You have a business problem.
Your team has several AI ideas. We compare their business value, available data and delivery risks, then identify the first workflow worth testing.
Consulting with the CloudHero team
AI readiness assessment
and implementation planning.
Decide whether an AI idea or pilot merits investment and what it will take to deliver. Working with your team, CloudHero’s consultants review the business case, data, systems and team, then estimate costs, identify risks and define the evidence needed to proceed.
Who it suits
A new idea or a pilot that needs a decision.
You have a prototype.
The demo works, but integration reliability, access controls, running costs or support remain unresolved. We identify what needs to change before you invest in a wider rollout.
The assessment
How does an AI readiness assessment work?
Agree the business decision
Identify the sponsor, users, available budget and target date. Measure the current workflow and agree an outcome that would justify further investment. Record assumptions when a baseline is missing.
Inspect the data and system boundaries
Review sample data, source ownership, freshness, permissions and integration constraints. Establish what the application can read, propose or change. Involve security and privacy owners to identify data-handling requirements.
Define the evidence for a release decision
Plan tests for answer quality, access controls, permitted actions and failure handling. Include adversarial inputs, the users’ languages, expected load, response time and cost. Assign each unresolved finding an owner and a condition for closure.
Plan delivery and team preparation
Compare architecture options and estimate implementation and operating effort. Set delivery milestones, training needs and support responsibilities. Recommend proceeding, testing a specific uncertainty, resolving a blocker or reconsidering the use case.
Assessment deliverables
What do you receive?
The assessment produces six connected outputs for the agreed workflows. We confirm their depth at scoping and identify any evidence still needed.
Business case and use-case priorities
Compare expected value, feasibility and risk. Set measurable goals against the current process. Estimate potential payback using cost scenarios and explicit assumptions about adoption.
Readiness findings and risk register
Document data quality, access and integration gaps with supporting evidence. Record security and privacy requirements, remediation owners and the issues that block the next stage.
Architecture and evaluation plan
Describe the proposed components, alternatives and trade-offs. Define human approvals, test scenarios and acceptance criteria for quality, safety, response time and cost.
Effort and cost estimates
Estimate the first release and later phases, with assumptions and confidence levels. Include data preparation, integration, human review, model usage, infrastructure and support.
Team readiness and enablement plan
Identify skill gaps and training needs for users, reviewers and the technical team. Assign ownership for adoption, evaluation and ongoing support.
Phased implementation and operating plan
Set milestones, dependencies and release decisions. Plan monitoring, model and data updates, incident handling, rollback and recovery, with responsibilities for each stage.
We agree the assessment scope, fee and schedule before starting. Missing evidence is recorded with a verification step. Application development, training delivery and ongoing operation are separately scoped.
Method and evidence
Every finding needs evidence and an owner.
Our AI readiness assessment methodology explains the questions, evidence and interpretation rules we use. It covers business value, data, architecture, risk, evaluation and team preparation.
For each finding, we record the evidence, business impact, next action, owner and acceptance condition. The initial online check gives you priorities based on your answers. A detailed assessment verifies those answers with your team.
Prepare for the conversation
Bring the workflow and the people who own it.
Start with the current process, its volume and the outcome you want. Useful inputs include a system diagram, a source inventory, sample tasks, an existing prototype and budget or timing constraints.
Involve the business sponsor, data owners and a technical contact. Include security, privacy and operations colleagues where relevant. We agree access before reviewing sensitive material. Please do not send credentials or confidential datasets through the enquiry form.
Questions grounded in delivery work.
Mindory shows AI integrations and operating controls. RaptorCI documents evaluation on a consistent test set. Our utility data platform work shows the data foundations behind a business workflow.
Each case has its own scope and results. We establish targets for your use case.
Questions before we start
AI assessment cost, timing and scope.
How much does an AI readiness assessment cost, and how long does it take?
The fee and schedule depend on the workflows, source systems, access arrangements and people involved. We agree both after scoping. Implementation estimates identify assumptions, dependencies and evidence still needed before a delivery commitment.
How is the assessment different from an AI proof of concept?
The assessment produces a decision and an implementation plan. A proof of concept tests a specific technical uncertainty by building something. We can recommend a focused experiment; any build work, access and acceptance criteria require an agreed scope.
Does the assessment include AI training for our team?
It identifies the skills, responsibilities and training needed to use and operate the solution. The deliverable is an enablement plan. Workshops, courses and training delivery are scoped separately.
Do we need to use AWS?
We start with your existing systems and data. CloudHero brings AWS AI implementation experience and data platform work involving Google Cloud, BigQuery and hybrid environments. We choose components according to the workflow’s requirements.
Can you assess an AI application that already exists?
Yes. We can assess answer quality, integrations, data access, action permissions and operating readiness. We also review the evidence for business value and running costs before recommending the next release.
Can we start before our data is ready?
Yes. The assessment can identify missing sources, unclear ownership and quality issues. The plan separates data preparation from application development and records which blockers must be resolved before a pilot.
Can AWS funding cover the assessment?
Funding depends on the applicable programme, project eligibility and AWS approval. We can discuss potential options during scoping. Funding is not guaranteed.
Illustrative assessment output
An example AI implementation plan.
This fictional example shows how an assessment informs a decision about an internal knowledge assistant. Its assumptions require verification. It is a sample format, not a customer result or a fixed delivery package.
The workflow and the investment decision
A support team wants to find approved procedures and prepare answers. The proposed first release retrieves information and cites its sources. Changes to customer records require a separate assessment of permissions and approvals.
Before estimating value, measure request volume, search time, correction time and the proportion of eligible tasks where staff would use the assistant. Compare those findings with implementation and operating costs. Record time saved separately from cash savings.
| Candidate | Evidence to collect | Proposed next step |
|---|---|---|
| Find an approved procedure | Task frequency, search time, source owners and access rules. | Test retrieval on one maintained collection. |
| Draft a customer reply | Representative questions, approved answers and review effort. | Evaluate drafts after retrieval checks pass. |
| Update a customer record | API permissions, approvals, duplicate-action handling and recovery. | Assess separately before enabling write access. |
How one finding changes the plan
- Finding to verify
- Some procedures may lack a named owner or a confirmed current version.
- Evidence needed
- A source inventory and a review of documents proposed for the pilot.
- Impact
- The assistant could cite an outdated procedure.
- Action and owner
- The business owner assigns document owners and confirms current versions.
- Condition to proceed
- Every document included in the pilot collection has an owner and an approved version.
Acceptance criteria and operating responsibilities
- Data and access: agree refresh, retention and deletion rules. Test that users cannot retrieve another team’s restricted documents.
- Answer quality: compare the current process with the assistant using representative questions, missing evidence and conflicting sources. Test the languages staff use.
- Team preparation: name a support lead and reviewers. Identify training for checking citations, correcting answers and reporting problems.
- Running costs: include model usage, indexing, storage, monitoring, human review and support. Set a budget and usage alerts.
- Release decision: agree quality, response-time and cost thresholds after measuring the baseline. Identify the approver, unresolved failures and a rollback procedure.
An implementation roadmap with decision points
Confirm the evidence
Measure the workflow, inspect sources and agree data handling. Resolve ownership and access blockers before connecting the collection.
Test a bounded pilot
Use one approved collection and selected users. Record answers, citations, failures, latency, review effort and running costs.
Prepare the team and operation
Evaluate against agreed criteria. Confirm training, monitoring, incident response, data refresh and recovery responsibilities before a release decision.
Review value before expanding
Compare observed usage and outcomes with the business case. Reassess cost and capacity before adding sources or actions that change records.
What determines the estimate?
Estimate data cleanup, integrations, interface work, evaluation, team preparation and support separately. Document dependencies and uncertainty for the first release and later phases. Any missing value stays marked as “to be measured”, with an owner and a verification step.
What decision do you need to make about AI?
Tell us about the workflow and your starting point. We’ll discuss the assessment scope and the evidence needed for your next step.
Working with teams in Romania, the United Kingdom and Ireland.