Prebuilt generative AI models

Increasingly, individual content creators and marketing professionals use prebuilt models such as ChatGPT to generate ideas and create first drafts of customer communications. Similarly, off-the-shelf generative AI-enabled marketing tools such as Adobe’s Generative Fill allow individuals to quickly alter creative assets by using natural language prompts. These AI solutions, built with versatility in mind and aimed at large audiences, increase day-to-day efficiency by decreasing the time employees spend on routine tasks. 

Customized generative AI models

Some organizations opt to lightly customize foundation models, training them on brand-specific proprietary information for specific use cases. This might include generating creative assets, recommending search engine optimization (SEO) keywords or analyzing data to forecast future customer behavior. Using these models, humans receive content from a generative AI technology and approve or take advantage of its input.

Large-scale AI transformation

A large-scale AI transformation combines multiple AI technologies, including customized generative AI solutions, to alter an organization’s core marketing processes. In addition to using models trained on proprietary data to increase efficiency and embedding key automations, this kind of transformative AI practice might generate entirely new ways of marketing. For example, by using generative AI to analyze consumer sentiment and develop new products or provide autonomous guidance to customers as they shop.

Chatbots and customer experience

Generative AI enhances customer interaction by providing instant, intelligent responses and support across various touchpoints. This might include an AI chatbot handling potential customer inquiries, providing product information and ushering consumers through a sale—all in natural, intuitive language. AI-driven virtual assistants also guide users through websites, recommend purchases and improve the overall user experience.

For instance, generative AI customer interaction tools might automatically respond to customer reviews and complaints in a brand’s voice, summarizing potential issues for an organization’s customer support team. Generative AI might even automate future discounts or product replacements.

Chatbots and virtual agents trained on an organization’s proprietary data provide round-the-clock assistance and global reach across time zones. Combined with Robotic Process Automation (RPA), they can trigger specific actions, such as initiating a sale or return process, without human intervention. As these generative AI tools “remember” interactions with customers, they can nurture leads over long periods, maintaining a cohesive relationship with an individual consumer. These highly personalized experiences engender loyalty and increase conversion rates.

Generative AI chatbots also collect crucial information for marketers about consumer preferences and behavior. They can analyze this vast and invaluable dataset to make recommendations and improve operations across a business. 

Text generation and image generation

Generative AI revolutionizes the content supply chain from end-to-end by automating and optimizing the creation, distribution and management of marketing content. Applications for AI in content marketing include automated content creation. Through these processes, AI tools generate high-quality blog posts, social media updates and ad copy based on specific keywords, topics and styles.

Generative AI also creates custom images and video tailored to brand aesthetics and campaign needs, enhancing visual content without the need for extensive design resources.

These models also significantly accelerate the creative production process, allowing marketing professionals to rapidly create and test various creative assets, creating fully fledged campaigns in a matter of hours or days. 

Personalization and segmentation

Where traditional AI might have helped marketing professionals segment audiences into broad groups according to purchasing history or taste, generative AI has ushered in an era of micro-segmentation. Micro-segmentation gives organizations the power to market to specific individuals in close to real-time. This type of personalization is a key strength of generative AI, allowing marketers to deliver highly targeted and relevant experiences to consumers at scale across channels.

For instance, generative AI might create customized recipes and meal-planning ideas based on customer grocery orders, or interpret an individual’s feedback to generate product recommendations.

Generative AI also enhances adaptive content, in which websites, emails and mobile apps adjust their displays in real-time based on individual user interactions and data, creating the most relevant possible experience for consumers. An AI tool trained on a brand’s specific messaging can craft individual creative assets for small audience segments, helping ensure marketing communications resonate as effectively as possible with diverse customer groups. 

Data analysis and predictive analytics

Generative AI excels at analyzing vast amounts of data to uncover customer insights and predict future trends, enabling data-driven decision-making. This might include market research analysis, a process through which AI algorithms interpret market data or competitor pricing to identify future consumer behavior and broader industry dynamics.

Other generative AI tools might use customer data to surface and target particularly relevant audiences. Using the technology, organizations can quickly and efficiently identify the best possible leads and predict future trends, helping marketers plan proactive campaigns and optimize their resources. 

Process automation

Generative AI streamlines marketing processes using automation. By automating repetitive and time-consuming tasks, organizations can achieve increased efficiency and productivity. Some AI-powered tools can automate various marketing workflows such as social media posting or email sequencing, freeing up human resources for more strategic initiatives. Some are used to manage specific marketing campaigns, monitor campaign data and optimize the delivery of advertisements or communications based on performance.

Generative AI is also used to translate content from one language to another, or convert files into several formats, streamlining marketing departments’ day-to-day operations and increasing a brand's reach.

The technology can also optimize the creative production process. Using generative AI, marketing departments can rapidly generate dozens of versions of a piece of content and then A/B test that content to automatically determine the most effective variation of an ad.

Idea generation

Generative AI can stimulate creativity and innovation by generating new ideas and content variations. Marketing departments might use generative AI to suggest search engine optimization (SEO) headlines or topics based on current trends and audience interests.

For example, according to the consultancy McKinsey, Kellogg’s uses AI technologies to scan viral recipes that incorporate breakfast cereal. Generative AI then uses that data to create creative assets and social media posts.⁴ During the ideation process, generative AI can also be used to suggest options for logos or advertisements, providing a vast trove of ideas for marketing departments to choose from and refine. 

1. Defining customer experience goals

Typically, decision-makers spend significant time outlining their organization’s goals before designing an AI implementation. This might include auditing existing processes that might benefit from enhancement, identifying workflows that might benefit from generative AI and outlining the wanted customer experience.

2. Collecting and analyzing data

During this phase, an organization typically gathers data from various customer touchpoints to understand their preferences, behavior and data points. A business might also collect and clean internal proprietary data, or engage trusted third-party data to create a cohesive dataset on which to train an AI.

3. Choosing generative AI tools

Depending on the scope of the AI implementation, an organization might decide on a prebuilt tool or identify what kind of model it will use to train a bespoke AI during this phase. Regardless of how customized the eventual solution will be, organizations generally research options thoroughly before coming to a decision.

4. Integrating and deploying generative AI

Integrations might take as little as a few weeks or a year. Large-scale AI transformations might require additional infrastructure and talent, while prebuilt off-the-shelf models might simply dictate that marketing departments input datasets they’ve previously identified. During the training and tuning period, the AI tool learns from third-party and internal data to function more effectively. 

5. Monitoring and improving generative AI

Typically, an organization consistently reviews its generative AI tools to identify areas for improvement and continuously feeds the AI new data to improve accuracy. 

Data quality and availability

Generative AI models require a huge amount of high-quality data to function effectively. Inaccurate data or data with bias can lead to poor performance and unreliable outputs. Also, gathering and managing the necessary data can be time-consuming and costly, especially for smaller businesses with limited resources. Organizations embarking on a generative AI project might hire additional data scientists and data engineers to help ensure the quality and consistency of a training corpus or engage a trusted third party with vetted data practices.

Data privacy and user trust

Using customer data for AI-driven personalization and content creation typically requires organizations to keep a close eye on data privacy rules and regulations. As mishandling data can lead to compliance issues and a loss of consumer trust, an organization might need to invest in advanced security infrastructure. Successful generative AI solutions are typically transparent and explainable, meaning the business designing the AI has clear documentation about how it was trained and tuned. Additionally, an organization using proprietary or user data might carefully design the AI tools with the customer’s level of comfort in mind, helping ensure customer experience solutions don’t appear invasive. 

Quality control and consistency across AI models and outputs

Ensuring that AI-generated content meets brand standards and maintains a consistent voice can be challenging. Choosing the right model and thoroughly auditing training data can be time-consuming. In the initial planning phase, an organization typically researches specific foundation models extensively, ensuring the basis of its AI solutions is the most appropriate to deploy for a specific use case. To ensure consistency over the long term, organizations typically monitor a model continuously to detect and correct errors. They might also feed a model more data to make sure it’s up-to-date.