AI Readiness: Complete Guide and Free Checklist to Assess Your Business

Artificial intelligence is reshaping the way businesses function. In 2024, McKinsey conducted a global survey that found the adoption of AI is accelerating rapidly. Nearly three-quarters of respondents said that they had adopted AI in at least one business function, while half had adopted AI in two or more business functions.

And the spike (sharp increase) in generative AI use was even more pronounced. In just one year, the percentage of respondents who reported regularly using generative AI to perform a business function doubled to 65 percent. From marketing and sales to product development, businesses are relying on AI to streamline processes, improve workflows, and inspire innovation across the organization.

Still, adopting AI comes with distinct challenges. While organizational leaders are eager to implement new tools, workers are a bit more hesitant. A separate survey released this year on digital work trends found that more than half of employees feel they haven’t been adequately trained to work with AI. And nearly half of employers admit they haven’t adopted AI because their company’s data is not ready.

AI readiness doesn’t happen overnight. Making the most of AI requires a thoughtful and strategic plan for implementation. But with the right investments in tools, training, and culture, organizations can harness the vast capabilities that these new technologies offer. Let’s explore what you need to consider to better prepare your organization to embrace AI.

What is AI readiness?

AI readiness refers to the degree to which a company is prepared to effectively implement and benefit from artificial intelligence. To become AI-ready, your organization needs the technical infrastructure with the necessary computing resources in place to support AI initiatives. You also need high quality, accessible data.

However, readiness isn’t limited to the technical arenas. To embrace the changes that come with the implementation of AI tools, you will need to consider what scaling AI means for your workforce. Do your people have the relevant skills or expertise to use AI well? What knowledge gaps need to be filled? And how open is your workforce to the transformational impact that AI can bring?

Why is AI readiness important?

AI is not a fad; it is the future. And leaning into AI can make your people more efficient and productive. Instead of wasting resources on repetitive, time-consuming tasks, AI frees up your employees to focus on strategic, creative thinking. Automating different processes can help streamline operations across departments, such as automating reporting or accelerating customer service response times. As a result, your teams can move through their tasks faster without sacrificing the quality of their work.

Prioritizing AI readiness can also set your team up to become leaders in innovation. AI tools provide teams with data-driven insights that can inspire new ideas and solutions. With advanced analytics and machine learning, companies can uncover data trends, predict market changes, and personalize products or services based on customer preferences. This ability to anticipate needs and rapidly adapt fosters a culture of continuous improvement and experimentation.

Of course, companies want to know how these tools will actually affect their bottom line. On that front, there’s good news. In a 2023 McKinsey survey, nearly 40 percent of companies that adopted AI saw their costs cut, while 60 percent experienced increased revenue.

How to become AI-ready

There are many schools of thought on AI readiness that emphasize different factors. Becoming AI-ready will really depend on the needs and goals of your company. One way to think through the process is to use the Intel model, which breaks down AI readiness into three phases: foundational readiness, operational readiness, and transformational readiness. Let’s explore how the phases differ.

1. Foundational readiness

If you want to become AI-ready, the first step in the Intel model is achieving foundational readiness. Many organizations at this stage may be new to AI and are only starting to research or experiment with what’s possible. Therefore, foundational readiness focuses on ensuring that organizations have the appropriate infrastructure and interfaces in place to support implementation.

Becoming foundationally ready requires a review of some of the following factors:

• Infrastructure platforms: Are your data center facilities capable of handling the storage and processing required to support AI and machine learning? The level of data processing will be needed to run AI, particularly as you move out of the experimental phase, can overburden your network if you don’t have the appropriate infrastructure in place.
• Data sources: Are your data sources both available and accessible? AI and machine learning can require large quantities of data to perform their assigned functions, and that data will need to be both complete and clean.
• Software packages: There are many different software packages to choose from including, open-source packages, commercial solutions, and cloud-based solutions. You will need to consider how software integrates with the current tools you use for data collection and management and whether there are opportunities for customization based on your specific needs.

2. Operational readiness

Once your organization has identified AI solutions that meet its needs, you’ll need to make sure that those solutions also work at scale. To achieve operational readiness, you therefore must establish effective management and governance mechanisms.

In this stage of AI adoption, Intel says that organizations should focus on a new set of factors, which include:

• Operational management: Your leaders should establish clear guidelines for how to effectively manage different data sources.
• Skills and expertise: Once you start to rely on AI for important business functions, it can be beneficial to have people on staff who are experts on the technology.
• Governance, compliance, and risk: Working with AI requires handling a lot of data and that data may include sensitive information.

3. Transformational readiness

You’ve found the tools you need, and you’ve figured out how to incorporate them into your business systems. Now, you need to extract as much value as possible from AI.

To achieve transformational readiness, Intel suggests reviewing a third set of considerations:

• Strategic leadership: Are the leaders at the top invested in leveraging AI to develop a strategic advantage over competitors?
• Clarity of business case: At this point in an organization’s journey, those championing AI have to move beyond merely identifying the benefits and begin to quantify the ROI of using different tools and platforms.
• Business acceptance: Implementing AI solutions will come with changes in how your staff perform their jobs.

How to make sure your data is AI-ready

While the model above focuses on making sure your entire business is AI-ready, there are also specific considerations you will need to make when preparing your data for use by AI platforms, tools, and systems.

Data is ultimately the foundation of any AI system, so having a strong data strategy is absolutely necessary for companies that want to realize the full potential of AI.

So, what does “good data” actually entail?

• Accuracy and consistency: It contains no errors, typos, or irrelevant entries.
• Completeness: It has minimal or no missing values and includes only essential columns.
• Proper labels and categories: It includes clear, descriptive column headers that specify what each data point represents.
• Structure and organization: Data is arranged in a tabular format if applicable, with rows as observations and columns as features.
• Management of outliers: Identified outliers have been addressed.

We know that preparing your data isn’t always a simple task, but we think it’s well worth the effort.

Making the most of Domo’s AI capabilities

Domo has powerful AI tools that can help you generate value for your company.

At Domo, we will continue to innovate to ensure that prepping your data for AI is clear, simple, and transparent. Be on the lookout for the launch of exciting new features this year.