Imagine a newer pastor sitting at a desk late in the week with a difficult biblical text open in front of them. There are notes from commentaries, a half-developed idea, and several questions that have not yet resolved themselves into a sermon. The pastor is tired. AI can turn that unfinished material into a coherent outline in seconds, suggesting interpretations, transitions, illustrations, and applications that sound more finished than anything currently on the page.
The result may even be good. But the ethical question is not exhausted by whether the sermon improves. Some of the unfinished work the pastor is tempted to bypass may be part of how a preacher learns to interpret Scripture, make theological judgments, understand a congregation, and decide what they are actually prepared to proclaim. AI can help with sermon preparation. The harder question is whether it is doing something the person still needs to practice.
The Better Result May Not Answer the Formation Question
One of the most important distinctions in responsible AI use is between producing a strong result and developing the person who produces it. Those can overlap, but they are not the same thing. A polished report does not prove that the person who submitted it understands the material. A persuasive argument does not tell us whether the writer could reason through the issue independently.
Many forms of work do more than produce an artifact. They form capacities. Reading difficult material teaches us how to stay with uncertainty. Working through a financial report can teach someone to notice relationships among numbers rather than simply repeat totals. Drafting and revising can sharpen judgment about what a person actually means. Preparing a sermon can develop theological and pastoral discernment even when the final manuscript looks less impressive than something generated more quickly.
Research on AI and learning reinforces the need for care without supporting a simple conclusion that AI makes people less intelligent. Some studies raise concerns about heavy reliance on AI during cognitively demanding work, while other research shows that carefully designed AI tutoring can improve learning. The useful distinction is whether the way the technology is used still requires the human being to engage in the practice through which learning or judgment develops.
Some Work Is Burden; Some Work Is Practice
There is no virtue in preserving every task simply because people used to do it themselves. A church administrator does not become less capable because software alphabetizes a list. A pastor may gain time by using transcription rather than typing every spoken note. A staff member who understands the material may use AI to reorganize a document or tighten repetitive language without losing the capacity the work requires.
The difficulty is that burden and practice can look similar from the outside. The slow part of a task may be the very part through which someone learns. A developing leader who wrestles with conflicting information is not merely taking longer to reach an answer. They may be learning how to weigh evidence, recognize ambiguity, revise an assumption, and carry responsibility when the answer remains imperfect. So the question cannot simply be, “Can AI do this faster?” Leaders also need to ask what doing this work is supposed to develop in the person.
The Same Tool Can Play Different Roles in Formation
An experienced professional may use AI to generate alternatives, challenge an interpretation, or expose weaknesses in an argument while retaining substantial command of the work. Someone still learning the field may receive the same output but interact with it differently. If the system supplies the reasoning before the learner attempts it, the person can appear more capable without yet having developed the capacity the output seems to represent.
That does not mean novices should be kept away from AI. A well-designed use of AI can make learning more active. It can ask questions instead of supplying conclusions, offer feedback on a first attempt, present counterarguments, or explain why an answer is incomplete. In those situations, the technology can structure the difficulty so that the learner remains involved in the work.
The same principle applies beyond formal education. A pastor can ask AI to critique an interpretation after doing substantial study. A staff member can draft an explanation and ask where it is unclear. A new administrator can compare their process with an AI-generated alternative. The issue is what role the system plays in relation to the human capacity that needs to develop.
Formation in Ministry Cannot Be Measured by the Finished Product
Return to the newer pastor. Suppose AI produces a sermon outline that is coherent, biblically plausible, and pastorally sensitive. The congregation may hear a better-organized sermon on Sunday. That outcome still does not tell us whether the pastor is becoming a better interpreter.
The concern is not that AI touched the sermon. It may be appropriate to use AI to identify questions, compare interpretations, test an argument, or notice assumptions. The formative problem arises when assistance becomes a substitute for sustained theological engagement. If the pastor repeatedly receives an interpretation before struggling with the text, an application before considering the congregation, and a structure before deciding what the sermon is trying to say, the finished product can improve while the preacher has less opportunity to develop the judgment the role requires.
Ministry formation has always involved repeated practice: studying, attempting, receiving correction, revising, observing consequences, and returning to the work. No practice guarantees wisdom or faithfulness, and formation does not establish superiority, infallibility, or divine favor. But capacities develop through use, and ministry that depends on judgment cannot ignore the practices through which judgment is cultivated.
Oversight Depends on Capacities That Have to Come From Somewhere
Consider a newer church treasurer. AI can help organize financial information, identify possible variances, draft an explanation for the board, and make a report easier to read. Some of that assistance may be exactly what a volunteer leader needs.
But imagine that month after month the system also does the interpretive work. The treasurer accepts its explanation of why cash changed or what deserves the board’s attention without learning how to trace those relationships independently. The reports may become more polished while the treasurer remains unable to explain what sits behind them without returning to AI.
The church eventually needs a human being who can recognize when something does not make sense, ask the next question, understand the underlying records, and know when outside expertise is required. Meaningful oversight depends on capacities that must be developed somewhere. The answer is not to force the treasurer to perform every clerical step by hand. The formative question is which parts of the role still need direct human practice so that the person becomes capable of carrying the judgment and responsibility that remain theirs.
Responsible Delegation Includes Deciding What to Keep Practicing
AI makes delegation unusually easy because the transition can happen one prompt at a time. A person asks for help with a paragraph, then a section, then an outline, then the reasoning itself. None of those steps necessarily creates a problem. The cumulative effect can be difficult to notice unless someone is asking what capacity the work is supposed to preserve.
That may mean designing workflows differently. A learner may make a first attempt before asking AI for critique. A developing professional may need regular opportunities to complete important parts of the work without assistance. A supervisor may automate routine burdens precisely so that more time can be spent on judgment-intensive practice. A team may use AI to generate alternatives while requiring the responsible person to explain the reasoning behind the final choice.
There is no universal percentage of work that must remain AI-free. The appropriate boundary depends on the activity, the person, the capacity being formed, and the consequences of losing it. Sometimes the hardest part of the work is simply waste, and technology should relieve it. Sometimes the difficulty is where learning is happening.
When AI offers to take over the next difficult piece, the useful question may not be whether it can do the work well. It may be whether this is one of the parts I still need to do, struggle with, understand, and practice if I am going to remain capable of carrying the responsibilities that come after it.