The conversation around AI has shifted quickly from experimentation to adoption. Leaders spent the last year encouraging their teams to use it, finding new applications, and pushing AI deeper into everyday workflows. But is any of it making the business better?
Our 2026 Growth Index surfaced an interesting disconnect. Roughly 90% of leaders reported that their teams were using AI effectively. Yet the companies reporting the strongest AI adoption weren't necessarily producing stronger financial results, which tells us that adoption isn't the finish line.
The mistake is treating AI as something that will inherently make an organization more productive. It won't.
AI accelerates the organization you already have. Give it to a company with clear priorities, strong cross-functional alignment, and disciplined execution, and it can amplify those strengths. Teams can move faster, surface insights sooner, and eliminate work that previously consumed valuable time.
Give it to an organization operating in silos, and those silos can move faster, too. Marketing can automate more activity. Sales can deploy its own tools. Product can accelerate development. Engineering can generate more code. Everyone can point to increased AI usage without necessarily producing a better outcome for the business.
That's the difference between using AI and creating value with it.
If AI is considered an investment, leaders should manage it like one. That means establishing what success looks like before celebrating adoption. In engineering, for example, that could mean comparing development throughput before and after implementing AI while also monitoring whether additional quality-control work offsets those productivity gains. The measurement will differ by function and company. The principle shouldn't.
What changed because of AI? Did it happen faster? Did quality improve? Did costs decline? Did revenue increase? If leaders can't answer those questions, AI usage itself isn't much of a KPI.
The strongest AI adopters we've observed are coordinating how AI is used across the organization.
Consider go-to-market. AI can make prospecting more efficient, but the greater opportunity comes when insights from marketing and sales conversations inform product priorities—and those priorities subsequently inform development.
The value compounds when information moves across the organization rather than staying trapped within individual functions. That requires executive leadership to treat AI as an organizational capability, not a collection of departmental experiments.
AI accelerates everything, including company culture.
In an organization already operating with constant priority changes, poor alignment, or unsustainable urgency, introducing technology that enables everyone to move even faster can make those problems worse. Employees worried about being replaced may feel pressure to demonstrate that they're using AI. Leaders may reward visible experimentation without understanding whether it produces meaningful results. Speed without alignment isn't healthy urgency. It's simply more activity.
The companies best positioned to create value from AI will therefore need to manage two things at once: the technology and the environment in which people are being asked to use it. The question for leaders isn't simply, 'How much AI are we using?' It's whether AI is accelerating an organization worth accelerating.
Want to learn more? Tune in to Episode 81 of the Electrifying Growth podcast.
Start with the business outcome AI is intended to improve and establish a pre-AI baseline. Depending on the function, that could include throughput, time saved, quality, costs, pipeline, or revenue. The specific metric matters less than establishing a measurable return rather than treating adoption itself as success.
Systemic adoption occurs when AI is coordinated across functions and the executive leadership team rather than deployed independently within departmental silos. Insights and improvements can then flow between areas like marketing, sales, product and development.
AI can amplify existing organizational conditions. In companies without healthy urgency and alignment, Chris argues that greater AI adoption can increase anxiety, burnout and employee churn rather than resolving underlying problems.