How mature is your organization with AI?

1. Our organization has a clear vision of where AI should create value in the next 12 to 24 months.

A written roadmap, even a short one, with business outcomes rather than technologies.

2. AI initiatives have an executive sponsor and a dedicated budget.

3. We have an AI usage policy that employees know and apply.

What data can be shared with which tools, and when a human must validate an AI output.

4. Someone is accountable for AI outcomes, not only for the technology.

A named owner who reports on results to leadership.

1. Our core business data (customers, products, finances, projects) lives in integrated systems, not in disconnected spreadsheets.

For example, the CRM and the ERP share the same customer record.

2. Our data is clean enough to be trusted: few duplicates, consistent naming, up-to-date records.

3. Key business metrics (revenue, margin, utilization, churn) have one agreed definition across departments.

Two teams asked for the same number give the same answer.

4. We can trace where a figure comes from, from the report back to the source transaction.

1. Our main business systems (ERP, CRM, PSA, HCM) are cloud platforms that receive regular updates.

2. We have activated the AI features already included in these platforms.

For example, assistants, predictive scoring, automated matching or text generation built into your ERP or CRM.

3. Our systems are connected through an integration platform, so information flows without manual re-entry.

iPaaS such as Boomi or Celigo, or native connectors.

4. Employees use AI tools approved by IT rather than personal accounts on public tools.

1. Finance uses AI or automation in at least one recurring process.

Invoice capture, bank reconciliation, forecasting, close checklists.

2. Sales and marketing use AI for lead scoring, content or customer insights.

3. Customer service or support uses AI for routing, suggested answers or self-service.

4. Operations (supply chain, projects, HR or manufacturing) use AI for planning or scheduling.

Demand planning, resource allocation, staffing forecasts.

1. Employees have received practical training on the AI tools available to them.

2. We have internal champions who help colleagues adopt AI in their daily work.

3. Changes to how people work are planned and communicated, not left to chance.

Change management is part of every AI initiative.

4. Employees actually use the AI capabilities we have deployed.

Adoption is measured, not assumed.

1. Each AI initiative has a measurable objective (time saved, error rate, revenue, satisfaction).

2. We track the return on our AI investments and report it to leadership.

3. A human reviews AI outputs before they affect a customer, a payment or a legal commitment.

Human-in-the-loop for high-stakes decisions.

4. Security, privacy and compliance requirements are checked before an AI tool is deployed.

Where the data goes, who can see it, how long it is kept.