The Spectrum and Domains of AI Usage
I want to start this chapter by acknowledging something that might feel more nuanced than what we've talked about so far: AI involvement in content creation isn't binary. It's not "AI or no AI." It's a spectrum. And different domains of usage have different implications.
Understanding both the spectrum and the domains helps us be thoughtful about how we use AI. It helps us be honest about where we are. It helps us set appropriate boundaries. And it helps us navigate complexity with wisdom.
So let's talk about both: the spectrum of AI involvement and the domains where AI can be used.
The Spectrum: From No AI to Full AI
Let me map out the spectrum for you. I think it helps to see the full range, from no AI involvement to full AI generation.
Category 1: Human-Created, AI-Assisted (Levels 1-6)
This category includes content where the human maintains primary authorship and voice. AI serves as a tool to enhance human work.
- Level 1: AI-Assisted Research — AI used to find sources, gather information, check facts. Human creates all content.
- Level 2: AI-Assisted Editing — AI used to edit, refine, or polish human-written content.
- Level 3: AI-Assisted Structuring — AI used to help organize, structure, or format content.
- Level 4: AI-Assisted Expansion — AI used to expand, develop, or elaborate on human ideas.
- Level 5: AI-Assisted Adaptation — AI used to adapt human content for different audiences or formats.
- Level 6: AI-Assisted Translation — AI used to translate human content (sermon to article, framework to guide, etc.).
- Level 7: AI-Generated with Human Review — AI generates content based on human input and direction. Human provides direction and reviews/approves output.
- Level 8: AI-Generated with Minimal Human Input — AI generates content with minimal human direction. Human provides basic parameters, AI does most of the work.
Implications by Category
Category 1: Human-Created, AI-Assisted
- Human voice and insight remain primary
- Credibility is maintained through human authorship
- Transparency is about disclosing assistance, not authorship
- Voice preservation is relatively straightforward
Category 2: Human-Directed, AI-Generated
- Human maintains editorial control but not primary authorship
- Credibility requires transparency about AI generation
- Voice preservation is more complex
- Human oversight is essential
Category 3: AI-Generated, Human-Published
- Human maintains minimal control
- Credibility is significantly diminished
- Voice is likely generic
- May not be appropriate for movement leaders
The Domains of AI Usage
Now let's look at the domains. Because different domains have different implications and requirements.
The Technical Domain
This is where AI handles tasks that are primarily mechanical or logistical.
What this includes: Formatting, grammar checking, organization, technical optimization (SEO, metadata), data processing.
Implications: Low risk to voice and credibility. High efficiency gain. Minimal transparency needs. Generic tools often sufficient.
Boundaries: AI handles mechanics, not meaning. Human maintains control over content.
The Research Domain
This is where AI helps with information gathering, fact-checking, and source finding.
What this includes: Finding sources, checking facts, gathering information, cross-referencing, organizing research.
Implications: Moderate risk if over-relied upon. High efficiency gain. Transparency needed about research assistance.
Boundaries: AI gathers information, human evaluates it. Human maintains responsibility for accuracy.
The Theological Domain
This is particularly important for movement leaders.
What this includes: Finding biblical passages, checking theological accuracy, organizing theological frameworks, adapting theological content, maintaining theological consistency.
Implications: High risk if not carefully managed. Requires specialized agents trained on movemental theology. Essential human oversight and verification.
Boundaries: AI assists with theological tasks, human maintains theological authority. Human maintains responsibility for theological accuracy.
The Creative Domain
This is where AI helps with creative expression, storytelling, and communication.
What this includes: Creative expression, storytelling, communication, creative adaptation, creative variation.
Implications: Moderate to high risk to voice and distinctiveness. Requires careful boundaries to preserve creativity.
Boundaries: AI assists with creative execution, human maintains creative vision. Human maintains responsibility for creative expression.
The Formation Domain
This is particularly sensitive for movement leaders.
What this includes: Discipleship resources, spiritual formation materials, pastoral care content, formation curriculum.
Implications: Very high risk if not carefully managed. Human presence is essential for formation. AI can assist with resources, but not replace relationship.
Boundaries: AI can assist with formation resources, but not replace formation. Human presence is essential.
The Practical Domain
This is often overlooked but straightforward.
What this includes: Practical application guides, implementation tools, step-by-step resources, practical frameworks.
Implications: Low to moderate risk. High efficiency gain. Transparency helpful but not always critical.
Boundaries: AI assists with practical structure, human provides practical wisdom.
Patterns Across Domains
Let me point out some patterns that emerge across these domains:
Risk increases with sacredness. Technical domain is low risk. Formation domain is very high risk. The more sacred or important the domain, the more careful we need to be.
Human oversight is essential in high-risk domains. Technical domain needs minimal oversight. Formation domain requires human presence.
Specialized agents matter in high-risk domains. Generic tools work for technical tasks. Specialized agents are essential for theological content.
Transparency requirements vary by domain. Technical domain needs minimal transparency. Theological domain requires full transparency.
A Framework for Navigation
For each content creation task, ask yourself:
Step 1: Identify the Domain
- What domain are you operating in?
- What is the risk level?
- What level of oversight is needed?
Step 2: Determine the Spectrum Level
- Where are you on the spectrum?
- What category are you in?
- What does that mean for transparency?
Step 3: Apply the Principles
- How does amplification vs. replacement apply here?
- How do you preserve voice?
- What transparency is required?
- What about theological integrity?
Step 4: Set Boundaries
- What will AI do?
- What will you do?
- What oversight is needed?
Step 5: Implement Transparency
- What disclosure is required?
- How will you be transparent?
- What language will you use?
Structural Transparency
Here's what I want you to understand: transparency doesn't have to be burdensome. It can be built into the process structurally.
How structural transparency works:
- When you publish content, you indicate the level of AI involvement
- The system generates appropriate disclosure
- Disclosure is presented clearly but not intrusively
- Readers can see what's real without it dominating the experience
Example disclosure language by spectrum level:
Levels 1-2 (Research/Editing): "This content was written by me. I used AI to assist with research and editing, but the ideas, insights, and voice are mine."
Levels 3-4 (Structuring/Expansion): "I created this content with AI assistance. I provided the core ideas and insights, and AI helped me structure and develop them. The voice and perspective are mine."
Levels 5-6 (Adaptation/Translation): "I created this content, and AI helped me adapt it for this audience and format. The original ideas, insights, and voice are mine."
Level 7 (AI-Generated with Review): "This content was generated by AI based on my direction and input. I reviewed and approved all content, and the ideas and framework are mine."
What This Means for Movement Leaders
Movement leaders operate across multiple domains, at different spectrum levels, with various principles applying. And having this framework helps you navigate that complexity.
The framework isn't rigid. It's a guide. It helps you think through AI usage thoughtfully. It helps you maintain boundaries. It helps you be transparent. It helps you preserve voice and credibility.
And when you use it well, you can use AI to amplify your impact while maintaining your voice, your credibility, and your calling.
A Word of Encouragement
I know this chapter has been about spectrums and domains and frameworks. And that might feel complicated or overwhelming.
But here's what I want you to know: it's simpler than it sounds. The spectrum just helps us understand that AI involvement isn't binary. The domains help us understand that different uses of AI have different implications. The framework just helps us navigate thoughtfully.
You don't need to be perfect. You just need to be thoughtful. You just need to understand the spectrum and domains, set appropriate boundaries, maintain oversight, and be transparent.
And when you do that, you can use AI well—amplifying your impact while maintaining your voice, your credibility, and your calling.
What's Next
In the next chapter, we're going to explore why personalization and context are required for agentic value. We're going to talk about why generic AI tools fail for formation-oriented work, and how specialized agents provide the context-awareness that movement leaders need.
For now, though, I want you to sit with what we've covered. The spectrum. The domains. The framework.
These aren't abstract concepts. They're affecting you right now. They're shaping how you use AI, how you create content, how you build credibility. And understanding that reality is the first step toward responding to it well.
So take a breath. Process what we've talked about. And when you're ready, we'll move forward together.
Reflection Questions:
1. Where do you typically fall on the spectrum of AI involvement? What does that look like in practice?
2. In which domains do you most commonly use AI? What does that look like?
3. How do you navigate the different risk levels across domains? What boundaries do you set?
4. Where do you need specialized agents? Where are generic tools sufficient?
5. How would structural transparency change how you use AI? What would that make possible?