The role of AI in marketing has gone from being a novelty to being a game-changing reality that’s reshaping how we connect with consumers and understand their needs. Yet, the power of these advanced technology systems hinges on one profoundly human factor: trust.
Trust has always been the cornerstone of any successful marketing strategy. And with the embedding of AI into nearly every aspect of marketing, it takes on an even greater significance. Our algorithms, models, and systems are more than merely tools — they’re extensions of our brand, our values, and our promise to our customers. The trust we build in the AI systems we develop, deploy, and use will ultimately determine their efficacy, shaping not just individual customer experiences, but the future of marketing itself.
Taking this a step further, trust is more than a bridge between your company and your customers. It’s also an essential pathway to consistent adoption of AI systems by your most important internal stakeholder communities.
Three Communities of Trust
When setting up and scaling AI for your marketing organization, it’s crucial that you commit to building trust into your approach from the outset, and in three broad areas.
In the Market
Lack of trust in the market erodes customer relationships and damages brand reputation. Build trust with your consumers by being transparent about your use of generative AI in your marketing campaigns. Demonstrate your commitment to data privacy and security. Proactively address potential biases. And consistently deliver accurate and valuable personalized experiences.
Among Your Marketers
Lack of trust among your marketing team members stands in the way of end user adoption. Here, it’s essential that you maintain human agency, autonomy, authority, and accountability in all key marketing decisions and in every marketing workflow.
By Your Company’s Leaders
Lack of trust by the leaders in your marketing organization and across the enterprise slows progress, kills your credibility as an AI changemaker, and makes it harder to sell in and scale AI implementations. Here, it’s important to build confidence in using AI systems for high stakes decision-making and market-facing communications.
10 Ways to Build Trust in AI Systems
Now, with a basic understanding of why trust is important to each of these three stakeholder communities, let’s explore 10 steps you can take to establish trust as you embed AI throughout your marketing operations.
- Assemble a diverse and multidisciplinary team to build, evaluate, and buy AI systems for your organization. This helps make sure that a wide range of perspectives and experiences are considered, reducing the risk of unconscious bias in AI training and output, promoting fairness, and increasing the system’s relevance and usability for a broader audience.
- Be intentional about the creation of inclusive and appropriate data sets. It may be necessary to collect additional data about underrepresented and marginalized groups to promote responsible and inclusive use of AI. Bear in mind that you won’t always control or have good visibility into the core data set — for example, when you buy or build systems that use popular generative AI foundation models like OpenAI’s GPT-4. In these cases, it’s important to conduct adequate diligence before you determine your level of comfort with a potential vendor’s data practices. Publicly available vendor risk profiles like the ones published by Credo.ai can be helpful. And our own discussion guide for technology partner evaluation provides a practical framework for asking the right data questions. And when you’re fine-tuning these third-party models or applications with your own proprietary data, pay attention to any unintentional bias that may have seeped in over time.
- Clearly explain the ways in which data is being combined and used within AI algorithms to validate the output of AI systems and correct for possible biases which may come up later. Here again, the burden of explainability may fall mainly on the developers of the underlying foundation models or the third party application companies that embed those models into the marketing AI systems that you buy. If you train or fine-tune any of these systems with your organization’s proprietary customer or campaign data, ensure that you have a solid understanding of how the addition of your own data affects the performance of the model and influences its outputs.
- Ask which groups will benefit from using the AI system, which will be harmed, and if the data being used is appropriate or fit for the intended purpose. Keep in mind that potential harms might be internal (for example, when a productivity-enhancing AI system might result in the elimination of headcount or the de-skilling of substantial marketing workstreams). Or they might be external. Consider inappropriate or even unethical uses of personal data, predatory or discriminatory marketing practices, or even low quality or inaccurate content produced by generative AI systems without adequate human oversight.
- Consider establishing an ethics board to provide holistic oversight on the ethical and responsible development of AI systems. Many organizations recruit outside advisors for their ability to lend diverse viewpoints to ethics discussions.
- Build in appropriate monitoring and validation mechanisms as the AI system is used over time. Maintain a registry of all active AI projects. Establish a robust AI governance program to continually evaluate and address potential organizational, regulatory, and reputational risks before they become problematic. As real issues inevitably arise, address them promptly and own up to any errors.
- Enlist independent third parties to conduct periodic audits. Doing so will help make sure that your AI systems are performing as intended and are producing accurate, fair, and unbiased outcomes. Independent outsiders can also evaluate the sufficiency and effectiveness of your organization’s overall AI governance model.
- Track both the performance of AI systems and the impact of decisions suggested by them. These are critical steps toward aligning intentions with outcomes, an early warning for any variations or degradations in performance over time, and a basis for ongoing discussions around risk and mitigation. When you clearly communicate performance issues to your internal leadership and key stakeholders, you create the kind of transparency that fosters trust and credibility. If you encourage others in your organization to report any performance issues that arise in their own experience with your AI systems, you create deeper engagement around responsible AI.
- Create standards to govern the development, purchase, and usage of AI systems. At CognitivePath, we tend to view these essential guardrails as “freedom within a frame” – a set of policies and practices that mitigate the most common and most egregious risks to protect your employees, company, customers, and brand, while empowering your marketing end users to experience the productivity gains, creativity boost, and engine for innovation that AI-powered workflows can offer.
- Safeguard users by following social norms, along with applicable laws and regulations. It’s equally important to safeguard internal users and external constituents. At the same time, marketing leaders should acknowledge the complexity inherent in adhering to norms and even regulations. Norms and even the notion of what constitutes bias or fairness vary by country and culture. Regulations and laws are nascent, open to interpretation, and vary by region. In other words, there’s no “perfect,” but your brand’s purpose and values can (and must) be your guide for responsible AI.
The Effort is Essential
Trust is the key to unlocking AI’s true potential in marketing. By proactively building trust with consumers, marketers, and leadership, we lay the foundation for responsible AI adoption and innovation. This demands inclusive data practices, explainable systems, governance, and auditing that center on human values. While complex, the effort is essential.
When our AI reflects our brand values and commitment to fairness and agency, it becomes a powerful engine of trust, strengthening customer connections and fueling organizational growth. Trusted AI allows us to transform marketing through more relevant, ethical experiences. The future of our profession depends on the trust we consciously build in AI today. With focus and care, we can drive responsible AI innovation that propels marketing forward.
