Businesses have leveraged the capabilities of chatbots for years now–much longer than the average consumer may expect. However, technological developments have led to a more advanced version of this solution: conversational AI.
From the simple capabilities of the chatbot, we have come a long way thanks to advanced NLU and AI. These technologies make it possible for virtual AI assistants to handle human variance–especially in dialogue.
So, how did we get from chatbots to conversational AI? It’s time for a history lesson.
The History of Chatbots
The first chatbot came into existence in 1966 from the minds at MIT. This chatbot, given the name ELIZA, was revolutionary at the time and served as the starting point for innovations in artificial human language. While at the time, ELIZA fell under the category of “chatterbots,” it and similar technologies soon adopted the name of chatbots.
ELIZA was of extraordinary capabilities upon its introduction, but by today’s standards, it fails very quickly. When asked a remotely complex question, ELIZA wouldn't be able to answer. This downfall isn’t surprising, considering the high variance of human interactions. That made it challenging for programmers to equip the chatbot with enough variations to understand “un-canned” responses.
Still, customers and employees alike loved the idea of delegating menial interactions to a chatbot. Suddenly, they had the opportunity to reduce their workloads or focus on more meaningful projects. Consequently, a wave of demand rolled in, and more and more chatbots came to life.
Many of the chatbots that followed ELIZA’s lead faced the same challenges–until a realization was made. Finally, a developer revolutionized the way we store and capture data using underlying modern database systems. As computer processing design grew more advanced, so did technologies such as chatbots. Eventually, a breakthrough in neural networks in computing became a pivotal stepping stone to artificial intelligence (AI).
The discovery of neural networks, which led to AI, finally gave humans a way to generate and train models to imitate and reproduce human interactions. This was a significant development with a language as complex and varying as the human language. Years later, conversational AI was born at the crossroads between Machine Learning (ML), Deep Learning (DL), Natural Language Understanding (NLU), and Natural Language Processing (NLP).
The Technological Becoming of Conversational AI
Since the introduction of ELIZA, conversational AI has come a long way in technological innovation. As a result of a crossroads of technologies, conversational AI can recreate human language in the most natural way possible. Using large volumes of data from your company’s database, conversational AI leverages NLU and NLP to recognize speed and text inputs. The solution then generates responses based on user intent–on any communication channel, whenever needed.
Unlike a traditional chatbot, conversational AI has the means to handle human variance. That’s the difference between choosing from a pre-programmed list of phrases and understanding human intent, processing it, and speaking plainly. Rather than being limited to rule-based, bounded systems with well-defined categories, conversational AI will respond in a natural, human-like manner.
Time Spared to Focus on Value-Added Projects
As a result, conversational AI is tremendously instrumental in providing access to self-service solutions. Whether a customer is seeking support or an employee is accessing helpful resources, conversational AI serves as a direct line to the answers they need. With self-service, your company can reinvest the time spared.
For example, if customers use conversational AI for support rather than a human agent, that time can be used for more meaningful tasks. Even for customers, conversational AI makes time-consuming tasks more efficient, returning spare time to them.
Data Unveiled at a Throw of a... Word!
Conversational AI is also beneficial in helping users sort through complex data. Your data is likely scattered across multiple enterprise systems and applications, as within any business. Consequently, finding the data you’re looking for can be time-consuming and error-prone–two things that don’t make for an efficient business. But, integrated with your company’s database, conversational AI can simplify these data processes.
Your conversational AI solution has the ability to access the precise data you want automatically. The solution will ensure that the data you gather is accurate and up-to-date, significantly reducing the risk of human error and especially the time spent on putting it together. With the support of conversational AI, you will bring data together and make it work for you so that you can make informed business decisions.
Automation Accelerated Through a Conversational Layer
Leveraging RPA, conversational AI helps businesses enhance process automation and accelerate digital transformation. Tedious processes can be automated and, consequently, streamlined. Additionally, as conversational AI can be integrated with your company’s database, it can work cohesively with other applications, ERPs, CRMs, etc.
It allows you to maximize your existing investments and increase collaboration between numerous systems. This ensures that conversational AI doesn’t just automate a few processes and systems–but every process within your organization. As such, you can achieve top-down automation and technical optimization.
Conversational Business Applications (CBA)
With top-down automation comes a unified user experience–something all modern businesses should strive toward. Applying conversational AI to your business in a holistic sense–meaning every process, interaction, and platform–allows you to focus on serving the internal user. When all processes are linked together, it creates a unified user experience and, ultimately, a more positive and productive one.
This “user experience” is an end-to-end process that applies to customers and employees. After all, happy employees mean happy customers. The more positive your user experience, the more likely your business will be to generate revenue, retain both customers and employees, and thrive as a business.
Even beyond happier users, conversational AI and, consequently, CBA foster:
- User experience (UX): Eliminate the need for users to operate every enterprise system or application individually. Instead, conversational AI blends all systems together for seamless usage.
- Automation: Maximize the automation mechanisms of your business through a conversational layer, achieving streamlined processes meant to reposition people into jobs grounded in the ineffable human condition and spirit.
- Digital transformation: Obtain a maximized ROI from all your investments in multiple digitization technologies. This reduces the need for expensive technology stacks that may fail to deliver results due to incompatible systems.
- Ease of scalability and quick deployments: CBA is easy and fast to deploy. Plus, ease of scalability means that the solution can be deployed and developed in any internal department by users that aren’t necessarily tech-savvy. As such, the solution is user-friendly for the whole company.
Conversational AI Today
For years businesses have tried to find the balance between technical innovation and automation without compromising the human experience. After all, a significant component of your brand is your customer relationships and voice as a business.
While technology can help you optimize operations and perform more effectively, its benefits are moot if you lose a human connection to customers–hence the value of conversational AI. Conversational AI offers users a balance between the speed and efficiency of technology with the thought, intuition, and connection of human language.
Conversational AI has come a long way from the introduction of the first chatbot, and its capabilities are only growing each year. Today, conversational AI can revolutionize the way that businesses operate with humans and technologies. Adopting conversational AI allows your business to streamline processes, leverage accurate data, facilitate human-like interactions, integrate enterprise systems, and so much more.
Not only are humans able to store data and process it, but they are now able to generate and train models to imitate and reproduce human interactions. Such as human language. The undecipherable, unreproducible human language. We are living in great times of conversational experience revolution!