Generative AI (genAI) is poised to transform almost every business sector. Here’s a high-level breakdown of the challenges—and opportunities—faced by businesses that use this technology.

Key Question: How can companies focus their genAI efforts to take full advantage of opportunities, and meet challenges head-on?

Executive Summary

• Generative AI (genAI) is very different from its AI predecessors. It puts the technology in the hands of consumers, can be used by those with no coding knowledge, and allows for bottom-up innovation. The benefits of its use can range from massively improved operational efficiencies to substantial cost savings.
• But companies must address challenges to take full advantage of the tech. GenAI leaders tend to lack expertise in internal processes, knowledge of which is typically spread throughout the organization. That makes it hard to know where and how genAI can prove its worth. Firms can use widespread training and internal partnerships to solve those problems.
• There’s no dominant use case for genAI—at least not yet. However, the tech is already speeding up or taking over mundane tasks. Marketers, for example, are using it for content creation and personalization efforts. But most businesses still struggle to find the highest-impact way to use genAI.

GenAI Use Cases: Introducing Our New Report Series

This AI is different

AI was already a buzzword when the term generative AI (genAI) entered the global lexicon in late 2022 with the release of ChatGPT. As far back as the early 2000s, numerous media outlets were touting AI as a potential global game changer. Ten years later, Big Tech companies were investing heavily in—and making mega-bets on—AI.

GenAI, however, proved to be very different from its AI predecessors.

• GenAI puts consumers in the driver’s seat. One of the biggest changes ChatGPT’s launch brought about was that it turned a behind-the-scenes tech powering corporate-wide rollouts into a consumer-facing app. Suddenly AI, which had been a vague concept for most people for years, was now directly delivering results to users within seconds. Although voice assistants like Siri and Alexa had given people a glimpse of what AI could do with a human prompt, genAI was both consumer-directed and infinitely more powerful.
• It can be used by those with no knowledge of coding. GenAI didn’t just open up AI’s power to end users; it can also be programmed by individuals with no technical skills. GenAI’s use of natural language prompts means software engineers often aren’t needed to make the tech useful: Consumers can learn, test, and refine results on their own. The reduced demands on engineering teams—combined with low monthly subscription costs—also make genAI a relatively affordable option. That opens up new innovation opportunities for small and medium-sized businesses, as well as cash-strapped ones.
• It enables widely distributed, bottom-up innovation. GenAI has disrupted the top-down model of selecting and deploying AI-based solutions. Now employees across an organization can identify use cases and deploy the tech. Emerging genAI leaders—most of whom sit on tech teams but can live anywhere in the organization—are aided by rank-and-file enthusiasts who help develop and iterate on use cases. As a result, CTOs and other senior tech executives who typically drive new deployments are often guiding and defining policy rather than owning and executing on genAI initiatives.

Companies must bridge a gap to harness the power of genAI

The distributed nature of genAI innovation means great ideas are bubbling up across the organization. But knowledge of internal processes and of genAI's potential are often siloed. Across different companies, we see that:

• GenAI leaders can’t be experts in all internal processes. Newly crowned genAI leaders are often given a broad mandate to help transform the company. However, it’s impossible for them to be familiar with every internal process across the organization that could be improved with genAI. They are typically experts in genAI tech and its potential rather than in internal operations. They must, therefore, seek out areas where their expertise can have the biggest impact.
• Process knowledge is spread throughout the organization. Thousands of different processes are required to develop, sell, and distribute products. Knowledge of those internal operations is spread across a variety of teams and levels within the organization; it’s challenging for those without strong genAI skills to identify which processes could be automated through the tech’s use. GenAI’s reputation for job elimination has also made employees hesitant to go all-in on identifying potential use cases.
• Both training and matchmaking are key to overcoming this disconnect. There’s no one right way to bridge the gap between these two knowledge centers. Most companies offer some type of genAI training programs: At a minimum, teams are taught how to build and refine prompts that can help with their daily tasks. Others tap individuals interested in becoming genAI champions for their teams. Another popular approach is to pair trained genAI experts with different teams to identify the most viable use cases. Perhaps the most common approach is “all of the above,” as organizations try to ensure they’ve checked the “AI-ready” box that investors and boards are looking for.

The lack of a dominant use case means companies are still experimenting

There’s been vast experimentation and billions of dollars of investment poured into genAI, yet no single dominant use case has emerged. Surveys asking executives about their existing or planned genAI deployments rarely show one use case outpacing others by a wide margin. The data indicates that:

• Overall, genAI is helping speed up and automate mundane tasks. GenAI has already proved indispensable in eliminating the more rote elements of many roles. Summarizing lengthy or dense content, facilitating data entry, drafting email responses, and answering common customer service questions are just a few of the areas where the technology has already had a significant impact.
• Marketers are finding efficiencies in content creation and personalization. For marketing teams, specific use cases are emerging. GenAI is now widely used in content marketing, creative processes, and personalizing customer messaging. Rare is the marketing team at a medium- or large-sized company that hasn’t yet used genAI to streamline or enhance its efforts.
• Businesses still struggle to determine the highest-impact use cases for their organization. Given that the technology can be used by so many different teams and for so many different purposes, companies face an embarrassment of genAI riches. Most businesses want to encourage widespread use across their organization, but determining the areas that should receive the most attention and investment isn’t always straightforward. Identifying where companies will get the biggest bang for their genAI buck has become a lucrative business for agencies and consulting firms alike.

Our upcoming series will help companies identify and prioritize use cases

To help companies determine where to focus their efforts, we are launching a new series focused specifically on genAI use cases. These short reports will outline the most widely used use cases across key areas of digital businesses, highlighting examples of companies that have rolled out initiatives in these areas and helping to prioritize genAI use cases.

Our first report is live: Check out “Generative AI for Personalization in Retail,” which dives into the six most dominant personalization use cases for retailers and brands selling online.

If you would like to read this series but are not an EMARKETER client, see our Contact Us page.

Sources

Association of National Advertisers (ANA)

Boston Consulting Group (BCG)

MIT Technology Review