AI is everywhere in customer support right now. But for Chief Customer Officers and Support leaders at B2B companies, the real question is not whether to use AI. It is how to use it in a way that improves efficiency without compromising trust, quality, or the customer experience.
That was the focus of our recent webinar with Rob Edmondson, Chief Customer Officer at Ironclad, who shared practical lessons from Ironclad’s journey to using AI in support. Rather than chasing hype, Rob described a grounded approach: start with a clear use case, run a pilot, learn what works, and measure value in ways the business actually understands.
You can watch the full webinar replay here: Practical Playbook for AI-Driven B2B Support.
And for leaders thinking more broadly about where they are on the journey, our AI Maturity Model offers a helpful framework for understanding how support organizations can move from early AI adoption to more strategic impact.
1
In B2B support, quality matters as much as speed
One of the clearest themes from the conversation was that support quality is not just about responding faster. In B2B, customers need answers they can trust. That is especially true when the product supports mission-critical workflows, regulated processes, or multiple stakeholders across an account.
For support leaders, that means AI success should not be measured only by speed-based metrics. It also needs to be measured by accuracy, confidence, and the ability to maintain a high-quality experience at scale.
2
Headcount alone is not a long-term scaling strategy
As companies grow, support volume grows with them. At some point, simply adding people is not enough. Rob spoke candidly about the reality many support leaders face: growth eventually puts pressure on teams to find leverage, not just add capacity.
That is where AI can play an important role. Used well, it can help teams handle more demand, reduce repetitive work, and create room for humans to focus on higher-value interactions.
3
The best AI strategy starts with a real customer problem
A great takeaway from the webinar was that Ironclad did not start with AI for AI’s sake. They started with a support experience customers already loved and asked how they could recreate more of that experience at scale.
That is a smart model for any B2B support organization. Instead of beginning with a tool, begin with a customer pain point or a valuable support moment. Then evaluate how AI can help deliver that experience more consistently and efficiently.
4
Cross-functional alignment matters more than most teams expect
Support AI initiatives are rarely owned by support alone. Success depends on coordination between support, knowledge management, content, operations, and often product or customer success as well.
One of the practical lessons from Rob’s experience is that even a seemingly straightforward AI initiative can quickly expose dependencies across teams. The organizations that move fastest are usually the ones that align early on ownership, goals, and the customer experience they want to create.
5
Run a pilot before rolling AI out broadly
This was one of the most important themes in the webinar, and one that deserves emphasis: conduct a trial or pilot before a broad rollout.
Ironclad started in a controlled way, first testing how AI could support internal agents and then introducing customer-facing capabilities with a limited cohort. That approach helped the team validate quality, learn where content needed improvement, and reduce risk.
For Chief Customer Officers and Support leaders, this is one of the best ways to build confidence across the organization. A pilot gives you the chance to:
- validate answer quality before broad exposure
- identify knowledge gaps and content issues
- understand which use cases perform best
- gather internal buy-in with real results
- refine workflows before scaling further
A pilot also makes the business case easier. Instead of debating hypothetical value, you can point to outcomes from your own environment.
6
Prioritize the use cases that can create the most value first
One of the most practical lessons from the webinar is that companies should start by identifying the top AI support use cases to prioritize based on their business, customer needs, and operational goals. For some organizations, the best first move may be agent assist or co-pilot tools that help teams respond faster and more consistently. For others, it may be customer-facing self-service that helps users get answers without opening a ticket. And for many, the right approach may be to pursue both over time.
The key is to avoid treating AI as one monolithic initiative. Instead, break it into clear use cases and evaluate which ones are most likely to deliver value first.
That was part of what made Ironclad’s approach so practical. The team was interested not only in agent assist and customer-facing self-service, but also in knowledge content creation. In its trial, Ironclad tested all three. That gave the team a broader view of where AI could drive the most impact — improving internal efficiency, expanding customer access to answers, and helping the organization create and refine knowledge content more effectively.
For Chief Customer Officers and Support leaders, that is an important takeaway: the goal is not just to “do AI.” It is to identify the highest-value use cases, test them in a focused way, and use what you learn to shape a smarter rollout.
7
Your knowledge base does not have to be perfect to get started
One important takeaway from the webinar is that strong knowledge content helps AI perform better, but perfection is not a prerequisite for starting. Most B2B support organizations do not have a flawless help center or fully standardized internal documentation, and that is okay.
The key is not to treat imperfect content as a reason to wait. In fact, AI can help teams identify where knowledge is thin, outdated, inconsistent, or missing altogether. As support teams begin using AI, they often gain visibility into the questions customers ask most, where answers are weak, and which topics need better documentation.
In that way, AI can do more than deliver answers. It can become part of a continuous improvement loop for your knowledge strategy. Instead of waiting until your content is “ready,” start with what you have, use pilots to learn, and let those insights help you steadily improve your support content over time.
8
ROI should be measured in practical business terms
Another critical takeaway: you need a realistic approach to calculating and measuring ROI.
Support leaders sometimes make the mistake of looking for a singular AI metric. In practice, ROI is usually more layered than that. Rob’s perspective pointed to a more useful framing: measure how AI helps the organization support growth more efficiently, reduce manual effort, and improve operational leverage.
That means ROI can show up in areas such as:
- reduced time spent drafting responses
- improved agent productivity
- lower cost to support growth
- faster knowledge creation and maintenance
- increased self-service success
- fewer repetitive tickets reaching the queue
This is especially important for executive stakeholders. When you can translate AI into staffing efficiency, capacity expansion, and time savings, the value becomes much easier to understand.
9
The best ROI models combine hard metrics with experience signals
When calculating ROI, support leaders should track both quantitative and qualitative measures.
On the quantitative side, consider metrics like:
- average handle time
- time to first response
- resolution time
- deflection or self-service success
- cost per ticket
- agent productivity
- content production velocity
- incremental capacity created without additional headcount
On the qualitative side, look at:
- customer confidence in answers
- consistency of support quality
- agent trust in the AI outputs
- customer sentiment in interactions
- whether customers seem to be getting to resolution faster and with less friction
This matters because not every AI win shows up as an immediate CSAT jump. Sometimes the true value is that you preserve service quality while scaling more efficiently, which is often the bigger strategic win.
10
The long-term opportunity is bigger than efficiency alone
Efficiency is a strong starting point, but it should not be the end goal.
The broader opportunity is to use AI to transform how support operates: not only answering questions faster, but helping teams improve workflows, identify friction, surface content gaps, and create more proactive customer experiences.
That is also why frameworks matter. Embrace.ai’s AI Maturity Model is designed to help support and customer leaders understand where they are today and what the next stage of AI adoption should look like. For some teams, that starts with answer automation. For others, it expands into operational automation and broader business impact.
The key is to match ambition with readiness.
Final thoughts
The biggest lesson from our webinar with Rob Edmondson is that successful AI adoption in B2B support is usually not about making one giant leap. It is about making the right first move.
Start with a clear problem.
Run a thoughtful pilot.
Invest in knowledge quality.
Measure ROI in practical terms.
Scale what works.
That is how support organizations can use AI to create efficiency without sacrificing quality.
Want to go deeper?
Webinar Replay
Watch the full webinar replay here: Practical Playbook for AI-Driven B2B Support.
Ironclad ROI Study
Read the Ironclad ROI study here.
AI Maturity Model
You can also explore Embrace.ai’s AI Maturity Model to see how leading B2B support organizations are thinking about the path from early AI adoption to broader transformation.
If your team is evaluating AI for support, we’d love to talk.
And if your team is evaluating AI for support, we’d love to talk. You can set time for a conversation, demo, or discussion about an unpaid trial.
Less ticket volume. Better quality. No extra headcount.
“Embrace has provided a treasure trove of data that we are only just seeing the benefits of.”
Rob Edmondson
CCO, Ironclad
