Create Quotes 70% Faster with AI: How It Works
Erwin BerkouwerWriting a quote takes an average of 90 minutes. With AI you do it in 15. Read how SME businesses are implementing this in practice.
Writing quotes is a time sink and a bottleneck in many SME businesses. Sales wants to respond quickly, but quality must remain high. The reality: an average quote costs a salesperson 60 to 120 minutes — from gathering product information to formatting the document and adding the right disclaimers. Multiply that by 10 quotes per week and you quickly lose more than two full working days to administrative work.
With AI you dramatically shorten that turnaround time. Not as a replacement for your salespeople, but as a smart assistant that builds the framework, so you can focus on the relationship and the deal.
Why AI Works So Well for Quotes
Quotes have a high degree of repeatability. The structure is usually the same: client information, scope of delivery, pricing breakdown, terms, references. Only the details vary per client and assignment. That is precisely the task where AI excels: recognising patterns, maintaining structure and filling in quickly based on available data.
An additional benefit: AI makes no typos in prices, does not forget a discount agreement stored in the CRM, and always applies the correct disclaimer per product category. Consistency goes up, the risk of errors goes down.
How an AI Quote Agent Works
A fully automated quote flow typically looks like this:
- Client request arrives — via email, web form, or meeting notes from a sales call
- AI analyses the request — recognises product categories, quantity, delivery terms and client-specific requirements
- Data retrieved from your systems — product information and current prices from ERP (Exact, AFAS), client data from CRM (HubSpot, Salesforce, Pipedrive)
- Draft quote generated — in your branded template, with the right tone for that client (new vs. existing, large vs. small)
- Review by sales — the salesperson checks, adjusts if necessary and sends
- Automatic follow-up — a reminder after 3 or 5 days if the client has not responded
The result: what used to take 90 minutes now takes 15 to 20 minutes. And for more standardised quotes, even less.
Concrete ROI: What Does It Deliver?
With SME clients we have guided through the implementation of AI quote automation, we consistently see the following results after 60 to 90 days:
- 60–80% shorter turnaround time per quote (from an average of 90 min to 15–20 min)
- 30–50% more quotes per FTE per month, without additional staff
- 5–15% higher win rate due to faster response time — clients appreciate receiving a quote within an hour rather than three days later
- Fewer errors in prices, discounts and product specifications through direct ERP integration
- Higher client satisfaction through consistent language and professional formatting
A concrete example: a technical wholesaler in the Utrecht region processed an average of 35 quotes per week before implementation with 3 FTE in sales. After 8 weeks, those same 3 people were handling 55 quotes per week — with a higher win rate due to faster response. The project payback period: 11 weeks.
What You Need to Get Started
You do not need to wait for a perfect technical infrastructure. The foundation is more likely already in place than you think:
Minimum requirements:
- A product database or ERP with current prices (Exact, AFAS, or even a well-maintained Excel sheet)
- A CRM with client data (HubSpot, Salesforce, Pipedrive — or even a structured inbox)
- Existing quote templates in Word, PDF or a quoting tool (Offorte, GetAccept, Quoter)
Recommended additions for a mature flow:
- API access to ERP and CRM
- An orchestration layer such as n8n, Make or a custom backend
- AI via API (OpenAI GPT-4o or Anthropic Claude — depending on tone and complexity)
- A review interface for sales (can be as simple as an email with an "approve" button)
Step-by-Step Plan: From 0 to a Working AI Quote Flow in 8 Weeks
Week 1–2: Inventory and Standardise
- Collect your 50 most recent quotes
- Analyse which blocks always recur: introduction, scope, pricing breakdown, terms, references
- Define standard modules per product type or client segment
- Identify the 20% of quotes that make up 80% of the volume — start with those
Week 3–4: Build the Basic Flow
- Connect AI to your ERP for current product information and prices
- Connect to CRM for client context (sector, previous orders, discount agreements)
- Build the generation flow: input → AI draft → review step → send
- Test with historical client cases: is the output correct?
Week 5–6: Pilot with 2–3 Salespeople
- Let a small group of salespeople create quotes via the new flow
- Collect structured feedback: what does the AI miss? What is incorrect?
- Adjust prompts and templates based on that feedback
- Measure two things: turnaround time and final win rate
Week 7–8: Roll-out and Optimisation
- Roll out the flow to the entire sales team
- Build a simple dashboard: number of quotes, average turnaround time, win rate per week
- Schedule a bi-weekly improvement cycle: new feedback → better prompts → better output
- Add automatic follow-up if it is not yet in your CRM
Three Pitfalls to Avoid
1. Starting immediately with complex quotes
The temptation is great to also automate large, custom assignments. Do not do this in the first phase. Start with the standard quotes that make up 80% of your volume. These are predictable, contain fewer exceptions and deliver results quickly. Custom work you tackle later, once the foundation is in place.
2. Removing the human review step
AI creates good drafts, but not perfect quotes. Especially in the first months, every AI output contains errors that only an experienced salesperson will notice — wrong tone for that client, a missing detail, a price that is just slightly off. Keep the review step mandatory, even as the technology improves. It is the quality check your client deserves.
3. Using outdated data as input
An AI quote is only as good as the data it is based on. If your ERP contains prices that are three months old, the AI will pass that error through. Invest before implementation in data hygiene: current prices, correct product descriptions, complete client profiles. This is not an AI problem — it is a data quality problem that AI makes visible.
Which Sectors Benefit Most?
AI quote automation works best in sectors with:
- High repetition in product offering: technical wholesale, building materials, IT services, manufacturing companies
- High quote frequency: more than 10 quotes per week per FTE
- Standardised pricing structures: fixed rates or simple discount structures
Sectors where more caution is advised: legal services, large infrastructure projects or highly personalised services. There AI can support (e.g. with intake analysis or formatting), but the core remains human work.
How to Get Started?
Most SMEs I speak to overestimate the technical threshold and underestimate the gains to be made. You do not need to start with a perfect, fully automated flow. Start with one product category, one type of quote, and measure the difference.
Want to know what AI quote automation would concretely deliver for your business? We are happy to conduct a quick scan of your current process and an initial sketch of a possible flow — without obligations.
[Get in touch for a free intake](/en/contact) or read more about how we guide SME businesses through AI implementations on the AI consultancy page.
