AI Readiness Assessment — Effibit Pulse

Data Readiness How usable, connected, and trustworthy is your data today?

1. How would you describe your organization's core operational data?

Scattered across spreadsheets and siloed systems, no shared source of truth Mostly centralized in a few systems, but inconsistent formats/quality Centralized in structured databases with reasonable data quality controls Centralized, governed, with documented schemas and quality monitoring

2. Can your team access and query the data they need without weeks of IT tickets?

No — access requests routinely take weeks and multiple approvals Sometimes, for a few key systems, with manual exports Yes, for most systems, via reporting tools or dashboards Yes, self-serve access via APIs or a data platform

3. Do you have a data retention/quality policy that's actually enforced?

No formal policy Written policy exists, enforcement is inconsistent Enforced for most critical systems Enforced org-wide with periodic audits

Existing Automation Maturity How much of your workflow is already automated or tooled up?

4. How much of your core operational workflow is automated today?

Almost entirely manual, ad-hoc processes A few scripts/macros handle isolated tasks Multiple workflows automated with dedicated tools (RPA, integrations) Automation is standard practice, monitored and maintained

5. Have you piloted any AI/ML tools (chatbots, copilots, forecasting, etc.) already?

No, never tried Informal experiments by individuals, no official pilot Ran at least one structured pilot with defined success criteria Have one or more AI tools in active production use

6. Do you have integration infrastructure (APIs, middleware) connecting your core systems?

No — systems are mostly standalone A few manual/scheduled integrations (file exports, etc.) API-based integrations for key systems Mature integration layer/middleware across most systems

Compliance & Security Posture How ready is your governance for introducing AI safely?

7. Do you have a defined data governance/security framework (e.g. ISO 27001, NIST, internal policy)?

No formal framework Informal guidelines, not audited Formal framework in place, partially certified/audited Certified and regularly audited framework

8. Does your organization operate in a regulated or sensitive domain (defense, finance, healthcare, critical infra)?

No specific regulatory constraints Some sector guidelines, moderate scrutiny Heavily regulated, but we have a compliance team in place Heavily regulated with no dedicated compliance resourcing yet

9. Do you have a defined process for vetting third-party/vendor software (including AI tools) before deployment?

No — tools get adopted informally Ad-hoc review by IT when someone raises a concern Documented vendor review process, applied inconsistently Formal, consistently-applied vendor security review process

Team Capacity Do you have the people and bandwidth to run an AI pilot?

10. Do you have in-house technical staff who could own an AI pilot (even part-time)?

No dedicated technical staff available General IT staff could help, but no AI/data specialists At least one person with relevant technical background Dedicated data/engineering team or function

11. Does leadership have bandwidth and appetite to sponsor a pilot in the next 2 quarters?

Not currently a priority Interested, but no budget/time allocated yet Budget identified, actively scoping Sponsored initiative already greenlit

12. How does your team generally react to new operational tools/processes?

High resistance — change adoption is historically slow/difficult Mixed — depends heavily on the team/individual Generally open, with some change-management support needed Change-ready — team adopts new tools quickly with light support

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