AI-Based Lead Generation Automation

AI-Based Lead Generation Automation


Digital transformation has driven increased innovation in marketing automation software with anticipated market size of about $50 billion by 2027. There are thousands of different categories including social media management, ad management, intent management, content management, contact databases and email marketing.

With AI-based knowledge work automation, we anticipate new solutions that will eliminate many manual tasks still required today. In this article, we outline the anticipated impact of such tools on lead generation.

“Lead generation can involve hundreds of tedious manual tasks. Many of them are of low complexity. This is a sizable opportunity for AI-powered marketing automation.”

Greg Ness, Marketing Executive

The Lead Generation Process

The lead generation process consists of four key steps – search, assessment, segmentation and engagement. It can involve looking at as many as 100,000 contacts as a starting point and then filtering them down to about 25,000 relevant ones. A monthly outreach to 2,000 contacts would result in about 20 leads per month per salesperson when all activities are handled properly.


The initial task is to identify relevant target accounts and potential buyers. This often starts with searching on LinkedIn and using database tools to retrieve contact details and company information.

While these sources of information are available, identifying relevant people is still a challenge. Titles and industries are not at all standardized. Searches are often keyword-based and result in many irrelevant people on the list.

AI, however, can easily scan, interpret and categorize available information about each person. This will lead to a much more accurate contacts database.


Once a database of relevant people is created, the information about people can be augmented with additional details. LinkedIn has introduced more transparency, but it is still useful to determine, for example, if: 1) a developer has any GitHub repository experience; 2) a data scientist has published any articles; and 3) a marketing executive has been a speaker at an industry conference. All of this is typically done manually and, therefore, only for a few people in a database and nearly impossible for every contact.

This is a great place to turn on AI augmentation. Let it collect and interpret additional information from multiple sources, including correlating industry events, analyzing press releases and other public sources to enrich data and build comprehensive people profiles.


After relevant people are identified, it is important to prioritize them based on revenue potential and allocate to different market segments. Micro-segmentation enables proper personalization of communication targeted at each category of buyers. This is effort intensive and error-prone manual process.

AI can handle these costly tasks with ease, including more granular people categorization based on collected data. This enables more individualized communications and results in a higher number of leads generated per month.


Contacts then need to be approached across multiple channels in a systematic fashion including email and LinkedIn. Lead generation systems should be able to pick up existing customers and prospects from the CRM, add competitors and any other companies that should be on “don’t contact” list. Messaging should be customized by micro-segment, which is tiresome activity when done manually. It would be important to quickly interpret the responses of people and handle them whenever possible in an automated way.

An AI-powered platform can easily address these requirements. Texting systems are getting more sophisticated and can fine tune messages, even appearing as authentic outreach. Natural language processing (NLP) tools can help with sentiment analysis and conduct preliminary categorization of interest level based on response. Such a system could also track email opens, clicks on links and track follow-on website interaction behavior. This allows much better tracking of ongoing communications and more accurate intent scoring.

Cost Savings

Typically, companies have sales development representative (SDR) or a junior salesperson dedicated lead generation. Some companies may not have this role and these activities need to be done either by a more senior marketing or business development person. For our business case we assume that an SDR spends 35% of their time on lead generation while the salary of these roles is about $5,000 per month. Related software typically costs about $250 per month per salesperson, a smaller component of the traditional cost calculation. The total cost to company to run the described above lead generation process is about $2,000 per month ($1750 portion of the SDR salary and $250 related software costs).

Taking into consideration all of the discussed automation opportunities we see a sizable opportunity to automate many manual tasks, while increasing the volume of activity and reducing the risk of error. The monthly cost of lead generation could be reduced from $2,000 today to $1,000 in year 1, $500 in year 2 and $250 in year 3. This would make it affordable for any business at any scale.

Business Impact

In addition to cost reduction, AI-powered demand generation promises to increase revenue. We estimate that with a larger contacts database and more personalized messaging we could increase the number of leads per month by a factor of two resulting in 20% increase in the number of business opportunities. Assuming an annual order booking target of $1.2 million per salesperson in the software industry, this 20% increase would mean about $240,000 in additional annual revenue per year from such higher performance outbound lead generation. This annual revenue increase represents 20 times the annual spend on AI-powered outbound lead generation during that year.


The time of AI in lead generation is here and now. Such automation, when integrated properly with other marketing channels (social media engagement, content marketing, intent capture systems), will fundamentally change the rules of engagement. Access to such systems will be a make-or-break scenario for growth-oriented companies. It will enable account-based marketing (ABM) across all potential buyers and provide the most accurate interest level tracking to avoid missing any opportunity on the market.

Marketing Automation Market Size And Forecast