AI promises transformation—but for many support leaders, it’s becoming a distraction. Misapplied, it can burn resources, frustrate teams, and erode customer trust. Successful AI initiatives in support require the right approach—not just the right technology.
95% of AI pilots fail with only about 5% of pilots making it into production delivering measurable value.
Source: The GenAI Divide STATE OF AI IN BUSINESS 2025: MIT’s Media Lab (Project NANDA)
Support and CX teams are racing to adopt AI —often applying them to processes they haven’t fully mastered. While AI opens doors to automation and capabilities previously out of reach, that doesn’t mean every new capability is worth pursuing. Just because AI makes something possible doesn’t mean it’s strategically valuable.
AI adoption in CX and Support is inevitable. It’s accelerating faster than any prior technology wave in this space. But speed must not come at the expense of alignment. For AI to deliver real value, it must fit your tech stack, enhance your workflows, and elevate—not sideline—your people.
AI success requires planning, piloting, and validation—not just chasing the next cool thing.
We’ve identified numerous practical AI use cases in support, and there are many more across the CX landscape. This isn’t a call to delay adoption—but a call to approach it thoughtfully. Here are four essential factors to drive thoughtful and successful AI initiatives:
1. AI Organizational Alignment
Should your AI efforts run as a separate “skunk works” team, or be integrated into your core support operations and governance? While standalone pilots may be easier to launch, they often struggle to scale or integrate effectively across critical functions. Don’t create AI silos. True value comes when the AI strategy is woven into broader planning, design, and governance frameworks.
2. Staffing and Human Infrastructure
AI doesn’t eliminate the need for people—it transforms it. To succeed, you need a staffing strategy that brings in the right skills: AI design, data training, model governance, and more. Support teams must rethink their staffing models. Some roles may be displaced, but the greater opportunity lies in creating new, high-leverage roles that drive AI success. Start building your AI-ready workforce now.
3. Use Cases That Matter
There’s no shortage of AI use cases. The key is to identify the ones that align with your business model, support strategy, and customer needs. Start where the impact will be meaningful and measurable—whether that’s automating repetitive resolutions, scaling self-help, or improving agent productivity. Choose the use cases for AI that will have the greatest impact within a reasonable amount of time without adding pressure to the budget. Here are a few support use cases to consider.
4. ROI and Executive Expectations
What outcomes do you expect from your AI efforts—and how will you measure them? More importantly, what expectations does your executive team have? AI is not just a cost-reduction lever. Done right, it drives retention, expansion, and customer lifetime value. Success depends on setting the right metrics—and the right narrative. Set clear metrics and craft a compelling narrative. Set and manage leadership expectations about the investments required, the impact anticipated, and the realistic timeline to get there.
A Guide to Successful AI Adoption for Support
If AI is a strategic priority for your team, let’s connect. We’re always open to a conversation—and committed to helping support leaders turn AI into a strategic win, not just another tech experiment.
We will continue to explore these topics in depth so stay tuned.
