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Conversational AI Platforms : Determining the Right Solution for Your Business

Conversational AI Platforms use natural language understanding capabilities to facilitate human-like conversations via voice, text, touch, or gesture input. They provide the artificial intelligence models required to build intelligent bots for various business requirements.

Today, businesses worldwide are embracing advanced Conversational AI solutions to automate customer-facing touchpoints. From social media platforms and websites to apps and voice assistants like Google Home, conversational AI solutions are everywhere. The AI-driven conversational platforms help to develop advanced conversational interfaces such as voice bots, voice + text assistants, and chatbots. These intelligent digital assistants provide Guidance, Recommendations, Advice, Consultation, and Expertise (G.R.A.C.E.) which isn’t possible in a “self-service” buying journey.

Recent market research shows the global conversational AI platform market is predicted to grow at a CAGR of 32.4% during the forecast period of 2019-2025. With this surge of Conversational AI platforms, the challenge now for many businesses is not about integrating Conversational AI. Rather, it is about selecting the most appropriate conversational AI platform to match their unique business requirements.

What Conversational AI can do for your business

Conversational AI platforms can be used to build a range of conversational interfaces - with capabilities to handle complex processes to simple if-else loops that guide users through a flowchart. While any investment in Conversational AI is expected to yield significant competitive advantage and improved financial returns, it is not only about profit margins. The decision to implement Conversation AI should be based on well-defined goals and objectives.

For example, in the context of eCommerce, Conversational AI can be deployed to achieve multiple aims, including customer acquisition, buyer engagement and customer support. However, when deployed within the enterprise, the same tools can be used to improve productivity, implement lean operating practices and drive greater collaboration between teams. As an enterprise, you need to determine which capabilities will work best for your business.

To decide which conversational AI platform is right for your business, you must start with the following:

  • Evaluate your current processes, products, and services and how the capabilities of conversational AI can be applied.
  • Identify processes that can use automation to save time and money. Automating time-consuming and laborious tasks can free your employees to focus on higher-value responsibilities instead.
  • Identify underlying tasks for each process and determine how they might benefit from the addition of cognitive services or custom AI logic.
  • Rank business needs and business cases in terms of risk, value, costs, and scalability and think iteratively to refine your use case within the newly acquired context.

As you narrow down to the many ways Conversational AI can add value to your business, consider which areas of AI best fit into your organization and how to select the right implementation partner.

Reality check: Big tech companies don’t have all the answers

Every organization must choose the platform that matches their needs, depending on where they are in their journeys. No two organizations can have the same business and technical requirements. Hence, it’s important to get in touch with the right vendor who can determine the scope of your business and offer a conversational AI platform whose features will meet all your requirements.

Classified by deployment

Deeply integrated into information systems, conversational AI platforms can communicate with most channels, including voice interfaces, text messaging, social media, and websites. Depending on the nature of your business, you can choose how to deploy Conversational AI solutions – on-premise, public cloud, or hybrid.

  • On-premise deployment - For tighter security

On-premise deployment of Conversational AI ensures overall control over security measures and gives you the flexibility to allow access or restrict anyone from accessing your data. In this model, an enterprise uses proprietary architecture and maintains its own data centres. It offers options to customize and to integrate the solution into existing workflows. However, on-premise deployment of Conversational AI comes with certain restrictions, especially integration with popular third-party applications and software.

  • Cloud deployment - For greater flexibility & lower cost

Conversational AI deployed on the cloud comes with a lot of flexibility and is relatively less expensive than on-premise deployment. In this model, enterprises don’t have to maintain servers in-house. It also ensures that enterprises have continual upgrades in the solution and access to pre-built machine-learning APIs instead of building their own.

  • Hybrid deployment - For seamless integration

Another way to deploy Conversational AI is hybrid-cloud. In this model, production infrastructure is on-premise and processes like Conversational AI-training and analytics are performed in the cloud. It supports the seamless movement of applications between on and off-premises infrastructure. Thus, it provides greater freedom at the infrastructure level while giving control over the conversational AI.

Classified by use case

Another important consideration is the types of industry models and engines offered by the platform. In the Conversational AI landscape, vendors cover the gamut from data science platforms enabled with machine learning capabilities to marketing automation tools that help optimize advertising. Without some sort of organizational framework, this vendor landscape would look like a confusing mess to an enterprise end-user or Conversational AI implementer.

Based upon Conversation AI’s broad applicability, we can arrange the vendor landscape as described below:

  • Function-specific platforms: Under this landscape, vendors provide conversational AI platforms that aren’t aimed at a specific industry or class of industry problem. They provide operations intelligence, predictive analytics, decision support, virtual assistants (including voice assistants), intelligent document processing, and task assistant models of all sorts.
  • Industry-specific platforms: Here vendors provide platforms with domain-specific AI and cognitive models that are tailored to the specific industry needs. These Conversational AI platforms leverage a combination of AI, cognitive technologies, and deep domain expertise to address industry-specific challenges. In this category, the AI vendor classification is extremely diverse, with dozens of further categories and distinctions. These solutions are for industries that are generally accepted as “verticals”: finance, healthcare, insurance, energy, and utilities. It also includes cybersecurity and physical security applications, sales and marketing, news and content production, education and knowledge management, and the like.

Classified by operational principle

Another key conversational AI differentiator lies within the features and capabilities of the platform. Currently, chatbots can be deployed on relatively simple rule-based principles or more complex AI-based platforms.

  • Rule-based - Following a simple if/then process, rule-based platforms allow you to build an interaction by asking a series of choice-based questions and then responding to each reply with a pre-programmed answer. Although simple to create, these solutions will often fail to provide a correct response if the user query is not present within its database. To eliminate the chances of encountering an off-script response, rule-based conversational solutions are best built around a predefined option menus or button-based responses.
  • Keyword-based - A step up from rule-based, these platforms use keyword recognition to select the correct response to each interaction. For example, if you asked the bot “How do I fix a leaky garden hose?”, the software would pick up on the words ‘how’, ‘fix’, ‘leaky’ and ‘hose’, to select the appropriate answer from a preconfigured library of responses. However, the limited AI capabilities inherent to these solutions mean that they often fail to understand non-standard modes of communication - for example, the wildly varied syntax used by non-native English speakers can confuse bots built on low-tech keyword-based platforms.
  • NLP-enabled and contextual platforms - The pinnacle of conversational technology, contextual platforms use AI, deep learning and natural language understanding (NLU) algorithms to deliver a near-human like interaction as far as possible. These platforms are designed to learn from every interaction, and to flawlessly mimic human modes of speech across linguistic and cultural boundaries. NLU is of particular importance to productivity-based functions and consumer-facing conversational platforms - being able to understand, intuit and react to natural human conversation is a key differentiator when saving man hours or addressing consumer problems via a conversational AI interface.

These classifications offer insights on how to evaluate, procure, and implement conversational AI solutions into your business and compare the different offerings at different layers in the stack.

AI meets business

After selecting one specific Conversational AI platform, you need to check if you are adopting all the right features into your business.

Can the Conversational AI solution guide the customer through the purchase journey by delivering status clearance pop-ups, notifications, reminders, cart abandonment messages, or internal mailers?

Can the solution be integrated with third-party channels?

Is there a limit on customization – limitation of the training data, character limits, user experience/ user interface restrictions, or backend integration capabilities?

Can the Conversational AI platform map the conversation flow/design and use smart algorithms to minimize ‘unanswered questions’?

Can the Conversational AI be deployed on-premise, pure-cloud or hybrid?

How seamless is the API integration of the AI platform with legacy systems?

If you are wondering whether one Conversational AI platform can provide you with every solution, then the answer is yes!

Senseforth’s proprietary platform A.ware provides pre-built, pre-trained extensive industry and functional AI models for various industries and domains. We are a full-stack service provider and allow our clients to self-host or integrate the Conversational AI on a public or hybrid cloud.

At Senseforth, we understand chatbots represent the next wave of user interaction for a wide range of use cases. Accordingly, we have developed both bots by function and bots by industry using A.ware. Our bots can be configured to suit various indicators and events like source, landing page, idle time, customer behavior, transactions performed, customer preferences, profiling, and point in the interactions journey.

All our Conversational AI integrations have bi-directional communication capabilities. They come with pre-built integrators for different CRM, Ticketing, Email, HRMS, ERP, Order Management systems using REST API, web services or RPA tools. When it comes to integration with third-party channels, solutions built using A.ware can be easily implemented across social media channels, IMs, voice-enabled assistants, mobile apps, and other collaboration tools.

One of our core beliefs is that as the business scales, so does the scope of implementation and improvisation. Our Conversational AI platform allows you to develop interfaces that can be customized to go beyond training data by learning and inferring knowledge and can be implemented to unseen data sets. So, at Senseforth there are no limits on UI customization, number of intents, entities, responses, languages or integrations.

Some of the chatbots which we have developed using A.Ware are tailored to provide solutions to specific operations as well as specific industries:

  • Conversational Banking Bot, Ila, addresses millions of customer queries and can perform hundreds of online banking transactions through Natural Language Interaction.
  • Another application of Conversational Banking is Eva, which addresses over 40,000 queries each day from hundreds of users. This omnichannel solution is available on the website module, Alexa, Google Assistant as well as on WhatsApp.
  • Human Resource Bot, Kyra, can handle several HR tasks like leave application, scheduling meetings, conducting surveys and even making company-wide broadcasts. It can do tailor-made surveys across the organization and visualize the results as per your needs.
  • TeeCee, Thomas Cook India’s Virtual Assistant, helps users plan their holidays, buy/ sell Forex and addresses visa-related queries. While there are several competitive and evolving AI platforms and NLP engines on the market, Senseforth’s A.ware capabilities provide a unique abstraction layer. It enables enterprises to leverage any of these technologies to ensure that you have the greatest flexibility and range of choice in your Conversational AI solutions. Here at Senseforth, the ease-of-use and speed of no-code application development enhance the overall conversational AI platform implementation. We help businesses to optimize existing processes and unlock new growth opportunities using the power of A.ware, our Conversational AI Platform.