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.

The most successful AI applications like ChatGPT are built ground-up. Incumbents trying to retrofit AI into existing products (e.g., Alexa Plus) are handicapped by their legacy architecture and success, a classic innovator's dilemma. True disruption requires a native approach.

A custom AI tool offers more value than a generic one like ChatGPT because it can be trained on a brand's unique, paywalled intellectual property. This creates a curated experience that aligns perfectly with your teachings and provides answers that cannot be found for free on the web, solidifying your expertise.

The key for enterprises isn't integrating general AI like ChatGPT but creating "proprietary intelligence." This involves fine-tuning smaller, custom models on their unique internal data and workflows, creating a competitive moat that off-the-shelf solutions cannot replicate.

The excitement around AI often overshadows its practical business implications. Implementing LLMs involves significant compute costs that scale with usage. Product leaders must analyze the ROI of different models to ensure financial viability before committing to a solution.

While it's tempting to build custom AI sales agents, the rapid pace of innovation means any internal solution will likely become obsolete in months. Unless you are a company like Vercel with dedicated engineers passionate about the problem, it's far better to buy an off-the-shelf tool.

For decades, buying generalized SaaS was more efficient than building custom software. AI coding agents reverse this. Now, companies can build hyper-specific, more effective tools internally for less cost than a bloated SaaS subscription, because they only need to solve their unique problem.

The true test for an AI tool isn't its initial, tailored function. The problem arises when a neighboring department tries to adapt it for their slightly different tech stack. The tool, excellent at one thing, gets "promoted into incompetency" when asked to handle broader, varied use cases across the enterprise.

According to an MIT report, enterprise AI projects led by external vendors are twice as likely to succeed as those built by internal teams. This is primarily due to a talent gap, as top-tier AI engineers and developers are concentrated in startups, not large corporations.