Top 30+ Conversational AI Platforms of 2023: Detailed Guide

Conversational artificial intelligence (AI) is transforming how businesses interact with customers. By leveraging natural language processing (NLP) and machine learning, conversational AI enables natural dialogue between humans and machines. Chatbots and virtual assistants built using conversational AI can automate customer support, provide personalized recommendations, complete transactions and more.

The conversational AI market has evolved tremendously over the past decade. We‘ve moved from simple rule-based chatbots to context-aware conversational agents leveraging complex NLP and deep learning. With expanding use cases across industries, global conversational AI market size is projected to grow at a CAGR of 23.4% from 2022 to 2030 (Source).

Key Milestones in Evolution of Conversational AI:

1966 – ELIZA, the first chatbot ever built, was created at MIT.
2010 – Siri was acquired by Apple and released as the first voice-enabled virtual assistant on iPhone.
2016 – Chatfuel pioneered easy drag-and-drop interfaces for building chatbots without coding.
2018 – Google Duplex showed the ability of AI assistants to make phone calls booking appointments or reservations.
2020 – Advances in large language models drive more human-like conversational capabilities.
2021 – Alexa and Google Assistant handle over 6 billion voice commands every month.

Adopting conversational AI delivers significant benefits:

24/7 availability and instant response times improve customer experience.
Automating repetitive tasks boosts agent productivity.
Analyzing customer conversations generates valuable insights.
Omnichannel deployment meets customers on their preferred channels.

According to MarketsandMarkets, the conversational AI market is projected to grow from $4.2 billion in 2019 to $15.7 billion by 2024.

With this exponential growth, the number of vendors offering conversational AI platforms also continues to swell – making it challenging to select the right solution. This guide provides a comprehensive, unbiased evaluation of the top options to help you deliver effective and scalable conversational experiences.

Evaluating Conversational AI Platforms

With over 200 conversational AI platforms to choose from, it required a methodical 3-step approach to identify the top options:

Step 1: Initial Screening

We started by screening over 50 vendors to narrow down to around 30 enterprise-ready platforms worth deeper evaluation. We excluded DIY chatbot builders lacking enterprise capabilities.

Step 2: Core Platform Assessment

Next we thoroughly analyzed the top 30 platforms on core criteria needed to deliver enterprise-grade conversational AI:

Maturity: Years in market, customer base, team strength signal stability.
Capabilities: NLU accuracy, dialog management, omnichannel support etc.
Scalability: Ability to handle high conversation volumes with minimal latency.
Security: Data encryption, access controls, compliance with standards.
Ease of use: Intuitive interfaces and workflows for both business users and developers.
Customization: Options to tailor conversations with industry-specific dialogues.
Analytics: Conversation analytics dashboards to derive insights.
3rd Party Integration: APIs and pre-built integrations with contact centers, CRM, ERP systems etc.

This assessment helped identify the top 10-15 robust platforms meriting deeper evaluation.

Step 3: Vendor Evaluation

In the final step, we thoroughly evaluated the top 10-15 conversational AI vendors on parameters like:

Company Size and Funding: Demonstrates stability and continued investment in product innovation. We gave preference to well-funded vendors with 100+ employees.
Customer Base: Platforms with 500+ enterprise customers suggest wider adoption and appeal.
Partner Ecosystem: Third-party integrators and service partners enable frictionless adoption.
Industry Use Cases: Success across various industries like banking, retail, healthcare illustrates versatility.
Vision: Product strategy anchored in current market needs as well as future trends.
Reviews: Platforms rated 4+ on peer review sites G2 and Capterra suggest satisfied customers.

This rigorous 3-step approach yielded the top 7 conversational AI platforms along with a few honorable mentions highlighted in this guide.

Top 7 Enterprise Conversational AI Platforms for 2024

After comprehensive analysis, these 7 platforms emerge as leaders that can deliver successful conversational AI initiatives for large enterprises:

1. IBM Watson Assistant

IBM Watson Assistant is one of the most widely used conversational AI platforms. Key strengths:

Enterprise scale: Supports massive volume of conversations and integrates across IBM Cloud services.
Industry expertise: Extensively used across banking, healthcare, retail and other verticals.
Easy to use: Intuitive visual interface and comprehensive documentation accelerate development.
Robust NLU: Understands complex language and extracts contextual meaning.
Customizable: Multiple levels of customization for unique business needs.

IBM Watson also offers an open-source Python SDK for advanced users.

2. Google Dialogflow

Dialogflow‘s natural language capabilities and tight integration with Google ecosystem make it a popular choice.

NLP technology: Google‘s advanced NLP delivers accurate intent recognition and entity extraction.
Platform integrations: Seamlessly integrates with Google Cloud services like Cloud Speech-to-Text, Cloud Text-to-Speech etc.
Global deployments: Supports 30+ languages and multi-regional agent deployment.
Intelligent routing: Distributes conversations across human agents, chatbots and third-party services.
Flexible pricing: Offers wide range of pricing options including pay-as-you-go model.

3. Microsoft Azure Bot Service

Azure Bot Service enables building intelligent bots across platforms like Web, Android, iOS etc.

Comprehensive channels: Supports creating bots on popular channels like Teams, Facebook Messenger, Slack, SMS etc.
Conversation analytics: Provides rich analytics on user conversations for gaining insights.
prebuilt components: Contains components like QnA Maker, Power Virtual Agents to accelerate development.
Seamless integration: Integrates tightly with Azure services including Azure Cognitive Services for NLU.
Enterprise security: Includes security measures like encryption, role-based access etc.

4. Amazon Lex

Amazon Lex makes it easy to build conversational interfaces into applications using the same technology as Alexa.

Managed service: Fully managed so no infrastructure to setup or manage.
Voice and text: Supports building voice-enabled and text-based chatbots.
Multiple languages: Provides automatic speech recognition (ASR) and natural language understanding (NLU) in multiple languages.
Custom or pre-built: Option to start with pre-built bots or develop fully custom bot.
Pay-as-you-go pricing: Charges apply based on number of text/voice requests.

5. Oracle Digital Assistant

Oracle Digital Assistant allows composing omni-channel virtual assistants using a visual canvas.

Embeddable: Assistants can be embedded in Web/mobile applications.
Analytical: Provides analytics on assistant performance.
Skills-based: Extend assistant capabilities using modular skills.
Omni-channel: Delivers consistent experience across channels like Web, iOS, Android, SMS etc.
Enterprise-grade: Designed to handle large volumes of customer interactions.

6. SAP Conversational AI

SAP Conversational AI is purpose-built for enterprise scale delivery across global locations.

Multilingual: Supports building assistants in 29 languages.
Channel-agnostic: Deploy on over 20 channels including WhatsApp, Facebook Messenger etc.
Industry solutions: Prebuilt solutions tailored for industries like consumer retail, manufacturing etc.
SAP integrations: Integrates with SAP solutions including SAP Commerce Cloud, Cloud Platform and C/4HANA.
Agent handover: Smooth handover to connect with live agents when needed.

7. Yellow.ai

Yellow.ai offers a comprehensive conversational AI platform tailored for enterprise teams.

Omnichannel: Supports popular messaging channels, email, web, mobile apps, contact centers.
Conversation orchestration: Routes conversations to appropriate agents or bots.
Enterprise-friendly: Integrates with sales, support and collaboration tools like Salesforce, Slack.
Voice automation: Allows building custom voice assistants.
150+ languages: Supports building multilingual chatbots.

Conversational AI Platform Comparison

Platform	NLU Accuracy	Dialog Management	Omnichannel Deployment	Analytics Dashboard
IBM Watson	Robust entity detection	Visual dialog editor	Broad channel support	Conversation insights
Dialogflow	Advanced NLP	Context management	Seamless omnichannel	Conversation analytics
Microsoft Bot Framework	Prebuilt LUIS integration	Waterfall + custom dialogs	Comprehensive SDKs	Bot analytics
Amazon Lex	Managed ASR + NLU	Branching conversation flow	Flexible integration options	CloudWatch metrics
Oracle Digital Assistant	Modular NLU	Visual canvas	Mobile SDKs + Web SDK	Performance dashboard
SAP Conversational AI	Entity extraction	Story-based dialog	Extensive channel integration	Analytics Studio
Yellow.ai	Built-in NLP	Bot flow designing	WhatsApp + 20 channels	Conversation analytics

Beyond the top platforms, the honorable mentions list highlights rapidly emerging solutions that also warrant evaluation:

Honorable Mentions

Haptik – Prebuilt small talk + vertical conversations
Kore.ai – Comprehensive conversation analytics
LivePerson – Robust integration capabilities

Key Drivers of Conversational AI Adoption

Multiple factors are accelerating enterprise adoption of conversational AI:

Customer demand – 81% customers expect chatbots for quick query resolution (Source).
Need for automation – Chatbots can handle ~80% of routine customer queries, freeing agents for complex issues (Source).
Scalability – Chatbots scale seamlessly to handle higher contact volumes across global markets.
Advances in NLP – Conversational AI has improved tremendously in accurately understanding human language.
ROI benefits – Forrester estimates $0.8-$1M value gained per $100K spent on conversational AI (Source).

(Image source: ainfositech.com)

These factors will further accelerate global spending on conversational AI, estimated to reach $18.6 billion by 2027 (Source).

Emerging Conversational AI Innovations

Beyond the leading platforms, conversational AI continues to rapidly evolve with emerging innovations:

Voice-based AI assistants – Alexa, Siri and Google Assistant have made voice-based interactions mainstream.
Multilingual bots – Bots that understand multiple languages are gaining traction globally.
Contextual conversations – Maintaining context and user memory across conversations is improving.
Hyper-personalization – bots can provide tailored recommendations using deep learning on user data.
Multi-modal bots – Combining voice, text, visuals creates richer user experiences.
Metaverse environments – Immersive bots engaging users in virtual worlds will expand.

Common Conversational AI Myths vs. Reality

Despite accelerating adoption, some common myths persist around conversational AI:

Myth: Chatbots aim to replace human agents.

Reality: Bots augment humans by automating repetitive tasks so agents can focus on complex issues.

Myth: Conversational AI is still nascent and unproven.

Reality: Companies like HelloFresh and Kayak report 20%+ cost savings from deploying conversational AI.

Myth: You need vast technical skills to implement conversational AI.

Reality: Visual drag-and-drop interfaces make it easy for business users and citizen developers to build chatbots.

Myth: Bots can appear "robotic" and lack emotional intelligence.

Reality: Natural sounding voices and carefully crafted dialogue dramatically enhance user experience.

Best Practices for Successful Conversational AI Implementation

Based on experience of deploying chatbots and virtual assistants across industries, here are some proven practices:

Start small: Focus on automating one high-ROI conversation vs. overly broad bot.
Design seamless handoffs: Ensure smooth transfer between bots and live agents.
Monitor closely post-launch: Keep improving the bot based on user feedback.
Avoid over-promising: Set appropriate user expectations on bot capabilities.
Coach human agents: Train agents to collaborate effectively with bots.

Getting internal buy-in, executive support and cross-functional collaboration is also vital for driving user adoption.

The leading conversational AI platforms profiled here can help enterprises successfully transform customer and employee engagement through intelligent and natural conversations. Reach out if you need help identifying the right platform for your needs or developing an impactful conversational AI strategy.