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AI should be judged not only by what a model can do, but by what its use does to people. That is the design argument made by Dr. Arthur C. Evans Jr., CEO of the American Psychological Association, at a side session during Salesforce Dreamforce in San Francisco: treat technology as a means to human flourishing, not as an end measured only by capability or speed.
What does it mean to flourish?
Evans’s answer is broader than feeling good. Flourishing includes functioning well and being connected to other people. Applied to AI, that means looking beyond whether a system performs a task and asking how it affects the people who use it and the human systems around them.
That “human layer” includes confidence, critical thinking, judgment, decision-making, trust, relationships, autonomy, agency, and the full breadth of people’s work. A faster process may still be a poor outcome if it weakens a person’s ability to judge, narrows their role, or damages trust.
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As Evans put it, “We can be intentional about designing into the technology things that actually could help our abilities and our skills.” The question is not whether AI can be designed to support people, but whether developers and organizations deliberately make that a measure of success.
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Why technical success can still fail people
A system can function as specified and still frustrate the person trying to use it. Evans uses the familiar push-versus-pull door as an example: the hardware works, but the experience communicates the wrong action. The broader lesson is to observe how people actually encounter and use a system, rather than assuming that technical correctness guarantees a good human experience.
That distinction matters in AI because performance measures such as speed or task completion do not, by themselves, show what happens to judgment, skill, autonomy, or relationships. A design review should examine those effects in real use, not infer them from model capability.
Questions to ask at each stage of AI adoption
For developers: What should be measured?
Technical evaluation needs a human-impact counterpart. Ask whether the system supports or undermines critical thinking, judgment, confidence, and decision-making. Evans’s own question is direct: “Is it helping us in terms of our critical thinking skills?” A favorable answer requires evidence about how people use the system, not simply a benchmark result.
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For deployers and leaders: What changes in the human system?
Before introducing AI, ask: “What is the impact on the human systems?” and “What is the impact on the humans?” Consider autonomy, agency, relationships, and the way responsibilities shift across a team. Map the full job rather than isolating one task that appears automatable: changing one task can alter the surrounding work and the judgment people are expected to exercise.
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For customer-facing organizations: What happens to trust?
When customers interact through AI, ask whether the arrangement builds or erodes confidence in the organization and its relationship with people. Evans’s question, “Is it creating more trust or less trust?”, is a practical test for deciding where AI belongs in a customer interaction and where human contact remains important.
What psychologists report about patients and chatbots
The APA’s 2026 survey offers a specific, clinician-reported view of AI in mental-health-related conversations. More than 1,200 licensed psychologists in the United States responded: 1,242 of more than 22,000 invitees, with a reported 6.3% completion rate. The online probability-based random sample was fielded April 9–26, 2026. The figures below describe psychologists’ reports about their patients, not the percentage of all people who use AI.
| Psychologists’ reported observations | Share |
|---|---|
| Had patients discuss using AI for diagnosis, emotional support, or conversation | 77% |
| Reported patients using AI to self-diagnose | 39% |
| Reported patients using AI as an additional mental health professional | 35% |
| Reported AI used to assist treatment | 33% |
| Reported AI used for self-discipline, affirmations, or behavioral reminders | 34% |
| Reported patients using chatbots for fun, friendship, or intimate relationships | 33%, 22%, and 13%, respectively |
| Among psychologists whose patients had ongoing chatbot conversations, said patients talked about or appeared to feel supported or validated | 68% |
| Among psychologists whose patients had ongoing chatbot conversations, said patients used chatbots to reinforce healthy coping skills | 41% |
| Reported observing a level of chatbot dependency among patients engaging with chatbots | 36% |
| Reported distorted thinking or delusions related to a chatbot | 15% |
| Comfortable with some patients engaging with chatbots; concerned about certain patients doing so | 54%; 93%, respectively |
These are observations and judgments reported by psychologists. They do not establish that chatbots caused dependency or delusions, or that they improved coping. The APA also cautions that the survey cannot show the full scope of people using chatbots for mental-health advice or companionship.
Keep health guidance within its scope
For mental-health needs, the APA says AI is not a safe or effective substitute for a qualified mental health provider. Its guidance also warns that AI can sound confident when it is wrong and recommends using it to complement a relationship with a licensed human professional. This is health-specific advice; it should not be stretched into a blanket conclusion about every use of AI.
A practical human-impact check
Before approving or expanding an AI system, decision-makers can make the human stakes explicit:
- Functioning and skill: Does the system help people think, learn, judge, and do their work, or does it merely complete a narrow task?
- Relationships and trust: How does AI-mediated interaction change confidence between colleagues, an organization and its customers, or people and professionals?
- Autonomy and agency: Can people understand, question, and meaningfully shape decisions that affect them?
- Work as a whole: What happens to the complete job and its human connections when a task is automated or reassigned?
- Actual experience: What do people do with the system in practice, and where does the designed interaction fail to match their needs?
These questions put Evans’s central claim into operational terms: the relevant measure is not capability alone, but whether the technology fits into people’s lives in ways that support functioning and connection. In Evans’s words in an APA Monitor article, “Psychology is essential to getting it right.”
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