RU
UZ
KZ
EN
Businesses managing customer inquiries via WhatsApp, Telegram, and Instagram Direct are shifting from rigid button-based bots to generative AI assistants. Standard tree menus often fail when clients ask complex questions in natural language. While dynamic models handle open-ended inquiries and multilingual dialogues with ease, they present fresh operational risks when discussing pricing.
The limits of knowledge bases
Unlike script-based bots that output fixed phrases, language models interpret uploaded manuals and product sheets to synthesize answers. However, if product catalogs or pricing tiers are not strictly synchronized with backend inventory systems, the assistant easily hallucinates. Outdated price lists, mixed-up currency rates, or conflicting wholesale thresholds lead to inaccurate promises in the chat.
Furthermore, conversational models often struggle with assertive bargaining. When a customer pushes for a discount, an unconstrained model may promise special rates or free delivery that the business does not support. When a human representative steps in or sends a payment link, the buyer produces screenshots of the bot's promise, forcing the company to either absorb the loss or deal with client friction.
Restricting AI to intake and triage
To mitigate financial discrepancies, companies are narrowing the scope of generative bots. Instead of allowing automated agents to finalize transactions, they use AI primarily for qualification, answering product specifications, and capturing project parameters. When the lead and chat summary are pushed directly into a unified interface like shamCRM, human sales reps review the context, verify stock levels, and issue the final invoice without discrepancies.
