AI Readiness Survey
68 Questions to understand strengths and opportunities for utilizing AI capabilities in your organization within the next 12 months.
1. Leadership Alignment — Is there a shared definition of AI success and an identified owner for initiatives?
2. Process Documentation — Are core workflows documented as they're actually performed today?
3. Data & Tech Infrastructure — Is customer/operational data centralized, trusted, and accessible for integration?
4. Workforce Readiness — Is training capacity available?
5. Change Readiness — Have frontline staff been part of the AI conversation?
6. Governance & Compliance — Does a policy (formal or informal) exist on acceptable AI use and review?
7. My Intake process is mostly
This would be your contact center, your order processing department areas.
8. My Transactional process is mostly
This would be your Claims, invoicing, data entry areas.
9. My Training and Workforce Enablement process is mostly
This would be your Onboarding, Upskilling, Knowledge Transfer areas.
10. My Product Development process is mostly
This would be your Sales, Field Ops, R&D, QA areas.
11. My Sales & Field Operations process is mostly
This would be your CRM, Dispatch, Account Management, Proposals areas.
12. Which level best describes where we are RIGHT NOW, organization-wide?
DEEP DIVE Tool Kit Section
13. Leadership has a shared definition of what "AI" means for this organization
14. An executive sponsor/owner is identified for AI initiatives.
15. AI adoption is tied to a specific business outcome (cost, capacity, revenue) rather than general pressure to "keep up"
16. Budget has been discussed or allocated (even informally)
17. Leadership can articulate what success looks like 12 months out.
18. There's tolerance for pilot failure / iterative learning vs. expecting one big rollout.
19. Core processes are documented (flowcharts, SOPs, work instructions).
20. Documentation reflects how work is actually done today.
21. Process owners are clearly identified.
22. A standard method exists for capturing process metrics (cycle time, volume, error rate).
23. Recent process improvement activity exists (Lean, Six Sigma, Shingo, kaizen) to build on.
24. Frontline input is captured when processes are designed or changed.
25. Exception cases are documented, not just tribal knowledge.
26. Data lives in systems that can be accessed/integrated (not siloed spreadsheets).
27. Data quality is trusted (accurate, deduplicated, current).
28. IT has capacity/bandwidth to support a pilot (internal or vendor-supported).
29. Security, privacy, and compliance requirements are documented.
30. A defined process exists for vetting new software/vendors.
31. Frontline staff have been part of the AI conversation, not just informed after.
32. There's a clear message about what AI is/isn't replacing.
33. Training capacity exists to build new skills as tools are introduced.
34. Union/labor considerations (if applicable) have been identified.
35. A feedback loop exists for staff to flag AI errors.
36. Middle management is bought in, not just executives.
37. A policy (formal or informal) exists on acceptable AI use.
38. A review process exists for AI-generated customer communications.
39. A policy (formal or informal) exists on acceptable AI use.
40. Vendor/third-party AI tools go through a risk review.
41. A plan exists for auditing AI decisions/outputs over time.
Industry Considerations for following question
☐ Healthcare — HIPAA, patient data, clinical accuracy standards
☐ Financial Services — SOC2, PCI-DSS, fair lending, recorded-line requirements
☐ Insurance — state regulatory variance, claims documentation standards
☐ Retail/E-commerce — PCI-DSS, consumer protection
☐ Government/Public Sector — ADA/508, procurement rules, data residency
☐ Manufacturing / Industrial — safety-critical documentation, union agreements
42. Industry-specific regulatory requirements are identified.
43. Volume & Scope of necessary AI integration is quantified.
44. Work types/categories have a defined classification.
45. Systems of record are documented and understood.
46. Records/data are centralized and accessible.
47. Performance metrics are tracked (cycle time, error rate, cost, satisfaction).
48. Escalation/exception paths are clearly defined.
49. Integration potential with AI tools is known.
50. Records are centralized and accessible (not paper-based or siloed)
51. Cycle time and error/rework rate are tracked.
52. Exception handling (what happens when a transaction doesn't fit the standard path) is clearly defined.
53. Training volume/frequency is quantified (new hires, recertifications, ongoing programs).
54. Training content is categorized by role/skill/topic.
55. LMS or training delivery system is documented and understood.
56. Training records/completion data are centralized and accessible.
57. Time-to-competency and knowledge retention are tracked.
58. A defined path exists for updating content when processes change.
59. LMS/content systems have integration potential with AI tools.
60. New Product development/content volume and cycle frequency are quantified.
61. Work types have a defined classification (feature dev, content types, QA categories).
62. Systems used (PM tools, doc repositories, version control) are documented.
63. Product/content records are centralized and accessible.
64. Time-to-market and quality/defect metrics are tracked.
65. Review/approval and exception paths are clearly defined.
66. Account/customer records are centralized and accessible.
67. Win rate, response time, and/or SLA performance are tracked.
68. Escalation/exception paths (complex deals, service issues) are clearly defined.
Your score is {score}
out of a possible score of 340.