How to Implement Chatbots for Business in 2025

Enterprise Chatbots

Enterprise chatbots are designed for large organizations that require deep integrations, scalability, and governance across complex business systems.

What distinguishes an enterprise chatbot?

• Enterprises tend to exist in many legal jurisdictions and handle more personal data, so their bots tends to have more stringent chatbot security requirements 
• Enterprises often use bespoke internal platforms (for communication, analytics, etc.), which requires custom integrations to connect with a bot
• A company with more departments will have more use cases for a chatbot

These key differences mean that an enterprise chatbot needs serious security and high flexibility (i.e. to build custom integrations and scale). They'll need to abide by GDPR, SOC 2, and any other relevant data protection frameworks.

Midmarket Chatbots

Midmarket chatbots are flexible chatbots that automate customer interactions and workflows while integrating with essential business tools without enterprise-level complexity.

A step away from enterprise chatbots, midmarket chatbots don't always need custom integrations (since many chatbot platforms come with pre-built integrations to major platforms).

Security is still integral to midmarket chatbots, and the ability to scale a chatbot as the business grows is especially important.

Small Business Chatbots

Small business chatbots are often chatbots that adapt to growing business needs without requiring extensive technical resources. 

Small businesses often use chatbots to scale their operations beyond what their small team can accomplish alone. Common use cases tend to include AI lead generation and customer service, since these tasks traditionally take a lot of human labor to scale. 

Because of their smaller scale, chatbots used by small businesses will always be cheaper than midmarket or enterprise chatbots. 

Customer Support

You’re probably (and unfortunately) unfamiliar with the terrible customer service bots of years past. You can breathe a sigh of relief, because those brittle bots are a solution of the past.

Now, the best teams use AI for CX, instead of outdated bots.

These days, customer support chatbots are powered by LLMs. So instead of repeating the same circular flow when it gets stuck on a request, an LLM chatbot will understand nearly any request (typos and all).

These types of chatbots don’t just hand off issues — they solve them. They can pull answers from a knowledge base, guide users through troubleshooting, and only escalate when necessary.

Customer support bots are often used as an AI ticketing tool, triaging incoming requests and autonomously deciding how to prioritize them. At scale, they can source entire AI contact centers for enterprise customer support needs.

Lead Generation

Most lead generation tasks will be AI lead generation in the next few years – 40% of businesses back in 2023 planned to increase their AI investment in lead gen.

Most chatbots you interact with are lead generation bots, even if you might not know it.

A lead gen bot might offer price estimates after collecting information about your project requirements. Or it might act as a consulting service, suggesting tailored solutions while qualifying the lead at the same time.

Lead gen is the most popular use case in an AI-enhanced sales funnel, given the large number of leads that need to be created for a healthy pipeline. Luckily, these bots can qualify leads too – so no funnel will fill up with unqualified candidates.

Sales

There are endless ways to use AI for sales – the only limit is your creativity.

A chatbot in sales can handle tasks like qualifying leads, answering product questions, and scheduling demos without needing a human rep to step in right away.

Sales chatbots can provide instant responses to common sales inquiries, like pricing, product comparisons, or feature breakdowns. 

If a lead isn’t ready to buy, the chatbot can follow up later with additional details or book a meeting with a salesperson. 

With the rise of online shopping, sales bots are especially popular in the e-commerce industry. E-commerce chatbots or retail chatbots are often little digital assistants for customers browsing online – they can recommend products or even offer personalized discounts. 

Marketing

Chatbots are a favorite amongst marketing teams, since many of their tasks can be scaled at will for better business results.

Marketing chatbots are diverse in task and ability. They can:

• Recommend products
• Host contests or giveaways
• Manage social media
• Act as digital BDRs
• Conduct personalized email campaigns

On the customer side, conversational marketing is a customer engagement strategy that uses real-time, personalized conversations to move prospects through the buying journey. 

By chatting back and forth – similar to a lead gen use case – a marketing chatbot can qualify potential customers, answer product-related questions, and guide them toward relevant content or offers.

Finance 

Errors in financial tasks can be deadly. Chatbots can prevent human error.

Chatbots for finance can streamline expense tracking, simplify tax calculations, send payment reminders, help predict financial forecasting, and keep the company up-to-date on regulatory updates.

The possible use cases get more specific across the wide domain of finance: 

• Insurance chatbots can assist with claims processing, like the Waiver Group’s Waiverlyn
• Accounting bots can process invoices and categorization expenses
• And at big institutions, banking chatbots can pre-screen loans and detect patterns of fraud 

Human Resources

Plenty of HR reps are sick of getting the same questions over and over again. From enterprises with HR departments to start-ups with no HR manager, HR chatbots can step in and level up the employee experience.

HR chatbots can be simple: They answer FAQs about policies and they take vacation requests. 

HR chatbots can also be complex: They can learn how often enterprise employees call in sick and then create a predictive schedule to avoid last-minute scrambling. 

Its range just depends on a company’s needs.

Note: Our business uses a lot of chatbots, but the most beloved is our HR bot named Harry Botter. It answers questions about benefits, insurance, time off, the employee directory, or other HR-related queries.

IT and Tech Support

IT chatbots are another common use case for businesses. Plenty of troubleshooting follows the same steps, so it makes sense to offload the most common issues to a bot.

For employees, IT support chatbots can reset passwords, onboard users to new software, or troubleshoot. 

But they can also help with overall IT strategy – they can simplify the distribution and management of IT resources and ensure proper safety and compliance.

Chatbots work 24/7 without downtime

Whether answering customer inquiries, handling internal requests, or processing transactions, chatbots ensure that businesses never miss an interaction, no matter the time zone.

Chatbots automate repetitive tasks at scale

From scheduling meetings to managing data entry, chatbots handle high-volume, time-consuming processes, freeing up employees for more strategic work.

Chatbots improve response times and efficiency

Instead of waiting on hold or for an email reply, users get instant answers, automated workflows, and seamless self-service options across multiple channels.

Chatbots capture valuable business insights

Chatbots track interactions, analyze customer behavior, identify trends, and optimize business processes, leading to smarter decision-making.

Chatbots enhance customer and employee experience

By streamlining workflows, reducing friction, and offering personalized, AI-driven interactions, chatbots make both internal operations and customer engagement smoother.

Rule-Based Chatbots

If you’ve ever had a frustrating experience with a chatbot, it was likely a static, rule-based chatbot.

These bots follow a simple procedure and are typically just FAQ bots. They’re given a list of questions, a list of answers, and unleashed to the world. 

While old-school, these bots may still have their time and place. Although I can’t name one off the top of my head. I’ll be honest, they suck.

LLM-Powered Chatbots

The perfect middle option: an LLM agent can respond to users with flexibility to answer a wide variety of user questions (even if they use slang, misspell words, or ask in a way that your chatbot has never seen before).

Because they’re powered by large language models, these AI chatbots can engage in complex conversations with your users. One you’ve probably used before is ChatGPT.

If all you want is a simple business bot, the LLM chatbot is the best option. They’re used by enterprises and small businesses alike for plenty of conversational tasks. 

AI Agents

The best of the bunch, an AI agent can go far beyond the typical capabilities of a chatbot. Powered by LLMs, it can take advanced actions in order to better inform its users. For example, an AI agent could:

• Pull up current stock availability 
• Suggest personalized products
• Refer back to previous conversations with the user
• Change a password
• Troubleshoot a technical issue
• Book a meeting with a sales representative 

Design for decision-making, not just conversation

A chatbot can do more than chat. Instead of just selling a product to a website visitor, it should qualify leads and book meetings. 

If it’s handling internal workflows, it should automate approvals, assign tasks, or update records, not just provide answers.

If you’re paying for a software, make the most out of it. Any platform worth its salt will have autonomous capabilities, so you should maximize their utility for your flows.

Make partial answers useful

If your chatbot can’t gather all the information it needs, it should still be able to provide a useful experience for the user.

For example, if a chatbot for an insurance company is asked, “What’s my monthly premium?” but doesn’t have the user’s full details, it could respond with:

"Based on similar policies, your estimated range is $80–$120 per month. To get a precise quote, I’ll just need your age and coverage preferences—want to continue?"

Similarly, a support chatbot handling a complex IT issue might not have an immediate fix but can log the issue, provide troubleshooting steps, and set expectations on response time, keeping the user informed rather than frustrated.

A chatbot that gives partial but useful answers keeps users engaged, reduces frustration, and ensures conversations remain productive.

Prioritize seamless human escalation

Automation is powerful, but some requests need a human touch — and when that happens, the handoff should be instant and seamless.

For example, if a B2B chatbot is handling an enterprise software issue, it shouldn’t just say, “I’m escalating this to support.” Instead, it should attach the full chat history, categorize the issue, and route it to the right specialist, so the user doesn’t have to repeat themselves.

Even better, the chatbot could suggest available meeting slots, allowing the user to book a live support session directly from chat instead of waiting for an email response. A smooth escalation reduces frustration, speeds up resolution, and keeps interactions productive.

Enable multi-modal interactions

Multi-modal interactions – like video, image, and voice – can create richer experiences and accommodate diverse user preferences. A multimodal business chatbot can:

• Handle voice commands for hands-free interactions in customer service or internal support
• Process document uploads for tasks like submitting forms, verifying identity, or handling applications
• Recognize images for product identification, barcode scanning, or troubleshooting issues
• Display interactive visuals to showcase analytics, reports, or step-by-step workflows
• Show instructional videos to guide users through onboarding, troubleshooting, or setup processes

The world is quickly becoming more and more multimodal. Build your chatbot to keep up with how your users communicate.

A finance bot that saved €530,000

VR Bank Südpfalz, a historic financial institution, leveraged AI chatbots to streamline loan applications and retirement planning, achieving €530,000 in cost savings and a 56% containment rate.

Handling over 3,000 real estate loan applications annually, the bank faced high processing costs and regulatory complexity. To address this, they introduced AVA, a chatbot that gathers applicant details, integrates with their CRM, and provides real-time assistance 24/7.

Following its success, VR Bank expanded AVA’s capabilities to retirement planning, allowing customers to track their investments and optimize savings. This automation saved an average of €53 per conversation, significantly cutting costs while enhancing customer experience.

Read the full case study.

A customer support bot with 0 hallucinations

RubyLabs, the company behind Able, a personalized health coaching platform, drastically reduced customer support ticket volumes by automating responses and subscription management. 

In the health sector, accuracy matters. A key success factor for RubyLabs was 0 AI hallucinations across 100,000 conversations, ensuring precise information in high-stakes health and payment-related interactions.

After introducing an AI-powered support system, RubyLabs saw a 65% reduction in manual tickets and saved $50,000 annually – all while maintaining accurate and reliable responses.

With automation handling routine inquiries, the company improved response times, eliminated the need for Tier 1 support, and streamlined operations across its entire product line.

Read the full case study.

A lead gen and booking chatbot that increased leads by 25%

Waiver Group needed a way to book more consultations without adding more work, so they launched Waiverlyn, an AI chatbot to answer questions, qualify leads, and automatically schedule meetings.

Instead of waiting for someone to reply to a contact form, potential clients could chat with Waiverlyn, get the info they needed, and book a consultation on the spot. 

The bot even fills out calendar invites, updates sales spreadsheets, and sends follow-up emails, making sure nothing slips through the cracks.

The impact was immediate: consultations shot up 25%, visitor engagement jumped 9x, and the chatbot paid for itself in just three weeks. It also helped filter out low-quality leads, so the sales team could focus on serious prospects instead of chasing down dead ends.

Now, Waiver Group is looking at how to make it even more powerful, turning a simple chatbot into a full-blown sales assistant.

Read the full case study.

1. Define use cases and objectives

2. Pick a platform and development approach

Who is going to build your chatbot, and how?

There are two main chatbot development approaches:

• In-house: Your development team builds the chatbot
• Outsource: You partner with an AI agency or freelancer to build the chatbot 

With low-code platforms, you don’t need a ton of technical expertise to put together a simple business bot. 

If a company has technical skills in-house, it can make sense to build in-house. It makes it easier to have a higher level of access to the person (or team) that built the bot. 

If there’s no in-house developer, most companies will opt to use a freelancer or an agency to build their business chatbot. This route means a business can automatically tap into a talent pool that’s familiar and experienced with building and deploying chatbots. 

3. Design the chatbot workflow

You know your chatbot’s goal – how is it going to best accomplish it?

If it’s resolving support requests using Zendesk and a knowledge base, then is it going to attempt to pull an answer from the knowledge base first and only escalate to Zendesk if needed, or log the issue in Zendesk first while searching the knowledge base in parallel to suggest an answer?

The developer will also need to consider:

• How to handle unresolved queries
• When to involve human agents
• What data should be logged
• How to track chatbot effectiveness 

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4. Integrate to platforms, channels, webhooks, and KBs

An AI agent without integrations is just your own version of ChatGPT. An AI agent's purpose is defined by its integrations.

There are many entities you can integrate with an AI agent — nearly infinite options if you use a flexible platform.

These integrations are what allow an AI agent to seamlessly integrate with existing workflows, rather than being an 'extra' with no connectors.

5. Test and reiterate

After building your AI chatbot, the next step is refining it. Testing and iteration are essential for success but are often overlooked by builders eager to launch.

Any chatbot platform should offer a simulator within its studio, allowing you to practice interactions with the bot before deployment. This is your first step in testing and a crucial part of fine-tuning your bot during the development process.

Once you’ve finished your initial build, you can share a sample version of your agent with friends or colleagues using a URL. Testing it this way helps ensure its functionality is ready before deployment.

As you test, you’ll be able to tweak your chatbot for the better. And be prepared: this process will continue even after you deploy your chatbot. It’s normal.

6. Monitor and update

Your chatbot project doesn’t end after deployment—in fact, deployment is just the beginning. Once it’s out in the world, your chatbot starts working for you.

A quality platform will offer ongoing chatbot analytics, providing insights into when people are using your agent, the topics they’re asking about, and the platforms they prefer to engage with.

1. Botpress

Botpress is a versatile AI chatbot platform, endlessly customizable and extensible. It’s always up-to-date with the latest LLM engines, ensuring its chatbots and AI agents are always powered by the most recent technology.

Botpress offers a visual drag-and-drop canvas for developers, automatic translations for over 100 languages, and endless customizability. 

The platform includes pre-built integrations to the most popular software and channels, but allows developers to connect their bot to any knowledge base or internal platform. This endless extensibility makes Botpress an excellent platform for professional, enterprise-grade AI agents. 

The company has over 750,000 active bots in production as of June 2024, processing over 1 billion messages. Their AI chatbots span customer service, HR, IT, governments, tech, and more.

Botpress comes with a thriving community. If you’re looking for a developer to build your chatbot, Botpress offers an extensive partner network of expert builders. And their active Discord community of 25,000 bot-builders provides 24/7 access to other developers. 

Learning the ins and outs of the platform is made simple with their YouTube video tutorials and by their  expert-curated courses at Botpress Academy.

2. IBM Watson Assistant

IBM watsonx Assistant is a conversational AI platform designed to build virtual and voice assistants for customer service applications. 

It leverages artificial intelligence and large language models to learn from customer interactions, aiming to improve issue resolution efficiency and reduce customer wait times.

Unlike traditional chatbots, watsonx Assistant can query knowledge bases, seek clarifications, or escalate to a human agent as needed. It’s adaptable for use in various environments, including cloud and on-premises setups.

The platform also offers voice capabilities, allowing integration into telephonic customer support systems. IBM promotes watsonx Assistant as a tool for improving the efficiency and effectiveness of customer service interactions.

3. Intercom

Intercom is a customer service platform that combines AI-powered automation with human support to enhance customer interactions. It provides businesses with a unified inbox, allowing teams to manage customer messages across websites, mobile apps, and email in one place. Intercom’s AI capabilities include Fin AI Agent, a chatbot designed to resolve common inquiries instantly, reducing support volume and response times.

The platform is known for its customizable messaging tools, enabling businesses to create targeted customer engagement campaigns. It also integrates seamlessly with popular CRM, helpdesk, and analytics tools, making it a flexible solution for scaling customer support.

4. ManyChat

ManyChat is a chat marketing platform that enables businesses to automate interactive conversations across Instagram Direct Messages, Facebook Messenger, WhatsApp, and SMS.

Founded in 2015, ManyChat focuses on simplifying customer engagement, lead generation, and sales automation without requiring coding expertise. The platform offers a visual interface for building chatbots, allowing users to broadcast messages, schedule communications, and automate responses based on keywords. 

ManyChat supports integration with various third-party applications, enhancing its versatility for marketing, e-commerce, and customer support functions. 

5. Chatfuel

Chatfuel is a no-code chatbot platform that enables businesses to automate customer interactions across messaging applications like Facebook Messenger, Instagram, and WhatsApp. 

Founded in 2015 and headquartered in San Francisco, Chatfuel empowers users to create AI-driven chatbots without any coding expertise.

6. Yellow.ai

Yellow.ai is an enterprise-grade AI chatbot platform designed to enhance both customer and employee conversational experiences. It specializes in customer service functions, including retail, BFSI, and healthcare. 

Yellow.ai allows for personalized interactions integrated across multiple channels, including websites, apps, and various messaging channels. 

Yellow.ai hosts a no-code/low-code bot builder, enabling quick deployment of AI chatbots and agents without extensive coding knowledge. The deployment time is further improved with Yellow.ai’s prebuilt templates and integrations.

The platform can support conversations in over 100 languages, and they offer services like campaign management and autonomous customer interaction.

The platform features DynamicNLP™, used to facilitate high intent accuracy and multilingual fluency – this can significantly reduce deployment time and enhance scalability for chatbot builders.

7. Kore.ai

Kore.ai is a leading conversational AI platform that enables enterprises to design, build, test, and deploy AI-powered virtual assistants and chatbots across various industries, including banking, healthcare, and retail.

Founded in 2013 by CEO Raj Koneru, the company is headquartered in Orlando, Florida, and employs approximately 1,000 people.

The platform offers a no-code development environment, allowing businesses to create and manage virtual assistants without extensive programming knowledge.

It provides pre-built templates and integrations, facilitating seamless deployment across multiple digital and voice channels. Kore.ai's solutions are designed to enhance customer and employee experiences by automating interactions and streamlining processes.