What Is Enterprise Conversational AI? A Non-Technical Guide

Enterprise conversational AI is no longer just a concept, it has been gaining momentum quickly. Today, the market for this specific AI use case is increasing in the enterprise world primarily because consumers and founders alike have grown accustomed to conversational AI. Chatbots and virtual agents with whom you can actually talk and get an answer from within a few microseconds have been a game-changer in the market.

Founders have come to the conclusion that smarter and quicker customer interactions can improve customer retention rates, improve sales, and make it easier for an enterprise to scale. Not only that, enterprises today are witnessing a significant level of growing demand for enterprise conversational AI. Thus, this guide will give you a clear, non-technical breakdown of what enterprise conversational AI is and how it works.

What is enterprise conversational AI?

Before we jump into dissecting enterprise conversational AI, let's first understand conversational AI. It is an AI use case that enables bots, machines, and online agents to converse like normal humans and understand their language via both chat and voice commands.

Enterprise conversational AI refers to advanced artificial intelligence systems that are specifically designed to simulate human-like conversations at an enterprise level and scale. These are scalable, secure, and robust versions that are specifically built for larger organizations. They are used across areas like customer service, sales, IT support, HR, and more to automate cross-interactions. 

Source: Salesforce Naturally, enterprise bots are quite different from consumer-level bots. There are some key and stark differences between the two. When it comes to consumer-level bots, they are good at handling basic tasks; they are typically made to serve a small user base or a singular platform; they can’t be easily integrated into a business, and since they are mostly free, your data is susceptible to hacks or infringement. 

Enterprise bots, on the other hand, are for complex business-related tasks; can cater to as large a user base as programmed; can be easily integrated with existing systems such as LMSs and CRMs; can easily handle sensitive data along with providing utmost security. Some examples of enterprise conversational AI include banking support bots, HR assistants, IT help desk chatbots, etc.

Why enterprises are adopting conversational AI now

A very pertinent question to ask is, “Why are enterprises adopting conversational AI now?”. Some key reasons and benefits have led to this phenomenon. Here are some of them. 

1. Market demand → There is a growing market demand for conversational AI today. Who wouldn’t want a 24/7 service with minimal rising support costs and global scalability? Only an enterprise that doesn’t want to witness growth in a short period would not take a bet on it. Today, companies and founders alike want to give customers the best user experiences, and that too, as quickly and swiftly as they can.

2. Strategic value → Enterprise conversational AI enhances customer experience and strengthens internal productivity. Its multilingual capabilities and ability to adapt to different users make it highly effective. These are not just features; these are what add strategic value to a startup or enterprise. Thus, this is the second reason why enterprises are adopting conversational AI.

3. Pandemic-driven digital acceleration → The pandemic gave a huge reality check to every online and offline business, as they are heavily dependent on humans. Now, human dependency is not wrong; however, companies usually have no backup plans in the wake of an exogenous shock such as the pandemic. Post-pandemic, there has been a digital acceleration. Firms today are prepared to fight against the challenges of the future, and conversational AI is their armor. Some real-world use cases that we currently have are customer service automation, employee onboarding, virtual agents for IT help desks, etc.

How enterprise conversational AI works 

Key components 

Now that you understand what conversational AI is, let’s understand its workings in a non-technical way. This form of AI is specifically trained for your business, and three key components allow it to do so. They are as follows:

1. Natural Language Processing (NLP)

NLP is the component that helps a bot understand everyday language. NLP recognizes the voice and converts human voice into an AI voice for it to understand, generate an AI answer, and convert it into a human voice to give an outcome. NLP also plays a huge role in helping AI identify the meaning of what is being said and not just pinpoint keywords. 

2. Machine Learning

Machine Learning (ML) is what allows AI to learn from its past conversations and enables it to improve its accuracy. The more data you feed an AI, the better the results get. This is why support agents used by enterprises are fed the most authentic, relevant, and accurate information to give the best output for an input.

3. Integrations with enterprise systems (CRM, ERP, etc.)

Enterprise conversational AI can easily be connected to tools such as CRM, ERP, etc. This component shows how AI is adaptive and how it can pull real-time data and complete actions such as updating a ticket or retrieving a customer’s order history.

The concept of training a bot using enterprise data

So, how does training a bot using enterprise data work? Training essentially means feeding everything about the enterprise/company. When we say everything, we mean EVERYTHING from docs, whitepapers, FAQs, chat logs, process logs, to product details. By doing so, you train a bot to adequately handle requests, respond accurately, and manage to take action in real-time. 

How to teach a smart assistant how a business works

Now, when it comes to training a bot on how to work, there is a protocol. A smart assistant is just like a new employee, except that it is a bot. You pave the way for it by feeding it what it needs to know. You practice giving commands and fetching data to an extent that it becomes ready to be launched in the real world.

Source: Getty

Must-have features in an enterprise chatbot platform

There are enterprise chatbot platforms of all kinds. Here are some must-have features in an enterprise-specific chatbot platform:

1. Omnichannel support → The idea behind this is that the AI chatbot for enterprises should work seamlessly across web, mobile apps, messaging apps, and other platforms to ensure a consistent experience.

2. Data privacy & security compliance (GDPR, SOC2) → The tech world has robust security standards and criteria such as GDPR and SOC2. These tools offer encryption, access controls, and audit trails to protect sensitive company and consumer data.

3. Analytics & reporting → A good enterprise chatbot platform comes with a built-in reporting and analytics mechanism. This helps you track bot performance, user satisfaction, and other parameters.

4. Workflow automation → The platform should be easier to integrate with tools such as CRM, etc. This way, the workflow will be automated.

5. Scalability & customization → This is a very important feature. An enterprise is bound to grow, and a platform that can keep up with this scalability is not only important but a necessity.

Benefits of using enterprise conversational AI 

So, enterprise conversational AI has some abstract benefits, but what are some of its quantifiable benefits? Here are some of the major ones:

1. Lower support costs → AI chatbots significantly reduce the support costs because with just a one-time investment, you don’t need to hire someone full-time.

2. Faster response time → It is humanly impossible to instantly reply to a query. AI chatbots greatly improve the response time.

3. Higher employee productivity → When mundane tasks such as customer support and responding to emails of customers sharing their complaints and queries are undertaken by chatbots, the employees get to work on more important tasks, improving their productivity.

4. Improved customer satisfaction → When all of these factors are present, customer satisfaction also skyrockets, leading to happier customers and better product/service reviews.

Studies show that enterprise conversational AI can reduce ticket volume by 35%. This happens because the chatbots are 24/7 available; there is multilingual support, proactive issue resolution, and instant response time.

Challenges and limitations 

Some challenges and limitations of enterprise conversational AI are as follows:

1. Integration complexity → Sometimes it may get a little difficult to integrate in-built systems into an AI chatbot for enterprises.

2. Bot training and maintenance → It is not just training the bot, but also putting in an effort to maintain it all.

3. Language accuracy and regional nuances → Languages are complex. There are multiple dialects and regional nuances. The bot may not always be able to pick them up.

Choosing the best conversational AI for your enterprise

How do you choose the best conversational AI for your enterprise? Here are some ways to determine that.

1. Use-case alignment (external support vs. internal automation) → For one, the platform has to align with the enterprise’s use cases. There are different kinds of AI platforms. While some focus on the external elements, others are focused on internal workflow automation.

2. Ease of deployment → Some platforms make it extremely easy to deploy and integrate built-in enterprise features, whereas there are platforms that are not very handy. Based on what your platform needs, you can choose either.

3. Vendor reliability and roadmap → Additionally, ensure that the vendor you are dealing with is reliable and there is a fool-proof, concrete long-term road map and track record in enterprise settings.

4. Support and SLA → Moreover, some platforms don’t offer strong support and SLAs, especially if it is the bot handling business-critical situations.

Lastly, some platforms are domain-specific. They operate and specialize in verticals like healthcare, finance, and may offer pre-trained bots, domain-specific NLP, etc.

Future trends in enterprise conversational AI 

When it comes to future trends in enterprise conversational AI, we are seeing voice-based enterprise assistants rapidly picking up the pace. There is immense focus on enabling hyper-personalized experiences, and the integration of generative AI and LLMs is further enhancing and honing the capabilities of these bots. As we move forward, we will see smarter multilingual AI agents.

Conclusion 

In conclusion, enterprise conversational AI is a powerful AI use case that helps businesses work smarter. It meets business goals like achieving efficiency, scalability, and a better user experience at a much faster rate. The best thing about conversational AI is that it does not require deep technical knowledge; you can take help from several experienced companies that provide AI chatbot development services, who can easily build and deploy for you.