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Conversational AI vs. Generative AI: Core Differences
AI has ushered in a new paradigm for businesses seeking enhanced efficiency and personalization via seamless human-machine collaboration. Two technologies helming this digital transformation are conversational AI and generative AI.
The AI impact on client engagement, marketing, content production and customer service is evident if you look at these projections by Gartner:
- 30% of all outbound marketing messages will be AI-synthesized by 2025.
- 10% of all data produced will be generative AI-originated by 2025.
No doubt, conversational AI and generative AI are among the most promising technologies in recent years, and they may look somewhat similar to the untrained eye, especially given the way they humanize and personalize interactions. However, they differ vastly in application, training methodology and output. Neglecting the differences between conversational AI and generative AI can restrict your returns and drive faulty tool selection.
In this article, we will explore the finer differences between conversational AI and generative AI and their synergies. But first, we touch on their definitions, benefits and challenges.
What is conversational AI?
Conversational AI is an advanced AI that enables natural two-way communication between humans and software applications like chatbots, voice bots and virtual agents. It leverages natural language processing (NLP) to interpret human input (text and voice), sentiment analysis to detect the underlying sentiment and natural language generation (NLG) to generate a human-sounding output.
Conversational AI adheres to certain core principles encompassing:
- Natural language understanding
- Intent recognition
- Context awareness
- Personalization
- Continuous learning
Conversational AI solutions augment every major customer-facing operation, including marketing, research, sales and support, with their speed, accessibility and ability to replicate human conversations. Apart from conversational AI for customer service, the technology finds widespread application in mainstream domains such as:
Retail: Conversational AI in retail helps with 24/7 order processing, customer engagement, feedback automation and conversational commerce. With e-commerce chatbots and voice bots, retailers are empowered to serve multilingual global audiences tirelessly in their preferred language and channel.
Banking: In FinServ, conversational AI platforms deliver transactional assistance to customers, helping them with account management, fraud prevention, loan application and other routine activities.
Read more: A Detailed Guide to Conversational AI in Banking
Insurance: Conversational AI in the insurance sector lends operational efficiency to processes such as claims processing, policy/product recommendations and customer query resolution.
Telecom: Telecom revenue hinges on subscriptions and plan renewals, which are the main applications of conversational AI. In addition, virtual agents can help subscribers with billing issues, account management, plan details and troubleshooting.
Benefits
When brands create a robust conversational AI strategy, there are many benefits to be reaped in the form of:
- Improved customer experience that is omnichannel, seamless and personalized
- Operational efficiency by automation of routine and recurrent tasks
- Cost savings in the form of enhanced agent productivity and saved manhours
- Conversational intelligence about customer preferences and key conversation themes
- Scalability with multitasking chatbots that handle multiple conversations asynchronously
- Voice consistency to build brand recall and nurture customer loyalty and trust
If you’re still skeptical of conversational AI’s power and potential, check out this real-life success story:
A Dubai-based transportation/logistics provider, Aramex, was struggling to scale its digital customer service and widen its client base while keeping costs in control. That’s when Aramex discovered Sprinklr Service and its multilingual chatbots that could converse in 4 regional languages. 400 Aramex agents implemented these nifty assistants in contact centers, serving global users on live chat, WhatsApp and email and solving routine cases in seconds at fractional costs. Famed for its customer-first approach, Aramex was able to outperform competitors and deliver matchless support while staying financially viable in a hyper-competitive industry that works on razor-thin margins. Read the full story here.
Challenges
There’s no doubt that conversational AI lends unimaginable scale to functions like customer support, engagement, lead generation and marketing. However, the technology is not devoid of drawbacks and challenges, mainly on account of:
Mishandling of complex queries and edge cases
Handling complex use cases requires intensive training and ongoing algorithmic updates. Faced with nuanced queries, conversational AI chatbots that lack training can get caught in a perennial what-if-then-what loop that frustrates users and leads to escalation and churn.
Did you know? Top conversational AI platforms offer verticalized use case libraries and plug-and-play intents for quick deployment. For example, Sprinklr conducts Discovery Runs to uncover frequent use cases from your historical transcripts, enabling you to build custom intents rapidly and build bots that decipher industry speak, deliver relevant responses and yield massive containment rates.
Ethical and regulatory compliance
“Responsible AI” is another challenge with conversational AI solutions, especially in regulated industries like healthcare and banking. If consumer data is compromised or compliance regulations are violated during or after interactions, customer trust is eroded, and brand health is sometimes irreparably impacted. Worse still, it can lead to full-blown PR crises and lost business opportunities.
Erroneous language processing
For hard-coded conversational bots, understanding finer linguistic nuances like humor, satire and accent can be challenging. Voice bots can struggle with fluctuating tone, pause and modulation on the user side. The result is garbled responses, dead air, cold handovers or poor customer satisfaction (CSAT) scores.
What is generative AI?
Generative AI is an advanced AI technology that uses deep learning models to generate original and contextual content in the form of text, images, video/audio clips, code and even product designs. Being a relatively new technology, generative AI is yielding innovative use cases every day, mainly related to content and creative services, including:
- Content augmentation
- Auto summarization
- Text and tone manipulation
- Categorization
- Simplification
Additionally, GenAI has a long-term impact and emergent application in code generation, product design and legacy code modernization. Synthetic AI data can flesh out scarce data, protect data privacy and mitigate bias issues proactively.
Benefits
Brands can reap multiple benefits from using generative AI or genAI that ultimately results in improved revenue, customer loyalty and competitive advantage. Let’s discuss them one by one.
GenAI enhances creativity. It generates novel ideas, approaches and designs that push the envelope on innovation and creativity, giving brands a unique and memorable identity.
GenAI boosts customer experience. The technology transforms routine customer-brand interactions into memorable moments, courtesy of astute personalization in content and targeting. In fact, 38% of business leaders bank on GenAI to optimize customer experience, according to Gartner.
GenAI uncovers revenue opportunities. Brands will be able to innovate at scale with GenAI, discovering new ideas, novel flavors, power-efficient gadgets, organic drugs and what not. Organizations with AI maturity are able to extract the best mileage from the technology.
GenAI multiplies cost savings and productivity. Through worker augmentation, process optimization and long-term talent identification, Generative AI empowers brands to reduce costs and boost productivity. For instance, by implementing genAI in customer service, your reps can simplify troubleshooting and moderate the tone on a case-by-case basis.
Challenges and risks
ChatGPT and other GenAI-based tools use publicly available data to draw insights and personalize output. This has led to deep fakes and intellectual property infringement, among other issues like:
- Lack of transparency owing to unpredictable LLMs scraping unstructured conversations
- Fabricated answers that lack accuracy and relevance at times
- Loose data governance over intellectual property and copyright
- Fraud and cybersecurity risk with rampant social engineering
While the risks and challenges associated with this disruptive technology are substantial, with rigorous oversight and robust tools, brands can protect their sensitive data and foster customer loyalty and trust.
Conversational AI vs. generative AI: A tabular comparison
Aspect | Generative AI | Conversational AI |
Primary Function | Generates new content, ideas, or designs. | Facilitates natural language interactions and responses in conversations. |
Use Cases | Content creation, design, code generation. | Chatbots, virtual assistants, customer support interactions. |
Output Type | Creatively generates diverse content. | Primarily focused on text-based or voice-based conversational outputs. |
Creativity Level | High, as it generates novel and original content. | Medium to high, depending on the sophistication of the model and training data. |
Applications | Design, art, content creation, innovation. | Customer support, virtual assistants, chatbot interactions, personalized marketing. |
Interaction Type | Output is usually one-sided (e.g., text, image). | Two-way interaction with users, responding to queries and providing information. |
Learning Approach | Trained on diverse datasets to learn patterns for creative output. | Trained on conversational datasets, learning to understand and respond to user queries. |
Examples | Artwork generation, language translation, creative writing. | Chatbots like Siri, Alexa, and Google Assistant are designed for conversation-based tasks. |
Sprinklr combines the power of conversational AI and generative AI
The two technologies offer distinct benefits and power in varied use cases. While genAI brings creativity and scale, conversational AI offers ecosystem familiarity to users. With their dual power, benefits and applications multiply exponentially for businesses, teams and end users.
Enter Sprinklr AI+.
Used by A-listers like Prada and Asahi, Sprinklr AI+ enhances agent productivity and CSAT with genAI prompts and tone moderation. It also enriches Sprinklr’s superlative conversational AI platform to resolve routine cases with zero human intervention. The two technologies entwine to uplift customer experience and engagement, unveiling new conversion opportunities and creative avenues for progressive brands.
Curious to learn more about Sprinklr AI+? Take it for a free spin today!
Frequently Asked Questions
Yes, there are some industries that favor Conversational AI over generative AI, mainly:
- Customer service for 24/7 chatbot assistance
- E-commerce for order assistance
- Healthcare for appointment scheduling and patient engagement
- Hospitality for automated reservations and guest experience
The security of generative AI and conversational AI systems depends on robust implementation and adherence to security protocols. Risks include biased outputs, data privacy concerns and potential exploitation, demanding rigorous measures to ensure secure and ethical usage.
Conversational and generative AI will witness huge advancements in the areas of:
- Multi-modal capabilities
- Advanced natural language processing (NLP)
- Ethical and explainable AI
- Realistic and diverse output
