AI is nothing new — it’s been around for decades. But until very recently, AI tools have been cost-prohibitive, requiring a significant financial investment that’s beyond the reach of most businesses.

Not anymore. AI is everywhere. It’s accessible, affordable, and already embedded in the tools that most companies use every day. Approximately 61% of marketers are using AI in their operations, according to a 2023 report from Influencer Marketing Hub. 

For B2B marketers, the arrival of everyday AI couldn’t have come at a better time. Faced with economic uncertainty and ever-tightening budgets, marketers have been under constant pressure to do more with less. Now, AI presents a real opportunity to elevate B2B demand generation while driving operational efficiency. 

Let’s cover a few of the key ways AI is posed to play a pivotal role in the future of demand generation.

Data is the lifeblood of successful demand generation strategies. Knowing precisely who you are targeting, where they spend time, what they care about, how they engage with content, and how to reach them are the kinds of actionable insights marketers need to execute engaging, personalized campaigns. 

These insights are the key components of account intelligence, the gold standard of high-quality marketing data. But achieving account intelligence is not easy. Teams need to collect and aggregate many types of data, from demographic and firmographic data to timely intent data that shows exactly how your prospects are engaging with content on any given topic at any given moment. 

AI tools make it possible to quickly and accurately collect, clean, and resolve discrepancies in this wide range of B2B marketing data. Since account intelligence data is aggregated from many disparate sources in different formats, it needs to be standardized and checked for errors before teams can access and use it. While that once required time-intensive manual processes, AI is now able to streamline and automate many of these steps. 

The result? More accurate account intelligence data that marketing teams can use to segment and sort their audience, reaching them with more personalized content and messaging that drives campaign results. 

In B2B marketing, predictive analytics uses past data — like the information in your CRM — and applies statistics and machine learning to build models that generate predictions about how prospects and accounts will behave as they move through their buying journey. These AI-powered models analyze multiple factors, including demographics, intent data, and digital behaviors, to arrive at these predictions. 

Marketers can use this information to improve their targeting strategies for demand generation campaigns. For example, predictive analytics can be used to look at existing customers and calculate their customer lifetime value (CLV), and identify similar prospects that have the potential to become your most profitable customers. That allows marketers to focus their efforts on high-value prospects, targeting them with tailored content and messaging, rather than spreading campaign resources too thin.

Marketers can also use predictive analytics to monitor and improve campaign performance in real-time. For instance, campaign managers can use predictive analytics to look at data from the first day or two of a campaign, and begin optimizing based on predictions of future outcomes and return on ad spend (ROAS) — instead of waiting weeks for more performance data to come in. 

B2B marketing and sales teams use lead scoring to quantify how “sales ready” a lead is at any given moment in their buying journey. Traditionally, lead scoring models are developed by assigning a certain number of points to specific demographic or behavioral data — like downloading a piece of content or visiting a pricing page. Then, when a lead accumulates enough points to cross a predetermined threshold, they are handed off to the sales team for one-on-one conversations.

When AI is incorporated into lead scoring, everything becomes more automated and objective. Rather than relying on subjective scores and static criteria, predictive AI models can analyze the behaviors that lead up to conversions and begin to identify nuanced patterns. Those patterns can be used to score leads dynamically, based on each lead’s interwoven behaviors, rather than treating each download or webpage visit as an isolated incident.

AI-powered lead scoring also helps identify the leads most likely to convert, so they can be prioritized accordingly by your sales team for ongoing conversations. Especially on busy teams with finite resources, this data-driven prioritization helps improve ROI and drive conversions across campaigns. 

The ultimate goals of gathering better data and segmenting audiences to a granular level are to deliver more personalized and relevant content and messages. And now, generative AI tools like ChatGPT and DALL-E have transformed the way B2B marketing teams create content. 

While most marketing leaders agree these tools still need human oversight to generate quality, on-brand assets, they give teams the transformative ability to go from a blank page to a first draft or early design prototype in moments. This allows teams to explore more concepts, ideate and brainstorm quickly, and spend more time on strategic and creative work rather than manual processes.

As more marketing platforms embed generative AI into their existing toolsets, it will become easier and faster for teams to customize content across campaigns. For example, email marketers are now able to generate an email draft directly in their marketing automation platform. And content marketers can customize an ebook cover with industry-specific imagery within their publishing tool. 

Here at DemandScience, we’ve been integrating AI into our suite of demand generation solutions for years. Recently, our Chief Strategy Officer Bill Harrigan sat down with Cyndy Sandor to discuss how AI is transforming demand generation for B2B teams — listen in to learn why Bill believes these first few applications are only the beginning.