12 questions. Instant AI maturity score — calibrated to where you actually stand.
3 minutes 12 questions
Pillar 01
Business Process Maturity
The engine AI improves
Pillar 02
Data & Platform Readiness
The fuel for AI
Pillar 03
AI Governance & Org Readiness
The guardrails for AI
Results include maturity breakdown + downloadable PPTX
ProgressSection 1 of 3
Pillar 01 — Business Process Maturity
Operations Engine
How well-defined, documented, and automation-ready your workflows are
1A — Workflow Documentation & Standards
How well are your core operational workflows documented and standardized across the organization?
Think about whether a new hire could follow your processes from documentation alone — or if they'd need to shadow someone for weeks before they could operate independently.
1Undocumented — lives in people's heads
2Partially documented, inconsistently applied
3Documented for most core processes, maintained
4Fully documented, owned, and automation-ready
TypicalLeading
1A — Workflow Documentation & Standards
When a process breaks down or produces errors, how quickly can your team identify the root cause?
Consider your most common operational failure — in ITSM, think about a P2 incident or a failed change request. Is there a clear audit trail and structured escalation path — or does troubleshooting rely on whoever happens to know the system?
1Days to weeks — no visibility into process steps
2Hours to days — depends on who's available
3Hours — process maps and logs help narrow it down
What percentage of your high-volume, repetitive workflows are currently automated?
Think about tasks your team performs more than 50 times a week — in ITSM, that might be incident routing or change approvals; in CRM, it could be case assignment or follow-up reminders. How many of those run without a human touching them?
1Under 10% — mostly manual execution
210–30% — isolated automations, no enterprise pattern
330–60% — automation program in place, expanding
460%+ — automation-first by design, scalable
TypicalLeading
1B — Automation Coverage & Scalability
Could your current process and technology double throughput on a core workflow in 90 days without proportional headcount increase?
This isn't about hiring — it's about headroom. If volume doubled tomorrow, would your operations scale gracefully or start to break down?
1No — we'd need to hire to scale
2Partially — some functions could scale, others can't
3Mostly — platform and process can absorb meaningful growth
Whether your data and systems can reliably power AI
2A — Data Quality, Accessibility & Stewardship
How would you describe the quality and accessibility of data for your most important operational workflows?
When your team makes a key operational decision, are they pulling from a trusted, centralized source — or reconciling exports and spreadsheets from multiple departments?
1Siloed, inconsistent, hard to access without IT
2Accessible with effort — quality varies by system
3Clean and accessible for core domains, gaps elsewhere
4Unified, high-quality, governed, and AI-pipeline ready
TypicalLeading
2A — Data Quality, Accessibility & Stewardship
Does your organization have a designated data owner responsible for data quality and governance across systems?
When data quality issues surface — in ServiceNow, think about stale CI records in your CMDB or missing asset relationships — is there a single named owner accountable for fixing them, or does it fall through the cracks?
1No — data ownership is informal or absent
2Informally — some IT ownership, no formal governance
3Yes, for key domains — gaps in coverage remain
4Yes — formal stewardship with documented policies enterprise-wide
TypicalLeading
Additional context — 2A Data Quality & Stewardship (optional)
2B — Platform Architecture & Integration
How integrated are your core business systems (CRM, ERP, workflow platforms, data warehouses)?
Think about how information flows between your key platforms today. Is data automatically synced in near real-time, or does your team manually move it between systems?
1Disconnected — heavy manual re-keying between systems
2Point-to-point integrations — brittle, hard to maintain
3Integration layer in place — most core systems connected
4Real-time API fabric — data flows without friction
TypicalLeading
2B — Platform Architecture & Integration
How would you describe your current platform architecture's ability to support complex integrations and automation at scale?
Consider whether your core systems expose APIs or support modern integration patterns — or if connecting new tools typically requires custom development and lengthy IT cycles.
1Legacy — predates AI use cases, significant retrofitting needed
2Mixed — modern in some areas, legacy bottlenecks persist
3Largely modern — could support AI with targeted investment
4AI-ready — architecture chosen to accelerate agent deployment
Whether your organization can deploy and sustain AI responsibly
3A — AI Policy, Risk & Control
Does your organization have documented policies governing AI use, model risk classification, and audit requirements?
Think about whether you've defined who is allowed to use automation tools, what approvals are needed before deployment, and how decisions made by automated systems are reviewed.
1No formal policy — AI decisions are not logged or governed
2Informal guidelines — not consistently applied
3Policy in draft or early deployment — gaps remain
4Formal framework — model risk tiers, HITL controls, audit trail
TypicalLeading
3A — AI Policy, Risk & Control
How prepared is your organization to respond to an AI-related regulatory audit?
If a regulator or internal auditor asked tomorrow how a specific automated decision was made, could you produce a clear audit trail and evidence of human oversight?
1Unprepared — no framework, no documentation
2Partially — some awareness, no structured response capability
3Moderate — compliance mapped, some gaps remain
4Prepared — regulator-ready documentation built in from day one
TypicalLeading
Additional context — 3A AI Policy & Risk (optional)
3B — Organizational Alignment & Capacity
How aligned are your executive sponsors (COO, CTO, CDO, CIO) on a shared definition of AI success?
When automation or digital transformation comes up in leadership meetings, is there a shared vocabulary and agreed priority — or do different leaders have competing definitions of success?
1Misaligned — each leader has a different AI agenda
2Partially — top-level agreement, diverges at execution
3Mostly aligned — shared roadmap, minor gaps
4Fully aligned — AI transformation managed as a strategic portfolio
TypicalLeading
3B — Organizational Alignment & Capacity
Does your organization have a dedicated team or center of excellence actively managing AI adoption?
This could be a formal CoE, an internal task force, or a single empowered owner. The key question is whether someone is accountable for driving adoption — and has the mandate to act.
1No — AI is a project, not an ongoing capability
2Ad hoc — a few champions, no formal program
3Emerging — program forming, resources being dedicated
4Yes — funded CoE or program office with executive sponsorship
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Your AI Maturity Tier
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Pillar Scores
Pillar 01
Business Process Maturity
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Pillar 02
Data & Platform Readiness
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Pillar 03
AI Governance & Org Readiness
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Key Findings
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