Many organizations continue to deliver substandard customer service
Too many companies have spent years throwing money at customer service tech, with little to show for it. Long wait times, poorly designed digital interfaces, automated agents that don’t solve anything, these are symptoms of a deeper issue: a fragmented approach to service. Technology has advanced. Customer expectations have grown. But most organizations are stuck with post-sales support models that haven’t changed much in a decade.
Customer experience doesn’t end when the deal closes. Unfortunately, many companies still treat support as a back-end function, a cost center to automate, shrink, or silo. That mindset kills retention and erodes brand trust. For customers, unresolved service issues and being bounced between disconnected systems and departments are clear signals of disinterest.
Adrienne DeTray, CIO at Universal Technical Institute, put it straight: “Once the deal is done, the relationship should not go quiet.” That insight is basic yet widely ignored. Danny Sit, CEO at NUU, highlighted a common mistake: organizations waste resources acquiring customers, only to neglect them afterward. When service sits outside the core business strategy, it becomes inconsistent and unreliable.
The numbers support this. Kathy Ross, Senior Director Analyst at Gartner, shared that only 30% of customer service interactions get resolved on first contact. That’s unacceptable in an age where customers expect speed and clarity. A company that can’t solve an issue in one shot doesn’t have a technology problem, it has a leadership problem. The issue isn’t capability. It’s poor integration and lack of prioritization.
Most strategies fail not because they lack the right tools, but because they underestimate the complexity of customer expectations today. Great tech alone won’t solve this. What closes the gap is intent, a commitment to place service at the strategic center of the business.
Effective utilization of customer data is crucial for delivering proactive and personalized service
Ask most organizations about customer data, and they’ll claim to have “a lot.” That’s not the issue. The problem is most of that data isn’t being used properly. It sits in silos, or worse, gets used without context. Focused organizations use captured data to predict needs, fix problems before they escalate, and deliver well-timed support that feels personal.
Adrienne DeTray made this clear when discussing Universal Technical Institute’s strategy. They use predictive analytics not just to respond, but to anticipate. They can detect when a student might start struggling or when an employer’s hiring patterns are shifting, triggering support before issues go live. That’s real use of data. It’s fast, it’s smart, and it minimizes friction for the end user.
Kathy Ross at Gartner emphasized something C-suites tend to overlook: data by itself has no value until you apply it to context. Service teams must know what products the customer uses, how they use them, and why. Without that context, data becomes noise, not insight.
This also means building strong internal knowledge bases. Think of it as centralized, always-updated content that support agents and AI assistants can use to resolve problems. That kind of infrastructure isn’t “nice to have.” It’s foundational. Without it, both humans and machines give inconsistent answers.
Here’s the nuance C-level leaders need to prioritize: scaling personalization doesn’t mean endless customer profiles, it means smarter workflows. High-value service happens when insights move fast and the next action is obvious. Teams need data that drives decisions. The rest is just clutter.
Organizations that align their tech stack, data infrastructure, and service strategy will pull ahead. Everyone else will keep spinning wheels, looking for answers they already have sitting in their systems.
Integrating diverse data sources enhances proactive issue resolution and improves first-response effectiveness
Most customer support teams still operate reactively. They wait for tickets, then scramble for answers. That’s broken. When data lives across disconnected systems like CRM software, ticketing platforms, website logs, or internal documentation, agents waste time hunting for basic details instead of resolving the issue immediately.
Fixing this starts with unifying data sources. Centralized, real-time visibility into customer interactions, usage patterns, and prior incidents allows support teams to respond with context. You’re not just answering a question, you’re resolving a problem with full awareness of past touchpoints and potential next steps. This speeds up the process and improves the quality of support.
Baris Zeren, CEO of Bookyourdata, talked about actively monitoring customer behavior and flagging early signs of friction. Their use of data analytics enables the company to reach out before a complaint happens. This shifts support from reactive to preemptive. Zeren also mentioned automated ticket triaging, a process where inquiries are grouped and prioritized by urgency. That matters because it ensures high-impact issues get fast resolution without being buried in queue volume.
At Avantra, CEO John Appleby said his team built a system that combines real-time incidents with historical solutions, technical documentation, and internal knowledge. When a new support ticket comes in, that system feeds highly specific data to the agent immediately. That context reduces average handle time, increases first-response accuracy, and cuts down on the number of touchpoints per case.
The strategic opportunity here is clear. Executives need to think beyond just faster response. Integration gives your support function real-time strategic awareness. Agents are not just reacting, they’re operating with intelligence. Tight incident resolution is a direct result of tight data workflows.
Agentic AI has the potential to fundamentally transform customer service
There’s a new phase of AI emerging, not just passive chatbots or linear scripts, but systems that can make decisions and act independently. It’s called agentic AI. This type of AI doesn’t wait to be told what to do. It understands context, sets goals, and executes multi-step tasks on behalf of the user.
Kathy Ross, Senior Director Analyst at Gartner, described how agentic AI can handle complex operations like account cancellations or researching service options. Instead of making the customer navigate the process, the AI completes it, end to end. The result is a smoother experience with less manual effort for everyone involved.
Baris Zeren from Bookyourdata discussed internal use of agentic AI to prioritize incoming tickets based on customer sentiment. The system doesn’t just read the words, it interprets the tone and urgency. That means angry or high-impact messages rise to the top of the support queue, making resolution faster and more targeted. He also noted that basic queries are handled entirely by AI, allowing live agents to focus on more advanced requests.
This is not about replacing humans, it’s about offloading routine work so that human agents can operate where it counts. Well-deployed agentic AI improves service quality, shortens response time, and scales customer capabilities instantly. If you rely only on traditional automation, you’re behind. If you design workflows around agentic AI, your service becomes faster, leaner, and more intelligent.
For leadership, the key decision isn’t just whether to deploy AI, it’s how to embed it so it drives strategic outcomes. Agentic AI brings automation into the execution layer. That’s where it starts to make real impact.
Maintaining adequate human staffing remains critical even in an era of advanced AI and automation
AI is improving fast, and companies are racing to integrate it across customer service operations. That makes sense. AI is great at handling repetitive tasks, managing high ticket volumes, and even analyzing sentiment. But there’s a point where machines hit a wall, especially when the customer issue is complex, emotional, or entirely new. That’s where people still matter.
Too many organizations are trying to cut support headcount by betting on a fully automated future. That’s a mistake. Kathy Ross at Gartner shared that only 14% of customers today are able to fully resolve their issues without human assistance. That number speaks for itself. Customers often look for nuance, empathy, or real-time judgment. AI doesn’t do that well, yet.
Even more telling, a recent Gartner report found that 50% of organizations who originally planned to slash customer service staff by 2027 are abandoning those reductions. Why? Because agentless strategies run into problems. Customers get frustrated. Complicated issues stall. Brand loyalty weakens. Technology leaders imagined full automation, then realized they underestimated human value.
That doesn’t mean companies shouldn’t invest in AI. It means they need to use it the right way. AI should empower, not eliminate, the support workforce. The best-performing models right now operate in hybrid mode: AI handles basic workflows, surfaces relevant data, and provides real-time recommendations, while human agents concentrate on strategic problem-solving and customer relationships.
Kathy Ross captured it clearly: success lies in a “digital-first, not digital-only” strategy. That distinction matters. Business executives who try to remove the human element entirely are not reducing costs, they’re increasing risk. Valuable customer experiences require both intelligent systems and skilled people. When these elements are balanced, service becomes faster, sharper, and more resilient. That’s where serious competitive value is created.
Key highlights
- Poor service despite tech spend: Many customer service ops still underperform because post-sale support is seen as secondary. Leaders should reframe service as a strategic growth function, not a cost center.
- Data is underutilized: Organizations collect extensive customer data but fail to apply it meaningfully. Prioritize data integration and contextual analytics to predict needs and improve personalization.
- Fragmented systems kill response time: Disconnected data and tools delay resolution and frustrate customers. Executives should mandate full integration across support systems to boost first-contact resolution and team productivity.
- Agentic AI changes the game: Autonomous AI can now complete multi-step tasks with minimal input, reshaping service efficiency. Invest in agentic AI to automate routine tasks and redirect human agents to high-touch interactions.
- Don’t cut people too fast: Most issues still require human judgment, especially in complex or emotional cases. Maintain a trained support workforce and use AI to enhance, not replace, their capabilities.
