Five AI Trends To Look Forward To In 2023 And Beyond

The artificial intelligence (AI) market has been growing at an exponential pace over the last couple of years, thanks in large part to consumer-ready products such as ChatGPT, Google Bard and IBM Watson that are now being used commonly across the globe. 

To this point, global management consulting firm McKinsey believes that anywhere between 50% and 60% of all organizations today are already making use of AI-centric tools, with this number expected to grow sharply in the near future.

Moreover, as per Forbes, AI is one of the fastest-growing industries in the world today, with the total market capitalization of this space set to expand at a compound annual growth rate (CAGR) of 37.3% until the end of the decade, reaching a cumulative valuation of $1.81 trillion over the said period.

This rise is not unfounded and is, in fact, being helmed by certain technological trends — such as generative AI and natural language processing (NLP) — which have led many experts to project that AI's contribution to the global economy will rise to $15.7 trillion by 2030, a figure that is more than the current gross domestic product (GDP) of global powerhouses India and China combined.

With the technology's growing importance, market and technological observers have noted several possible trends affecting the AI sector or driven by AI.

Increased use of AI assistants

As the tech paradigm has continued to expand and grow, the use of AI assistants seems primed to help automate and digitize a wide range of service sectors. Paweł Andruszkiewicz, chief operating officer of VAIOT — a developer of AI-powered digital services — told Cointelegraph that legal services, public administration and citizen services are just some domains that can be completely revamped using AI. 

"AI Assistants offer increased availability, lower costs and ease of use for the end-user. Let's take legal services as an example; they are often scary, unavailable or simply too expensive for regular people [...] AI assistants, as a sort of 'natural user interface,' with [24/7] availability via a mobile device, disenchant this area, making it possible to access and obtain legal support for anyone, anytime," he said.

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Andruszkiewicz believes AI assistants can streamline formal legal documentation, process digital signatures or payments, provide users with possible outcomes of various cases, prepare tailor-made agreements, and even deliver corporate services related to compliance or due diligence.

Similar benefits, as per Andruszkiewicz, can be extended to the realm of public administration, including formal processes such as setting up a company, applying for a visa, registering properties or even obtaining various licenses, which are often complicated and require lots of paperwork.

Lastly, he believes AI assistants are great at "deciphering" more complicated technologies such as the blockchain and smart contracts. "With the use of AI, a person doesn't have to be a developer to create stuff on the blockchain. You can simply specify what you want to achieve, and the AI assistant will do the complicated part for you," he said.

More adoption among Fortune 500 companies

Miguel Machado, CEO and co-founder of Keenfolks — an AI consulting firm — told Cointelegraph that over the next few months, people will be startled by the speed of innovation and how fast AI products are able to scale and reach a wider audience. As an example, he alluded to OpenAI and how its ChatGPT interface did not go live until March 2022, yet today, it has over 100 million users.

"The ease of experimenting through different pilots will foster innovation, enabling Fortune 500 companies to swiftly iterate and refine their AI-driven strategies. Communities, too, will play a pivotal role, harnessing the knowledge of language models to create platforms that facilitate collaborative learning and skill enhancement," he said.

Moreover, he even sees a growing number of C-suite executives adopting AI to propel their businesses to new heights, especially within spaces such as law, HR and finance.

"The emergence of no-code solutions is set to democratize AI adoption, allowing brands to integrate advanced technologies into their operations without requiring extensive technical expertise," he added.

The continued rise of generative AI 

Over the last couple of years, most AI-based applications have predominantly relied on the use of predictive models, which, as the name suggests, emphasize making predictions or providing insights based on existing data sets. To put it another way, the results produced by these frameworks are derived or recycled and are free of any new content. 

On the other hand, generative AI uses machine learning and deep learning to produce original information that has been computed independently using newer patterns built atop existing training data. Over the past year, these models have been extensively used to generate texts, images, and audio and video content.

Talking about the potential of this technology, Henry Ajder, generative AI expert and tech adviser to Meta and Ernst & Young, said, "We're still in the nascent stages of this generative revolution; the future will be one where synthetic media is ubiquitous and democratized in daily life, not as a frivolous novelty, but powering groundbreaking advances in entertainment, education, and accessibility."

Growth of natural language processing systems

Another domain of AI that is primed to gain traction over the coming months is that of natural language processing (NLP). This technology serves as the backbone for various tech products that thousands interact with on a daily basis, be they search engines or voice-activated assistants.

Through the use of NLP platforms, it is possible to make machines understand, interpret and respond to human language in a lifelike manner. In fact, the technology utilizes language modeling, parsing, sentiment analysis, machine translation and speech recognition to provide realistic responses for users operating in different business sectors.

The potential of this still-nascent market is highlighted by Grand View Research in its recent report, which suggests that it will grow at a compound annual growth rate of 40.4% from 2023 to 2030, reaching a total capitalization of $439.85 billion by the end of the decade.

AI in healthcare

According to Forbes, AI's use in healthcare will grow immensely, particularly when it comes to how doctors diagnose and treat patients with various ailments. Moreover, the use of machine learning is projected to rise within domains such as drug discovery and medical research.

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The use of AI in drug discovery is expected to reach $4 billion by 2027 (growing at a CAGR of 45.7%). Similarly, more than 50% of all American healthcare providers have either deployed or are planning to use AI tools, such as robotics process automation, as part of their internal medical processes.

Therefore, as we head toward a future driven by technologies such as AI, machine learning, deep learning and NLP, it stands to reason that their use will grow across various industries, helping usher in a digitized, more automated future.

The Rise Of NLP Startups

Artificial intelligence has been the king of recent tech advancements. Breakthrough after breakthrough, individuals, small and medium-sized businesses, and enterprises have been the lucky beneficiaries of the current wave of AI madness – from automation to generative AI. 

Today's AI high is just the beginning. In fact, experts are anticipating an annual growth rate of 37% from 2023 through 2030, signaling a promising era of innovation, disruption, and transformative possibilities in the years ahead.

One of the most notable AI subfields is Natural Language Processing (NLP). Coined as linguistic AI, NLP focuses on the interaction between computers and human language. Since its emergence, a remarkable number of NLP startups have taken over the scene, overhauling how people communicate, and making technology more accessible, intuitive, and responsive to human needs.

A Brief History of NLP

While NLP has only seemingly emerged in recent years, its history roots back to the 1950s when Alan Turing introduced the concept of the "Turing test" in an article titled "Intelligence." This test has since become widely recognized as a benchmark for assessing AI's capacity for human-like intelligence, including linguistic capabilities.

During the 1980s, IBM emerged as a prominent player in advancing NLP through the development of several intricate and successful statistical models. This shift towards statistical models represented a departure from rigid rule-based systems and paved the way for more flexible, data-driven approaches in the field of NLP.

Welcoming Siri

In 2011, Apple introduced Siri, marking a significant milestone as the world's first NLP/AI assistants. Siri's innovative system was among the earliest to achieve widespread success. Its functionality revolves around a sophisticated automated speech recognition module that translates the user's spoken words into digital concepts. Subsequently, its voice-command system matches these concepts with predefined commands, thereby initiating specific actions. This capability heralded a new era in human-computer interaction, making technology more accessible and user-friendly for a broader audience. 

This breakthrough became one of the catalysts that spearheaded a multitude of NLP startups that are currently revolutionizing the current global business ecosystem.

Disrupting Global Industries

The worldwide market for NLP is anticipated to undergo substantial expansion, with a projected increase from $24.10 billion in 2023 to a significant $112.28 billion by the year 2030. This growth trajectory reflects a robust CAGR of 24.6%, signifying the increasing significance and adoption of NLP technologies across various industries and applications.

Today, NLP startups are underway to overhaul business processes, streamline workflows, and optimize organizational revenue generation. One of today's key players is Novacy, a behavioral intelligence platform that utilizes NLP to help B2B revenue teams close more deals by deciphering prospects' non-verbal cues, understanding their prospects' underlying perceptions, and decoding the things that are often left unsaid. The company aims to turn psychology into technology and empower humans to understand humans. Moreover, Novacy cuts 85% of the call analysis time through AI-powered summaries, transcripts, call snippets, and seller insights. 

Another NLP capacity is to convert speech to text with unmatched accuracy. Deepgram is leveraging this power to make voice intelligence available to all with faster, more accurate, and more scalable speech recognition made through end-to-end deep learning.

The healthcare industry has seen unprecedented improvements in patient care and treatment plans with the rise of NLP. One startup that leads this movement is Marigold Health, which employs innovative NLP techniques to seamlessly merge text-based peer support groups with substance use and behavioral health care services. This groundbreaking approach provides individuals facing stigmatization with round-the-clock access to tailored care. Simultaneously, it empowers existing care managers and peer coaches, enabling them to effectively handle a tenfold increase in patient capacity.

Conclusion

The momentum that NLP startups have created these past several years is just the tip of the iceberg. With the pace of these breakthroughs, progressive founders are poised to continue reshaping human interaction with technology – from streamlining processes to addressing long-standing challenges – promising a future marked by sustained innovation and transformative possibilities.

Top 10 Conversational AI Platforms 2023

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By leveraging natural language processing and generative AI, conversational AI platforms enable businesses to build intelligent AI chatbots and virtual assistants that can understand and respond to user queries seamlessly.

We highlight the top Conversational AI platforms empowering enterprises to deliver personalized, efficient, and engaging customer experiences.

Here are our picks for the best conversational AI platforms:

Top Conversational AI Platform: Comparison chart

Here is a head-to-head comparison summary of the best conversational AI platforms.

Best for Multi-channel support Top feature Natural language processing Starting price IBM Watson Assistant Advanced features Yes Supports up to 1000 concurrent call capacity Yes $140 per month Kore.Ai Automation Yes Auto-dialog generation Yes 20¢ per conversation Avaamo.Ai Conversational analytics Yes Branded AI voice capability Yes Available upon request Amazon Lex Affordability Yes Contact center integrations with Amazon Connect, Genesys Cloud CX, Amazon Chime SDK and AWS Contact Center Intelligence (CCI) Yes $0.004 per speech request &

$0.00075 per text request

Oracle Digital Assistant Performing operational tasks Yes Prebuilt chatbots for Oracle Cloud applications such as Oracle Cloud ERP and Oracle Cloud SCM Yes $0.0232 unit price per request Microsoft Bot Framework Developers Yes Native integration with Azure cognitive services Yes $0.50 per 1,000 messages Cognigy.AI Contact center automation Yes Generative AI capability and AI-based voice gateway services Yes Available upon request Yellow.Ai Customization Yes No code builder and Text automation capability Yes Available upon request Acquire Ease of use and simplicity Yes One-way and two-way video capability; dynamic prioritization and intelligent routing Yes $500 per month billed annually plus $25 per agent per month Clinc Financial service companies Yes Omni-channel support – Alexa, web messenger and mobile apps; context retention capability Yes Available upon request

IBM Watson Assistant: Best for advanced features

Built on deep learning, machine learning, and natural language processing (NLP) models, the IBM Watson Assistant conversational AI platform enables your teams to build AI-powered voice agents and chatbots that deliver automated self-service support across all channels and touchpoints. It can search and find answers to customer inquiries in existing documents, websites, and knowledge bases in order to complete the user's intended action.

Figure A: IBM Watson Assistant analytics dashboard

The platform allows you to build an AI chatbot that can be trained to understand user requests and adapted to your business scenarios – it also can recognize plain-language responses from your customers, like synonyms, dates, times, and numbers. It integrates with various third-party services, including WhatsApp, Slack, Facebook Messenger, Kustomer, Zendesk and more.

Pricing

IBM Watson is available for free with basic features and paid versions with advanced features.

Lite: Available at no cost. It includes 1,000 unique monthly active users (MAUs), up to 10,000 messages per month, up to five skills (Dialog, Action, Search), seven days of usage analytics, session inactivity timeout of 5 minutes, and services deleted after 30 days of inactivity.

Plus: Starts at $140 per month. It supports phone and SMS integration and includes 1,000 MAUs. Additional MAUs are billed at $14 per 100 MAUs.

Enterprise: Quotes available on request.

Features

Supported channels include webchat, telephony and voice add-ons, SMS and MMS, messaging channel integrations and custom channel API.

Customer service desk integrations.

Supports up to 100 concurrent call capacity for its plus plan and up to 1,000 for its enterprise plan.

Deploy on any cloud, including IBM, Amazon, Google, Microsoft, or on-premises environments.

Pros

Natural Language Processing (NLP) capability.

Multi-language support.

Language, acoustic and voice customization options.

Cons

Limited duration for its analytics data retention capability.

Onboard support is only available to enterprise users.

Also see: Best Artificial Intelligence Software 2023

Kore.Ai Experience Optimization (XO) Platform: Best for automation

Kore.Ai Experience Optimization (XO) Platform is designed to help businesses build, train, and deploy intelligent chatbots and virtual assistants that can understand and respond to customer queries in a natural, conversational manner.

Figure B: Kore.Ai bot administration real time status view

Aside from the XO platform, the company also offers various solutions such as SmartAssist, an AI-native CCaaS solution; AgentAssist, which are virtual assistants supporting agents in real time; and BankAssist, which is an AI-native, omnichannel, conversational banking assistant pre-trained on 400+ retail banking use cases.

Pricing

Standard: 20 cents per conversation. This plan also includes 10 cents per conversation for pre-built apps and 4 cents per minute for voice automation.

Enterprise: Custom quotes.

Korea.Ai offers optional enhanced support at an additional cost – $2,000 per month for the standard plan and a custom quote for the enterprise plan.

Features

Support over 100 languages, including English, Arabic, Japanese, French, and Malay.

Auto-dialog generation.

On-premise, cloud and hybrid deployment options.

Conversational IVR (Interactive Voice Response) capability.

Pros

Integrates with over 75 prebuilt workflows, including CRM, ERP, ITSM, and NLM tools.

Compliant with industry regulations like SOC2 Type2, HIPAA, GDPR and PCI.

Easy to use.

Cons

Advanced support costs extra.

The documentation can be improved.

Avaamo.Ai: Best for conversational analytics

Figure C: Avaamo.Ai analytics view

Avaamo offers a skills builder that includes a flow designer for designing conversation, dynamic dialog, conversational IVR, and other tools that enable you to automate complex enterprise use cases.

Pricing

Avaamo doesn't advertise pricing on its website; the company encourages users to request a demo to learn about the platform and get custom quotes based on their needs.

Features

It supports integration using APIs, Web Services, ESBs, MQs, and Custom Adapters, including over 150 pre-built integrations.

Branded AI voice capability.

Conversational analytic tools.

Compliance with industry regulations such as SOC3, SOC2(Type Ⅱ), CCPA, HIPAA and more.

IVR channel integration.

Pros

Supports over 114 languages.

Flexible deployments – on-premise and cloud deployment.

It supports over 70 channels across various chats and voice mediums.

Cons

Some users reported that Avaamo is somewhat expensive.

Amazon Lex: Best for affordability

Amazon Lex is a chatbot-building technology by AWS, so it benefits from the cloud giant's extensive platform. It's a service that can help you build conversational interfaces using voice and text. Amazon Lex uses Automatic Speech Recognition (ASR) to convert spoken language into text and Text-to-Speech (TTS) to convert text into spoken language. The platform has various components that work together to enable users to build intelligent conversational AI solutions for their businesses. They include:

Intent: It represents the desired action.

Utterance: Phrases or sentences that users input to interact with the chatbot.

Prompt: Request data.

Slot: Required data.

Fulfillment: Amazon Lex can trigger backend business logic to fulfill user requests.

Figure D: Conversational analytics platform view for Amazon Lex Pricing

Amazon Lex only charges you for what you use. You can also try the tool for free. You can process up to 10,000 text requests and 5,000 speech requests or speech intervals per month at no cost for the first year of trying the tool.

Request and response interaction pricing

$0.004 per speech request.

$0.00075 per text request.

Streaming conversation pricing

$0.0065 per unit per speech interval.

$0.0020 per unit per text request.

Automated chatbot designer pricing

You can use the AWS pricing calculator to calculate your monthly expense or contact an AWS specialist for pricing assistance to get an estimate.

Features

Natural conversation capability includes speech recognition and natural language understanding, context management, 8 kHz telephony audio support and multi-turn dialog.

Builder productivity capability that allows you to build visual conversations, streamline conversations and deploy to multiple platforms.

Integration with AWS services like Amazon Kendra, Amazon Polly and AWS Lambda.

Contact center integrations with Amazon Connect, Genesys Cloud CX, Amazon Chime SDK and AWS Contact Center Intelligence (CCI).

Pros

Offers use cases for financial services, insurance, retail, telecom, and travel.

Easy to train and configure chatbots.

Positive reviews from current and past users.

Users find the solution highly affordable.

Natural language understanding.

Cons

Documentation can be improved.

Some users say the confidence score isn't always accurate.

Also see: Top Generative AI Apps and Tools

Oracle Digital Assistant: Best for performing operational tasks

With the Oracle Conversational AI platform, you can build chatbots that can engage in natural language conversations, understand user intents, and provide relevant responses and actions. The platform lets you connect with a chatbot through channels like Microsoft Teams or Facebook on your website or embedded inside your mobile app.

Figure E: Oracle Digital Assistant insights Pricing

Oracle Digital Assistant platform pricing is available per request and as a subscription for SaaS customers by employee, named users, or sessions.

Oracle Digital Assistant Cloud Service: $0.0232 unit price per request. This plan requires a minimum of 250 requests per hour.

Oracle Digital Assistant Platform for Oracle SaaS – Hosted Employees: $3 per month.

Oracle Digital Assistant Platform for Oracle SaaS – Hosted Named User: $6 per month.

Oracle Digital Assistant Platform for SaaS – 1,000 Sessions: $275 per month.

Features

NLU's capability to understand conversations to derive intent and context.

AI-powered voice.

Prebuilt chatbots for Oracle Cloud applications such as Oracle Cloud ERP, Oracle Cloud SCM, Oracle Cloud HCM, and Oracle Cloud CX.

Pros

Multilingual support.

Multi-channel support: web, mobile, SMS texting, smart speakers, Slack, and Microsoft Teams.

Users applaud its seamless integration with messaging apps.

Cons

Limited language support.

Voice recognition can be improved.

Microsoft Bot Framework: Best for developers

Supporting both code-first and no-code approaches, Microsoft Bot Framework provides a platform for creating conversational agents capable of interacting with users via channels such as online sites, Slack, and Facebook. The framework comprises various components:

A language component that enables you to build natural language experiences.

Speech component that can reply to customers in a natural language with a branded voice, hear commands, translate, and identify individual speakers.

QnA Maker, which is a question-and-answer bot.

Vision components that leverage the capabilities of computer vision services to recognize faces, moderate content, and index images and video.

Search functionality allows you to search across defined domains or the web.

Figure F: Microsoft Bot Framework connected channels view Pricing

Standard channels: Free for unlimited messages.

Premium channels: Free for up to 10,000 monthly messages or $0.50 per 1,000 for the S1 tier.

Features

It can integrate into existing ecosystems such as Slack, Cortana, Skype, Telegram, Messenger, and SMS.

Native integration with Azure cognitive services.

Bot Builder SDK.

Pros

Offers open source SDK and tools to connect your bot to popular channels and devices.

Supports integration with Microsoft Azure Language Understanding (LUIS) for entity recognition and intent understanding.

Rich development ecosystem.

Cons

Limited customization options.

Steep learning curve for beginners.

Also see: Generative AI Companies: Top 12 Leaders 

Cognigy.AI: Best for contact center automation

Enhanced with generative AI, Cognigy's low code Conversational AI platform enables enterprises to automate contact centers for customer and employee communications. The platform offers customer service solutions like Conversational IVR, Smart Self-Service, and Agent + Assist. With Cognigy, users can design conversational flows, integrate with backend systems, and customize the behavior of their chatbots or virtual assistants to suit their specific business needs.

Figure G: Cognigy.AI insights dashboard Pricing

The platform doesn't advertise its pricing on its website. To get quotes, businesses are required to contact the company for a demo to discuss their needs. Publicly available information revealed the platform costs as follows.

Basic 5K pm: Platform & Setup fee, 60K conversations pa, Standard Support costs $43,080 for 12 months.

Basic 5K pm + VG: Platform & Setup fee, 60K conversations pa, 5 VGs, Standard Support costs $53,916 for 12 months.

Features

Generative AI capability.

AI-based voice gateway services.

Omni-Channel Support – deployment across various channels, including websites, messaging apps, and voice assistants.

Integrates with messaging apps like Teams, Slack, andWhatsApp; CCaaS and CPaaS such as Avaya, 8X8, NICE, and GENESYS; enterprise tools like Salesforce, ServiceNow, SAP, Zendesk, and Microsoft Dynamic 365.

Pros

Reporting and analytics capabilities.

Real time insight into your operations.

Cons

Some users reported difficulty with building custom extensions.

Limited documentation.

Yellow.Ai: Best for customization

Our analysis found that Yellow.Ai is a battle-tested conversational AI platform used by over 1,000 enterprises across 70 countries. Yellow.Ai dynamic automation platform is designed to automate customer and employee interaction and conversations across text, email, and voice. The platform is fluent in over 135 languages and supports over 35 channels.

Figure H: Yellow.Ai overview dashboard Pricing

Yellow.Ai AI chatbot pricing are as follows:

Free: Available at no cost for 100 MTU per month and limited to two agents.

Enterprise: Usage-based pricing. Quotes are available upon request.

Publicly available information on the AWS marketplace reveals that the Yellow.Ai Basic Plan, which includes a basic chatbot with one use case and tiered conversation limits, costs $10,000 for 12 months, while the Standard Plan, which includes a Standard chatbot with four use cases and tiered conversation limits, costs $25,000 for 12 months.

Features

No code builder.

Integrates with 100+ third-party services, including Zendesk, Freshdesk, Jira, ServiceNow, HubSpot, and Jira.

Supports up to 135 languages.

Text automation capability.

Supports 35+ channels, including Slack, Messenger, Instagram, and WhatsApp.

Pros

Natural language understanding capability.

Highly customizable.

Drag and drop studio for building bots.

Cons

Advanced features may be complex to implement.

Some users reported that the integration of Yellow.Ai with other tools is somewhat complex.

Acquire.Io: Best for ease of use and simplicity

Featuring live chat, video and voice calling, AI chatbots, co-browsing and centralized interaction management, Acquire conversational AI platform empowers users to help customers resolve complex issues in real time. The platform aims to improve customer satisfaction, increase conversions, and enhance customer support efficiency.

Figure I: Acquire.Io admin dashboard Pricing

Self service: This plan costs $500 per month billed annually plus $25 per agent per month.

Integration solution: This plan costs $2,000 per month billed annually plus  $45 per agent per month.

Features

One-way and two-way video capability.

Live chat messaging.

Translate conversations into 25+ languages.

Dynamic prioritization and intelligent routing.

Pros

Voice and video call options.

Call deflection capability.

Allows users to manage multiple chats simultaneously.

Cons

Some users reported that the notification capability doesn't function well, causing them to miss live chats.

It may be somewhat expensive for small businesses.

Clinc: Best for financial service companies

Clinc's AI platform is designed to provide personalized and natural language-based experiences for applications like virtual assistants, chatbots, and voice-controlled devices. The company has been particularly involved in developing  AI systems for the financial and banking sectors, where their technology is used to create virtual banking assistants that allow customers to check balances, report lost or stolen cards, transfer funds, and gain deep insights into their spending habits.

Pricing

Pricing is available upon request.

Features

Flexible deployment – cloud or on-premise.

IVR capability.

Advanced Natural Language Processing.

Omni-channel support – Alexa, web messenger and mobile apps.

Pros

Voice and text support.

Multi-lingual support.

Context retention capability.

Cons

Initial setup complexity.

Documentation can be improved.

Key Features of Conversational AI Platforms Natural Language Processing

The computer's ability to understand human spoken or written language is known as natural language processing. NLP combines computational linguistics, machine learning, and deep learning models to process human language. This feature enables the conversational AI system to comprehend and interpret the nuances of human language, including context, intent, entities, and sentiment.

Omini-channel experience

The best conversational AI platform allows you to engage with your customers across multiple channels and touchpoints such as websites, mobile apps, messaging platforms (like Facebook Messenger, Instagram or WhatsApp), voice assistants (such as Amazon Alexa or Google Assistant), and more. The tools we analyzed in this guide allow you to engage with your customers across various channels.

Sentiment Analysis

The best conversational AI tools are trained to analyze digital text to deduce the emotional tone of the message – which could be positive, negative, or neutral. This capability allows chatbots to respond to customers in a more personalized way or empathetic manner.

Integration with third-party services

Integration capability is an important feature of any modern-day digital solution, especially for conversational AI platforms. Seamless integration with third-party services like CRM systems, messaging platforms, payment gateways, or ticketing systems allows businesses to provide personalized experiences.

Analytics and insights

Conversational AI platforms often provide analytics and insights into user interactions. This data can help businesses understand user behavior, identify common queries, and improve the effectiveness of the AI system.

How to Choose the Best Conversational AI Software for Your Business

While there are hundreds of conversational AI platforms marketed as the best system, each tool has its strengths and areas of weaknesses. What seems to be a strength for company A may be a weakness for company B and vice versa. Choosing the right conversational AI software for your business can significantly impact customer interactions, operational efficiency, and overall success. So, how do you select the best conversational AI platform for your business?

Identify your conversational AI needs – the current gap in your existing system, and also define your expectations.

Research various platforms and narrow your list to the best three that stand out.

Evaluate these tools' features and compare them to your requirements list.

Select the platform with features that align with your needs.

Consider the affordability of the tool, ease of use/usability and scalability.

Research the vendor reputation – a well known, established vendor is more likely to provide reliable software and support. Then again, there are eager start-ups that provide a lot of value for the money.

Keep in mind that the best conversational AI software for your business will depend on your unique needs, goals, and the preferences of your customers.

How We Evaluated the Best Conversational AI Platforms

The best conversational AI platform is easy to use, offers features that meet the intended users' needs, balances quality service and affordability, and allows businesses to integrate with tools and services they already use.

Core features – 30%

We evaluated each platform's core offerings and their ability to serve the needs of businesses in various industries. Our analysis considered features like NLU, multi-channel support, flexible deployment, multi-lingual and other essential features.

Cost – 25%

We researched each conversational AI platform pricing strategy and model. We also checked for pricing transparency and the availability of free demos and trials to allow potential buyers to test out the platform before making a purchase decision.

Integration – 15%

We checked whether the conversational AI platform integrates with third party services such as CRM, ITSM, and various communication channels such as websites, messaging apps, voice assistants, and social media platforms.

Ease of use – 15%

We reviewed each platform's usability to determine if it's beginner-friendly. A user-friendly dashboard makes it easier for non-technical team members to manage the AI. So we checked if the platform has an intuitive interface for setting up and managing conversational flows.

Customer support – 15%

To score each conversational AI platform for this category, we analyzed user feedback on review sites and considered the types of support offered by each company.

Frequently Asked Questions (FAQs) What features should I look for when evaluating conversational AI platforms?

Consider the following features when shopping for a conversational AI platform:

Natural Language Understanding (NLU)

Multi-turn dialog management

Intent recognition

Entity recognition

Integration with existing systems

Analytics and reporting

Multi-channel support

Language support

Testing and debugging tools

What are conversational AI platforms and how do they work?

Conversational AI platforms are software solutions that leverage the innovations of AI, deep learning, and NLP technologies to enable automated, human-like interactions between computers and users through natural language.

These platforms facilitate seamless communication across various channels, such as chatbots, voice assistants, messaging apps, websites, and more. They are designed to understand user inputs, interpret their intentions, and provide relevant and contextual responses.

Which industries can benefit from adopting conversational AI platforms?

The potential applications of conversational AI are vast. Any industry that involves customer interactions, information dissemination, and process automation can benefit from leveraging conversational AI platforms.

Bottom Line: Conversational AI Platforms

As businesses seek to enhance their customer interactions and improve overall efficiency, conversational AI platforms offer organizations the opportunity to forge stronger connections with their audience, provide instant assistance, and gather valuable insights from user interactions. From understanding user intent to generating coherent responses, conversational AI platforms help business create lifelike conversations that meet customer needs efficiently.

Read Next: Top Natural Language Processing Companies

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