The contact center industry has battled various challenges over recent years, such as high employee turnover, low first contact resolution rates, an excessive number of technology tools flooding the workplace, and the resultant customer dissatisfaction.
The widespread adoption of digital devices and perpetual connectivity is changing how customers want to communicate with organizations.
A growing gap exists between customers’ expectations and contact centers’ capability to meet them.
Conversations haven’t been limited to traditional voice channels for some time now, and customers expect to engage with companies seamlessly across multiple channels like email, webchat, messaging, social, video, and chatbots.
At the same time, with growing consumer demand, call volumes, email inquiries, and average hold times have skyrocketed.
A growing gap exists between customers’ expectations and contact centers’ capability to meet them. Contact center leaders have increasingly turned to automation tactics to facilitate service and reduce agent stress. Consequently, they are under tremendous pressure to accelerate modernization.
As a result of its numerous uses and compelling benefits, artificial intelligence (AI) plays a vital role in transforming contact centers and helping organizations modernize processes.
Favored by the increasing adoption of machine learning tools and cloud services, AI in contact centers has gained significant traction in recent years.
The adoption of AI in contact centers has accelerated as businesses look for better ways to improve customer satisfaction and internal operations, and its proliferation is only expected to increase. According to a report by Allied Market Research, the AI market is expected to reach $9.95 billion in 2030, with an annual growth rate of 26%.
But it has its challenges in a contact center.
Current Challenges AI is Combatting
While contact centers are focused on delivering great results for customers they interact with, they often lag in helping those who make those interactions great – agents.
Lack of engagement amongst agents, high agent turnover, long wait times, and lack of efficient technological solutions to resolve these issues have led to poor customer satisfaction and loss of business. Not surprisingly, 91% of customers reported a worsening customer experience (CX) in the second half of 2021, according to a Replicant survey.
In today’s world, consumers expect to have their inquiries resolved quickly, but call volumes, as well as chatbot and email inquiries, have significantly increased both volume and agent workload complexity, with fewer employees to field requests.
As consumer demands become more complex and employee turnover continues, the agent’s role gets more difficult. Organizations need more effective ways to develop top-performing customer-facing teams to solve complex problems and deliver superior CX.
Legacy-based IVR systems still define most forms of self-service, but with today’s customer expectations, that service standard needs to be higher. A poor IVR experience raises customers’ frustration levels and adds extra burden on agents to handle inquiries that shouldn’t be coming their way.
Fortunately, when properly implemented, different types of AI can vastly improve the delivery of existing self-service options and increase customer acceptance. They can streamline workflows to shorten the time to resolution and help combat agent fatigue and turnover rates.
Choosing the Right Applications
As a result of technological advancements and AI automation, companies can now respond to customer needs through various channels, including email, text messaging, phone calls, and chatbots, to name a few.
From Conversational AI and chatbots to predictive call routing and AI-powered agent assist applications, AI is helping contact centers transform workflows for agents on the back end and boosting customer experience on the front end.
These applications are also becoming savvier as access to pools of data increases, making them even more efficient in resolving customer-related issues.
Predictive routing helps connect customers to specific customer service agents who can best handle their inquiry, while sentiment AI can model a customer’s behaviors during an interaction and can give supervisors feedback in real-time about how a customer is feeling about them.
As a result of technological advancements and AI automation, companies can now respond to customer needs…
Some AI applications are better suited for certain types of customer interactions. For instance, chatbots can use natural language processing (NLP) AI to systematically narrow down the customer’s choices and determine the best probable outcome. But it cannot answer two requests looped into the same sentence. It will usually answer one of the questions and dismiss the other.
As well, generally available NLP models have yet to conquer a simple negative in a sentence. Keeping the job simple and directed helps improve chatbot accuracy and customer satisfaction.
Automated email handling is a different scenario – but will also help improve operations. Email is an entirely different machine learning application that is designed to evaluate multiple paragraphs and include all the information in one shot.
So, when choosing AI applications, it’s important not to rely on one general jack-of-all-trades AI for any specific function.
While AI is helpful in many ways, it can damage a contact center’s operations and make or break a customer interaction if the applications are not trained carefully. Worse, it can negatively affect your staffing and further increase attrition rates, repeat calls, and agent complexity.
AI applications must pair up well with agents’ different communication channels and agents must be able to move across them seamlessly, as customers have communication preferences.
It’s essential to avoid making the mistake of adding AI applications to the contact center without researching if they’re the correct fit.
Paramount to any AI decision is how it will interact with or affect your agents – adding new communication channels and AI applications can add new silos and complicate your agent’s workspace.
If agents can’t supply good customer service across all channels equally, all you will do is drive your agent and customer satisfaction down. And those customers will also connect via different channels for a single business transaction, so the AI applications in play must be able to create a seamless transition between those channels.
It’s essential to avoid making the mistake of adding AI applications to the contact center without researching if they’re the correct fit for the services and channels being used in the agents’ desktop portals. Too many disjointed AI applications can cause agent fatigue and even complicate processes further by creating data silos on the back end.
Impact on Agent Performance
AI benefits agent performance on various levels. Most notably, it can increase speed to competency and establish a shorter learning curve for agents.
Contact centers have increasingly turned to omnichannel platforms, and agents must shift seamlessly from channel to channel based on customer preferences. In most cases, there’s a learning curve when it comes to navigating different channels as each channel has its nuances.
When channels are added to agents’ desktops, they must adapt quickly to meet customer needs, which is only possible with the proper training tools.
AI can harness vast amounts of data that come with digital interactions and process that data into applicable information that enable agents to provide highly personalized customer service in real-time. With AI, agents can know far more about every customer than ever before, which goes a long way toward elevating the CX.
Agents deal with customer interactions all day, every day. They must be prepared to handle any situation that arises at any given moment, which always requires access to the correct information. According to Forrester, agents currently spend 35% of their time searching for information.
AI applications can help gather as much information from customers as possible and can assist agents during customer interactions with intent, sentiment, and suggested or automated responses. AI pattern recognition identifies which behaviors produce the best outcomes for specific agent situations.
Predictive analytics can also help evaluate behavioral patterns, enabling contact centers to provide better up-sales and improvements by analyzing historical customer behavior and predicting possible future actions.
This is why having a large base of data sets, or either structured or unstructured data, is crucial since it enables AI-based guidance to become more accurate and gives agents more confidence when speaking with customers.
Regarding training, AI applications can help agents upskill in less time and more effectively than with other supplemental learning tools. Contact centers can use AI conversation simulation to create various scenarios that agents may encounter, which helps them practice problem-solving and build confidence before taking on live customers.
AI as a technology is massively overhyped, but the various applications that are commercially available can still add a significant amount of value. But only if the right AI is chosen for the right job, and only if they are easily accessible and integrated in a way that simplifies processes and empowers agents to perform efficiently and confidently.
There is a large pool of AI vendors that contact center leaders can choose from. With so many types of AI, it can take time to select suitable options for the contact center’s unique needs. So, make sure you approach your AI project carefully and systematically to achieve the results you seek.