CRM SCENE Digital Transformation • Business Operations • AI Agents
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Free source-backed diagnostic

Is your CRM operation ready for AI agents?

Score ten controls across knowledge, integrations, automation, governance, and AgentOps. Receive a practical priority list and live recommendations from CRM Scene Knowledge.

Start the assessmentHow the score works
10 controlsAbout 5 minutesNo login requiredAnswers stay in your browser

A readiness gate, not an AI personality quizMeasure the operating system the agent would depend on.

The score tests whether an agent would receive trustworthy knowledge, dependable system state, controlled permissions, measurable outcomes, and a safe route back to accountable people.

Assess

Score evidence, not ambition

Rate each control from unknown to defined, owned, tested, and reviewed.

Ground

Query CRM Scene Knowledge

The weakest area selects a public CRM Scene checklist and service source through the read-only MCP.

Act

Leave with a sequence

Use the score, three priority actions, and source-backed controls to frame a focused review.

CRM and support AI readiness

Assess the controls that should exist before autonomy.

Choose the answer that reflects current operating evidence—not the intended future process.

Knowledge and retrieval

Knowledge and retrieval

Can an agent find approved, current guidance without mixing public, internal, and uncertain content?

01Does every major knowledge area have a named owner or review group?

AI cannot resolve stale or disputed guidance when nobody owns the correction decision.

02Are high-risk articles reviewed on a defined, risk-based schedule?

Billing, security, policy, and compliance guidance needs faster maintenance than low-risk reference content.

Systems and integration

Systems and integration

Are sources of truth, data movement, failure handling, and operational ownership explicit?

03Is one authoritative source named for every critical customer field and business status?

Agents become unreliable when CRM, billing, identity, and support systems disagree about the same state.

04Do integrations have documented retries, alerts, degraded states, and failure owners?

A successful API call is not an operating model; support needs to know what happens when synchronisation breaks.

Automation and exception control

Automation and exception control

Can automated or agent-driven changes fail safely, hand work to people, and be reversed?

05Are automation and AI changes staged, tested, approved, and reversible before production release?

Automation increases the speed and reach of both good logic and hidden mistakes.

06Do exception paths transfer context, authority, and next actions cleanly to a human?

Human-in-the-loop is not a control if the person receives an unexplained failure with no decision context.

Measurement and governance

Measurement and governance

Can leaders see whether the system is accurate, safe, useful, and improving the intended business outcome?

07Can the team measure accuracy, escalation quality, operational impact, cost, and customer outcomes?

Activity volume does not prove an agent is correct, useful, safe, or economically justified.

08Are permissions, approvals, audit evidence, and change authority explicitly governed?

Agent capability should expand only when access and decision rights are inspectable and controlled.

Agent role and operating rhythm

Agent role and operating rhythm

Is the agent treated as an owned operational capability rather than a one-off chatbot experiment?

09Is the agent's job defined through responsibilities, inputs, outputs, limits, and service expectations?

A generic assistant has no reliable definition of success or a defensible boundary when evidence is weak.

10Is there an ongoing cadence for incidents, evaluations, knowledge updates, and controlled expansion?

Models, prompts, tools, policies, and business operations change after launch.

All ten controls are required. Scoring happens locally in your browser.