The Role of Artificial Intelligence in Call Centers
Artificial intelligence (AI) automates tasks, provide real-time assistance, and analyze vast amounts of data and enhances customer service, improve efficiency and productivity in call centers.
This guide will analyze the advantages of deploying artificial intelligence in call centers while sharing insights on how OpenQuestion and Teneo integrate the technology.
The Impact of AI on Customer Service in Call Centers
In the past few years, advancements in artificial intelligence (AI) have been rapidly transforming many fields, including the customer service sector.
AI is allowing companies to improve their customer service with optimized customer service workflows and automated processes, which are resulting in improved customer satisfaction and boosted efficiency from call centers.
While AI can be utilized in various customer service applications, one of its most effective uses is within the call center environment.
Call centers are the cornerstone of customer service as they enable businesses to quickly respond to customer inquiries, provide support, and resolve any issues, 24/7.
Benefits of AI in Customer Service
Traditional call centers depend heavily on human agents. These agents can handle only one customer at a time. AI significantly changes this dynamic.
AI-powered tools like IVRs, chatbots, and virtual assistants can handle multiple customer inquiries at once. This drastically reduces wait times.
This multitasking ability not only speeds up service delivery but also frees human agents. They can tackle more complex customer issues that require nuanced understanding and emotional intelligence.
For instance, AI can facilitate automated conversations between customers and company representatives.
Before AI
In the era prior to AI, one particular business faced numerous operational hurdles and lost almost $26 per call with a 9% rate of call misdirection.
Customers had to wait over two minutes on average, and customer satisfaction was barely satisfactory. The previous touch-tone IVR system was unable to adequately serve customers and patients due to excessive demand.
Not only did this affect customers, but agents also suffered, having to deal with elementary troubleshooting and the repercussions of misdirected calls.
Results with AI
Upon implementing the OpenQuestion IVR, the company experienced significant improvements including a decrease in the number of misdirected calls, reduced customer waiting times, and a boost in customer satisfaction.
Furthermore, it improved service levels, deflection rates, and a noteworthy 42% increase in calls being correctly routed to the right agents was observed.
Successfully managing over a million IVA sessions, the company saved an annual amount of $6M and elevated customer satisfaction by 6%.
One of the critical aspects of this transformation was the effect it had on the call agents. The AI-fueled system equipped the agents with the necessary context about the customer’s problem, making it unnecessary for the customer to repeat their issues.
This efficiency was achieved through predictive analytics, which identified potential problems based on patterns and usage, enabling a more personalized and effective customer service experience.
As AI is very adept at recognizing patterns and quickly responding to questions, it can completely transform customer service.
With new technological advancements in AI with generative AI, large language models (LLM) are reshaping the way businesses operate. However, the decision to integrate LLMs into a business strategy should be a calculated move, not a blind gamble.
In the context of call center automation, for instance, LLMs can significantly enhance the efficiency and effectiveness of customer service.
However, it’s equally critical not to corner yourselves into a single technology or approach.
The future is invariably uncertain, and the key to thriving is to build for an open architecture to be able to stay on top of new advancements in AI, such as generative AI.
How to harness the power of generative AI in customer service
When embracing generative AI in a Contact Center, an orchestration is fundamental to maintaining a harmonious technological ecosystem where each element enhances the other, preventing discord and disruption when introducing a new tool like an LLM.
When it comes to contact centers, an orchestration ensures that the customer experience remains smooth and consistent, even as new technologies and methodologies are introduced.
So, whatever platform you choose, AWS, Google, Microsoft or Genesys, make sure to add an orchestration layer to reap the benefits to be able to future proof your call center.
One example of an orchestration platform is Teneo, which plays the role of the conductor, ensuring each component operates in harmony.
Teneo integrates various technologies and systems, including generative AI, into a single cohesive and efficient operational framework.
Read how Teneo can orchestrate your call center, here.
Self-service: Improving Call Center Interactions with Generative AI
As businesses strive to enhance customer experiences, the adoption of AI-powered tools, such as generative AI is skyrocketing. Generative AI can significantly improve the effectiveness and efficiency of call centers in various ways.
7 challenges with Generative AI for Call Centers
The implementation of generative AI poses several challenges for businesses, and we will explore the key obstacles associated with the adoption of generative AI and discuss strategies to overcome them.
Technical Complexity
Generative AI models are incredibly complex, often containing billions or trillions of parameters.
Training such models requires extensive computational resources and expertise, making it impractical for most organizations.
As a result, businesses are likely to rely on cloud APIs for consuming generative AI, with limited customization and tuning capabilities.
This concentration of power in a few entities may raise concerns about data privacy, control, and accessibility.
Legacy Systems
Integrating generative AI into existing technology environments can pose additional challenges.
Legacy systems may have well-established processes and workflows that do not align with the capabilities and thinking of generative AI models.
Enterprises must decide whether to integrate the new technology with their legacy systems, use an orchestration tool like Teneo or invest in replacing outdated infrastructure.
Finding ways to create integrations or adopt new capabilities becomes crucial to achieving desired outcomes efficiently.
Avoiding Technical Debt
Generative AI should not be treated as a mere optimization tool but rather as a means to bring about significant changes in business processes.
Deploying AI models for customer support, for example, should involve reducing the number of human agents handling cases, not simply redistributing the workload.
Failing to achieve substantial improvements can result in generative AI becoming another burden of technical debt, adding complexity without true optimization.
Reshaping the Workforce
Generative AI holds the potential to reshape work across various industries. This raises concerns about job displacement.
While some roles may disappear, new positions will emerge. These will oversee and improve AI-assisted processes. Employees can shift from being “doers” to trainers. They will be responsible for refining AI algorithms and leveraging the technology’s capabilities.
Organizations must take proactive steps. They need to identify and create new job opportunities. This ensures a smooth transition and avoids workforce obsolescence.
Monitoring Misuse and Misinformation
Generative AI models have the ability to create content at a reduced cost, but this also introduces the risk of misuse by threat actors. AI models themselves can produce misinformation and generating false facts.
Legal Concerns and Algorithmic Bias
Generative AI models may inadvertently infringe upon intellectual property rights by using training data without proper authorization, potentially leading to copyright issues.
Additionally, algorithmic bias is a critical concern when training generative AI models. Biased or incomplete training data can perpetuate discriminatory results, leading to legal repercussions and societal harm.
Implementing robust governance frameworks and ensuring diverse and representative training data are essential steps to mitigate legal and ethical risks.
Providing Coordination and Oversight:
To navigate generative AI challenges effectively, organizations should set up centers of excellence (CoE). These centers focus on the technology’s adoption and governance.
CoEs play a crucial role. They understand the capabilities of generative AI and develop policies for its use. They also involve key stakeholders from legal, IT, risk, and other relevant departments.
CoEs ensure coordination and oversight. They’re responsible for deploying generative AI within organizations. They foster innovation while managing associated risks.
Generative AI in Contact Centers – How to use it
Generative AI is pivotal in redefining customer interactions in contact centers.
However, despite its immense benefits, there are certain challenges and limitations that need to be addressed for seamless communication to boost accuracy in the language understanding models. These challenges can be solved with the Teneo Linguistic Modeling Language (TLML).
The Teneo Linguistic Modeling Language (TLML) is a state-of-the-art, deterministic language understanding system capable of recognizing and deciphering word patterns in customer speech.
It adds an extra layer of determinism on top of Natural Language Understanding (NLU), Language Learning Model (LLM), and machine-learning classification, attaining precision that eludes probabilistic models.
By producing matches that instigate specific actions based on the customer’s intent, TLML enables the use of LLM and accurately interpret spoken language and enhance contact center operations.
The Working Mechanism of TLML
Teneo can identify a customer’s response, assign an intent to the call, and then extracts crucial information from the text to decide the appropriate response.
For instance, without Teneo, a match on “unlock” would only process the request and route the call into an account support queue.
Alternatively, it might need the creation of a new intent and workflow to probabilistically respond to clarifications like “my device is locked”, “password is wrong”, or “login says my account is locked”.
However, with Teneo, the system matches “unlock”, asks for clarification, and accurately matches the language on “device is locked”, “password is wrong”, or “login says locked”, thus routing the call appropriately.
This strategy employs a single flow, unified clarification, and simple language constraints to assure correct routing. Learn more about TLML, here.
Teneo’s Unique Strength
Teneo stands out by using the TLML system. This system understands and interprets spoken language with more precision than traditional techniques. It uses a unified workflow that depends on words and context to determine conditions. This bypasses the need for manually constructed rulesets.
As a result, TLML streamlines bot and dialogue management. It standardizes deployments and reduces reliance on ad hoc solutions and human intervention.
Other platforms depend on metadata markup or custom scripts. This leads to the need for static and ever-expanding rulesets. Such an approach increases the demand for resources, infrastructure, and personnel dedicated to maintenance and support.
Key Advantages of TLML
Accuracy Booster
With TLML on top of ML, you can boost accuracy rates from 80% to 95%.
Guardrails for Generative AI and Precision
Implement safety measures at scale to ensure the accuracy and relevance of AI-generated content as well as the correct interpretation of closely related intents.
High-Functioning and Critical Component
TLML is designed to optimize and scale performance across all aspects of bot and dialogue management.
Teneo: Enhancing Customer Engagement
Teneo enhances both agent and customer engagement and accelerates competency by merging TLML with other AI-based technologies. The advanced language understanding capabilities of TLML enable more accurate and efficient handling of customer calls, making it an essential asset for any contact center.
Case Studies: Successful Implementations of Artificial Intelligence in Call Centers
Artificial intelligence in call centers is a new topic to most people, but the technology has been quietly developed over a long period and has been delivering results for businesses all over the world.
Find out how, in any of the following success stories.
Telefónica Germany Creates Industry-Leading IVR Solution
This system provided 24/7 real-time answers that were personalized covered an omnichannel approach.
As a result, there was a 6% increase in IVR resolution rate, and the system handled over 900,000 monthly calls and 200,000 monthly text requests.
The use of AI in this way allowed Telefónica to provide better customer service and improve customer engagement.
Swisscom Successfully Implements & Operates Europe’s Leading Conversational IVR Project
Swisscom, in collaboration with Teneo, used OpenQuestion as the first point of contact for customers.
The AI solution was multilingual, covering German, Italian, French, and English, and integrated seamlessly with Swisscom’s pre-existing systems.
With this implementation, Swisscom was able to support over 9 million calls per year and increase its transactional Net Promoter Score by 18 points.
Tech Giant Saves $39M with OpenQuestion
A top global tech company implemented OpenQuestion and estimated a $39M ROI.
The AI solution saved agent call time by referring 55% of callers to web resources, reduced misrouted calls by 30%, and decreased average handle time by two minutes per call.
OpenQuestion was also scalable, allowing the company to expand its use to high-priority commercial customers.
Healthcare Call Center Transformation
These examples demonstrate the significant positive impact AI can have on call center operations, from improving customer service to reducing operational costs and increasing overall efficiency.
Note that the specific outcomes will depend on the particular context and implementation approach of each company.
If you would like more information on how to incorporate artificial intelligence in call centers, get in touch here.