Are you a smartphone user? Then you’d know that setting a simple alarm the traditional way takes 4-6 steps, about 20-30 seconds.
Today, it doesn’t have to be. With the widespread integration of AI in our daily lives, you can just say “hey Siri” or “Ok Google” and speak out your instructions—alarm set in 2 seconds flat!
From rule-based basic chatbots to sophisticated conversational AI, tech has come a long way. In this blog post, we map that journey. We explore chatbot vs conversational AI, and then see how they are changing customer experience as we know it.
Let’s begin!
Chatbot vs Conversational AI: What’s the Difference?
Summarize this article for me pleaseDefining Chatbots and Conversational AI
A chatbot is a software that can have a conversation with a user in natural language through messaging applications, websites, mobile apps, or the telephone.
Conversational AI refers to technologies that enable machines to understand, process, and respond to human language naturally and engagingly.
For example, a customer service chatbot on an e-commerce website can help users find answers about products, place orders, and process returns and refunds. Its ability to converse with customers is enabled by predefined rules and machine learning algorithms, together known as conversational AI.
Some of the technologies that form the foundation of conversational AI solutions are as follows.
Artificial intelligence
AI is an umbrella term for machines that can carry out tasks in ways we consider “smart.” It is a combination of technologies that mimic human reasoning to solve problems.
AI is the foundation for creating systems that learn from data, recognize patterns, and make decisions. For instance, YouTube’s ability to understand your preferences and recommend related videos is a result of AI tech.
Machine learning (ML)
ML is a subset of AI that builds self-learning systems that are designed to learn from data and improve performance over time without being explicitly programmed.
In a chatbot, this technology helps gain knowledge about the customer through continued conversations (and other behavioral data) to adapt accordingly.
Natural language processing (NLP)
NLP is a field within AI that focuses on interacting with computers and humans through natural language. To put it simply, with NLP, users can interact with a computer in English, for example, instead of C++, Java, or Python.
NLPs are the foundation of chatbots. They enable the bots to understand human language and respond appropriately.
Virtual assistants
Virtual assistants, like chatbots, are an application of conversational AI technology. They are software agents that can perform tasks based on user commands or answer user questions. Apple’s Siri, Google Assistant, and Amazon Alexa are popular virtual assistants.
With the integration of generative AI, we now have text, voice, and image-based virtual assistants across platforms. The Meta AI bot integrated into WhatsApp is a great example.
Summarize this article for me pleaseThe Evolution of Chatbots to Conversational AI
While the penetration of smartphones and the Internet gave an extraordinary boost to chatbots and conversational AI applications, it is hardly a new phenomenon.
Early chatbots
In the mid-1960s, MIT researchers created ELIZA, a conversational program. It used pattern matching and scripted responses. So, conversations with ELIZA were rudimentary and did not involve anything by way of artificial intelligence.
Rule-based chatbots and early AI
With the rise of the Internet, rule-based systems emerged with conversational interfaces able to have simple conversations based on pre-programmed knowledge. This gave way to more sophisticated approaches that used statistical models and algorithms to improve response accuracy and relevance.
This is one of the most exciting times for conversational AI, which inspired the advancements we use today.
Deep learning
Deep learning is a subset of machine learning that uses neural networks with many layers (deep networks) to model complex patterns in data. It enables the system to understand and generate human language with high accuracy.
Deep learning models are trained on large datasets to recognize patterns and make predictions. For instance, AI assistants like IBM Watson use deep learning to analyze medical data and provide preliminary diagnoses, assisting doctors in decision-making.
Context awareness
When you Google the word ‘Python,’ how does Google show you details about the programming language and not the snake (or vice versa)?
The answer is Context awareness.
Context awareness is the system’s ability to understand and remember the context of a conversation over multiple interactions. Context-aware systems track the history of interactions and relevant user data to provide personalized responses.
Based on your past searches and building on natural language understanding, Google’s deep learning model, BERT, makes a guess about what you might be looking for and optimizes your search results and conversational responses.
This is also the same technology that understands you when you say, “play my favorite song” or “set an alarm for 8” (though you don’t specify AM or PM).
Generative AI (and ChatGPT)
ChatGPT is a generative AI solution developed by OpenAI. Like all GenAI, it is a language model that uses deep learning and context awareness to generate human-like text based on user input. It can have extended conversations, generate creative content, and answer complex questions.
In essence, GenAI predicts the next word in a sentence based on the context provided by previous words. In this way, ChatGPT can draft articles, summarize content, generate ideas, etc.
Bonus: Optimize your interactions with GenAI with some of these AI prompt templates.
All of these technologies have a wide range of use cases. From personal productivity to autonomous cars, conversational AI is making inroads across industries. However, its most significant impact is in customer experience. Let’s explore that.
Summarize this article for me pleaseConversational AI and Chatbots: Their Role in Customer Service
Traditionally, marketing and customer service tend to be tech-savvy teams. They are open to new tech and have the ability to adopt them quickly. Teams have known how to use AI for lead generation for a while now. This applies to conversational AI and chatbots as well. Let’s look at how.
Customer queries and ticket management
Businesses use AI tools for customer service on websites, apps, and e-commerce platforms as the first port of call for a number of customer inquiries. They act as an intercom alternative, more suitable for millennial and GenZ customers, who prefer to troubleshoot themselves instead of speaking to someone.
As a result, businesses can:
- Provide instant answers
- Be available 24×7
- Minimize wait times for customers
- Scale customer service operations quickly and cost-effectively
For example, H&M uses a chatbot to help customers with order tracking, product searches, and returns, providing a seamless shopping experience.
Bonus: More tips and strategies on how to use AI in customer service.
Customer experience
When we speak of AI in customer experience (CX), we think of bots speaking to users as the only approach. It doesn’t have to be. As Starbucks’ Deep Brew has shown, conversational AI makes the lives of support staff easier, which in turn translates to better CX. It can automate inventory management, supply chain, stock replenishment, etc., freeing their time to engage in building deeper human connections with customers.
Workflow efficiency
Every business has hundreds of customer-facing processes. With AI workflow automation, you can make these processes more efficient and effective.
In CRM systems, AI streamlines routine tasks such as data entry, call scheduling, etc. In communications, AI in the workplace can help send automated follow-up emails with the right message. For instance, after a discovery meeting, a good AI tool can summarize the discussion and create action items automatically.
In marketing project management, AI can automate manual and repetitive tasks. For example, ClickUp Automations allows marketing project managers to automatically:
- Create tasks based on templates
- Update status or add tags based on customer behavior
- Move tasks down the workflow based on user input
- Send reminders based on upcoming deadlines
When you’re using ClickUp, just add action items for every trigger point, and let the AI do the work for you. Create your custom workflow or use the existing 100+ templates to automate your work.
Data-driven decision making
Conversational AI is a great way for you to glean timely contextual insights about your customer interactions. For starters, a feature like the ClickUp Dashboard is a fantastic way to customize and unify visibility for early detection and remediation of problems.
With some thought and experimentation, you can do a lot more with conversational AI. For example, here are the questions you might ask:
- How many customers have abandoned their carts in the last 3 days?
- Of them, how many had applied the coupon code?
- Of them, how many have visited the e-commerce website or app since the first abandonment?
- Of them, who are the customers with more than 20 purchases?
- And what are their customer satisfaction scores?
This would give you a highly specific list of audiences for whom you can hyper-personalize communication.
AI tools provide actionable insights that help businesses make informed decisions not just about customer service but also marketing, product, sales, and more.
Customer relationship management
Last but not least, a good conversational AI chatbot can have a significant impact on customer relationship management. AI tools for CRM can perform a number of tasks that were so far manual and time-consuming.
Process automation: AI can handle customer queries, order processing, appointment booking, etc. It can automatically take care of everything in the background, clearing up the space for sales/customer success to have meaningful conversations.
Personalization: AI can personalize at scale. Think of how Netflix and YouTube offer personalized recommendations to millions of users. A good conversational AI tool can add similar capabilities to your chatbots. It can have deep conversations, and offer tailored responses/recommendations.
Team efficiency: An AI tool can be the superpower in your marketing toolkit. With ClickUp Brain, your team members can get instant answers to queries, status updates on tasks, be reminded of deadlines, summarize notes and more!
All these applications and use cases are simply the beginning of a powerful phase in modern history. Conversational AI has the potential to dramatically change the way we do this for the better across domains and use cases. Let’s see what that might look like.
Summarize this article for me pleaseThe Future of Chatbots vs Conversational AI
Chatbots and conversational AI are here to stay. In the future, they will be so intricately intertwined into the fabric of our lives that we might not even notice it’s conversational AI. Some of these impacts might be dramatic, others more muted. Let’s take a look.
Specialization
AI specialization is already underway. Given the variety, volume, and velocity of data available today, it is no longer possible for ML models to ‘know everything.’ So, chatbots will become more specialized, focusing on specific industries and tasks.
For example, specialized healthcare chatbots will integrate into patient care. Investment recommendation systems will play the role of an advisor.
Better emotional intelligence
Early bots spoke like, well, robots. Today, conversations are more nuanced than that. For instance, Apple’s Siri is known to be an amusing conversationalist. It is also a serious virtual assistant. For instance, it can identify mentions of self-harm or suicide and offer a helpline number.
The next generation of conversational AI chatbots will have improved emotional intelligence, allowing them to understand better and respond to users’ emotional states. This could involve recognizing tone, sentiment, and context to provide more empathetic and appropriate responses.
Multi-lingual and cross-cultural competence
Today, most conversational AI is in English. There are some applications emerging in languages, such as Korean, Japanese, and French. . In the future, there will be conversational AI systems in almost every language people speak.
They will understand cultural nuances and be able to switch between languages seamlessly, enhancing usability for diverse user bases.
When such technology matures, chatbots can become personalized tutors for students and learners. It can customize lesson plans and methods to best serve the learner’s needs, strengths, pace, and style. This can also make education more widely accessible across the globe.
Multi-sensory experiences
Currently, most of our interactions with conversational AI are text-based. Even with tools like ChatGPT, users are expected to type in. The future will change that.
Users will be able to have multi-sensory experiences. You will be able to share images or videos or input and receive output in the form you prefer. You might also be able to engage with gestures, making it more accessible for those who use sign language.
As you can tell by now, the future does look bright. So does the present. Today’s conversational AI and chatbot technologies are advanced enough to make your lives much simpler and easier than ever before. Don’t wait too long.
Summarize this article for me pleaseGet Started on Your Conversational AI Journey With ClickUp
How many alarms do you set every morning to wake you up? Your virtual assistant is more likely to know the right answer to that than you do yourself.
In modern life, conversational AI already plays a part. Especially with the growth of GenAI, every tool has some form of AI feature integrated into it.
However, the best AI is one that is put to good use. While AI can draw unicorns and create ebooks alike, sucessul use of conversational AI depends on what you need for your business.
Before you get a chatbot tool or think about how to integrate AI into a website, think of what you’d like to achieve. Would you like to increase lead generation or resolve customer requests efficiently or setup real-time reports? Start small and build up.
ClickUp’s AI tools are designed to help you achieve exactly that. The beauty is you can use it as a chatbot to get information about your projects and as a conversational AI to brainstorm ideas. You can also summarize notes, get instant answers, automate manual tasks, and more.
What are you waiting for? Try ClickUp today!
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