AI Readiness Assessment - Data Ideology

General Business Information

What industry are you in?

Technology

Healthcare

Finance

Retail

Manufacturing

Size of the Company (Number of Employees):

1-250

251-500

501-1000

1001-5000

5001+

Annual Revenue:

Less than $250M

$250M - $500M

$500M - $1B

Greater than $1B

Section 2: Current Technology Infrastructure

Do you have cloud computing capabilities?

Yes

Planning to implement

How is data stored today?

On-premises

Cloud-based

Hybrid

None

Are your systems scalable and flexible for future expansions?

Yes

Not sure

Section 3: Data Management and Quality

How do you currently collect, manage, and store data?

Manually

Automated

Combination of both

Rate the quality and cleanliness of your existing data (1-5 scale):

1 (Poor)

5 (Excellent)

How frequently is your data updated and refreshed?

Daily

Weekly

Monthly

Rarely

Never

How centralized is your organization's data, facilitating easy access for AI initiatives?

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

Moderately centralized: Majority of data is in a unified location, but some silos remain

Partially fragmented: Some centralization, but many department-specific silos exist

Highly fragmented: Data is scattered across different silos

To what extent is your data processed, cleansed, and ready for AI projects?

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

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

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

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

How effectively does your organization track the source and lineage of data used in your data models?

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

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

We have basic tracking but lack comprehensive lineage details

We do not actively track data sources and lineage

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

Section 4: Talent and Expertise

Do you have employees with expertise in AI or data science?

Yes

In the process of hiring

Is there a willingness to invest in training for existing staff?

Yes

Unsure

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

Section 5: AI Understanding and Expectations

How would you rate your organization’s understanding of AI and its capabilities? (1-5 scale):

1 (Poor)

5 (Excellent)

Have you identified specific business areas or processes for AI implementation?

Yes

In progress

Section 6: Strategy and Goals

Do you have a strategy to deploy AI powered solutions in your organization?

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

Is it clear who/what team is leading the AI strategy for your company or is it being managed in a more organic and decentralized manner?

There is clear leadership/ownership of our organization's AI strategy

More organic and decentralized

How urgently is your organization 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 organization 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 organization

Section 7: Challenges and Concerns

What are your main concerns regarding AI implementation? (Select all that apply)

Cost

Data privacy and security

Lack of expertise

Technological limitations

Other:

How prepared is your business to address these challenges?

Fully prepared

Somewhat prepared

Not prepared

Section 8: Regulatory Compliance and Ethics

Are you aware of the legal and ethical considerations surrounding AI?

Yes

Somewhat

How does your organization 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

Section 9: Investment and Budget

Is there a dedicated budget for AI initiatives?

Yes

Planning stage

How do you plan to fund AI projects?

Internal budget

External funding

Partnerships

Not decided yet

How is your company prioritizing 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

Section 10: Final Thoughts

Are there any specific AI technologies or applications you are particularly interested in?

Machine Learning

Natural Language Processing

Robotics

Computer Vision

Other:

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Find out if your organization is ready for AI.

General Business Information

Section 2: Current Technology Infrastructure

Section 3: Data Management and Quality

Section 4: Talent and Expertise

Section 5: AI Understanding and Expectations

Section 6: Strategy and Goals

Section 7: Challenges and Concerns

Section 8: Regulatory Compliance and Ethics

Section 9: Investment and Budget

Section 10: Final Thoughts