4 Practical Questions to Ask About AI’s Marketing Applications

There has been plenty of talk about the excitement (and fear) of how AI will completely transform businesses, forever changing the game and revolutionizing every industry. And while there’s certainly plenty to anticipate, we’re already beginning to see the hype fade as AI market leaders move on from experimentation and into the nitty-gritty.

It’s time to have a practical conversation about AI and how your business can integrate it into your marketing strategies.

Given that AI technology will only grow and evolve over time, it makes the most business sense to invest cautiously, focusing particularly on the aspects that will deliver value today, rather than the potential “maybes” and “what ifs” down the line. It’s a less flashy approach than going all in, sure, but it’s one that will save your organization heartache in the long run.

Don’t just hop onboard the AI hype train because you have FOMO; use the prospect of AI as an opportunity to examine your business processes and how marketing is currently done. Only then can you determine how and whether AI can drive genuine business value, and what practical applications will make sense for your unique organizational needs.

Here are four practical questions marketing leaders need to ask about integrating AI.

1. Which processes do we automate first?

We’ve seen that AI can be incredibly helpful when it comes to process automation, taking repetitive tasks off the table for your team and freeing them up to focus on core competencies. But, before you purchase every tool under the sun to help with content generation, lead nurturing, audience interaction, predictive analysis, personalization, marketing campaigns, workflow enhancement and more, it’s critical to ask whether you actually need AI to automate all these processes.

Consider instead that perhaps you need to optimize that tech stack you’ve already invested in but aren’t using to its full extent. Do you need to generate more content, or do you need to better target customers? Will a chatbot really solve your customer service and sales problems, or do you need better training and synergy for your teams?

Keep in mind that automating a bad process won’t make that process better. Take a keen eye to what’s currently in need of improvement, then ask the hard questions around how that can be enhanced before dropping budget on AI solutions.

2. Have we collected enough data for custom AI to make financial sense?

The research is in, and we’re seeing that custom AI solutions that build on your current discrete data will likely deliver the best results and have the most advantages, eliminating redundancy, integrating with your current tools, protecting your IP and giving you an edge in your industry. But this is assuming your organization has enough data to leverage a personalized AI solution.

An underlying issue many businesses may be facing is a data organization problem—one that AI cannot help with until you’ve addressed what’s broken. Take a look at important data processes in your marketing department—customer data, list hygiene, even content and audience analytics—and see how you can first optimize these. Then, you’ll be able to better prep for future AI adoption.

It’s a big decision, and one that shouldn’t be made without significant deliberation. Work first on your data issues (which may also be training, turnover and human error issues), then assess whether building or implementing AI solutions will deliver on the investment.

3. How will we measure and analyze the outcomes of our AI usage?

If you’ve determined that implementing AI will help with areas of lack in your company, you’ll need a plan to determine your ROI on investment. Set a baseline from which to measure improvement, and choose key metrics where you want to see growth—perhaps audience identification, lead close rate, customer engagement, click-through rates and customer lifetime value.

Then, determine where AI can drive the most value for customers. You’ll need to set up mechanisms for measuring impact and outcomes so that you have tangible evidence of where AI is creating efficiencies (or where it may be put to better use). If you’re using AI to produce more marketing content, you’ll need to know the unit cost of producing marketing materials, and how your organization determines that cost.

Spending budget on AI is worthless if you’re not maximizing it for your business.

4. How will we integrate AI with our human staff?

Let’s be clear: AI is not meant to replace your valuable team members. Although it may eventually help you save on your staffing budget, the recipe for success is “AI + Human = Value.” We’re not trying to replace people; we’re trying to better support them. Your people are the arbiters of brand integrity and the ultimate decision-makers of any sort when AI provides recommendations; don’t undervalue these critical skills.

If you’re implementing AI, keep in mind that this is an investment for the long-haul, not a one-off, and it needs nurturing. This means allocating and devoting resources to training, deployment, management, optimization and maintenance of the technology. You can’t just introduce a novel solution and call it a day; your team will need to spend time experimenting and learning—and, notably, failing—so you can truly hone your usage of AI.

One example is with content generation: Generative AI is wonderful for helping the content writers you already have ideate and execute better and faster on your marketing campaigns, so they gain back more time to focus on creativity, brand personalization and the human touch. But none of that can happen if you don’t first create the necessary space for trial and error.

Overall, AI can be an invaluable investment for your business—if you first think practically about its implementation and usage. Consider it the perfect excuse to examine and optimize your organizational processes and how marketing is currently done. Then, you’ll be able to see far more clearly where AI offers real business value.

Vikram Ramachandran is Principal of AI for 2X.