The 3 Stages of AI Adoption: Where Does Your Organization Stand?

Everyone is “using AI” right now, but that statement means very different things depending on the organization.

For some, it is a handful of employees experimenting with ChatGPT. For others, it is embedded into daily workflows. And for a growing number, AI is beginning to take action on their behalf.

The challenge is that many organizations do not have a clear understanding of where they actually stand.

That lack of clarity creates problems quickly. Organizations risk applying the wrong controls too early, underestimating exposure at later stages, or investing in tools that do not align with how AI is actually being used.

Ryan Spelman, Managing Director of Cyber Risk at K logix explains: “The organizations that succeed will not necessarily be the fastest. They will be the ones that understand where they are, align their strategy accordingly, and scale with intention.”

Most companies fall into one of three stages of AI adoption: Getting Started, AI as a Ride-Along, and Agentic AI.

Stage 1: Getting Started

At this stage, organizations are exploring AI but have not fully committed.

You might see small pilot programs, informal experimentation, or internal discussions about potential use cases. Employees may be trying tools on their own, but there is little structure or governance in place. Ryan points out: “Most of our customers are still getting started.”

Even without formal adoption, AI usage is often already happening throughout the organization. Employees are testing tools on their own, sharing information with public models, and creating workflows outside traditional oversight.

This creates a growing “shadow AI” problem where organizations lack visibility into how AI is being used and what data may be exposed.

The opportunity here is to build strong foundational governance:

• Establish clear policies early
• Educate employees on appropriate use
• Identify high-value, low-risk use cases

Organizations that do this well can scale faster and more safely when they are ready.

Stage 2: AI as a Ride-Along

This is where most organizations are today.

AI is deployed more broadly, but primarily as an assistive tool. Employees use it to generate outputs based on inputs such as summarizing documents, drafting emails, analyzing data, or conducting research. Ryan summarizes:“More people are starting to get into that AI as a ride-along zone, where they ask questions, get answers, and it’s helping employees do their job.”

In other words, AI is helping people work faster, but it is not acting independently.

At this stage, the biggest risks shift from visibility to control. Employees are actively feeding information into AI systems and receiving outputs that may or may not be accurate, secure, or appropriate.

Key risks include:

• Sensitive data exposure through prompts
• Lack of oversight into what is being shared or generated
• Over-reliance on AI outputs without validation

• Monitoring prompts and responses
• Strengthening data governance
• Ensuring visibility into usage patterns.

Organizations that treat AI purely as a productivity boost without addressing these controls often underestimate the risk they are introducing.

Stage 3: Agentic AI

This is where AI begins to move from assistant to operator.

Instead of simply generating outputs, AI systems can now take action across workflows, applications, and business systems with limited human involvement.

While adoption is still early, momentum is increasing rapidly. Ryan notes: “Not as many are in that AI agent space, but we’re starting to see more of it.”

This stage introduces a fundamentally different level of risk. AI systems are no longer just responding but acting, and often with the same access and permissions as the users who created them.

Key risks include:

• Over-permissioned AI agents
• Lack of visibility into agent behavior
• Misaligned or unintended actions
• Workflow disruptions or business impact

Security priorities shift to:

• Identity and access control for AI agents
• Monitoring and auditing agent activity
• Establishing governance around automation
• Defining where humans remain in the loop

Organizations that underestimate these risks can quickly face unintended consequences at scale.

The Biggest Mistake: Misaligned Strategy

One of the most common pitfalls in AI adoption is applying the wrong strategy to the wrong stage.

Overengineering controls too early can slow adoption. Underestimating risk at later stages can create serious exposure.

AI is not just a technology shift but an operational one. Each stage requires a different approach to governance, security, and investment. The key is understanding where you are today and aligning your strategy accordingly.