The Librarian and the Freelancer: The Correct Way to Think About AI in Your Business

TL;DR:

Treating your LLM like an oracle for proprietary insights is a strategic error. The real competitive advantage comes from treating it like a new employee: one that requires high-quality, proprietary context to create strategic value. This is an operational discipline, not a magic button.

Asking a Large Language Model (LLM) for proprietary insights is like asking a librarian to reveal a publisher’s secret formula for a bestseller. The librarian has read every book in the building; they can summarize plots, identify themes, and tell you what’s popular. But they weren’t in the boardroom where the strategic decisions were made. They don’t know about the confidential market research, the author negotiations, or the cover design debates.

This is the critical misunderstanding I see executives make. They ask their AI tools questions like:

• “How does Google really rank content?”
• “What makes a brand truly authoritative in the algorithm’s eyes?”

The LLM will provide a confident, well-structured answer. However, that answer isn’t insider information from Google’s engineering team. It’s a summary of the vast public corpus of articles, blogs, and forum posts written about that topic – The AI is a pattern-matching engine reflecting the public conversation.

Basing your strategy on these answers is equivalent to building your business plan on industry gossip. To truly unlock the value of AI, we must shift our mental model from treating it as an oracle to managing it like a new type of employee.

Helpful Reading

• Harvard Business Reviews – Generative AI
• Mckinsey & Co – The economic potential of generative AI

Your AI is a Freelancer, Not an Oracle

Think of using an LLM like hiring a freelancer. That freelancer has no context about your business until you provide it. They are incentivized to work quickly, and they will take your initial request and run with it. If the input is vague, the output will be generic.

When you ask an LLM to “rewrite the meta titles for our top landing pages,” it will do it. But the titles will be stripped of crucial context: your current ranking data, the competitive SERP landscape, your specific brand voice, and the strategic intent of each page.

To get expert-level work, you must provide expert-level context. Unless you are willing to build processes and systems that feed the LLM high-quality, proprietary inputs, your results will be inconsistent and unreliable. The real competitive advantage doesn’t come from using AI; it comes from integrating it into your operations with precision.

Here are some examples of how we leverage this “AI-as-a-freelancer” model to drive business results.

1. Content Strategy: Provide Context to Your Process

Instead of asking for generic content, we build solutions that give our AI the context it needs to perform at a high level. This means providing higher quality inputs into the content creation process, such as a vectorized crawl of your website, established brand guidelines, real-time SERP data, and competitor intelligence. By treating the AI like a strategic partner and giving it the same materials you’d give a new marketing director, you elevate its output from generic prose to strategically-aligned content.

• What this means for you: You can scale content production without sacrificing quality or brand integrity. The AI becomes an extension of your marketing team, executing within predefined strategic guardrails.

2. Technical Operations: Build Deterministic Assets

One of the biggest risks in business is relying on a probabilistic tool for a deterministic need. You wouldn’t want your financial reporting to be “mostly accurate.” We avoid this by using AI not to run the process, but to build the machine that runs the process. For example, instead of a flimsy prompt for a recurring report, use the LLM to write a robust Google Apps Script that automatically pulls API data into a spreadsheet. That script is a deterministic asset—it will run the same way every time, for free.

• What this means for you: You reduce operational risk and manual overhead. Your team uses AI to build stable, automated workflows, freeing up valuable engineering resources for more complex challenges.

3. Strategic Planning: Create a Custom AI Brain Trust

This is where the model truly shines. We use tools like Gemini’s custom “Gems” to create an AI strategist fed with our own proprietary data. By connecting a Gem to our Google Drive, we can train it on our internal process documents, past client deliverables, market research, and financial models. We prime it with a custom instruction set:

• “You are an expert SEO strategist. Your methodology combines the strategic frameworks of Roger Martin and Michael Porter with First-Principles thinking. Access the provided documents in Google Drive to help me build a clear, coherent, and actionable SEO strategy based on our firm’s proven approach.”

Suddenly, the “freelancer” has the full context of a seasoned employee. It can generate V1 strategic outlines, synthesize research, and organize thinking in a way that is deeply aligned with our business.

• What this means for you: This transforms AI from a public information tool into a secure, private repository of your company’s institutional knowledge—an interactive brain trust that can accelerate strategy and decision-making.

Key Takeaways for C-Suite Leaders

• Shift Your Mental Model: Treat AI as a capable team member that requires context, not an oracle with secret knowledge.
• Advantage Comes from Inputs, Not the AI Itself: Your proprietary data, strategic documents, and brand guidelines are what transform generic AI outputs into a competitive weapon.
• Build Deterministic Assets: Use AI to write code and build stable, automated workflows. This reduces operational risk and frees up high-cost engineering resources.
• Create a Private Brain Trust: Leverage custom AI capabilities to build a secure, interactive repository of your institutional knowledge, accelerating strategy and decision-making.

The Leadership Imperative

Many leaders get frustrated with AI. That’s likely why 95% of AI initiatives fail. They try it once, the result isn’t perfect, and they dismiss it. This is like hiring a talented freelancer, giving them a one-sentence brief, and then being disappointed with the first draft.

Leveraging AI for a true competitive advantage is an operational discipline. It requires patience, a commitment to providing context, and a willingness to iterate. The models will change, and your workflows will need to adapt. But the leaders who embrace this process—who treat their AI less like a magic eight ball and more like a capable team member—are the ones who will build a lasting strategic advantage.