By Gerard Cafaro, Senior Director, Customer Support at Precisely
The transformative potential of AI continues to be top of mind for organizations across industries.
And increasingly, that potential is now paired with executive pressure to act. Leaders want teams to adopt AI, improve efficiency, and create more time for higher-value work. But one question keeps coming up: how do you turn AI’s promise into measurable business value?
This has been a focus for our team at Precisely, too. And in customer support, we’ve already seen meaningful ROI from a focused, practical AI strategy.
Our experience shows that the right AI deployment can do more than remove bottlenecks. Even in a function as human-centered as customer support, it can reshape how you scale, serve customers, and strengthen your team.
When Growth Creates Pressure
Like many fast-growing companies, Precisely faced a familiar challenge: how do you maintain a high standard of service as the business expands?
We had grown to support more than 12,000 customers across 100 product lines. Our traditional support model, built around email and phone, struggled to keep pace.
At the same time, we faced an accessibility paradox. Key support documents sat behind a login, but customers often had to contact support first to get access. That pushed them back to phone and email, which increased queues and slowed response times.
Our knowledge base also spanned roughly 150,000 documents across multiple sources, including websites, forums, and internal systems. Even when the answer existed, it was often hard for customers to find on their own.
This was more than a user experience issue. It was a scalability issue. Customers wanted self-service, but our support infrastructure was not built to deliver at the level they expected. That challenge became the catalyst for change.
Building a Frictionless Self-Service Experience
Adding more support agents was not going to solve the problem long term. Real scale comes from helping customers find answers on their own. The shift towards self-service is not just a preference but an accelerating trend: Gartner predicts that by 2027, self-service and live chat will surpass traditional channels like phone and email as the leading customer service technologies.
That expectation is already standard in B2C experiences. Industry research indicates that B2B customers expect the same. To get there, we had to move beyond simply organizing support content and start delivering it in a way that was intelligent, secure, and easy to use.
Using Coveo’s AI platform, we can now provide direct, personalized answers to complex natural language queries. Those answers draw from our indexed enterprise content and respect the permissions already in place. Customers get accurate, consistent, context-aware information faster, without unnecessary friction.
We also avoided a large, disruptive content migration. Instead, our new AI-powered search platform gave us a unified search layer across distributed content sources, while preserving document ownership and access controls across teams.
That mattered for two reasons:
- We were able to improve the self-service experience without overhauling our existing systems.
- Our support team gained more control. We could launch A/B tests, make weekly updates, and improve the experience without waiting on IT tickets or long development cycles.
That approach helped us build a test-and-learn culture grounded in evidence.
Every change, from search ranking to UX updates, could be measured, reversed, and tied to clear outcomes such as click-through rates, engagement, case deflection, and answer accuracy.
The Business Impact: Lower Costs and Higher-Value Work
AI is not only helping us resolve cases faster, it is lowering the number of cases opened overall.
After running a search, more than 80% of users do not continue to the case form to submit a ticket. That has led to nearly 50% explicit case deflection overall.
That improvement contributed to a 10% drop in total case volume over the last year, even as the business has continued to grow. Today, we are deflecting about 425 more cases per month than before.
When we rolled out generative answering, we saw another 13.3% lift in case deflection over three months by delivering direct, citation-backed answers at scale.
AI-powered search also helped us reduce literal content gaps to less than 1% by surfacing relevant information based on meaning, not just keywords. That capability has since helped us identify conceptual content gaps, where content technically exists but does not fully answer what customers need. That insight gives us a clear path for continuously improving the knowledge base.
Better Support for Customers and for Teams
The value goes beyond the metrics. As noted by McKinsey, AI’s impact extends to fundamentally reshaping roles. In customer support, teams often spend too much time answering repetitive questions. That work is necessary, but it limits your ability to focus on more strategic issues. By deflecting routine inquiries, AI has freed our team to spend more time on higher-value work. That includes curating knowledge, identifying content gaps, and helping customers solve more complex problems.
This shift changes the role of support in a meaningful way. Instead of staying in reactive mode, we can take a more proactive role in shaping the customer experience. That makes the work more engaging and helps us create greater impact.
What Customer Experience Leaders Should Take Away
For leaders exploring AI in customer service, a few lessons stand out.
- Treat self-service as a product for scalable growth
Growth puts pressure on support teams fast. AI can help you meet that demand without weakening the customer experience. Invest in its usability, intelligence, and continuous improvement as you would any core offering, turning a scaling challenge into a competitive advantage.
- Give customers control over the experience
AI is not only about internal efficiency. It is also about giving customers faster access to the answers they need. By delivering direct, personalized, and context-aware responses – drawn from all available knowledge sources – you reduce friction, improve the customer experience, and naturally decrease operational strain.
- Measure and address content gaps
You do not need a perfect system on day one, but you do need a rigorous test-and-learn culture. Our results came from steady iteration, frequent testing, and careful measurement of outcomes like case deflection and answer accuracy. Use AI to identify both explicit content gaps (missing information) and conceptual content gaps (where existing content doesn’t fully answer the user’s intent), creating a clear path for continuous knowledge base improvement.
- Elevate your support team to strategic contributors, not just ticket responders
AI should transform, not diminish, your team’s role. By handling routine tasks, AI frees support professionals to focus on the work that calls for judgment, context, and expertise. This shifts them from reactive problem-solvers to proactive curators of knowledge and strategic contributors, empowering them to drive broader customer experience improvements.
Turning AI Into Real ROI
At Precisely, our experience has shown that tangible AI ROI is possible when you take a strategic, grounded approach.
You do not need to rebuild everything from scratch. You need a clear problem to solve, a scalable way to build on what already exists, and a commitment to learning from the data.
When you get that right, the payoff extends well beyond cost savings. You create better customer experiences, stronger operations, and more room for your team to focus on the work that matters most.
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ABOUT THE AUTHOR
Gerard Cafaro is a Senior Director of Customer Support at Precisely, where he leads global support strategy with a strong focus on modernizing the customer experience through AI and automation. In his role, Gerard drives operational excellence, scales self-service and knowledge management, and aligns support outcomes with broader business and customer value goals. He is passionate about turning data into actionable intelligence and leveraging AI to deliver scalable, high-impact customer outcomes.
