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<\/a><\/p>\nUsing AI in your digital customer interactions is the best (and dare I say \u201ceasiest\u201d) way to make those interactions feel more human, which in turn makes engaging with them a better experience for your customers, offering up responses that are tailored to your customer\u2019s unique needs.<\/p>\n
In this article, I\u2019ll discuss:<\/strong><\/p>\n <\/a> <\/p>\n A chatbot is a software application that uses artificial intelligence to simulate human-like conversation.<\/p>\n The goal of a chatbot is to help customers by processing requests, generating answers, and providing general support. You may hear the term \u201cconversational AI\u201d floating around. This term essentially summarizes the experience of using AI to create communication that feels smooth and conversational in tools like chatbots, voice assistants, email writing tools, and more.<\/p>\n Chatbots typically use natural language processing models<\/a> (NLP) to create that human-like communication.<\/p>\n Here are some of our favorites<\/a>.<\/p>\n An AI agent is a software program that uses artificial intelligence to perform tasks and make decisions on behalf of a user.<\/p>\n AI agents use advanced large language models<\/a> (LLMs) to understand and respond to user inputs, and they\u2019re able to handle multi-step processes.<\/p>\n AI agents go beyond just gathering information needed to make a decision or solve a problem \u2014 they can interact with external sources to obtain the required data and then autonomously complete the task for you. (Example: Rebooking your hotel room reservation for another day, or generating a report).<\/p>\n \n<\/a> <\/p>\n When you hear AI agents vs. chatbots, you may be tempted to use them interchangeably. While both utilize AI to help businesses or users complete a task, they differ in their level of knowledge and their ability to execute.<\/p>\n I spoke to Ben Gardner<\/a>, VP of customer care at AvidXchange<\/a>, about how he explains the differences between AI chatbots and AI agents. Here\u2019s what he said.<\/p>\n \u201cChatbots are simply conversational and are providing answers or instructions on how to do things yourself,\u201d he said.<\/p>\n In contrast, Gardner said that, \u201can agent can do more than just have a conversation and provide information \u2026 [it] can look up data in other systems, perform actions, and do things that typically a person could do.\u201d<\/p>\n \u201cBoth options are useful but they have different focuses,\u201d he concluded.<\/p>\n Here are a few key ways I\u2019ve learned that they differ.<\/p>\n At their core, AI chatbots are designed to offer support within the context of a conversation by providing information or offering next steps for a user to complete their request.<\/p>\n An AI chatbot is more advanced than a traditional chatbot in that it can understand language outside of pre-programmed commands and offer AI-generated responses based on data it has been trained on. However, an AI chatbot needs to ingest large quantities of information (like conversational data) for it to analyze and use to formulate responses.<\/p>\n However, when it comes to multi-step interactions, AI chatbots have limited capabilities.<\/p>\n AI agents function more like a real customer service rep would operate.<\/p>\n Since AI Agents are built on LLMs, they can learn and adapt in real time while also understanding context and sentiment. So instead of solely relying on keywords or pattern matching to provide a resolution, the agent can understand more nuanced questions and even help solve multi-step interactions.<\/p>\n This not only makes the conversation feel more natural, but it also allows for the agent to solve more complex problems.<\/p>\n Here\u2019s an example I like to use to pull it all together.<\/p>\n Imagine you\u2019re visiting a shoe store in person. Here are your different interactions.<\/p>\n Chatbot interaction:<\/strong> The AI chatbot is similar to the greeter at the front door who says hello, asks if they can help you find something, and then points you in the right direction. While they can detect things like your intent and your sentiment, if you asked this person which brand you should buy or if a shoe runs true to size, they\u2019d have to pull in another employee to help you.<\/p>\n Agent interaction: <\/strong>The AI agent is more like the shoe store employee who specializes in the type of shoes you\u2019re looking for and can be consultative in their guidance. They can answer your questions about brands or functionality, pull sizes from the back for you to try on, suggest a pair of matching socks to go along with your new shoes, and then ring you up at the register to complete your transaction.<\/p>\n Conclusion<\/strong>: While the chatbot and agent both helped you on your journey to buy new shoes, their roles and responsibilities are limited to their level of knowledge and their ability to execute on certain tasks.<\/p>\n AI chatbots typically require more manual training and review, and any findings that you uncover can also require manual updates to the decision trees and bot flows.<\/p>\n For example, if you uncover a question that the bot is struggling to answer, you\u2019ll need to go in and review that customer question, see how the bot is responding, and train the model on a better response. You may even need to A\/B test question and answer flows to see which performs better.<\/p>\n Since AI chatbots are constantly learning from conversations, the more conversations the bot is having and the more feedback that you give it, the more the bot adapts its future responses (and its suggested replies for your team).<\/p>\n Pro tip:<\/strong> Your support teams should also be training the model by giving it feedback on the conversations. Most chatbots will have a place for a human to do things like approve, deny, or edit a suggested response as well as rate a conversation that the bot had.<\/p>\n AI agents typically come with built-in models or algorithms that help the agent learn and adapt.<\/p>\n As with the chatbot, the more conversations your agent has, the more it is learning and changing to fit your customers’ needs.<\/p>\n Once an AI agent is trained on your datasets and connected to your tech stack, it can do things like look at past conversations or interactions that your human reps have had and offer similar responses.<\/p>\n As your company\u2019s knowledge base, product offerings, or policies change, your AI Agent can automatically adapt its responses to reflect those changes, removing the need for manually updating tags or conversation flows.<\/p>\n Training an AI Agent still includes the responsibility of reviewing customer interactions and providing feedback, but the beauty of an agent is that it usually doesn\u2019t require a manual adjustment (like adding a new response to multiple bot flows) in order for the bot to implement the changes necessary.<\/p>\n Additionally, many AI Agents will continually analyze data to find gaps and make suggestions for new or revamped flows. Some agents can even go beyond just suggesting changes and can actually write the new flows and implement them for you.<\/p>\n Chatbot Knowledge<\/p>\n Your AI chatbot is only as intelligent as the information you feed it. Your bot\u2019s knowledge domain will be limited to the information you\u2019ve given it access to via site mapping, integrations, or APIs.<\/p>\n When possible, using federated search provides a better customer experience, but it requires more lift to set up with an AI chatbot.<\/p>\n To enable federated search in a standard AI chatbot, you may be looking at doing things like manual tagging and even requesting help from your engineering team to connect various data sources, training the bot, and defining the rules for relevant content types.<\/p>\n AI Agent Knowledge<\/p>\n In contrast, an AI agent not only scans your website, data, and integrations for answers, but it can also use broader language models, external resources, and historical context to inform the answers it provides.<\/p>\n But it goes beyond just pulling that variety of information \u2014 it can then combine those inputs to make a logical assumption, creating a more robust and nuanced response for customers.<\/p>\n Additionally, vertical AI agents<\/a> are likely to increase in popularity due to the increasing need for industry- and domain-specific knowledge in AI workflows.<\/p>\n <\/a> <\/p>\n You\u2019d be hard pressed to find a company that isn\u2019t trying to balance scaling their organization while also ensuring they deliver a great customer experience. AI agents excel at helping organizations achieve both of those goals simultaneously.<\/p>\n The market for AI agents in tech is expected to grow at least 45%<\/a> over the next five years, with Boston Consulting Group predicting that businesses will transition to a model that includes having much smaller teams of humans working alongside an agent \u2014 specifically for complex disciplines like customer service, analytics, and software development.<\/p>\n Next, I\u2019m going to look at a few ways AI agents can benefit different teams across an organization.<\/p>\n Customer service and support teams continue to look to AI to help enhance the customer experience while decreasing ticket volume and response time. A recent study by Gartner found that 80% of companies<\/a> are either using AI or planning to use AI in customer service by 2025.<\/p>\n AI agents can transform the support experience by not only handling Tier 1 support requests, but by helping customers fully resolve their own issues (which leads to quicker resolution times).<\/p>\n As we\u2019ve discussed earlier, AI agents can revolutionize the customer experience by doing things that typically require multiple steps, systems, or human intervention, such as:<\/p>\n Implementing AI agents can help retailers drive more revenue and enhance the customer experience. AI agents can also help retailers make buying decisions by providing real-time analysis on customer data and inventory.<\/p>\n AI Agents could help ecommerce teams with things such as:<\/p>\n Ask any customer success manager what they wish AI could take off their plate, and chances are they\u2019ll have an entire list. If you can find an agent that helps account teams spend more time managing relationships and less time on admin work, you\u2019ll have a happier team!<\/p>\n Josh Schachter<\/a>, the CEO of UpdateAI<\/a>, told me that in his experience, \u201cAI agents are redefining customer success and account management by automating busywork, scaling customer knowledge, and delivering white-glove service at scale.\u201d<\/p>\n Similarly, Erin Gehant<\/a>, an account manager at TrueLearn<\/a>, said that their AI agent streamlines their internal processes and does things like \u201ctranscribing meetings and summarizing key takeaways, and then drafting follow-up emails based on meeting insights.\u201d<\/p>\n Gehant says this has \u201chelped boost efficiency in my workflow which gives me more time to focus on relationship building.\u201d<\/p>\n Here are a few ways account teams can benefit from AI agents:<\/p>\n Example: An agent could notify a CSM that their top account has dropped in product adoption, then offer to draft an email inviting that customer to an upcoming webinar the customer could benefit from.<\/p>\n When it comes to finding creative ways to use AI, I\u2019d bet money that marketing teams already have a long list of clever use cases. From lead scoring to data analysis to content creation \u2014 the possibilities are endless.<\/p>\n Today, between 40-50% of marketers are already using some form of AI<\/a> in their workflow and the majority of marketers surveyed<\/a> expressed excitement about what AI can help them with in their jobs.<\/p>\n Here are just a few ways that AI agents are being used by marketing teams today:<\/p>\n While this list highlights just a few different teams that can benefit from AI agents, I have no doubt we\u2019ll continue to see new and creative use cases for AI agents expand across entire organizations.<\/p>\n <\/a> <\/p>\n When it comes to how consumers interact with a business, chatbots have become table stakes in today’s digital landscape. Customers not only expect to have a chatbot option, but in some cases, they prefer using a chatbot, with 74% of internet users<\/a> saying they prefer a chatbot for help resolving simple questions.<\/p>\n However, currently only 58% of B2B companies<\/a> and 42% of B2C companies are using chatbots, which I find surprising. If the majority of consumers want (and expect) a quick, 24\/7 chat experience to be available, then it sounds like companies have some catching up to do.<\/p>\n It\u2019s worth noting that chatbots aren\u2019t limited to just helping with support questions \u2014 businesses can use chatbots to increase conversion and revenue as well. Data shows<\/a> that on average, businesses that use chatbots see more high quality leads captured, an increase in transactions, and more upsell revenue attributed to chatbot interactions.<\/p>\n Let\u2019s look at a few ways that teams can use AI chatbots.<\/p>\n When you use an AI chatbot to manage repetitive and commonly submitted support requests, you free up your support team to handle more complex customer issues and deliver white-glove service.<\/p>\n Additionally, by creating these support flows with an AI chatbot, you provide your customers with 24\/7 support, which, for the majority of consumers<\/a>, is the most helpful feature of a chatbot.<\/p>\n Here are a few key ways that AI chatbots can help customer service and support teams:<\/p>\n Offering your customers support with their new purchase is a great way to make setting up a new product, or onboarding to a new software, feel personal. If the product or service you sell requires your customer to set it up on their own, an AI chatbot can offer quicker time to value for your customers and take some of the lift off of your implementation teams.<\/p>\n Gardner sees even more opportunity here: \u201cI foresee AI expanding more into onboarding and customer success in the near future. We can use AI to think about helping customers set things up or be more strategic in their implementation.\u201d<\/p>\n I\u2019ve seen how onboarding bots offer value in both the D2C and B2B space:<\/p>\n Here are a few ideas on how to use a chatbot for onboarding:<\/p>\n Pro tip:<\/strong> Go one step at a time, set a positive and encouraging tone, and require the customer to engage with the chat before moving on to the next step. Avoid just linking help docs in this bot \u2014 actually write out the steps and embed video tutorials in the chat flow if a step is especially technical or complicated.<\/p>\n Pro tip<\/strong>: Take time to review this and see how it\u2019s performing. I would be sure to ask myself: Is this bot helping me drive down support tickets?<\/em><\/p>\n Pro tip<\/strong>: I suggest you get creative with this by pulling in your customer marketing or customer advocacy team to feature existing customers showing off their tips and tricks.<\/p>\n AI chatbots are a great tool for helping businesses move customers through the different stages of the sales cycle.<\/p>\n Chatbots can capture and qualify leads, offer a prospect the chance to book a demo right away, and curate content that resonates with prospects no matter where they\u2019re at in the funnel.<\/p>\n Here are a few key ways AI chatbots can benefit sales teams:<\/p>\n Pro tip<\/strong>: If you set qualifying criteria in the chatbot flow, like company size, business email, etc., you could have the chatbot drop your calendar for qualifying visitors and give them the opportunity to book time with you.<\/p>\n Pro tip:<\/strong> I\u2019ve seen AE\u2019s create hyper-personalized chat experiences for their \u201cwhite whale\u201d accounts, including a chatbot that presents a video of the AE addressing that company by name and introducing themselves.<\/p>\n AI chatbots are a great resource for every business, and while I highlighted a few different teams above that could benefit, I thought it would be worth visiting a few additional industry-specific use cases.<\/p>\n Service and Hospitality Industries<\/strong><\/p>\n Pro tip:<\/strong> If Millennials are part of your target demographic, you should really have a chatbot option. Data shows millennials overwhelmingly prefer a chatbot option<\/a> for contacting businesses, booking services, etc. (I\u2019m a millennial, and trust me \u2014 we don\u2019t pick up the phone unless it\u2019s an emergency.)<\/p>\n Recruiters<\/strong><\/p>\n Realty\/Property Management<\/strong><\/p>\n <\/a> <\/p>\n Now that I\u2019ve covered the major differences and use cases between AI agents and chatbots, how do you decide which one is the best option?<\/p>\n In general, I think it comes down to a few key factors like budget, bandwidth, your specific use case, and your desired outcome.<\/p>\n Reasons to go with a chatbot:<\/strong><\/p>\n Pro tip:<\/strong> In my time working for a leading chatbot company, I\u2019ve found that many companies will throw \u201cAI\u201d in front of their rule-based chatbot and call it an AI chatbot.<\/p>\n But just because a chatbot connects to your knowledge center and can surface help articles does not make it an AI chatbot. If you truly want an AI chatbot experience, look for a tool that uses NLP, can respond and adapt to changes in language, and does not rely solely on rule-based decision trees.<\/p>\n Reasons to go with an agent:<\/strong><\/p>\n No matter which option you choose, here are a few final recommendations to consider before you sign the dotted line:<\/p>\n\n
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AI Agents Versus Chatbots<\/h2>\n
What is a chatbot?<\/h3>\n
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<\/p>\nWhat is an AI agent?<\/h3>\n
The Differences Between AI Chatbots and AI Agents<\/h2>\n
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Interacting With Customers and Helping With Requests<\/h3>\n
What AI Chatbots Can Do<\/strong><\/h4>\n
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What AI Agents Can Do<\/strong><\/h4>\n
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Example of How AI Chatbots and AI Agents Interact Differently With Customers<\/strong><\/h4>\n
Learning and Adapting<\/strong><\/h3>\n
How AI Chatbots Learn<\/strong><\/h4>\n
How AI Agents Learn<\/strong><\/h4>\n
Level of Knowledge<\/strong><\/h3>\n
AI Agent Use Cases<\/h2>\n
Use Case #1: Customer Service<\/h3>\n
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Use Case #2: Ecommerce<\/h3>\n
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Use Case #3: B2B Account Management\/Customer Success<\/h3>\n
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Use Case #4: Marketing<\/h3>\n
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AI Chatbot Use Cases<\/h2>\n
Use Case #1: Customer Service<\/h3>\n
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Use Case #2: Product Setup\/Onboarding<\/h3>\n
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Use Case #3: B2B Sales Teams<\/h3>\n
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Use Case #4: Industry-Specific Use Cases<\/h3>\n
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How to Choose Between AI Agents and Chatbots<\/h2>\n
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