CEO
Which workflow, if improved, would change speed, operating leverage, service quality, or strategic focus?
AI readiness checklist
AI readiness checklist for companies.
Check whether your company is ready to turn AI pressure into a buildable project. The key is knowing which workflow matters, who owns it, which data can be used, and what result would justify moving from MVP to production.
- Workflow owner
- Data access
- Security constraints
- Production path
Readiness signals
The company is ready when one workflow can be scoped clearly.
- A senior sponsor can name the workflow, the current bottleneck, and why it matters now.
- The team knows where the relevant documents, databases, dashboards, tickets, or tools live.
- Users are available to test whether the AI system actually improves the current process.
- Security, GDPR, permissions, hosting, and review constraints can be discussed before build starts.
- There is a measurable proof target: time saved, reporting speed, margin insight, quality, risk, or adoption.
Executive checklist
Different buyers need different readiness answers.
CFO
Which manual reporting, pricing, forecasting, or anomaly loop has enough value to justify funding?
CTO
Which systems, permissions, evaluations, logs, and ownership model are needed to run this safely?
Users
What would make the AI system meaningfully better than the current process in daily work?
Implementation checklist
The first project email should answer these ten points.
1. Business area
Which part of the company owns the workflow: finance, operations, legal, sales, project delivery, support, product, or compliance?
2. Workflow
What repeated process, decision loop, knowledge task, reporting flow, or approval path should AI improve?
3. Current friction
Where is the work slow, manual, expensive, duplicated, hard to search, or dependent on a few experts?
4. Data sources
Which documents, databases, dashboards, ERP, CRM, SharePoint, tickets, email, or internal tools are involved?
5. Users
Who will use the first MVP, how often, and what level of accuracy or review do they need?
6. Security
Which permissions, GDPR constraints, hosting requirements, confidential data, and audit needs shape the system?
7. AI pattern
Is the likely system a RAG assistant, agentic workflow, finance dashboard, Company Brain layer, or AI-native internal tool?
8. Success metric
What proof would justify moving forward: faster cycle time, better decisions, fewer manual steps, adoption, or lower reporting drag?
9. Usage profile
How many users, how much concurrent usage, what data volume, and what production availability should be expected?
10. Scale decision
If the MVP works, what should happen next: production hardening, broader rollout, more workflows, or a reusable AI platform?
Choose the right route
The checklist points to the next commercial step.
The objective is to reduce ambiguity before any large AI transformation commitment. The right first step depends on how many answers are already clear and how quickly the company needs a working proof.
- Few answers
Start with an AI opportunity assessment to find the workflow and map the data landscape.
- Workflow clear
Use an AI integration roadmap to define architecture, security, governance, and delivery sequence.
- Data accessible
Build an MVP or PoC around one workflow with realistic users, constraints, and measurement.
- Proof achieved
Harden the system into production infrastructure, a Company Brain layer, or AI-native internal software.
Common gaps
Not ready does not mean stop. It means scope the missing piece.
No clear owner
Find the executive or operating sponsor before starting a build. AI projects drift when nobody owns the workflow.
Too many ideas
Rank opportunities by value, feasibility, data readiness, risk, and delivery speed before choosing the MVP.
Data uncertainty
Use discovery to confirm which sources are usable and where synthetic or sampled data is enough for early validation.
Security unclear
Bring permissions, hosting, privacy, review, and audit constraints into architecture before the first prototype scales.
AI readiness questions
Questions companies ask before moving from interest to implementation.
How can a company know whether it is ready for AI implementation?
A company is ready when at least one workflow has a clear business owner, accessible data or documents, defined users, measurable value, security constraints, and a path from MVP to production.
What should be ready before an AI MVP or proof of concept?
The company should identify the workflow, target users, data sources, success metric, permissions, expected usage, reviewer process, and the decision criteria for scaling.
Does the company need perfect data before starting AI consulting?
Perfect data is not required, but the first AI workflow needs enough usable data or documents to test value. Data gaps can become part of the roadmap if the business case is strong enough.
Who should answer the AI readiness checklist?
The checklist is strongest when answered by a business sponsor, a technical owner, and the people closest to the workflow. CEO, CFO, CTO, operations, finance, data, security, and compliance may be involved.
Readiness next step
Bring one workflow and the checklist answers you already have.
Share the business area, workflow, current friction, data sources, expected users, security constraints, timeline, and what the first AI MVP should prove.