Generative AI is powering a new world of creative, customized communications, allowing marketing teams to deliver greater personalization at scale and meet today’s high customer expectations. The potential of this powerful new tool spans the entire end-to-end marketing process, from internal communications and productivity to customer-facing channels and product support. In a May 2023 survey carried out by IBM and, 67% of CMOs reported that they plan on implementing generative AI in the next 12 months and 86% plan to do so within 24 months.

AI for business has long been able to achieve multiple marketing functions: seamless connection across any platform or device, immediate response when experiencing issues and customization based on current location and purchase history. But generative AI solutions can provide new capabilities for customer-facing teams in marketing to provide even greater personalization at scale and improve employee skills and performance.

Enterprise marketing teams stand to benefit greatly from generative AI, yet introduction of this capability will require new skills and processes. According to the IBM survey, when CMOs were asked what they thought the primary challenges were in adopting generative AI, they listed three top concerns: managing the complexity of implementation, building the data set and brand and intellectual property (IP) risk.

With the right generative AI strategy, marketers can mitigate these concerns. The journey starts with sound data.

Generative AI needs the right data

As with all AI implementations, generative AI requires attention to sourcing and maintaining the underlying data. The familiar IT adage, “garbage in, garbage out,” still applies; high-quality data is essential to yield a high-quality result. If the training data is biased or incomplete, the models may generate inaccurate content.

For marketing in particular, generative AI can help with content development and audience targeting. Data curation is key, along with setting guardrails and supervision to address bias and ensure consistency of brand voice and accuracy of product and service information.

For example, a retail clothing company might use generative AI to customize email or online experiences tailored for different customer personas. The advanced capabilities of generative AI for text, visuals and video have the potential to create a more personalized and engaging experience. This might include a virtual model wearing outfits that match the customer’s body type, fashion choices and activities of interest. The generative AI tool can also incorporate external factors like weather, upcoming events or the shopper’s location.

But what if the generative AI tool recommends the customer buy a bathing suit in the middle of winter or a snow parka in the summer? Because various generative AI solutions are trained on large swaths of data, they have the capability to pull and interpret existing data incorrectly. Thus, the tool has the potential to provide unexpected results.

When an AI foundation model generates off-topic or incorrect content, that behavior is referred to as a hallucination. To mitigate this scenario, teams must ensure they customize their models with proprietary datasets, rather than relying solely on open-source internet data.

Create a data-driven generative AI marketing strategy

Before your marketing organization can introduce effective generative AI solutions, you need a strategy to implement AI foundation models. Given the vast landscape of available data (both external and internal), it’s essential to define your use cases in advance of sourcing and training your models. Understanding the benefit and risk of each use case will help to create a step-by-step path that prioritizes the model training process.

Marketers also need to work closely with IT to align on the data architecture needed to securely build and deploy foundation models while following necessary protections for intellectual property and confidential data. The appropriate usage guardrails will help monitor and safeguard your IP and the integrity of your brand.

Generative AI needs human marketing teams

Once deployed, your generative AI data journey isn’t over. Foundation models are continually being refined as they interact with customers, collecting increasing amounts of data, which in turn improves their capabilities. Human supervision (such as supervised fine-tuning with human annotations and reinforcement learning from human feedback) is required to align the output of generative AI apps running on foundation models with human intentions, ensuring they are helpful, ethical and reliable.

Even though generative AI can produce customer-facing work that seems humanlike, it still requires a human guide with expertise in navigating ethical and legal concerns regarding data use. Human reviewers can also identify and correct any instances of bias or hallucination that could have seeped into the content.

Add generative AI to your marketing toolkit

In the IBM survey, CMOs cited content creation and editing, SEO and social media marketing as the top B2B use cases for generative AI capabilities.1 In regard to B2B marketing function, these leaders called out lead generation and sales nurturing as the top use cases.1

When asked about their biggest concerns regarding generative AI, leaders were focused on data accuracy, privacy management and having the skilled resources to build this solution.1 To that end, adopting generative AI technology requires a practical approach to build, test and learn about its capabilities. This will ensure that proprietary data is protected, customer experiences are relevant and rewarding, and the marketing process is streamlined and cost effective.

For decades, IBM has been at the forefront of AI for business. We provide solutions and services that help marketers implement generative AI responsibly and effectively. Watsonx, IBM’s enterprise-ready AI and data platform, is designed to help marketing and other business leaders confidently move into the generative AI arena. The platform includes three powerful components:

  1. an enterprise studio for AI builders to train, validate, tune and deploy generative AI
  2. an open hybrid data store built on an open lakehouse architecture, designed to help scale generative AI workloads
  3. watsonx.governance: a toolkit that accelerates AI workflows that are built with responsibility, transparency and explainability

IBM Consulting™ and its diverse, global team of more than 20,000 AI experts help marketing organizations quickly and confidently design and scale AI and automation across their business. We work in concert with IBM watsonx technology and an open ecosystem of partners to deliver any AI model, on any cloud, guided by ethics and trust.

Take the first step toward generative AI with the right data sources and architecture to support the access, quality, richness and protection of your brand.

Get the CEO’s guide to generative AI for customer & employee experience

1 “CMOs and Generative AI,” IBM, May 2023. n count (200)


More from Business transformation

Child support systems modernization: The time is now

6 min read - The majority of today’s child support systems are dated, first-generation systems that are now more than 25 years old. These systems need modernization to meet the evolving needs of children and families in the 21st century. With more than 20% of families and children supported by these systems, the impact is significant. Today’s constituents are interested in engaging with services using modern, consumer-friendly technologies, platforms and devices. Families also expect interactive experiences that drive outcomes tailored to their needs. The…

Unleashing the power of Presto: The Uber case study

7 min read - The magic behind Uber's data-driven success Uber, the ride-hailing giant, is a household name worldwide. We all recognize it as the platform that connects riders with drivers for hassle-free transportation. But what most people don't realize is that behind the scenes, Uber is not just a transportation service; it's a data and analytics powerhouse. Every day, millions of riders use the Uber app, unwittingly contributing to a complex web of data-driven decisions. This blog takes you on a journey into…

“Teams will get smarter and faster”: A conversation with Eli Manning

3 min read - For the last three years, IBM has worked with two-time champion Eli Manning to help spread the word about our partnership with ESPN. The nature of that partnership is pretty technical, involving powerful AI models—built with watsonx—that analyze massive data sets to generate insights that help ESPN Fantasy Football team owners manage their teams. Eli has not only helped us promote awareness of these insights, but also to unpack the technology behind them, making it understandable and accessible to millions.…

Generative AI as a catalyst for change in the telecommunications industry

4 min read - Generative artificial intelligence (AI) burst into the mainstream in 2023, lighting a fire under businesses to integrate enterprise-grade versions into their processes. By 2024, 60% of C-suite executives are planning to pilot or operate generative AI in some way, indicating that generative AI's public-facing platforms have awakened the world to its groundbreaking capabilities For Communications Service Providers (CSPs) and Network Equipment Providers (NEPs), in particular, generative AI holds tremendous potential to help improve all manner of operations and customer engagement.…