Feature
The Gist
- Rapid adoption. Generative AI is quickly becoming a competitive necessity in marketing, with major brands leading the way.
- Versatility showcased. The technology enhances customer service, content creation, and data analysis, offering a multifaceted advantage.
- Proceed with caution. Despite its potential, generative AI has limitations such as algorithmic bias and data privacy concerns.
Generative AI is transforming marketing and customer engagement, from content creation to data analysis to personalized interactions. These large language models produce human-like content at scale, optimize campaigns, and enhance customer self-service. With major brands rapidly adopting generative AI, its capabilities have quickly become thought of as a competitive necessity. This article will examine the ways that brands are using generative AI today, and will seek to answer the question: is it vital to embrace generative AI?
How Are Brands Using Generative AI?
When OpenAI introduced ChatGPT in November 2022, it was the only generative AI application available that could be used by the public. OpenAI had not yet released access to the Generative Pre-trained Transformer (GPT) API yet, and there weren’t any other generative AI models that programmers could work with. Soon after the release of ChatGPT, OpenAI worked with Microsoft to integrate GPT technology into the Bing search engine. Not to be outdone, Google soon announced that it, too, had plans for a generative AI application called Bard.
It quickly became apparent that the era of generative AI is upon us. According to a recent Statista report, the global market revenues of AI in marketing are predicted to grow from $27.4 billion in 2023 to $107.4 billion in 2028, and over 80% of industry leaders have integrated some form of AI technology into their marketing tactics and methodologies. Additionally, a recent Acrolinx survey revealed that 95.3% of those polled have adopted or will adopt generative AI.
The first generative AI applications to be released were, quite naturally, chat apps — more specifically, customer service chatbots. Brands had been using conversational AI chatbots as well as linear, rule-driven chatbots for years, but generative AI chatbots such as Five9’s Agent Assist took the quality of customer service up several notches while enabling customers to control their own narrative.
Beyond customer service, generative AI is transforming content creation and campaign optimization for brands. Tools like Copy.ai and Jasper.ai allow marketers to generate high-quality copy and creative concepts in seconds based on prompts. This enables greater experimentation to find messaging that truly resonates with target audiences. Companies such as Grammarly are using generative AI to enable brands to create hyper-personalized, grammatically correct business communications. Grammarly allows these brands to create customized and stylized emails that are tailored to the brand voice.
On the back end, generative AI is being applied to improve campaign performance through multi-armed bandit testing and algorithmic optimization. Brands such as Persado and Phrasee use AI to generate hundreds of variations for subject lines, ad copy and other marketing assets. By automatically testing across audiences, the best performing versions are quickly identified and scaled up.
Ankit Prakash, founder of Sprout24, a contextual data platform provider, told CMSWire that through their AI marketing solutions, they've seen a surge in engagement levels and a significant shortening of sales cycles. “Our SaaS partners have witnessed a 45% increase in conversion rates and a 30% reduction in sales cycles, all thanks to AI-powered insights. While these numbers might sound impressive, they're simply the results of harnessing generative AI's potential,” said Prakash. “Thus, embracing it isn't just vital — it's inevitable for those seeking growth.”
The pace of innovation in AI applications shows no signs of slowing down. As generative AI models become more powerful and accessible, virtually every facet of marketing from ideation to execution can be augmented and enhanced. Brands that embrace this transformation will gain an advantage in cost-efficiency, agility and customer resonance. Those that neglect it risk losing relevance and losing ground to competitors.
Related Article: AI in Business: How Company Leaders Are Taking the Plunge
The Advantages of Using Generative AI
With generative AI applications exploding on to the market in recent months, many brands have begun to test the waters of AI to see if it lives up to the hype. Depending on the application, there are many advantages for brands who wish to leverage the immense power of this technology, including:
- Increased productivity and efficiency: Generative AI can rapidly produce high-quality marketing content, allowing teams to do more with less time and resources, freeing up budget and employees for other initiatives.
- Faster iteration and experimentation: The ability to instantly generate hundreds of variations of copy, ads, emails etc. enables brands to test and optimize at speeds not otherwise possible. This rapid experimentation creates opportunities for enhanced and improved performance.
- 24/7 productivity: Generative systems are able to work continuously without downtime, enabling brands to scale output and implement ideas at any time of day. This flexibility is especially valuable for global teams.
- Consistent brand voice and tone: AI models can be fine-tuned using a combination of customized training data, explicit guidelines, and iterative human feedback that helps ensure the AI's generative capabilities are aligned with a consistent brand voice in order to absorb and mimic a brand's unique style. This ensures messaging remains on-brand across regions and campaigns.
- Granular personalization: Marketing automation platforms like HubSpot, Adobe Marketo, and Salesforce Marketing Cloud are integrating generative AI capabilities into their platforms. Since these tools already contain extensive customer profiles and activity data, the AI can leverage this to tailor its outputs and experiences. This builds stronger engagement and loyalty.
As generative AI is deployed for a wider variety of applications, the benefits will undoubtedly continue to grow, and more brands will see the advantages of using this emerging technology. John Mazur, CEO of Chatmeter, an AI-powered reputation management platform provider, told CMSWire that AI has the potential to responsibly transform the marketing industry for the better. "Deep listening through AI-powered access to unstructured data trends and sentiment pattern analysis in concert and alignment with omnichannel marketing approaches can pave the way to developing more human-centered, authentic relationships with consumers. And that leads to the kind of champions who readily and freely share their brand love and spend their dollars."
Related Article: 5 CX KPIs Companies Are Improving With AI
The Limitations of Generative AI
Although there are many potential opportunities for brands to capitalize on the advantages of generative AI, it does not come without risks or challenges. Here are some potential disadvantages or risks of using generative AI for marketing:
- Lack of judgment: AI systems currently lack the full nuance and discretion of human marketers, which can lead to tone-deaf or problematic content.
- Algorithmic bias: Generative models can propagate and amplify existing societal biases if they are not properly monitored and adjusted.
- Loss of human creativity: While AI can produce high volumes of content, some feel over-reliance on generative systems leads to formulaic, less creative work.
- Need for oversight: Because generative AI is apt to hallucinate or simply “make stuff up” when prompted about a subject it does not have enough data for, AI-generated content requires human review to catch such nonsensical or incorrect outputs before publication. This offsets some of the efficiency gains.
- Legal uncertainties: The legal standing around AI-generated content and intellectual property is still emerging. Brands take on new liabilities when using AI-generated content.
- Data privacy concerns: Generating highly personalized content often requires access to detailed customer data, raising privacy and regulatory compliance concerns.
- Implementation costs: While implementation costs are rapidly decreasing, effectively integrating generative AI still requires investment in technology, training and staffing.
- Algorithmic manipulation: The power of synthetic media raises concerns about propagating misinformation or manipulating audiences. All it takes is a quick search to find numerous instances in which generative AI has been manipulated, tricked, or jailbroken to generate offensive, false, or defamatory content.
- Job impact: Some roles like copywriters, programmers, and designers could face redundancy from automated content production.
The key for brands is finding the right balance — leveraging the advantages of generative AI while minimizing risks through governance, oversight and gradual integration. Responsible usage that combines both AI and human strengths is highly recommended.
Related Article: How to Pick the Right Flavor of Generative AI
The Case Against Using Generative AI
Once a brand has an understanding of the benefits and limitations of generative AI, it really comes down to the goals of a business or marketing campaign. Some brands may wish to hold off, to see how things play out over the next year in regards to the AI space. Similar to how consumers felt when LASIK laser eye surgery first became available, some brands are cautious, and feel like there is a significant risk when a business begins to rely upon new or unproven technology.
Iliya Rybchin, partner at Elixirr Consulting, the Challenger Consultancy, told CMSWire that some brands who jump onto the AI bandwagon may be at greater risk than companies who remain on the sidelines. "The reason is that the pace of AI innovation and the hype surrounding it makes it too difficult for most companies to discern truly impactful applications of AI from snake oil and hype that may do more harm than good."
Rybchin suggested that most companies are currently engaging in what he calls “AI Theater,” i.e., issuing press releases about cool uses of AI, proclaiming partnerships with AI platforms, announcing investments in AI products, etc. “All good, but mostly done for the benefit of public relations and investor relations,” Rybchin said. “AI Reality is often far less sexy and solves very specific challenges deep inside the organization that the public will rarely see.”
While the potential of generative AI is enticing, some brands may still opt for a wait-and-see approach given the technology's nascency. Conservative industries such as financial services and healthcare tend to be slower adopters of emerging technologies until capabilities and risks are well-understood. Additionally, heavily regulated sectors may have compliance concerns around utilizing AI before clear regulatory guidance is established.
“The challenge is that even the most sophisticated AI solutions are still very immature and highly flawed,” said Rybchin. “I'm seeing many companies starting to go ‘all in’ on AI without fully understanding the risks. In the case of driverless cars, failed AI can lead to a car crash. What is the business equivalent of a car crash when failed AI produces incorrect content, offers customers the wrong advice, screws up an ad buy, optimizes for the wrong SEO, utilizes copyrighted material, etc.?”
For businesses where the use of generative AI would present a marked difference in ROI, agent burnout, creativity, time spent, or content creation, it may be worth a calculated risk. Beyond industry-specific considerations, individual brands may have strategic reasons to delay full integration of generative AI.
Startup costs may be prohibitive for smaller brands or those with tight budgets. Taking a phased approach allows budgeting for the investment that is required. Brands focused on mass market consistency may be reluctant to risk deviations from proven creative approaches, especially in high-volume campaigns. Additionally, brands with strong internal creative teams and processes may see less urgency if human workflows are performing well, i.e., there is no need to fix what is not broken.
Finally, there may be limited in-house AI expertise, and brands may need to wait to develop necessary technical skills before adopting. Staff should be encouraged to become certified in generative AI and related skills before implementation.
Max Wesman, COO of GoodHire, a self-service background check platform that helps HR people screen candidates, told CMSWire that while generative AI provides automation that can heavily speed up the process, it comes with a huge risk. "Machine Learning is STILL learning and there is always a huge risk of error, inefficiency and risking your brand's reputation and trust at the cost of generative AI,” said Wesman. “That's why the smartest way to go about generative AI is to use it only to a certain degree that helps you come up with innovative ideas for content strategies, helps you gain insights about your consumers or gives you a head start, but doesn't do the whole job for you.”
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Final Thoughts on Generative AI & Brands
The rise of generative AI presents both unique opportunities and risks for brands. While early adopters are already achieving gains in efficiency, personalization and creativity, cautious governance is required to address concerns around bias, legal liabilities and job impact. Ultimately, finding the right balance — leveraging AI's strengths while maintaining human oversight — will separate the strategic integrators from those who lag behind. The generative genie is out of the bottle, but wise brands will be careful in how they implement its magic.
