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. We’ll return to this important topic a bit later. 
• 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. You must also identify how the effectiveness of AI will be measured in order to showcase the business value of different tools.
• 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. These specialists can help you decide where to make AI investments and can evaluate whether tools are working. You might need to hire new talent, but upskilling your current workforce can also be a great option.
• Governance, compliance, and risk: Working with AI requires handling a lot of data and that data may include sensitive information. With data moving across multiple systems, you’ll need to ensure that your tools and processes prioritize data privacy and meet compliance requirements. This includes establishing strong cybersecurity measures that can protect against data breaches.

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. This stage focuses on identifying where businesses can see the most positive change from AI and ensuring that everyone in your organization is on board with that change.

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? If so, it may be easier to secure resources and budgets for the types of technology that drive business growth.
• 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. Ultimately, the success of AI is dependent on frontline workers embracing it as a tool to automate mundane tasks, gain better insights, improve decision-making, and enhance customer experiences.

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. Entering poor data into an AI tool is not going to provide you with valuable insights. As the saying goes, “garbage in, garbage out.”

However, when you train AI models on good data, they can better understand the relationships within your data sets, leading them to make reliable predictions. And reliable predictions ultimately provide the information you need to make better decisions for your organization, all grounded in quality data.

So, what does “good data” actually entail?

• Accuracy and consistency: It contains no errors, typos, or irrelevant entries and has consistent formats across similar data types like dates.
• 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. Has consistent data types within columns.
• Management of outliers: Identified outliers have been addressed either by removal, transformation, or flagging.

We know that preparing your data isn’t always a simple task, but we think it’s well worth the effort. To help make the process a bit easier, our data scientists have put together a checklist that you can use to avoid common pitfalls. Download our Readiness Checklist for essential tips on polishing up your data for any AI application, including our AI tools in Domo.

Making the most of Domo’s AI capabilities

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

Domo’s AI Chat and AI Readiness features have just become generally available to our consumption customers. AI Chat allows you to ask questions in natural language about your data and quickly generate answers. It provides step-by-step breakdowns of how it answers your questions and can create charts or visualizations that enable you to explore your data more deeply and extract new insights.

And if you’re not getting the responses you expect, AI Readiness can help. This new feature lets you add metadata and select fields that enhance the quality of generative AI by supplying context that boosts accuracy and ensures the use of relevant data, significantly improving the precision and value of AI-generated content. You can use our AI Readiness functionality and add an AI Dictionary to the datasets in question.

Finally, Domo recently switched our LLM to DomoGPT, a collection of Domo Cloud private models. The update keeps your data securely in our ecosystem and out of the hands of third parties.

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.