Being AI-first for non-technical tasks: a specific examplePermalink

Right now I’m working on securing grant funding for research projects that I’m interested in working on in 2026. Instead of approaching this traditionally, I’m using AI workflows throughout the entire process. Here’s how I’m being AI-first in a non-technical task.

I’ve been building out my AI tooling and workflow system over the past few months, and this grant writing process was a perfect test case for applying these tools to non-technical work. Many of the personas and task instructions I’ve developed were directly applicable to this workflow.

Use case 1: Have ChatGPT summarize my research interestsPermalink

I fed ChatGPT all my recent blog posts, research papers, and project descriptions. Instead of manually synthesizing my interests, I asked it to identify patterns and create a coherent narrative about my research focus.

The AI output was surprisingly insightful. It identified that my work centers around “AI-augmented research workflows” and “empirical evaluation of AI tools in academic contexts,” but these were themes I hadn’t explicitly articulated before. It generated a summary like: “Your research demonstrates a systematic approach to integrating AI tools into academic workflows, with particular focus on creating reproducible evaluation frameworks and developing AI expert personas for domain-specific tasks.”

Actually, here’s what it came up with for my research interests:

“I study how AI and social media algorithms interact with human psychology and sociology to shape social learning online. As a research engineer, I build large-scale data systems, AI-driven simulations, and LLM-based methods to understand and redesign digital environments for healthier discourse.”

This was much cleaner than anything I’d written myself.

This approach is dramatically different from how I’d normally handle this. Traditionally, I’d spend days re-reading my own work, trying to manually extract themes, and often getting lost in the details. The AI gave me a bird’s-eye view that I could then validate and refine, rather than starting from scratch. It’s like having a research assistant who’s read everything you’ve written and can immediately spot the throughlines you might miss when you’re too close to the work.

Use case 2: Having AI agents do lit reviewsPermalink

I used my Research Synthesizer persona for this task. This persona specializes in combining findings across analyses and creating comprehensive research narratives. I gave it my research questions and it systematically searched for relevant papers, summarized key findings, and identified gaps in the literature. The AI expert followed a structured rubric for evaluating paper relevance and quality.

The AI expert didn’t just find papers, but it created a structured analysis. For each paper, it provided: relevance score (1-10), key findings, methodology assessment, and how it connected to my research questions. For example, it flagged a paper as “Highly relevant (9/10) - demonstrates similar AI workflow methodology but focuses on industry applications rather than academic research contexts. Key gap: no evaluation framework for academic productivity metrics.”

This transformed what would normally be weeks of manual literature review into a systematic, structured process. Instead of reading papers one by one and trying to remember connections, the AI created a searchable knowledge base with cross-references and gap analysis. It’s like having a research librarian who not only finds relevant papers but also immediately understands how they fit into your specific research context and can identify exactly where your work would contribute something new.

Use case 3: Analyze and break down examples of project proposals onlinePermalink

I found several successful grant proposals online and had ChatGPT analyze their structure, language patterns, and argumentation strategies. The AI extracted specific rhetorical patterns that I never would have noticed manually.

For instance, it identified that successful proposals consistently use “problem escalation” language like: “While existing approaches address X, they fail to account for Y, leading to Z consequences.” It also found that impact statements follow a three-part structure: immediate outputs → intermediate outcomes → long-term societal benefits. The AI created a template with these patterns built in.

Normally, I’d read a few proposals and try to intuitively understand what makes them work. This approach is hit-or-miss and heavily influenced by my own biases. The AI systematically analyzed dozens of proposals and identified patterns that human reviewers might not even consciously notice. It’s like having a grant writing coach who’s analyzed thousands of successful proposals and can immediately tell you the specific language and structure patterns that work.

Use case 4: Refine and iterate on my project ideasPermalink

I used my Critical Analysis Prompt to play devil’s advocate with my initial ideas. This framework is designed to challenge assumptions, identify over-engineering, and provide evidence-based recommendations. The AI was brutally honest in ways that human colleagues often aren’t. When I proposed “developing AI tools for academic research,” it immediately challenged: “This is too vague. What specific research tasks? What’s your target user - PhD students, postdocs, or established researchers? How will you measure success - time savings, quality improvements, or adoption rates?” It forced me to get specific and defend every assumption.

This systematic challenge process is something I rarely get from human feedback. Colleagues are usually supportive and focus on helping you polish existing ideas rather than fundamentally questioning them. The AI had no social constraints and would immediately flag logical inconsistencies, unrealistic timelines, or weak justifications. It’s like having a skeptical peer reviewer available 24/7 who’s not afraid to tell you your idea needs work before you waste time developing it further.

Use case 5: Have AI agents write out a proposed outlinePermalink

Based on the successful proposal patterns and my refined ideas, I had an AI agent create a detailed outline. The AI didn’t just create a generic outline, but it tailored it specifically to my research focus and the patterns it had identified in successful proposals. For example, under “Methodology,” it suggested: “1. User study design (300 words) - recruit 20 PhD students, mixed methods approach, pre/post productivity metrics. 2. AI tool development (400 words) - focus on literature review automation, specify technical stack. 3. Evaluation framework (200 words) - define success metrics, comparison baselines.” Each section had specific prompts and reminders.

Creating outlines is usually where I get stuck. I know what I want to say but struggle with organization and flow. The AI solved this by applying proven structural patterns to my specific content. Instead of staring at a blank page wondering how to organize my ideas, I had a detailed blueprint that I could follow or modify. It’s like having a professional grant writer who knows exactly how to structure your ideas for maximum impact.

Use case 6: Have AI agents write out a first draftPermalink

Using the outline and my research materials, I had an AI agent write initial drafts for each section. The key was providing it with specific examples, data points, and quotes from my previous work to ground the writing in my actual expertise.

The AI generated surprisingly good first drafts that captured my voice and expertise. For instance, for the problem statement, it wrote: “Current academic research workflows remain largely manual and inefficient. A recent study by Torres et al. (2025) found that PhD students spend an average of 40% of their time on literature review and data organization tasks rather than actual analysis. This inefficiency compounds across the academic ecosystem, with researchers spending disproportionate time on administrative tasks rather than generating new knowledge.” It seamlessly integrated my specific research findings with broader context.

This approach transformed writing from a creative bottleneck into a collaborative process. Instead of staring at blank pages, I had working drafts that I could refine and improve. The AI handled the heavy lifting of structuring arguments and connecting ideas, while I focused on adding my unique insights and ensuring accuracy. It’s like having a skilled research assistant who can write in your style and knows your work well enough to make connections you might miss.

Use case 7: Have AI agents double-check my reasoning as I writePermalink

As I wrote my own sections, I had an AI agent review each paragraph for logical consistency, evidence quality, and argument strength. The AI caught logical flaws that I would have missed. For example, when I wrote “Our AI tools will increase research productivity by 50%,” it immediately flagged: “This claim lacks supporting evidence. Do you have preliminary data? What’s your baseline measurement? Consider rewording to ‘We expect to demonstrate measurable improvements in research productivity through systematic evaluation.’” It also caught weak evidence like when I cited a study without explaining its relevance to my specific research context.

This real-time feedback prevented me from making embarrassing mistakes that would undermine my credibility. Normally, I’d write an entire section and only catch logical inconsistencies during final review, if at all. The AI acted like a critical peer reviewer who could spot weak arguments, unsupported claims, and logical gaps as I wrote. It’s like having a meticulous editor who’s also an expert in your field and can immediately identify when your reasoning doesn’t hold up.

Use case 8: Having AI agents trained on specific personas give pointed feedback on my draftsPermalink

I created different AI experts using my persona creation framework to represent typical grant reviewers: a technical expert and a domain specialist. The persona-based feedback was remarkably specific and actionable. The technical expert focused on methodology: “Your evaluation framework needs more detail on statistical power analysis. How will you handle participant dropout? What’s your plan for inter-rater reliability in qualitative coding?” The domain specialist questioned novelty: “How does this differ from existing AI writing tools? What’s the specific academic research angle that hasn’t been explored?”

This multi-perspective approach revealed blind spots that single-person feedback would miss. Normally, I’d get feedback from colleagues who share my perspective and concerns. The AI personas represented different stakeholder viewpoints with different priorities and expertise. It’s like having a diverse review committee available on demand, each bringing their own professional concerns and evaluation criteria to your proposal.

Use case 9: Have the AI agents simulate a mock grant review committee for my proposalPermalink

Finally, I had the AI experts simulate a grant review committee meeting. The simulated committee meeting was surprisingly realistic. The technical expert argued: “The methodology is sound but lacks detail on how they’ll handle the Hawthorne effect when measuring productivity improvements.” The domain specialist countered: “I’m concerned about generalizability - this only studies PhD students in one discipline.”

This final simulation revealed issues that individual feedback missed. The committee dynamic created new concerns that emerged from the interaction between different perspectives. It also gave me confidence, but the proposal passed the review, which meant my arguments were fundamentally sound. It’s like having a dress rehearsal for the actual review process, complete with the kind of committee dynamics and competing concerns that real reviewers would bring to the table.

ConclusionPermalink

Being AI-first doesn’t mean replacing human judgment, but rather it means leveraging AI to amplify your capabilities. I still made all the final decisions, but AI accelerated every step of the process. The key was creating structured workflows and using AI experts with specific expertise rather than relying on generic AI responses.

This approach isn’t limited to grant writing. You can apply similar workflows to any complex, multi-step task where you need research, analysis, writing, and review capabilities. The fundamental insight is that AI excels at structured, systematic tasks that humans find tedious or time-consuming, while humans excel at creative synthesis and strategic decision-making. By combining both, you get the best of both worlds.

The traditional approach to complex writing tasks is largely sequential and individual. You research alone, write alone, and get limited feedback from a few colleagues. The AI-first approach makes every step collaborative, systematic, and iterative. Instead of hoping your first draft is good enough, you can rapidly iterate through multiple versions with different perspectives and systematic feedback. It’s the difference between working with a single research assistant versus having an entire research team at your disposal.

There’s no free lunch here, though. The hard work comes before you start asking ChatGPT to write anything. You still need to articulate why you’re doing the task, clearly define what you need, and know what success looks like. But once you’ve done that foundational work, AI can dramatically accelerate the execution phase. I’ve been doing this for mostly technical and software-related tasks, but the workflows, as shown here, apply to, and particularly shine for, non-technical tasks.

The key was having a structured approach. I used tools from my AI agent system throughout the process: the Research Synthesizer for literature review, Critical Analysis Prompt for challenging my ideas, and persona creation framework for the reviewer simulations. These weren’t generic AI responses, but rather were systematic workflows designed to amplify specific capabilities.