Top 5 AI models transforming B2B customer support 2025

Written by

Govind Kavaturi

Published on

Oct 8, 2025

In this article, you can find:

Table of Content

Table of content

Customer support has officially entered its intelligence era. In 2025, AI models are no longer just digital assistants that help agents type faster — they’re reasoning systems that manage conversations, execute actions, and learn from every interaction. The question has shifted from “Should we use AI?” to “Which AI model powers our customer experience?”

For B2B companies, that choice carries real weight. Enterprise clients expect accuracy, consistency, and context across every channel — Slack, Teams, email, or web chat. They don’t just want answers; they want resolution, transparency, and insight.

This is where the latest AI models redefine what’s possible. Systems like GPT-5, Claude Opus, Gemini 2.5, Llama 4, and Qwen 3 blend reasoning, retrieval, and automation to deliver outcomes that were unimaginable just a year ago. They don’t just respond — they act.

Platforms like Thena are already leading this change by integrating these AI models into multi-channel support ecosystems. Instead of siloed chatbots, B2B teams now operate with AI copilots that understand account context, update CRMs, escalate issues, and close tickets autonomously.

What is an AI model?

An AI model is the engine that understands and generates human language, processes data, and performs actions. Trained on massive datasets of text, code, and interactions, these models learn how to reason, write, summarize, and solve problems.

Modern AI models — built on large-scale transformer architectures — can analyze long conversations, interpret images or structured data, and maintain context across channels. In B2B customer support, they act as digital teammates: understanding intent, retrieving information, and taking follow-up actions like updating systems or tagging tickets.

How can you use AI models in B2B customer support?

B2B customer support is complex, multi-layered, and deeply contextual. AI models simplify this world by understanding language, recalling past interactions, and performing actions that previously required manual effort.

Here’s how leading B2B teams are using AI models today:

Automated query resolution
AI models instantly interpret complex support requests, suggest or deliver solutions, and pull relevant documentation.
Smart ticket detection and routing
AI identifies ticket intent and urgency, tagging and routing issues to the right teams with minimal manual effort.
Proactive account intelligence
By analyzing ticket frequency, sentiment, and behavior, AI detects risk patterns and alerts success teams before churn risk grows.
Knowledge retrieval and summarization
Instead of manually searching documentation, teams can ask AI questions in natural language and get accurate, referenced answers.
Action execution
Modern AI doesn’t stop at talking. It performs — logging issues, updating CRM fields, or creating tasks based on the conversation.
Unified support across channels
Whether customers reach out on Slack, Teams, email, or web chat, AI maintains full context and continuity.

Platforms like Thena make this orchestration effortless — combining AI understanding, ticketing, and customer context into a single, account-centric support view.

What are the advantages of AI models?

AI models have become the operational core of modern customer support. They combine intelligence, automation, and analytics to deliver better service and cleaner operations.

1. 24/7 responsiveness

Always-on service with accurate, on-brand responses across time zones.

2. Operational efficiency

AI handles internal operations like tagging tickets, updating CRMs, summarizing threads, and escalating automatically. Every message improves your data quality and reporting.

3. Consistent quality

AI maintains tone, compliance, and factual consistency across agents and channels.

4. Infinite scalability

AI scales instantly with customer volume — no additional headcount required.

5. Deep contextual understanding

Models remember conversation history, configurations, and SLAs, creating seamless experiences.

6. Proactive intelligence

AI identifies sentiment changes or repeated issues, predicting churn or dissatisfaction before escalation.

7. Agent augmentation

Instead of replacing people, AI empowers them — drafting replies, summarizing context, and automating repetitive work.

8. Data-driven visibility

Every conversation becomes structured insight, helping leaders identify trends and optimize operations.

9. Multilingual and omnichannel reach

AI can handle multiple languages and unify tone across Slack, Teams, and email.

10. Reduced cognitive load

By removing repetitive tasks, AI gives human teams the space to focus on relationship-driven interactions.

In essence: AI turns customer support into a proactive, intelligent, data-rich function — not a cost center.

What are the disadvantages?

AI adoption in B2B support comes with practical considerations.

Hallucination risk: Even advanced models can produce inaccurate statements if not grounded in data. Pair them with retrieval-augmented generation (RAG) and human review.
Data privacy: Enterprise data demands secure environments and compliance with SOC 2, GDPR, and customer contracts.
Integration complexity: Connecting AI with CRMs, chat tools, and knowledge bases requires careful design.
Prompt dependency: The quality of AI responses depends on structured prompts and clean context injection.
Cost: High-end models like GPT-5 are powerful but come with high inference costs.
Change management: Teams must adapt to working with AI copilots rather than static chatbots.

Gartner predicts that by 2026, 60% of enterprise support functions will use AI orchestration as their core support layer — but only those with strong governance will realize its full potential.

Top 5 AI models for B2B customer support in 2025

1. OpenAI GPT-5

OpenAI’s GPT-5 is the most advanced general-purpose AI model available in 2025. It offers superior reasoning, persistent memory, and multimodal understanding — meaning it can process text, images, and data simultaneously.