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Looking to add AI to your demand generation mix?
Can you start by testing AI tools for random tasks? Sure. But if you’re looking to optimize across programs—and repeat results—consider your existing workflows.
Scaling isn’t chasing shiny new objects; it’s real-world implementation.
“The challenge that all of us encounter is, really, how do you optimize your demand gen when AI can do so many things?” says marketing consultant and author of The Modern AI Marketer Pam Didner. “The question we encounter in terms of optimization is… what should AI do for you?”
Her recommendation? Look inward at what you’re already doing and optimize that first.
Her recent MarketingProfs webinar detailed her seven steps for scaling AI-powered efforts across your existing demand gen programs.
Step 1: Understand your demand generation channels
First, the easy part: create a list of all the channels and campaign types you currently use in your demand gen programs.
Email, social media, PPC, original content—list them all, even if you use them infrequently, like in-person roadshows.
Step 2: Document the processes and workflows
“The next thing is you need to document the processes and workflows,” says Pam. “It will give you an idea of what to optimize.”
Perhaps you already have a playbook that outlines the replicable activities you undertake and how you do them. Great!
Don’t have documented processes? Don’t worry, many businesses don’t. But you’ll need to take a few minutes to map out those workflows so you can identify where AI can deliver the most value. A one-slide flow chart for each campaign type works fine.
Detail the key activities and steps for each program. For instance, if you are using webinars, document the pre-event, during-event, and post-event activities.
Pam explains: “Let’s assume you have a quarterly webinar and it’s a big deal. You might have LinkedIn paid ads, you run several email campaigns, and you also do a lot of social media posts to drive traffic to the registration page. Then a day comes, you have the webinar, and after the webinar you send a thank you email.”
Although that’s a fairly typical webinar process, it may not be your process. Document what works for you, not what you think other people are doing.
If your campaigns include lead nurturing, sales enablement, or related down-funnel processes, document those as well.
Step 3: Evaluate where AI can fit in
Now’s time to layer on AI. According to Pam, “there are many ways that you can do it,” and only part of it involves writing.
Back to your process flow charts: circle all the elements with an AI use case. “You need to write LinkedIn ads, email copy, social media posts, and even the thank you post webinar email. So that’s one way to automate the process.”
Check out this clip from Pam’s recent MarketingProfs presentation, “Optimize Lead Generation With AI,” for more ideas on where and how AI can fit into your programs:
Step 4: Inject the tools
It’s time to select your tools. Many marketers mistakenly begin their AI journey at this step—because shiny new tech is fun. But selecting and buying any new tool should come after you understand your existing programs and what problems you need to solve.
Pam suggests using existing AI tools and integrated features that your organization already has before considering new tools. “Focus on using existing tools” in your martech stack. “See if the existing tools can satisfy all the needs.”
For instance, if you are already using a chatbot, say ChatGPT or Google Gemini, continue using it and testing its performance. Or, if your CRM has built-in AI features, try those.
Step 5: Test and modify
Next up: test, measure, and optimize.
“You may not get it right the first time. Give yourself some time to correct or make adjustments based on the workflow,” Pam advises. “Set up a control environment, test your hypothesis through A/B testing, and start small.”
If it doesn’t work—or doesn’t work as well as your traditional methods—modify what you’re doing.
Step 6: Think like a data analyst
Measure and analyze your results. “You need to think like a data analyst,” Pam says.
“When you are using AI to help you for demand gen, you want to make sure that AI actually helps you. That it’s not a detriment to your demand gen efforts.”
Quantify the results. “That’s the key. That’s the very important part that we marketers tend to overlook,” she emphasizes.
Step 7: Retest and remodify
Get ready. Get set. Do it all over again.
Pam says, “Test results based on specific changes”:
- First, does it actually make your workflow more efficient?
- “The other one, because it’s demand gen, is SQLs and leads,” and any other conversion or financial KPIs you’re accountable for.
“Then you have to retest and remodify.”
Who’s the boss?
Finally, consider who’s doing all the thinking. If you’re considering delegating strategic demand gen work to AI (or—a step further—downsizing your staff and employing AI instead), ask yourself: Can AI think like a demand generation marketer?
It can perform tasks. It can even help you generate new ideas for your campaigns. But to achieve meaningful KPI results, you need to own the strategic thinking. AI’s just a tool to help you get there.
Want to explore more of Pam’s ideas for AI use cases and steps for scaling AI, including using AI for demand gen planning and budget allocation? Check out her AI for Demand Gen Marketers presentation.
More Resources From the AI for Demand Gen Marketers Series
Can AI Save You From Marketing Inferno?
AI Use Across the Customer Journey Means Aligning Across Teams
Using AI to Build Your Personas: Don’t Lose Sight of Your Real-World Buyers
AI Can’t Write Thought Leadership (But It Can Do Something Else)
Your AI Needs a Human Copyeditor
AI Can Do Hard Things for You (Like Forecasting Future Success)
