How SAS and The Nature Conservancy Leverage AI to Drive Revenue

AI adoption and understanding in marketing: A significant gap exists between planned AI investments and marketers’ understanding of the technology’s potential impact on business processes.

• A global market survey by SAS and Coleman Park revealed that while 9% of organizations plan to invest in generative AI by 2025, 90% of CMOs admit they don’t fully understand the technology or its implications.
• This knowledge gap raises concerns amid the ongoing AI hype, highlighting the need for marketers to deepen their understanding of generative AI.

Practical applications of generative AI: The Nature Conservancy and SAS are demonstrating revenue-generating applications of generative AI in various areas of marketing and business operations.

• The Nature Conservancy, an environmental nonprofit, is using generative AI to optimize marketing fundraising decisions and craft digestible messaging from scientific papers.
• SAS is leveraging AI for branding, lead scoring, and tailoring customer journeys, as discussed at ADWEEK’s Brandweek event in Phoenix, Arizona.

The Nature Conservancy’s AI-driven success: Generative AI has helped the nonprofit organization scale its environmental efforts and achieve significant fundraising growth.

• The organization is using AI to optimize marketing decisions, such as identifying which lapsed donors to reach out to.
• The Nature Conservancy has grown its revenue by 8% annually, outperforming peers in the sector who have experienced an 8% drop in donations year over year.
• The nonprofit is now raising about twice as much as similar organizations compared to five years ago.

Innovative AI strategies: Both The Nature Conservancy and SAS are implementing cutting-edge AI techniques to enhance their operations and decision-making processes.

• The Nature Conservancy is using synthetic data to supplement its limited pool of high-dollar donors, allowing for the creation of predictive models to identify potential major donors from smaller contributions.
• SAS is exploring the use of digital twins, or virtual simulations, to test and refine event strategies in real-time.

AI-driven customer engagement: SAS is leveraging predictive modeling and generative AI to automate and enhance customer interactions at scale.

• The company is using AI for lead scoring and creating next-best offers to drive its customer engagement strategy.
• These AI applications have helped SAS maintain a competitive edge in the market.

Ethical considerations and guidelines: As generative AI use accelerates, both organizations are addressing ethical concerns and implementing guidelines to ensure responsible AI usage.

• SAS has established guidelines to uphold ethical standards, prioritizing privacy, incorporating a human-in-the-loop approach, and ensuring transparency in all AI applications.
• The company has implemented a “nutritional label” that discloses how AI is used in decision-making processes, which has proven effective in maintaining client trust.
• The Nature Conservancy acknowledges environmental concerns surrounding generative AI and has redirected cost savings from reduced direct mail marketing towards environmental initiatives.

The importance of data foundation: Both organizations emphasize the critical role of a strong data foundation in successfully implementing AI strategies.

• John Blackwell, director of strategic analytics at The Nature Conservancy, stressed that none of their AI initiatives would work without a robust data foundation.
• This underscores the importance of data quality and management in driving successful AI implementations.

Balancing AI hype and practical applications: As the AI landscape evolves, organizations must navigate the line between hype and practical, revenue-generating applications.

• The experiences of The Nature Conservancy and SAS demonstrate that generative AI can drive tangible business results when applied strategically.
• However, the knowledge gap among marketers highlights the need for continued education and careful consideration of AI’s potential impacts and limitations.