AI & Automation · Assessment
AI Readiness Assessment
A grounded view of where AI and automation would genuinely reduce effort in your business — and what has to be true in your data and systems before it will work.
The hard part of applied AI is not the model. It is knowing which process is worth automating, whether the data behind it is usable, whether the systems involved can be integrated, and who stays accountable for the output.
This assessment works through those questions in business terms first, so any technology decision follows a clear operational case rather than leading it.
Assessment framework
The five dimensions we assess
The automated scoring model is being connected. Your selections here are a preview of the framework — a specialist reviews the same dimensions with you and responds with observations, not a generated score.
What this assessment covers
Repetitive processes
Where staff time goes into predictable, rules-based steps that a system could reliably perform or pre-fill.
Data availability and quality
Whether the information a use case depends on exists, is accessible and is consistent enough to trust.
Support and sales workflows
Triage, response drafting, quoting and follow-up — usually the fastest place to remove manual effort.
Internal knowledge
How documentation, policies and past work are stored, and whether they can support reliable retrieval.
Integration readiness
API availability across CRM, helpdesk, ERP and internal tools, and where exports are the only option.
Governance and handoff
Approval thresholds, audit logging, escalation to a person, and how errors are detected and corrected.
Quick-win opportunities
Contained automations that can ship in weeks, prove value and inform whether wider work is justified.
Cost and effort realism
An honest read on which ideas are viable now, which need groundwork first, and which are not worth doing.
What you'll learn
- Which of your processes are realistic candidates for automation today.
- What has to change in data or systems before the harder use cases become viable.
- Where a contained pilot would prove value fastest.
- How human oversight and handoff should be designed for each candidate.
- Which ideas we would advise against, and why.
- A sequence for the next two to three initiatives rather than one large programme.
This is an assessment, not a forecast. We do not publish efficiency percentages or return figures for work that has not been scoped — any numbers we discuss come from your own volumes and process times.
How it works
- 1
Describe the operation
Tell us your size, the workflows that consume the most time, and the systems currently involved.
- 2
We review readiness
Processes, data, integrations and governance are assessed together to separate viable from premature.
- 3
You get a shortlist
A small set of candidate automations with prerequisites, oversight requirements and sequencing.
Frequently asked questions
- What is an AI readiness assessment?
- A structured review of whether your processes, data and systems can support useful AI or automation yet — and which specific workflows would benefit first. It is a business and technology assessment, not a technology demonstration.
- Do we need clean, large datasets to start?
- Not for every use case. Assistive work over documents, email drafting and support triage can start with what most companies already have. Forecasting and analytics work genuinely depend on data quality and history.
- Which processes usually qualify first?
- Repetitive, high-volume, rules-heavy work with a clear correct outcome: support triage, quote preparation, document extraction, internal knowledge search, routine reporting.
- How is automation kept safe?
- Through scoping and handoff design — deciding what the system may do unattended, what needs human approval, what gets logged, and how a person takes over when confidence is low.
- Is this about replacing staff?
- In the projects that succeed, no. The value comes from removing repetitive steps so the same team handles more volume with fewer errors and less manual copying between systems.
- Does the assessment produce a score?
- Not on this page. The scoring model is being built alongside the assessment backend; until it is connected, a specialist reviews your answers and responds with observations rather than a generated number.
Move from assessment to a working pilot.
Our AI and intelligent solutions team builds the shortlisted automations with proper integration, logging and human handoff.
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