How AI is Streamlining Customer Service Across the Globe

By 2026, one in ten call center interactions are expected to be automated, leading to savings beyond $80 billion in labor costs. As consumers continue to demand faster, hyper-personalized experiences, and CX leaders fight to do more with less, the advantages of the AI-driven support model become impossible to ignore.

In this article, we’ll explore how AI and automation are helping global businesses streamline operations, reduce agent labor and meet the ever-changing needs of the customer.

Customers expect a lot – and aren’t forgiving when they don’t get what they need. In a world where experience actively outranks product and price, meeting ever-changing expectations is both testing and expensive – and the stakes are incredibly high. Customers switching companies due to poor service is estimated to cost U.S. businesses between $75 billion and $1.6 trillion each year.

With cost pressures rising as quickly as customer expectations, the obvious response for businesses – offshoring, outsourcing or hiring more trained agents – isn’t sustainable, especially with attrition rates soaring year over year. 

In the face of such high intensity, businesses have turned to generative and predictive AI to survive and thrive – and data suggests that it’s working:

In 2020, McKinsey estimated that AI technologies could potentially deliver up to $1 trillion in additional value for businesses each year. With global research seemingly pointing in one direction, it’s no surprise to see CX leaders across the world putting budget behind AI investment. 

In fact, global spending on conversational AI is set to surpass $8.4 billion by 2031 – a CAGR of 21%. So what justifies that level of investment?

Aside from driving down overwhelming labor costs, implementing AI within customer support generates huge benefits for businesses and their customers. In the following sections, we’ll break down AI’s key advantages, and how it is expediting customer support across the world.

66% of customers now expect companies to proactively understand their needs and expectations according to Salesforce – but personalization at significant scale is never easy. Predictive AI can automatically harvest and analyze huge quantities of customer data, prompting agents or bots to instantly offer context-aware assistance based on a customer’s previous behaviors, orders, warranties, browsing habits and interactions. 

Beyond just issue resolution, these capabilities can help businesses to present customers with ultra-targeted, relevant recommendations and next-best actions, based on their recognized intent or purchasing habits – something 60% of consumers find valuable.

81% of contact center executives are invested in AI for agent-enabling capabilities and operational efficiency – and for good reason. Conversational intelligence and natural language processing (NLP) can handle a wide variety of repetitive tasks – including everyday customer conversations – without human involvement, reducing workload and freeing up agent time to address more complex customer issues. In these situations, tools like Quiq Compose – or Magic Message – can improve agent response time and quality by automatically rewriting their messages to be on-brand, grammatically accurate and empathetic.

Additionally, with machine learning (ML), chatbots and IVRs can be configured to continually learn from best-performing agents and become capable of handling more complex interactions independently, further reducing the need for physical resources.

With improved accuracy and instant responses, AI can greatly increase first contact resolution rate (FCR). Using intelligent sentiment analysis, tools like Amazon Contact Lens can interpret the reason for contact (RFC) and route the conversation toward the most suited agent and channel for that specific disposition, based on historical engagement data.

Agent-facing AI tools like Quiq Suggest can even be used to provide real-time conversational cues in more complex situations, based on what top CSAT performers have said and done in the past. This can help agents to reduce escalation and resolve issues within the first interaction.

Long waiting times are the bane of the customer. Nearly 60% say it’s the most frustrating part of customer service – and the longer they wait, the more you jeopardize CSAT. Using generative AI, LLM chatbots can learn from the entirety of a company’s conversation history to instantly provide solutions automatically – without hallucinations or inaccuracies (when guardrails are used).

When a need escalates – and a customer reaches out for agent support – predictive AI can direct them toward the most appropriate contact channel for a timely response, or even restrict overwhelmed contact channels from displaying as an option. This can be automated in real time based on call volume, ticket requests and agent availability.

Proactive AI understands and engages with customers based on who they are, where they are, and what they are trying to do. Certain sophisticated tools can be configured to make real-time decisions based on a customer’s current emotional state and session information – making recommendations tailored to their immediate needs. This can include expensive product assistance, add-to-cart upsell, and checkout assistance to maximize conversion opportunities and order value.

For example, AI can let customers know that it’s time to renew their subscription, or that they’ve left something in their shopping cart. It can even be agent-facing, prompting team members with the next best actions to take with customers once an engagement has ended. Using generative AI, conversations can be proactively summarized and flagged as an identified knowledge gap, helping CX teams layer their self-service content with real-time customer insights.

Despite all of its evolutionary capabilities, one of the greatest advantages to AI in customer support is simple, routine automation. Using AI, CX teams can proactively collect and structure more customer data than ever before – 100% of it to be exact. They can use this data to support marketing efforts, bot interactions, self-service content and agent training.

Robust AI solutions can even help retain customers. By predicting the context of user interactions, businesses can anticipate future behaviors and potential churn. At-risk or frustrated customers can be flagged by tracking support requests and gauging user sentiment after each interaction. This can enable CX leaders to segment customers based on their likelihood to churn and take the necessary actions to retain their business before it’s too late.

Multilingual support is critical when serving an international customer base – but building a global support team isn’t easy, nor does it come cheap. Still, 75% of customers from 29 countries say they’re more likely to purchase from a brand if it offers support in their language. Now, the application of deep-learning AI to natural language understanding (NLU) has made it possible for chatbots and IVRs to understand and reply meaningfully in hundreds of languages.

With embedded language recognition, NLU software can seamlessly translate foreign inputs and respond with communications that are in the user’s native tongue, and with the same level of personalization as any domestic interaction. Not only is this good for customer retention, it also means businesses can scale support outside of their geographical boundaries, without heavy investment or offshoring.

According to a 2021 ProcedureFlow report, less than 10% of contact centers have agents hitting full speed in under two months. Over a third take five to seven months. Clearly, the old-school training approach needs refining – and thankfully, AI can make all the difference.

With 100% interaction coverage, AI can help businesses track KPIs more accurately and use scorecards to pinpoint issues that contribute to poor CSAT, NPS and AHT. With routine analysis, CX leaders can identify where agents are falling short – individually or teamwide – and deliver coaching that addresses clear trends rather than isolated incidents. Plus, with tools like Quiq Suggest, businesses can coach with highly accurate solutions, based on interactions with the highest first-time resolution rates for specific dispositions and situations.

Customers harbor big expectations for generative AI – its use across all industries and sectors is not a well-kept secret. But, as consumers interact with generative AI on a more regular basis, their expectations evolve. Now, they demand authentic, informed and accurate responses from bots that speak on their terms, through their channel and in their voice.

Certain generative AI tools have been configured to go beyond text-based responses and create sensory-rich customer experiences with interactive media such as photos, videos, gifs, emojis and more. We call this rich messaging. It’s commonly used in the context of SMS, where it can encourage customers to migrate from the expensive phone channel in favor of a more personalized, relevant and empathetic messaging experience on a handheld device.