While the discussion around Generative AI (Gen AI) is widespread, many brands remain cautious about large-scale deployment due to concerns about control and parameters. Despite the buzz surrounding Gen AI, including notable launches like ChatGPT, brands hesitate due to a lack of control over model outputs.
Customer service organizations, for example, can’t simply use a model like ChatGPT out of the box because they have no idea what it will say to their customers. They don’t know if it will spew out nonsense or misinformation, or even hate speech.
Amidst this cautious approach, a new report from Nash Squared indicates that nearly half of companies are exploring small-scale AI projects, with one-third considering Gen AI. The reality is that technology is progressing quickly, and it’s essential for organizations that want to survive and thrive to embrace the possibilities presented by Gen AI.
Here’s a simple three-step approach for successfully getting started with Gen AI.
Step 1: Find a partner with a solutions-based approach
So where should you begin on your AI journey?
Look for partners who are thoughtful when it comes to deploying Gen AI and who are willing to listen to what you want and work with you on making it happen. This may sound overly simplistic, but the reality is that many tech providers take a product-based approach rather than a solutions-based approach.
Why? It means that they’re more interested in imposing their existing products on your business rather than figuring out what problems you have, and then tailoring solutions that specifically address your needs. You don’t want a vendor who is only interested in selling you, say, an AI voice bot, you want a vendor who will listen, comprehend your pain points, and then build a solution for them.
The best partners start by conducting a thorough assessment of your situation and then infusing technology where applicable to solve your problems. They don’t take a cookie-cutter approach or try to jam the customer’s problem into their already existing product suite. When it comes to AI, you also want to work with forward-thinking partners that are constantly assessing, training and refining their models, and who are committed to always enhancing their capabilities.
Step 2: Find the low-hanging fruit
A great place to start implementing AI is in the contact center. Contact centers serve as crucial touchpoints for businesses aiming to enhance their customer experience. Utilizing AI systems can help organizations filter through high volumes of call data and develop key learnings and strategies for improving their business.
Recently, we helped a client implement AI-powered language-translation capabilities in its contact center. Language translation has existed in the chatbot sphere for several years but most exciting now is the prospect of AI enabling live agents to converse in different languages in real time.
This client was attempting to serve its customers in 45 different languages. But that’s a costly proposition. It is also not very effective, especially if you have to rely on a translation service company. In the traditional setup, there is a customer on one end, an interpreter in the middle and an agent at the other end. This arrangement prolongs the process and often leads to misunderstandings because the translator might not grasp the nuances of the business and can’t convey messages back and forth in all their detail.
We worked with our client to implement an AI solution that eliminates the need for a human translator in the middle. Instead, the AI engine operates seamlessly between the customer and the agent. It swiftly translates what the customer is saying into English for the agent. Then, the agent’s response is processed through the translation engine, which quickly translates it into the customer’s preferred language. So even if the client has customers speaking 50 different languages, it can support all of them with just one English-speaking agent.
This real-time language translation in the call center is a huge cost saver for the business. It also enhances the customer experience by eliminating communication barriers, reducing response time and improving efficiency.
Step 3: Tap into the Expertise of a BPO
Business process outsourcers, or BPOs, make great partners for brands looking to deploy AI effectively. Why? Because BPOs typically have extensive access to customer data, including voice recordings and conversations. This data offers a significant opportunity to leverage AI for applications like sentiment analysis.
Sentiment analysis gauges the mood of customers in real time. One of our clients is using sentiment analysis as part of its quality assurance (QA) process. The client runs all calls through an AI analysis engine, which enables QA to quickly identify agents who provoke negative sentiment in callers. By monitoring calls and listening for keywords like “supervisor” and “escalate,” the engine can efficiently pinpoint struggling agents. This enables prompt identification of performance issues and the development of tailored coaching plans.
In the past, a process like this would take weeks, relying on random surveys asking customers about agent performance. By leveraging AI in sentiment analysis, companies can quickly identify issues and intervene, sometimes within hours of a problematic call.
Final takeaway
As AI technology evolves, more industries will benefit from it—but some can do so now. In particular, AI is fundamentally transforming contact center operations, providing businesses with powerful tools to enhance customer service, improve operational efficiency and strengthen their overall performance.
Written by Eric Guarro.
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