We’ve approached the peak of Gartner’s Hype Cycle for AI. It doesn’t matter where you look—AI is everywhere, in many shapes and forms. But with massive hype comes inflated expectations and disappointment when things don’t work as expected, which is why it’s essential to manage your expectations and be realistic about what these AI tools can actually do.
There’s huge potential for AI in customer experience, and you can maximize its effectiveness by finding out what your call center needs and how AI can help. In existing use cases, we’ve seen AI capable of:
In fact, 32% of attendees surveyed during our recent AI customer experience webinar are already using AI for customer service, with another 45% planning on using it.
But, in reality, half of AI projects today fail because of poor execution or unmet expectations. In this blog post, we’ll clear up some of the misconceptions about AI in customer experience, explain how to avoid pitfalls, and provide some best practices for getting the most out of your AI.
The hype surrounding AI may be strong, but don’t get blinded by it. AI can be a very powerful tool, but it’s not going to immediately become a solution to all of your problems. While AI in the customer experience can make a significant impact, it also needs careful planning and implementation before you’ll see any benefit.
With any new tool, the only way you’ll get the best results is by planning, finding the relevant use cases for your business or industry, and setting clear objectives to guide you along the way.
Take your time, assess how AI can solve your problems, and remember that it won’t be an instant fix. You’ll still need to deal with growing pains, train your staff to use it, and find the perfect balance between AI and human effort to make it work.
Unlike those late-night TV commercials, AI is not something you can just set and forget. These tools take time to implement into your system. When you’re evaluating AI customer experience solutions, you need to consider the time it’ll take to integrate into your existing systems and make sure that your chosen AI solution can actually meet your call center’s needs.
This means configuring and tweaking settings and algorithms, and those take time. Not all AI tools are one-stop solutions, and it may take weeks or months of tweaking to get them just right for your use case. This means you’ll also need to partner with a vendor that understands your business's unique needs and is willing to customize and adjust as needed.
You don’t need to be a tech whiz to start using AI within a call center or customer service context. Many of the tools available should be implemented and adjusted by your vendor or your internal IT teams.
And with the massive demand for generative AI tools, vendors have made them far more accessible for the everyday person. They come with plenty of user-friendly interfaces and tutorials, letting you rely on your vendor for the technicals.
One major hurdle you might have to deal with is data siloing. This is when your data is scattered across different systems and departments, making it difficult to access and integrate into your AI. In fact, 26% of our AI customer experience webinar attendees said that handling these integrations is their biggest challenge when trying to integrate.
If you want to get the most out of your AI implementation, it’s vital to make sure that your data is easily accessible and well-organized. That means auditing and consolidating your data, finding out where it lives, and getting it into one central location.
By eliminating silos, you improve the accuracy and effectiveness of AI-driven insights, leading to better customer experiences.
Whether it’s IT, the C-suite, operations, or agents, each group might have their own vision and priorities for AI in customer experience. And when they don’t all align, it can result in miscommunication, delays, or even failed AI implementations.
You’ll need to create open communication and collaboration right from the start to avoid these issues. That means setting up regular meetings where stakeholders can voice their concerns and expectations. By ensuring everyone is on the same page, you pave the way for a smoother integration process, allowing you to minimize any potential negatives.
Additionally, you can create a shared roadmap that outlines each department’s role and how their input contributes to the overall success. Just like open communication, clear expectations of what still needs to be done (and who’s responsible) can help improve transparency and push for a more unified effort.
Without clear objectives, you can’t gauge if your AI implementation is having the impact you wanted. And this ambiguity can make it tough to sustain the business case for AI and maintain support from your stakeholders. You need to set SMART goals—specific, measurable, achievable, relevant, and time-bound.
First, start by identifying what you want to accomplish with your AI customer experience. Typical examples include:
Next, pinpoint the specific key performance indicators (KPIs) that align with these goals, such as:
These call center KPIs will help you better understand the quality of your customer experience.
Then, regularly analyze your KPIs to measure progress and make data-driven adjustments. With clear goals and actively monitoring how to get there, you can drive successful AI adoption and show its value to stakeholders.
One unfortunate truth about modern AI is that it can still be incredibly flawed. It can often reflect biases present in its training data, which can lead to unfair treatment of customers. These biases can have real-world impacts, with anything from lopsided call routing to biased assistance eroding customer trust and satisfaction.
To combat this, it’s vital that you understand how your AI models work and why they make certain decisions so you can maintain transparency. You’ll also want to create an AI governance framework that can proactively monitor your AI systems with regular audits to identify and correct biases.
Implementing a framework can create a fairer, more reliable AI system that enhances the customer experience. Instead of letting these biases scare you away—or worse, impact the customer—take a proactive approach that ensures equality and fairness within your AI implementations.
When you don’t implement and monitor your AI tool, it can cause massive damage to your brand and your business. Inaccurate or inappropriate responses can lead to a tarnished brand image, causing you to lose customers, suffer decreased revenue, and possibly face public backlash.
To mitigate this risk, start small with your AI customer experience. Implement AI gradually with pilot programs, ensuring it performs as intended before scaling up. Begin with less critical tasks and introduce more complex functions as your teams become more confident in their accuracy and reliability./p>
Once it’s fully deployed, it’s vital to continuously monitor its performance to maintain quality and consistency. Regular assessments can help you catch potential issues early, allowing you to make adjustments before they impact your customer, like writing a poem about your bad customer service.
Adding AI to your call center can dramatically enhance the customer experience, but it needs a strategic, measured approach to be successful. These five tips from call center industry leaders can help guide you to a smooth AI customer experience.
Before adopting AI tools, make sure you understand the problem you’re trying to solve and how AI contributes to your desired outcome. Set well-defined goals—what do you want to achieve? Is it, for example, reducing call handling time, improving customer satisfaction, or increasing efficiency?
Panos Karagiannis, Co-Founder and CEO of Moveo.ai, has two suggestions on how to begin:
Derek Corcoran, Founder and CEO of Scorebuddy, suggests starting small with something non-critical to test the waters without jeopardizing critical aspects of your operations.
For example, you can consider using AI to handle routine queries or provide decision support for a small number of agents and then scale up as confidence grows. These applications can deliver quick wins and build confidence in the technology before scaling up to more complex tasks.
Navigating AI tech can be tricky. With the sudden and rapid growth of generative AI, the market is packed with new vendors of varying skill, quality, and features. Look for partners with a proven track record in the industry and expertise in AI-driven solutions for call centers, which means you need to do your research.
While AI may be new, many of these companies have been in the customer experience industry for years. Derek suggests that you should work with a skilled vendor who can not only help define the use case but also help you test the value proposition and make sure the AI solution fits your existing workflows.
It’s essential that you monitor your AI tool over time to ensure it’s performing as expected. Both Derek and Panos highlight the importance of this, not just to make sure you hit your goals but to ensure it isn’t causing harm with inaccurate or inappropriate interactions.
By taking advantage of a QA auto-scoring tool, you can achieve 100% interaction coverage and avoid mishaps (like poetic justice from package company AI) and make sure that your customers are getting consistent high-quality experiences across every single interaction.
Your employees are key to the ongoing success of your AI customer experience. Which means you should be providing them with the comprehensive training needed to help them understand how to work alongside AI tools effectively and help quell their fears about this new technology.
Ongoing support is essential, too, as the AI field is constantly evolving, with new features and use cases popping up all the time. This means they also need to keep up with the times as your tools add new features and AI capabilities over time, whether it’s through targeted training sessions or broad concepts for different teams.
Let’s face it. The AI hype is real, but that doesn’t mean we can rush in, plug it into our systems, and expect miracles. Implementing AI in customer experience journeys requires a strategic, thoughtful approach that includes real use cases and goals that align with your business needs.
Starting small, working with trusted vendors, carefully planning and monitoring your progress is the best way to have a successful integration. Hype is through the roof, but if you follow the best practices we’ve mentioned—and the advice from industry leaders—you can completely revamp your customer experience.
If you really want to ensure a positive AI impact on your customer experience, we’ve got a QA tool to guarantee next-level CX.
Scorebuddy’s GenAI Auto Scoring empowers you to automatically audit 100% of interactions, giving you the full picture of your contact center and how your customers feel. It lets your teams find insights quickly, spot patterns and trends, create new coaching opportunities, and more—all while drastically reducing your QA costs.
Sign up for a free demo today to see exactly how it would work in your organization.
