Content generation and finding the right tone and voice of copy is a common use case for generative AI, as it can help businesses meet the complex content challenges across writers, languages, and cultures.

Before experimenting with generative AI, a technology that has the ability to transform business processes, boost productivity and streamline repetitive tasks, businesses first need to understand how the technology fits the context of their operations and their risk appetite for experimenting with, then deploying generative AI at scale, says a new report from Forrester.

“While there are many short-term opportunities for experimentation with generative AI, businesses need to think about the long-game when experimenting with the technology,” says Chuck Gahun, a principal analyst for Forrester and lead author of the report. “Generative AI is about making value delivery systems more efficient for businesses to impact business outcomes such as efficiency, worker productivity, profitability, and top-line revenue growth.”

Forrester Research study on generative AI

Forrester, which interviewed more than 25 business executives, data scientists, and content management team members, defines generative AI as a set of technologies and techniques that leverage large amounts of data, including large language models, to generate new content such as text, images, video, audio, and code.

Businesses are already adapting generative AI at a rapid pace. 50% of participants polled during a webinar use the technology on a daily basis in an experimental or incidental usage capacity, according to the report. Of those respondents, some 71% were likely to leverage generative AI for content and product information creation use cases. Meanwhile, just 14% use the technology for creative expression or code-based use cases.

Content generation and finding the right tone and voice of copy is a common use case for B2B marketers implementing generative AI. It can help businesses meet the complex content challenges across writers, languages and cultures. Lloyds Banking Group in the United Kingdom, for example, uses Acrolinx to find the right tone and voice for 13 brands. Acrolinx is an AI-powered software platform that improves the effectiveness and consistency of enterprise content.

“We discuss tone and voice standards to teach the tool, such as using the word ‘select’ instead of ‘click’ and following overall design standards [in conjunction with tone and voice],” Chris Whitwam, senior product owner for Lloyds, says in the report. “This is a new learning process for our teams, but they are getting used to managing the tool as it suggests alternatives [and helps reduce the risk of inconsistent content].”

The human element of AI

While the use of generative AI helps expedite copywriting, more time is needed for human copy editing of the AI enhanced copy because of the risk of errors, according to Gahun. Such errors can include using words in the wrong context or poor language translations.

“As generative AI gets put into place, one of the challenges facing businesses is what to do with copywriters as they can become displaced by the technology as it learns,” Gahun says. “One option is to turn copywriters into copy editors.”


Generative AI use cases

Opportunities also exist for B2B marketers using generative AI to personalize content using behavioral data. For example, the technology can create product descriptions that fit the interests of a specific customer segment. As a result, businesses can generate more intelligent product descriptions across multiple layers of the customer base.

Translating copy into new different language to support geographic expansion is another use case for generative AI, although it is in the intermediate stage of experimentation.

“Generative AI capabilities are quickly accelerating beyond traditional translation plug-ins, such as Google Translate, to keep pace with professional translation services that have been providing these services for decades. This reduces the risk of inaccurate translations and simplifies expanding a business geographically,” the report says.

Uploading data into a product information solution is an emerging use case for generative AI as today’s solutions require businesses to map the data provided by the supplier into a predetermined format. This can be a time-consuming task that is prone to error when done manually.


fonQ, a Netherlands-based home décor retail, is using ChatGPT to onboard products. ChatGPT, from consortium OpenAI, is an AI-based chatbot that uses natural language processing to create humanlike conversational. The technology is enabling each of the company’s content specialist to onboard 100 products a week, compared to 30 to 35 per week manually.

“Manually uploading products to a PIM requires a lot of human brain power and time,” Gahun says. “Generative AI speeds the onboarding of suppliers and products, can show where there is broken product content, and can enable sellers to expand their product catalog. If sellers have more products, they can increase sales.”

B2B marketers using generative AI

Business applications for generative AI still in the early stages of experimentation include using the technology for creative tasks, such as producing product videos. While generative AI can embed previously approved brand assets within newly generated image backdrops to help enterprises reconcile creative quality with scale and speed to market, one shortcoming of using the technology for creative tasks is that it cannot yet create images. That is especially important for businesses to keep in mind when it comes to using generative AI for the development of brand assets.

“Brands tend to be careful about their brand assets,” Gahun says. “And while generative AI can help content creators stitch together images, it can’t create images from scratch, which raises concerns about its impact on brand assets.”


A word of caution concerning AI

As the potential use cases for generative AI expand, Gahun stresses that businesses need to thoroughly evaluate the speed at which their organization should move from experimentation to implementation of the technology.

“Business context influences which generative AI use cases are most valuable, which is why businesses need to be thinking about what implementation of the technology means to their business,” Gahun says.

Factors such as industry, geography, strategy, client base, and values, can influence the business context, Gahun adds. Forrester interviewed one company that applied generative AI for use on managing one product sold through one sales channel in one in geographic area for six months to see understand the impact of the technology on its operations.

“This was a way for the company to put the risk around the technology into the context of its business,” Gahun. “Companies that research and identify the risks around adding a new technology such as generative AI into their operations can think through their appetite for piloting the technology, then scaling the technology on a trajectory that is healthy for their business. The goal is to be methodical and put more structure around the potential impact the technology can have on the business.”


Peter Lucas is a Digital Commerce 360 contributing editor covering B2B digital commerce technology and strategy.

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