How Conversational AI Really Works
Blog: ProcessMaker Blog
With chatbots expected to save consumers and businesses a whopping 5 billion hours by 2023, better conversation clearly packs an unmissable value. But legacy text chatbots and interactive voice response systems (IVRs) won’t be the drivers of this time-savings. The secret sauce? Artificial intelligence (AI).
Conversational AI expands the scope of today’s chatbots from stiff preset replies to intelligent, adaptive action.
Many organizations will want to explore the path from simple customer support to agile frontend and backend operations. To be among those that get the most optimal results, consider learning what your business can expect from today’s AI bot solutions.
In this article, we’ll help you explore these key questions:
- What is conversational AI?
- How is it different from chatbots?
- Why would I use conversational AI for business?
- How does conversational AI work?
- What do I need to know about neural conversational AI?
- Where does conversational AI fit within iBPMS solutions?
What is conversational AI and how does it differ from chatbots?
Conversational AI is any software that learns to allow humans and computers to talk and work together in a more natural way. Chatbots are an older, scripted version of the services AI technologies intend to expand and augment.
Both chatbots and AI-based tools strive to automate the manual gaps between human conversations and follow-up actions. Intelligent conversational tech intends to transition businesses from legacy chatbots to better accomplish this goal.
The wider scope of artificial intelligence bots features more than just customer service tools. These intelligent digital assistants serve a variety of use cases, either via typing or voice dialogs.
Making the distinction between automated conversation tools is essential to getting the right systems for your needs.
Task-based chatbots handle simpler tasks they’ve been preset to understand.
Historically, humans have always had to “speak” to computers at their preprogrammed limits of understanding. Precise keywords and choppy phrasing are nothing like how we speak with other humans, yet many modern chatbots require this.
Examples of these chatbots include legacy call routing Interactive Voice Response systems and FAQ tools on business websites.
Basic predictable actions like getting business hours fit well into this chatbot model.
However, this one-size-fits-all bot approach doesn’t get why someone is initiating the conversation. These bots can’t learn why either. Combined with an absence of memory, each conversation starts from scratch rather than carried over from previous interactions.
Conversational AI is designed to be predictive and personal for more complex, fluid responses and those that lack a predefined scope.
The goals are to understand users better, take more effective action with fewer steps, and feel natural to work with. The result is a step closer to mirroring human decision-making.
To exceed the common chatbots you’re likely familiar with, AI-driven tech is enhanced to:
- Observe user-specific traits such as location, mood, gender, etc.
- Remember and recall available existing data like CRM databases and past conversations.
- Learning via patterns in past conversations with each user.
- Taking complex action by integrating into business operations tools like Robotic Process Automation (RPA) and Business Process Management Software (BPMS).
Types of conversational AI
AI dialogue technology has been used to support various advanced digital assistants — or intelligent virtual assistants (IVAs). These interact with human users in one of two forms:
- Active communication via human-to-machine (H2M) interaction
- Passive observation of human-to-human (H2H) interactions
Within these areas, conversational AI is often packaged to serve a specific type of user.
Digital personal assistants handle individual needs like smart home interactions and daily schedule queries (weather, calendar, etc). Commercial assistants like Amazon Alexa and Google Assistant fall into this category.
Digital customer assistants connect a customer directly to business services without human interaction. These include custom assistants on business websites, branded apps, or social messaging apps to order a pizza or quickly file a customer support ticket.
Digital employee assistants manage tasks internal to a business, such as curating key information in meetings, addressing HR support requests, and automated IT password resets.
Why use conversational AI?
Conversational AI intends to save businesses from a few major problems by:
- Automating redundant or draining tasks.
- Augmenting human teams with more information and efficient tools.
With tons of labor hours tied up in verbal or written chats, it’s important to quickly and reliably figure out how to move words to action with the best results.
Whether managing employee or customer needs, AI assistant tools open these previously human-only activities up to automation. Each dialog unpacks what’s important, what needs to be done, and takes the best actions to complete the job.
Offloading repetitive, limited-value communications work from human staff is a major motivation in using chatbots. Where your teams are understaffed or underutilized for monotonous tasks, you can see a worthy boost to your operations.
Yet despite many businesses currently using chatbots, an artificial intelligence upgrade can extend these efforts to more tasks and dramatically enhance their efficiency.
As a result, organizations find their operations are:
- Connected via end-to-end automation for fast, consistent results at-scale.
- Personalized for better user satisfaction.
- Streamlined for cutting unneeded actions and simplifying the rest.
- Adaptive due to self-learning tools, meaning less preprogramming.
- Augmented by equipping teams with better insights via cleaner data.
- Refocused as employees reclaim time and energy for higher-impact tasks
- Available 24/7 with AI bots working even outside business hours.
Ultimately, teams find they can gain and leverage more information and faster action to save money, elevate operational efficiency, and keep their business scalable.
What are some ways to use Conversational AI?
Marketing can sort through leads faster to focus on leads that are more likely to convert to customers. Interactions with artificial intelligence bots can carry between devices and learn about a customer’s needs and wants. As data is stored into integrated CRMs and databases, IVAs automatically leverage this metadata to provide predictive product offers and even handle end-to-end sales processing via RPA directly in-chat.
Customer support can offload their most common, repetitive troubleshooting requests to conversational AI tools for 24/7 self-service availability, such as password resets or package tracking. AI bots can intelligently escalate more complex tasks for a seamless handoff to live human service, versus forcing customers to repeat themselves and fight through clunky legacy chatbot menus.
Management can glean insights from team meetings by automatically gathering key information and identifying possible action items via H2H meeting monitoring. Full machine-generated meeting transcripts can be made available for further review. Multilingual translations can make these insights available for higher management globally across enterprise sites — bridging gaps that result from cultural and language barriers.
How conversational AI works
At its simplest, conversational AI processes words into action. The system of components allows it to understand, respond, and adapt to each interaction.
Setup is simple: pre-production training feeds the tool to get it started, then teams will fine-tune based on real user feedback.
What are the core components of conversational AI?
AI digital assistants share core components similar to those of basic chatbots you may be familiar with.
Inputs are provided by speakers that aim to make use of the AI bot. This is the root of every conversational AI interaction.
- Text-based input functions by typing to interact. This method is discreet while being very consistent due to direct input.
- Voice-based input works by speaking to interact. This input form offers more versatility via hands-free use, despite being less privacy-friendly.
Automated Speech Recognition (ASU) listens to voice queries. If conversing via text-only, the system will exclude this piece of tech.
Natural Language Processing (NLP) breaks strings of dialog into cohesive sentences and shapes them to be easily read and acted upon by the AI bot. It also attributes other features like emotion to the input.
Natural Language Generation (NLG) offers a reply. As a more advanced step from older chatbots, this version of NLG can glean business databases like CRMs to personalize responses. This effectively acts as memory for the digital assistant.
Text-to-Speech (TTS) to give the reply via machine-generated voice, if applicable.
Non-AI chatbots use these basic question-response tools for FAQ-like uses.
Beginning with a simple request, a bot will convert speech or text into a computer-readable form. NLP matches the keywords of a request with a preprogrammed action. NLG takes this assigned action, sometimes with a confirmation prompt to ensure it is accurate. This process is stiff, to say the least.
What makes conversational AI intelligent?
Complex predictive actions require learning about ideal responses over time. To augment these systems, conversational AI adds the following parts to increase its abilities:
Natural Language Understanding (NLU) is used to expand the NLP operations and help the AI comprehend what a speaker means to say. It pulls from various available subject areas, and uses language devices like synonyms to navigate possible meanings. Using preset rules and AI as a foundation, NLU learns new responses.
Artificial Intelligence (AI) technologies learn from each finished interaction. A given digital assistant will typically make use of either Machine Learning (ML) or Deep Learning (DL). They work within and post-conversation to become better every time.
Integrations allow these systems to execute end-to-end action via Application Programming Interfaces (APIs) and other business operations tools. These features permit more autonomous actions.
The more frequently conversational AI is used, the faster it learns the most efficient solutions.
Neural approaches to conversational AI
As a primary feature of conversational AI, adaptive learning attempts to mimic the neural logic of a human’s mind. ML and DL are part of the effort to recreate fully authentic human thought processes.
Real artificial intelligence would be able to fully think and respond as a human would — including thinking abstractly and generally.
Neural conversational AI is the next frontier towards the limitless dialog of a hypothetical human-like artificial intelligence.
Machine learning and deep learning
Today’s ML and DL only process patterns they’ve been exposed to, and are limited to being highly domain-specific. Despite its adaptability, conversational tools based around this tech is deeply specialized on dedicated topics and use cases.
Both ML and DL work post-interaction to learn from each success and mistake, offering more precise responses in the future. These also work within NLU to sort and group new and existing dialog traits for more reliable response matching. Their NLU functions differ in the following ways:
- Machine Learning requires manual gathering of features like CRM customer information such as gender, emotion, and intent. This intervention can slow down model training and be somewhat disruptive but comes at a lower cost.
- Deep Learning does not require human intervention to identify, sort, and categorize traits of an interaction. As such, it can intake and process more dialog at-scale, albeit at a higher cost.
Response generation and conversation scope
Older chatbot models operate under rules-based retrieval of responses from existing databases. This leaves non-programmed, generative responses to conversational AI. Since we’ve explored simple task-based chatbots already, let’s focus on intelligent conversation models.
Closed-domain generative models are commonly seen within today’s intelligent digital assistants. These are specialized for a limited spectrum of tasks depending on the AI bot’s design. While difficult to design, these are possible through ML and DL.
Open-domain generative models can be described as “true AI.” Fully open to any topic or task beyond the scope of scripted actions. This is out-of-reach today, but there is hope to one day reach this new era of machine interaction.
Conversational AI companies
Among the many companies offering intelligent virtual assistants and conversational AI platforms, here are some of the more prominent ones:
These companies have distinguished themselves with features like multi-lingual conversations and low-code IVA development. Organizations seeking an AI assistant solution will find the best benefits in tools designed for ease of use and versatility for global use.
Conversational AI’s role in the digital operations toolbox and how it’s used with an iBPMS like ProcessMaker
Intelligent virtual assistants are a larger push towards complete digital transformation. This concept is not just technical but an operations mindset shift for stronger, leaner results across the whole organization.
In other words, conversational AI has opportunities to both benefit from and give support to other tools with your digital business operations. These changes revolve around two key areas:
- Business process management (BPM): Assessing, optimizing, and iterating your business processes.
- Hyperautomation: Integrating into your end-to-end business systems and operations.
Effective use of IVAs relies on first digging through how your business operations and current tech strategies work. Finding the day-to-day problems, fixing them, and elevating your existing automation methods brings out the best in your conversational AI strategy.
Business Process Management (BPM)
AI digital assistants don’t fix your processes — and all existing flaws are baked into your efforts until they’re mended. Business process analysis (BPA) works to help you understand and resolve the inefficiencies within your business.
Intelligent business process management software (iBPMS) makes your investigation simple. Teams find that low-code platforms like ProcessMaker help them create enterprise-level business processes. When a process is mapped and analyzed, it can be better optimized and ultimately improve the functioning of your IVAs.
Many businesses considering conversational AI already have partial automation. However, these improvements are often segmented and required manual handoffs to progress across processes. The next stage is bridging these automations together for organization-wide efficiency.
Hyperautomation seeks to bridge fragmented gaps in your business for streamlined operations.
After planning with iBPMS, siloed uses of RPA and other tools can be united through IVAs. By bringing natural language into the umbrella of automation, friction at various points-of-interaction is relieved.
As a result, front-end customer interactions can trigger back-end processes across departments with less human effort. Fewer of these cross-process manual handoffs means teams can reclaim more time for their business. This means a lighter need for staff numbers, labor hours, and ultimately, lower costs.
Businesses globally are discovering the competitive advantage of including IVAs in their digital transformation. More reliable outcomes and less time wrapped up in low-priority labor can allow teams to refocus employees on meaningful work.
Undoubtedly, you should always assess the costs and complexity of any new solution. However, you’ll join many teams that find conversational AI to be important in the future of the global competitive landscape — regardless of industry.