Machine Learning Boosts AI Quotes for Maximum Conversions

Executive Summary

AI-driven machine learning boosts quoting speed, accuracy, and conversions, transforming sales with explainable AI and learning about machine learning.

In an intensely competitive B2B environment, a mid-sized enterprise found its manual quoting process becoming a significant bottleneck. Preparing quotes took over three hours on average, discounting practices were inconsistent across different regions, and forecasting accuracy was unreliable. These challenges culminated in lost revenue and missed growth targets. Notably, the company’s competitors did not necessarily have superior products but were winning by moving faster and executing with greater precision.

To reverse this downward trend, the company adopted a practical and explainable AI-driven quoting solution, built upon advanced machine learning techniques and seamlessly integrated with their existing systems. Within six months, the speed of quoting more than doubled, conversion rates improved by over 34%, and quoting errors almost entirely disappeared. Most importantly, the quoting process transformed from an operational burden into a strategic driver of revenue, delivering more than five times the return on investment in less than a year.

Client Challenges

The quoting workflow exposed critical operational vulnerabilities. The average turnaround time for producing a quote was 3.2 hours, causing delays that undermined high-intent sales conversations. Discounting practices varied widely by region—sometimes by as much as 15 to 18 per cent—resulting in customer confusion and erosion of profitability. The company’s proposal templates were generic and rigid, failing to connect with prospects, especially during competitive bids. Over eight per cent of quotes contained pricing or configuration errors, which damaged trust and necessitated costly rework. Additionally, revenue forecasts were off by approximately 23 per cent, undermining internal confidence and complicating planning. Without decisive intervention, the company risked falling behind faster-moving rivals—not due to product weakness but inefficiency in execution.

Project Details

The project involved developing a web application leveraging machine learning and learning machine learning models such as predictive analytics and reinforcement learning, alongside natural language generation (NLG) and APIs. The initiative ran from November 2023 to May 2024, with a budget structured to be SME-friendly yet scalable for future expansion.

Why They Chose Us

The client valued our unique blend of deep technical expertise in machine learning and operational pragmatism. With a strong track record of improving sales pipelines by between 20 and 45 per cent and proven experience embedding AI and machine learning into complex quoting environments, we offered both credibility and capability. We placed strong emphasis on model transparency, low-friction adoption, and delivering measurable early wins, thus fostering stakeholder confidence that improvements would be swift, sustainable, and aligned with existing sales behaviours.

Solution

Our approach prioritised rapid business value over theoretical perfection in learning about machine learning implementation. Machine learning models provided predictive win scoring by assigning live probability scores to sales opportunities, helping the sales team to prioritise deals with the highest potential. Natural language generation was used to produce personalised, sector-specific proposals tailored to each prospect’s profile. Smart quoting workflows incorporated guardrails around discounting, compliance checks, and auto-approval triggers, reducing friction while protecting profit margins. Models were retrained quarterly, drawing on fresh data, A/B test results, and sales feedback to ensure the system remained relevant and effective. Clear, explainable AI outputs helped build trust within the sales team, encouraging adoption rather than resistance.

Key Features

Live win probability scores highlighted deals with a high likelihood of closing based on deal size, sector, and historical patterns. Real-time dynamic discounts adjusted thresholds according to region and customer type, improving margin consistency by 14 per cent. Industry-specific proposal automation employed tailored language and sector-relevant case studies, increasing engagement by 21 per cent. Pipeline risk dashboards surfaced deals at risk of delay or profit erosion, improving forecasting accuracy by 17 per cent. The explainable AI interfaces rendered recommendations transparent, which lifted adoption rates from 54 to 88 per cent within six months.

Results

The transformation generated substantial, measurable outcomes. Quote-to-deal conversion rates increased by 34.7 per cent, raising win rates from 31 to over 41 per cent. The time taken to prepare quotes was reduced by 58 per cent, from 3.2 hours down to 1.3 hours on average. Quoting errors dropped dramatically by 98 per cent, falling from 8.4 to less than 0.2 per cent. Topline revenue grew by 6.8 per cent, with 4.3 per cent directly attributable to quoting optimisation. Forecast accuracy improved by 17 per cent, tightening prediction errors from ±23 to ±6 per cent. Sales productivity increased by 28 per cent, enabling representatives to handle 22 per cent more opportunities without increasing headcount.

Implementation Challenges

Early sales adoption proved challenging, with only 54 per cent of sales representatives initially engaging with the AI and machine learning features. This was overcome through targeted workshops, incentives, and initiatives focused on explainability, driving adoption to 88 per cent. Data quality issues surfaced, as 16 per cent of historical data was unusable. A six-week sprint was executed to remediate, align, and enrich datasets. Initial API latency caused middleware delays but was addressed by optimising backend processes, reducing quote generation latency to under one second.

Lessons Learned

Human trust emerged as a non-negotiable foundation for success in deploying machine learning solutions. Transparent AI recommendations built confidence and accelerated adoption. Explainability was critical, as sales teams required not only insights but clear understanding of the rationale behind them. An iterative approach with continuous learning cycles proved far more effective than static design, preserving momentum and ensuring sustained performance.

Next Steps

With quoting now a strategic revenue enabler, the client is advancing further initiatives. These include negotiation intelligence to support live discount decisions in real time and intent-driven quote customisation using CRM and behavioural signals to tailor content dynamically. Plans are underway to globally expand quoting intelligence across APAC, LATAM, and EMEA. Furthermore, the client aims to develop customer lifetime value-focused pricing models that optimise long-term value rather than solely deal size.

Final Thoughts

This transformation demonstrates that quoting is far more than a sales administrative task; it is a powerful lever for growth. By embedding explainable AI and machine learning into the sales process and focusing on adoption, the client accelerated deal velocity and unlocked scalable, sustainable value. The success was not driven by technology alone but was founded on collaboration, clarity, and continuous learning. This approach created a quoting engine that does not merely keep pace with the market but improves quarter by quarter.

Ready to transform your quoting process? Get in touch to see how our AI-powered solutions can cut quote time, boost conversions, and deliver real business impact — fast. Let’s talk.