AI Adoption Should Start with a Business Problem, Not Fear of Missing Out

Over the past year, organizations have raced to adopt AI. In many cases, the conversation begins with a simple concern: "What are our competitors doing?" While understandable, I believe this is the wrong starting point.

Organizations should not implement AI simply to say they are using AI. They should implement it because it solves a meaningful business problem.

The most successful AI initiatives I've seen focus on improving efficiency, enhancing effectiveness, creating a better customer experience, or generating new revenue opportunities. AI should help employees make better decisions, eliminate repetitive work, accelerate service delivery, or unlock insights that were previously difficult to obtain. If the business outcome isn't clear, the technology itself is unlikely to deliver meaningful value.

At the same time, organizations must recognize that AI is only as good as the data and access behind it. As companies connect AI tools to enterprise systems, many are discovering long-standing data governance and access management challenges. Understanding where sensitive data resides is important. Understanding who can access that data, why they have access, and whether those permissions remain appropriate is equally critical.

AI often exposes issues that already existed. Overly broad access, inconsistent data classification, and weak governance become significantly more impactful when AI can rapidly search, summarize, and surface information across an organization.

As security leaders, our responsibility is not to slow innovation. It is to help ensure that innovation is purposeful, secure, and aligned to business objectives. The organizations that will realize the greatest value from AI will not be those that adopted it first. They will be the ones that implemented it thoughtfully, with a clear business purpose, strong governance, and a solid understanding of the data and access that power it.