Generative AI has the potential to revolutionize marketing capabilities.

Generative AI, also known as gen AI, is poised to revolutionize the world of consumer marketing.

It has the potential to permanently alter the idea generation process, automate processes, and power hyper-personalization, thus bringing the holy grail of hyper-personalization at scale close to reality.

Companies incorporating gen AI into their marketing strategies can reap significant benefits, including increased customer insights, improved customer engagement and satisfaction, and increased efficiency gains from automation and automated content generation.

Gen AI enables marketing campaigns to be rolled out in weeks or even days, with at-scale personalization and automated testing. Website development and customer service tasks, often bottlenecks in interactions with individual consumers, can be executed well to induce greater engagement and improve satisfaction.

Marketers can simultaneously analyze and interpret text, image, and video data to better understand innovation opportunities. Gen AI is powering granular personalization in impossible ways.

These productivity gains from gen AI are beginning to ripple across the global economic marketplace.

A recent McKinsey report estimates that general AI could contribute up to $4.4 trillion in annual global productivity, with marketing and sales being one of the functional groups that could reap an estimated 75 percent of that value.

The marketing productivity alone due to gen AI could increase between 5 and 15 percent of total marketing spend, worth about $463 billion annually. This article explores three ways consumer companies can create value with gen AI. Companies are already exploiting existing gen AI models that are publicly available.

The next step for them will be to differentiate themselves, propelling unequaled customization and greater capabilities by integrating those models with their data and systems.

Finally, we look at the long-term opportunities for companies that want to push even further by reinventing their end-to-end processes with gen AI. Current uses of gen AI in marketing mostly consist of off-the-shelf pilots integrated into existing workflows.

These efforts deliver immediate value by helping companies generate copy and images in less time, personalize campaigns, and respond to and learn from customer feedback.

Personalization of marketing campaigns is one of the common use cases of gen AI. For example, a crafts retailer, Michaels Stores, uses gen AI to deepen customer engagement through more personalized and frequent interactions with its shoppers.

The company built a content generation and decision-making platform to help with copy development and better understand how customer segments engage with different messages. Michaels has gone from personalizing 20 percent of its email campaigns to personalizing 95 percent, lifting the click-through rate for SMS campaigns by 41 percent and email campaigns by 25 percent.

Hyper-personalization efforts also benefit from more granular analyses of consumer behavior, which gen AI can augment. Personal-clothing service Stitch Fix, for example, uses gen AI to help stylists interpret customer feedback and provide product recommendations. Instacart uses gen AI to offer customers recipes and meal-planning ideas and generate shopping lists.

Marketers have always played a core integrating role across enterprises, and there are opportunities for companies to automate interactions between marketing and other functions. For instance, a direct-to-consumer retailer uses gen AI to help resolve customer tickets, such as order-taking or repair requests and has seen a more than 80 percent decrease in time to first response and a four-minute reduction in average time to resolve a ticket. Gen AI is also used to analyze competitor moves, assess consumer sentiment, and test new product opportunities.

Rapid generation of response-ready product concepts can improve the efficiency of successful products, increase testing accuracy, and accelerate time to market. For instance, Mattel uses AI in Hot Wheels product development to generate four times as many product concept images as before, inspiring new features and designs. Kellogg’s is scanning trending recipes that incorporate (or could incorporate) breakfast cereal and using the resulting data to launch social campaigns around creative and relevant recipes. And L’Oréal is analyzing millions of online comments, images, and videos to identify potential product innovation opportunities.

As companies explore opportunities with gen AI, they will want to ensure that whatever efforts they launch align with their overall marketing goals. Attempting to incorporate too many different-gen AI initiatives in the hope that something sticks can become costly, diffuse, and difficult to track, making it hard to incorporate whatever lessons are generated across the launches. Instead, companies can focus on two or three use cases wherein off-the-shelf gen AI tools can immediately impact priority domains.

While applying and adopting gen AI, marketers must ensure that measures are in place to mitigate risks such as “hallucinations,” biases, data privacy violations, and copyright infringement. Gen AI is typically unsuited for high-stakes decision-making, regulated environments, or applications involving a heavy volume of requests or numerical reasoning. Establishing an accountable leader, as well as a technology oversight board, is an important first step.

Other guardrails may include working in a level of human review for anything going directly to a customer or limiting the topics that gen AI can address for marketing campaigns. Lots of companies have started developing use cases like the ones listed above.

However, companies seeking to differentiate themselves create unique, customized solutions for customers by adapting off-the-shelf models trained on smaller, task-specific data sets.

This is when companies can see exponential improvements in customizing everything for customers, from campaigns to products. In marketing, fine-tuning an existing gen AI model might mean training an open-source model with proprietary data to generate bespoke content.

This semi-custom gen AI solution can regularly update with new company data and ongoing learning.

The result is a continually improving, bespoke-gen AI solution that helps increase a company’s competitive advantage as it develops. In addition to using off-the-shelf marketing tools and customized solutions, companies may want to consider what a marketing function transformed by gen AI would look like in the long run. In this transformed future, nearly all marketing tasks could be assisted by Gen AI; if marketers need to write copy, they could begin with a draft written by Gen AI.

If marketers need to do research, they could start by asking gen AI for democratically sourced inputs. But while the future marketing function has the potential to be more innovative with gen AI, there must be guardrails to ensure that personally identifiable information isn’t exposed, copyrighted materials aren’t misused, and other risks are mitigated.

To get started with gen AI in marketing, companies can create a North Star vision and road map, build the team to get it done, and get some quick wins going. For prioritized, low-complexity use cases where off-the-shelf gen AI tools can be applied, initiate a few efforts to learn and identify where gen AI can deliver the most value, what talent and skills are needed to sustain this capability, and what the operating model requirements to scale effectively are. Leaders in gen AI marketing can also start building high-value use cases. These are often complex and will likely require fine-tuning gen AI foundation models and significant refinements to any first draft of the solution.