Innovation IQ: AI Readiness

Is your business AI ready?

Innovation IQ: AI Readiness

Is your business AI ready?

The Disruptors Co Innovation IQ: AI Readiness assessment helps you evaluate your AI readiness, identify gaps, and develop a roadmap for AI-driven success.

Please answer all 30 questions in accordance with the rubric for each question.

Please enable JavaScript in your browser to complete this form.

Name

First

Last

Company name

AI Vision & Strategic Alignment

1. To what extent is AI embedded in your company's overall strategic vision?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
AI is not mentioned in strategy; no clear AI initiatives.	AI is considered in isolated projects but lacks a formal plan.	AI is part of strategic planning, but execution is inconsistent.	AI is a key pillar in business strategy with defined objectives.	AI is fully embedded in corporate strategy and drives long-term goals.

Q01 score

Selected Value: 0

2. Does your leadership team actively support and prioritise AI initiatives?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
No or minimal executive support; AI is an ad-hoc effort.	Some executives support AI, but no clear leadership buy-in.	Leadership has AI teams and initiatives but lacks full alignment.	Executives actively fund and support AI, with clear accountability.	Leadership champions AI, integrating it across business functions.

Q02 score

Selected Value: 0

3. How well are AI-driven insights incorporated into decision-making?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
AI is rarely used in decision-making.	AI is used for basic reporting, but not for strategic decisions.	AI insights influence some decision-making but lack real-time application.	AI insights are widely used across functions to guide strategy.	AI-driven insights are central to decision-making at all levels.

Q03 score

Selected Value: 0

Q18 Q16 Q30

4. Do you have a structured roadmap for AI adoption aligned with business objectives?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
No formal roadmap; AI efforts are scattered.	An AI roadmap exists but is not consistently followed.	A structured AI roadmap exists but is not fully integrated into business goals.	AI roadmap is well-defined and actively followed.	AI roadmap is continuously refined and fully aligned with business strategy.

Q04 score

Selected Value: 0

5. How frequently does your organisation review and update its AI strategy?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
AI strategy is not reviewed or rarely updated.	AI strategy is reviewed occasionally but without structured updates.	AI strategy is reviewed periodically based on business needs.	AI strategy is updated proactively based on market shifts.	AI strategy is continuously refined using real-time data and insights.

Q05 score

Selected Value: 0

Data Ecosystem & Governance

6. How well-structured and accessible is your organisation’s data for AI applications?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
Data is mostly siloed, unstructured, and difficult to access.	Some data is centralised, but accessibility is limited.	Data is structured and supports AI use cases in some areas.	Well-structured, AI-ready data systems enable real-time access.	Fully AI-optimised data ecosystem with seamless accessibility.

Q06 score

Selected Value: 0

7. Do you have a centralised data management system that supports real-time AI processing?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
No centralised data management system (1), or centralised system in planning stage (2-4)	Basic centralised storage exists, but real-time AI processing is not possible.	AI-ready data management system is in place but lacks real-time capabilities.	AI data infrastructure supports real-time processing and analytics.	Fully real-time AI-driven data management system, integrated enterprise-wide.

Q07 score

Selected Value: 0

8. How robust are your organisation’s data governance policies?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
No data governance policies exist or being planned.	Basic policies exist but are inconsistently enforced.	Strong governance framework with some compliance mechanisms.	AI-driven data governance is well-established and enforced.	Fully developed AI governance framework with automated compliance.

Q08 score

Selected Value: 0

9. How effectively does your organisation report AI use to regulators, industry bodies, or customers?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
No or minimal AI use reporting; no transparency with regulators, industry bodies, or customers.	AI reporting is ad-hoc, with some internal documentation but no structured disclosure.	AI reporting is formalised but occurs infrequently and lacks depth.	AI use is regularly reported, with structured disclosure mechanisms for compliance and transparency.	AI reporting is fully embedded, proactive, and a standard part of governance, ensuring full transparency and trust.

Q09 score

Selected Value: 0

10. How effectively does your organisation measure and improve data quality for AI applications?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
Data quality is not actively managed or measured.	Data quality initiatives exist but are reactive.	Data quality is measured but lacks real-time improvements.	AI models actively monitor and improve data quality.	AI continuously enhances data quality with self-learning systems.

Q10 score

Selected Value: 0

AI Skills & Workforce Readiness

11. How widespread is AI literacy across teams?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
AI knowledge is minimal or non-existent across the organisation.	Some teams are AI-aware, but adoption is low.	AI literacy is increasing across multiple departments.	Most teams are AI-literate, and AI training is common.	AI literacy is embedded across all functions, and continuous AI education is a priority.

Q11 score

Selected Value: 0

12. Do you have a strategy for recruiting and retaining AI talent?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
No AI recruitment strategy exists or is in planning stage.	AI hiring happens ad-hoc, with no formalised strategy.	AI talent recruitment is structured but not fully aligned with business needs.	AI hiring and retention are a key business priority with dedicated investment.	AI talent strategy is industry-leading, attracting and retaining top talent.

Q12 score

Selected Value: 0

13. To what extent does your organisation provide AI upskilling programmes?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
No AI training programmes exist or are being planned.	Some AI training is available, but participation is low and inconsistent.	AI upskilling programmes are structured and accessible to key employees.	AI training is part of a structured learning programme across business units.	AI upskilling is continuous, with role-based learning pathways and career development.

Q13 score

Selected Value: 0

14. How well are AI skills integrated into job roles?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
AI skills are not required or encouraged in job roles.	AI skills are relevant to a few specialised roles but not broadly applied.	AI is integrated into some job roles, but skill application is inconsistent.	AI competencies are embedded in most roles, with clear expectations.	AI capabilities are a fundamental part of job functions across all departments.

Q14 score

Selected Value: 0

15. Do you use AI talent analytics to identify skill gaps?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
No AI talent analytics exist for the company, or are being designed.	AI skills are assessed informally or sporadically.	AI skills are periodically assessed, but insights are not actioned effectively.	AI talent analytics drive learning and hiring decisions.	AI analytics continuously monitor skill development, shaping workforce strategy.

Q15 score

Selected Value: 0

AI Infrastructure & Deployment

16. What level of AI infrastructure does your organisation currently have in place?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
No or minimal AI infrastructure, experimental tools only.	Basic AI infrastructure exists, but it is not scalable or integrated.	AI infrastructure supports multiple use cases but is not fully automated.	AI-powered automation is in place, and AI models are widely deployed.	AI infrastructure is enterprise-wide, supporting real-time and self-improving AI systems.

Q16 score

Selected Value: 0

17. How frequently do you deploy AI models into production, and how well are they maintained?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
AI models are not deployed beyond experiments.	Some AI models are in production but are inconsistently updated.	AI models are deployed at scale, but automation and maintenance are limited.	AI deployment is streamlined, with automated updates and performance monitoring.	AI models are continuously deployed and optimised, ensuring peak performance.

Q17 score

Selected Value: 0

18. Do you have automated AI-driven processes that improve operational efficiency?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
No or minimal AI-driven automation exists.	Some automation is in place, but it is not widely used or optimised.	AI-driven automation is implemented in key processes, but gaps remain.	AI automation is embedded in workflows, improving efficiency across teams.	AI automation is fully integrated, continuously learning and improving operations.

Q18 score

Selected Value: 0

19. How resilient and scalable are your AI systems in handling large volumes of data and real-time decision-making?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
AI systems generally lack resilience and scalability.	AI systems can handle some data loads but struggle with scaling.	AI systems are scalable but require significant manual oversight.	AI systems are highly resilient and scale efficiently across business operations.	AI infrastructure is enterprise-grade, ensuring resilience and real-time decision-making at scale.

Q19 score

Selected Value: 0

20. To what extent does your organisation monitor and optimise AI system performance over time?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
No or minimal AI performance monitoring is in place.	AI performance is monitored occasionally but lacks structured optimisation.	AI performance is regularly assessed, but improvement strategies are limited.	AI performance is proactively monitored, with structured optimisation processes.	AI performance is continuously optimised using self-learning algorithms and automation.

Q20 score

Selected Value: 0

Responsible & Ethical AI

21. How comprehensive is your organisation’s AI ethics framework?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
No AI ethics framework exists or is in planning.	A basic AI ethics framework is in place, but implementation is weak.	AI ethics policies are in place but inconsistently followed.	AI ethics framework is robust, regularly reviewed, and enforced.	AI ethics is a core business priority, with a transparent and accountable framework.

Q21 score

Selected Value: 0

22. Do you have policies in place to mitigate bias in AI decision-making?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
No bias mitigation policies exist or are being designed.	Some policies exist but are not enforced or monitored effectively.	Bias mitigation policies are in place but need refinement.	AI bias monitoring is standard practice, with proactive mitigation strategies.	AI bias prevention is automated, transparent, and continuously improved.

Q22 score

Selected Value: 0

23. How transparent are your AI models in explaining their decision-making processes to stakeholders?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
AI models lack capacity to be explained, and may also lack transparency.	Some explanations are provided, but clarity is limited.	AI models provide transparency, but technical complexity hinders full comprehension.	AI models are interpretable, with stakeholder-focused explanations.	AI decision-making is fully explainable, transparent, and aligned with regulatory expectations.

Q23 score

Selected Value: 0

24. To what extent does your organisation comply with AI-related regulations and industry standards?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
No or minimal AI regulatory compliance measures exist.	Compliance is minimal, with potential regulatory risks.	Compliance is managed, but gaps in adherence remain.	AI regulatory compliance is a priority and actively managed.	AI compliance is industry-leading, shaping regulatory best practices.

Q24 score

Selected Value: 0

25. How frequently does your organisation conduct ethical audits on AI deployments?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
No ethical audits are conducted or are being planned.	AI ethical audits are rare and informal.	Ethical audits occur, but not at regular intervals.	AI ethical audits are standard practice, with clear accountability.	AI ethical audits are frequent, proactive, and drive continuous ethical improvements.

Q25 score

Selected Value: 0

AI Value & Business Impact

26. How well does your organisation measure the return on investment (ROI) from AI initiatives?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
No AI ROI measurement exists or is being planned.	Some ROI tracking exists but is inconsistent.	AI ROI is measured but not linked to business strategy.	AI ROI is clearly defined, with business-aligned KPIs.	AI ROI is a core business metric, driving strategic investments.

Q26 score

Selected Value: 0

27. To what extent has AI contributed to revenue growth, cost reduction, or operational efficiency?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
AI has no or minimal measurable business impact.	AI provides some cost savings but no major revenue gains.	AI contributes to revenue growth and operational improvements.	AI significantly enhances profitability and efficiency.	AI is a major driver of new revenue streams and innovation.

Q27 score

Selected Value: 0

28. How well does your organisation integrate AI insights into its business innovation and product/service development?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
AI is not considered or is only poorly considered in innovation strategy.	AI is used for limited product or service improvements.	AI supports innovation but is not consistently leveraged.	AI is a key driver of innovation in product and service development.	AI continuously fuels new business models and market leadership.

Q28 score

Selected Value: 0

29. Do you track AI’s impact on customer satisfaction, employee productivity, or market competitiveness?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
AI's impact on customers, employees, or competitiveness is not measured or only minimally measured.	Some tracking exists, but AI's impact is not clearly linked to outcomes.	AI-driven customer and employee insights are tracked but not fully optimised.	AI metrics influence business decisions, improving customer experience and workforce efficiency.	AI insights directly shape strategy, enhancing customer engagement, workforce performance, and competitive advantage.

Q29 score

Selected Value: 0

30. How frequently does your organisation reassess and refine AI strategies based on business performance metrics?

1-4 (Initiating)	5-8 (Developing)	9-12 (Scaling)	13-16 (Optimising)	17-20 (Leading)
AI strategy is rarely reviewed and is not tied to performance metrics.	AI strategy is reviewed sporadically but lacks structured performance tracking.	AI strategy is periodically adjusted based on performance, but updates are slow.	AI strategy is refined regularly using performance-driven insights.	AI strategy is continuously optimised in real-time, ensuring maximum business impact and adaptability.

Q30 score

Selected Value: 0