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- AI integration created a ripple effect internally, giving the software company’s team new roles as AI trainers and quality monitors.
- In an environment where speed and accuracy are paramount, quantifying the impact of AI using both KPIs and AI-specific metrics to evaluate performance is essential.
- Embracing AI fully is about giving teams the room to think bigger, act faster, and deliver more.
For Sterling Parker, Executive Vice President of Global Support at Ivanti, customer support has never been a static function.
He told me in our interview about witnessing firsthand during his nearly 14-year journey with the company — starting from taking support calls on the front lines to now overseeing a global operation — how technology can transform not just how support is delivered, but also how teams think, collaborate, and innovate. Today, generative AI is at the heart of that transformation.
Turning Curiosity Into Capability
Ivanti’s journey with AI in customer support didn’t begin with the Gen AI boom of late 2022. The company started weaving machine learning into its support infrastructure as early as 2018. The initial goal was straightforward: automate low-effort, repetitive support tasks to improve the self-service experience and free up human agents to focus on higher-value interactions.
This early integration used machine learning to analyze a customer’s behavior just before submitting a support case — where they’d been, what documentation they had already accessed, what entitlements they had — and then recommended related support articles. This modest beginning already produced measurable impact, deflecting up to 12% of incoming support requests.
But Parker and his team weren’t satisfied with marginal gains.
“We really wanted to take it to the next level,” he said.
With the maturity of large language models, Ivanti moved to embed Gen AI into customer-facing forums and community platforms. Now, users — whether logged in or not — can pose questions and receive dynamically generated responses drawn from a federated search across forums, product documentation, and internal knowledge bases.
And unlike earlier iterations, these responses are not just links; they’re full explanations, augmented with traceable source references and immediate feedback mechanisms so customers can rate the AI’s helpfulness.
This evolution didn’t just enhance the user experience. It created a ripple effect internally, giving Parker’s team new roles as AI trainers and quality monitors.
“It shifted from curiosity to capability,” Parker noted. “Now it’s something my team can’t live without.”
The Human-AI Partnership
Any implementation of AI in a human-dominated space invites questions about job displacement. But Parker’s team met the technology with open arms.
“Because we’re a tech-first culture, the excitement outweighed the fear,” he explained.
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Still, skepticism lingered. Would Gen AI really create value? That question was answered as soon as the technology began tackling some of the most frustrating tasks in the support process: Case summarization.
Before AI, engineers would spend hours post-call compiling notes from marathon troubleshooting sessions. Now, Gen AI auto-summarizes voice calls, assigns action items, and prepares documentation.
This automation eliminated what Parker calls “low work”—necessary, yet soul-sapping administrative duties—freeing agents to focus on solving complex problems.
“It made my team’s work more meaningful,” he said. “They’re happier, more engaged, and their job satisfaction has increased because they get to spend their time on higher-value contributions.”
This partnership between human expertise and AI augmentation is now a critical part of the team’s identity. As Parker put it, “AI isn’t replacing us. It’s helping us become better versions of ourselves.”
Guardrails for Accuracy and Trust
One of the most pressing challenges in using Gen AI in customer support is managing the risk of hallucinations — those confidently incorrect answers AI systems sometimes produce. Ivanti has built a robust system of human-in-the-loop oversight to combat this.
The backbone of this process is its Knowledge-Centered Support (KCS) framework. Dedicated KCS coaches continuously monitor generative outputs and customer feedback to ensure the AI’s responses are grounded in verified knowledge, not assumptions or speculation.
“It’s a heavy lift,” Parker admitted. “But it’s absolutely necessary.”
Ivanti’s use of a proprietary internal LLM also adds a layer of control, reducing exposure to the unpredictability of public models. This closed-loop training approach ensures responses remain accurate, relevant, and policy-compliant.
The result? AI that customers can trust — and that Parker’s team can stand behind.
Measuring What Matters
In an environment where speed and accuracy are paramount, quantifying the impact of AI is essential. Parker uses a blend of traditional KPIs and AI-specific metrics to evaluate performance.
Deflection rate remains a cornerstone — tracking the percentage of support requests resolved without human intervention. But satisfaction is equally critical.
“We marry deflection with CSAT,” he said. “If AI handles a case but frustrates the customer, that’s not success.”
Another powerful metric is the “effort score,” captured at the close of every human-handled incident. This tells Parker whether the interaction felt seamless and easy from the customer’s perspective, an essential measure in the support experience.
On the learning and development side, AI has dramatically reduced content production timelines. What once took four days to localize, caption, segment, and embed into training modules can now be done in minutes.
Parker tracks this efficiency gain as another indicator of Gen AI’s value, helping his team accelerate onboarding and internal upskilling.
And there’s a financial side, too. Ivanti calculates cost savings by estimating the average cost per incident and comparing it to the number of cases deflected by AI. These hard numbers bring clarity to AI’s business impact.
Looking Ahead: The Rise of AI Agents
As the capabilities of AI continue to evolve in the future of work, Parker’s vision for the next phase of support is centered on proactive, intelligent AI agents.
In his ideal future, customers no longer need to explain the basics — product versions, tenant information, environment details. The AI agent already knows. It identifies the customer, understands the context, flags known issues, and even initiates next steps like sending notifications or preparing post-mortem documentation.
“It’s not just about reducing complexity for my team,” Parker explained. “It’s about reducing complexity for the customer.”
The most powerful use cases, he believes, lie in automating the low-effort, high-volume interactions that clog up the pipeline. But even as AI takes on more of these roles, Parker emphasizes the importance of the handoff.
When a problem needs human insight, the transition must be frictionless and well-informed. The AI should not only pass along logs and diagnostic information, but also suggest likely causes — ensuring that the human agent enters the interaction equipped and ready.
This is not about removing people from the equation. It’s about giving them room to think bigger, act faster, and deliver more.
Redefining the Role of Support
In an era where customer expectations are rising and loyalty is hard-won, the support experience can make or break a brand. By leaning into Gen AI, Sterling Parker and his team at Ivanti are proving that technology isn’t a threat — it’s a tool for transformation.
AI has shifted their operating model, reframed their metrics, and re-energized their workforce.
Most of all, it’s created space. Space to innovate. Space to connect. Space to think bigger.
And in the fast-changing world of customer support, that may be the most valuable outcome of all.
