The AI Readiness Assessment by OA

Welcome to the AI Readiness Assessment!

This assessment is best completed as a team or by a leader familiar with the technology stack within the organization. The aim of this assessment is not to label your organization in terms of maturity but to give you an idea of where your organization stands when it comes to AI readiness. Every organization is different, so don't use this tool and feel left behind, but instead use it to help spot areas on which to focus.

Fill in your contact information and press the next button to proceed. Please note that by taking this quiz, you agree to have your responses analyzed (automatically and by experts).

Name

Are you aware of the available data assets in your organization?

Data assets are the stored data sources that you can leverage for AI and analytics. Is there documentation of these data assets? Are you able to find the data assets when you need them?

Yes

Some

Is your company aggressive about data collection?

Does your company collect data from the daily running of the business? How about customer interactions? Think about the data that’s generated from the day-to-day running of your business. For example, sensor data, support tickets, medical records, purchase orders. Is this data collected and stored?

Yes

Some

Are you able to access your organization’s data stores as and when needed for decision making, reporting, or analytics?

For example, if you're a CEO, can you quickly lookup pertinent information across company locations and branches?

Yes

Some

1 out of 5

Do teams within your organization work cross-functionally or in silos?

Integrating AI into your products and business processes is a team sport. You need business leaders, data scientists, data engineers, software engineers, and support staff. Do your teams have experience working cross-functionally?

Yes, we're quite used to cross-functional team collaboration

We do some cross-functional team collaboration, but not a whole lot.

We rarely collaborate with other teams.

Are teams within your organization comfortable with projects with uncertain outcomes?

Experimentation and iteration are vital parts of AI. And AI projects could potentially fail. Is your organization used to that level of uncertainty?

Yes, we've worked on many projects with unpredictable outcomes.

We've had experience with a few projects with some uncertainty.

We don't attempt projects unless we're sure it's going to work.

Do most employees understand AI and how it can help the organization at a high-level ?

Most employees in the company—technical or not—should have a base understanding of AI. What is this thing? How does it work? What are we as a company planning to do with AI? How does it impact job security?

Yes, most of us understand AI at a very high level.

Some employees understand what AI is, but most don't.

Most employees know nothing about AI.

Are most business decisions in your organization anchored on data?

Decisions around AI initiatives require the ability to make data-driven decisions. For example, if you’re using AI as an alternative solution to an existing problem, is this driven by data (perhaps customer feedback)? Or is it based on gut feel?

Most of our business decisions are data-informed.

Some business decisions are anchored on data, but many are based on gut feel.

We don't usually use data for decision making.

Do you have an ethics and accountability committee or legal counsel to advise you on the use of data, AI, and technology in general?

As regulations are limited around data and AI, the onus falls on companies to use AI and data responsibly.

Yes, we have a rock star team for this.

We get some legal and ethics advise, but we don't have a team we can go to for every project.

We don't have any such thing set up.

2 out of 5

Have you successfully deployed AI solutions at your company?

Yes, several.

No, we're still experimenting.

We've not reached the experimentation stages.

10.

Do you feel that your company has a solid AI infrastructure to build, deploy, and monitor AI models as initiatives arise?

Add description here!

Yes we do.

We are still exploring options.

No, we are yet to look into this.

3 out of 5

11.

If you are an executive, do you feel that you and your peers understand what AI is and are savvy enough to spot AI opportunities and assess the relevance and success of AI? If you’re not an executive, do you think your organization’s executives are AI-savvy?

Yes, our executives have a pretty good understanding of AI and see the potential in it.

Some of our executives are AI-savvy. Some aren't.

None of our executives have a good understanding of AI.

12.

Are the key innovation managers in your organization, such as product managers, and engineering managers, AI-savvy?

Innovation managers don’t have to know implementation-level AI. However, they should know how to manage AI initiatives and spot AI opportunities as they arise.

Yes, most are skilled in AI.

Some are AI-savvy.

Most of our innovation managers lack AI understanding.

13.

Does your organization have a good number of skilled implementation-level AI personnel? These can be data engineers, data scientists, and machine learning engineers.

Yes, we have a decent number to get us through implementation.

We have a few people to help, but nothing solid.

We don't have anyone to help us with implementation as yet.

4 out of 5

14.

Does your organization have an allocated budget for training innovation managers, executives and technical employees?

Yes, we have a sizable budget for this.

They're already trained, so this doesn't apply.

We have some budget. We need to prioritize who gets trained.

We don't have any training budget.

15.

Does your organization have a budget carved out for investing in AI and data infrastructure?

Yes, we have a sizable budget.

We already have the infrastructure, so this is not applicable.

We have a small budget for AI infrastructure investment.

We cannot afford to spend on AI infrastructure at this moment.

16.

Does your organization have an allocated budget for hiring new personnel for the integration of AI?

For example, data scientists, machine learning engineers, data engineers, or consultants.

Yes, we have a generous hiring budget.

We've already hired these employees, so this is not applicable.

We have a tight budget. We can hire some key personnel only.

We don't have a budget for new hires at this moment.

5 out of 5

Time is Up!

Time's up