Transforming Support Centers with AI and Automation

Improving the efficiency, cost, and satisfaction structure for agents and customers has always been a difficult balancing act in support center evolution. Innovative trends like automation, AI/machine learning (ML), omnichannel customer support, and self-service portals are poised to make that balance easier.  

The challenge of delivering on that promise for most organizations is seeing the two sides of implementation and integration via a cohesive digital strategy. The key for organizations is to start by looking by at these trends and how they intersect. This helps to drive a sober approach to implementation that delivers more than it disrupts.

AI and Automation as the "Swiss Army Knife" of Support Center Innovation

In a climate of all things AI, it’s easy to forget that AI is a tool for building a variety of support center outcomes. AI is a broad term that can apply to a variety of ends rather than an end. It requires closer inspection and specific application to see how it can affect support centers in ways that matter to: 

• Agents
• Customers
• Improved efficiency and insights for better outcomes
• Stronger businesses, brands, and bottom lines

The real evolution, trends, and innovation of AI in support centers is happening at the intersection of generative AI, ML, large language models (LLMs), and automation/RPA. The right combinations can drive major support center advances in countless ways, including:

• Automating repetitive task (database, processes, and interactions)
• Gathering and leveraging customer data for smart knowledge bases
• Predictive analytics
• Real-time sentiment and emotional analysis
• Conversational AI support
• Fulfillment of the customer demand for flexible self-services across channels
• Real-time customer needs and emotions prediction and analysis

The broad goal is to give agents deeper understanding and personalization of customer journeys and experiences. It's challenging to achieve these outcomes at this intersection of data and analytics in the support center with such complex tool interactions and integrations. Organizations can see this complexity reflected in the statistic that only one in ten agent interactions are projected to be automated by 2026, according to Gartner.  

Understanding the intersection of AI and ML in the support center is the first step in planning its practical, affordable, and incremental implementation for real ROI. This is where natural language processing (NLP), robotic process automation (RPA) and large language models (LLM) come into play in multiple ways.  

Natural language processing (NLP) uses the AI subset of machine learning (ML) to understand (and use) spoken language. RPA automates data collection, interpretation, and agent/client interaction support/routing, among other things.  

LLMs (powerful generative AI tools) play a key role in data integration across text, voice, and even images to support next generation analytics and RPA bots. Using AI-driven RPA bots is a growing trend with 64% of CX leaders increasing their investments in these next gen chatbots, according to the Zendesk 2024  CX Trends report.  

This next level of generating naturally sounding text, contextual interactions, understanding, and support is the first level. The ability to make them work globally and across customer channels requires moving to the next level of innovation: omnichannel and cloud customer support.

Omnichannel and Cloud Customer Support

The intersection of cloud-based customer support solutions is changing to enable organizations to meld them with AI, ML, LLM, and automation/RPA. There are countless advanced RPA business possibilities that also apply to support center services. These approaches can take customer/agent interactions, analysis, and satisfaction to another level by:

• Unifying all customer communication channels into one platform
• Maximizing agent skills, resources, and location diversity for a global workforce across support center services and internal marketing and sales
• Delivering the flexibility to meet changing customer volume and support needs

Reaching the goal of becoming an omnichannel support center requires the ability to give customers a seamlessly integrated experience across all channels and touchpoints, including:

• Phone
• Web
• Live chat
• Email
• Social media, and more

The benchmark is providing a consistent, contextual experience that allows customers to switch between channels with all interaction data intact. This is a growing trend that customers are already demanding, but only 31% of support centers have implemented so far, according to CX Today. 

This intersection of cloud, AI/ML, and automation delivers the next generation of automation-powered workflows that fuel omnichannel support center services via the potential involvement of: 

Contact center as a service (CCaaS) platforms that globally connects customers and agents and support customers across multiple channels

Unified customer experience management (UCXM) platforms to gather, consolidate, and analyze customer feedback data for delivering and tracking omnichannel experiences and interactions.

Organization support centers are beginning to integrate CCaaS, Unified CXM, AI, LLMs and RPA, such as the recent ServiceNow and Genesys strategic partner announcement. Integrations like these taps into even more granular omnichannel interactions that:

• Improve scalability and flexibility to meet customer need and complexity fluctuation
• Automate support request categorization by need, importance, language, and other vital factors like sentiment
• Allow customers to switch between channels without repeating information while lowering response time and speeding optimum resolution
• Route routine queries to self-service options so agents can handle complex interactions for overall operational cost savings
• Boost agent efficiency by giving them complete history at their fingertips
• Enhance customer insights/experience by optimizing agent efficiency by capturing and analyzing valuable customer data across all channels
• Match customers to the right agents 
• Track every interaction 
• Enable accurate forecasting for distributed call routing to agents
• Send out tailored customer opinion queries or predefined replies
• Monitor agent performance and quality through sentiment mapping and call recording
• Reduce after call work for agents by gathering pertinent call information for post call actions
• Provide a consistent brand experience to customers everywhere, anywhere.

Organizations cannot choose and implement support center automation solutions in a vacuum. It requires understanding and planning how AI/ML, omnichannel/self-service, and cloud will work holistically to deliver a hyper-personalized agent and customer experience.  

Support centers can take this hyper-personalization to the next level by centralizing customer data using real-time customer relationship management (CRM) software and/or a customer data platform (CDP). This enables organizations to provide and draw from customer insights across support centers and departments like sales, marketing, and field service. These automated, guided workflows enable:

• Repetitive tasks elimination, such as inputting accurate customer information into CRM CDP databases
• Hyper-personalization via predictive and prescriptive analytics
• Driving self-service bots and knowledge databases
• LLM data set input for RPA bot training, improved agent management/training, and customer satisfaction
• Data validation by cross-referencing information from multiple systems or databases, ensuring data accuracy and consistency

Making the most of these advances in automation and AI in support centers is about more than implementation. It’s about planning and creating applications and solutions engineered for optimum user experience. This makes for a transparent blend between people, processes, and technology. 

The key is finding a consulting and implementation partner that understands the challenges and possibilities of these advances in support center environments. 

Turning Support Center Evolution Complexity into Manageable Reality

Understanding and integrating these innovative approaches to support center technologies can be daunting for any company. The intersection of people, processes, and technologies may start with automating one or many processes that require bespoke solutions or selecting from countless vendors. 

It can often be best to start incrementally, but that approach can also fail without a long-term plan for implementation of AI, automation, and cloud solutions. To deliver the defined ROI and KPIs, they must be highly integrated. This is about more than fast, simple, and cost-effective implementation of new tools and support center integration approaches.  

A skilled support consulting and engineering partner can bring a vendor agnostic viewpoint based on broad company project successes and vendor solution implementations. They understand the limitations of AI, automation, LLMs and other emerging technologies that drive support center evolution. This requires an engineering, implementation, training, and ethical understanding of how these technologies should work where people and data are concerned to:

• Ensure generative AI systems and LLMS comply with industry regulations like GDPR, CCPA, or HIPAA to avoid legal ramifications and fines
• Overcome existing systems integration complexity and intensive resource needs via in-depth assessment of the current infrastructure and strategic planning 
• Provide comprehensive agent training to ensure understanding of AI tools, automation, and their limitations to improve interactions

Many organizations do not have the in-house expertise to see the limitations of LLMs and how to implement safeguards and alternate data training/cleaning models. This can avoid ethical and regulatory challenges that result in fines, brand damage and loss of market share. The goal is to implement AI/LLM model training free of biases or personal data risks, whether it is via a vendor solution or bespoke. 

Balancing the benefits of these technologies and approaches with the realities of how best to choose, implement and define the ROI outcomes is one side of the equation. The other is developing a realistic timeline, budget and impact on the workforce and workflows based on change management needs.  

Establishing the balance between human touch and machine interaction/automation and service optimization is crucial to the bottom line and workforce comfort. It takes a highly experienced team with diverse yet interconnected expertise in digital strategy and engineering to forge and guide the organization down that path to a leading customer support center. 

To see just a select few of the many Solugenix support Center case studies, click here.

Then learn more about how Solugenix can customize and meet all your support center services and needs by clicking here.