AI Readiness Assessment

Artificial intelligence has permeated across industries for years. And today. it is revolutionizing organizations and the anatomy of work Nevertheless. it’s important to recognize that not every company, despite their optimism, is fully equipped to adopt this technology. Being Al-ready requires combining six critical pillars – Strategy, Infrastructure, Data. Governance. Talent. and Culture. This assessment tool helps companies understand their level of readiness across each of these pillars.

Strategy

Infrastructure

Culture

Governance

Data

Name

First

Last

Company

Strategy

Strategy forms the backbone of Al 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 to deploy Al powered solutions in your organization?

Yes - we have a well-defined Al strategy
No - we are currently in the process of developing an Al strategy
No - we have not yet started to develop an Al strategy
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Is it clear who / what team is leading the Al strategy for your company or is it being managed in a more organic and decentralised manner?

There is clear leadership I ownership of our organization's Al strategy
More organic and decentralised

Do you have a process in place to measure the impact of the deployment of Al / Al-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
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Has your company established a financial strategy to ensure sustainable funding for Al deployment initiatives?

Yes - A short and long-term financial strategy is in place
Yes - Only a short-term financial strategy is in place
No - But we are currently underway with developing a financial strategy
No - We have no plans presently to develop a financial strategy
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How is your company prioritizing budget allocation between Al deployment and other technological initiatives?

Al deployment is the highest priority for budget allocation. and we have been given an additional budget for it
Al deployment is given equal priority alongside other technological initiatives. We have some additional funding available
Al deployment is important, but we will have to cut spending across other technical initiatives to fund it
Al deployment is important, but depends on other technical initiatives to be in place first
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Infrastructure

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

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

Fully adaptable: can instantly accommodate any Al computational needs
Highly scalable: designed with growth and future Al demands in mind
Moderately scalable: can handle current projects but need enhancements for more complex applications
Limited scalability: might need significant updates for large Al projects
Not scalable at all

Does your organization have dedicated GPU resources available and integrated for processing of Al workloads?

Robust GPU infrastructure available for current and future Al workloads
Just enough GPIJ resources to cater to ongoing projects
Limited GPU resources for experimental purposes only
No, we don't have a dedicated GPU resources available currently

How efficiently can your organization allocate compute resources for Al tasks based on their demand?

Our systems are mostly automated and efficiently allocate resources based on Al demand
We have some automated resource allocation processes, but manual intervention is often required
Resource allocation for Al tasks is done manually and might not be optimal
We do not have a structured approach to resource allocation for Al

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

Optimal: Minimal issues and tailored for the most demanding Al 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 Al workloads
Not optimal: We experience frequent issues and bottlenecks

As your Al 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 Al projects instantly
Highly scalable: Designed with significant Al growth in mind
Somewhat scalable: Potential bottlenecks for very large Al projects
Not scalable: significant upgrades are required for large Al projects

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

High-level integration ensuring efficient data flow for most Al tasks, ensuring seamless operations across all Al 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 Al systems operate mostly in silos

How would you assess your organization's awareness and understanding of cybersecurity threats specific to Al 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 Al workloads

How does your organization ensure the protection of data utilized in Al 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

How equipped is your organization to detect and prevent unauthorized tampering or adversarial attacks on your Al 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 risk and have basic sorted but lack robust measures
Not equipped: have not considered the cybersecurity aspect of Al workloads

How does your organization manage access control to Al 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

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

Highly prepared: we have dedicated infrastructure in place to optimize power consumption in Al deployment
Somewhat prepared: some measures in place to address power consumption concems in Al deployment
Not prepared: no specific measures or considerations for power consumption in Al deployment
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Data

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

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

Fully centralized: Data is consistently managed and readily accessible organization-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

To what extent is your in-house data pre-processed, cleaned, and ready for Al projects?

Consistently pre-processed: our data strategy ensures data is always Al-ready
Mostly pre-processed: most of our data is primed for Al use
Occasionally pre-processed: some datasets are Al-ready, but many require additional work
Rarely pre-processed: significant time is needed to clean and organize data for Al

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

Facilitative: procedures actively promote efficient data access for Al
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

How well-integrated are your analytics tools with the data sources and Al platforms used within your organization?

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

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

Excellent: Majority of our tools are Al-optimized and cater to advanced tasks
Good: a balance of general-purpose and Al-specific analytics tools
Fair: some tools are Al-enhanced, but there's significant reliance on general tools
Basic: tools are more general-purpose and don't cater specifically to Al

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

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

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

Proficient: staff are adept at leveraging tool capabilities to their fullest
Moderate: most staff can handle regular Al analytics tasks efficiently
Intermediate: staff can use tools but often need guidance for advanced functions related to Al
Beginner: significant training is required

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

Most of our Al 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 Al projects
We have basic tracking but lack comprehensive lineage details
We do not actively track data origins

How does your organization ensure and verify the accuracy of the data being used in Al 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 intemal verification

Governance

Governance in Al 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 Al practices.

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

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

Does your organization have mechanisms to actively detect biases and lack of faimess in data used for Al?

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

How does your organization 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

How transparent are the algorithms used in your Al 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

Does your organization have mechanisms to detect biases and ensure fairness in Al 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

What is the level of understanding across your organization about global data privacy standards (like GDPR, CCPA, etc.) and ensuring adherence to these in Al 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

How does your organization handle data anonymization to protect user privacy in Al datasets?

Consistent anonymization techniques across all datasets
Advanced anonymization techniques for most Al datasets
Basic anonymization techniques applied inconsistently
No anonymization: data is used as is

In case of a data breach or privacy violation, how prepared is your organization 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

How well-versed is your organization 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

How does your organization 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

How comprehensive are the Al policies and protocols in your organization overall?

Highly comprehensive policies
Moderately comprehensive
Limited
We do not have any

Talent

Talent in Al readiness hinges on the depth and proficiency of in-house expertise, complemented by ongoing training investments. Inclusivity remains key, with policies ensuring Al 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 Al deployment?

Very well resourced
Moderately well resourced
Moderately under resourced
Significantly under resourced
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How would you describe the proficiency level of your staff in adopting and fully leveraging the Al technologies that you are deploying? )

Proficient: staff are adept at leveraging tool capabilities to their fullest
Moderate: most staff can handle regular Al related tasks efficiently
Intermediate: staff can use tools but often need guidance for advanced functions
Beginner: significant training is required

Has your company invested in training programs to upskill existing employees in Al-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
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When it comes to talent management, has your company started to think about 'accessibility' of Al technologies for employees who are differently abled?

Yes. it is a core part of our Al 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 Al tools so we can't control this aspect
No. this is not a consideration at this time