Five Principles For Turning AI Agent Adoption Into Measurable Impact

Dr. Chih-Han Yu is the CEO and co-founder of Appier, an AI-native agentic-AI-as-a-service (AaaS) company.

​AI agents are moving from pilots into everyday business operations. According to McKinsey’s The State of AI in 2026 survey, 40% of respondents at organizations with more than $1 billion in annual revenue said they were scaling AI agents in at least one business function, up from 27% a year earlier. Yet among all respondents, the share reporting that AI contributed positively to their organization’s EBIT held at 37%, essentially unchanged from 2025.

Adoption is rising, but enterprise-level impact has yet to follow at the same pace.

In my experience, this gap is often less about the technology itself. Today’s models are highly capable, and many enterprises have already done the hard work of getting agents into their workflows. What often lags is how those agents are operated once they’re in place: how strategic goals are set, how decisions are checked, how tasks are assigned and how systems improve over time.

Scaling agentic AI, in other words, depends as much on how it’s operated as on how widely it’s adopted. Here are five principles I believe enterprises should build around.

1. Strategy: Set Clear Business Goals And Metrics

Every AI deployment should begin with a clear answer to one question: What is this meant to achieve? Revenue growth, customer conversion, cost reduction and productivity improvement each call for different metrics and different timelines.

Without that clarity, teams tend to measure what’s easiest to see, such as activity, usage or the number of agents deployed. Those numbers can rise steadily while business results stay flat. It’s far more useful to agree on the outcome and the evaluation criteria before deployment, so AI is judged on what it delivers rather than how busy it looks.

This also makes later decisions easier. When the goal is clearly defined, it’s much simpler to tell whether an agent should be expanded, refined or retired.