What AI teams need to measure beyond task success
AI products are changing the relationship between people and technology. Traditional digital products have largely been evaluated around how effectively people can use them. Conversational and agentic AI can interpret intent, offer guidance, make recommendations, and increasingly take action on a user’s behalf. That expands what teams need to evaluate.
Usability and technical performance remain essential. Teams also need to understand how people experience the AI itself: whether they understand it, trust it appropriately, feel in control, achieve the outcome they intended, and want to use it again.
AI changes how people relate to technology
Conversational and agentic AI create a different kind of interaction with technology. People are increasingly engaging with systems that appear to understand what they mean, respond in natural language, make recommendations, and sometimes take action on their behalf.
That changes how people judge the experience.
Consider a customer using an AI support agent. Traditional AI evals can measure whether the system gives an accurate response, follows policy, completes the task, or behaves safely. Those measures are essential. The customer is also forming judgments about the experience that traditional AI evals do not fully capture:
- Did the AI understand what I meant?
- Do I trust its recommendation?
- Did I feel in control?
- Did I get the outcome I needed?
- Would I want to use this experience again?
These judgments shape whether an AI experience feels effective to the people using it and whether they choose to rely on it again.
As conversational and agentic AI take on a larger role in customer experiences, AI quality needs to account for both system performance and the human side of the interaction.
Agentic AI raises the stakes
Agentic AI increases the level of responsibility people can delegate to technology. An agent may move beyond providing information to making recommendations, coordinating steps, or taking actions on a user’s behalf.
As that level of autonomy increases, people need to understand what the AI is doing, know when to trust it, feel able to guide or correct it, and remain confident that its actions reflect their intent.
That makes the human side of AI quality increasingly important to how these experiences are designed and evaluated.
What AI teams need to measure
Five elements help shape how people relate to an AI experience:
- Understanding. Do people feel they understand what the AI is doing, what it intends to do, and where its boundaries are?
- Trust. Do people place an appropriate level of trust in the AI, relying on it when warranted and questioning it when needed?
- Control. Can people steer the AI, correct it, and recover when something goes wrong? This becomes especially important as agents take more autonomous action.
- Outcome. Does the AI help people achieve the result they intended, and does that result feel relevant and useful?
- Affinity. How do people respond to the AI’s personality, tone, and way of communicating? Do those qualities make them want to continue interacting with it?
Together, these elements help explain whether people will feel confident relying on an AI experience, continue using it, and ultimately find it valuable.
As conversational and agentic AI become more capable, these elements are becoming part of what it means to evaluate AI quality. Technical evals remain essential for understanding system performance. Teams also need ways to measure how people experience and relate to the AI itself.
Together, these measures can give teams a more complete view of whether an AI experience is working as intended for the people using it.
