Despite the hype, LinkedIn found that third-party AI tools for coding and design don't work out-of-the-box on their complex, legacy stack. Success requires deep customization, re-architecting internal platforms for AI reasoning, and working in "alpha mode" with vendors to adapt their tools.

Companies feel immense pressure to integrate AI to stay competitive, leading to massive spending. However, this rush means they lack the infrastructure to measure ROI, creating a paradox of anxious investment without clear proof of value.

Fragmented data and disconnected systems in traditional marketing clouds prevent AI from forming a complete, persistent memory of customer interactions. This leads to missed opportunities and flawed personalization, as the AI operates with incomplete information, exposing foundational cracks in legacy architecture.

The conventional wisdom that enterprises are blocked by a lack of clean, accessible data is wrong. The true bottleneck is people and change management. Scrappy teams can derive significant value from existing, imperfect internal and public data; the real challenge is organizational inertia and process redesign.

Marketing leaders pressured to adopt AI are discovering the primary obstacle isn't the technology, but their own internal data infrastructure. Siloed, inconsistently structured data across teams prevents them from effectively leveraging AI for consumer insights and business growth.

Many leaders mistakenly halt AI adoption while waiting for perfect data governance. This is a strategic error. Organizations should immediately identify and implement the hundreds of high-value generative AI use cases that require no access to proprietary data, creating immediate wins while larger data initiatives continue.

Enterprises struggle to get value from AI due to a lack of iterative, data-science expertise. The winning model for AI companies isn't just selling APIs, but embedding "forward deployment" teams of engineers and scientists to co-create solutions, closing the gap between prototype and production value.

For years, access to compute was the primary bottleneck in AI development. Now, as public web data is largely exhausted, the limiting factor is access to high-quality, proprietary data from enterprises and human experts. This shifts the focus from building massive infrastructure to forming data partnerships and expertise.

The effectiveness of an AI system isn't solely dependent on the model's sophistication. It's a collaboration between high-quality training data, the model itself, and the contextual understanding of how to apply both to solve a real-world problem. Neglecting data or context leads to poor outcomes.