Navigating the responsible use of AI in marketing

Generative AI is transforming the way marketing is done by making content creation more accessible and affordable. Similar to how machine learning democratized pattern recognition, generative AI is now bringing powerful new capabilities to marketers in all industries.

With such wide-spread adoption of this new technology, marketers and the organizations they represent should be aware of any unintended consequences of AI adoption. While AI has the power to boost productivity, it could also bring unforeseen impacts that may only become clear later. Recognizing and managing these effects are critical for the responsible use of generative AI in marketing—and this starts at the organizational level.

How is generative AI being leveraged in marketing?

Generative AI has had many impacts on the marketing landscape, including:

• Hyper personalization: Generative AI allows marketers to tailor content with unprecedented precision, helping to meet specific customer needs more effectively. In a world where consumers have expectations for tailored content, this is a huge game changer.
• Data-driven insights: These insights are particularly helpful for pricing strategies. Tailoring offers based on individual customer history and loyalty helps enhance customer interactions and loyalty, creating a more seamless and productive consumer experience.
• Marketing process automation: Many tedious tasks, such as editing, managing creative assets or connecting campaign data may now be able to be automated, freeing up time for marketers to focus on more valuable tasks, such as strategy.
• Creative content generation: Generative AI also revolutionizes creative content generation through text-to-image and text-to-video technologies, opening new possibilities for content creation.

Currently, marketers are experimenting with these tools, balancing utility with potential risks, and exploring tailored solutions using advanced techniques like fine-tuning AI models. While some predict a major transformation in the next year or more, marketers should be prepared for a range of scenarios.

The importance of responsible AI application

The use of AI in marketing should be approached responsibly. This technology offers vast opportunities but also creates room for human error, malintent and misuse. As marketing professionals and service providers explore AI applications, it's essential to be mindful of the potential consequences.

Currently, without strict regulations, companies are left either relying on virtuous leadership or preparing for future compliance risks. Both approaches could detract from the true purpose of using AI: improving business outcomes and productivity. In addition to focusing on external metrics or imposed guidelines, businesses should also ensure AI is being used effectively to achieve their specific goals. The responsible use of AI must also consider the long-term impact on society and the environment. Ignoring the broader consequences of AI, such as environmental costs, could lead to significant ethical issues in the future.

As organizations navigate this evolving landscape, they must consider how to manage risk across AI use cases. While not all generative AI applications pose the same risk, those involving customer data require more stringent oversight than those using internal data.

Creating responsible AI frameworks

Building responsible AI frameworks requires establishing the right structures across the organization. This starts with a clear strategy. Begin by defining key principles—such as fairness, inclusion and eliminating bias—and integrate them into policies, procedures and processes.

When developing marketing programs, it's crucial to involve teams from risk, compliance and technology to thoroughly assess the program. Creating a sandbox environment for testing outcomes helps ensure AI applications meet ethical standards. Governance structures, such as an AI Risk Council, are essential to evaluate use cases and identify "no-fly zones." For instance, your organization may decide to avoid any marketing programs that use biometric data, aligning with both internal values and potential regulatory requirements.

This responsible framework should flow from strategy to policies, technology implementation and company culture. To succeed, all these elements must be aligned, helping the organization's approach to responsible AI remain consistent and actionable.

Key principles of AI responsibility

Establishing a solid governance structure for AI responsibility begins with identifying clear principles, such as:

• Protecting customer data: This means not using public data to train models without securing it internally first.
• Applying security techniques like tokenization: This is crucial to ensure no personal identifiable information (PIA) escapes during model training.
• Keeping a human in the loop: Before applying AI externally, it's critical to understand and verify the model's outcomes with internal teams.
• Fostering a shared purpose: The partnership between security, compliance and other departments is vital in implementing compliance by design. However, true progress comes from educating the entire organization about AI—its potential, risks and the responsibility that comes with it.

Embracing intelligence for a better future

Reports indicate that generative AI could generate an economic value of $2.6 to 4.4 trillion annually. Additionally, research from BCG and Harvard shows that use of generative AI for creative work could potentially increase performance by 40%.

Despite past fears surrounding AI, the conversation has taken a shift towards the positive. While risks remain, the potential benefits of well-managed AI outweigh these concerns. In the words of Andrew Ng, a globally-recognized leader in AI, “…having more intelligence in the world—be it human or artificial—will help all of us better solve problems.” Properly regulated, this technology could lead to more fulfilling lives for current and future generations.

The real challenge lies in the mindsets of those deploying AI-powered marketing strategies. Businesses must decide whether to adopt a marginal approach, aiming for incremental improvements, or to embrace disruptive thinking. By reconsidering marketing from first principles, companies are open to envisioning new possibilities. While incremental gains are important, the greatest rewards will come from a willingness to reimagine not just marketing but every function within an organization.

Note: This blog was informed by a panel of the same name held during Deluxe Exchange 2024.