The AI Playbook for B2B Support Teams: Key Insights from Our Webinar

In B2C customer service, teams primarily use AI to deflect high volumes of simple tickets from consumers. But in B2B, there’s a lot more that support teams can automate: Besides fielding common questions, B2B support teams handle high-complexity issues and often collaborate with other customer-facing teams to build long-term relationships.

Last week our co-founders Marty and Advith shared how B2B companies can leverage AI beyond basic ticket deflection. If you missed it live or want to revisit key insights, here are our most valuable takeaways from the webinar.

6 Key Insights on Implementing AI for Support Teams

1. B2B support is fundamentally different from B2C

The biggest mistake B2B companies make is applying consumer-focused AI strategies to their support operations. Advith explained that the workflows are fundamentally different.

In consumer support, you typically have:

• One customer-facing team handling all inquiries
• High volume of relatively simple tickets
• Focus on speed and deflection rates

In B2B support, there generally are:

• Multiple teams involved with customers (support, customer success, sales, solutions engineering, etc.)
• Lower volume but significantly higher complexity
• Greater emphasis on relationship management and revenue impact

So instead of just deflecting tickets, AI workflows in B2B will focus on enriching account context, facilitating cross-team collaboration, and surfacing insights that help teams support their customers strategically.

2. Cross-team workflows are where AI delivers the most value

One of the most powerful AI applications for B2B support actually isn't customer-facing.

Support teams in B2B companies work closely with customer success, account management, product, professional services, and even pre-sales teams. The challenge is they need context from all these cross-functional partners to do their job effectively.

"For example, imagine looking at a ticket and knowing, hey, the customer's renewal is coming up soon. They had a really poor call with their customer success manager a week ago. We talked about some ongoing issue, and then they write in about that issue," Advith shared. "How can we use AI … to help surface that information to someone on the support team, so they don't need to go digging?"

Key cross-team workflows where AI adds value:

• Support to CS: Flagging expansion opportunities when customers ask about new features or products
• Support to product: Automatically categorizing and prioritizing feature requests based on customer value and frequency
• Support to engineering: Detecting anomalies and grouping related issues before they become major incidents
• All teams to support: Providing account context like sentiment, recent calls, renewal dates, and risk indicators

3. Context switching is killing your support team's productivity

Advith emphasized that one of the biggest drains on B2B support teams isn't ticket complexity. It's the constant context switching teams have to do to get information.

The typical workflow might look like this:

1. Customer submits a vague question
2. Support team is working on another ticket, so doesn’t respond for up to a few hours
3. Support team asks follow-up question
4. Support team switches to another ticket
5. Customer responds
6. Support team has to rebuild context
7. Support team searches internal docs, pings other teams, checks multiple tools
8. Support team finally has enough information to solve the issue

With AI, this process can be streamlined:

• AI asks initial clarifying questions immediately, even outside of normal business hours
• When support teams open the ticket, there’s already relevant documentation there
• AI suggests similar past tickets for support teams to reference
• Support teams can ask AI to bring up account context from other calls, Slack threads, or CRM data—right alongside the ticket

"The goal is for there to be as much information as possible where you don't need to still consult other people or other sources to try and answer the question. You can just see all the information you need," Advith explained during the demo.

4. Build an AI support engine, don't just deploy AI tools

Rather than thinking about AI as a tool you "turn on," Advith recommends adopting an engineering mindset. That means building a support engine where AI handles repetitive workflows while your team focuses on complex problem-solving and relationship building.

Here’s how you can approach it:

1. Map your ticket lifecycle for common support scenarios from start to finish
2. Identify bottlenecks where manual work slows things down
3. Hand off repetitive tasks to AI like initial triage, clarifying questions, documentation searches
4. Iterate continuously as you find new opportunities for automation

"You're essentially trying to build this engine where … you're like the puppet master, essentially, or folks on your team … are puppet masters for the AI," Advith explained.

5. Start with a measured approach to customer-facing AI

When it comes to AI interacting directly with customers, B2B companies need to be more careful than B2C teams.

Advith's advice is to start with internal support workflows first, then deploy customer-facing AI in very specific, low-risk scenarios.

Good use cases to start with customer-facing AI:

• Asking simple clarifying questions
• Collecting information upfront
• Routing to the right team member
• Suggesting relevant documentation (with your support team’s review)

Scenarios to approach carefully:

• End-to-end ticket resolution
• High-value or at-risk accounts
• Complex technical troubleshooting
• Sensitive situations that you’ll need your team’s judgment for

You can also segment your AI deployment:

• Only use it for certain customer tiers (lower-value accounts)
• Deploy only during off-hours when your team isn't available
• Restrict to specific ticket types that are well-documented and straightforward

Advith demoed a workflow where AI asks a clarifying question, fetches internal data via API, then routes the ticket to a support specialist—without trying to fully resolve the issue. This way teams help customers faster, but there’s still a human in the loop for the actual resolution.

6. Train AI on good data sources

Because Pylon already has data across your live and async customer conversations, our AI can pull context from multiple sources to create a complete customer 360:

• Past support tickets across all channels (email, Slack, Discord, etc.)
• Call recordings from tools like Gong or Fathom
• Internal Slack channels where teams discuss accounts
• CRM data like ARR, renewal dates, and account owners
• Product usage data and custom metrics
• Documentation and knowledge base content

This means AI surfaces complete context and customer insights to your team. You can create custom AI fields with prompts like, "What CRM does this customer use?" or "Who is the biggest detractor at this account?" and AI will automatically populate the info by analyzing all the available data.

Answers to Top Audience Questions

Q: How do you monitor hallucinations and AI agent responses?

Advith explained that in Pylon, you get extensive visibility into what AI agents are doing and how they’re responding to customers. For every action the AI takes, you can see:

• What decision the AI made and why
• Which sources it referenced
• API calls it executed and responses it received
• Suggestions for improving content to make future responses better

"We try to surface as much information to you as possible to help you understand exactly what the AI is doing," he said. Pylon also includes testing tools so you can validate AI behavior before deploying it to customers.

Q: How do AI agents impact first response time metrics?

When AI answers customers, it technically counts as a first response. But Advith noted that you might want to shift your focus to resolution time or synthetic CSAT scores (where AI evaluates customer satisfaction based on the conversation).

Alternatively, you can split your reporting to show "human first response time" vs. "AI first response time" separately, especially if you're not deploying AI on every interaction.

Q: With AI bridging the gaps on gathering context, what signals should support teams still keep an eye on?

The most useful signals are business-specific, Advith explained. That could be:

• Customers mentioning competitors
• Upcoming renewal dates
• Security concerns for security-focused companies
• Ticket volume patterns from a specific customer segment

Identify signals that would change how you handle customer interactions based on your specific business model and priorities.

Q: What are use cases where we shouldn't be trying to implement AI or where it still isn't a great fit?

Advith emphasized that internal workflows are often a great fit for AI. "Things like helping you write your knowledge base, helping you find all the common things customers are saying, help you figure out whether customer sentiment is poor, helping surface key information about a customer automatically."

For customer-facing AI, be very intentional about deployment. Consider using AI only for:

• Specific customer tiers (like lower-value accounts or PLG customers with simpler questions)
• Off-hours when your support team isn't available
• Specific types of questions that are well-documented and straightforward
• Onboarding phases when questions tend to be simpler

The key is to be thoughtful about it instead of trying to automate everything at once.

Accelerate Your B2B Support Operations with AI

B2B teams can accelerate and improve support operations with AI—as long as you approach implementation strategically. Start with internal workflows that automate real bottlenecks, gradually deploy AI for specific customer-facing tasks, and keep iterating to improve how it performs for your team.

Pylon is the modern B2B support platform that offers true omnichannel support across Slack, Teams, email, chat, ticket forms, and more. Our AI Agents & Assistants automate busywork and reduce response times. Plus, with Account Intelligence that unifies scattered customer signals to calculate health scores and identify churn risk, we're built for customer success at scale.

Book a demo today.

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