SHAPE Worked Example: A Freelance Copywriter

Module 3 · Section 9 of 10

SHAPE Worked Example: A Freelance Copywriter

Applying SHAPE to a real workflow problem:

Sarah is a freelance copywriter who spends too much time on client research and first drafts. She has four regular clients and picks up project work. Her bottleneck is the front end of every project: understanding the client’s industry, competitors, and audience well enough to write something credible.

Situation:

• Spending 2-3 hours per client on background research before writing begins
• Research quality is inconsistent — sometimes thorough, sometimes rushed depending on deadlines
• Using Google, industry publications, and client-provided materials manually
• Already paying for ChatGPT Plus but mostly using it for brainstorming, not structured research
• Readiness Check: Comfortable with AI tools (8/10), clear on what to automate (7/10), willing to test (9/10)

Hypothesis:

• “AI-assisted research will cut my per-client research time from 2-3 hours to 45 minutes”
• “Research briefs will be more consistent across clients because I’m using a structured prompt”
• “First drafts based on AI research briefs will need less revision than my current process”

Action (Takers Approach):

• Use ChatGPT with a structured research prompt — no custom GPT, no integrations, just a well-crafted prompt
• Test on next three client projects over two weeks
• Track time spent on research and number of revisions per draft
• Compare to her baseline from the last three projects

Process (Scaling):

• Research prompt worked well — adapt it for competitor analysis briefs and content strategy summaries
• Create a saved prompt template for each recurring research type
• Teach her part-time VA to run the research prompts and prepare the briefs before Sarah starts writing

Evaluation (After One Month):

• Research time dropped from 2.5 hours average to 50 minutes
• Research briefs are more consistent — she’s catching industry details she used to miss under time pressure
• First drafts need slightly less revision, but the bigger win is she’s starting each project with better context
• Next step: apply the same approach to her content strategy deliverables

What made this work: Starting with the tools she already had (Takers approach), testing on real client work instead of hypothetical projects, and measuring against a specific baseline. No custom GPTs, no automation tools, no integrations — just a better prompt and a system for using it.

For larger teams: The same SHAPE logic applies at larger scale. A financial services firm rolling out AI for loan processing would follow the same phases — assess current state honestly, define specific hypotheses (“reduce processing time by 30% while maintaining approval accuracy”), pilot with a Takers approach using an established vendor solution, scale step by step, and measure against baseline metrics. The framework is identical; the scope is different.

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Next: You have a method. Chapter 4 puts it on a timeline — a 90-day cycle that forces specificity, prevents drift, and builds the kind of momentum that compounds.