Measuring AI Readiness

AI readiness assessments measure an organization’s preparedness for implementing AI initiatives. In this blog, we examine common assessment areas, the different readiness levels, and how to interpret the results

Rapid advancements in AI precipitated a breakneck race for organizations to weave this technology into their company fabric, but are organizations prepared to meet the demands of AI initiatives?

AI readiness ensures the organization has the proper elements in place to enable AI across the business. A simple way to measure AI readiness is through an assessment: a series of questions across different areas of business that generates an AI readiness score.?

Why Assess AI Readiness

A cohesive implementation of AI is a monumental task, and as with most tasks, it is easier to accomplish when broken down into smaller parts. The AI readiness assessment allows for a detailed view into each area that contributes to implementing AI.?

Once you know the current state of AI readiness, an accurate roadmap can be created and executed. An AI readiness assessment can inform roadmap activities as it covers the most important aspects of preparing for AI rather than encountering issues during deployment. For example, if the assessment asks about the training plan for introducing new AI technology, that aspect can be incorporated into your roadmap if it is missing.?

Assessing AI readiness early into the initiative reduces potential roadblocks and barriers. With the large scope of implementing AI initiatives, roadblocks can grind all progress to a devastating halt. Anticipating potential requirements by assessing AI readiness can help address these roadblocks before they majorly impact innovation.?

Common Areas of Assessment

Many iterations of AI readiness assessments are available, but they all ultimately assess the organization’s AI strategy, workforce preparedness, data and governance maturity, and tech infrastructure capacity. Each area’s questions add additional nuance. Here is how OvalEdge’s AI Readiness Assessment is organized.

Strategy

• AI Use Case: When adopting AI technologies, organizations must keep in mind the goals and objectives that drive their AI ambitions and fully understand why AI is an important addition to their business. Generally speaking, what is the use case for introducing AI into the organization?

People

• Train Existing IT Workforce: A strong team is necessary to execute an AI roadmap, and the need for a data-literate workforce is higher than ever. Utilizing AI models requires a solid understanding of AI’s technical, ethical, and regulatory aspects, with retraining and upskilling opportunities.?
• Hire AI Specialists: Developing and implementing custom AI solutions takes experts. Hiring AI specialists might be necessary to create, implement, and maintain AI in the organization.
• Implement Change Management: Don’t forget change management! Prepare a company culture that encourages and fosters AI innovation and experimentation amongst employees. The business must be fully engaged and know how to interpret, analyze, and use data effectively.

Data

• Centralize Data: Centralizing data in a data catalog or within a data lake/warehouse ensures the most important data sources critical to the organization are identified and documented.?
• Define Roles and Responsibilities: Data governance roles and responsibilities play an important part in managing the organization’s data, as these individuals are the experts in their respective domains. Clear ownership of data assets paves the path to curated metadata.
• Curate Metadata: Curating metadata provides context, consistency, and understanding of data assets. Creating correct descriptions, definitions, and metrics provides a high-quality base for training AI models.
• Ensure High-Quality Data: Poor-quality data leads to poor-performing AI. A well-rounded data quality program that pays attention to the four aspects of data quality implementation increases the trust of AI models since people can trust the data.
• Classify Data: Establish ethical AI frameworks and ensure their integrity by classifying data. Additional benefits are improved searchability and reduced privacy risks.?

Infrastructure & Tech

• Find the Right Technology: You must have the right technology and platforms to support your AI ambitions. Consider the hardware, software, and scalability needed for company-wide AI initiatives.
• Buy/Rent Infrastructure: Creating tech infrastructure to accommodate the demands of AI can be costly and time-consuming. Buying or renting infrastructure opens opportunities for timely data delivery at scale.

Levels of AI Readiness

Most AI readiness assessments assign a score out of five, similar to measuring data governance maturity. OvalEdge’s AI Readiness Assessment uses five levels to score each area of readiness mentioned previously.?

Level 1: Unprepared?

AI and how to utilize it effectively is not addressed within the organization. Activities such as investing in tech infrastructure, preparing data via data governance, and training the existing workforce to adapt to AI initiatives aren’t considered when discussing the overall direction of the organization.

Level 2: Planning?

Discussions and plans about how AI can benefit the organization are forming. Creating an AI readiness roadmap can greatly assist in moving from the planning phase to the developing phase (although it is not required) to ensure activities for AI implementation are considered in conjunction with the proposed timeline.

Level 3: Developing?

The details for successfully implementing AI within the organization are being finalized, and implementation has begun. The organization is in the process of enacting the planned course of action for the AI use cases defined in strategy discussions.

Level 4: Implemented?

The planned course of action is implemented within the organization, but the AI initiatives are not fully embedded. This could be due to a slow shift in company culture, troubleshooting tech infrastructure challenges as they arise, or completing data governance implementation for high-quality data and other benefits.?

Level 5: Embedded?

AI initiatives are optimized and fully embedded in the organization’s culture. Company culture is considered data-driven, and the processes/procedures developed while implementing AI initiatives are being maintained and improved.

Interpreting Results

Once all the questions are answered, the individual scores for each area are presented. For the most accurate representation of the organization’s AI readiness, scores should have been rounded down. Rounding up hides potential areas of improvement and impacts the priorities for improving readiness. So, if the average of answers in the strategy area is 2.4, or even 2.9, the strategy score is 2.??

To understand the overall readiness of the organization to implement AI, average the individual scores. If one area severely lags behind the others, it’d be a great place to focus first for quick progression.?

Next Steps

After receiving the assessment results, it’s time to make a plan! Review questions where the organization scored lower and use this information to inform the AI roadmap. Sections of the roadmap could be as simple as displaying the section titles of the assessment but can be as detailed as necessary.?

There are many dependent overlaps across the assessment categories. Trying to progress AI readiness evenly by focusing on lower-scoring areas can save time and energy. Strategy might be a high-scoring area, but it can only progress to a certain point if the data’s readiness score is significantly lower.?

Overall, an AI readiness assessment provides a wealth of information useful for planning and deploying AI. A defined strategy, high-quality and well-governed data, a trained workforce, and the right technology create a solid foundation for successful implementation.

Click here to take the OvalEdge AI Readiness Assessment to see if your organization is ready.?