Creating quotes is time-consuming and slows down the sales process. AI can automate a large part of the structure and initial elaboration. Here is how a service provider reduced its quoting process from five days to 24 hours.
An IT services company received twelve to fifteen quote requests per week. Every quote was assembled manually: processing information from the intake conversation, writing a proposal, calculating prices, handling formatting. On average that took two to three hours per quote. The result was a turnaround time of three to five working days. Potential clients drop out sooner the longer it takes.
The manual quoting process had three structural problems. First, it was time-consuming: sales consultants spent a considerable part of their week writing quotes rather than talking with clients. Second, there was inconsistency in quality: one consultant wrote more comprehensive proposals than another, leading to an uneven client experience. Third, the client was waiting: three to five days is a long time in a market where competitors can respond faster.
The solution consisted of two core elements.
Element 1: Structured intake Instead of a free-form intake conversation, a structured intake form was introduced that prospects completed before the first meeting. The form asked about project goal, scope, timeline, budget indication, technical environment and success criteria. This delivered standardised input directly usable as a basis for AI generation.
Element 2: AI-driven proposal structure Based on the completed intake, an AI model automatically generated a first version of the proposal. That version included:
The consultant reviewed and adapted the generated version: added specific details, adjusted the approach based on the conversation and completed the final formatting.
Before automation: two to three hours per quote, turnaround three to five working days.
After automation: forty to fifty minutes per quote, turnaround less than 24 hours for 80 percent of requests.
The remaining 20 percent involved complex or atypical requests that required more customisation and still took around two hours.
Beyond time savings, the quality of proposals also improved. The structured intake ensured consultants asked the right questions earlier. The modular structure of the proposal made it easier to be consistent in approach and completeness.
Clients indicated in feedback conversations that proposals better matched their question and were clearer than before. Not because the consultant had improved, but because the structure had.
Price generation was the most sensitive component. The AI model generated prices based on an internal pricing model, but that model did not account for client-specific factors such as relationship history, strategic importance or competitive position. Every price required human validation before going to the client.
The first version of the intake was also too long: prospects abandoned forms with more than twelve fields. After reducing to seven core fields, the completion rate rose from 54 to 78 percent.
The faster turnaround had a noticeable effect on conversion. For requests where the proposal was available within 24 hours, the conversion rate was 23 percent higher than for requests with a turnaround of more than three days. Speed of response is a quality signal for prospects.
Automating the quoting process with AI significantly accelerates turnaround time and improves the consistency of proposals. The critical success factors are a well-designed structured intake and a pricing model that the consultant always validates before sending.
Mach8 helps organisations automate sales and quoting processes with AI. View our AI agents service or get in touch.
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