GenAI IN DIGITAL COMMUNICATIONS

Communications teams spend countless hours drafting press releases, blog posts, social media captions, newsletters, and video scripts to support digital communication efforts. Teams executing digital campaigns are expected to tailor messages for different audiences, maintain a consistent brand tone, and remain relevant in real-time. They must also manage rapid news cycles, engage with diverse audiences across multiple platforms (such as Facebook, LinkedIn, TikTok, Instagram, X/Twitter, and YouTube), and respond promptly to comments and crises. Additionally, they often have to localise content for different regions and languages, repurpose stories for various formats, and support internal and executive communications. All of this must be done under tight deadlines, with the added pressure of standing out in a crowded, noisy digital landscape.

A McKinsey report (2023) highlights that organisations leveraging GenAI in content workflows saw up to a 60% increase in production speed. McKinsey describes Generative artificial intelligence (AI) as algorithms (such as ChatGPT, Google Gemini and Meta AI) that can be used to create new content, including audio, code, images, text, simulations, and videos. McKinsey research indicates that generative AI applications stand to add close to 5 trillion dollars to the global economy, increasing the impact of all artificial intelligence by 15 to 40 percent. In the technology, media, and telecommunications (TMT) space, new gen AI use cases are expected to unleash between $380 billion and $690 billion in impact—$60 billion to $100 billion in telecommunications, $80 billion to $130 billion in media, and about $240 billion to $460 billion in high tech. In fact, it seems possible that within the next three years, anything not connected to AI will be considered obsolete or ineffective.

According to The Future of Professions report, The AI adoption curve within professional organizations is beginning to shift from the innovation to the early adoption phase. This signals an emerging trend. This transition aligns with the Diffusion of Innovations theory by Everett Rogers (2003), which explains how new ideas and technologies spread within a social system through distinct adopter categories: innovators, early adopters, early majority, late majority, and laggards. As AI tools (such as generative AI and automation platforms) demonstrate practical benefits, more organizations are moving beyond experimentation toward broader implementation, driven by influential early adopters who act as opinion leaders and role models within their sectors. While brands and organisations based in countries like the US, China, and Japan lead the way, African-based brands and companies are quickly catching up. Arakpogun et al. (2021) argue that African countries lag in readiness but also highlight the opportunity to leapfrog traditional communication methods directly to AI-enhanced practices.

A myriad of challenges face the development and adoption of AI technologies in Africa. These issues include a lack of a structured data ecosystem, insufficient infrastructure and digital divide, lack of enough capacity building on AI, and limited venues for innovation skills acquisition. By embracing GenAI, communications managers will guarantee that their brands stay ahead in the global race for audience attention, as witnessed by leading global brands such as Coca-Cola, Heinz, Sephora and Nike.

Founded in 1886, Coca-Cola is a leading beverage company and one of the top brands readily available and easily recognisable across the world. Coca-Cola has been an early adopter and a leader in the generative AI space the last few years pre-GPT launch, pre-hype cycle of generative AI. The Beverage giant recently used Gen AI in 2024 during its Christmas campaign dubbed “Holidays are coming”. Instead of relying solely on traditional static ads, Coca-Cola leveraged AI to create dynamic, video content that could resonate with diverse global audiences in real time. The AI-generated videos were designed to be easily adapted and shared across various digital platforms, including social media, YouTube, and TV display ads, increasing reach and engagement globally.

The "Holidays Are Coming" campaign is one of Coca-Cola’s most iconic and beloved campaigns worldwide, and its popularity is due to its glowing Santa painted red trucks decorated with sparkling lights moving through a dark, snowy landscape somewhere in Europe. The Coca-Cola team leveraged on Gen AI abilities to refresh a popular 1990 ad and develop imagery and storytelling techniques that boosted emotional connection with their audiences through an immersive experience with those who remembered the advert. According to Pratik Thakar, global vice president and head of generative AI at Coca-Cola, the TV commercial drew a lot of attention and became a lightning rod for some of the controversy over the use of gen AI. Coca-Cola didn’t simply type a single prompt into an AI tool and instantly receive a ready-made ad. Instead, the process was highly collaborative and carefully guided by human creative decisions.

To reimagine their iconic 1990s “Holidays Are Coming” commercial, they began with the original film as a creative foundation — a film they had rights to use. They shared this as a creative brief with three different AI studios: Secret Level, Silverside AI and Wild Card to create three distinct versions of the festive ad. Each made use of distinct Coca-Cola-branded assets and followed the same concept. They used a range of AI models, including Runway. Runway is renowned for introducing Gen-2, the first commercially available AI video generator. These teams were able to expand the original European village setting in the original video into a broader, more international visual story. Gen AI enabled them to create fantastical, highly realistic imagery showing different parts of the world, going beyond what traditional production methods or the original footage could achieve.

While the AI generated visuals and certain creative elements, the overall storytelling choices —such as which scenes to include, what emotional tone to set, and how to structure the ad — were made by humans. The music was still composed by real artists, and the final product was refined and shaped through human judgment and design direction.

Another example is Heinz, a renowned ketchup brand that leveraged GenAI in affirming brand leadership by driving viral marketing that attracted its millennials and Gen Z. By using AI-generated images such as “ketchup in space” and “ketchup as a superhero,” Heinz created visually striking, shareable content perfectly suited for digital channels like Instagram, TikTok, and Twitter. The campaign encouraged user-generated content by inviting fans to create and share their own AI ketchup artworks, transforming passive audiences into active brand participants using GenAI. Both campaigns demonstrate how GenAI empowers brands to move beyond traditional communication techniques into inclusive, data-driven digital techniques that foster deeper emotional connections with diverse audiences.

The Transmission Model of Communication, also known as the Sender and Receiver theory, is considered the most foundational and widely recognised framework for understanding communication. It was originally developed by Claude Shannon and Warren Weaver in 1949. It outlines the basic components — sender, encoder, channel, decoder, receiver, and noise — that can be applied to nearly any communication context, including modern digital and AI-driven campaigns. GenAI challenges traditional models of communication by blurring the lines between human and artificial actors, altering perceptions of authenticity, and potentially undermining trust and social ties.

In addition to challenging the Transmission Model, generative AI also disrupts Berlo’s SMCR Model (Source-Message-Channel-Receiver), developed by David Berlo in 1960. This model emphasizes the skills, attitudes, knowledge, and social systems of the source and receiver as crucial factors influencing message effectiveness (Berlo, 1960).

In Coca-Cola’s campaign, GenAI didn’t just create one ad and send it to everyone. Instead, the GenAI helped make different versions of the video and could even adjust or personalize content for different audiences or regions. In Berlo’s original model, Coca-Cola would act as the sender, creating one message (the video) that everyone receives in the same way. But with GenAI, the AI becomes part of the "sender," helping adapt the storyline and visuals to fit local cultures or personal preferences. This makes the communication product more interactive and tailored, rather than being a single, one-way message.

In Artificial Communication, Elena Esposito argues that drawing such analogies between algorithms and human intelligence is misleading. If machines contribute to social intelligence, it will not be because they have learned how to think like us but because we have learned how to communicate with them. Esposito proposes that we think of “smart” machines not in terms of artificial intelligence but in terms of artificial communication. On content workflows, GenAI automates tasks such as idea generation, drafting, visual design, and repurposing for different platforms. While this enhances speed and scalability, it challenges traditional creative processes that rely on human originality and editorial quality control. Generative AI solutions are not merely tools for meeting rising content demands; they are strategic enablers for outpacing them. By collaborating with AI, organisations can produce high-quality, hyper-personalised content more efficiently, thereby gaining a competitive advantage in today’s dynamic and crowded digital landscape (Brinker, 2024).

In the traditional model, the sender (Coca-Cola) formulates the message in the form of a video script, and an encoder simply translates it (e.g., turning text into a video). AI tools like Runway Gen-2 and the three creative studios became co-creators, not just encoders. They introduced new creative possibilities and influenced visual styles to appeal to different audiences, meaning the "encoding" step was no longer purely mechanical but part of a creative, iterative loop.

As Kaplan and Haenlein (2019) argue, AI not only supports efficiency but also fundamentally redefines customer interaction, raising new questions about authenticity and trust. GenAI models frequently exhibit biases reflective of the human-generated data they are trained on, perpetuating stereotypes and prejudices present in the training data (Bail, 2024). Despite the efficiency gains, GenAI introduces risks that threaten brand reputation and public trust. Feuerriegel et al. (2024) identify "AI hallucinations" or errors where AI-generated content appears plausible but is factually inaccurate or misleading. Recent examples of when artificial intelligence generates distorted information include, in 2024, Air Canada was ordered to compensate a customer who was misled by a chatbot on its website. In 2023, an AI-generated image of an explosion near the Pentagon in the United States was widely circulated, resulting in a significant dip in the U.S. stock market due to the impact of the photo, which was posted on the X platform.

Hicks et al. (2024) warn that unchecked use of AI can erode credibility if inaccurate content reaches audiences. Copyright violations also pose a significant limitation, as GenAI diffusion models can produce outputs that resemble existing works without permission or attribution to the original creators (Feuerriegel et al., 2024). Diffusion models are a type of generative artificial intelligence (GenAI) used to create new content, especially images, by learning from large datasets of existing visuals. Such models have been the focus of outrage because they are trained on work from real artists (typically, without compensation or consent), with allusions to their provenance emerging in the form of repeating art styles or mangled artist signatures (Xiang, 2023).

Organizations must carefully balance these gains with the need to maintain brand voice and uphold content standards. In terms of audience engagement, GenAI enables messaging and dynamic content adaptation based on user data and real-time interactions. A good example is Sephora, a French multinational retailer of personal care and beauty products. Sephora leverages GenAI for content generation across multiple languages and customer segments; this has resulted in a 40% reduction in creative production time.

Using Generative AI in content and campaigns empowers brands to build stronger connections and foster loyalty. However, it also raises ethical concerns around data privacy, algorithmic transparency, and the potential loss of perceived authenticity if audiences discover content is machine-generated. Finally, the roles of communication professionals are evolving. Rather than focusing solely on content creation, communicators now act as strategic supervisors and ethical stewards of AI-assisted narratives. This demands new competencies in areas such as prompt engineering, data analytics, AI governance, and crisis management. Together, these dimensions demonstrate that GenAI is not merely a technological trend but a profound catalyst for rethinking digital communication strategy, organizational structure, and audience relationships.

Generative AI (GenAI) has emerged as a transformative force in increasing capacity and efficiency in digital communications, reshaping how organizations create, deliver, and personalize content at scale. By automating and enhancing creative processes, GenAI enables organizations to generate personalized social media captions and ad copy for different audience segments using tools like Copy.ai and Jasper. It helps create dynamic visuals and images for campaigns with AI image generators such as DALL·E and Runway. GenAI also supports rapid drafting of video scripts or storyboards using tools like Synthesia, and automates email newsletter designs and content blocks through platforms like Mailchimp’s AI-powered content tools. Additionally, it powers virtual try-on experiences and interactive digital assets, as seen with Sephora’s use of ModiFace, and enables the generation of multiple ad variations for A/B testing to optimize campaign effectiveness.

Critics warn that the automation and efficiency offered by GenAI may lead to significant job displacement within digital communications teams. Roles that focus on repetitive tasks—such as drafting basic social media posts, video scripts and creating standard graphic templates, writing routine email newsletters, and moderating simple customer comments—are particularly at risk. GenAI offers unmatched efficiency and the ability to tailor messages dynamically to diverse audiences, as demonstrated by pioneering brands like Coca-Cola and Sephora. However, this technological evolution also introduces significant challenges, including risks of bias, privacy violations, copyright infringements, and potential erosion of authenticity and trust.

However, with generative AI, machines are no longer just tools that will replace humans or translating human ideas; they now actively co-create content alongside humans. For example, AI might generate images, write captions, or suggest storylines. This means machines are influencing what the final message looks like, rather than just delivering it.

Because AI can adapt and personalize content in real time (based on user data), communication is no longer strictly linear or fixed. Messages become dynamic, changing based on who receives them and how they interact. As a result, communications and marketing professionals need to rethink their strategies, focusing less on controlling a single, one-way message and more on managing an evolving, interactive conversation that combines human creativity with AI adaptability. According to PwC (2023), future business models will integrate GenAI across creative, operational, and strategic workflows to improve scalability and global reach.

In the near future, communication professionals must evolve beyond content production, campaign planning and moderation. Adopting roles as ethical stewards and strategic overseers of AI-driven narratives. They must also act as strategic overseers, guiding AI-driven narratives to build trust and deepen audience connection. Communication professionals can develop these essential skills through specialized online courses offered for free or through nominal fees by platforms like Coursera, edX, and LinkedIn Learning, which provide training in AI governance, prompt engineering, and data analytics. According to AU NEPAD, Generative AI presents a powerful tool for shaping a dignified future of work in Africa. Africa-centric AI platforms designed with local expertise can address the continent’s specific challenges. Collaboration among stakeholders is key for responsible AI development that respects local knowledge and traditions.

As organizations across the globe move from experimentation to full-scale adoption, it is critical to embed GenAI thoughtfully and transparently, ensuring that technology aligns with brand values and builds rather than diminishes audience trust. Ultimately, by strategically integrating GenAI, communication teams can maintain relevance, deepen connections, and secure a competitive edge in an increasingly crowded digital landscape while upholding ethical and creative integrity. Looking ahead in 2026 and the coming years, more businesses and brands are expected to integrate generative AI into everyday workflows so employees with little to no technical knowledge have the opportunity to leverage AI-powered productivity and creativity tools to create high-quality engaging digital marketing material in the shortest time possible.