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Tailoring Generative AI for the Contact Center

Tailoring Generative AI for the Contact Center

Tailoring Generative AI for the Contact Center

Why a vertical solution is the best choice.

As industries evolve, so do customer expectations. Customer service is more critical than ever for maintaining customer loyalty and business success.

Enter Generative AI (artificial intelligence): a groundbreaking technology that promises to revolutionize the contact center and customer service industry, transforming how businesses interact with their customers, and elevating the overall customer experience (CX).

The Essence of Generative AI

Generative AI is not just a buzzword. It’s a powerful tool that harnesses existing data to create fresh content across various mediums, from text and audio to software code and images. Think of it as a digital artisan, capable of weaving new narratives, composing harmonious melodies, and even crafting intricate lines of code.

Generative AI...has the potential to revolutionize and simplify the way businesses interact with their customers.

At the forefront of this Generative AI wave stand platforms like ChatGPT, where human-like conversations emerge from algorithms that understand the nuances of human language. As one can imagine, this kind of innovation has the potential to transform CX for contact centers across the globe.

Generative AI’s potential is not limited to casual chitchat. It has the potential to revolutionize and simplify the way businesses interact with their customers.

Through a sophisticated understanding of language and context, Generative AI can provide tailored responses that address customer queries in ways that no pre-programmed script can achieve.

However, the impact of using Generative AI that is combined with existing AI methods is the key differentiator, namely for organizations that need it for niche, esoteric use cases.

The Two Faces of Generative AI

As Generative AI continues to mature, it branches into two distinct offerings: horizontal and vertical models (SEE CHART 1).

Horizontal models, exemplified by ChatGPT, offer a broad understanding of various industries and scenarios. These versatile tools can be used across departments and industries, but their scope might fall short when it comes to hyper-targeted solutions for specific categories.

On the other hand, vertical AI models bring deep domain expertise to the table. These models delve into the intricate nuances of specific businesses, offering solutions that are finely tailored to individual needs.

Imagine a customer service representative armed with an AI-powered assistant that understands the intricacies of their industry. Guiding them to offer solutions that go beyond the generic responses found in horizontal AI systems.

Many people think that Generative AI is the main change-maker for organizations. But it’s actually the combination of vertical AI solutions with Generative AI capabilities that are making a bigger impact.

Because vertical AI solutions are industry-specific, any and every form of business can harness the power of Generative AI for their workforce.

Vertical AI platforms are typically built upon essential data models, (described later), that form the foundation of the technology. This sets it apart as a vertical model rather than a horizontal one.

1. Industry-specific language processing. This teaches the technology to understand and communicate like people in the industry using the platform. For service industries, it helps the model read, comprehend, and offer suggestions using common industry terms by technicians.

2. Data contextualization. This process is exactly what it sounds like. It involves giving context to the data and ensuring the data knows its place and connections. It’s all about adding the “who, what, where, and why” to data, making the output more meaningful.

3. Expert knowledge. Some vertical AI solutions go even beyond historical data by gathering knowledge from a company’s top workers to enhance the model’s intelligence. This approach includes the expertise of skilled individuals in the dataset, resulting in more accurate and personalized outputs.

On top of this foundational model lies the Generative AI element of the technology found in vertical AI offerings. This enables the model to provide elevated and tailored responses that address queries just like a customer service chatbot, but it does so in ways that no pre-programmed script can achieve.

Five Reasons Why Vertical AI Solutions is a Game Changer

1. Meeting the bar for excellence: In customer service, every interaction is an opportunity to build or tarnish brand loyalty. Service organizations demand solutions that exceed expectations and address problems with precision. This is where vertical AI models shine - equipped with the expertise to provide accurate solutions, even in complex situations - like tech support.

2. Unlocking expert knowledge. As noted earlier, the best solutions often lie within the minds of seasoned experts. Vertical AI models tap into this wealth of knowledge, enabling companies to utilize the expertise of their employees effectively. Instead of relying solely on historical data or generic internet answers, service/contact center-focused AI harnesses real-world scenarios to provide accurate and contextually relevant solutions.

3. Guiding effective problem-solving. Our 2024 Field Service Benchmark Report, which analyzed data from 145 service organizations, found that if every employee had the knowledge and skills to perform, like the top 20% of the workforce, service costs would be reduced by as much as 22%.

Diagnosing and solving complex issues often involves asking the right questions. AI built for service can guide technicians through a step-by-step process, leading them to the correct solution. This not only improves the quality of service but also streamlines the problem-solving process, making it cost-effective.

4. Coordinating complex situations. Service involves multi-layered coordination between customers, support agents, field technicians, and various assets. Vertical AI models break down data silos, standardize metrics, and create models that drive actionable decisions, ensuring that the right resources are allocated to each situation.

5. Consistency and sustainability: Learnings from our report reveal that a smaller skills gap has a direct impact on an organization’s performance.

On average, top-performing organizations resolve issues in 2.4 days. In contrast, bottom-performing companies take four times longer to resolve issues and have approximately three times more visits per asset compared to their high-performing counterparts.

With the increasing complexity of machines and products, maintaining a consistent service standard becomes challenging. AI enables the sharing of expert knowledge, ensuring that both tenured technicians and new hires can make informed decisions efficiently.

Because vertical AI solutions are industry-specific, any and every form of business can harness the power of Generative AI for their workforce.

As businesses embrace the transformative potential of Generative AI, customer service organizations stand on the brink of unprecedented advancement. The technology’s prowess in delivering tailored, expert-driven solutions is poised to redefine customer interactions. Thus ushering in a new era of elevated customer satisfaction, enhanced brand loyalty, and resounding business success.

The integration of Generative AI promises to illuminate the path ahead, propelling customer service within the contact center and beyond into an era of unparalleled excellence.

Gartner predicts that by 2025, 80% of customer service and support organizations will be applying Generative AI technology in some form to improve agent productivity and CX.

According to the analyst firm’s research and insights, “application leaders responsible for customer service should partner with customer service technology vendors to evaluate and adopt the generative AI product innovations that deliver the most value in the near term.”

Generative AI Success Stories

Generative AI is no longer a technology that exists only on virtual paper and in marketing hype. It has become a real-world tool that is having strong positive results for those organizations that have implemented it. Here are two examples.

AI in Customer Service: Ricoh’s Transformation

Ricoh, a prominent player in the global printing industry, faced a surge in churn rates and the pressing need for remote onboarding due to the COVID-19 pandemic.

Turning to AI for solutions, they embarked on a journey to redefine their customer service landscape in July 2021. Through the strategic implementation of vertical AI solutions, Ricoh not only revolutionized its customer service but also achieved remarkable results.

  • The challenge. Ricoh confronted the dual challenge of high churn rates and the necessity of remote onboarding caused by the pandemic’s impact. These hurdles prompted a search for innovative solutions to enhance customer service and create a more streamlined and efficient process.
  • The solution. With a commitment to elevate their customer service game, Ricoh turned to AI for answers. Leveraging AI solutions, they embarked on a transformative journey. By capturing and democratizing the expertise of their top technicians, they equipped their resolution agents with accurate information to tackle challenges effectively. The outcome was nothing short of remarkable.
  • The transformation. Ricoh’s dedication to innovation and excellence yielded tangible results. After integrating AI into their work processes, they achieved a notable 17% increase in remote resolution rate. Furthermore, the deployment of AI led to reduced onboarding times and an outstanding 31% boost in first call resolution (FCR) rates, solidifying their commitment to exceptional customer service.

Streamlining Medical Device Service Organizations

In the world of complex biomedical testing equipment, precision and efficiency are critical. A leading medical device manufacturer faced an all-too-common challenge: dispatching field technicians at an unexpectedly high rate, particularly for cases demanding return visits within a tight five-day window.

Predictive analytic capabilities found in vertical AI offerings identified opportunities for remote resolutions, paving the way for substantial cost savings by reducing dispatches.

This strain on resources prompted a search in fall 2021 for accurate remote resolutions within the contact center.

  • The challenge. Notably, 26% of this particular organization’s cases required return visits within five days, highlighting the urgency of the issue. Additionally, a lack of insight into consistently misdiagnosed product issues by their call center agents further complicated matters.
  • The solution. In December 2021, the manufacturer embraced a suite of AI-powered solutions, implementing both call center troubleshooting and field troubleshooting products. These tools elevated technician skills, refining remote resolutions. Predictive analytic capabilities found in vertical AI offerings identified opportunities for remote resolutions, paving the way for substantial cost savings by reducing dispatches.
  • The transformation. Since adopting vertical AI solutions, the manufacturer experienced a remarkable overhaul. Tools like call center troubleshooting became integral to agent processes, used in an impressive 92% of cases. Over 10 months, this partnership led to a notable 22% reduction in return visit rates. Identifying remote resolution chances boosted cases resolved by the call center by 3%, culminating in a 7% reduction in resolution cost per case.

These success stories underscore AI’s power to enhance not just the experience within the contact center, but rather throughout the entire service journey. The result: better resource allocation, heightened customer service, and substantial cost savings: a trifecta in the demanding field of medical device manufacturing.

Assaf Melochna

Assaf Melochna

Assaf Melochna’s experience incorporates strong leadership skills built upon a strong technical foundation. He is an expert in service and has business and technical expertise in enterprise software. Assaf started Aquant with his co-founder Shahar with the vision of helping service companies transform the way they deliver service and serves as president.

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