Is Your Organisation
AI-Ready?

Leverage the power of Artificial Intelligence strategically. Evaluate your organisation’s AI awareness, skills, adaptability, and ethical preparedness to drive innovation and maintain a competitive edge.

Trusted by the world’s most ambitious teams.

Why Assess Your Organisation's AI Readiness?

AI presents transformative opportunities but requires careful integration.

Understanding your organisation’s collective readiness – from workforce skills and strategic alignment to ethical considerations – is vital for successful AI adoption.

An assessment provides the baseline needed to build effective strategies, mitigate risks, and harness AI’s full potential.

Simple Steps to Insight

1

Start the Test

Click the button to begin the organisational assessment

2

Answer Strategically

Complete the survey in approximately 10-15 minutes. It evaluates key areas like awareness, skills, adaptability, and ethics across your organisation.

3

Receive Your Insights

Get a report identifying organisational strengths and areas needing development for successful AI integration.

Empower Your Organisation

Future-Proof Your Organisation

Gain the critical insights needed to navigate the AI revolution successfully. Understand your readiness and build your roadmap for AI integration.

Your AI Journey Starts Here

Don’t get left behind. Take the AI Readiness Test today and step into the future with confidence.

AI Readiness Assessment Form - Organisational/Institutional Assessment

1 / 72

Category: Demographic Information

This section collects basic demographic information to help us analyse AI adoption trends across different roles, industries, and organisational levels.

Surname

2 / 72

Category: Demographic Information

This section collects basic demographic information to help us analyse AI adoption trends across different roles, industries, and organisational levels.

First Name

3 / 72

Category: Demographic Information

This section collects basic demographic information to help us analyse AI adoption trends across different roles, industries, and organisational levels.

Email Addresses (Private and Official)

4 / 72

Category: Demographic Information

This section collects basic demographic information to help us analyse AI adoption trends across different roles, industries, and organisational levels.

Mobile number

5 / 72

Category: Demographic Information

This section collects basic demographic information to help us analyse AI adoption trends across different roles, industries, and organisational levels.

Name of your organisation

6 / 72

Category: Demographic Information

This section collects basic demographic information to help us analyse AI adoption trends across different roles, industries, and organisational levels.

Role / Title in your organisation

7 / 72

Category: Demographic Information

This section collects basic demographic information to help us analyse AI adoption trends across different roles, industries, and organisational levels.

Number of years you have spent your current organisation

0 - 5 years

5 - 10 years

10 - 15 years

Over 15 years

8 / 72

Category: Demographic Information

This section collects basic demographic information to help us analyse AI adoption trends across different roles, industries, and organisational levels.

What is the nature of organisation in which you work

Public Sector

Private Sector

Academia

Civil Society

Other…

9 / 72

Category: Demographic Information

This section collects basic demographic information to help us analyse AI adoption trends across different roles, industries, and organisational levels.

What is your current role or level of responsibility within your organisation?

Board Member / Non-Executive Director

Executive Committee (ExCo) Member / C-Suite Executive (e.g., CEO, CFO, COO)

Senior Management (e.g., General Manager, Director, Head of Department)

Middle Management (e.g., Manager, Team Lead, Supervisor)

Technical/Professional Staff (e.g., Specialist, Analyst, Consultant)

Administrative/Support Staff

Other…

10 / 72

Category: Strategy

Strategy forms the backbone of AI readiness, emphasizing a well-defined approach, clear ownership, and sustainable financial planning. It's not just about having a strategy but ensuring its clarity, measurable outcomes, and long-term viability.

Do you have a strategy for using AI in your organisation?

Yes — we have a well-defined AI strategy

No — we are currently in the process of developing an AI strategy

No — we have not yet started to develop an AI strategy

Unsure

11 / 72

Category: Strategy

How is your organisation's AI strategy managed? Is there a specific team or individual responsible for leading it, or is it handled in a more organic and decentralized manner.

Clear leadership / ownership of our organisation’s AI strategy

More organic and decentralized

12 / 72

Category: Strategy

Do you have a process in place to measure the impact of the deployment of AI-powered solutions?

Yes, we have a process and clearly defined metrics

Yes, we have a process but are still working on actual metrics

No, we don’t have a process or metrics, but we are likely to have this in the next 12 months

No, we don’t have a process of metrics and we are unlikely to have this in the next 12 months

Unsure

Other…

13 / 72

Category: Strategy

How does your organisation prioritize budget allocation between AI deployment and other technological initiatives?

AI deployment is the highest priority for budget allocation, and we have been given an additional budget for it

AI deployment is given equal priority alongside other technological initiatives. We have some additional funding available

AI deployment is important, but we will have to cut spending across other technical initiatives to fund it

AI deployment is important, but depends on other technical initiatives to be in place first

Unsure

Other…

14 / 72

Category: Infrastructure

AI readiness heavily depends on a solid infrastructure, which includes powerful computing resources and networks that can scale. A strong security foundation is also essential, as is ensuring the sustainability of the entire AI implementation. Each of these components is crucial for the success of AI.

How would you rate your organisation's current IT infrastructure in terms of scalability and flexibility to accommodate the evolving computational needs of AI projects?

Fully adaptable: can instantly accommodate any AI computational needs

Highly scalable: designed with growth and future AI demands in mind

Moderately scalable: can handle current projects but need enhancements for more complex applications

Limited scalability: might need significant updates for large AI projects

Not scalable at all

Other…

15 / 72

Category: Infrastructure

Does your organisation have dedicated GPU resources available and integrated for processing of AI workloads?

Robust GPU infrastructure available for current and future AI workloads

Just enough GPU resources to cater to ongoing projects

Limited GPU resources for experimental purposes only

No, we don’t have dedicated GPU resources available currently

Other…

16 / 72

Category: Infrastructure

How efficiently does your organisation allocate compute resources for AI tasks based on their demand?

Our systems are mostly automated and efficiently allocate resources based on AI demand

We have some automated resource allocation processes, but manual intervention is often required

Resource allocation for AI tasks is done manually and might not be optimal

We do not have a structured approach to resource allocation for AI

Other…

17 / 72

Category: Infrastructure

How would you assess your current data center's network performance in terms of latency and throughput, especially for AI workloads?

Optimal: minimal issues and tailored for the most demanding AI workloads

Moderately optimal: rare hiccups with current workload, but will need improvement to cater to future demand

Sub optimal: we have occasional latency issues, especially with large AI workloads

Not optimal: we experience frequent issues and bottlenecks

Other…

18 / 72

Category: Infrastructure

As your AI projects grow in complexity and data volume, how prepared is your network to adapt to these accordingly?

Fully flexible and adaptable: can accommodate any scale of AI projects instantly

Highly scalable: designed with significant AI growth in mind

Adequately scalable: might need periodic updates

Somewhat scalable: potential bottlenecks for very large AI projects

Not scalable: significant upgrades are required for large AI projects

Other…

19 / 72

Category: Infrastructure

How seamlessly is your network infrastructure integrated with your AI systems to facilitate efficient data flow and processing?

High-level integration ensuring efficient data flow for most AI tasks, ensuring seamless operations across all AI projects

Moderate integration: we've optimized major pathways but still have occasional hiccups

Some basic integrations, but often require manual adjustments

No integration: our network and AI systems operate mostly in silos

Other…

20 / 72

Category: Infrastructure

How would you assess your organisation's awareness and understanding of cybersecurity threats specific to AI and machine learning systems?

High awareness: have a comprehensive understanding and / or regularly update our security protocols based on new threats

Moderate awareness: aware and have taken preliminary precautions

Limited awareness: have some basic understanding but no specific measures in place

Unaware of security threats specific to AI workloads

Other…

21 / 72

Category: Infrastructure

How does your organisation ensure the protection of data utilized in AI models, especially during transit and at rest?

End-to-end encryption with regular checks and security audits, continuous monitoring and instant threat response

Advanced encryption measures in place, but may lack regular audits

Basic encryption measures in place

No specific encryption or protection measures

Other…

22 / 72

Category: Infrastructure

How equipped is your organisation to detect and prevent unauthorized tampering or adversarial attacks on your AI models?

Fully equipped: have proactive monitoring and tamper detection with timely counter measures

Moderately equipped: have protective measures in place but lack real-time monitoring

Somewhat equipped: we are aware of the risks and have basics sorted but lack robust measures

Not equipped: have not considered the cybersecurity aspect of AI workloads

Other…

23 / 72

Category: Infrastructure

How does your organisation manage access control to AI systems and datasets?

Dynamic and granular access controls that adjust based on project needs and security levels, with real—time monitoring

Advanced role-based access controls with periodic audits

Basic role-based access in place but may lack regular updates

Access is largely open and not specifically restricted

Other…

24 / 72

Category: Infrastructure

How ready is your company to deploy AI from a power consumption perspective?

Highly prepared: we have dedicated infrastructure in place to optimize power consumption in AI deployment

Somewhat prepared: some measures in place to address power consumption concerns in AI deployment

Not prepared: no specific measures or considerations for power consumption in AI deployment

Unsure

Other…

25 / 72

Category: Data

Quality data is the cornerstone of AI, with an emphasis on centralized, cleaned-in-house datasets and reliable external sources. Empowering staff proficiency further ensures the seamless integration and effective utilization of this data.

How centralized is your organisation's in-house data, facilitating easy access for AI initiatives?

Fully centralized: data is consistently managed and readily accessible organisation-wide

Moderately centralized: majority of data is in unified databases, but some silos remain

Partially fragmented: some centralized databases, but many department-specific silos exist

Highly fragmented: data is scattered across different silos

26 / 72

Category: Data

To what extent is your in-house data preprocessed, cleaned, and ready for AI projects?

Consistently pre-processed: our data strategy ensures data is always AI-ready

Mostly pre-processed: most of our data is primed for AI use

Occasionally pre-processed: some datasets are AI-ready, but many require additional work

Rarely pre-processed: significant time is needed to clean and organize data for AI

27 / 72

Category: Data

How would you describe the procedures and protocols in place for AI teams to access and use in-house data?

Facilitative: procedures actively promote efficient data access for AI

Balanced: while there are protocols, they don't overly impede access

Somewhat restrictive: procedures exist but are not streamlined so there can be occasional issues

Restrictive: cumbersome protocols hinder timely access

28 / 72

Category: Data

How well integrated are your analytics tools with the data sources and AI platforms used within your organisation?

Fully integrated: almost all tools have direct, automated interactions with data sources and operate in complete harmony

Moderately integrated: most tools connect seamlessly with our main data sources

Somewhat integrated: some tools interface directly with data sources, but many require manual bridging

Not integrated: manual processes dominate tool-data interactions

29 / 72

Category: Data

How would you rate the sophistication of your analytics tools in terms of handling complex AI-elated data sets?

Excellent: majority of our tools are AI-optimized and cater to advanced tasks

Good: a balance of general-purpose and AI-specific analytics tools

Fair: some tools are AI-enhanced, but there's significant reliance on general tools

Basic: tools are more general-purpose and don't cater specifically to AI

30 / 72

Category: Data

How adaptable and scalable are your analytics tools to cater to evolving AI project needs?

Highly adaptable: tools are frequently updated and scaled based on project demands and can be rapidly tailored to any AI analytics demand

Moderately adaptable: tools cater to most AI projects, with occasional need for third-party solutions

Somewhat adaptable: tools can handle current tasks but might struggle with larger, more complex projects

Not adaptable: tools often lag behind project requirements

31 / 72

Category: Data

How would you describe the proficiency level of your staff in leveraging these analytics tools for AI projects?

Proficient: staff are adept at leveraging tool capabilities to their fullest

Moderate: most staff can handle regular AI analytics tasks efficiently

Intermediate: staff can use tools but often need guidance for advanced functions related to AI

Beginner: significant training is required

32 / 72

Category: Data

What level of quality checks and processes do you have in place to check the quality and reliability of the external data used for AI training?

Advanced: external data undergoes rigorous quality checks and peer reviews

Intermediate: we have a systematic process for any external data we incorporate

Basic: we do some manual checks

We have no systematic processes

33 / 72

Category: Data

How effectively does your organisation track the origins and lineage of data used in your AI models?

Most of our AI projects have detailed data lineage tracking incorporating end-to-end data traceability, ensuring complete transparency.

We have a structured system for tracking data origins, but it's not integrated with all AI projects.

We have basic tracking but lack comprehensive lineage details

We do not actively track data origins

34 / 72

Category: Data

How does your organisation ensure and verify the accuracy of the data being used in AI models?

We have a continuous data accuracy validation system integrated with real-time corrections

We have dedicated teams that periodically verify data accuracy

We do occasional checks but lack a systematic verification process

We rely on external data providers without internal verification

35 / 72

Category: Talent

Talent in AI readiness hinges on the depth and proficiency of in-house expertise, complemented by ongoing training investments. Inclusivity remains key, with policies ensuring AI technologies are accessible to all, irrespective of abilities.

How well resourced is your company with the right level of in-house talent necessary for successful AI deployment?

Very well resourced

Moderately well resourced

Moderately under resourced

Significantly under resourced

Unsure

36 / 72

Category: Talent

How would you describe the proficiency level of your staff in adopting and fully leveraging the AI technologies that you are deploying?

Proficient: staff are adept at leveraging tool capabilities to their fullest

Moderate: most staff can handle regular AI related tasks efficiently

Intermediate: staff can use tools but often need guidance for advanced functions

Beginner: significant training is required

37 / 72

Category: Talent

Has your company invested in training programs to upskill existing employees in AI-related competencies?

Yes, but we hire external vendors to train our staff

Yes, we have comprehensive internal training programs

No, we have not implemented training programs yet but plan to in the future

Unsure

38 / 72

Category: Talent

When it comes to talent management, has your company started to think about the ‘accessibility’ of AI technologies for employees who are differently abled?

Yes, it is a core part of our AI strategy and talent planning

Yes, we have thought about it, but there are no clear answers

Yes, we are aware but we don’t build these AI tools so we can’t control this aspect

No, this is not a consideration at this time

39 / 72

Category: Governance

Governance in AI demands a rigorous commitment to data privacy, sovereignty, and fairness. Ensuring transparency in algorithms, adherence to global privacy standards, and safeguarding data sovereignty are paramount to fostering trust and ethical AI practices.

Does your organisation have an AI governance framework or policy in place?

Yes

Not sure

40 / 72

Category: Governance

Who is responsible for overseeing AI strategy and governance in your organisation?

Board of Directors

Executive Management

AI Governance Committee

IT/Technology Department

No specific oversight

41 / 72

Category: Governance

Who is responsible for overseeing AI strategy and governance in your organisation?

Board of Directors

Executive Management

AI Governance Committee

IT/Technology Department

No specific oversight

42 / 72

Category: Governance

How aligned is your AI strategy with your organisation’s overall business strategy?

Not aligned

Partially aligned

Fully aligned

43 / 72

Category: Governance

Does your organisation have defined ethical principles for the development and use of AI?

Yes

Not sure

44 / 72

Category: Governance

Is there clear accountability for decisions made by AI systems in your organisation?

Yes

Partially

45 / 72

Category: Governance

Does your organisation have data governance policies that address data quality, security, and ethical use for AI applications?

Yes

Partially

46 / 72

Category: Governance

What is the level of awareness across your organisation regarding potential biases and fairness in data sets used for AI?

High awareness: regular training sessions and active discussions around biases

Moderate awareness: occasional training or awareness programs in place

Limited awareness: sporadic discussions but no formal understanding

Not aware: haven't considered biases in our data

47 / 72

Category: Governance

Does your organisation have mechanisms to actively detect biases and lack of fairness in data used for AI?

Diversity in external data is a priority; regular checks for biases are conducted with continuous monitoring and adjustment

We actively seek diverse data sources and occasionally audit for biases

We try to use diverse data but don't have systematic checks in place

This isn't a focus for us currently

48 / 72

Category: Governance

How does your organisation handle and rectify identified biases and lack of fairness in data?

Systematic process for bias and fairness correction with dedicated teams and proactive strategy for bias prevention and rectification, and ensuring fairness, ingrained in data management

Biases are addressed on a project-by-project basis

Acknowledge biases but lack systematic correction mechanisms

No formal process for rectification

49 / 72

Category: Governance

How transparent are the algorithms used in your AI systems in terms of their decision-making processes?

Highly transparent: can trace most decisions back to specific factors

Moderately transparent: essential decision factors are known

Limited transparency: some understanding but lacks depth

Completely black box: no understanding of decision mechanisms

50 / 72

Category: Governance

Does your organisation have mechanisms to detect biases and ensure fairness in AI algorithms?

Regular comprehensive automated checks with continuous monitoring for algorithmic biases complemented with manual reviews.

Automated bias detection tools in place but not used consistently

Sporadic manual reviews

No mechanisms in place

51 / 72

Category: Governance

What is the level of understanding across your organisation about global data privacy standards (like GDPR, CCPA, NDPR etc.) and ensuring adherence to these in AI projects?

High understanding: strict adherence with regular audits and review and a proactive strategy to stay ahead of global privacy norms and regulations

Moderate understanding: have protocols in place, but occasional lapses occur

Basic understanding, but no systematic adherence

Unaware of global privacy standards

52 / 72

Category: Governance

How does your organisation handle data anonymisation to protect user privacy in AI datasets?

Consistent anonymization techniques across all datasets

Advanced anonymization techniques for most AI datasets

Basic anonymization techniques applied inconsistently

No anonymization: data is used as is

53 / 72

Category: Governance

In case of a data breach or privacy violation, how prepared is your organisation to address and rectify the situation?

Advanced protocol: regularly reviewed with mock drills and updates , continuous monitoring and rapid response teams

Structured protocol with designated teams but rarely reviewed

Basic protocol, but not comprehensive or tested

No established protocol for breaches

54 / 72

Category: Governance

How well-versed is your organisation in data sovereignty laws and regulations across different regions/countries?

Detailed knowledge of varied jurisdictions with experts on board

Good understanding of major regions/countries

Basic awareness but lacks depth

Not aware of data sovereignty laws

55 / 72

Category: Governance

How does your organisation ensure that data storage and processing align with local data sovereignty requirements?

Strict protocols with data mapped and stored according to local laws and sovereignty rules

Advanced Protocols: regular checks to ensure compliance with major regions, sovereignty laws

Basic protocols: some alignment with sovereignty laws, but not consistent

No specific protocols: data is stored wherever convenient

56 / 72

Category: Governance

How does your organisation handle cross-border data transfers, ensuring they adhere to data sovereignty laws?

Rigorous checks ensuring every transfer aligns with local sovereignty laws

Structured protocols for most cross-border transfers

Aware but might have occasional lapses in adherence

We don't consider sovereignty during cross-border transfers

57 / 72

Category: Governance

How comprehensive are the AI policies and protocols in your organisation overall?

Highly comprehensive policies

Moderately comprehensive

Limited

We do not have any

58 / 72

Category: Governance

What challenges does your organisation face in implementing effective AI governance?

59 / 72

Category: Culture

AI-driven cultural transformation emphasizes urgency, adaptability across all organsational tiers, and robust change management. Embracing AI requires not just technological readiness but a cultural shift supported by deep and qualitative planning.

How urgently is your organisation looking to embrace AI?

High urgency: the move to embrace AI is seen as highly important and urgent

Moderate urgency: embracing AI is seen as important but the organisation is not acting with urgency

Limited urgency: embracing AI is seen as an inevitable driver of some change but not important or critical

No urgency: there is no discussion or momentum around embracing AI within the organisation

60 / 72

Category: Culture

How receptive is your Board to embracing the changes brought about by AI?

High receptiveness: widespread acceptance and willingness to adopt

Moderate receptiveness: general acceptance and willingness to adopt

Limited receptiveness: only limited teams / stakeholders accepting and willing to adopt

Not receptive: resistant to change and will struggle to adapt

Unsure: I don’t know

61 / 72

Category: Culture

How receptive is your leadership team to embracing the changes brought about by AI?

High receptiveness: widespread acceptance and willingness to adopt

Moderate receptiveness: general acceptance and willingness to adopt

Limited receptiveness: only limited teams / stakeholders accepting and willing to adopt

Not receptive: resistant to change and will struggle to adapt

Unsure: I don't know

62 / 72

Category: Culture

How receptive is your middle management to embracing the changes brought about by AI?

High receptiveness: widespread acceptance and willingness to adopt

Moderate receptiveness: general acceptance and willingness to adopt

Limited receptiveness: only limited teams / stakeholders accepting and willing to adopt

Not receptive: resistant to change and will struggle to adapt

Unsure: I don’t know

63 / 72

Category: Culture

How receptive are your employees to embracing the changes brought about by AI?

High receptiveness: widespread acceptance and willingness to adopt

Moderate receptiveness: general acceptance and willingness to adopt

Limited receptiveness: only limited teams / stakeholders accepting and willing to adopt

Not receptive: resistant to change and will struggle to adapt

Unsure: I don’t know

64 / 72

Category: Culture

Do you have a change management plan in place to address the changes brought about by deploying AI technologies?

Yes

Maybe

65 / 72

Category: Culture

How would you assess the quality and depth of the change management plan?

Comprehensive: have thought through every aspect

In progress: we have some areas fully covered; others are under review

Draft: just started developing

66 / 72

Category: AI Use Cases

This section aims to assess the current state of AI adoption within your organisation, explore existing use cases, and identify opportunities for future AI applications. Your insights will help uncover key trends, challenges, and areas where AI can deliver the most value.

Is your organisation currently using any AI technologies or solutions? (tick all that apply)

Generative AI

Machine Learning (ML)

Natural Language Processing (NLP)

Computer Vision

Predictive Analytics

Robotic Process Automation (RPA)

Chatbots/Virtual Assistants

Product/Service Recommendation Systems

I am not aware of any of these technologies

68 / 72

Category: AI Use Cases

How would you rate the impact of AI on your organisation’s operations so far?

Very High Impact

Moderate Impact

Low Impact

No Impact

Unsure: I don’t know

69 / 72

Category: AI Use Cases

Are there business processes or areas where you believe AI could add value but has not yet been implemented?

70 / 72

Category: AI Use Cases

What are the key challenges your organisation faces when identifying new AI use cases?

Lack of technical expertise

Limited data availability or quality

Budget constraints

Unclear business value

Regulatory or ethical concerns

Resistance to change

Other…

71 / 72

Category: AI Use Cases

How does your organisation identify potential AI use cases?

Through leadership directives

Employee suggestions

External consultants/vendors

Data-driven insights

Not actively identifying new use cases

Other…

72 / 72

Category: AI Use Cases

What factors are critical for scaling AI initiatives in your organisation?

Your score is

About AIQTest.io

AIQtest.io is your gateway to understanding your AI literacy, problem-solving abilities, and overall readiness for an AI-driven future. As artificial intelligence continues to transform industries, workplaces, and daily life, having a solid grasp of its principles, applications, and impact is more important than ever.

Our comprehensive assessment is designed to evaluate key competencies across multiple dimensions, including critical thinking, data interpretation, ethical reasoning, and adaptability in AI-powered environments. By taking this test, you’ll gain valuable insights into your strengths, uncover areas where you can improve, and receive personalized feedback to help you stay ahead in an increasingly AI-centric world.

Whether you’re a student, professional, entrepreneur, or lifelong learner, AIQtest.io empowers you with the knowledge and awareness needed to navigate the future with confidence. Stay competitive, enhance your skill set, and prepare for the opportunities AI will bring.

Take the test today and take control of your AI future!

Framework

At the core of AIQtest.io is our AI Readiness Framework, designed to measure five key dimensions of AI competency:

AI Literacy – Understanding AI fundamentals, machine learning, and key technologies.
Data Reasoning – Ability to interpret AI-generated insights and make data-driven decisions.
Ethical AI Awareness – Recognizing AI bias, privacy concerns, and responsible AI use.
Problem-Solving with AI – Applying AI tools effectively in professional and real-world scenarios.
AI Adaptability – Learning to integrate AI seamlessly into daily work and business strategies.

By assessing these areas, we provide actionable insights that help individuals and businesses build AI confidence, enhance skills, and stay ahead in the AI-driven future.

Are you a researcher?

FAQ

What is AIQtest.io?

AIQtest.io are platforms designed to assess AI literacy, problem-solving skills, and AI readiness for individuals and businesses.

How does the AI Readiness Test work?

The test evaluates your AI knowledge across multiple dimensions through multiple-choice questions, scenario-based assessments, and practical AI challenges.

Who should take the AI Readiness Test?

Anyone looking to understand their AI proficiency, including professionals, students, educators, and organizations assessing workforce AI capabilities.

How long does the test take?

The test typically takes 30-60 minutes, depending on the level of assessment.

What do the test results mean?

Your results place you in one of four categories: Novice, Aware, Proficient, or Expert, helping you understand your AI readiness level.

Can I retake the test?

Yes! You can retake the test to track your progress and improve your AI readiness over time.

Is the test free?

Yes, upon successful completion, you receive a certification that validates your AI proficiency.

How can businesses use AIQtest.io?

We offer both free and premium versions. The premium version provides a more detailed analysis and certification.

How is the test structured?

We offer both free and premium versions. The premium version provides a more detailed analysis and certification.

How do I get started?

The test includes multiple-choice questions, real-world AI scenarios, and interactive challenges to gauge AI application skills.

Is Your Organisation
AI-Ready?

Why Assess Your Organisation's AI Readiness?

Simple Steps to Insight

1

Start the Test

2

Answer Strategically

3

Receive Your Insights

Empower Your Organisation

Benchmark Readiness

Identify Skill Gaps

Inform Strategy

Guide Training Initiatives

Assess Ethical Preparedness

Drive Competitive Advantage

Future-Proof Your Organisation

Your AI Journey Starts Here

About AIQTest.io

Framework

Are you a researcher?

FAQ

Assess, Understand, and Elevate Your AI Literacy

Is Your Organisation AI-Ready?

Why Assess Your Organisation's AI Readiness?

Simple Steps to Insight

1

Start the Test

2

Answer Strategically

3

Receive Your Insights

Empower Your Organisation

Benchmark Readiness

Identify Skill Gaps

Inform Strategy

Guide Training Initiatives

Assess Ethical Preparedness

Drive Competitive Advantage

Future-Proof Your Organisation

Your AI Journey Starts Here

About AIQTest.io

Framework

Are you a researcher?

FAQ

Assess, Understand, and Elevate Your AI Literacy

Are you a researcher?

Is Your Organisation
AI-Ready?