CRAFT Content report
Thought Leadership Series: your result
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Thought Leadership Series
Draft a thought leadership article series
For founders and CMOs building authority. The decision: what do we publish to make our point of view known?
Give it a topic, your own take on it and the reader you want, and get a short first draft for each article, promotional posts and a publishing plan.
What you get
This sample is a made up example, built from the inputs behind Fill in an example. It shows part of one section of the answer.
Article 1 of 1
Headline: The Case Against the Usual View of AI in Customer Support
The Case Against the Usual View of AI in Customer Support
By the author. Written for Support leaders at consumer apps with growing ticket backlogs.
Support automation should be measured by tickets resolved, not tickets deflected. For support leaders at consumer apps with growing ticket backlogs, the measure to watch is automated resolution rate.
See the full example answer or run it with your own details.
Result
When a tool is not enough: if the decision needs more than one answer, talk it through in a call. Book 45 minutes with Shashwat.
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Example answer
Generated on 2 November 2026 from this branch, with the inputs behind Fill in an example. Your own inputs give a different result.
CRAFT Content report
Example: Thought Leadership Series
Show the full result
Thought Leadership Series
Article 1 of 1
Headline: The Case Against the Usual View of AI in Customer Support
The Case Against the Usual View of AI in Customer Support
By the author. Written for Support leaders at consumer apps with growing ticket backlogs.
Support automation should be measured by tickets resolved, not tickets deflected. For support leaders at consumer apps with growing ticket backlogs, the measure to watch is automated resolution rate.
The argument
The argument of this piece rests on one point: support automation should be measured by tickets resolved, not tickets deflected. For support leaders at consumer apps with growing ticket backlogs, an idea like this is only worth the time if it shows up in the numbers the work is judged on, automated resolution rate, so that is where it is tested here. The sections below take it in order: how the parts fit, what the evidence shows, the objection it has to survive and a way to try it on your own work.
The objection: AI gets answers wrong
Someone at the table will say: "AI gets answers wrong". A fair way to settle it is to agree an evaluation on your own history before go-live, with a person approving any action that moves money or changes a record.
Who has to agree
Whoever reads this will not decide alone. The owner of the workflow being automated signs; the data or AI lead evaluates the model; security and legal review data use and privacy; the operations team that hands work to the AI uses it.
What to do next
Measure it on automated resolution rate, before and after the change.
Then take this question into the next meeting: "Which cases or decisions would you trust an AI to handle first, and which never?"
Evidence to add
To add before publishing: one result or quote from a customer of yours that bears on this part, with the figure, the period and the customer's approval. Nothing is invented here to fill the gap, so this piece argues from the position alone until you add it.
The takeaway
If you keep one thing from this piece, keep the point it opened with, and the test that checks it. The pushback, "AI gets answers wrong", is fair, and it is answered by a test on real work rather than by argument.
Notes on this article, not part of it:
Lead proof point: none given
Counter-argument it answers: "AI gets answers wrong"
Draft length: about 354 words
Promotional Posts
Promo Post 1 (for Article 1)
New article: "The Case Against the Usual View of AI in Customer Support".
The short version: Support automation should be measured by tickets resolved, not tickets deflected.
A question for support leaders at consumer apps with growing ticket backlogs: "Which cases or decisions would you trust an AI to handle first, and which never?"
Publishing Order
- Article 1, "The Case Against the Usual View of AI in Customer Support", then its promo post.
Link each promo post to its article. Hashtags for the posts: #AINative.
Proof Points Used
None were given.
Sector Notes: AI native
- Counter-arguments your readers will raise: ai gets answers wrong; data privacy; we can build this on a model api ourselves; cost grows with volume. The articles answer them in turn.
- Measures your readers use: automated resolution rate, accuracy on an evaluation set, escalation rate, handling time, cost per resolution. Cite the ones your proof points really move.
- Terms your readers use: resolution rate, evaluation set, human in the loop, guardrails, accuracy, hallucination.
- A proof point that lands: Results on an evaluation set built from the buyer's own history, then live results with human review switched on.
Notes for you
Sector: read from your inputs as AI native. Business model: not stated. Tell us how you charge and the plan will adjust.
Billing dependent steps are conditional: trials, seats and recurring renewals require a subscription; a statement of work requires a services contract; site or link commitments require connectivity billing. Confirm the applicable choice before using those steps.
One article in the contrarian style, written for support leaders at consumer apps with growing ticket backlogs.
Nothing in your inputs needed flagging.
What this series was built from
| Element | Detail |
|---|---|
| Topic | AI in customer support |
| Your Take | Support automation should be measured by tickets resolved, not tickets deflected |
| Target Reader | Support leaders at consumer apps with growing ticket backlogs |
| Author | not given |
| Articles | 1 in the contrarian style |
To sharpen this, give: proof points (it would change every article, which now quotes no result, customer or figure and names instead what would carry the argument in this field: results on an evaluation set built from the buyer's own history, then live results with human review switched on); author background (it would change the bylines, which now read "the author").
How the answer is laid out, using the sections the tool prints. Where you gave no proof, the answer suggests proof points to add.
When a tool is not enough: if the decision needs more than one answer, talk it through in a call. Book 45 minutes with Shashwat.
How to read the answer
The tool turns your Topic, Your take, and Target reader into a multi-part article series (The argument, The objection, Who has to agree, What to do next, Evidence to add, and The takeaway), paired with Promotional Posts, a Publishing Order table, and Sector Notes. It does not write full prose paragraphs for you or invent proof; if you leave Proof points or Author background empty, each article marks Evidence to add and Notes for you explains what to supply. Treat the output as a structured editorial spine that needs your real customer examples and author experience before publishing.
What to do next
- Founder: Add your own lived experience to Author background and fill the Evidence to add block in each article with a real customer baseline and result.
- CEO: Review The argument and The objection in Article 1 to confirm the point of view challenges the status quo in your market without naming confidential clients.
- CMO: Schedule the series using the Publishing Order cadence (one article every two weeks) and assign an editor to turn each outline into a finished draft.
- CRO: Have account executives send Promotional Post 1 and the article link to prospects in the Target reader role who are stuck in "do nothing" or manual processes.
- Demand generation head: Distribute each published article across LinkedIn, your newsletter, and targeted outbound sequences using the Promotional Posts hooks.
Why come back
Re-run the tool with Proof points and Author background filled in to weave your verified numbers directly into the outlines, or return next quarter when you want a new 3-to-5-part series for a different buyer role or market thesis. The tool applies the same fixed rules every time. It does not call an AI model, and this site does not keep what you enter.
Who is the Thought Leadership Series tool not ideal for?
- Anyone who cannot give the inputs listed above, because the answer is built only from them.
- Teams that want finished copy in their brand voice: each tool returns a structured draft to edit, with placeholders wherever your inputs did not supply a fact.
- Anyone who needs research or quotes gathered for them: the tools do not look anything up. They work from the story and facts you give, and anything they add themselves is marked as an example, a suggestion or a placeholder.
How do I use the Thought Leadership Series tool with an AI assistant?
Add CRAFT Content once (see Use it in Claude, ChatGPT or Gemini), then ask in plain words. Here is a prompt built from the tested example:
My role is not stated. Ask me for my role first. Use CRAFT Content's Thought Leadership Series tool. topic: AI in customer support; your take: Support automation should be measured by tickets resolved, not tickets deflected; target reader: Support leaders at consumer apps with growing ticket backlogs; number of articles: 1; article type: Contrarian. If this tool is unavailable, ask me to open its web page; do not invent its output. In Gemini CLI or Antigravity, use the Helix GTM Suite skills package for related guidance, without claiming to run this connector.
How does the Thought Leadership Series tool work?
You do not need to sign in. A web browser is enough, and the answer comes back in a second or two.
- Fill in the form above, or press Fill in an example.
- Press Run Thought Leadership Series.
- Read the result on this page, then copy it and edit it before you use it.
It applies fixed rules in the CRAFT Content code to the inputs above and returns text. It does not call an AI model, does not look anything up, and does not store what you type. The web form and the AI assistant connector run the same tool code, so the same input gives the same answer in both.
For developers
MCP tool name: thought_leadership_series. REST: POST https://craft-content.gtmhelix.com/api/tools/thought_leadership_series with this JSON body (see How it works):
{
"input": {
"topic": "AI in customer support",
"your_take": "Support automation should be measured by tickets resolved, not tickets deflected",
"target_reader": "Support leaders at consumer apps with growing ticket backlogs",
"num_articles": 1,
"article_type": "contrarian"
}
}
Choose your app
Run a Helix tool in your browser, then take the result to ChatGPT, Claude or Gemini. Connectors work with supported plans in Claude, ChatGPT, Cursor, VS Code and Windsurf. The Helix GTM Suite skills package supports Claude Code, Gemini CLI, Antigravity, Cursor and VS Code, and is available through skills.sh. Each plugin has its own installation steps, below or on its own page.
Connector setup guides · Skills package setup guide · Find the skills on skills.sh