Aug 19, 2026
The first phase of enterprise AI adoption was largely about experimentation. You gave teams access to tools, identified use cases, and measured where productivity improved. But as AI became embedded in day-to-day operations, the question shifted from "Who is using AI?" to "How are we using AI across the organization?"
The Problem With Pockets of AI Adoption
There are several ways organizations can approach AI successfully. Some companies are making sweeping changes, choosing their preferred tools and establishing an expectation that there is no going back, while others are taking a more structured approach in asking each function to identify AI initiatives and report on their progress. Though the models are different, they have something important in common: everyone is participating.
The more concerning model is one where AI adoption becomes concentrated. For example, the CEO tells the CTO to own AI strategy. Maybe engineering and marketing race ahead while finance, customer success, or other functions remain largely untouched. Or perhaps a handful of enthusiastic employees become known internally as the "AI people." This lack of structure can lead to a major alignment gap.
As a result, teams begin operating at different speeds, and AI experimentation may become underutilized as something to showcase rather than something connected to shared business priorities. Eventually, the organization has plenty of AI activity without necessarily having an AI operating model.
Start With the Business Outcome
The first step is requiring every function of your business to connect AI to an actual business outcome. For one department, that may mean reducing repetitive work and improving efficiency. For another, it may mean accelerating the product roadmap. The specific application matters less than the organizational alignment behind it.
There is no single AI adoption curve that works for every growth-stage company. But there is an increasingly important distinction between organizations where AI adoption is broadly connected to business priorities and those where experimentation remains fragmented.
Download our 2026 Growth Index for steps to create healthy urgency within your organization, while maintaining the alignment, efficiency and sustainability required to turn your activity into business performance.
Frequently Asked Questions
How can companies use AI to improve profitability?
AI can improve profitability by increasing efficiency, reducing expenses, and helping teams execute faster, but it cannot compensate for a fundamentally weak business model. Companies still need sustainable growth, expanding margins, and strong customer economics for AI-driven efficiencies to translate into long-term value.
What is the best approach to AI adoption for growth-stage companies?
There isn't one AI adoption model that works for every growth-stage company, but adoption should extend across the organization rather than remain isolated within technology or a few departments. Each function should identify how AI can contribute to outcomes such as greater efficiency, lower costs, faster innovation, or improved execution.
Who should own AI strategy within a growth-stage company?
AI should not be treated exclusively as a CEO, CTO, or technology team initiative. Organizations can take different approaches to adoption, but every function should participate and connect its AI initiatives to business outcomes such as efficiency, profitability, or innovation.
