Key highlights:
- Gartner expects conversational AI to help reduce costs associated with human agents at contact centers by $80 billion by 2026.
- Businesses are deploying conversational AI tools for round-the-clock, personalized support to customers, making relevant recommendations, and boosting the omnichannel experience.
- Implementation best practices include adopting an iterative approach to learning and scaling, a smart deployment strategy, safeguarding user data, and continuously optimizing systems for better results.
Conversational AI- a sophisticated data-driven technology enabling human-like dialogue across digital platforms–is empowering businesses from diverse industries to deliver quick, friendly, scalable, and personalized customer experiences round the clock.
Perhaps the biggest breakthrough of conversational AI is its ability to lend more authenticity to AI-led interactions. Unlike a traditional bot that relies on predetermined questions and answers, conversational AI offers tailored responses based on context, sentiment, and intent. The conversational AI market is estimated to grow at a compound annual growth rate of 20.9% from 2023 to 2030 and reach USD 34.7 billion by 2030.
Behind conversational AI are complex and sophisticated algorithms that utilize Natural Language Processing (NLP) to understand and interpret human speech. These models leverage vast datasets and deep learning techniques to continuously refine this understanding, facilitating accurate and contextually relevant responses in real-time. Recent breakthroughs in deep learning, particularly with Generative AI (Gen AI) models like Generative Pre-trained Transformer (GPT), have further enhanced the conversational abilities of AI systems, enabling more nuanced and human-like interactions. Gartner estimates that by 2025, generative AI will be embedded in 80% of conversational AI solutions.
New application scenarios unfolding
Businesses are integrating conversational AI in their contact center operations and anticipating increased productivity, accessibility, cost savings, superior service levels, and scalability. The benefits of AI in the contact center have already been established, with MIT Technology Review reporting that over 80% of respondents to a survey of 599 executives from across the world have observed improved call volume processing with AI. Conversational AI is expected to take contact center efficiency and productivity to a new level. Gartner predicts that by 2026, conversational AI will reduce costs associated with human agents by $80 billion.
Round-the-clock, personalized support
AI-powered customer support is revolutionizing customer engagement and satisfaction by offering prompt, personalized assistance round-the-clock. Consider a scenario where a customer encounters an issue with an online banking account outside regular business hours. The customer leaves a message on the bank’s mobile app. A conversational AI bot comprehends the query and provides personalized assistance by analyzing the customer’s transaction history, identifying the discrepancy, and offering tailored solutions such as initiating a refund or investigating further. The chatbot’s ability to offer prompt, personalized assistance and round-the-clock availability enhances the customer’s experience.
Leveraging data insights to make recommendations
Consider a streaming platform that utilizes conversational AI to enhance the user experience. When a subscriber interacts with the platform, AI algorithms analyze their viewing history, genre preferences, and previous interactions to personalize recommendations. Additionally, if a user encounters an issue while streaming, the AI-powered customer support provides tailored troubleshooting steps based on the user’s device and viewing habits.
Omnichannel strategies for seamless interactions
Customers today effortlessly transition between devices during a buying journey. They might begin by browsing products on their mobile phones, consult social media reviews, and finalize their purchase on a laptop. Conversational AI enhances omnichannel strategies by providing a consistent and personalized experience across various touch points, while maintaining context throughout the journey.
Use cases from different industries
1. Healthcare
Healthcare organizations can leverage conversational AI for purposes such as virtually educating patients about available care and treatment procedures, booking appointments, and receiving medication reminders. For instance, Northwell Health’s chatbot assists patients by taking care of any concerns related to colonoscopies, thereby reducing no-shows for medical examinations.
2. Education
AI-driven chatbots and virtual assistants can provide personalized guidance and tutoring to students. For instance, 324 students of Georgia State University claim that they would have dropped out of college had it not been for the ability of Pounce, the university’s AI-powered chatbot, which promptly responds to their questions on topics such as scholarship applications, financial aid, and course enrollment.
3. Travel
Air Asia's implementation of conversational AI reduced customer wait times by 98% while offering multilingual support. Moreover, travel companies such as TEZ Tour utilize chatbots to assist customers with trip planning, resulting in significant time savings and increased customer satisfaction.
4. Hospitality
Hotels leverage conversational AI chatbots to engage with guests. For instance, Casa di Fiore SPA & Medical Hotel’s chatbot Miss Fiore helped save its hotel employees 7,000 working hours, with 545,000 interactions, including chatting, scrolling, and searching in its first year of implementation.
5. Retail
Retail giant Walmart employs conversational AI to help customers shop through voice shopping and text messaging. It also leverages the technology via its voice assistant, Ask Sam, which enables its in-store associates to find items, navigate through store maps, look up pricing and sales information, and more.
Addressing implementation challenges
The adoption of conversational AI is growing, yet implementation bottlenecks remain. Gartner states that setting up complex conversational AI systems can take years as it involves building and refining call flows. It estimates integration costs at $1,000-1,500 per AI agent. Moreover, there are significant cybersecurity risks associated with the technology, with hackers looking to exploit AI tools to propagate misinformation and target customers with convincing, personalized phishing emails.
Addressing these challenges necessitates a comprehensive approach that considers not just the technological aspects but also regulatory frameworks, responsible practices, and expert advice.
Best practices in implementing conversational AI
1. Start small, iterate, scale
Selecting a use case that offers optimum results is the key to success. Customer interactions that constitute a bulk of contact center activity yet are relatively simple to accomplish, such as providing information or carrying out transactions, are a good starting point. Define success metrics, monitor progress, and use the learnings to inform the strategy for bots to carry out complex problem-solving tasks.
2. Decide your implementation strategy
Designing and deploying a conversational AI tool in-house can be prohibitive because of the costs involved and the need for resources trained in niche skills such as data analytics, AI, NLP, and data management. Conversational AI platform providers offer cost-effective solutions to onboard a tool. But to maintain and finetune it requires continuous support that may not be offered by the platform provider. IT partners who have a deep understanding of the technologies and the industry context offer strong consulting, implementation, and maintenance support.
3. Data protection
As conversational AI systems collect and process sensitive information during interactions, safeguarding user data becomes paramount. Some important steps include being transparent with users on what data is being used, how it is being collected, shared, or stored, implementing encryption protocols, anonymizing data whenever possible, incorporating features like user consent mechanisms, transparent data handling policies, and security audits to bolster data security. Equally important is to monitor the regulatory environment and ensure compliance.
4. Continuous optimization of AI systems
Optimal performance and user satisfaction require continuous finetuning of the model. There is a need to enhance the tool’s results by regularly analyzing user feedback, conducting A/B testing, and leveraging advanced analytics to enhance the system’s capabilities until the results match the expected level of quality.
Implementation best practices
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Define clear objectives |
Clearly define your goals for implementing conversational AI. Determine what you aim to achieve, whether it's improving customer service, increasing efficiency, or enhancing user experience.
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Personalization |
Incorporate personalization into your conversational AI to make interactions more engaging and relevant to individual users. Utilize user data and preferences to tailor responses and recommendations.
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Human handoff |
Integrate mechanisms for seamless handoff to human agents when necessary. Design escalation paths for complex queries or situations that require human intervention.
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Scalability and performance |
Design your conversational AI solution with scalability and performance in mind. Ensure that it can handle increasing user volumes and maintain responsiveness under peak loads.
Fig: High-level AI framework based on the customer care lifecycle
As customer preference for omni-channel interactions rises, the relevance of conversational AI is set to grow. But sub-optimal performance will erode the customer’s confidence and expose an organization to business, regulatory, and reputational risks. To ensure customers place their trust in a conversational AI tool, the tool must not only offer accurate results and effective resolution but also guarantee an easy and personalized experience and the safety of users’ personal data.
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Panchalee ThakurIndependent Consultant |
