AI Sales Agents | Use Cases, Industries, and Top Tools

Lead generation

Harvard Business Review found that using AI in sales can increase leads by 50%.

Acting as 24/7 'lead machines,' lead generation chatbots use conversational AI and agentic workflows to engage prospects, qualify them, and integrate their information into the sales process. 

So, on an online clothing store’s website, these agents can:

• Greet visitors with a friendly message like, ‘Looking for something specific? Let me help!’
• Capture customer details such as email addresses, favourite styles, or sizes.
• Use this information to personalize the shopping experience, creating a smoother path to conversion.

Personalize recommendations

Like a personal shopper, AI sales agents offer tailored suggestions based on what a customer has browsed, bought, or liked before. 

The clothing store’s website might suggest outfits based on what a customer has previously browsed or purchased, and say, ‘I noticed you liked the red jacket, how about pairing it with these black boots?’

They can chat naturally, guide customers to the right products, and adjust recommendations as the conversation goes on. 

Marcus Chan, the President and Founder of Venli Consulting Group, sums it up perfectly: "AI gives guidance that’s so personal and precise, we’re always presenting the most relevant solutions — no pushing required.”

Answer questions

Handling customer inquiries is another area where AI sales agents come in handy. They immediately respond to customer inquiries, offering quick and accurate information about products, services, or policies. 

An AI sales agent might:

• Answer questions like, ‘Do you have this dress in a size 6?’ or ‘How long does shipping take?’
• Check stock availability and respond with, ‘Yes, the dress is available in size 6.’
• Provide instant updates on shipping policies, saying, ‘Standard shipping takes 3-5 business days.’

Follow up 

AI agents send reminders about abandoned carts, promotional offers, or scheduled meetings to re-engage potential customers.

If a customer leaves items in their cart, the AI can send a friendly follow-up message like, ‘You left your favourite jacket behind, complete your purchase now and get 10% off!’ 

Close deals

They guide customers through the sales funnel, from initial inquiry to final purchase, reducing friction in the buying process.

For example, after a customer selects items and asks for assistance, the agent might say, ‘Great choices! Let me walk you through a quick checkout process.’ 

Cost of starter AI solutions

If you want to experiment with an AI agent for your small business and just need a no-fuss way to dip your toes into AI, you can find basic plans for free or scale up to $30-$90 dollars a month. 

These starter options typically offer basic automations and light analytics. They’re a great way to try out AI without spending much.

Cost of mid-range AI solutions

Now if you're looking for something a bit more advanced, mid-tier AI plans typically range from $200 to $1,000 per month, depending on the features included. 

These plans usually support more advanced use cases like custom workflows, deeper analytics, integrations with third-party tools, and higher usage limits.

Cost of enterprise AI solutions

On the enterprise side, pricing generally starts at $15,000 per year and goes up based on scale and customization needs. 

These plans often include things like advanced analytics, audit logs, custom SLAs, and hands-on support from technical specialists.

1. Define Clear Objectives

The first step in building an AI sales agent is deciding exactly what it’s meant to do.

A clear scope acts as the blueprint for your agent: shaping what capabilities it needs and how it fits into the sales process.

AI sales agents can take on a wide range of roles, depending on the sales team’s structure and goals. Some examples include:

• An outbound assistant who sends personalized messages via email or messaging platforms like WhatsApp, follows up automatically, and nurtures prospects until they’re ready to talk to a rep
• A sales rep copilot that surfaces relevant deal insights, tracks prospect engagement, recommends next steps, and automates note-taking during calls or chats
• A pipeline manager who monitors deal health, flags at-risk opportunities, and syncs with CRMs to ensure no lead slips through the cracks

In more complex environments, a single AI agent might do a mix of these tasks, acting as both a customer-facing assistant and a behind-the-scenes sales enabler.

2. Choose a Platform

With your goals in place, the next step is picking a platform that can actually support them.

Some important questions to ask when you're exploring different platforms include: 

• Does this platform allow me to automate tasks?
• Can this platform connect with prospects on multiple channels?
• Does this platform offer built in analytic tools? 
• What third-party integrations can this platform integrate with?

Lastly, make sure to pick a platform that fits both today’s needs and tomorrow’s growth. This allows you to scale without having to start over as your sales processes evolve.

3. Build the Agent

Once the platform is in place, it's time to build your AI sales agent.

Make sure it’s trained on real-world sales data and situations, such as:

• Lead qualification conversations across different personas
• Common objections and rebuttals for specific products
• Email and chat-based follow-up sequences tailored to deal stages
• Product positioning and competitive differentiators

Connect the agent with knowledge bases, sales playbooks, and objection-handling libraries. This ensures that the agent responds accurately in real-world sales conversations.

The better the agent understands the product and the customer journey, the more helpful it will be.

4. Integrate with Core Sales Tools

For an AI sales agent to be truly effective, it must integrate with your sales tech stack.

Key integrations include CRMs and sales enablement platforms.

These connections ensure the agent can operate fluidly within existing workflows, surfacing the right information at the right moment.

5. Test and Refine

Sales conversations aren’t always predictable and errors can cost deals.

Run simulations and edge-case scenarios to stress test the AI’s performance.

This might include:

• Responding to unusual objections or urgent budget-related questions.
• Handling long sales cycles with multiple stakeholders and decision points.
• Managing large-volume outreach during campaign launches.

Use A/B testing and sandbox environments to refine tone and timing.

6. Deploy and Monitor Performance

Once live, monitor the bot's performance using analytics.

Some Key Metrics to Monitor Post-Launch:

• Most common intents
• High dropout nodes
• Repeated phrases that hit fallbacks
• Time per session / success rates

Pro Tip: Create a "Bot Improvement Log". 

I recommend reviewing this log biweekly. Track updates and what impact they had. Retrain your intent recognition as new patterns emerge. 

1. Botpress

Botpress is a powerful, flexible platform for building AI-powered sales agents and LLM-driven solutions. Designed for developers and businesses, it combines adaptability with advanced features like natural language understanding (NLU), multilingual capabilities, and omnichannel support.

With Botpress, users can create highly customizable and scalable AI sales agents tailored to diverse applications, including lead generation, sales automation, and customer engagement.

These agents seamlessly integrate with tools such as CRMs, marketing platforms, and ticketing systems, making them an ideal choice for businesses aiming to enhance their sales processes.

2. HubSpot 

HubSpot provides a comprehensive CRM platform with integrated AI sales agents designed for small to medium-sized businesses. These agents excel at lead qualification, meeting scheduling, and automating routine tasks, all while seamlessly integrating with the CRM to streamline sales workflows.

3. Zoho SalesIQ

Zoho SalesIQ is tailored for lead generation and customer engagement. It features real-time visitor tracking, automated chat responses, and robust analytics to optimize sales strategies.

4. Freshworks Freddy AI

Developed by Freshworks, Freddy AI assists with overall sales processes. It provides AI-driven insights, automates repetitive tasks, and integrates with various communication channels to enhance customer interactions.

5. Salesforce Einstein

Salesforce Einstein is an AI-powered platform offering enterprise-level automation. It delivers predictive analytics, personalized customer experiences, and deep integration with Salesforce CRM for comprehensive sales management.

1. What is the difference between AI voice agents and AI sales agents?

The difference between AI voice agents and AI sales agents is that AI voice agents communicate through spoken language over phones or smart devices using speech recognition and synthesis, while AI sales agents typically operate via chat interfaces and focus on product recommendations or conversion in text-based channels. 

2. How do AI agents balance automation with a personalized customer experience?

AI agents balance automation with personalization by using user data – like purchase history, location, browsing behavior, or CRM profiles – to craft context-aware responses. 

3. What kinds of AI models are typically used to power these agents?

AI agents are powered by natural language processing (NLP) engines, large language models (LLMs), and sometimes reinforcement learning or decision tree models to drive context handling and personalization within workflows.

4. What are common benchmarks for conversion rate improvements using AI sales agents?

Conversion rate improvements from AI sales agents commonly range between 15-30%, but it depends on the industry. Retail, SaaS, and travel sectors often see strong gains when agents proactively engage leads and reduce drop-off.

5. How often do AI sales agents need to be updated?

AI sales agents should be updated at least monthly to reflect changes in products, promotions, FAQs, and customer behavior patterns.