Sales reps spend only 28% of their time actually selling. According to Salesforce's own data, the remaining 72% goes to data entry, configuration, approvals, and administrative work — a disproportionate chunk of which lives in the quoting process. Industry analysis identifies that the average B2B sales cycle is 25% longer today than it was in 2019, with quoting bottlenecks named as a leading contributor to that drag.
The quoting problem
Configure, price, quote (CPQ) tools were supposed to fix this. For large enterprises willing to absorb six-to-twelve-month implementations, dedicated RevOps teams, and external consultants charging $200–400 per hour, they delivered partial value. But research finds that fewer than 30% of mid-market B2B companies have deployed CPQ at all. Most still quote from spreadsheets and email threads.
The structural problem is that traditional CPQ software builds the environment for quoting — it does not build the quote. It requires humans to configure, maintain, and operate the rules engine and the product catalogue, and then manually approve. For every dollar spent on CPQ licences, companies typically spend ten or more on the people needed to make the system work: deal desk analysts, RevOps managers, Salesforce admins, and implementation consultants. Across B2B companies, the total annual labour spend on quoting exceeds $50 billion.
Why AI CPQ is different from CPQ with AI features
Most legacy CPQ vendors have added AI-copilots that suggest pricing, assistants that streamline configuration and chat interfaces layered onto the same underlying rules engine. But these are still tools that help humans work faster — they do not change who does the work. AI CPQ, in its more capable forms, is different in kind, not just degree.
The distinction worth making here is between intelligence and judgement in quoting. Intelligence covers roughly 80% of the process: validating product configurations, applying pricing tiers, checking discount guardrails, routing approvals, and enforcing compliance with business rules.
Judgement is the remaining 20%. It is where experienced reps excel. Tasks like structuring terms around a tight buyer budget, deciding when to push back on a discount request, and reading competitive dynamics mid-deal stay with the human.
AI CPQ is still maturing. Truly agentic implementations are where AI builds the quote end-to-end, and the human only reviews it. Such workflows remain the exception rather than the norm.
Dynamic pricing optimisation
Static pricing rules capture pricing logic as it stood at the last configuration update. A price list accurate six months ago may be losing deals to more adaptively priced competitors, or approving discounts that erode margin.
Dynamic pricing optimisation addresses this by continuously adapting recommendations based on real-time signals: competitor benchmarks, deal history, buyer segmentation, and margin thresholds. But the bigger opportunity is AI identifying losses and plugging them.
About 70% of sales teams using AI have seen deal sizes grow, and 79% report greater profitability as a direct result.
Closing the loop: quote-to-cash automation
Revenue leakage is not confined solely to the active sales cycle. Revenue leaks between the sale and the invoice, in manual handoffs from CPQ to ERP, version conflicts, and gaps between what was quoted and what was eventually billed. Quote-to-cash automation connects the full chain: from initial configuration through contract execution, billing, and revenue recognition, without manual re-entry at each stage.
Nealy 78% of sales professionals using AI tools report shorter sales cycles, and 76% attribute higher win rates to AI-powered quoting. Industry research estimates that effective AI implementation in sales can deliver 5–10% revenue increases alongside 10–20% cost reductions. For an organisation running $200 million through its sales function, even a conservative 5% revenue rise amounts to $10 million.
How can Infosys BPM help with quote-to-cash automation?
When quotes are handled by systems that learn from every deal rather than rigid rules, there is a quantifiable revenue growth. Infosys BPM helps businesses modernise their sales and commercial operations, applying process intelligence and automation capability to turn quote-to-cash into a competitive advantage.
Frequently asked questions
Traditional CPQ builds the environment for quoting — humans still configure, maintain, and approve every output using rules engines and product catalogues. AI CPQ handles the intelligence layer autonomously: validating configurations, applying pricing tiers, enforcing discount guardrails, and routing approvals without manual intervention. The structural distinction is who does the work, not how fast they do it. AI CPQ reduces the labour overhead that makes traditional CPQ implementations cost ten dollars in people for every dollar in licence fees.
Substantial. Revenue leaks between the sale and the invoice through manual re-entry errors, version conflicts between quoted and billed terms, and gaps in contract execution handoffs. These losses are structurally invisible in organisations where CPQ and ERP operate disconnectedly. Quote-to-cash automation closes this chain — from initial configuration through billing and revenue recognition — eliminating the manual intervention points where discrepancies between committed and collected revenue accumulate silently.
Static pricing rules reflect market conditions at the last configuration update — which may be months old. Dynamic pricing optimisation continuously adapts recommendations using real-time competitor benchmarks, deal history, buyer segmentation, and margin thresholds. Organisations approving discounts against outdated static rules systematically erode margins on deals that current market conditions would support pricing higher. Industry data shows 79% of sales teams using AI-driven pricing report greater profitability as a direct result.
Agentic quoting workflows that bypass human review create accountability gaps, compliance exposure on regulated products, and audit trail deficiencies that procurement and legal teams require for contract defensibility. Standard enterprise architectures for AI CPQ retain human judgement for the 20% of quoting decisions requiring contextual deal structure, competitive positioning, and terms negotiation — while automating the 80% that is rule-bound configuration and validation. Removing human oversight entirely from high-value deals increases commercial risk materially.
Measurable within the first full sales cycle post-implementation. Industry research estimates effective AI implementation in sales delivers 5–10% revenue increases alongside 10–20% cost reductions. For an organisation processing $200 million through its sales function, a conservative 5% revenue improvement represents $10 million. Additional ROI derives from cycle compression — the average B2B sales cycle is 25% longer than in 2019, with quoting bottlenecks cited as a leading contributor — and from recovery of revenue previously lost to manual billing discrepancies.
