AI, Large Language Model
Exploring Business Use Cases for Generative AI Solutions
A New Wave of Business Innovation led by Generative AI
The business use cases for Generative AI (Gen AI) solutions cannot be ignored as businesses across multiple industry sectors are turning to the ever-advancing landscape of artificial intelligence (AI) technologies to revolutionize the way they engage with customers, streamline operations, and drive innovation.
Among these AI technologies, conversational AI and generative AI (powered by Large Language Models or LLMs) are emerging as game-changers, reshaping the future of business in profound ways that would have been unthinkable a few years ago.
In recent years, Conversational AI, based on traditional natural language understanding (NLU) technology, became a cornerstone of modern digital engagement, enabling businesses to provide more automated and personalized customer interactions through chatbots and voice-enabled interfaces.
Large Language Models (LLMs), such as OpenAI’s GPT and Google’s Bard, have accelerated AI-led innovation, augmenting the capabilities of traditional chatbot-based engagement models and opening up a whole new set of business use cases, powered by generative AI. These go beyond the traditional chatbot use cases that focus on customer and employee engagement to encompass a broader range of workplace and automation applications that are not only conversational in nature.
Note: The AI terms Generative AI and Large Language Models (LLMs) are used throughout this blogpost but really are mostly interchangeable in the context of the use cases. A helpful definition of the distinction between the terms is offered on a Google search as:
“Generative AI is a good fit for creative fields like art, music, or content creation. LLMs are well-suited for applications in natural language processing, including chatbots, content summarization, and language translation.”
Let’s explore some general use cases for LLM-enabled conversational and generative AI, breaking them down into customer-facing applications and internal business use cases.
Customer-facing Business Use Cases for Generative AI.
- Automated & Personalized Customer Support
One of the key trends in AI adoption is the integration of generative AI into customer-facing applications to deliver more personalized and intuitive experiences. An AI Assistant, powered by Gen AI, can understand user intent and context and generate intuitive and intelligent responses to a wide range of queries.
The ability of generative AI to better understand and classify customer intent and generate responses, even to complex and multi-step queries, offers a more dynamic and fluid conversational AI experience. Added to this, the ability for gen AI assistants to better retain the context of a conversation and to adapt accordingly is game-changing for customer support operations.
Generative AI is also used to analyze customer interactions and data, such as customer preferences, so that a Gen AI Assistant can tailor recommendations and content, providing personalized responses in real-time. This is a big improvement on traditional intent-led chatbots that rely on more predefined intents and rules.
By harnessing the power of generative AI in secure and enterprise-ready ways, businesses can deliver highly enhanced and hyper-personalized self-service experiences for their customers. This helps increase revenue, improve customer satisfaction, and lower the cost to serve.
- Marketing & Sales Support
Generative AI is also revolutionizing marketing and revenue-generating initiatives by empowering businesses with innovative tools to engage customers, generate leads, and drive conversions.
Gen AI enables businesses to more efficiently create engaging and relevant content for their target audiences, driving better customer engagement and retention. Using generative AI, social media posts, blogs, ad copy, website content, and campaign emails are more easily generated.
In lead generation, Gen AI plays a crucial role in creating personalized and compelling content that converts leads generated from marketing campaigns, social media ads or a website in real time. By analyzing customer data and behavior patterns, businesses can leverage gen AI to interact with potential leads in real-time, answer inquiries, and guide them through the sales funnel towards conversion.
These AI-driven solutions also enable proactive outreach, capturing leads’ attention and nurturing relationships through personalized interactions across various channels.
With advanced sentiment analysis capabilities, businesses can gauge audience sentiment and tailor their marketing strategies accordingly, ensuring messages resonate with the intended audience and drive desired outcomes.
Explore more detail on how LLMs can improve marketing and lead conversion initiatives.
Internal Workplace Use Cases for Generative AI
- Agent Copilot
Generative AI offers significant advantages for assisting agents when it comes to back office tasks. It can efficiently distill complex information into concise and actionable insights, in real time.
Using Generative AI, it is now possible to identify key themes, extract relevant details, and produce succinct summaries, enabling agents to quickly grasp the essence of large volumes of information without the need for manual review. This not only saves time and effort but also facilitates improved compliance. For example, Gen AI can be used to monitor agent responses and prompt them to include compliance-related information to customers, in real time. In highly regulated environments, where agent turnover and training costs are high, this copilot assistance offered by AI is invaluable.
Large Language Models also revolutionize knowledge access by leveraging techniques like Retrieval Augmented Generation (RAG) to index and retrieve information from external knowledge sources or documents. By utilizing RAG techniques, agents can easily access accurate information from predetermined and trusted sources. This enables them to provide the most accurate responses and mitigates the risks often associated with the hallucination or inaccuracies of generated responses from LLMs, such as ChatGPT.
Additionally, Generative AI excels in summarizing conversational history, allowing agents to gain insights into past interactions with customers. By summarizing previous conversations and extracting key information, agents can more easily understand customer preferences, concerns, and previous resolutions, facilitating more informed and personalized interactions. With quicker access to relevant information, agents can resolve customer issues more efficiently, leading to higher customer satisfaction and retention.
- Workplace Knowledge Assist & Summarization
Just like in the contact center where Gen AI can better assist agents with relevant, timely and personalized information, they can also be applied to knowledge management in the workplace by providing employees with access to relevant information, summarizing lengthy complex documents, contracts, and other materials. This ultimately leads to improved decision-making and workplace productivity.
For example, in financial services, back office processes such as risk management, compliance, and legal benefit significantly from better knowledge access facilitated by generative AI. Risk management teams can quickly analyze and assess complex financial data, regulatory requirements, and market trends, enabling more informed decision-making and proactive risk mitigation strategies. Similarly, compliance departments can stay updated on regulatory changes, interpret complex regulations, and ensure adherence to compliance standards across the organization.
Overall, the integration of generative AI in the workplace transforms how employees work by providing timely access to relevant knowledge, streamlining document processing, and enabling faster decision-making across various departments and roles. This not only enhances productivity but also strengthens organizational agility and competitiveness.
- Compliance & Risk Insights
Another emerging trend for the use of Generative AI is in risk management and compliance. These models can be leveraged to analyze vast amounts of unstructured data, such as regulatory documents and legal contracts, to identify potential risks, ensure compliance with regulations, and mitigate breaches and losses.
The technology can also be used to analyze the conversational history with customers at scale and search for any potential compliance breaches. This lookback application of LLMs is especially relevant for industries that are highly regulated and need to report on compliance to regulatory bodies.
Conclusion
Despite the enormous benefits, the adoption of AI technologies also raises concerns and risks that need to be carefully addressed as they adopt LLM solutions in their organizations. One of the main concerns is the potential for biased or inaccurate outcomes, especially in LLMs, which may perpetuate existing biases present in training data.
Additionally, there are concerns about data privacy, security, and regulatory compliance, particularly regarding the use of AI in sensitive transactions and decision-making processes. Compliance and security safeguards help mitigate these risks for enterprises. Additionally, the rapid pace of innovation and advancements in the area of generative AI is also expected to address some of these industry concerns in the near future.
In conclusion, the adoption of Generative AI is transformative, enabling businesses to deliver more personalized, efficient, and innovative services to customers and more automated workplace processes to help increase productivity, drive revenue, and lower costs.
By embracing these technologies and addressing associated risks, businesses can unlock new opportunities for growth, differentiation, and competitive advantage. Those that shy away from AI may get left behind.