Everyone is saying it, so we’ll say it too: AI is transforming the way companies deliver customer service, offering new ways to connect with people faster and more efficiently. Yet, many organizations jump into AI hoping it will magically solve their challenges—only to realize that preparation makes all the difference.

In our End of Year Insights Report: The Future of AI in CX, we surveyed 100 senior CX leaders to learn what truly sets AI successes apart from AI failures. Spoiler alert: it’s all about clear goals, real-world testing, and iterative improvements.

“AI isn’t just about efficiency—it’s about transforming how we connect with customers at scale.”

Below, we’ll cover the biggest takeaways from our findings and show you how to lay the groundwork for AI initiatives that actually deliver on their promises.

For a deeper dive, be sure to check out the white paper, where you’ll find more data, quotes, and practical tips from the pros.

Why AI in CX matters

Growing adoption

According to our report, 65% of companies surveyed plan to expand their AI use in the next year—especially in areas like customer support, automated ticket triage, and sentiment analysis.

Even though AI is a hot topic, nearly half (44%) of respondents have been using it for less than six months, underscoring how new these technologies are for most teams.

Organizations are motivated by tangible outcomes: cutting operational costs, improving agent productivity, speeding up resolution, and boosting customer satisfaction.

But they’re also discovering a crucial truth: if you don’t have your processes and goals in order, AI can’t fix what’s fundamentally broken.

“Teams fail when they expect AI to fix broken processes. The foundation has to come first.”

The role of clear goals

Clear goals = better results

One of the biggest insights from the report is the direct link between goal setting and success. Almost half the participants said they had very clearly defined goals (a 4 or 5 on a 5-point scale), and 50% of those well-prepared teams saw the AI outcomes they expected.

Translation: the clearer your vision, the better your results.

“Set measurable, realistic goals that allow you to track progress and refine your approach over time.”

Top CX goals driving AI adoption

• Reducing operational costs (26%)
• Improving agent productivity (26%)
• Speeding up resolution times (26%)
• Increasing customer satisfaction (17%)

When organizations anchor their AI initiatives to specific targets—whether that’s cutting response times or improving agent workflows—they’re in a much stronger position to see real wins. This focus on measurable results is also at the heart of any solid AI preparation best practices playbook.

‍Real-world AI testing

Start small to minimize risk

‍Defining success is half the battle. The other half? Real-world testing. Our survey shows that teams who pilot AI in one function first (like a chatbot or auto-routing feature) build internal trust faster and spot potential issues before they escalate.

“Test it out hands-on, on your own data, with your own team.”

By taking the time to validate AI’s performance in a controlled environment, you reduce the risk of surprises later on. Plus, you can gather valuable feedback from agents and customers early in the process.

This is a prime example of success with AI tools—starting small and scaling up once you know it works.

‍Team buy-in

‍It’s not just about the technology. You also need the team to believe in it. The best AI implementations prioritize employee trust through transparent communication, pilot programs, and a clear outline of how AI will make work easier, not harder.

“Keep the feedback loop between administrators and users wide open. Have a process for feedback and separate feedback (wins/bugs) from feature requests.”

‍Iterative improvements

Staying flexible

‍AI isn’t one-and-done. It evolves quickly—new updates, new features, and new data streams—which means iterative improvements are crucial. The top-performing teams in our survey regularly evaluate their AI tools, adjust settings, and add new capabilities based on real-world feedback.

“Flexibility is key—AI is evolving fast, and staying adaptable ensures you can keep pace with changes.”

‍Continuous feedback loops

‍Don’t just set up an AI tool and hope it runs perfectly forever. Gather feedback from customers, analyze agent performance, and review service quality metrics to see where tweaks are needed.

When you consistently refine, your AI solution matures alongside your organization. This ongoing approach is a core element of effective CX strategies and ensures your AI in customer experience keeps delivering value.

‍Key insights and next steps

• Define measurable outcomes
  Align AI initiatives to specific metrics (cost reduction, time-to-resolution, etc.) for clear accountability.
• Pilot AI in one channel
  Rather than deploying everywhere at once, start with a single function (like chatbots), then expand once you see tangible results.
• Engage your team
  Encourage open communication and clarify how AI supports—not replaces—the humans doing the work.
• Invest in training
  Provide user-friendly resources (videos, SOPs, group demos) so your team feels confident about the technology.
• Iterate and adapt
  Keep your AI roadmap flexible. Technology (and your needs) will change, so plan for continual updates.

Want to see the complete breakdown—survey methodology, in-depth quotes, real-world success stories, and the exact steps top CX leaders are taking? Check out the full The Future of AI in Customer Experience report. Inside you’ll find:

• Detailed findings on AI’s current role in CX
• How teams are tackling challenges like data privacy and compliance
• The one-year, three-year, and five-year goals leaders have for their AI journeys
• Best practices around training, integration, and service quality assurance

Click here to access the full white paper and take your CX strategy to the next level!

‍Conclusion

‍At the end of the day, AI in customer service isn’t about chasing the latest trend. It’s about preparation, testing, and ongoing refinement that align with your unique business needs—three pillars that define any robust AI implementation.

Whether you’re just beginning or looking to optimize your existing AI stack, staying focused on clear goals and careful rollouts is what truly drives success with AI tools.

Define your AI success criteria and start with a small-scale pilot today. Set up time with our team and get ready to transform your customer experience one step at a time.

‍Pro tip

‍Looking for more details on outsourcing customer service or implementing nearshore or offshore customer support alongside AI?

Our AI implementation guide covers how companies balance external resources with in-house teams to maximize efficiency and deliver exceptional customer outcomes.