples or daily workflows.
Operational histories that may support forecasting, anomaly detection or risk scoring
Knowledge scattered across procedures, reports and shared drives
Data cleanup that needs prioritization by workflow and decision value
Scope and deliverables
What I review, change and hand over
The scope stays close to the systems, data and workflows that affect day-to-day work. The deliverables are designed to support a clear next decision.
Scope
Operational decisions, workflows and measurable value
Structured data, documents and representative historical records
Data meaning, quality, ownership and access
Evaluation criteria, risk and human guardrails
Deliverables
Prioritised list of use cases
Data and process gap register
Risk and guardrail checklist
Pilot scope with explicit go/no-go criteria
How it works
How the engagement works
A defined sequence keeps the work focused. Representative examples and access to the right owners make the review faster and more reliable.
Steps
Define the workflow, decision and useful outcome
Inspect representative data or documents and how they are created
Compare feasible use cases, risks and evaluation needs
Recommend a pilot, preparatory work or a clear decision to stop
Fit and boundaries
Good fit and clear boundaries
A focused scope makes the work easier to evaluate, deliver and hand over.
Good fit
- Teams considering a first practical AI/ML use case
- Organisations unsure whether the available data can support the intended decision
- Data, operations or automation leads who need a pilot scope they can justify
Not included
- Not a generic AI strategy workshop
- Not a promise that poor data can be bypassed with AI
- Not a vendor-led technology selection exercise
- Not cleanup work without a defined operational purpose
Related services
Useful next steps and related services
Operational problems can cross system, data, interface, workflow and AI/ML boundaries. These services are often relevant together, but each can also stand alone.
Applied AI/ML Pilot A specific AI/ML use case needs to be proven against real data and daily work. Interface Reliability Sprint An interface is live, but users still correct records, investigate mismatches or distrust downstream results. Work Apps & Workflow Automation Manual office workflows slow work down, or a defined operational process needs a lightweight tool without replacing the core system.
Does this service fit your case?
Bring the current workflow or problem, a few representative examples and the business context. The first step is to decide what is worth changing, testing or leaving alone.