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Generative AI and Marketing Within Enterprise.

Andreessen Horowitz (aka @a16z) recently shared some findings from their latest exploration into the world of Generative AI. We dive into the implications for marketers.

Andreessen Horowitz (aka @a16z) recently shared some findings from their latest exploration into the world of Generative AI. They talked to a bunch of Fortune 500 companies, getting the inside track on how these giants are integrating Generative AI into their operations. The insights they’ve come back with are telling for marketers navigating the world of Generative AI today. Here are a few takeaways;

Explicit Budget Growth with Strategic Shifts

Enterprises reported an average spend of $7M on generative AI in 2023, planning increases of 2x to 5x in 2024 due to early successes. Investment is shifting from “innovation” budgets to standard software lines, signaling genAI’s core role in operations.

  • Takeaway: Adapt marketing budgets to mirror the growing emphasis and potential returns of genAI, aligning with the shift towards seeing it as a fundamental operation tool for competitive edge.
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Broadened ROI Measurement Approaches

Enterprises are moving beyond productivity to include metrics like customer satisfaction (NPS), revenue, cost savings, efficiency, and accuracy to evaluate genAI’s ROI, acknowledging the evolving nature of ROI measurement.

  • Takeaway: Refine ROI measurement to encompass the comprehensive impact of genAI on marketing, from enhancing customer interaction to improving cost effectiveness.

Talent Gap and Implementation Costs

The scarcity of specialized genAI talent is a major hurdle, with a significant budget portion going towards implementation, highlighting the reliance on external services.

  • Takeaway: Focus on acquiring or nurturing specialized genAI expertise within the marketing department to foster more efficient and creative campaigns.

Adoption of a Multi-Model and Open-Source Strategy

The trend towards using multiple and open-source AI models indicates a preference for customization, control, and avoiding vendor lock-in.

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Cloud Provider Choices for Enhanced Security and Efficiency

Enterprises choose AI models based on cloud provider relationships to balance data security with ease of procurement, indicating strategic cloud alliances to optimize genAI initiatives.

  • Takeaway: Align genAI projects with strategic cloud partnerships to enhance data security and operational efficiency, maintaining customer trust.

Convergence of Model Performance

The narrowing performance gap among fine-tuned AI models suggests a wider selection for specific needs, reflecting a shift in focus from performance benchmarks to practical applicability.

  • Takeaway: Leverage the growing ecosystem of high-performing AI models to diversify marketing tools and platforms, boosting customization and effectiveness.

Cautious Deployment in Customer-Facing Applications

Enterprises are careful about using genAI in external marketing due to brand and accuracy concerns, showing a preference for in-house development given the market’s experimental nature.

  • Takeaway: Deploy genAI in external marketing judiciously, balancing innovation with risk management to safeguard brand integrity and ensure message precision.

Strategic Budget Reallocation Reflects genAI’s Core Role

The reallocation of AI investments to regular software budgets reflects the recognition of genAI as an essential technology across functions.

  • Takeaway: Advocate for reallocating budgets towards genAI within marketing, acknowledging its transformative potential on customer interaction and content creation.

Full article and post – https://a16z.com/generative-ai-enterprise-2024/

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