We're well past the point where artificial intelligence in customer service is novel. The question with which organizations are now grappling is far more practical: Why do some teams see value from AI in weeks while others stall out in pilots that never seem to graduate into production?
At Valoir we have looked at hundreds of AI deployments over the past two years and built some AI agents of our own. We've found with early AI deployments in customer service that the difference isn't sophistication of the model, ambition of the project, or the cleverness of the technology. Instead, it really comes down to a handful of fundamentals. The organizations getting real value aren't doing more AI; they're doing the basics better, and they're treating AI as a catalyst for change not just another layer of automation.
Leverage a platform.
Although it might seem obvious at this point, do-it-yourself AI projects are often doomed to fail because they take too much time, money, and expertise to get to an acceptable level of accuracy. The fastest path to value comes from building AI on top of systems that already run customer service today. We found that organizations that had already invested in shared data models, workflow automation, and standardized integrations were able to move from experimentation to production far more quickly than those starting from scratch.
That doesn't mean that your existing CRM or contact center vendors' AI is the one you must, or should, use. You should be looking for a platform that provides the capabilities that AI agents need to thrive, like permissions, process logic, data access rules, and operational context, as well as capabilities to integrate data from external sources and other agents and manage and monitor them. When those guardrails exist, AI can inherit them. When they don't, teams end up spending more time plumbing than innovating.
Get your data and processes in order.
There are lots of tools to build agents out there, with varying levels of ease of use and sophistication, but building an agent is the easy part. The hard part is getting it to deliver consistently accurately responses, and that is nearly impossible if you don't have your data and processes and order. AI has an uncanny ability to surface issues with which organizations have been living for years, inconsistent processes, conflicting definitions, or messy data.
With AI agents, teams that saw rapid time to value had already invested in process clarity and data hygiene. They knew how work was supposed to flow, even if humans sometimes improvised around it. That clarity allowed AI agents to perform more predictably out of the gate and made it easier to diagnose when things went wrong. We're now seeing organizations get to 70 percent to 80 percent accuracy with AI agents, even with more complex queries, provided they have done the data, knowledge, and process homework.
However, if your knowledge is fragmented or your processes are inconsistent, that doesn';t mean you're out of the AI game. The upside is that AI can accelerate data cleanup, and process mapping tools can make those messy process conversations into process maps and definitions. Rapidly built agents can be tested, monitored, and refined, helping organizations pinpoint where data or process refinement is needed. The key is recognizing that trial and error is part of the journey and planning for it rather than being surprised by it.
Rethink what good work looks like and how to talk about it.
AI doesn't just change workflows; it changes roles. Supervising automated work, correcting AI output, and knowing when to intervene are learned skills, and few of us have been at AI long enough to have honed those skills yet.
The most successful teams treated AI adoption as a change management exercise. They invested in policies that defined acceptable use, training that focused on collaboration rather than replacement, and leadership structures that acknowledged the growing importance of digital labor alongside human teams.
They also created safe environments to experiment, running AI during off-peak hours, testing small workflows and case queues, and closely monitoring outcomes. They recognized that moving from pilots to production meant trust and confidence above all, and that trust was a moving target that they had to continuously have in their sights. Visibility mattered, and dashboards that showed accuracy, sentiment, and topic trending helped teams build confidence and improve performance over time.
AI can absolutely drive productivity gains in customer service, but if you're aiming for productivity boosts, you're selling AI short. The real payoff and exponential ROI comes when AI enables better decisions, reduces friction, and supports meaningful changes in customer and employee experience.
We consistently see that organizations moving beyond pilots aren't just layering AI onto existing workflows, they're rethinking them. They're choosing projects where AI can eliminate manual effort, accelerate insight, or improve consistency at scale, and they're tying those efforts to clear business outcomes.
Yes, technical and data readiness matter, but intent matters most. Organizations that treat AI as a strategic lever and prepare their people for the change it brings unlock faster time to value, better customer and employee experiences, and stronger long-term results.
AI doesn't transform customer service on its own, organizations do.
Rebecca Wettemann is founder, CEO, and lead analyst of Valoir.