For roughly the last eighteen months, I have spent a great deal of time learning how to use generative artificial intelligence well. I have used it across nearly every part of my work, from research and writing to presentations and routine administration. I have tried different systems, watched them succeed and fail, changed how I prompt, and become more careful about what responsibility I am willing to place in their hands.
A friend eventually challenged me about that use. They were concerned that using AI itself might be unethical. I did not dismiss the concern. AI raises legitimate questions about privacy, authorship, environmental cost, and what happens when people surrender judgment to systems they do not fully understand. In churches, those concerns become especially important when technology enters pastoral care, confidential conversations, governance, or discernment.
So I decided to research the question rather than defend what I was already doing. I wanted to know whether my own practice was ethically responsible and what needed to change. I found that the question I began with was too broad. Asking whether “using AI” is ethical treats every use as though it carries the same purpose, consequence, and degree of human responsibility. My research led me instead to examine what AI is being asked to do, what could go wrong, and whether the human beings involved remain genuinely responsible for the work.
The Question Was Too Simple
Saying that someone “used AI” tells us remarkably little. A church staff member might use it to tighten a congregational announcement, while a pastor organizes presentation notes and a board chair asks for possible discussion questions. Those uses can remain fairly ordinary when the person understands the subject, reviews what the system produces, and takes responsibility for what is communicated.
The ethical stakes rise when the work carries consequences the user cannot responsibly hand over. A leader might ask AI whether a board has authority to sign a contract and accept the answer without checking the governing documents. A pastor might submit a sensitive pastoral situation in identifiable detail because the added context produces a more specific response. A finance chair might place an AI-generated explanation of restricted funds into a board packet without knowing whether it is correct. The morally significant features are the purpose of the task, the consequences of error, and the responsibilities being exercised or avoided.
That changed the way I framed the problem. AI can extend human capacity, but it can also begin doing work whose value depends on human judgment, relationship, or responsibility. The ethical task is to tell the difference in a particular setting rather than assume the technology itself settles the matter.
Start With What the Human Activity Is For
Imagine a church administrator preparing an email about changes to the Sunday schedule. The administrator knows what has changed, why it changed, and where people may become confused. After drafting the basic information, they ask AI to make the announcement clearer and shorter, then review it before sending.
AI may improve the communication without carrying much of the underlying responsibility. The administrator still determines what is true, what needs to be said, and whether the final wording accurately represents the church. The situation becomes more complicated when the activity itself depends on judgment that cannot be reduced to polished language.
A system can generate a pastoral response, theological argument, or governance recommendation that sounds thoughtful and complete. That does not establish that the person using it has done the work the situation requires. Sometimes the goal is clearer communication or faster organization. At other times, the work matters partly because a human being needs to exercise judgment, develop understanding, encounter another person, or carry responsibility through uncertainty.
Human Responsibility Has to Be More Than a Final Click
One of the strongest conclusions from my research was that human accountability cannot be reduced to reviewing a finished product. For consequential work, the responsible person needs enough understanding to question what AI produced, revise it, reject it, explain why it is sound, and answer for the decision to use it.
That standard has changed the way I work. I often use AI to discover possibilities, organize information, test an argument, or synthesize material. When the matter is consequential, I expect evidence to substantiate what is being claimed, and I remain responsible for deciding what I accept and reject. I do not need to type every sentence myself, but I do need to understand and own what I put into the world.
That matters especially in church leadership because people may rely on us in matters ranging from finances and polity to personnel and pastoral care. AI can contribute substantially without becoming an authority in its own right. The more others may depend on the result, the more serious the obligation to know why it should be trusted.
The Ethical Burden Should Rise With the Consequences
Responsible use also requires proportionality. Editing an announcement or organizing meeting notes does not require the same scrutiny as using AI to interpret governing authority, advise on personnel, analyze church finances, or handle confidential pastoral information. The consequences are different because people may act on the result.
Consider a board weighing a significant property agreement. A leader might use AI to identify relevant sections of the bylaws and generate questions the board should consider. That could be useful as a research aid. The final determination of the board’s authority, however, has to rest on the governing documents, applicable polity or law, qualified expertise when necessary, and accountable human judgment.
An incorrect answer in church life can affect money, employment, property, legal obligations, pastoral trust, or the legitimacy of a governing decision. As the consequences become more serious, verification and oversight have to become more demanding.
Serious Concerns Do Not Require a Blanket Verdict
My research gave me reasons to be more careful about AI, not less. Generative systems can produce incorrect or unsupported information while sounding confident, and they can misrepresent sources. That means fluency cannot substitute for verification when someone may act on the answer. Privacy concerns also become more serious when confidential or identifiable information enters an external system, because convenience does not remove the responsibility to protect what has been entrusted to us.
The research also complicated some broader claims I had encountered. AI has real environmental and material costs, but there is no single meaningful environmental cost that can be assigned to every prompt; impact varies greatly with the system, workload, hardware, location, and kind of output. Research on learning and formation likewise did not support the claim that AI inevitably destroys human thinking. The more useful distinction was whether it assists a person who remains engaged in the work or substitutes for the practice through which judgment, skill, or understanding develops.
That pattern appeared across the research. Questions about labor, authorship, bias, disclosure, and increasing autonomy remain legitimate, but none produced a simple rule that the presence of AI makes an activity ethical or unethical. What mattered repeatedly was what human good the activity serves, what responsibility AI is being given, what could happen if it is wrong, and whether an accountable person remains able to understand and answer for the result.
The Question I Carry Now
The friend who challenged me was right to make me examine practices that had become familiar. It is easy to move from “this is useful” to “this is acceptable” without doing enough work in between, especially when a tool saves time and often produces good results.
The research did not persuade me to stop using AI. I continue to use it extensively because it helps me think more clearly, develop my work, and accomplish things I could not do as efficiently on my own. What changed is the way I evaluate that use. I am less interested in asking whether AI participated in a piece of work and more interested in what I asked it to carry, what responsibility remained mine, what evidence supports the result, who could be affected if it is wrong, and whether I could genuinely answer for what leaves my hands.
Those questions require me to keep examining my own practice as the technology changes, rather than relying on a general judgment about AI itself.