Applying AI to B2B Tech Sales and Demand Gen Marketing

AI is all the rage these days, and with good reason. AI continues to enamor a global audience across countless use cases, and the timing couldn’t be better for B2B sales and marketing pros.

For B2B sales and demand gen marketers, AI promises to scale target account identification and engagement, quickly mine and organize actionable insights, and create a fast lane to pipeline and accelerated revenue growth. These are very exciting times.

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Here are just some of the ways B2B sales and demand gen pros can consider leveraging AI:

1. AI-driven ABM for Top-Funnel Engagement – Scaling Account Lists

B2B marketers know that attracting and engaging top of funnel buyers requires a list of key prospect accounts that fit their ideal customer profile, and relevant content to engage them.

AI can assist in the heavy lift of expanding target account lists using interest and intent signals captured through paid media investments, namely publisher direct and other ABM awareness campaign executions. Deeper insight into buyer stage and even content recommendation for demand gen outreach may be reached by adding in contextual data, if offered by partners.

As B2B tech becomes increasingly competitive, AI will help marketers intelligently expand target accounts and fuel always-on brand-to-demand strategies that drive results.

2. AI to Identify Mid-Funnel Buyers – Next Level Lead Scoring

Once marketers are scaling top of funnel account expansion, it quickly becomes important to engage this group to segment those who are researching information from those who are actively considering a change.

Enriching the AI model along the way with interaction data from outbound calling efforts takes lead scoring to the next level, and helps sales pros prioritize outreach and optimize their time focused on leads who are ready to move to the next stage of the sales process/buyer journey.

Marketers can tackle this with trusted demand gen partners that have long-standing expertise in high quality calling programs and/or deep funnel demand gen offerings that include call confirmation as a final pre-qual step.

The key to success is the calling team’s ability to connect, unambiguously establish the buying stage, and thus maintain high data veracity for the AI model.

3. AI to Convert Bottom-Funnel Leads to Opportunity

According to HubSpot’s 2023 State of AI Report: “71% of B2B sales pros say AI/automation tools have impacted how they plan to sell in 2023.” Which is supported by the sheer volume of (AI-based) sales enablement tools and product extensions being released into the market.

Today AI is used to define ICPs, create cohorts, conduct prospecting research, and help sales pros more quickly establish the knowledge base they need to create pipeline.

AI is also used to instruct and automate multi-touch sales cadences, identify upsell and cross-sell opportunities, provide customized product demos, real-time call coaching, meeting analysis and smart notes to nail next steps follow-up.

The use cases for AI in sales motions will only grow, all with the goal of driving efficiency and effectiveness and allowing sales pros to spend more time in front of prospects and customers.

4. Aspects of AI to be Wary of

It’s still early days for generative AI, and there are many things to be mindful of when integrating AI into sales and marketing activities.

For content creation such as personalized email, technical white papers, thought leadership, etc., AI can create starter content to overcome cold starts and writer’s block. Just plan on a healthy amount of review and editing before you’ve got a finished product ready to ship.

Larger concerns loom around data security, as AI becomes a popular target for hackers. It’s been widely reported that between June 2022 and May 2023 more than 100,000 ChatGPT account credentials were leaked onto the dark web, providing access to a cache of sensitive intelligence.

Data protection and privacy risks may also exist. AI models are a long way from being reconciled with data protection laws. Unlike the unanswered legal questions around fair use and AI models trained on copyright-protected data, users have the right to be removed from AI models trained on their data. What happens when data subjects start to exercise their right to be forgotten and the right to erasure? Ponder that for a moment.

Finally, a fundamental danger to manage for is the risk of model collapse. Model collapse happens when new AI models train on other AI-generated content and break down, forgetting the underlying data set over time. There are ways to manage for this, but they require planning documentation and ongoing maintenance over the lifespan of the AI model.

Conclusion

AI can automate the process of creating deeper engagement with your prospects and customers. It can also deliver efficiencies by identifying and prioritizing accounts and contacts that have the greatest propensity to convert.

As long as you’re mindful of AI’s limitations, it would be prudent to take advantage of all AI promises—to speed processes and scale like never before.