Actionable insights for adopting genAI in marketing, from establishing a cross-functional AI council to improving data governance.
GenAI’s transformative power accelerates marketing objectives and unlocks growth. The benefits are clear: enhanced productivity, deeper data analysis, personalized experiences, content generation and more.
However, tapping into genAI’s full potential requires more than just adding another tool to the martech stack. Shifting from pilot projects to widespread implementation demands a strategic approach. One that supports change management across every element of the current workflow that needs to evolve.
Eight out of 10 marketing leaders expect genAI to positively impact marketing investment and strategy this year, per Gartner research. This growing optimism highlights the need to establish a clear roadmap for adopting genAI in marketing. The following four recommendations address how to manage these challenges.
1. Establish an AI council and demonstrate marketing’s role
Marketers don’t have to work on genAI implementation alone. Work cross-functionally to assemble an AI council to provide direction and drive strategy. This multidisciplinary team of decision-makers and experts throughout the organization should encourage responsible and effective adoption, focusing on everything from risk management to upskilling talent to building trust.
Actively engage in the council to show how AI adds business value. Focus on brand ownership and crafting unique customer messages and experiences as key benefits of genAI, linking AI investments to real business results. Position genAI as both a people and tech solution, emphasizing training and upskilling to prepare your team for AI.
2. Ensure data is AI-ready
Business leaders often underestimate the impact of data on outcomes. But for successful AI implementation, your data must be clean, consistent and well-structured. Data governance and metadata management should be directed at the enterprise level with guidance from the AI council.
Marketers should also ensure data quality aligns with marketing goals by following these steps:
- Identify your top marketing use case for genAI, honing in on the data critical to that case.
- Assign a marketing data champion to build best practices, encourage data sharing and oversee data quality.
- Collaborate with stakeholders and data owners to set data quality standards and plan to close gaps between current and ideal quality. Leaders should anchor data governance for genAI in business value, emphasizing accountability for customer data and brand intellectual property.
Many mistakenly believe you can simply feed an organization’s data into a genAI tool and get results. In reality, large language models and other AI engines rely heavily on metadata — data about data that comprises various forms of descriptions and attributes.
Metadata provides essential context and understanding of the underlying data. Your marketing data champion should work closely with data, analytics and technology leaders to identify and link metadata that will drive marketing’s productivity and effectiveness, focusing on use cases that can be augmented by genAI.
3. Refine technology and talent mix
Assembling the right technology and talent mix depends on the organization’s:
- Desired outcomes.
- Risk appetite for investing in customization.
- Resources available to support implementation.
Carefully weigh the pros and cons of buying a genAI model or building your own. Most organizations take a hybrid approach to the tech mix as they experiment and move into pilots and implementation.
Whichever option you choose, upskilling talent is a must. The buy approach needs minimal training due to user-friendly models, while the build approach requires extensive training to design, develop and deploy the model.
4. Build customer trust
Building trust and long-term customer loyalty is crucial for marketing success. But concerns about content authenticity are growing in today’s AI-driven world. Gartner found that 70% of consumers believe AI-based content generators could spread false or misleading information.
Marketers must be transparent about genAI-created content. Deploy safeguards and establish risk mitigation policies to avoid high-threat scenarios undermining brand trust with this three-pronged approach:
Certify
- Establish processes to ensure the authenticity and accuracy of genAI-produced content to maintain consumer trust.
- For example, brands should disclose when using photographs of real people and places versus synthetic compositions.
Listen
- Broaden the scope of coverage for brand reputation management and monitoring, especially within social media.
- Prepare marketing teams to respond to and quickly address concerns over fake content.
Engage
- AI tools optimize workflows and enable companies to respond quickly to customer queries and concerns.
- Leaders must communicate the benefits of genAI implementation to showcase its value to both customers and stakeholders.
By following these steps to implement genAI and address its challenges, you can fully unlock its positive impact.
The post A marketer’s guide to implementing generative AI appeared first on MarTech.
About the author
Nicole Greene is a VP, Analyst for Gartner for Marketers, based in Virginia, US. She lead’s Gartner’s Marketing Channels and Content research and is a key contributor to Gartner’s research on Generative AI spanning across practices. Nicole analyzes marketing strategy, trends and practices with an emphasis on artificial intelligence, content marketing and digital experience. She delivers presentations to C-Suite leaders across industries on the strategy, operating models and governance required to successfully implement AI, including Generative AI (genAI), across the enterprise. Her cross-functional work on AI helps Gartner clients who are preparing their organizations for disruption. This includes strategic direction on moving from pilots to implementation, prioritization of use cases, the impact of genAI on content and the customer experience, responsible use and how to mitigate bias, among others. She is the lead author for Gartner’s Magic Quadrant on Content Marketing Platforms providing a wide-angle view for CMOs whose goal is to identify providers looking to create, manage and measure content at scale. Nicole also co-authors the Digital IQ for Website and Mobile Marketing Benchmarks helping leaders to amplify customer experience on these channels.
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