CASAN · Level Diagnosis

Where are you on the
AI-Native journey?

Answer 16 quick questions across eight dimensions to pinpoint your current CASAN maturity level, see your biggest capability gaps, and get the concrete actions needed to reach the next level. This runs entirely in your browser — your answers are never sent anywhere until you choose to email your result.

A team in a working session reviewing their AI strategy

Answer 16 questions to benchmark your organization — then turn the result into a plan with our team.

Leadership & Organization

How does leadership treat AI?

1 Individual curiosity — no leadership mandate. 2 Leaders endorse AI tools and fund enterprise licenses. 3 An AI governance board sets standards and owns the roadmap. 4 Leaders restructure teams around human + AI-agent operations. 5 The board runs the business with AI as the core operating model.

Is there clear ownership for AI outcomes?

1 No owner — AI usage is ad hoc. 2 Informal champions in a few teams. 3 Named AI product, data, and security owners. 4 Owners run agent workflows against KPIs and SLAs. 5 Cross-organization accountability with adaptive governance.

Strategy & Use Cases

How are AI use cases chosen?

1 Random, individual experiments. 2 A few productivity use cases per team. 3 A central portfolio ranked by impact, feasibility, and risk. 4 Workflows re-engineered around agents with measured ROI. 5 New AI-native products and revenue streams.

How is AI value measured?

1 Not measured. 2 Time saved at the individual level. 3 Standard KPIs per use case with cost monitoring. 4 Business outcomes (P&L impact) tracked per workflow. 5 Revenue growth, customer experience, and learning speed.

Data & Data Governance

How ready is your data for AI?

1 Fragmented, multiple copies, no lineage. 2 Some internal data connected via official connectors. 3 Classified and access-controlled, with lineage and audit logs. 4 Real-time pipelines feed governed inference. 5 Enterprise knowledge + agent memory as architectural layers.

How is sensitive data protected when using AI?

1 No policy — data may leak to public tools. 2 Sensitivity labels and endpoint DLP in place. 3 RBAC, data classification, and minimization enforced. 4 Per-agent access scoping with full audit trails. 5 Adaptive, multi-jurisdiction data governance.

Technology Platform

What AI platform do you rely on?

1 Public chat tools only. 2 Licensed vendor copilots (M365, GitHub, etc.). 3 Standardized RAG, prompt libraries, reference architecture. 4 Agent control plane, orchestration, and AgentOps. 5 Multi-agent platform operating as an enterprise OS.

How do you build and validate AI solutions?

1 Ad hoc prompts, no validation. 2 Vendor defaults with manual spot checks. 3 Reusable templates and an evaluation harness. 4 Validation-gated deployment with regression suites. 5 Continuous evaluation and a self-improving harness.

Operations & Process

How automated are your workflows?

1 Manual — AI assists only individual tasks. 2 AI speeds up steps within existing processes. 3 Processes standardized and ready for automation. 4 Agents operate end-to-end workflows within guardrails. 5 Core processes are event-driven and agent-orchestrated.

What is the human role in AI-assisted work?

1 Humans do everything; AI is a side tool. 2 Humans use AI to draft and accelerate. 3 Humans review outputs and maintain standards. 4 Humans handle exceptions and high-risk decisions. 5 Human–agent teams co-create; humans set intent and ethics.

Talent & Skills

How AI-skilled is your workforce?

1 A few enthusiasts self-learning. 2 Broad basic AI literacy and safe-use training. 3 Dedicated AI roles (engineers, validators, data owners). 4 Harness / AgentOps engineers run agents in production. 5 AI-native architects design operating and business models.

How is AI capability developed?

1 No formal training. 2 Tool and prompt training rolled out. 3 Role-based upskilling tied to real projects. 4 Certified specialists across the delivery chain. 5 Continuous research and a thought-leadership culture.

Governance, Responsible AI & Security

How is AI governed and secured?

1 No policy — awareness only. 2 Acceptable Use Policy and basic controls. 3 Documented AI lifecycle, audit logs, ISO 42001 readiness. 4 Agent identity, prompt-injection defense, kill switch, rollback. 5 Proactive red-teaming and adaptive, multi-region compliance.

How much authority do you delegate to AI agents?

1 No agents in real operations. 2 AI drafts; humans approve everything (L1). 3 AI executes bounded low-risk tasks within clear limits (L2–L3). 4 Agents run guarded workflows with audit and exceptions (L4). 5 Restricted high-autonomy agents under strict control (L5).

Ecosystem & Measurement

How do you work with AI partners and platforms?

1 No strategy — whatever individuals pick. 2 Vendor tools adopted team by team. 3 A standard reference architecture and partner stack. 4 An integrated agent ecosystem across enterprise systems. 5 A co-innovation ecosystem sustaining long-term advantage.

Do you have an AI maturity baseline and roadmap?

1 None. 2 An informal sense of where you are. 3 A documented current level with a target and roadmap. 4 Dashboards track maturity, ROI, and risk per workflow. 5 Continuous reinvention guided by live metrics.

Please answer every question to see your result.

Your CASAN level

Standard

— The Smart Factory

Overall maturity score: 3.0 / 5.0

Breakdown

Your score by dimension

Your biggest gaps

How to level up

Moving to the next level

Next step

Turn this into a plan

Share your result with our team and we'll map it to a concrete level-up proposal — the right service packages, AI-adoption projects, and a 90-day pilot for your situation.

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A team collaborating on their AI adoption plan

Where are you on theAI-Native journey?

Standard

Your score by dimension

Moving to the next level

Turn this into a plan

Where are you on the
AI-Native journey?