Of course, leaders in every industry are most concerned about the potential held by AI. According to Goldman Sachs researchers, AI investments are expected to reach $100 billion in the U.S. alone by 2025, yet many leaders are getting caught up in AI without fully understanding its potential. AI is not the first large-scale technological disruption to drive organizational change, and there will be other technological disruptions in the future. What leaders must do is ask themselves where AI fits within their workforce, operations and broader digital transformation strategy.

Here are five questions to help you determine how you should implement your AI strategy.

1. Why should we use AI?

Many leaders are consumed by the idea of leveraging AI to grow their business, but fail to think about why their business needs it. As with any discussion around a new digital or technology initiative, leaders must start with why. Do you want to automate your process? Are you looking to accelerate product development? Are you trying to generate better insights? If a leader cannot clearly articulate the reasons behind an AI initiative, there may be a misalignment between the rational and true motivations behind it.

2. What will we do with AI?

Once you understand why, you must consider what it is that your business hopes to improve or grow. Are you looking to reduce time-consuming processes by automating repeatable actions? Are your developers trying to better identify bugs in your code base? Need to identify patterns in a data set? Does your business want to accelerate the product or process development life cycle? All AI initiatives are inherently part of a process. AI does not constitute a standalone function, nor should it be viewed as a specialized expense.

3. How will we implement AI?

Once you understand the why and the what, you can then consider how your business can use insights from AI to better achieve its goals. How will your employees respond, and how will they benefit? Enterprises today have multiple technology partners, and many of them may say they can do AI. But how will your business work with all these partners to bring AI solutions together? Many businesses are developing AI policies to define how AI will be used. With these guardrails in place, ensure your business is ethical, ethical, and legal when using AI.

4. Do we have the right data?

This is the most important question leaders fail to ask themselves. We continue to see that despite large data management initiatives, many organizations are still facing the challenge of data disconnect. AI is only as good as the data you have. Inaccurate data will lead to AI informing poor decisions, which remains the biggest concern in the market, whether it is open or closed AI. Incomplete data or data that includes patterns of behavior that historically occurred based on bad decisions will cause the AI to learn these behaviors and provide inaccurate insights.

5. Is our enterprise ready to operate with AI?

People, process, and technology are equally important pillars in the context of any digital implementation, but businesses often overlook the people and process aspects. Companies that overemphasize technology efficiency and functionality may not consider the impact on end users or core operational functions. Before deciding to implement AI at scale, it’s important to consider whether your business is truly ready for AI at the enterprise or departmental level. Pilot projects can help you determine whether the implementation is producing the desired results and better understand how end users will interact with the process. If you can't enable customization and personalization across the enterprise, AI initiatives will be harder to implement.

The world of AI is vast, and we are still developing our full understanding of the potential that AI holds at the enterprise level. However, it is clear that the purposeful use of AI to create better insights from the data a company has can have a profound impact on its business. The journey simply starts by taking a step back and asking the right questions at the beginning.