🎮 The Next Input — Issue #174

In partnership with

⚡ The Briefing — 60 sec

• Image AI models now drive app growth, beating chatbot upgrades
  Didn’t see this stat coming but interesting takeaway nonetheless. Turns out shiny visual creation may be doing more to move app growth than another round of “chatbot got smarter” updates.

• OpenAI, Anthropic launch separate joint venture PE partnerships
  Big 4 might become the Mighty 2 if they pull this off right. OpenAI and Anthropic are not just selling tools anymore — they are trying to become the operating layer PE firms push through entire portfolios.

• Canadian musician sues Google after AI Overview wrongly claimed he was a sex offender
  Pretty horrifying considering the implications. If AI search can falsely attach that kind of claim to someone’s name and cost them work, “hallucination” stops sounding cute very quickly.

🛠️ The Playbook — The AI Reputation Risk Engine

Mission
Build AI workflows that protect people, brands, and businesses from false claims, synthetic mistakes, and automated reputational damage.

Difficulty
Intermediate

Build time
3–5 hours

ROI
Lower legal exposure, faster incident response, and a much stronger trust layer around AI-generated outputs.

0) Why This Matters

This is where AI gets sharp.

Visual models are driving growth because people can see the output instantly. Private equity wants AI deployed across entire portfolios because the upside scales fast. And Google’s AI Overview reportedly misidentifying Canadian musician Ashley MacIsaac as a sex offender shows what happens when AI’s speed meets reputational harm. MacIsaac has filed a $1.5 million civil lawsuit after the false claim allegedly contributed to a cancelled concert. (theguardian.com)

So the move is not:

• generate faster

• deploy wider

• trust the summary

• hope the brand survives

The move is:

• verify claims before publication

• classify reputational risk before automation

• monitor AI-generated mentions

• create a correction path before damage compounds

1) Architecture

2) Workflow

1. Identify where AI-generated outputs could mention people, customers, partners, staff, or public figures.

2. Classify each output by reputational risk before it is published, acted on, or circulated.

3. Require evidence for any claim involving misconduct, legality, health, finance, employment, or identity.

4. Route high-risk claims to human review before they move anywhere public.

5. Monitor external AI-generated search results or summaries for damaging false claims.

6. Create a correction workflow with evidence, escalation owner, and response timing.

3) Example Prompts

Reputation Risk Classifier

You are reviewing an AI-generated claim for reputational risk.

Classify the claim as:
- low risk
- medium risk
- high risk
- critical risk

Check whether it involves:
- criminal allegations
- professional misconduct
- health, finance, or legal claims
- personal identity
- public reputation

Claim:
[insert claim]

Evidence Check Prompt

You are fact-checking an AI-generated statement.

For each claim:
- identify whether it is supported by evidence
- identify what source is required
- identify whether it should be removed, revised, or escalated
- flag any claim that could cause reputational harm

Text:
[insert text]

AI Search Monitoring Prompt

You are monitoring AI-generated search or summary outputs for reputational risk.

Review the result below and identify:
- false or unsupported claims
- damaging implications
- required corrections
- who should be notified

Result:
[insert AI-generated summary]

Incident Response Prompt

You are preparing a response to an AI-generated false claim.

Include:
- what the false claim was
- what evidence disproves it
- who needs to be contacted
- what correction should be requested
- what public or internal statement may be needed

Keep it clear and calm.

4) Guardrails

• Never publish high-risk claims without evidence.

• Treat criminal, legal, medical, financial, and identity claims as critical by default.

• Do not let AI summaries become source material for other AI summaries.

• Keep a correction path for false AI-generated claims.

• Log every disputed claim and response.

• Review reputational risk before scaling AI-generated content across large audiences.

5) Pilot Rollout — 3 hours

1. Pick one workflow that generates or republishes claims about people, companies, or partners.

2. Add a reputational risk classifier before publication or action.

3. Define which categories require evidence and human review.

4. Create a correction template for false or unsupported AI-generated claims.

5. Run 10–15 real examples through the process.

6. Tighten the workflow based on what gets flagged.

6) Metrics

• Number of high-risk claims detected

• Percentage of claims with supporting evidence

• Human review rate for reputational content

• False-claim correction time

• Number of disputed AI outputs logged

• Escalation rate to legal or comms

• Public-facing correction rate

Pro Tip: The most dangerous AI error is not the weird image or the bad paragraph. It is the false claim that looks official enough for someone else to believe it.

🎯 The Arsenal — Tools & Platforms

• Google Alerts / media monitoring · track public mentions before AI-generated falsehoods snowball · Google Alerts

• Airtable · simple register for disputed claims, risk level, evidence, owner, and resolution · Airtable

• Claude / ChatGPT · useful for claim extraction, risk classification, and incident response drafting · Anthropic · ChatGPT

• Private equity AI rollout trackers · necessary if AI tools are being pushed across many portfolio companies at once; OpenAI and Anthropic are both pursuing PE distribution plays. (axios.com)

• Human review queues · boring until one AI-generated sentence turns into a legal problem

Copy-paste prompt block:

You are helping me build an AI Reputation Risk Engine.

For the workflow below:
1. identify where AI may generate claims about people, companies, or partners
2. classify the reputational risk of those claims
3. identify what evidence is required before publication or action
4. identify where human review is mandatory
5. design a correction path for false claims
6. list the top 5 failure modes
7. define the key metrics to track

Workflow:
[insert workflow here]

Return the answer in markdown with sections for:
- Workflow summary
- Claim map
- Risk classification
- Evidence requirements
- Human review points
- Correction path
- Metrics

💡 Free Office Hours

If your organisation is using AI to generate content, summaries, or public-facing claims, I run free office hours to help map the reputation risk layer before one wrong sentence becomes the whole story.

4x more context into every prompt. Zero extra effort.

You think faster than you type. Which means every typed prompt leaves out the constraints, examples, and edge cases that would have made the output actually useful.

Wispr Flow turns your voice into paste-ready text inside any AI tool. Speak naturally — include "um"s, tangents, half-finished thoughts — and Flow cleans everything up. You get detailed, structured prompts without touching a keyboard.

89% of messages sent with zero edits. Used by teams at OpenAI, Vercel, and Clay. Free on Mac, Windows, and iPhone.

Try Wispr Flow free

Book here: https://calendly.com

🕹️ Game Over

AI can scale creation. It can also scale damage. Verify before the machine starts writing cheques your reputation has to cash.

— Aaron Automating the boring. Amplifying the brilliant.

Subscribe: link