Pillar 1: Technological Readiness
1. How would you describe the quality and usability of your company’s data?
Mostly unstructured, incomplete, or hard to access (Sales, Customer and employee records are kept on paper and/or scattered spreadsheets; key fields are missing and files are hard to access). Some structured data exists, but quality is inconsistent (Basic business systems exist like ERP and/or CRM but are working in isolation, with inconsistent formats and standards). Data is fairly structured with basic cleaning processes (Some Data Analytics exist and a basic data warehouse collects most core data and routine cleaning scripts run weekly). Data is reliable and used in most business areas (Structured data feeds a governed business systems (like CRM, ERP, eCommerce, Supply Chain) with automated quality checks across departments). Data is fully clean, integrated, and governed company-wide (A centralized data lake integrates all business systems with automated validation and full data lineage. Business is not only dependent on Data Insights for decision making, but for Innovation and Growth Opportunities).
2. How is AI currently being applied in your core business functions (e.g., operations, finance, HR, sales, customer service)?
No AI use cases in business functions ( all analysis and operations are handled through spreadsheets and basic analysis tools ). Only basic pilots or automation experiments ( A single pilot chatbot answers simple customer questions on the website). Some structured use cases in 1–2 business functions ( AI models forecast payroll costs and assist with admissions scoring in some departments). Multiple AI applications across different functions( AI optimizes marketing campaigns for company’s eCommerce, AI manages automatic call-center routing to increase Human-Agent productivity, AI Handles Candidate Screening for recruitment). AI deeply embedded across most major business functions ( AI supports risk management, HR planning, operations scheduling, and new-product design company-wide).
Pillar 2: Organizational Readiness
7. Do you have governance rules for AI projects (e.g., defined responsibilities, ethical guidelines, risk management, and audits for compliance with regulations, who accesses data) ?
No rules or processes in place (There are no policies, roles, or oversight for AI projects). Informal, inconsistent guidelines (Scattered Excel sheets containing guides on how-to and basic pilot documentation, but are applied inconsistently). Basic governance defined for some projects (Some Pilot AI Projects are documented, including roles & responsibilities, guidelines, privacy checks, and audits exist). Clear governance with accountability (A formal governance framework assigns accountability and manages risk for all AI initiatives). Robust, ethical, and transparent governance system (A transparent, ethical governance system includes external audits and clear escalation paths).
Pillar 3: Human Readiness
11. How supportive is the organizational culture toward change management and overall openness to change (including AI adoption as one example)?
Resistant to change, tends to maintain the status quo (Leadership consistently dismisses new initiatives. When a new workflow or system is proposed, managers delay decisions and employees openly say, “This will never work here.” Past transformation projects were cancelled or quietly ignored). Occasional curiosity, but mostly cautious or skeptical (A few tech-savvy managers discuss AI in meetings, but pilots rarely move beyond talk. Staff attend presentations but afterwards say changes are “too risky” or “not for us.” Previous small projects stalled because approvals were never granted). Mixed culture — some openness but noticeable resistance (Certain departments (e.g., marketing or R&D) experiment with new tools, while others (e.g., finance or operations) insist on old procedures. A data-automation pilot is running, yet several teams still rely on manual spreadsheets and question the value of automation). Generally positive culture, most staff are receptive to change (Cross-department change teams exist, and leadership communicates clear reasons for transformation. Employees volunteer for pilot programs, such as testing a new CRM or AI-based forecasting tool, and training sessions are well attended). Strong change-positive culture — experimentation and continuous improvement are encouraged and celebrated (Senior leaders openly reward experimentation. Hackathons and “innovation days” are part of the calendar. Teams share lessons from failed pilots, and successful projects—like a company-wide shift to an AI-assisted support platform—are scaled rapidly).
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