Generative AI (GenAI) is an exciting topic that’s created a lot of buzz across the IT landscape. Despite all the chatter, and that GenAI is not completely new, most businesses are in the early stages of investigating and trialing how GenAI can best serve their organizations and customers. In fact, according to recent research from Enterprise Strategy Group, more than half of organizations are in the consideration, planning, piloting or early production stages of GenAI adoption. Like these businesses, TechTarget’s own marketing team is investigating how GenAI might help with specific challenges we’re facing, specifically when it comes to fueling our content engine to scale our demand gen program. As we work to chart our own generative AI path, there are a few things we’ve learned along the way that we think will be useful for others getting started. Here are three of our greatest learnings from the past year.
#1 – Define your GenAI goals and align your team to them.
Just as when starting any new project, it’s important to meet with your team at the onset to identify the goals you want to achieve by using generative AI and use cases you want to test. To identify those goals, we found it helpful to ask ourselves, “What are critical outputs to hit our goals?” and “What do we spend a lot of time on today?” For our team, we wanted to explore how GenAI might help us scale our content engine. Like many B2B marketing teams, we spend a lot of resources fueling our content engine because it’s central to the success of our demand gen program. We take a video-first approach to our content strategy – in the past year, we produced over 80 thought leadership webinars. And because our target audience prefers a mix of content formats, we create derivative content from our webinars, including e-books, infographics and blogs. The challenge for our team is to scale this process so we can extend the ROI of every webinar we create, without growing the size of our team.
Once your team is fully aligned on the goals and tasks where GenAI will be used, you can map out the use cases you want to test and the timelines for testing. To ensure we’re staying on track with our objectives, we hold bi-weekly check-ins to review outputs for specific use cases, map out timelines for future use case testing and even flag when use cases aren’t successful.
#2 – Think of GenAI as another person on your team: training and assistance is necessary before you can see success.
Since our main GenAI goal centered on scaling content, many of our use cases focus on creating long-form derivative content while shortening the time spent writing and editing. We’ve often heard GenAI sold as this magic solution to content, where you’ll get a complete and final e-book or blog post at the click of a button. In our experience, this isn’t true – and it can also be a dangerous way of thinking, especially because our research shows 91% of tech buyers trust expert content over AI-generated content.
Like someone new to your team, GenAI isn’t going to get your brand’s tone, messaging and style right on the first try. It must be trained – and even then, it won’t be perfect. While we can have content drafts ready on the same day, it’s important to still take the time to review and edit each GenAI output. For our team, we’ve found success by approaching an AI-generated piece of content as the first draft, which we then review, edit and hone down to a polished final piece. By approaching GenAI tools as a partner, we can increase our content outputs, without sacrificing the high level of quality that’s integral to our brand.
#3 – Be flexible and get creative when testing GenAI use cases throughout your organization.
In business, there are often multiple ways to address a problem. Sometimes the most intuitive path will produce the best output, but as it was in our case testing GenAI for content production, often the less obvious route can lead to a better result. And finding the more elegant solution often requires some trial and error. This is where having an open mind and putting on your “experimenter” hat is really important to finding success within a specific use case, as well as identifying other areas of your business that could benefit from GenAI.
While our team started testing the creation of long-form e-books from webinars, from those outputs, we saw a greater opportunity to create derivative blogs and pivoted our testing to that use case. This also required testing and refining a prompt within our GenAI tool that produced the best blog output possible. We trialed more than five different prompt iterations before landing on one that created a strong first draft and worked for our team. Always be willing to adapt and don’t be afraid to get creative – by keeping your goals front and center, no matter what pivots you take, you’ll be on the path to better outcomes for your business.
Any GenAI success won’t be achieved overnight – it takes clear goals, an open mind and a dedicated team to get it right. Over the past year of testing different generative AI use cases, our team has learned so much – both about what works, and what doesn’t work for us. We know GenAI isn’t a quick fix to all our content woes, but we remain energized by the opportunities we’ve seen. We hope that with these tips, your own team will be on its way to seeing GenAI success more quickly.
If you want to learn more about how to use GenAI to support your content engine, watch How TechTarget Scales Content Using Writer’s Enterprise Generative AI Platform from our virtual event, Reach.
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