Enterprises are rethinking the role of their contact centers. GenAI-driven automation is poised to make contact centers not only much more efficient, but the technology is also creating opportunities to transform the customer experience and even boost revenue through more precise, targeted cross-selling and upselling.
While digital channels like self-service portals and virtual agents are gaining traction, especially among younger generations, in-person contact channels like phone calls and texting (live chat) continue to dominate the volume across all age groups. Clearly, customers continue to value a human touch, personalization, and empathy. For businesses, this is not just about resolving queries; it’s an opportunity to connect with customers on a deeper level, building trust and loyalty.
In this context, enterprises must not only maintain but enhance the quality of these personal interactions. Of course, few customers need or expect a person-to-person connection for every problem. The goal is to ensure that customer problems are resolved efficiently (often through automation) while simultaneously identifying opportunities for cross-selling and up-selling that make the most of human, high-touch interactions. Every human interaction should become a moment of potential value creation, both for the customer and the business.
To meet these multidimensional goals, a holistic transformation of the contact center is essential. This transformation must span the entire customer journey, ensuring that interactions are seamless, personalized, and effective, whether they occur through virtual channels or direct, in-person communication. The key to unlocking this more nuanced, responsive approach to contact centers will be AI, and particularly GenAI.
The best AI-powered contact centers of the future will not seek to automate every interaction through virtual channels. Rather, they will view AI as also a powerful tool for maximizing the impact of human interactions. For in-person contact channels, contact centers can equip agents with AI tools that provide seamless, accurate information. Agents should be empowered to answer customer queries quickly and accurately, leveraging AI insights to better understand the customer’s sentiment and emotional state. This allows agents to provide a more empathetic response, ensuring that the interaction is productive and that the customer leaves the interaction satisfied.
The Role of GenAI in Contact Center Transformation
Now that GenAI has advanced far beyond the initial prototype phase, it offers a range of capabilities that can significantly enhance contact center performance and impact, including:
- Expanded Self-Service Capabilities: GenAI can improve the scope and effectiveness of self-service options, enabling customers to resolve their issues independently while reserving human interaction for more complex queries.
- Agents Knowledge Assistance: GenAI can provide agents with accurate, almost instantaneous answers backed by trusted enterprise knowledge sources. GenAI streamlines the information retrieval process, allowing agents to quickly find relevant data without needing to sift through multiple search results or navigate numerous documentation pages. This improves the agent's productivity while also boosting the quality and efficiency of customer conversations.
- Precise Sentiment Analysis: GenAI excels in understanding customer sentiment and emotions in real-time, helping agents modulate their tone and approach accordingly. This leads to more empathetic and effective communication, which is crucial in maintaining customer satisfaction.
- Intelligent Segmentation and Recommendations: By analyzing customer interactions, GenAI can provide agents with timely recommendations, ensuring that they present the most relevant solutions and offers to the customer. This not only increases the likelihood of a successful resolution but also enhances the overall customer experience and, in many cases, increases revenue through cross-selling and up-selling.
- Post-Call Documentation: GenAI can automate much of the after-call work, such as summarizing interactions and updating records. This allows agents to focus on what matters most — engaging with customers — while ensuring that all necessary data is accurately captured.
As businesses continue to explore and implement GenAI, the potential for innovation in the contact center space is immense. By integrating these advanced capabilities, enterprises can exceed customer expectations, driving both satisfaction and revenue growth.
Our Approach to the GenAI Contact Center
Wipro’s GenAI Contact Center solution is built in collaboration with Databricks. The Databricks Mosaic AI suite stands out as a comprehensive platform for developing Generative AI solutions for contact centers, offering a holistic environment to manage data ingestion, data preparation, customizing GenAI models with vector search or model training, as well as model deployment, and operations.
Mosaic AI is a suite of tools developed to simplify the use of LLMs by integrating capabilities for training, fine-tuning, and serving LLMs. It provides access to pretrained LLMs that can be further fine-tuned on domain-specific language, product inquiries, or support requests. It supports the four most common GenAI architectures (prompt engineering, RAG, fine-tuning, and pre-training) and is designed to work seamlessly with the Databricks Lakehouse architecture, which helps users manage vast amounts of data.
Our GenAI Contact Center provides critical GenAI capabilities that include:
- Data Unification. GenAI solutions for contact centers should integrate various cross-channel data sources (CRM, support tickets, chat logs, audio transcripts, emails, etc.) to create a unified view of customer interactions. To accomplish this, AI models must have access to complete, real-time contextual information, not just about an individual customer but about trends across the customer base. Databricks provides a scalable platform for this.
- Data Governance. Data quality, accuracy, and compliance are critical for GenAI's success in contact centers. Regulations like CCPA and GDPR necessitate careful handling of personal data. Data lineage tracking can ensure visibility into data origin and transformations, which in turn enhances trust in Gen AI outputs. Unity Catalog within Databricks provides robust data governance, which is essential for compliance, including capabilities such as personally identifiable information (PII) and Role-Based Access Control (RBAC).
- Data Engineering. Advanced techniques like distributed text cleaning and tokenization allow enterprises to handle massive volumes of data efficiently. Strong data engineering capabilities are also essential for appropriately managing PII data in contact center conversations. NER (Named Entity Recognition) models can recognize names, addresses, and phone numbers and mask them appropriately to protect sensitive information and avoid passing that data to downstream tasks. Meanwhile, domain-specific fine-tuning of GenAI models improves the relevance of responses to customer queries. Databricks Mosaic AI fine-tuning capability efficiently adapts models to better understand company and domain specific terminology improving the quality and accuracy of the LLM responses.
- Efficient Orchestration. End-to-end automation is critical for minimizing manual intervention and errors; Databricks provides significant automation in the GenAI pipeline. Continuous integration/continuous delivery (CI/CD) models are also essential, given how frequently new policies, emerging issues, and product launches require updates to GenAI programs. Efficient model serving is also critical for delivering quick and accurate responses to customer queries. Databricks model serving infrastructure enables autoscaling so that models can handle peak time loads effortlessly in a contact center environment while reducing costs during periods of low activity. Moreover, optimized model serving also reduces latency, enabling rapid responses when using the model APIs.
- Security and Privacy. LLM security begins with end-to-end encryption of data both at rest and in motion. Databricks’ data encryption processes ensure that any data moving between storage or though cloud remains secured. Also critical: strict access controls (including token-based authentication) when models are exposed to users via APIs.
- LLMOps. When it comes to GenAI solutions, one of the biggest stumbling blocks for enterprises is the potential for hallucination (incorrect responses) and model drift (uneven performance). To solve these concerns, many enterprises are thinking more carefully about the operational aspects of managing large language models (LLMs) that underlie GenAI tools. Best-in-class LLMOps should emphasize efficient model versioning and tracking, allowing teams to continuously experiment and search for the best model. Monitoring and drift detection tools can track the model performance in real-time using performance metrics like bilingual evaluation understudy (BLEU) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE). Retrieval Augmented Generation (RAG) approaches have been shown to mitigate hallucinations as well. In the Databricks ecosystem, MLflow is a core component for managing the end-to-end lifecycle of ML models, providing visibility and flexibility in experiment tracking, model management, model deployment, and model security.
- Tools and Accelerators. Contact center Gen AI solutions will benefit from tools and accelerators that address key GenAI challenges like hallucination, bias, lack of explainability, and privacy breaches. Our GenAI Contact Center solution is equipped with a technology foundation to quickly onboard and synthesize new tools and accelerators in the GenAI pipeline.
Even so, optimizing GenAI for contact centers is no simple task. Wipro’s GenAI Contact Center solution is emblematic of the path forward. When a best-in-class AI-optimized data platform meets deep functional contact center expertise and AI integration experience, contact centers can quickly achieve GenAI results without building bespoke solutions for each emerging GenAI challenge. With the right GenAI capabilities in place, contact centers will become more responsive, efficient, and personalized.
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