- AI Call Center Solutions that Drive Value
- Will AI in call centers replace agents?
- The Future of AI in Call Centers
Call centers are busier than ever. According to a 2021 survey, 65% of U.S. consumers have required assistance during the COVID-19 pandemic. These growing call volumes – coupled with the continuous need for cost optimizations – have driven the demand and adoption of artificial intelligence (AI) in call centers. AI is moving forward rapidly, with contact center AI software continuously evolving and dramatically improving, and ultimately delivering more value.
AI Call Center Solutions that Drive Value
Robotic Process Automation
A standard contact center is chock full of repetitive and dull tasks that are definitely necessary but rarely require a human level of decision making. Automating these activities, known as Robotic Process Automation (RPA), takes call center AI automation to the next level by interpreting, triggering responses, and communicating with other systems just like agents would, via a combination of user interfaces and commands. There are two forms of RPA, attended and unattended, and both have value for a contact center.
Attended RPA are automated processes launched by the agents themselves as part of their daily workflow, for example, logging in to multiple systems at once, or launching a series of post-call tasks such as customer emails and future follow-up tasks.
Unattended RPA generally work autonomously in the background, such as monthly invoice processing or customer reminders.
Both types of RPA make contact center agents more effective by offloading repetitive tasks and giving them the space to focus on more value-added areas – namely, pleasing customers.
Next-step suggestion: Work with frontline staff to select the right use cases — along with the relevant measurement criteria — and properly communicate the expected changes to the contact center team.
AI-Based Collaboration Tools
With many call centers adopting the remote working model, the common practice of one agent asking a question over his cubicle wall has come to an end. Cost optimizations have also cut back on floor managers circulating the room to answer questions and provide assistance.
Intelligent collaboration tools are trending in the call center to meet this need of connecting agents with their colleagues and supervisors in order to better serve their customers. Technology plays a large role in facilitating the collaborative call center. Collaboration can come through basic messaging platforms like Slack or Microsoft Teams, or via more advanced AI-driven solutions that can loop in subject matter experts from across departments based on their skill set and availability.
Next-step suggestion: Consider implementing a pilot program to determine how collaboration between customer service agents and subject matter experts can improve service, based on KPIs such as CSAT and FCR rates.
AI-Driven Agent Decision Support
Beyond team collaboration, the interconnection of humans with contact center AI technology can be utilized to provide practical decision support during the agent-customer interaction. The agent and machine collaborate, with the agent’s performance enhanced by the computer’s ability to provide faster resolutions. The bot learns from the agent’s feedback and improves the automated responses over time.
This model is especially effective when the contact center must handle large call volumes or highly complex calls. AI call center solutions are expected to reduce agent training time and streamline the entire support process, resulting in a more satisfying customer experience.
Next-step suggestion: Determine the workflows that are most common, and train the machine accordingly.
Computer Vision AI-Based Self-Service
AI is reshaping the enterprise approach to self-service. AI enhances existing self-service capabilities, such as smart FAQ and IVR, with the new cognitive capabilities in chatbots or virtual agents. Using these bots to automate answers to basic customer questions lowers incoming call volumes and has been shown to decrease the average agent handle time (AHT) by 10% or more while improving CSAT scores and freeing up agents to focus on more complex inquiries.
Augmenting self-service with computer vision AI technologies such as object recognition, facial recognition, image to text, and image similarities can add significant value to a company’s self-service platform, allowing customers to visually interact with bots that can guide them to self-resolution. Computer vision can add essential data to a customer’s profile based on visual data, help predict issues before they even happen, and effectively route the customer’s case to the relevant agent if self-service is unsuccessful.
Next-step suggestion: Choose several use cases where computer vision AI can simplify the agent-customer interaction. Measure the difference in time and effort of each interaction and be prepared to fine-tune as you go.
AI-Based Prediction of Customer Behavior via Speech Analytics
Thanks to structured data analysis, predictive analytics can now be performed by extracting information from massive amounts of data and using it to predict trends and future behavior patterns, such as customer churn. Predictive analytics for customer relationship management can be applied throughout the customer’s lifecycle – acquisition, relationship growth, retention, and win-back.
One trending approach for AI in call centers is a focus on speech analytics. With speech analytics, call centers are equipped with a wide range of AI call center solutions that enable recognition of a customer’s accent, gender, and emotion, and power conversational IVRs and voice-based virtual assistants. Natural Language Processing (NLP) algorithms have enabled AI-powered tools to understand context, power smart classification, routing of customer inquiries, and create conversational chatbots.
Once deployed, the data from the analysis can facilitate intelligent quality management on 100% of agent interactions, thereby improving performance via targeted training.
Next-step suggestion: Check the feasibility of partnering with another department — such as CX or Marketing — to share the cost of deployment for predictive voice analytics.
Will AI in call centers replace agents?
Contact center AI technology not only makes the call center more efficient, but the technology is capable of augmenting the human agent’s overall performance, making them smarter and expanding their possibilities for career development. Far from doomsday predictions that robots are taking over their jobs, enterprises are more likely to use AI in call centers to help their agents succeed in their roles.
According to research, of the 78% of US-based contact centers that plan to deploy AI in their contact center in the next three years, 97% of them plan to use AI to support agents as opposed to 7% who plan to use AI to replace some or all of their current staff. A Forrester report underscores this sentiment by discussing how AI trends will transform agents’ roles in the contact center by giving them the tools they need to succeed.
The Future of AI in Call Centers
The future of customer service is human-machine collaboration. As call volumes increase and agents must handle more complex customer inquiries, contact center AI automation is becoming a force multiplier. AI equips call centers with computer vision AI self-service tools, RPA, agent decision support, and predictive tools, all of which simplifies call center processes and interactions, delivering a value that both enhances call center KPIs and results in happier agents…. a definite requirement for a successful contact center.