Rushing in to adopt generative AI (genAI) in marketing without setting the right foundation or assessing readiness is a mistake. Harnessing the benefits of genAI effectively requires huge amounts of data, both structured and unstructured, possibly from various different sources.
Imagine this scenario. Your marketing team is working to launch a genAI-powered campaign that’s designed to include personalized content for your audience. genAI is to be used for creating compelling blog posts, social media posts, and email newsletters. However, if you need the average age of your target audience and the channels they frequent to determine the tone, structure and timing of the messaging, it can be a challenge for genAI if your data is fragmented across different sources and is available in different inconsistent formats. This seemingly simple requirement suddenly becomes a complex problem to solve. This is a classic example of a lack of data readiness before adopting genAI.
Data Readiness: The Foundation for B2B Marketing genAI Success
CMOs and key marketing leaders must understand that genAI’s capabilities are only as good as the data that gets fed into it. AI, and particularly generative AI (genAI), has an endless thirst for data that is essential for training proprietary LLMs (large language models). Data serves as the lifeblood of AI-generated content, and there are multiple challenges to overcome while establishing data readiness.
Challenge 1: Data Silos
First, data silos often exist across teams and departments, each holding a piece of the data puzzle. Overcoming these silos necessitates collaboration and the implementation of well-defined data governance practices. Taking proactive steps by centralizing and organizing the data from both structured and unstructured sources is critical to ensuring data accuracy and consistency.
Challenge 2: Data Security and Privacy
Second, data security and privacy pose crucial concerns. Using customer data for AI-generated marketing materials demands compliance with data protection laws such as the European Union’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA) and more. CMOs must be vigilant and work in collaboration with legal and compliance teams to ensure that data usage is transparent, aligns with regulatory requirements, and customers have the option to opt out. genAI’s insatiable thirst for data makes it a prime target for cyberattacks, thus making the CMO’s collaboration with the Chief Information Security Officer and team critical.
Challenge 3: Data Skills Gaps
Finally, skill gaps in advanced data handling and AI expertise may surface during genAI implementation. Bridging these gaps becomes pivotal, whether through training programs or the hiring of experts as employees or consultants. Investing in people before investing in technology is a critical success factor.
Parting Words
So, what should B2B CMOs do now? How should they approach adoption? How do they steer their marketing teams, and where do they start? Read more in the full report, B2B CMOs: Lead, Don’t Follow Your Marketing Organization’s Generative AI Efforts.
genAI is likely to be a powerful ally in modern marketing, capable of creating personalized, engaging content at scale. However, CMOs must understand that genAI’s potential is unlocked only when supported by robust data readiness. It’s also imperative to acknowledge that data readiness is not a one-time task; it’s an ongoing effort demanding consistent maintenance and updates to remain effective.
