AI Lead Generation for Commercial Real Estate

Artificial intelligence in lead generation is becoming increasingly common, as it can help vendors and suppliers sort through thousands of prospects, analyzing and reaching out to them at scale. AI prospecting and lead gen are exciting to explore in CRE because they enable small to mid-size businesses to launch campaigns that would normally be out of their staffing scope. 

However, if you’re thinking of using AI in your commercial real estate lead generation strategy, proceed with caution.

AI lead gen tools don’t yet understand the nuances of the CRE industry. Say your commercial cleaning business has experience working in CRE. You know that cleaning for a healthcare asset class dramatically differs from cleaning an office building. Those nuances may be lost on an AI tool, resulting in wasted time or resources pursuing an irrelevant lead. AI tools do have costs associated with how many credits are used generating results, so the costs can quickly add up!

For your B2B CRE business, there are a few best practices for using AI lead generation tools. 

What Is AI Lead Generation?

An artificial-intelligence-powered lead generation tool uses AI to assist your business in finding prospects, sorting or segmenting them, and crafting automations to engage with them. 

In a separate post, we define the difference between lead generation and prospecting. In short, all prospects start as leads, but not all leads become prospects. Prospects have been vetted against some sort of fit criteria, such as asset class, location, functional area, etc. 

Effectively, there’s an AI tool to enhance every step of the business development, sales, and marketing processes. What really sets AI lead generation apart is the scale at which it can process lead lists. AI lead generation can quickly rank, score, and even provide information about the leads themselves for marketing teams to develop personalized messages that are more likely to secure a customer. 

This shakes up prospecting a lot. The job shifts from manually qualifying leads to defining the criteria an AI assistant uses to score them, and because AI can handle that scoring at scale, the line between the marketing and sales sides of the pipeline starts to blur. AI will make mistakes, misinterpret your prompt, or not fully understand what a prospect does. Human verification is still essential, especially in technical or specialized fields.

Types of AI Lead Generation Tools

AI lead generation tools cover one or more of the following:

Data/contact AI generation

AI data and contact generation is an agent that combs the web for leads. The process is generally picking a data source like LinkedIn, Google Maps, or a lead list, and having the agent automatically gather contact information. You set the qualifications for a viable lead, and the AI finds them for you. 

An AI tool that finds contacts for you is probably the most expensive use of AI in terms of the sheer amount of credits needed for the agent to find, sort, and qualify potentially thousands of leads. The wider the net that you cast, the more time and credits you’ll need to spend to find good leads.

If you don't have access to lead lists — but you do have a niche data source to point the AI at, or a larger marketing budget — generating AI contacts can be a viable option. 

AI data enrichment

Data enrichment is proving to be an effective use of artificial-intelligence tools. Instead of having AI find leads from scratch, you give the agent a lead list that already has contacts and business information. You provide the agent with instructions on how to improve the data, such as verifying contact information or checking if people’s employment is current. The result is that old data gets updated and enriched to be more relevant to your business goals.

Because you control the data that the AI sorts through, an AI agent can be a cost-effective way to revitalize outdated or incomplete data without your marketing team spending hours verifying contact data.  

Lead scoring with AI

Artificial intelligence agents can be used as a scoring or ranking tool for leads. These AI tools analyze lists of potential leads and predict which are the highest quality based on the criteria that you determine. Using AI for scoring is usually most effective when it’s done in conjunction with lead generation and data enrichment, as it’s the next logical step in outreach.

Scoring leads works well if you have established your ideal customer profile (ICP). In other words, if you’re aware of the qualifications for a good lead, such as asset class, job role, or business type, lead scoring adds value to your business development process. The ICP, in short, represents the characteristics of customers who have easily converted, been loyal customers, or made high-value purchases.

For newer businesses, ICP may be more difficult to define until you’ve collected valuable customer data. So, manual scoring may be necessary. The advantage of using these tools (AI scoring) is that as you gather more data, you can update your scoring criteria. 

AI outreach 

AI outreach involves instructing an agent to collect relevant information about a business, its goals, and anything else that matters to your business. In CRE, an example would be gathering the asset classes a property management company specializes in or finding contacts for multiple decision-makers at a company based on job role or functional area. 

AI outreach can also include automating outbound communication with AI-generated email messaging. CRM tools can already schedule outbound email marketing and automate follow-up messaging, so the real game-changer is personalized outreach at scale. But if you’re generating outreach based on a lead’s goals, we recommend having a step of human verification. AI can misinterpret a contact’s business goals, and that creates a bad first impression. 

Why Generic AI Lead Gen Falls Short for CRE

Generic AI can misinterpret the nuances of commercial real estate. For example, if it conflates residential with commercial real estate, it may provide you with contacts not relevant to B2B. It also may lack a nuanced understanding of the different types of asset classes. As an example, Biscred identifies 24 asset types in CRE. Depending on the model you use, the AI may only have an idea of broad categories like multi-family, office, industrial, or retail. 

Additionally, when finding contact information, a generic AI lacks the CRE context for what different job roles are responsible for. For example, an AI may interpret “asset manager” and “property manager” as the same, but they have very different roles and responsibilities. 

AI lead generation tools lack CRE-specific data filters. They’re built for general use. Your job up from the start is to teach the tool about your business. This can increase your upfront costs. A CRE-specific tool like Biscred can minimize upfront costs.  

AI-Integrated Lead Gen Workflow in CRE (Example) 

Now that we’ve outlined how AI is being used for lead generation, you’ve probably got an idea of where AI could fit into your business’s marketing workflow. What part of lead generation do you need AI for? Here is an example of an AI-integrated workflow. 

1. Define an ICP by CRE-specific criteria (asset class, geographical region, seniority, etc.). 

2. Prompt AI to construct lead lists or sort through existing customer data to identify prospects. 

3. Enrich your data by instructing your AI to score or sort leads based on criteria relevant to your business.

4. Push the enriched data to your CRM with an AI-assisted marketing tool to write and schedule outreach. 

What to Look for in an AI Lead Gen Tool

If generating leads with AI, what matters most is the data you're using and the criteria you're using to instruct an agent. In general, the more data that you want an AI to find or enrich, the more credits it will use, which means more expenses for your business. Biscred's approach combines AI-driven data collection and synthesis with human data analysis. This hybrid model ensures your data is both comprehensive and accurate. AI handles the scale and speed, while human analysis catches nuances that generic tools miss.

Biscred's AI-powered query builder lets you search the way you think. Instead of navigating complex filters, you simply type what you need in plain language. For example, "find me CEOs of multifamily companies in California with email addresses." The tool interprets your request and returns verified, actionable results. Having this natural-language interface built directly into the platform saves your team time in learning yet another tool and translates your business needs into data results without technical overhead.

See how Biscred combines AI and human expertise to deliver CRE-specific data at scale; consider setting up a demo today to search your own prospects and see the results firsthand.