Why generic AI agents fall short in quote creation
A sales support employee receives a customer inquiry by email: ten line items, different product numbers, and a handwritten bill of materials attached as a photo. By the time the quote has been entered into the ERP system, they have spent twenty minutes searching for products, checking prices, and manually transferring data.
The problem is rarely a lack of knowledge. Instead, the issue is that this knowledge is not structured or connected to the relevant systems. For the person responsible for inside sales, the case is easy to understand because they know where to look – in the ERP, the CRM, or the customer’s most recent email. For an AI agent, things become much more difficult when exactly these connections are missing.
What can a generic AI agent really do – and what can’t it do?
A generic AI agent can draft quotes, format content, and turn predefined requirements into a linguistically polished document. That is where its real strength lies.
But without connections to the relevant systems and a clearly defined process behind it, that is not enough to create a reliable quote. The agent lacks access to validated product data, current prices, and the context stored across the ERP, CRM, and email. The result: fabricated or incorrect product numbers, unvalidated information, and no adherence to the actual sales process. In the end, the quote still has to be manually entered into the ERP – meaning the time savings the AI was supposed to deliver simply do not materialize.
The challenge begins when a process depends heavily on company-specific rules, data, and systems – because these are not automatically part of a generic model. Typical scenarios include:
Relevant knowledge may already exist digitally – but it is not structured or accessible to an AI agent. Special agreements, individual customer requirements, or employees’ experience-based knowledge may be stored in emails, Excel spreadsheets, or distributed across multiple systems. In this case, a generic agent often does not even know how to handle this information, even if it theoretically exists.
The data is available, but many rules interact with one another. An ERP system may contain standard prices, special pricing conditions, volume discounts, small-quantity surcharges, and delivery terms. A generic agent may well be able to find this information – but the question is whether it can reliably derive the correct decision from it. Is the special price still valid? Does it take precedence over the volume discount? What happens when two pieces of information do not clearly match?
There is no clearly defined quote-creation process – including integration with ERP, CRM, and email. Without this integration, an agent can generate text, but it cannot provide a reliable basis for the quote itself. The result is a quote containing incorrect products or unvalidated information – and one that ultimately still has to be manually entered into the ERP.
The more such rules, exceptions and missing system integrations interact, the less sufficient it becomes to simply connect an AI agent to the available data. The challenge is no longer understanding the information, but reliably applying the underlying business logic and processes.
That's why enmit goes a step further
At enmit, we connect the data available in the ERP with the specific business logic of each company – including special pricing conditions, small-quantity surcharges, delivery prices, framework agreements, and the rules governing how this information interacts. enmit is directly connected to the ERP and email, follows a clearly defined quote-creation process, and creates the quote directly in the ERP – instead of merely generating text that then has to be manually entered.
If information is missing or a quote is ambiguous, the system does not simply make an automatic decision. Instead, it recognizes the uncertainty and specifically escalates the case to a human for review.
Benefits our customers gain from this:
Faster processing: more quotes handled in the same amount of time, without additional headcount – faster responses increase close rates and revenue from existing inquiries
Centralized knowledge: special terms and business rules live in one place, instead of on sticky notes, in spreadsheets, or in individual employees' heads
More time for active sales: less manual double-checking means more room for customer relationships and closing deals