From creating personalized content to enhancing productivity, generative AI has the potential to help companies transform marketing operations
Generative AI has made its way into nearly every industry, promising to change the way organizations do business. However, while generative AI can be a catalyst for creativity and innovation, it does not replace human oversight and input.
As leaders invest in implementing generative AI to enhance their marketing operations, it’s critical that they invest in the right foundations to help ensure success. One such foundation is an AI-ready data strategy.
Successful generative AI projects rely on large amounts of data to work effectively. To be useful, that data must be trustworthy, secure, accessible, and organized so that AI projects can produce meaningful insights and outputs. Since generative AI models build outputs based on the data that was used to train them, organizations should ensure they have an effective data strategy in place.
To do so, leaders can consider four questions.
Is the customer data reliable, indexed, and usable by a large language model?
Before launching a generative AI project, leaders should examine their data quality strategy. They can begin by conducting a thorough analysis of the organization’s data infrastructure, understanding how data is collected and where it’s collected from, and creating a plan for data cleanup.
To make data easier to consume by both AI and human collaborators on the project, leaders can consider developing a metadata strategy and data dictionary. A metadata strategy can enable organizations to tag their data and thus improve searchability. A data dictionary provides a business-friendly collection of descriptions of the data objects or items in the model and is crucial for data management consistency and accuracy.
With a metadata strategy and data dictionary in place, leaders can then assess the data infrastructure for integration and organization. Often, the data relevant to different use cases sits across various functions. Leaders can map out where the data needed for a project is located and eliminate silos or manual processes that may slow or prevent access to the data required.
How can organizations maintain customer trust and ensure regulatory compliance?
Leaders can prioritize customer trust and regulatory compliance by establishing a clear set of guidelines and usage rules around data. For example, they can consider what governance they have in place to ensure that AI projects will comply with relevant regulations; what types of information they can feed into generative AI models; and what boundaries they need to draw to ensure that inappropriate data is not collected, stored, or used in their AI projects. By establishing such guidelines, leaders can help ensure that their AI deployments can be trusted.
Leaders can prioritize customer trust and regulatory compliance by establishing a clear set of guidelines and usage rules around data.
Customers expect to be able to trust that the data they share with organizations will be used ethically and without bias. Leaders can prioritize transparency by sharing how they are using customer data. Furthermore, they can implement privacy and security protections on data systems to mitigate breaches of customer trust and help ensure regulatory compliance.
Leaders can also consider implementing anonymization techniques to help protect individual privacy while still allowing the AI to learn from the data. Additionally, organizations can put processes in place to monitor who has access to the data and regularly review these processes to ensure they are aligned with the latest regulatory changes.
Is the customer data compiled into a single platform?
Organizations that already compile their data from marketing, sales, and service into a single unified view of the customer tend to have an easier time implementing AI-enabled personalization journeys than do businesses whose data is siloed in separate systems across the enterprise.
Even in the absence of a data cloud or customer data platform, organizations can still explore and experiment with generative AI as they continue to develop and refine their data strategies. Doing so can help companies identify gaps in their data and build the necessary connection points between their AI projects and existing business processes.
How can organizations monitor generative AI outputs for accuracy and relevance?
Because generative AI models can potentially produce inaccurate or false insights, it is important for organizations to monitor outputs. To do so, leaders can implement real-time tracking and verify the generative AI model’s results for accuracy, relevance, and potential risks like false statements, bias, or regulatory violations.
Organizations can also use surveys, A/B testing, and test-and-learn strategies, as well as include human judgment in the AI decision-making process. By incorporating human oversight and AI governance teams, companies can monitor and evaluate the performance of their AI systems, mitigate reputational and brand risk, provide feedback, and make corrections as needed. This strategy can help ensure that the AI system’s outputs are reliable and aligned with the intended goals.
AI is not a magic wand. It requires a clear strategy, a solid foundation of good data practices, the right operational model and talent to review and assess outputs, and an overarching culture that understands and values data. As leaders start to build their generative AI strategy, it is imperative that they prioritize a strong data foundation.
—by Trinadha Kandi, managing director and Advertising, Marketing & Commerce practice leader; David Chan, Customer Data Platform practice leader; Sai Medi, machine language specialist and data science manager; Jenny Kelly, head of content, all with Deloitte Digital, Deloitte Consulting LLP
Published on Mar 12, 2024 at 3:00 PM
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