Purpose: Evaluate the strategic readiness of your business to adopt and operationalise AI automation, AI Agents and AI Workforce effectively.

This assessment identifies whether your current systems, data, and leadership alignment provide a strong foundation for implementing AI that drives measurable business outcomes.

Readiness Levels

Scoring: Each question scores 0–3 points. Your total score determines your organization’s readiness level and recommended next steps for implementation.

🔵 0–10: Foundational Readiness – strategic groundwork required

Your organisation is in the early stages of AI readiness. Core systems, data architecture, or process visibility require development before large-scale automation can be implemented confidently and compliantly.

🟡 11–20: Developing Readiness – opportunities emerging

Digital systems and workflows are beginning to connect, and early automation may already exist. Focus on strengthening integration, governance, and data strategy to prepare for high-impact AI initiatives.

🟢 21–30: Advanced Readiness – strong foundation for AI adoption

Your business has the infrastructure, leadership commitment, and operational maturity to deploy AI solutions at scale. The next step is to design enterprise-wide use cases that deliver measurable ROI and competitive advantage.

Section 1: Process Documentation

1: How well documented are your core business workflows and standard operating procedures (SOPs)? *

No formal documentation — processes are largely informal or team-dependent

Some workflows documented, but not consistently followed across teams

Most critical workflows documented and occasionally reviewed for consistency

All workflows documented, standardized, and consistently applied across departments

Effective AI and Automation implementation depends on process visibility. Consistent, standardised documentation creates the foundation for automation and AI-driven optimisation.

Section 1: Process Documentation

2. How predictable and repeatable are your key business processes? *

Processes vary widely; outcomes are inconsistent and unpredictable

Some processes are repeatable, but many remain ad hoc or dependent on individuals

Most processes are predictable, with only minor variations or exceptions

All critical processes are standardized, predictable, and continuously optimized

AI thrives on repeatable processes. Predictable workflows make it possible to automate, analyze, and optimize performance across departments.

Section 2: Data Quality & Systems

3. How mature is your CRM or customer management system? *

No CRM in place or reliance on ad-hoc spreadsheets

Basic CRM exists but is used inconsistently or by limited teams

CRM actively used for most sales and customer-facing processes

CRM fully integrated across departments with automated workflows, analytics, and real-time reporting

A well-structured CRM is the backbone of any AI-ready business. It centralizes customer information, automates workflows, and creates visibility across sales, marketing, and service functions.

Section 2: Data Quality & Systems

4. How would you describe the quality and reliability of your business data across key systems? *

Data is inconsistent, fragmented, or duplicated across systems

Some structured data exists, but many gaps or silos remain

Data is mostly structured, reliable, and shared between some departments

Data is accurate, consistent, and centrally managed with clear governance practices

AI relies on clean, consistent, and accurate data. This includes customer, financial, operational, and project data — and how well it’s maintained, validated, and accessible for decision-making.

Section 2: Data Quality & Systems

5. How connected and accessible is your data across systems? *

Data is scattered across disconnected systems and spreadsheets

Some systems share data, but manual work or exports are often needed

Most key systems are connected, though some data still needs cleaning or alignment

Systems are fully integrated, with reliable, real-time data flow ready for automation and AI

Even the best data loses value if systems don’t communicate. Integration between tools like CRM, finance, marketing, and operations enables automation, analytics, and AI to work effectively.

Section 3: Sales & Customer Onboarding

6. How standardised are your customer onboarding processes? *

Manual, inconsistent, no formal steps

Some steps defined, but inconsistent implementation

Processes mostly standardised with occasional variation

Fully standardised onboarding with clear documentation and compliance

Consistent and automated sales and customer onboarding processes are vital for scaling your business and preparing for AI-driven customer management.

Section 3: Sales & Customer Onboarding

7. How defined and repeatable are your sales processes? *

Sales activities are mostly ad hoc, with no consistent process

Some sales steps are defined, but implementation varies by person or team

Most key sales steps are documented and generally followed

Sales process is fully defined, tracked through metrics, and regularly optimised for performance

Structured sales processes enable consistent results, easier automation, and clearer visibility into performance metrics — all essential for scaling and integrating AI-driven insights.

Section 3: Sales & Customer Onboarding

8. How automated and consistent are your sales and customer communication workflows? *

Communications and follow-ups are mostly manual and vary by person or team

Some automation exists (e.g., email templates or CRM reminders), but usage is inconsistent

Key sales and customer communication steps are automated, with data tracked in the CRM

Sales and customer communication workflows are fully automated, integrated, and continuously optimised using analytics or AI insights

Automation in sales and communication reduces manual effort, increases conversion consistency, and creates reliable data for forecasting and AI-driven insights. It’s a strong indicator of your organization’s scalability and operational maturity.

Section 4: Leadership, Resources & Continuous Improvement

9. Do you have internal resources (subject-matter experts or process owners) to support AI and automation initiatives? *

No dedicated internal owners or automation champions

Some staff are aware of AI opportunities but have limited time or expertise

Staff partially dedicated to automation or process improvement initiatives

Clear internal owners for each key process, actively driving improvement and adoption

Dedicated ownership ensures projects don’t stall once external consultants or vendors step back. Internal champions drive adoption, maintain systems, and ensure automations stay aligned with evolving business goals.

Section 4: Leadership, Resources & Continuous Improvement

10. Do you have monitoring and feedback mechanisms to measure process performance? *

No formal tracking or performance monitoring

Minimal tracking in place, reviewed inconsistently

Some monitoring and feedback occur, but not integrated into business rhythm

Comprehensive performance monitoring with continuous feedback embedded into regular operations

AI and automation only add value when performance is tracked and lessons are fed back into the system. Continuous measurement enables optimisation and supports data-driven decision-making.

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