Win Objection Response

---
name: win-objection-response
description: Prepares your answer to one live client objection with LAER, the objection in their exact words, an honest acknowledgement, exploring questions to find the concern under it, a response built only from your real proof, and what you will not say, including "AI could do this" and "too expensive". Use for "run win-objection-response", "the client says AI could do this", "they said it is too expensive", "handle this objection", "how do I answer this pushback", "why are you charging so much", part of the Claude Guide for Finding Clients Pack by Polar Bear.
---

# Objection Response

## When To Use
A client says "AI could do this in ten seconds, why are you charging so much?", or "too expensive", or "we need to think about it". You want to answer the real concern honestly, from proof you have, without a rebuttal script and without selling against AI.

## When Not To Use
If no one has objected yet and you want your position on AI ready in advance, run AI Use Statement. If the answer turns out to be that the scope or price should change, run Pricing Options to rebuild it rather than defending the old number.

## Inputs
- The objection, in the client's exact words, and where it came (call, email, walk-through)
- Your proof: case sources or one-pagers, your AI Use Statement, your proposal and options
- What you already know about their decision and budget from discovery
If you have none of this, I start from the objection alone, give you the exploring questions, and leave the response as a first draft marked [needs proof].

## Approach
LAER, from Carew International (carew.com): Listen, Acknowledge, Explore, Respond. The judgement sits in Explore, the step people skip: the stated objection is rarely the whole concern, and answering it straight turns the method into a rebuttal script. "AI could do this" may mean a real alternative they are weighing, a budget squeeze, or a boss asking the question. The failure it prevents is a confident speech about why AI cannot do your job, delivered to a client who was actually worried about risk.

## Workflow
1. Ask three questions: what exactly did they say (word for word if you can); what happened just before; and is there any part of the work AI genuinely could do for them.
2. Listen: write the objection in their exact words, not paraphrased into the one you would rather answer.
3. Acknowledge: one honest line that shows you understood, without agreeing to what is untrue and without "great question". For the AI objection, acknowledging that AI does change some of this work is usually true and disarms more than denial.
4. Explore: one to three open questions to find the concern under it: budget, risk, trust, internal pressure, a real AI alternative, timing. You ask them and listen; I do not guess the answer and state it as fact.
5. Respond, per likely concern, only from real proof: a case with a defended figure, your AI Use Statement, the options. If AI really can do part of it, say so, take that part out or price it accordingly, and name what remains that they pay you for (judgement, accountability, the decision you stand behind). For "too expensive", offer less scope at a lower price, never the same work cheaper.
6. Write what you will not say: no selling against AI, no hiding your own AI use, no fake scarcity, no claim you cannot prove.

## Output Format
```markdown
# Objection Response: [project]
## Listen
"[Objection in their exact words]" ([where and when])
## Acknowledge
[One honest line]
## Explore
1. [Open question]
2. [Open question]
## Respond, by the concern you find
| Concern under it | Your response | Proof it rests on |
|---|---|---|
| [budget / risk / trust / AI alternative] | [what you would say] | [case, AI Use Statement, option] |
## What I will not say
- [line you are tempted to use and will not]
## Decision
You choose which response fits once you have heard the answers to Explore, and decide by [date] whether the scope or price changes.
```

## Done When
- The objection is recorded in the client's words, not yours.
- At least one exploring question comes before any response.
- Every response points to proof you actually have, or is marked [needs proof].
- The "will not say" list is written.

## Quality Bar
- No judgement of the client; the concern is about the work, the price or the risk, never the person.
- No invented results, client quotes or claims about what AI cannot do.
- A price change always comes with a scope change, said plainly.
- Answer from real proof; never sell against AI and never hide its use.

## Next
Run win-win-loss-review (Win/Loss Review) to learn from the outcome, either way.

## About the makers

This pack is made by Polar Bear, a consultancy built by ex-McKinsey founders with a dream to make AI work for People, not instead of them. We help our clients build people systems and AI-first ways of working, and we run our own company on Claude. If your team has outgrown the self-serve version, message Pauline (linkedin.com/in/paulinebertry).