Real-World Case Studies

Example 1: AI Coding Tools ROI Decision

Context: Deciding whether to invest in AI coding tools for a development team.

Research Question (v1.2):

"What is the measured productivity impact of AI coding assistants (GitHub Copilot, Tabnine, Cursor) on professional software developers, based on quantitative studies from 2022-2025?"

Workflow Timeline:

Stage 1 (Clarify) took 12 minutes—the initial question was too broad: "Are AI coding tools worth it?" Stage 2 (Collect) took 18 minutes and found 24 papers. Stage 3 (Extract) took 25 minutes, processing papers in batches of 6. Stage 4 (Synthesize) took 8 minutes. Stage 5 (Write) took 15 minutes. Total time: 78 minutes.

Output:

The final synthesis was 2,400 words with 24 citations (18 peer-reviewed papers, 4 industry reports, 2 pre-prints). Key finding: 30-55% productivity improvement for code generation tasks, 15-25% for overall development workflow. Decision made: Invested in tools. ROI achieved in 6 weeks.

What Made It Work:

The narrow scope focused specifically on coding rather than "all AI tools," which kept the research manageable and actionable. The quantitative focus sought numbers rather than opinions, providing concrete data for decision-making. The practical outcome meant this informed a real business decision with measurable results. By limiting the scope to productivity metrics for professional developers, the researcher avoided getting lost in broader debates about AI's role in software development. The specificity of the question led directly to actionable insights that justified the investment decision.

Example 2: Algorithmic Management Literature Review

Context: Research paper on algorithmic management for an academic publication.

Research Question (v1.2):

"What are the documented impacts of algorithmic management systems on worker autonomy, job satisfaction, and turnover, based on empirical studies across industries?"

Workflow Timeline:

Stage 1 (Clarify) took 15 minutes working collaboratively to refine from "how do algorithms affect workers?" Stage 2 (Collect) took 22 minutes and found 31 papers. Stage 3 (Extract) took 35 minutes with the researcher reviewing each extraction for accuracy. Stage 4 (Synthesize) took 12 minutes. Stage 5 (Write) took 20 minutes. Quality control added 30 minutes for an additional verification pass. Total time: 2 hours 14 minutes (longer due to academic rigor requirements).

Output:

The literature review was 4,100 words with 31 citations (all peer-reviewed). It was organized by outcome variable (autonomy, satisfaction, turnover) and incorporated into the paper's introduction and related work sections.

What Made It Work:

The researcher did the critical evaluation while AI did the extraction, creating an effective division of labor that leveraged both human expertise and computational efficiency. An extra verification pass ensured citation accuracy, which is essential for academic publications where citation errors can undermine credibility. Thematic organization by outcome variable made writing easier by creating natural sections that aligned with the paper's analytical framework. The collaborative approach meant the researcher maintained intellectual control while delegating time-consuming extraction tasks. This balance between automation and human oversight produced a literature review that met rigorous academic standards while saving substantial time.

Researcher Feedback:

"This would have taken me two weeks. We did it in an afternoon. And honestly, it's more comprehensive than I would have done manually because I wouldn't have had the patience to read 31 papers."

Example 3: SaaS Market Competitive Analysis

Context: SaaS product launch requiring market landscape understanding.

Research Question (v1.2):

"What AI-powered sales automation tools launched in 2023-2024, what features do they offer, how are they priced, and what do early user reviews say about effectiveness?"

Workflow Timeline:

Stage 1 (Clarify) took 8 minutes. Stage 2 (Collect) took 20 minutes using Product Hunt, G2, Crunchbase, and tech blogs. Stage 3 (Extract) took 18 minutes (shorter because sources were blog posts rather than academic papers). Stage 4 (Synthesize) took 15 minutes building a comparison matrix. Stage 5 (Write) took 10 minutes. Total time: 71 minutes.

Output:

The market analysis was 1,800 words analyzing 15 competitors. It included a feature comparison matrix (12 dimensions), pricing analysis ($49-$499/month range), and gap analysis identifying features underserved by existing tools.

What Made It Work:

Mixed sources included not just academic papers but industry blogs, user reviews, and pricing pages, providing a comprehensive view of the competitive landscape. Structured extraction applied the same questions to each competitor, ensuring consistent analysis across all 15 tools and making comparison straightforward. The deliverable was a decision matrix rather than an essay, which better served the business context where quick comparison and decision-making were priorities. By combining quantitative data (pricing, feature counts) with qualitative insights (user reviews), the analysis provided both breadth and depth. The focus on recent launches (2023-2024) ensured the analysis reflected current market dynamics rather than outdated information.

Outcome:

The entrepreneur identified an underserved niche, pivoted product positioning, and launched successfully.