In the previous article, AI's Coming of Age(nts), I highlighted the five most popular categories of AI agents. So the next question now is,...

"How can we use them in the sales process to increase revenue (IR), reduce cost (RC), or expand market share (EM)?"

Well, let’s go through each of the 5 types of agents, with an example, an application, and data access points the agent will need to take action.

1. Reactive Agent:

Example: A virtual assistant who schedules appointments.

How it works: The assistant responds to commands like, "Schedule a meeting for next Tuesday at 2 PM." It checks the salesperson's calendar and suggests available times, with no human involvement.

Data Access: Salesperson’s calendar and email account.

2. Memory-based Agent:

Example: A social media listening tool that monitors customer conversations.

How it works: The tool tracks brand or product mentions on social media platforms. Sales teams can use this data to identify leads, address customer concerns, and refine marketing strategies. (Note: These ‘signals’ can also help improve existing products and processes.)

Data Access: Social Media accounts or reservoir.

3. Goal-based Agent:

Example: A sales forecasting model that predicts future revenue.

How it works: The model analyzes historical sales data, market trends, and economic indicators to forecast sales revenue for a specific period. Sales leaders can use this information to set realistic goals and allocate resources.

Data Access: CRM or Revenue Intelligence Platform (RIP), Market research site(s)/reports, Nasdaq, S&P, etc.

4. Utility-based Agent:

Example: A product recommendation engine for upsells or cross-sells; like Amazon.

How it works: The engine analyzes customer purchase history, browsing behavior, and product attributes to suggest additional products. This can increase average order value and improve customer satisfaction.

Data Access: Inventory and Purchase History; ERP system, Web analytics.

5. Learning Agent:

Example: A lead-scoring model that prioritizes leads based on their likelihood to convert.

How it works: The model analyzes lead data (i.e., demographics, company size, and engagement with marketing materials) to assign a score. Sales teams can then focus on higher-scoring leads to improve conversion rates.

Data Access: CRM, Lead Gen Systems, Content Management System, and Website Analytics.

So far, I’ve highlighted five types of AI agents and how each can enhance the sales process by enhancing specific sales issues or functions.

This is where we are today!

Yet, the future of AI Agents lies in the NEXT step of evolutionary AI; their ability to be collaborative!

Next: Prospecting AI Agents (Part 4)

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