Generative AI has an amazing amount of momentum. Major brands are stepping up, displaying how text generation, image generation, and video generation have a role in the future of marketing. Each week we see a new brand put out an “email written by ChatGPT” or a “portrait generated by Dall-E.” Major marketing technology ecosystems like Salesforce are incorporating AI directly into their platforms, their social posts, emails, and even scripts for sales agents are not created by people, but by the collective learning of extensively trained content generation engines.
AI In MarTech
For professionals in the marketing technology space, the topic of AI applications occupies a lot of our thoughts. We want to push the boundaries of how we conceive, develop, and deliver campaigns for our clients using the latest innovation. We also want to set our teams up for success in this rapidly evolving digital world, so at GALE, understanding the new emerging AI tools and their capabilities is something we constantly keep up to date on in the market.
While tech can lead us to new frontiers, there is equally significant concern with regard to how generative AI is used. Questions over data security and intellectual property arise often, with companies’ strategies and code becoming more vulnerable to new kinds of ‘solicitation attacks’ and data breaches. While data privacy standards are still in development, companies must deal with a new set of challenges unique to the application of AI, including realizing that control over input may be dubious at times. Further, when generative AI outputs are sent to consumers without proper controls, the shortcomings and imperfections can cause the loss of brand affinity. Poorly created content, data security and brand risk skyrocket in a world where models replace the humans at the heart of every organization.
A Strategy for Using AI
For this reason, a deliberate approach to utilization of generative AI within the marketing sphere is needed.
Content generation capabilities show promise as an accelerator and multiplier of the creative process, allowing for substantial increases in relevancy, but only to the extent that they’re supervised by a talented and capable set of art directors and copywriters. There’s a delineation here that allows the team to speed up the development of creative assets, but that process is still controlled by human experts, as is the delivery of the final work. Similarly, integrating generative AI into coding activities can help brands scale with better quality, while still requiring capable engineers to steer the ship.
Companies shouldn’t forget that developing audiences, models, and strategies based on first or zero party data still leads to the best value proposition. The analysis of this data can streamline when and where AI technologies are used and increase the probability that generative AI outputs are relevant to target consumers. At GALE, our foundation of asking meaningful questions and knowing how to obtain outcomes using data has positioned us well for the age of generative AI. We’re known for executing outstanding work in this space, even developing our own customer data platform, Alchemy, to help us drive results and dynamic value for our clients.
Generative AI has the power to improve and expedite countless tasks, and with a measured and deliberate strategy to guide the way, we can use it to supplement and elevate the work we do across business disciplines.