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?
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?
1 out of 5
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?
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?
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?
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?
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?
2 out of 5
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.
Have you successfully deployed AI solutions at your company?
3 out of 5
Do you feel that your company has a solid AI infrastructure to build, deploy, and monitor AI models as initiatives arise?
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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?
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.
4 out of 5
Does your organization have a good number of skilled implementation-level AI personnel? These can be data engineers, data scientists, and machine learning engineers.
Does your organization have an allocated budget for training innovation managers, executives and technical employees?
Does your organization have a budget carved out for investing in AI and data infrastructure?
5 out of 5
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.